Effects of Referent Token Variability on L2 Vocabulary Learning

ERIC Educational Resources Information Center

Sommers, Mitchell S.; Barcroft, Joe

2013-01-01

Previous research has demonstrated substantially improved second language (L2) vocabulary learning when spoken word forms are varied using multiple talkers, speaking styles, or speaking rates. In contrast, the present study varied visual representations of referents for target vocabulary. English speakers learned Spanish words in formats of no…

Catecholaminergic Regulation of Learning Rate in a Dynamic Environment.

PubMed

Jepma, Marieke; Murphy, Peter R; Nassar, Matthew R; Rangel-Gomez, Mauricio; Meeter, Martijn; Nieuwenhuis, Sander

2016-10-01

Adaptive behavior in a changing world requires flexibly adapting one's rate of learning to the rate of environmental change. Recent studies have examined the computational mechanisms by which various environmental factors determine the impact of new outcomes on existing beliefs (i.e., the 'learning rate'). However, the brain mechanisms, and in particular the neuromodulators, involved in this process are still largely unknown. The brain-wide neurophysiological effects of the catecholamines norepinephrine and dopamine on stimulus-evoked cortical responses suggest that the catecholamine systems are well positioned to regulate learning about environmental change, but more direct evidence for a role of this system is scant. Here, we report evidence from a study employing pharmacology, scalp electrophysiology and computational modeling (N = 32) that suggests an important role for catecholamines in learning rate regulation. We found that the P3 component of the EEG-an electrophysiological index of outcome-evoked phasic catecholamine release in the cortex-predicted learning rate, and formally mediated the effect of prediction-error magnitude on learning rate. P3 amplitude also mediated the effects of two computational variables-capturing the unexpectedness of an outcome and the uncertainty of a preexisting belief-on learning rate. Furthermore, a pharmacological manipulation of catecholamine activity affected learning rate following unanticipated task changes, in a way that depended on participants' baseline learning rate. Our findings provide converging evidence for a causal role of the human catecholamine systems in learning-rate regulation as a function of environmental change.

An Exploratory Analysis of Personality, Attitudes, and Study Skills on the Learning Curve within a Team-based Learning Environment

PubMed Central

Henry, Teague; Campbell, Ashley

2015-01-01

Objective. To examine factors that determine the interindividual variability of learning within a team-based learning environment. Methods. Students in a pharmacokinetics course were given 4 interim, low-stakes cumulative assessments throughout the semester and a cumulative final examination. Students’ Myers-Briggs personality type was assessed, as well as their study skills, motivations, and attitudes towards team-learning. A latent curve model (LCM) was applied and various covariates were assessed to improve the regression model. Results. A quadratic LCM was applied for the first 4 assessments to predict final examination performance. None of the covariates examined significantly impacted the regression model fit except metacognitive self-regulation, which explained some of the variability in the rate of learning. There were some correlations between personality type and attitudes towards team learning, with introverts having a lower opinion of team-learning than extroverts. Conclusion. The LCM could readily describe the learning curve. Extroverted and introverted personality types had the same learning performance even though preference for team-learning was lower in introverts. Other personality traits, study skills, or practice did not significantly contribute to the learning variability in this course. PMID:25861101

An exploratory analysis of personality, attitudes, and study skills on the learning curve within a team-based learning environment.

PubMed

Persky, Adam M; Henry, Teague; Campbell, Ashley

2015-03-25

To examine factors that determine the interindividual variability of learning within a team-based learning environment. Students in a pharmacokinetics course were given 4 interim, low-stakes cumulative assessments throughout the semester and a cumulative final examination. Students' Myers-Briggs personality type was assessed, as well as their study skills, motivations, and attitudes towards team-learning. A latent curve model (LCM) was applied and various covariates were assessed to improve the regression model. A quadratic LCM was applied for the first 4 assessments to predict final examination performance. None of the covariates examined significantly impacted the regression model fit except metacognitive self-regulation, which explained some of the variability in the rate of learning. There were some correlations between personality type and attitudes towards team learning, with introverts having a lower opinion of team-learning than extroverts. The LCM could readily describe the learning curve. Extroverted and introverted personality types had the same learning performance even though preference for team-learning was lower in introverts. Other personality traits, study skills, or practice did not significantly contribute to the learning variability in this course.

Determining a Difference in Self-Directed Learning Readiness Using the Survey of Adult Learning Traits

ERIC Educational Resources Information Center

Ezell, Diana

2013-01-01

The purpose of this study was to measure the self-directed learning of educators and explore the differences between and among the variables of age, level of education, position, school district ratings, levels of poverty and affluence, and gender. The Survey of Adult Learning Traits (SALT) authored by Hogg was used as the instrument to measure…

Verbal Knowledge, Working Memory, and Processing Speed as Predictors of Verbal Learning in Older Adults

ERIC Educational Resources Information Center

Rast, Philippe

2011-01-01

The present study aimed at modeling individual differences in a verbal learning task by means of a latent structured growth curve approach based on an exponential function that yielded 3 parameters: initial recall, learning rate, and asymptotic performance. Three cognitive variables--speed of information processing, verbal knowledge, working…

Different slopes for different folks: alpha and delta EEG power predict subsequent video game learning rate and improvements in cognitive control tasks.

PubMed

Mathewson, Kyle E; Basak, Chandramallika; Maclin, Edward L; Low, Kathy A; Boot, Walter R; Kramer, Arthur F; Fabiani, Monica; Gratton, Gabriele

2012-12-01

We hypothesized that control processes, as measured using electrophysiological (EEG) variables, influence the rate of learning of complex tasks. Specifically, we measured alpha power, event-related spectral perturbations (ERSPs), and event-related brain potentials during early training of the Space Fortress task, and correlated these measures with subsequent learning rate and performance in transfer tasks. Once initial score was partialled out, the best predictors were frontal alpha power and alpha and delta ERSPs, but not P300. By combining these predictors, we could explain about 50% of the learning rate variance and 10%-20% of the variance in transfer to other tasks using only pretraining EEG measures. Thus, control processes, as indexed by alpha and delta EEG oscillations, can predict learning and skill improvements. The results are of potential use to optimize training regimes. Copyright © 2012 Society for Psychophysiological Research.

Physiological Factors in Adult Learning and Instruction. Research to Practice Series.

ERIC Educational Resources Information Center

Verner, Coolie; Davison, Catherine V.

The physiological condition of the adult learner as related to his learning capability is discussed. The design of the instructional process, the selection of learning tasks, the rate at which instruction occurs, and the nature of the instructional setting may all be modified by the instructor to accomodate the variable physiological conditions of…

Human θ burst stimulation enhances subsequent motor learning and increases performance variability.

PubMed

Teo, James T H; Swayne, Orlando B C; Cheeran, Binith; Greenwood, Richard J; Rothwell, John C

2011-07-01

Intermittent theta burst stimulation (iTBS) transiently increases motor cortex excitability in healthy humans by a process thought to involve synaptic long-term potentiation (LTP), and this is enhanced by nicotine. Acquisition of a ballistic motor task is likewise accompanied by increased excitability and presumed intracortical LTP. Here, we test how iTBS and nicotine influences subsequent motor learning. Ten healthy subjects participated in a double-blinded placebo-controlled trial testing the effects of iTBS and nicotine. iTBS alone increased the rate of learning but this increase was blocked by nicotine. We then investigated factors other than synaptic strengthening that may play a role. Behavioral analysis and modeling suggested that iTBS increased performance variability, which correlated with learning outcome. A control experiment confirmed the increase in motor output variability by showing that iTBS increased the dispersion of involuntary transcranial magnetic stimulation-evoked thumb movements. We suggest that in addition to the effect on synaptic plasticity, iTBS may have facilitated performance by increasing motor output variability; nicotine negated this effect on variability perhaps via increasing the signal-to-noise ratio in cerebral cortex.

Searching for Variables and Models to Investigate Mediators of Learning from Multiple Representations

ERIC Educational Resources Information Center

Rau, Martina A.; Scheines, Richard

2012-01-01

Although learning from multiple representations has been shown to be effective in a variety of domains, little is known about the mechanisms by which it occurs. We analyzed log data on error-rate, hint-use, and time-spent obtained from two experiments with a Cognitive Tutor for fractions. The goal of the experiments was to compare learning from…

Measures of Heart Rate Variability and How They Relate to Age, Gender, Emotional Behavior, and Academic Achievement in Elementary School Children in Adventist and Public Schools

ERIC Educational Resources Information Center

Dalton, Marilee Serns

2013-01-01

The analysis of heart rate variability (HRV) is one tool shown to be of value in examining heart-brain interactions. HRV is remarkably responsive to emotion, and the importance of emotional state in cognitive function is increasingly being recognized and socio-emotional learning strategies being utilized in the classroom. Consequently, the…

Frontal Alpha Oscillations and Attentional Control: A Virtual Reality Neurofeedback Study.

PubMed

Berger, Anna M; Davelaar, Eddy J

2018-05-15

Two competing views about alpha oscillations suggest that cortical alpha reflect either cortical inactivity or cortical processing efficiency. We investigated the role of alpha oscillations in attentional control, as measured with a Stroop task. We used neurofeedback to train 22 participants to increase their level of alpha amplitude. Based on the conflict/control loop theory, we selected to train prefrontal alpha and focus on the Gratton effect as an index of deployment of attentional control. We expected an increase or a decrease in the Gratton effect with increase in neural learning depending on whether frontal alpha oscillations reflect cortical idling or enhanced processing efficiency, respectively. In order to induce variability in neural learning beyond natural occurring individual differences, we provided half of the participants with feedback on alpha amplitude in a 3-dimensional (3D) virtual reality environment and the other half received feedback in a 2D environment. Our results showed variable neural learning rates, with larger rates in the 3D compared to the 2D group, corroborating prior evidence of individual differences in EEG-based learning and the influence of a virtual environment. Regression analyses revealed a significant association between the learning rate and changes on deployment of attentional control, with larger learning rates being associated with larger decreases in the Gratton effect. This association was not modulated by feedback medium. The study supports the view of frontal alpha oscillations being associated with efficient neurocognitive processing and demonstrates the utility of neurofeedback training in addressing theoretical questions in the non-neurofeedback literature. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

The utility of kindergarten teacher ratings for predicting low academic achievement in first grade.

PubMed

Teisl, J T; Mazzocco, M M; Myers, G F

2001-01-01

The purpose of this study was to assess the predictive value of kindergarten teachers' ratings of pupils for later first-grade academic achievement. Kindergarten students were rated by their teachers on a variety of variables, including math and reading performance, teacher concerns, and amount of learning relative to peers. These variables were then analyzed with respect to outcome measures for math and reading ability administered in the first grade. The teachers' ratings of academic performance were significantly correlated with scores on the outcome measures. Analyses were also carried out to determine sensitivity, specificity, and predictive values of the different teacher ratings. The results indicated high overall accuracy, sensitivity, specificity, and negative predictive value for the ratings. Positive predictive value tended to be lower. A recommendation to follow from these results is that teacher ratings of this sort be used to determine which children should receive cognitive screening measures to further enhance identification of children at risk for learning disability. However, this recommendation is limited by the lack of empirically supported screening measures for math disability versus well-supported screening tools for reading disability.

The role of context in preschool learning: a multilevel examination of the contribution of context-specific problem behaviors and classroom process quality to low-income children's approaches to learning.

PubMed

Domínguez, Ximena; Vitiello, Virginia E; Fuccillo, Janna M; Greenfield, Daryl B; Bulotsky-Shearer, Rebecca J

2011-04-01

Research suggests that promoting adaptive approaches to learning early in childhood may help close the gap between advantaged and disadvantaged children. Recent research has identified specific child-level and classroom-level variables that are significantly associated with preschoolers' approaches to learning. However, further research is needed to understand the interactive effects of these variables and determine whether classroom-level variables buffer the detrimental effects of child-level risk variables. Using a largely urban and minority sample (N=275) of preschool children, the present study examined the additive and interactive effects of children's context-specific problem behaviors and classroom process quality dimensions on children's approaches to learning. Teachers rated children's problem behavior and approaches to learning and independent assessors conducted classroom observations to assess process quality. Problem behaviors in structured learning situations and in peer and teacher interactions were found to negatively predict variance in approaches to learning. Classroom process quality domains did not independently predict variance in approaches to learning. Nonetheless, classroom process quality played an important role in these associations; high emotional support buffered the detrimental effects of problem behavior, whereas high instructional support exacerbated them. The findings of this study have important implications for classroom practices aimed at helping children who exhibit problem behaviors. Copyright © 2010 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

Individual differences in implicit motor learning: task specificity in sensorimotor adaptation and sequence learning

PubMed Central

Raza, Meher; Ivry, Richard B.

2016-01-01

In standard taxonomies, motor skills are typically treated as representative of implicit or procedural memory. We examined two emblematic tasks of implicit motor learning, sensorimotor adaptation and sequence learning, asking whether individual differences in learning are correlated between these tasks, as well as how individual differences within each task are related to different performance variables. As a prerequisite, it was essential to establish the reliability of learning measures for each task. Participants were tested twice on a visuomotor adaptation task and on a sequence learning task, either the serial reaction time task or the alternating reaction time task. Learning was evident in all tasks at the group level and reliable at the individual level in visuomotor adaptation and the alternating reaction time task but not in the serial reaction time task. Performance variability was predictive of learning in both domains, yet the relationship was in the opposite direction for adaptation and sequence learning. For the former, faster learning was associated with lower variability, consistent with models of sensorimotor adaptation in which learning rates are sensitive to noise. For the latter, greater learning was associated with higher variability and slower reaction times, factors that may facilitate the spread of activation required to form predictive, sequential associations. Interestingly, learning measures of the different tasks were not correlated. Together, these results oppose a shared process for implicit learning in sensorimotor adaptation and sequence learning and provide insight into the factors that account for individual differences in learning within each task domain. NEW & NOTEWORTHY We investigated individual differences in the ability to implicitly learn motor skills. As a prerequisite, we assessed whether individual differences were reliable across test sessions. We found that two commonly used tasks of implicit learning, visuomotor adaptation and the alternating serial reaction time task, exhibited good test-retest reliability in measures of learning and performance. However, the learning measures did not correlate between the two tasks, arguing against a shared process for implicit motor learning. PMID:27832611

A Study to Determine Non-Academic Traits of a Successful Student in an Open Learning Center Environment at John Wood Community College.

ERIC Educational Resources Information Center

Kronquist, Shirley; And Others

John Wood Community College's Open Learning Center (OLC) offers an alternative to traditional classroom approaches using one-to-one instruction, a competency-based learning format, and flexible scheduling. Due to concern over the high attrition rate in OLC courses, a study was conducted to identify the characteristics and variables contributing to…

Optimal control in microgrid using multi-agent reinforcement learning.

PubMed

Li, Fu-Dong; Wu, Min; He, Yong; Chen, Xin

2012-11-01

This paper presents an improved reinforcement learning method to minimize electricity costs on the premise of satisfying the power balance and generation limit of units in a microgrid with grid-connected mode. Firstly, the microgrid control requirements are analyzed and the objective function of optimal control for microgrid is proposed. Then, a state variable "Average Electricity Price Trend" which is used to express the most possible transitions of the system is developed so as to reduce the complexity and randomicity of the microgrid, and a multi-agent architecture including agents, state variables, action variables and reward function is formulated. Furthermore, dynamic hierarchical reinforcement learning, based on change rate of key state variable, is established to carry out optimal policy exploration. The analysis shows that the proposed method is beneficial to handle the problem of "curse of dimensionality" and speed up learning in the unknown large-scale world. Finally, the simulation results under JADE (Java Agent Development Framework) demonstrate the validity of the presented method in optimal control for a microgrid with grid-connected mode. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

STDP allows fast rate-modulated coding with Poisson-like spike trains.

PubMed

Gilson, Matthieu; Masquelier, Timothée; Hugues, Etienne

2011-10-01

Spike timing-dependent plasticity (STDP) has been shown to enable single neurons to detect repeatedly presented spatiotemporal spike patterns. This holds even when such patterns are embedded in equally dense random spiking activity, that is, in the absence of external reference times such as a stimulus onset. Here we demonstrate, both analytically and numerically, that STDP can also learn repeating rate-modulated patterns, which have received more experimental evidence, for example, through post-stimulus time histograms (PSTHs). Each input spike train is generated from a rate function using a stochastic sampling mechanism, chosen to be an inhomogeneous Poisson process here. Learning is feasible provided significant covarying rate modulations occur within the typical timescale of STDP (~10-20 ms) for sufficiently many inputs (~100 among 1000 in our simulations), a condition that is met by many experimental PSTHs. Repeated pattern presentations induce spike-time correlations that are captured by STDP. Despite imprecise input spike times and even variable spike counts, a single trained neuron robustly detects the pattern just a few milliseconds after its presentation. Therefore, temporal imprecision and Poisson-like firing variability are not an obstacle to fast temporal coding. STDP provides an appealing mechanism to learn such rate patterns, which, beyond sensory processing, may also be involved in many cognitive tasks.

STDP Allows Fast Rate-Modulated Coding with Poisson-Like Spike Trains

PubMed Central

Hugues, Etienne

2011-01-01

Spike timing-dependent plasticity (STDP) has been shown to enable single neurons to detect repeatedly presented spatiotemporal spike patterns. This holds even when such patterns are embedded in equally dense random spiking activity, that is, in the absence of external reference times such as a stimulus onset. Here we demonstrate, both analytically and numerically, that STDP can also learn repeating rate-modulated patterns, which have received more experimental evidence, for example, through post-stimulus time histograms (PSTHs). Each input spike train is generated from a rate function using a stochastic sampling mechanism, chosen to be an inhomogeneous Poisson process here. Learning is feasible provided significant covarying rate modulations occur within the typical timescale of STDP (∼10–20 ms) for sufficiently many inputs (∼100 among 1000 in our simulations), a condition that is met by many experimental PSTHs. Repeated pattern presentations induce spike-time correlations that are captured by STDP. Despite imprecise input spike times and even variable spike counts, a single trained neuron robustly detects the pattern just a few milliseconds after its presentation. Therefore, temporal imprecision and Poisson-like firing variability are not an obstacle to fast temporal coding. STDP provides an appealing mechanism to learn such rate patterns, which, beyond sensory processing, may also be involved in many cognitive tasks. PMID:22046113

The Relationship among Correct and Error Oral Reading Rates and Comprehension.

ERIC Educational Resources Information Center

Roberts, Michael; Smith, Deborah Deutsch

1980-01-01

Eight learning disabled boys (10 to 12 years old) who were seriously deficient in both their oral reading and comprehension performances participated in the study which investigated, through an applied behavior analysis model, the interrelationships of three reading variables--correct oral reading rates, error oral reading rates, and percentage of…

Learning to be different: Acquired skills, social learning, frequency dependence, and environmental variation can cause behaviourally mediated foraging specializations

USGS Publications Warehouse

Tinker, M.T.; Mangel, M.; Estes, J.A.

2009-01-01

Question: How does the ability to improve foraging skills by learning, and to transfer that learned knowledge, affect the development of intra-population foraging specializations? Features of the model: We use both a state-dependent life-history model implemented by stochastic dynamic programming (SDPM) and an individual-based model (IBM) to capture the dynamic nature of behavioural preferences in feeding. Variables in the SDPM include energy reserves, skill levels, energy and handling time per single prey item, metabolic rate, the rates at which skills are learned and forgotten, the effect of skills on handling time, and the relationship between energy reserves and fitness. Additional variables in the IBM include the probability of successful weaning, the logistic dynamics of the prey species with stochastic recruitment, the intensity of top-down control of prey by predators, the mean and variance in skill levels of new recruits, and the extent to which learned Information can be transmitted via matrilineal social learning. Key range of variables: We explore the effects of approaching the time horizon in the SDPM, changing the extent to which skills can improve with experience, increasing the rates of learning or forgetting of skills, changing whether the learning curve is constant, accelerating (T-shaped) or decelerating ('r'-shaped), changing both mean and maximum possible energy reserves, changing metabolic costs of foraging, and changing the rate of encounter with prey. Conclusions: The model results show that the following factors increase the degree of prey specialization observed in a predator population: (1) Experience handling a prey type can substantially improve foraging skills for that prey. (2) There is limited ability to retain complex learned skills for multiple prey types. (3) The learning curve for acquiring new foraging skills is accelerating, or J-shaped. (4) The metabolic costs of foraging are high relative to available energy reserves. (5) Offspring can learn foraging skills from their mothers (matrilineal social learning). (6) Food abundance is limited, such that average individual energy reserves are low Additionally, the following factors increase the likelihood of alternative specializations co-occurring in a predator population: (1) The predator exerts effective top-down control of prey abundance, resulting in frequency-dependent dynamics. (2) There is stochastic Variation in prey population dynamics, but this Variation is neither too extreme in magnitude nor too 'slow' with respect to the time required for an individual forager to learn new foraging skills. For a given predator population, we deduce that the degree of specialization will be highest for those prey types requiring complex capture or handling skills, while prey species that are both profitable and easy to capture and handle will be included in the diet of all individuals. Frequency-dependent benefits of selecting alternative prey types, combined with the ability of foragers to improve their foraging skills by learning, and transmit learned skills to offspring, can result in behaviourally mediated foraging specialization, and also lead to the co-existence of alternative specializations. The extent of such specialization is predicted to be a variable trait, increasing in locations or years when intra-specific competition is high relative to inter-specific competition. ?? 2009 M. Tim Tinker.

Teachers' Pedagogical Content Knowledge and Mathematics Achievement of Students in Peru

ERIC Educational Resources Information Center

Cueto, Santiago; León, Juan; Sorto, M. Alejandra; Miranda, Alejandra

2017-01-01

After improving enrolment rates significantly, many developing countries such as Peru are facing the challenge to increase learning levels among students. Over the past few years, many researchers have turned to teacher-related variables as a way to better understand classroom processes that may help increase learning levels among students. In…

Exploring the Impact of Structured Learning Assistance (SLA) on College Writing

ERIC Educational Resources Information Center

Giraldo-García, Regina J.; Magiste, Edward J.

2018-01-01

This study determined that the addition of Structured Learning Assistance (SLA) attendance increased passage rates (from 66.5% to 82%) of first year students in English 101 courses. The model predicts first year students' performance in college writing, controlling for variables such as American College Test scores, and gender. A…

Python and Roles of Variables in Introductory Programming: Experiences from Three Educational Institutions

ERIC Educational Resources Information Center

Nikula, Uolevi; Sajaniemi, Jorma; Tedre, Matti; Wray, Stuart

2007-01-01

Students often find that learning to program is hard. Introductory programming courses have high drop-out rates and students do not learn to program well. This paper presents experiences from three educational institutions where introductory programming courses were improved by adopting Python as the first programming language and roles of…

[Heart rate variability of subjects when the instruction reading and their interrelations with the effectiveness of the follow-visual-motor activities].

PubMed

Murtazina, E P

2015-01-01

Investigation of the processes of studying human instructions relevant follow-up in terms of systemic mechanisms of learning and memory processes, and moreover affects such a fundamental issue as psychophysiology focused attention, understanding the meaning of the information provided and the formation of social motivation in human activities. Analysis of heart rate variability in reading the instructions compared to the initial state of operational rest showed that this stage of the activity causes pronounced emotional stress, which is manifested in increased heart rate, decrease in variability and pronounced changes in the spectral characteristics of heart rate. Besides, it was revealed that heart rate variability in a state of operational rest before testing, and in the process of reading instructions positively correlated with the duration of the instruction reading and inversely correlated with effectiveness and the level of resistance of the subjects to the error after error when follow-up activities. Showing pronounced gender differences in the relationships between changes in the variability of heart rate when reading the instructions and the subsequent execution indicators of visual-motor test.

Prediction and early detection of delirium in the intensive care unit by using heart rate variability and machine learning.

PubMed

Oh, Jooyoung; Cho, Dongrae; Park, Jaesub; Na, Se Hee; Kim, Jongin; Heo, Jaeseok; Shin, Cheung Soo; Kim, Jae-Jin; Park, Jin Young; Lee, Boreom

2018-03-27

Delirium is an important syndrome found in patients in the intensive care unit (ICU), however, it is usually under-recognized during treatment. This study was performed to investigate whether delirious patients can be successfully distinguished from non-delirious patients by using heart rate variability (HRV) and machine learning. Electrocardiography data of 140 patients was acquired during daily ICU care, and HRV data were analyzed. Delirium, including its type, severity, and etiologies, was evaluated daily by trained psychiatrists. HRV data and various machine learning algorithms including linear support vector machine (SVM), SVM with radial basis function (RBF) kernels, linear extreme learning machine (ELM), ELM with RBF kernels, linear discriminant analysis, and quadratic discriminant analysis were utilized to distinguish delirium patients from non-delirium patients. HRV data of 4797 ECGs were included, and 39 patients had delirium at least once during their ICU stay. The maximum classification accuracy was acquired using SVM with RBF kernels. Our prediction method based on HRV with machine learning was comparable to previous delirium prediction models using massive amounts of clinical information. Our results show that autonomic alterations could be a significant feature of patients with delirium in the ICU, suggesting the potential for the automatic prediction and early detection of delirium based on HRV with machine learning.

Single neuropsychological test scores associated with rate of cognitive decline in early Alzheimer disease.

PubMed

Parikh, Mili; Hynan, Linda S; Weiner, Myron F; Lacritz, Laura; Ringe, Wendy; Cullum, C Munro

2014-01-01

Alzheimer disease (AD) characteristically begins with episodic memory impairment followed by other cognitive deficits; however, the course of illness varies, with substantial differences in the rate of cognitive decline. For research and clinical purposes it would be useful to distinguish between persons who will progress slowly from persons who will progress at an average or faster rate. Our objective was to use neurocognitive performance features and disease-specific and health information to determine a predictive model for the rate of cognitive decline in participants with mild AD. We reviewed the records of a series of 96 consecutive participants with mild AD from 1995 to 2011 who had been administered selected neurocognitive tests and clinical measures. Based on Clinical Dementia Rating (CDR) of functional and cognitive decline over 2 years, participants were classified as Faster (n = 45) or Slower (n = 51) Progressors. Stepwise logistic regression analyses using neurocognitive performance features, disease-specific, health, and demographic variables were performed. Neuropsychological scores that distinguished Faster from Slower Progressors included Trail Making Test - A, Digit Symbol, and California Verbal Learning Test (CVLT) Total Learned and Primacy Recall. No disease-specific, health, or demographic variable predicted rate of progression; however, history of heart disease showed a trend. Among the neuropsychological variables, Trail Making Test - A best distinguished Faster from Slower Progressors, with an overall accuracy of 68%. In an omnibus model including neuropsychological, disease-specific, health, and demographic variables, only Trail Making Test - A distinguished between groups. Several neuropsychological performance features were associated with the rate of cognitive decline in mild AD, with baseline Trail Making Test - A performance best separating those who declined at an average or faster rate from those who showed slower progression.

Students’ Covariational Reasoning in Solving Integrals’ Problems

NASA Astrophysics Data System (ADS)

Harini, N. V.; Fuad, Y.; Ekawati, R.

2018-01-01

Covariational reasoning plays an important role to indicate quantities vary in learning calculus. This study investigates students’ covariational reasoning during their studies concerning two covarying quantities in integral problem. Six undergraduate students were chosen to solve problems that involved interpreting and representing how quantities change in tandem. Interviews were conducted to reveal the students’ reasoning while solving covariational problems. The result emphasizes that undergraduate students were able to construct the relation of dependent variables that changes in tandem with the independent variable. However, students faced difficulty in forming images of continuously changing rates and could not accurately apply the concept of integrals. These findings suggest that learning calculus should be increased emphasis on coordinating images of two quantities changing in tandem about instantaneously rate of change and to promote conceptual knowledge in integral techniques.

Learning from adaptive neural dynamic surface control of strict-feedback systems.

PubMed

Wang, Min; Wang, Cong

2015-06-01

Learning plays an essential role in autonomous control systems. However, how to achieve learning in the nonstationary environment for nonlinear systems is a challenging problem. In this paper, we present learning method for a class of n th-order strict-feedback systems by adaptive dynamic surface control (DSC) technology, which achieves the human-like ability of learning by doing and doing with learned knowledge. To achieve the learning, this paper first proposes stable adaptive DSC with auxiliary first-order filters, which ensures the boundedness of all the signals in the closed-loop system and the convergence of tracking errors in a finite time. With the help of DSC, the derivative of the filter output variable is used as the neural network (NN) input instead of traditional intermediate variables. As a result, the proposed adaptive DSC method reduces greatly the dimension of NN inputs, especially for high-order systems. After the stable DSC design, we decompose the stable closed-loop system into a series of linear time-varying perturbed subsystems. Using a recursive design, the recurrent property of NN input variables is easily verified since the complexity is overcome using DSC. Subsequently, the partial persistent excitation condition of the radial basis function NN is satisfied. By combining a state transformation, accurate approximations of the closed-loop system dynamics are recursively achieved in a local region along recurrent orbits. Then, the learning control method using the learned knowledge is proposed to achieve the closed-loop stability and the improved control performance. Simulation studies are performed to demonstrate the proposed scheme can not only reuse the learned knowledge to achieve the better control performance with the faster tracking convergence rate and the smaller tracking error but also greatly alleviate the computational burden because of reducing the number and complexity of NN input variables.

Colour in Learning: Its Effect on the Retention Rate of Graduate Students

ERIC Educational Resources Information Center

Olurinola, Oluwakemi; Tayo, Omoniyi

2015-01-01

Cognitive psychologists have discovered different design principles to enhance memory performance. It has been said that retrieving process depends on many variables and one of them is colour. This paper provides an overview of research on colour and learning. It includes the effect of colour on attention, retention and memory performance, and…

Attribution as a Predictor of Procrastination in Online Graduate Students

ERIC Educational Resources Information Center

Rakes, Glenda C.; Dunn, Karee E.; Rakes, Thomas A.

2013-01-01

Online courses are growing at a tremendous rate, and although we have discovered a great deal about teaching and learning in the online environment, there is much left to learn. One variable that needs to be explored further is procrastination in online coursework. In this mixed methods study, quantitative methods were utilized to evaluate the…

Student Perceptions of Classroom Learning Environments: Development of the ClassMaps Survey

ERIC Educational Resources Information Center

Doll, Beth; Spies, Robert A.; LeClair, Courtney M.; Kurien, Sarah A.; Foley, Brett P.

2010-01-01

The purpose of this study was to describe the means, variability, internal consistency reliability, and structural validity evidence of the ClassMaps Survey, a measure of student perceptions of classroom learning environments. The ClassMaps Survey is a 55-item student rating scale of eight important classroom characteristics. The survey provides a…

Individual differences in implicit motor learning: task specificity in sensorimotor adaptation and sequence learning.

PubMed

Stark-Inbar, Alit; Raza, Meher; Taylor, Jordan A; Ivry, Richard B

2017-01-01

In standard taxonomies, motor skills are typically treated as representative of implicit or procedural memory. We examined two emblematic tasks of implicit motor learning, sensorimotor adaptation and sequence learning, asking whether individual differences in learning are correlated between these tasks, as well as how individual differences within each task are related to different performance variables. As a prerequisite, it was essential to establish the reliability of learning measures for each task. Participants were tested twice on a visuomotor adaptation task and on a sequence learning task, either the serial reaction time task or the alternating reaction time task. Learning was evident in all tasks at the group level and reliable at the individual level in visuomotor adaptation and the alternating reaction time task but not in the serial reaction time task. Performance variability was predictive of learning in both domains, yet the relationship was in the opposite direction for adaptation and sequence learning. For the former, faster learning was associated with lower variability, consistent with models of sensorimotor adaptation in which learning rates are sensitive to noise. For the latter, greater learning was associated with higher variability and slower reaction times, factors that may facilitate the spread of activation required to form predictive, sequential associations. Interestingly, learning measures of the different tasks were not correlated. Together, these results oppose a shared process for implicit learning in sensorimotor adaptation and sequence learning and provide insight into the factors that account for individual differences in learning within each task domain. We investigated individual differences in the ability to implicitly learn motor skills. As a prerequisite, we assessed whether individual differences were reliable across test sessions. We found that two commonly used tasks of implicit learning, visuomotor adaptation and the alternating serial reaction time task, exhibited good test-retest reliability in measures of learning and performance. However, the learning measures did not correlate between the two tasks, arguing against a shared process for implicit motor learning. Copyright © 2017 the American Physiological Society.

Resting-state qEEG predicts rate of second language learning in adults.

PubMed

Prat, Chantel S; Yamasaki, Brianna L; Kluender, Reina A; Stocco, Andrea

2016-01-01

Understanding the neurobiological basis of individual differences in second language acquisition (SLA) is important for research on bilingualism, learning, and neural plasticity. The current study used quantitative electroencephalography (qEEG) to predict SLA in college-aged individuals. Baseline, eyes-closed resting-state qEEG was used to predict language learning rate during eight weeks of French exposure using an immersive, virtual scenario software. Individual qEEG indices predicted up to 60% of the variability in SLA, whereas behavioral indices of fluid intelligence, executive functioning, and working-memory capacity were not correlated with learning rate. Specifically, power in beta and low-gamma frequency ranges over right temporoparietal regions were strongly positively correlated with SLA. These results highlight the utility of resting-state EEG for studying the neurobiological basis of SLA in a relatively construct-free, paradigm-independent manner. Published by Elsevier Inc.

Efficacy of Incorporating Experiencing Exercises into a Smoking Cessation Curriculum.

ERIC Educational Resources Information Center

Watt, Celia A.; Manaster, Guy

2003-01-01

Examines the impact of experiential exercises, combined with a traditional smoking cessation intervention, on quit rates and social learning theory variables known to impact smoking cessation. Measures of self-efficacy and locus of control did not significantly differ between the experimental and control conditions. Quit rates did not differ…

Dopamine Modulates Adaptive Prediction Error Coding in the Human Midbrain and Striatum.

PubMed

Diederen, Kelly M J; Ziauddeen, Hisham; Vestergaard, Martin D; Spencer, Tom; Schultz, Wolfram; Fletcher, Paul C

2017-02-15

Learning to optimally predict rewards requires agents to account for fluctuations in reward value. Recent work suggests that individuals can efficiently learn about variable rewards through adaptation of the learning rate, and coding of prediction errors relative to reward variability. Such adaptive coding has been linked to midbrain dopamine neurons in nonhuman primates, and evidence in support for a similar role of the dopaminergic system in humans is emerging from fMRI data. Here, we sought to investigate the effect of dopaminergic perturbations on adaptive prediction error coding in humans, using a between-subject, placebo-controlled pharmacological fMRI study with a dopaminergic agonist (bromocriptine) and antagonist (sulpiride). Participants performed a previously validated task in which they predicted the magnitude of upcoming rewards drawn from distributions with varying SDs. After each prediction, participants received a reward, yielding trial-by-trial prediction errors. Under placebo, we replicated previous observations of adaptive coding in the midbrain and ventral striatum. Treatment with sulpiride attenuated adaptive coding in both midbrain and ventral striatum, and was associated with a decrease in performance, whereas bromocriptine did not have a significant impact. Although we observed no differential effect of SD on performance between the groups, computational modeling suggested decreased behavioral adaptation in the sulpiride group. These results suggest that normal dopaminergic function is critical for adaptive prediction error coding, a key property of the brain thought to facilitate efficient learning in variable environments. Crucially, these results also offer potential insights for understanding the impact of disrupted dopamine function in mental illness. SIGNIFICANCE STATEMENT To choose optimally, we have to learn what to expect. Humans dampen learning when there is a great deal of variability in reward outcome, and two brain regions that are modulated by the brain chemical dopamine are sensitive to reward variability. Here, we aimed to directly relate dopamine to learning about variable rewards, and the neural encoding of associated teaching signals. We perturbed dopamine in healthy individuals using dopaminergic medication and asked them to predict variable rewards while we made brain scans. Dopamine perturbations impaired learning and the neural encoding of reward variability, thus establishing a direct link between dopamine and adaptation to reward variability. These results aid our understanding of clinical conditions associated with dopaminergic dysfunction, such as psychosis. Copyright © 2017 Diederen et al.

Reward-Based Learning as a Function of Severity of Substance Abuse Risk in Drug-Naïve Youth with ADHD.

PubMed

Parvaz, Muhammad A; Kim, Kristen; Froudist-Walsh, Sean; Newcorn, Jeffrey H; Ivanov, Iliyan

2018-06-20

Attention-deficit/hyperactivity disorder (ADHD) is associated with elevated risk for later development of substance use disorders (SUD), specifically because youth with ADHD, similar to individuals with SUD, exhibit deficits in learning abilities and reward processing. Another known risk factor for SUD is familial history of substance dependence. Youth with familial SUD history show reward processing deficits, higher prevalence of externalizing disorders, and higher impulsivity scores. Thus, the main objective of this proof-of-concept study is to investigate whether risk loading (ADHD and parental substance use) for developing SUD in drug-naïve youth impacts reward-related learning. Forty-one drug-naïve youth, stratified into three groups: Healthy Controls (HC, n = 13; neither ADHD nor parental SUD), Low Risk (LR, n = 13; ADHD only), and High Risk (HR, n = 15; ADHD and parental SUD), performed a novel Anticipation, Conflict, and Reward (ACR) task. In addition to conventional reaction time (RT) and accuracy analyses, we analyzed computational variables including learning rates and assessed the influence of learned predictions of reward probability and stimulus congruency on RT. The multivariate ANOVA on learning rate, congruence, and prediction revealed a significant main Group effect across these variables [F(3, 37) = 3.79, p = 0.018]. There were significant linear effects for learning rate (Contrast Estimate = 0.181, p = 0.038) and the influence of stimulus congruency on RTs (Contrast Estimate = 1.16, p = 0.017). Post hoc comparisons revealed that HR youth showed the most significant deficits in accuracy and learning rates, while stimulus congruency had a lower impact on RTs in this group. LR youth showed scores between those of the HC and HR youth. These preliminary results suggest that deficits in learning and in adjusting to task difficulty are a function of increasing risk loading for SUD in drug-naïve youth. These results also highlight the importance of developing and applying computational models to study intricate details in behavior that typical analytic methodology may not be sensitive to.

Evaluation of the Stress Resilience Training System

DTIC Science & Technology

2014-10-30

enhanced by combining cognitive learning methodologies grounded in learning theory and biofeedback techniques based on heart rate variability ( HRV ) with...to reduce arousal. Biofeedback has been shown to reduce subjective stress, lower depression scores, decrease anxiety in athletes, and reduce...user’s biology (e.g., HRV -controlled games) provide a unique and highly immersive gaming experience (Prensky, 2001). These findings have been adopted

Supervised machine learning algorithms to diagnose stress for vehicle drivers based on physiological sensor signals.

PubMed

Barua, Shaibal; Begum, Shahina; Ahmed, Mobyen Uddin

2015-01-01

Machine learning algorithms play an important role in computer science research. Recent advancement in sensor data collection in clinical sciences lead to a complex, heterogeneous data processing, and analysis for patient diagnosis and prognosis. Diagnosis and treatment of patients based on manual analysis of these sensor data are difficult and time consuming. Therefore, development of Knowledge-based systems to support clinicians in decision-making is important. However, it is necessary to perform experimental work to compare performances of different machine learning methods to help to select appropriate method for a specific characteristic of data sets. This paper compares classification performance of three popular machine learning methods i.e., case-based reasoning, neutral networks and support vector machine to diagnose stress of vehicle drivers using finger temperature and heart rate variability. The experimental results show that case-based reasoning outperforms other two methods in terms of classification accuracy. Case-based reasoning has achieved 80% and 86% accuracy to classify stress using finger temperature and heart rate variability. On contrary, both neural network and support vector machine have achieved less than 80% accuracy by using both physiological signals.

Individual differences in heart rate variability are associated with the avoidance of negative emotional events.

PubMed

Katahira, Kentaro; Fujimura, Tomomi; Matsuda, Yoshi-Taka; Okanoya, Kazuo; Okada, Masato

2014-12-01

Although the emotional outcome of a choice generally affects subsequent decisions, humans can inhibit the influence of emotion. Heart rate variability (HRV) has emerged as an objective measure of individual differences in the capacity for inhibitory control. In the present study, we investigated how individual differences in HRV at rest are associated with the emotional effects of the outcome of a choice on subsequent decision making using a decision-making task in which emotional pictures appeared as decision outcomes. We used a reinforcement learning model to characterize the observed behaviors according to several parameters, namely, the learning rate and the motivational value of positive and negative pictures. Consequently, we found that individuals with a lower resting HRV exhibited a greater negative motivational value in response to negative pictures, suggesting that these individuals tend to avoid negative pictures compared with individuals with a higher resting HRV. Copyright © 2014 Elsevier B.V. All rights reserved.

Motor Variability Arises from a Slow Random Walk in Neural State

PubMed Central

Chaisanguanthum, Kris S.; Shen, Helen H.

2014-01-01

Even well practiced movements cannot be repeated without variability. This variability is thought to reflect “noise” in movement preparation or execution. However, we show that, for both professional baseball pitchers and macaque monkeys making reaching movements, motor variability can be decomposed into two statistical components, a slowly drifting mean and fast trial-by-trial fluctuations about the mean. The preparatory activity of dorsal premotor cortex/primary motor cortex neurons in monkey exhibits similar statistics. Although the neural and behavioral drifts appear to be correlated, neural activity does not account for trial-by-trial fluctuations in movement, which must arise elsewhere, likely downstream. The statistics of this drift are well modeled by a double-exponential autocorrelation function, with time constants similar across the neural and behavioral drifts in two monkeys, as well as the drifts observed in baseball pitching. These time constants can be explained by an error-corrective learning processes and agree with learning rates measured directly in previous experiments. Together, these results suggest that the central contributions to movement variability are not simply trial-by-trial fluctuations but are rather the result of longer-timescale processes that may arise from motor learning. PMID:25186752

Counter-propagation network with variable degree variable step size LMS for single switch typing recognition.

PubMed

Yang, Cheng-Huei; Luo, Ching-Hsing; Yang, Cheng-Hong; Chuang, Li-Yeh

2004-01-01

Morse code is now being harnessed for use in rehabilitation applications of augmentative-alternative communication and assistive technology, including mobility, environmental control and adapted worksite access. In this paper, Morse code is selected as a communication adaptive device for disabled persons who suffer from muscle atrophy, cerebral palsy or other severe handicaps. A stable typing rate is strictly required for Morse code to be effective as a communication tool. This restriction is a major hindrance. Therefore, a switch adaptive automatic recognition method with a high recognition rate is needed. The proposed system combines counter-propagation networks with a variable degree variable step size LMS algorithm. It is divided into five stages: space recognition, tone recognition, learning process, adaptive processing, and character recognition. Statistical analyses demonstrated that the proposed method elicited a better recognition rate in comparison to alternative methods in the literature.

Voluntary Management of Residential Water Demand in Low and Middle-Low Income Households: Case Study of Soacha (colombia)

NASA Astrophysics Data System (ADS)

Acosta, R.; Rodriguez, J. P.

2016-12-01

Water resources availability is a global concern due to increasing demands, decreasing quality and uncertain spatio-temporal variability (United Nations, 2009). In urban contexts research on efficient water use is a priority to cope with the future vulnerability of water supplies as a result of the impacts of climate change (Bates et al, 2008). Following the proposed methodologies of He and Kua (2013) for implementing programs to promote sustainable energy consumption, we focused on the use of educational strategies to promote a voluntary rationalization of residential water demand. We collaborated with three schools in Soacha (Colombia) where students ranging from 12 to 15 years participated in the project as promoters of educational campaigns inside their families, covering 120 low and middle-low income households. Three intervention or treatment strategies (i.e. e-learning, in-person active learning activities and graphical learning tools) were carried out over a period of 5 months. We analyzed the effects of the treatments strategies in reducing water consumption rates and the dependence of this variable on socio-demographic, economic, environmental, and life quality factors by using personal interviews and self reported water saving technics. The results showed that educational campaigns have a positive effect on reducing consumption in the households. Graphical learning tools accounted for the highest reduction in water consumption. Moreover, the results of the study suggests that socio-economic factors such as type of house, social level, income, and life quality variables significantly affect the variability in water consumption, which is an important fact to consider in similar cases where communities face difficult socio-economic conditions, displacement or high rates of urban growth.

Faculty motivations to use active learning among pharmacy educators.

PubMed

Rockich-Winston, Nicole; Train, Brian C; Rudolph, Michael J; Gillette, Chris

2018-03-01

Faculty motivations to use active learning have been limited to surveys evaluating faculty perceptions within active learning studies. Our objective in this study was to evaluate the relationship between faculty intrinsic motivation, extrinsic motivation, and demographic variables and the extent of active learning use in the classroom. An online survey was administered to individual faculty members at 137 colleges and schools of pharmacy across the United States. The survey assessed intrinsic and extrinsic motivations, active learning strategies, classroom time dedicated to active learning, and faculty development resources. Bivariate associations and multivariable stepwise linear regression were used to analyze the results. In total, 979 faculty members completed the questionnaire (23.6% response rate). All motivation variables were significantly correlated with percent active learning use (p < 0.001). Intrinsic motivation demonstrated the highest correlation (r = 0.447) followed by current extrinsic motivations (r = 0.245) and ideal extrinsic motivations (r = 0.291). Variables associated with higher intrinsic motivation included the number of resources used (r = 0.233, p < 0.001) and the number of active learning methods used in the last year (r = 0.259, p < 0.001). Years of teaching experience was negatively associated with intrinsic motivation (r = -0.177, p < 0.001). Regression analyses confirmed the importance of intrinsic and extrinsic motivations in predicting active learning use. Our results suggest that faculty members who are intrinsically motivated to use active learning are more likely to dedicate additional class time to active learning. Furthermore, intrinsic motivation may be positively associated with encouraging faculty members to attend active learning workshops and supporting faculty to use various active learning strategies in the classroom. Copyright © 2017 Elsevier Inc. All rights reserved.

Effect of Heart Rate Variability Biofeedback on Sport Performance, a Systematic Review.

PubMed

Jiménez Morgan, Sergio; Molina Mora, José Arturo

2017-09-01

Aim is to determine if the training with heart rate variability biofeedback allows to improve performance in athletes of different disciplines. Methods such as database search on Web of Science, SpringerLink, EBSCO Academic Search Complete, SPORTDiscus, Pubmed/Medline, and PROQUEST Academic Research Library, as well as manual reference registration. The eligibility criteria were: (a) published scientific articles; (b) experimental studies, quasi-experimental, or case reports; (c) use of HRV BFB as main treatment; (d) sport performance as dependent variable; (e) studies published until October 2016; (f) studies published in English, Spanish, French or Portuguese. The guidelines of the PRISMA statement were followed. Out of the 451 records found, seven items were included. All studies had a small sample size (range from 1 to 30 participants). In 85.71% of the studies (n = 6) the athletes enhanced psychophysiological variables that allowed them to improve their sport performance thanks to training with heart rate variability biofeedback. Despite the limited amount of experimental studies in the field to date, the findings suggest that heart rate variability biofeedback is an effective, safe, and easy-to-learn and apply method for both athletes and coaches in order to improve sport performance.

Performance Discrepancies on the California Verbal Learning Test-Second Edition (CVLT-II) in the Standardization Sample

ERIC Educational Resources Information Center

Donders, Jacobus

2006-01-01

The standardization data for the California Verbal Learning Test-Second Edition (CVLT-II; D. C. Delis, J. H. Kramer, E. Kaplan, & B. A. Ober, 2000) were used to evaluate the base rate of 6 specific discrepancies between various key variables. The results indicated that CVLT-II performance discrepancies should equal or exceed 1 or 1.5 z score…

Predicting Pre-planting Risk of Stagonospora nodorum blotch in Winter Wheat Using Machine Learning Models.

PubMed

Mehra, Lucky K; Cowger, Christina; Gross, Kevin; Ojiambo, Peter S

2016-01-01

Pre-planting factors have been associated with the late-season severity of Stagonospora nodorum blotch (SNB), caused by the fungal pathogen Parastagonospora nodorum, in winter wheat (Triticum aestivum). The relative importance of these factors in the risk of SNB has not been determined and this knowledge can facilitate disease management decisions prior to planting of the wheat crop. In this study, we examined the performance of multiple regression (MR) and three machine learning algorithms namely artificial neural networks, categorical and regression trees, and random forests (RF), in predicting the pre-planting risk of SNB in wheat. Pre-planting factors tested as potential predictor variables were cultivar resistance, latitude, longitude, previous crop, seeding rate, seed treatment, tillage type, and wheat residue. Disease severity assessed at the end of the growing season was used as the response variable. The models were developed using 431 disease cases (unique combinations of predictors) collected from 2012 to 2014 and these cases were randomly divided into training, validation, and test datasets. Models were evaluated based on the regression of observed against predicted severity values of SNB, sensitivity-specificity ROC analysis, and the Kappa statistic. A strong relationship was observed between late-season severity of SNB and specific pre-planting factors in which latitude, longitude, wheat residue, and cultivar resistance were the most important predictors. The MR model explained 33% of variability in the data, while machine learning models explained 47 to 79% of the total variability. Similarly, the MR model correctly classified 74% of the disease cases, while machine learning models correctly classified 81 to 83% of these cases. Results show that the RF algorithm, which explained 79% of the variability within the data, was the most accurate in predicting the risk of SNB, with an accuracy rate of 93%. The RF algorithm could allow early assessment of the risk of SNB, facilitating sound disease management decisions prior to planting of wheat.

Motor Experts Care about Consistency and Are Reluctant to Change Motor Outcome.

PubMed

Kast, Volker; Leukel, Christian

2016-01-01

Thousands of hours of physical practice substantially change the way movements are performed. The mechanisms underlying altered behavior in highly-trained individuals are so far little understood. We studied experts (handballers) and untrained individuals (novices) in visuomotor adaptation of free throws, where subjects had to adapt their throwing direction to a visual displacement induced by prismatic glasses. Before visual displacement, experts expressed lower variability of motor errors than novices. Experts adapted and de-adapted slower, and also forgot the adaptation slower than novices. The variability during baseline was correlated with the learning rate during adaptation. Subjects adapted faster when variability was higher. Our results indicate that experts produced higher consistency of motor outcome. They were still susceptible to the sensory feedback informing about motor error, but made smaller adjustments than novices. The findings of our study relate to previous investigations emphasizing the importance of action exploration, expressed in terms of outcome variability, to facilitate learning.

Potential of new machine learning methods for understanding long-term interannual variability of carbon and energy fluxes and states from site to global scale

NASA Astrophysics Data System (ADS)

Reichstein, M.; Jung, M.; Bodesheim, P.; Mahecha, M. D.; Gans, F.; Rodner, E.; Camps-Valls, G.; Papale, D.; Tramontana, G.; Denzler, J.; Baldocchi, D. D.

2016-12-01

Machine learning tools have been very successful in describing and predicting instantaneous climatic influences on the spatial and seasonal variability of biosphere-atmosphere exchange, while interannual variability is harder to model (e.g. Jung et al. 2011, JGR Biogeosciences). Here we hypothesize that innterannual variability is harder to describe for two reasons. 1) The signal-to-noise ratio in both, predictors (e.g. remote sensing) and target variables (e.g. net ecosystem exchange) is relatively weak, 2) The employed machine learning methods do not sufficiently account for dynamic lag and carry-over effects. In this presentation we can largely confirm both hypotheses: 1) We show that based on FLUXNET data and an ensemble of machine learning methods we can arrive at estimates of global NEE that correlate well with the residual land sink overall and CO2 flux inversions over latitudinal bands. Furthermore these results highlight the importance of variations in water availability for variations in carbon fluxes locally, while globally, as a scale-emergent property, tropical temperatures correlate well with the atmospheric CO2 growth rate, because of spatial anticorrelation and compensation of water availability. 2) We evidence with synthetic and real data that machine learning methods with embed dynamic memory effects of the system such as recurrent neural networks (RNNs) are able to better capture lag and carry-over effect which are caused by dynamic carbon pools in vegetation and soils. For these methods, long-term replicate observations are an essential asset.

Selected Influences on Solo and Small-Ensemble Festival Ratings: Replication and Extension

ERIC Educational Resources Information Center

Bergee, Martin J.; McWhirter, Jamila L.

2005-01-01

Festival performance is no trivial endeavor. At one midwestern state festival alone, 10,938 events received a rating over a 3-year period (2001-2003). Such an extensive level of participation justifies sustained study. To learn more about variables that may underlie success at solo and small ensemble evaluative festivals, Bergee and Platt (2003)…

The contribution of a MOOC to community discussions around death and dying.

PubMed

Tieman, Jennifer; Miller-Lewis, Lauren; Rawlings, Deb; Parker, Deborah; Sanderson, Christine

2018-02-20

Advances in medicine have helped many to live longer lives and to be able to meet health challenges. However death rates are anticipated to increase given the ageing population and chronic disease progression. Being able to talk about death is seen to be important in normalising death as part of life and supporting preparedness for death. Massive Open Online Courses (MOOCs) provide opportunities for the community to engage in collaborative learning. A 5 week MOOC was developed covering four main topics (language and humour, representations of death, medicalisation of dying, and digital dying) aiming: To enable participants to openly and supportively discuss and learn about issues around living, death and dying, To explore the normally unheard opinions and views of Australians around death and dying, and To determine what effect online learning and discussions offered through the MOOC had on participants' feelings and attitudes towards death and dying. Data was captured on engagement rates in the various MOOC activities. Death Attitudes were measured by five items representing the MOOC's learning objectives and completed at enrolment and conclusion. MOOC Satisfaction was measured with six items at the end of the MOOC. Descriptive statistics were produced for each variable and Chi-Square Tests of Independence assessed the extent of the relationship between categorical variables. Socio-demographic variables were examined as predictors of the outcome variables of MOOC engagement, MOOC satisfaction, and death attitudes. Ethical approval was received from Flinders University Social and Behavioural Research Ethics Committee (Project No. 7247). One thousand one hundred fifty six people enrolled in the Dying2Learn MOOC with 895 participating in some way. Enrolees were primarily female (92.1%). Age ranged from 16 to 84 (mean = 49.5, SD = 12.3). MOOC satisfaction scores were high. Responses to the experience of participating in the MOOC were very positive, with mean scores ranging from 4.3 to 4.6 (aligning with agreement and strong agreement to statements on the value of participating). Death Attitudes were positive at commencement but increased significantly following participation. The Dying2Learn MOOC provided an environment that enabled open and supportive discussion around death and dying and influenced attitudinal change.

Adaptive social learning strategies in temporally and spatially varying environments : how temporal vs. spatial variation, number of cultural traits, and costs of learning influence the evolution of conformist-biased transmission, payoff-biased transmission, and individual learning.

PubMed

Nakahashi, Wataru; Wakano, Joe Yuichiro; Henrich, Joseph

2012-12-01

Long before the origins of agriculture human ancestors had expanded across the globe into an immense variety of environments, from Australian deserts to Siberian tundra. Survival in these environments did not principally depend on genetic adaptations, but instead on evolved learning strategies that permitted the assembly of locally adaptive behavioral repertoires. To develop hypotheses about these learning strategies, we have modeled the evolution of learning strategies to assess what conditions and constraints favor which kinds of strategies. To build on prior work, we focus on clarifying how spatial variability, temporal variability, and the number of cultural traits influence the evolution of four types of strategies: (1) individual learning, (2) unbiased social learning, (3) payoff-biased social learning, and (4) conformist transmission. Using a combination of analytic and simulation methods, we show that spatial-but not temporal-variation strongly favors the emergence of conformist transmission. This effect intensifies when migration rates are relatively high and individual learning is costly. We also show that increasing the number of cultural traits above two favors the evolution of conformist transmission, which suggests that the assumption of only two traits in many models has been conservative. We close by discussing how (1) spatial variability represents only one way of introducing the low-level, nonadaptive phenotypic trait variation that so favors conformist transmission, the other obvious way being learning errors, and (2) our findings apply to the evolution of conformist transmission in social interactions. Throughout we emphasize how our models generate empirical predictions suitable for laboratory testing.

Pedagogical monitoring as a tool to reduce dropout in distance learning in family health.

PubMed

de Castro E Lima Baesse, Deborah; Grisolia, Alexandra Monteiro; de Oliveira, Ana Emilia Figueiredo

2016-08-22

This paper presents the results of a study of the Monsys monitoring system, an educational support tool designed to prevent and control the dropout rate in a distance learning course in family health. Developed by UNA-SUS/UFMA, Monsys was created to enable data mining in the virtual learning environment known as Moodle. This is an exploratory study using documentary and bibliographic research and analysis of the Monsys database. Two classes (2010 and 2011) were selected as research subjects, one with Monsys intervention and the other without. The samples were matched (using a ration of 1:1) by gender, age, marital status, graduation year, previous graduation status, location and profession. Statistical analysis was performed using the chi-square test and a multivariate logistic regression model with a 5 % significance level. The findings show that the dropout rate in the class in which Monsys was not employed (2010) was 43.2 %. However, the dropout rate in the class of 2011, in which the tool was employed as a pedagogical team aid, was 30.6 %. After statistical adjustment, the Monsys monitoring system remained in correlation with the course completion variable (adjusted OR = 1.74, IC95% = 1.17-2.59; p = 0.005), suggesting that the use of the Monsys tool, isolated to the adjusted variables, can enhance the likelihood that students will complete the course. Using the chi-square test, a profile analysis of students revealed a higher completion rate among women (67.7 %) than men (52.2 %). Analysis of age demonstrated that students between 40 and 49 years dropped out the least (32.1 %) and, with regard to professional training, nurses have the lowest dropout rates (36.3 %). The use of Monsys significantly reduced the dropout, with results showing greater association between the variables denoting presence of the monitoring system and female gender.

Improving the Prediction of Mortality and the Need for Life-Saving Interventions in Trauma Patients Using Standard Vital Signs With Heart-Rate Variability and Complexity

DTIC Science & Technology

2015-06-01

Trauma 69:S10YS13, 2010. 2. Liu NT, Holcomb JB, Wade CE, Darrah MI, Salinas J: Utility of vital signs, heart-rate variability and complexity, and machine ... learning for identifying the need for life-saving interventions in trauma patients. Shock 42:108Y114, 2014. 3. Pickering TG, Shimbo D, Hass D...Ann Emerg Med 45:68Y76, 2005. 8. Liu NT, Holcomb JB, Wade CE, Batchinsky AI, Cancio LC, Darrah MI, Salinas J: Development and validation of a machine

Hydrologic Impacts of Oak Harvesting and Evaluation of the Modified Universal Soil Loss Equation

Treesearch

Charlette R. Epifanio; Michael J. Singer; Xiaohong Huang

1991-01-01

Two Sierra foothill watersheds were monitored to learn what effects selective oak removal would have on watershed hydrology and water quality. We also used the data to generate sediment rating curves and evaluate the modified universal soil loss equation (MUSLE). Annual sediment rating curves better accounted for the variability in precipitation events from year to...

Developing renal nurses' buttonhole cannulation skills using e-learning.

PubMed

Blackman, Ian R; Mannix, Trudi; Sinclair, Peter M

2014-03-01

It has previously been shown that nurses can learn clinical nursing skills by e-learning (online), and that many variables will influence how well nurses adopt learned clinical skills using distance education. This study aimed to identify and measure the strength of those factors which would simultaneously influence registered nurses' (RNs') beliefs about their own learning about buttonhole cannulation, using e-learning. An online Likert style survey consisting of a list of statements related to knowledge and skill domains considered crucial in the area of buttonhole cannulation was distributed to 101 RNs before and after completing an e-learning programme. Participants were required to identify their current level of self-confidence in relationship to each of the statements. Measures of RNs' self-rated abilities to assess and implement buttonhole cannulation after completing a related e-learning program were tested using a Partial Least Squares Analysis (PLS-PATH) programme. The study's results strongly identify that the nurses' ability to meet both clinical and educational outcomes of the renal e-learning module can be predicted by six variables, none of which are directly related to the participants' demographic or clinical backgrounds. These findings support the use of e-learning to teach clinical skills to RNs, and demonstrate the value of Partial Least Squares Analysis in determining influential learning factors. © 2014 European Dialysis and Transplant Nurses Association/European Renal Care Association.

Anhedonia and anxiety underlying depressive symptomatology have distinct effects on reward-based decision-making.

PubMed

Harlé, Katia M; Guo, Dalin; Zhang, Shunan; Paulus, Martin P; Yu, Angela J

2017-01-01

Depressive pathology, which includes both heightened negative affect (e.g., anxiety) and reduced positive affect (e.g., anhedonia), is known to be associated with sub-optimal decision-making, particularly in uncertain environments. Here, we use a computational approach to quantify and disambiguate how individual differences in these affective measures specifically relate to different aspects of learning and decision-making in reward-based choice behavior. Fifty-three individuals with a range of depressed mood completed a two-armed bandit task, in which they choose between two arms with fixed but unknown reward rates. The decision-making component, which chooses among options based on current expectations about reward rates, is modeled by two different decision policies: a learning-independent Win-stay/Lose-shift (WSLS) policy that ignores all previous experiences except the last trial, and Softmax, which prefers the arm with the higher expected reward. To model the learning component for the Softmax choice policy, we use a Bayesian inference model, which updates estimated reward rates based on the observed history of trial outcomes. Softmax with Bayesian learning better fits the behavior of 55% of the participants, while the others are better fit by a learning-independent WSLS strategy. Among Softmax "users", those with higher anhedonia are less likely to choose the option estimated to be most rewarding. Moreover, the Softmax parameter mediates the inverse relationship between anhedonia and overall monetary gains. On the other hand, among WSLS "users", higher state anxiety correlates with increasingly better ability of WSLS, relative to Softmax, to explain subjects' trial-by-trial choices. In summary, there is significant variability among individuals in their reward-based, exploratory decision-making, and this variability is at least partly mediated in a very specific manner by affective attributes, such as hedonic tone and state anxiety.

Reduced variability of visual left ventricular ejection fraction assessment with reference images: The Japanese Association of Young Echocardiography Fellows multicenter study.

PubMed

Kusunose, Kenya; Shibayama, Kentaro; Iwano, Hiroyuki; Izumo, Masaki; Kagiyama, Nobuyuki; Kurosawa, Koji; Mihara, Hirotsugu; Oe, Hiroki; Onishi, Tetsuari; Onishi, Toshinari; Ota, Mitsuhiko; Sasaki, Shunsuke; Shiina, Yumi; Tsuruta, Hikaru; Tanaka, Hidekazu

2018-07-01

Visual estimation of left ventricular ejection fraction (LVEF) is widely applied to confirm quantitative EF. However, visual assessment is subjective, and variability may be influenced by observer experience. We hypothesized that a learning session might reduce the misclassification rate. Protocol 1: Visual LVEFs for 30 cases were measured by 79 readers from 13 cardiovascular tertiary care centers. Readers were divided into 3 groups by their experience: limited (1-5 years, n=28), intermediate (6-11 years, n=26), and highly experienced (12-years, n=25). Protocol 2: All readers were randomized to assess the effect of a learning session with reference images only or feedback plus reference images. After the session, 20 new cases were shown to all readers following the same methodology. To assess the concordance and accuracy pre- and post-intervention, each visual LVEF measurement was compared to overall average values as a reference. Experience affected the concordance in visual EF values among the readers. Groups with intermediate and high experience showed significantly better mean difference (MD), standard deviation (SD), and coefficient of variation (CV) than those with limited experience at baseline. The learning session with reference image reduced the MD, SD, and CV in readers with limited experience. The learning session with reference images plus feedback also reduced proportional bias. Importantly, the misclassification rate for mid-range EF cases was reduced regardless of experience. This large multicenter study suggested that a simple learning session with reference images can successfully reduce the misclassification rate for LVEF assessment. Copyright © 2018 Japanese College of Cardiology. Published by Elsevier Ltd. All rights reserved.

Normal rates of cognitive change in successful aging: the freedom house study.

PubMed

Royall, Donald R; Palmer, Raymond; Chiodo, Laura K; Polk, Marsha J

2005-11-01

We determined the rates of cognitive change associated with twenty individual measures. Participants included 547 noninstitutionalized septuagenarians and octogenarian residents of a comprehensive care retirement community who were studied over three years. Latent growth curves (LGC) of multiple cognitive measures were compared to a LGC model of the rates of change in Activities of Daily Living (ADL) and Instrumental Activities of Daily Living (IADL). All curves were standardized relative to each variable's baseline distribution. Baseline scores were within their expected normal age-specific ranges. Most measures showed significant rates of change over time. There was also significant variability about those rates, suggesting clinical heterogeneity. Many deteriorated over time, as did ADLs and IADLs. However, performance on some measures improved, consistent with learning effects. The rates of change in two measures, the Executive Interview and the Trail Making Test, were closely related to decline in IADLs. These results suggest that age-related cognitive decline is a dynamic longitudinal process affecting multiple cognitive domains. Heterogeneity in the rates of cognitive change may reflect the summed effects of age and comorbid conditions affecting cognition. Some measures may be ill-suited for measuring age-related changes in cognition, either because they are insensitive to change, or hindered by learning effects. Nonverbal measures appear to be particularly well suited for the prediction of age-related functional decline. These observations are relevant to the definition and diagnosis of "dementing" conditions.

Formation of Community-Based Hypertension Practice Networks: Success, Obstacles, and Lessons Learned

PubMed Central

Dart, Richard A.; Egan, Brent M.

2014-01-01

Community-based practice networks for research and improving the quality of care are growing in size and number but have variable success rates. In this paper we review recent efforts to initiate a community-based hypertension network modeled after the successful Outpatient Quality Improvement Network (O’QUIN) project, located at the Medical University of South Carolina. We highlight key lessons learned and new directions to be explored. PMID:24666425

Sex is not everything: the role of gender in early performance of a fundamental laparoscopic skill.

PubMed

Kolozsvari, Nicoleta O; Andalib, Amin; Kaneva, Pepa; Cao, Jiguo; Vassiliou, Melina C; Fried, Gerald M; Feldman, Liane S

2011-04-01

Existing literature on the acquisition of surgical skills suggests that women generally perform worse than men. This literature is limited by looking at an arbitrary number of trials and not adjusting for potential confounders. The objective of this study was to evaluate the impact of gender on the learning curve for a fundamental laparoscopic task. Thirty-two medical students performed the FLS peg transfer task and their scores were plotted to generate a learning curve. Nonlinear regression was used to estimate learning plateau and learning rate. Variables that may affect performance were assessed using a questionnaire. Innate visual-spatial abilities were evaluated using tests for spatial orientation, spatial scanning, and perceptual abilities. Score on first peg transfer attempt, learning plateau, and learning rate were compared for men and women using Student's t test. Innate abilities were correlated to simulator performance using Pearson's coefficient. Multivariate linear regression was used to investigate the effect of gender on early laparoscopic performance after adjusting for factors found significant on univariate analysis. Statistical significance was defined as P < 0.05. Nineteen men and 13 women participated in the study; 30 were right-handed, 12 reported high interest in surgery, and 26 had video game experience. There were no differences between men and women in initial peg transfer score, learning plateau, or learning rate. Initial peg transfer score and learning rate were higher in subjects who reported having a high interest in surgery (P = 0.02, P = 0.03). Initial score also correlated with perceptual ability score (P = 0.03). In multivariate analysis, only surgical interest remained a significant predictor of score on first peg transfer (P = 0.03) and learning rate (P = 0.02), while gender had no significant relationship to early performance. Gender did not affect the learning curve for a fundamental laparoscopic task, while interest in surgery and perceptual abilities did influence early performance.

Instructional Variables that Make a Difference: Attention to Task and Beyond.

ERIC Educational Resources Information Center

Rieth, Herbert J.; And Others

1981-01-01

Three procedures for increasing the disabled students' academic learning time(ALT)by maximizing allocation time, engagement time, and success rate are discussed, and a direct instructional model for enhancing ALT in both regular and special education environments is described. (CL)

Investigating speech motor practice and learning in people who stutter.

PubMed

Namasivayam, Aravind Kumar; van Lieshout, Pascal

2008-03-01

In this exploratory study, we investigated whether or not people who stutter (PWS) show motor practice and learning changes similar to those of people who do not stutter (PNS). To this end, five PWS and five PNS repeated a set of non-words at two different rates (normal and fast) across three test sessions (T1, T2 on the same day and T3 on a separate day, at least 1 week apart). The results indicated that PWS and PNS may resemble each other on a number of performance variables (such as movement amplitude and duration), but they differ in terms of practice and learning on variables that relate to movement stability and strength of coordination patterns. These findings are interpreted in support of recent claims about speech motor skill limitations in PWS. The reader will be able to: (1) define oral articulatory changes associated with motor practice and learning and their measurement; (2) summarize findings from previous studies examining motor practice and learning in PWS; and (3) discuss hypotheses that could account for the present findings that suggest PWS and PNS differ in their speech motor learning abilities.

[English as a foreign language (EFL) homework diaries: evaluating gains and constraints for self-regulated learning and achievement].

PubMed

Rosário, Pedro; Mourão, Rosa; Trigo, Luisa; Suárez, Natalia; Fernández, Estrella; Tuero-Herrero, Ellián

2011-11-01

Although homework completion is said to be rather important to achievement, nowadays there is a growing concern of educators about the increasing number of students who do not engage properly on doing the homework tasks and the subsequent impact on school failure rates. Focusing on English as a Foreign Language (EFL) and using a sample of 591 Portuguese fifth and sixth graders, the present study analyses the role played by a number of homework variables on students' achievement (proximal and distal), and their mediating role on the use of self-regulated learning strategies and perceived self-efficacy in the domain. Data confirm the indirect effect of homework on school achievement, by means of the referred cognitive and motivational variables (use of self-regulated learning strategies and self-efficacy). These findings are further discussed in order to highlight the significant role homework completion can play on fighting school failure.

Adaptive enhancement of learning protocol in hippocampal cultured networks grown on multielectrode arrays

PubMed Central

Pimashkin, Alexey; Gladkov, Arseniy; Mukhina, Irina; Kazantsev, Victor

2013-01-01

Learning in neuronal networks can be investigated using dissociated cultures on multielectrode arrays supplied with appropriate closed-loop stimulation. It was shown in previous studies that weakly respondent neurons on the electrodes can be trained to increase their evoked spiking rate within a predefined time window after the stimulus. Such neurons can be associated with weak synaptic connections in nearby culture network. The stimulation leads to the increase in the connectivity and in the response. However, it was not possible to perform the learning protocol for the neurons on electrodes with relatively strong synaptic inputs and responding at higher rates. We proposed an adaptive closed-loop stimulation protocol capable to achieve learning even for the highly respondent electrodes. It means that the culture network can reorganize appropriately its synaptic connectivity to generate a desired response. We introduced an adaptive reinforcement condition accounting for the response variability in control stimulation. It significantly enhanced the learning protocol to a large number of responding electrodes independently on its base response level. We also found that learning effect preserved after 4–6 h after training. PMID:23745105

Effects of Comparison and Game-Challenge on Sixth Graders' Algebra Variable Learning Achievement, Learning Attitude, and Meta-Cognitive Awareness

ERIC Educational Resources Information Center

Sun Lin, Hong-Zheng; Chiou, Guey-Fa

2017-01-01

This study examined the effects of comparison and game-challenge strategies on sixth graders' learning achievement of algebra variable, learning attitude towards algebra variable learning, and meta-cognitive awareness of algebra variable learning. A 2 × 2 factorial design was used, and 86 students were invited to participate in the experimental…

Formation of community-based hypertension practice networks: success, obstacles, and lessons learned.

PubMed

Dart, Richard A; Egan, Brent M

2014-06-01

Community-based practice networks for research and improving the quality of care are growing in size and number but have variable success rates. In this paper, the authors review recent efforts to initiate a community-based hypertension network modeled after the successful Outpatient Quality Improvement Network (O'QUIN) project, located at the Medical University of South Carolina. Key lessons learned and new directions to be explored are highlighted. ©2014 Wiley Periodicals, Inc.

Evolution and plasticity: Divergence of song discrimination is faster in birds with innate song than in song learners in Neotropical passerine birds.

PubMed

Freeman, Benjamin G; Montgomery, Graham A; Schluter, Dolph

2017-09-01

Plasticity is often thought to accelerate trait evolution and speciation. For example, plasticity in birdsong may partially explain why clades of song learners are more diverse than related clades with innate song. This "song learning" hypothesis predicts that (1) differences in song traits evolve faster in song learners, and (2) behavioral discrimination against allopatric song (a proxy for premating reproductive isolation) evolves faster in song learners. We tested these predictions by analyzing acoustic traits and conducting playback experiments in allopatric Central American sister pairs of song learning oscines (N = 42) and nonlearning suboscines (N = 27). We found that nonlearners evolved mean acoustic differences slightly faster than did leaners, and that the mean evolutionary rate of song discrimination was 4.3 times faster in nonlearners than in learners. These unexpected results may be a consequence of significantly greater variability in song traits in song learners (by 54-79%) that requires song-learning oscines to evolve greater absolute differences in song before achieving the same level of behavioral song discrimination as nonlearning suboscines. This points to "a downside of learning" for the evolution of species discrimination, and represents an important example of plasticity reducing the rate of evolution and diversification by increasing variability. © 2017 The Author(s). Evolution © 2017 The Society for the Study of Evolution.

Statistical interpretation of machine learning-based feature importance scores for biomarker discovery.

PubMed

Huynh-Thu, Vân Anh; Saeys, Yvan; Wehenkel, Louis; Geurts, Pierre

2012-07-01

Univariate statistical tests are widely used for biomarker discovery in bioinformatics. These procedures are simple, fast and their output is easily interpretable by biologists but they can only identify variables that provide a significant amount of information in isolation from the other variables. As biological processes are expected to involve complex interactions between variables, univariate methods thus potentially miss some informative biomarkers. Variable relevance scores provided by machine learning techniques, however, are potentially able to highlight multivariate interacting effects, but unlike the p-values returned by univariate tests, these relevance scores are usually not statistically interpretable. This lack of interpretability hampers the determination of a relevance threshold for extracting a feature subset from the rankings and also prevents the wide adoption of these methods by practicians. We evaluated several, existing and novel, procedures that extract relevant features from rankings derived from machine learning approaches. These procedures replace the relevance scores with measures that can be interpreted in a statistical way, such as p-values, false discovery rates, or family wise error rates, for which it is easier to determine a significance level. Experiments were performed on several artificial problems as well as on real microarray datasets. Although the methods differ in terms of computing times and the tradeoff, they achieve in terms of false positives and false negatives, some of them greatly help in the extraction of truly relevant biomarkers and should thus be of great practical interest for biologists and physicians. As a side conclusion, our experiments also clearly highlight that using model performance as a criterion for feature selection is often counter-productive. Python source codes of all tested methods, as well as the MATLAB scripts used for data simulation, can be found in the Supplementary Material.

Classification of ROTSE Variable Stars using Machine Learning

NASA Astrophysics Data System (ADS)

Wozniak, P. R.; Akerlof, C.; Amrose, S.; Brumby, S.; Casperson, D.; Gisler, G.; Kehoe, R.; Lee, B.; Marshall, S.; McGowan, K. E.; McKay, T.; Perkins, S.; Priedhorsky, W.; Rykoff, E.; Smith, D. A.; Theiler, J.; Vestrand, W. T.; Wren, J.; ROTSE Collaboration

2001-12-01

We evaluate several Machine Learning algorithms as potential tools for automated classification of variable stars. Using the ROTSE sample of ~1800 variables from a pilot study of 5% of the whole sky, we compare the effectiveness of a supervised technique (Support Vector Machines, SVM) versus unsupervised methods (K-means and Autoclass). There are 8 types of variables in the sample: RR Lyr AB, RR Lyr C, Delta Scuti, Cepheids, detached eclipsing binaries, contact binaries, Miras and LPVs. Preliminary results suggest a very high ( ~95%) efficiency of SVM in isolating a few best defined classes against the rest of the sample, and good accuracy ( ~70-75%) for all classes considered simultaneously. This includes some degeneracies, irreducible with the information at hand. Supervised methods naturally outperform unsupervised methods, in terms of final error rate, but unsupervised methods offer many advantages for large sets of unlabeled data. Therefore, both types of methods should be considered as promising tools for mining vast variability surveys. We project that there are more than 30,000 periodic variables in the ROTSE-I data base covering the entire local sky between V=10 and 15.5 mag. This sample size is already stretching the time capabilities of human analysts.

High variability impairs motor learning regardless of whether it affects task performance.

PubMed

Cardis, Marco; Casadio, Maura; Ranganathan, Rajiv

2018-01-01

Motor variability plays an important role in motor learning, although the exact mechanisms of how variability affects learning are not well understood. Recent evidence suggests that motor variability may have different effects on learning in redundant tasks, depending on whether it is present in the task space (where it affects task performance) or in the null space (where it has no effect on task performance). We examined the effect of directly introducing null and task space variability using a manipulandum during the learning of a motor task. Participants learned a bimanual shuffleboard task for 2 days, where their goal was to slide a virtual puck as close as possible toward a target. Critically, the distance traveled by the puck was determined by the sum of the left- and right-hand velocities, which meant that there was redundancy in the task. Participants were divided into five groups, based on both the dimension in which the variability was introduced and the amount of variability that was introduced during training. Results showed that although all groups were able to reduce error with practice, learning was affected more by the amount of variability introduced rather than the dimension in which variability was introduced. Specifically, groups with higher movement variability during practice showed larger errors at the end of practice compared with groups that had low variability during learning. These results suggest that although introducing variability can increase exploration of new solutions, this may adversely affect the ability to retain the learned solution. NEW & NOTEWORTHY We examined the role of introducing variability during motor learning in a redundant task. The presence of redundancy allows variability to be introduced in different dimensions: the task space (where it affects task performance) or the null space (where it does not affect task performance). We found that introducing variability affected learning adversely, but the amount of variability was more critical than the dimension in which variability was introduced.

A novel application of deep learning for single-lead ECG classification.

PubMed

Mathews, Sherin M; Kambhamettu, Chandra; Barner, Kenneth E

2018-06-04

Detecting and classifying cardiac arrhythmias is critical to the diagnosis of patients with cardiac abnormalities. In this paper, a novel approach based on deep learning methodology is proposed for the classification of single-lead electrocardiogram (ECG) signals. We demonstrate the application of the Restricted Boltzmann Machine (RBM) and deep belief networks (DBN) for ECG classification following detection of ventricular and supraventricular heartbeats using single-lead ECG. The effectiveness of this proposed algorithm is illustrated using real ECG signals from the widely-used MIT-BIH database. Simulation results demonstrate that with a suitable choice of parameters, RBM and DBN can achieve high average recognition accuracies of ventricular ectopic beats (93.63%) and of supraventricular ectopic beats (95.57%) at a low sampling rate of 114 Hz. Experimental results indicate that classifiers built into this deep learning-based framework achieved state-of-the art performance models at lower sampling rates and simple features when compared to traditional methods. Further, employing features extracted at a sampling rate of 114 Hz when combined with deep learning provided enough discriminatory power for the classification task. This performance is comparable to that of traditional methods and uses a much lower sampling rate and simpler features. Thus, our proposed deep neural network algorithm demonstrates that deep learning-based methods offer accurate ECG classification and could potentially be extended to other physiological signal classifications, such as those in arterial blood pressure (ABP), nerve conduction (EMG), and heart rate variability (HRV) studies. Copyright © 2018. Published by Elsevier Ltd.

Retrospective North American CFL Experience Curve Analysis and Correlation to Deployment Programs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Smith, Sarah J.; Wei, Max; Sohn, Michael D.

Retrospective experience curves are a useful tool for understanding historic technology development, and can contribute to investment program analysis and future cost estimation efforts. This work documents our development of an analysis approach for deriving retrospective experience curves with a variable learning rate, and its application to develop an experience curve for compact fluorescent lamps for the global and North American markets over the years 1990-2007. Uncertainties and assumptions involved in interpreting data for our experience curve development are discussed, including the processing and transformation of empirical data, the selection of system boundaries, and the identification of historical changes inmore » the learning rate over the course of 15 years. In the results that follow, we find that that the learning rate has changed at least once from 1990-2007. We also explore if, and to what degree, public deployment programs may have contributed to an increased technology learning rate in North America. We observe correlations between the changes in the learning rate and the initiation of new policies, abrupt technological advances, including improvements to ballast technology, and economic and political events such as trade tariffs and electricity prices. Finally, we discuss how the findings of this work (1) support the use of segmented experience curves for retrospective and prospective analysis and (2) may imply that investments in technological research and development have contributed to a change in market adoption and penetration.« less

The Educational Climate Inventory: Measuring Students' Perceptions of the Preclerkship and Clerkship Settings.

PubMed

Krupat, Edward; Borges, Nicole J; Brower, Richard D; Haidet, Paul M; Schroth, W Scott; Fleenor, Thomas J; Uijtdehaage, Sebastian

2017-12-01

To develop an instrument to assess educational climate, a critical aspect of the medical school learning environment that previous tools have not explored in depth. Fifty items were written, capturing aspects of Dweck's performance-learning distinction, to distinguish students' perceptions of the educational climate as learning/mastery oriented (where the goal is growth and development) versus performance oriented (where the goal is appearance of competence). These items were included in a 2014 survey of first-, second-, and third-year students at six diverse medical schools. Students rated their preclerkship or clerkship experiences and provided demographic and other data. The final Educational Climate Inventory (ECI) was determined via exploratory and confirmatory factor analysis. Relationships between scale scores and other variables were calculated. Responses were received from 1,441/2,590 students (56%). The 20-item ECI resulted, with three factors: centrality of learning and mutual respect; competitiveness and stress; and passive learning and memorization. Clerkship students' ratings of their learning climate were more performance oriented than preclerkship students' ratings (P < .001). Among preclerkship students, ECI scores were more performance oriented in schools with grading versus pass-fail systems (P < .04). Students who viewed their climate as more performance oriented were less satisfied with their medical school (P < .001) and choice of medicine as a career (P < .001). The ECI allows educators to assess students' perceptions of the learning climate. It has potential as an evaluation instrument to determine the efficacy of attempts to move health professions education toward learning and mastery.

Near-term fetal response to maternal spoken voice

PubMed Central

Voegtline, Kristin M.; Costigan, Kathleen A.; Pater, Heather A.; DiPietro, Janet A.

2013-01-01

Knowledge about prenatal learning has been largely predicated on the observation that newborns appear to recognize the maternal voice. Few studies have examined the process underlying this phenomenon; that is, whether and how the fetus responds to maternal voice in situ. Fetal heart rate and motor activity were recorded at 36 weeks gestation (n = 69) while pregnant women read aloud from a neutral passage. Compared to a baseline period, fetuses responded with a decrease in motor activity in the 10-seconds following onset of maternal speech and a trend level decelerative heart rate response, consistent with an orienting response. Subsequent analyses revealed that the fetal response was modified by both maternal and fetal factors. Fetuses of women who were previously awake and talking (n = 40) showed an orienting response to onset of maternal reading aloud, while fetuses of mothers who had previously been resting and silent (n = 29) responded with elevated heart rate and increased movement. The magnitude of the fetal response was further dependent on baseline fetal heart rate variability such that largest response was demonstrated by fetuses with low variability of mothers who were previously resting and silent. Results indicate that fetal responsivity is affected by both maternal and fetal state and have implications for understanding fetal learning of the maternal voice under naturalistic conditions. PMID:23748167

Changes in verbal learning and memory in schizophrenia and non-psychotic controls in midlife: A nine-year follow-up in the Northern Finland Birth Cohort study 1966.

PubMed

Rannikko, Irina; Haapea, Marianne; Miettunen, Jouko; Veijola, Juha; Murray, Graham K; Barnett, Jennifer H; Husa, Anja P; Jones, Peter B; Isohanni, Matti; Jääskeläinen, Erika

2015-08-30

Findings on longitudinal change of cognitive performance in schizophrenia are extremely variable in the case of verbal learning and memory, and it is still unclear which dimensions of verbal learning and memory exhibit possible deterioration over the long-term. Our aim was to compare the change in verbal learning and memory in individuals with schizophrenia 10-20 years after the illness onset and healthy controls during a nine-year follow-up in a general population sample. Our sample included 41 schizophrenia spectrum subjects and 73 controls from the Northern Finland Birth Cohort study 1966. The California Verbal Learning Test (CVLT) was used to estimate the degree of change in verbal learning and memory during a nine-year follow-up from age 34-years to 43- years. Both cases and controls deteriorated. There was statistically significant decline in two out of 20 CVLT items among cases and in 13 out of 20 CVLT items among controls. With the exception of two variables, the decline in verbal learning and memory over nine years was not significantly larger in cases. We conclude that during midlife verbal learning and memory in schizophrenia mostly declines in a normative fashion with aging at the same rate as the general population. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Predicting Survival From Large Echocardiography and Electronic Health Record Datasets: Optimization With Machine Learning.

PubMed

Samad, Manar D; Ulloa, Alvaro; Wehner, Gregory J; Jing, Linyuan; Hartzel, Dustin; Good, Christopher W; Williams, Brent A; Haggerty, Christopher M; Fornwalt, Brandon K

2018-06-09

The goal of this study was to use machine learning to more accurately predict survival after echocardiography. Predicting patient outcomes (e.g., survival) following echocardiography is primarily based on ejection fraction (EF) and comorbidities. However, there may be significant predictive information within additional echocardiography-derived measurements combined with clinical electronic health record data. Mortality was studied in 171,510 unselected patients who underwent 331,317 echocardiograms in a large regional health system. We investigated the predictive performance of nonlinear machine learning models compared with that of linear logistic regression models using 3 different inputs: 1) clinical variables, including 90 cardiovascular-relevant International Classification of Diseases, Tenth Revision, codes, and age, sex, height, weight, heart rate, blood pressures, low-density lipoprotein, high-density lipoprotein, and smoking; 2) clinical variables plus physician-reported EF; and 3) clinical variables and EF, plus 57 additional echocardiographic measurements. Missing data were imputed with a multivariate imputation by using a chained equations algorithm (MICE). We compared models versus each other and baseline clinical scoring systems by using a mean area under the curve (AUC) over 10 cross-validation folds and across 10 survival durations (6 to 60 months). Machine learning models achieved significantly higher prediction accuracy (all AUC >0.82) over common clinical risk scores (AUC = 0.61 to 0.79), with the nonlinear random forest models outperforming logistic regression (p < 0.01). The random forest model including all echocardiographic measurements yielded the highest prediction accuracy (p < 0.01 across all models and survival durations). Only 10 variables were needed to achieve 96% of the maximum prediction accuracy, with 6 of these variables being derived from echocardiography. Tricuspid regurgitation velocity was more predictive of survival than LVEF. In a subset of studies with complete data for the top 10 variables, multivariate imputation by chained equations yielded slightly reduced predictive accuracies (difference in AUC of 0.003) compared with the original data. Machine learning can fully utilize large combinations of disparate input variables to predict survival after echocardiography with superior accuracy. Copyright © 2018 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Association of suicide rates, gun ownership, conservatism and individual suicide risk.

PubMed

Kposowa, Augustine J

2013-09-01

The purpose of the study was to examine the association of suicide rates, firearm ownership, political conservatism, religious integration at the state level, and individual suicide risk. Social structural and social learning and social integration theories were theoretical frameworks employed. It was hypothesized that higher suicide rates, higher state firearm availability, and state conservatism elevate individual suicide risk. Data were pooled from the Multiple Cause of Death Files. Multilevel logistic regression models were fitted to all deaths occurring in 2000 through 2004 by suicide. The state suicide rate significantly elevated individual suicide risk (AOR = 1.042, CI = 1.037, 1.046). Firearm availability at the state level was associated with significantly higher odds of individual suicide (AOR = 1.004, CI = 1.003, 1.006). State political conservatism elevated the odds of individual suicides (AOR = 1.005, CI = 1.003, 1.007), while church membership at the state level reduced individual odds of suicide (AOR = 0.995, CI = 0.993, 0.996). The results held even after controlling for socioeconomic and demographic variables at the individual level. It was concluded that the observed association between individual suicide odds and national suicide rates, and firearm ownership cannot be discounted. Future research ought to focus on integrating individual level data and contextual variables when testing for the impact of firearm ownership. Support was found for social learning and social integration theories.

Industrial Landscapes: Perception and Classification as Learning Activities

ERIC Educational Resources Information Center

Peters, Gary; Larkin, Robert P.

1977-01-01

Suggests a high school or college level program of subjective perception and evaluation of industrial landscapes. Slides of local or national industrial sites can be rated and classified as pleasing or unpleasing in terms of variables such as architectural style of building, smokestacks, age, and visible pollution. (AV)

Attitudes of medical students toward communication skills learning in Western Saudi Arabia.

PubMed

Alotaibi, Fawaz S; Alsaeedi, Abdullah

2016-07-01

To explore medical students' attitudes towards communication skills learning in Western Saudi Arabia and to examine impact of socio-demographic variables on the attitudes towards learning these skills.   In this cross-sectional study, sample of medical students were recruited from Taif University, Taif, Kingdom of Saudi Arabia during the second semester (January-May 2014). Participants were all year 2 (197 students) and year 5 (151 students). The study utilize the Communication Skills Attitude Scale (CSAS) to measure students' attitudes toward communication skills learning. The response rate was 93.9%.  The study showed that Taif medical students hold highly positive attitudes towards learning communication skills. Positive attitude score (PAS) was significantly higher in level 5 students, older age group.   Significant positive attitude toward learning communication skills clearly observed in target group. Students with more positive attitudes towards communication skills learning tended to be higher level and older age.

Active Learning to Overcome Sample Selection Bias: Application to Photometric Variable Star Classification

NASA Astrophysics Data System (ADS)

Richards, Joseph W.; Starr, Dan L.; Brink, Henrik; Miller, Adam A.; Bloom, Joshua S.; Butler, Nathaniel R.; James, J. Berian; Long, James P.; Rice, John

2012-01-01

Despite the great promise of machine-learning algorithms to classify and predict astrophysical parameters for the vast numbers of astrophysical sources and transients observed in large-scale surveys, the peculiarities of the training data often manifest as strongly biased predictions on the data of interest. Typically, training sets are derived from historical surveys of brighter, more nearby objects than those from more extensive, deeper surveys (testing data). This sample selection bias can cause catastrophic errors in predictions on the testing data because (1) standard assumptions for machine-learned model selection procedures break down and (2) dense regions of testing space might be completely devoid of training data. We explore possible remedies to sample selection bias, including importance weighting, co-training, and active learning (AL). We argue that AL—where the data whose inclusion in the training set would most improve predictions on the testing set are queried for manual follow-up—is an effective approach and is appropriate for many astronomical applications. For a variable star classification problem on a well-studied set of stars from Hipparcos and Optical Gravitational Lensing Experiment, AL is the optimal method in terms of error rate on the testing data, beating the off-the-shelf classifier by 3.4% and the other proposed methods by at least 3.0%. To aid with manual labeling of variable stars, we developed a Web interface which allows for easy light curve visualization and querying of external databases. Finally, we apply AL to classify variable stars in the All Sky Automated Survey, finding dramatic improvement in our agreement with the ASAS Catalog of Variable Stars, from 65.5% to 79.5%, and a significant increase in the classifier's average confidence for the testing set, from 14.6% to 42.9%, after a few AL iterations.

Context effects on second-language learning of tonal contrasts.

PubMed

Chang, Charles B; Bowles, Anita R

2015-12-01

Studies of lexical tone  learning generally focus on monosyllabic contexts, while reports of phonetic learning benefits associated with input variability are based largely on experienced learners. This study trained inexperienced learners on Mandarin tonal contrasts to test two hypotheses regarding the influence of context and variability on tone  learning. The first hypothesis was that increased phonetic variability of tones in disyllabic contexts makes initial tone  learning more challenging in disyllabic than monosyllabic words. The second hypothesis was that the learnability of a given tone varies across contexts due to differences in tonal variability. Results of a word learning experiment supported both hypotheses: tones were acquired less successfully in disyllables than in monosyllables, and the relative difficulty of disyllables was closely related to contextual tonal variability. These results indicate limited relevance of monosyllable-based data on Mandarin learning for the disyllabic majority of the Mandarin lexicon. Furthermore, in the short term, variability can diminish learning; its effects are not necessarily beneficial but dependent on acquisition stage and other learner characteristics. These findings thus highlight the importance of considering contextual variability and the interaction between variability and type of learner in the design, interpretation, and application of research on phonetic learning.

An exploration of gender differences in tertiary mathematics

NASA Astrophysics Data System (ADS)

Watson, Jane M.

1989-02-01

Data from 400 students in a tertiary mathematics course were analysed to explore gender differences on a number of variables associated with learning mathematics. It was concluded that while differences did occur on variables associated with confidence, self-concept, test anxiety and quantitative ability indicating a detrimental effect for women, compensating behaviour by women, including increased assignment work and tutorial attendance, resulted in comparable final course performance for women and men. These findings are discussed in light of participation rates of women in mathematics.

Neuropsychological Test Selection for Cognitive Impairment Classification: A Machine Learning Approach

PubMed Central

Williams, Jennifer A.; Schmitter-Edgecombe, Maureen; Cook, Diane J.

2016-01-01

Introduction Reducing the amount of testing required to accurately detect cognitive impairment is clinically relevant. The aim of this research was to determine the fewest number of clinical measures required to accurately classify participants as healthy older adult, mild cognitive impairment (MCI) or dementia using a suite of classification techniques. Methods Two variable selection machine learning models (i.e., naive Bayes, decision tree), a logistic regression, and two participant datasets (i.e., clinical diagnosis, clinical dementia rating; CDR) were explored. Participants classified using clinical diagnosis criteria included 52 individuals with dementia, 97 with MCI, and 161 cognitively healthy older adults. Participants classified using CDR included 154 individuals CDR = 0, 93 individuals with CDR = 0.5, and 25 individuals with CDR = 1.0+. Twenty-seven demographic, psychological, and neuropsychological variables were available for variable selection. Results No significant difference was observed between naive Bayes, decision tree, and logistic regression models for classification of both clinical diagnosis and CDR datasets. Participant classification (70.0 – 99.1%), geometric mean (60.9 – 98.1%), sensitivity (44.2 – 100%), and specificity (52.7 – 100%) were generally satisfactory. Unsurprisingly, the MCI/CDR = 0.5 participant group was the most challenging to classify. Through variable selection only 2 – 9 variables were required for classification and varied between datasets in a clinically meaningful way. Conclusions The current study results reveal that machine learning techniques can accurately classifying cognitive impairment and reduce the number of measures required for diagnosis. PMID:26332171

Development of a computer-based clinical decision support tool for selecting appropriate rehabilitation interventions for injured workers.

PubMed

Gross, Douglas P; Zhang, Jing; Steenstra, Ivan; Barnsley, Susan; Haws, Calvin; Amell, Tyler; McIntosh, Greg; Cooper, Juliette; Zaiane, Osmar

2013-12-01

To develop a classification algorithm and accompanying computer-based clinical decision support tool to help categorize injured workers toward optimal rehabilitation interventions based on unique worker characteristics. Population-based historical cohort design. Data were extracted from a Canadian provincial workers' compensation database on all claimants undergoing work assessment between December 2009 and January 2011. Data were available on: (1) numerous personal, clinical, occupational, and social variables; (2) type of rehabilitation undertaken; and (3) outcomes following rehabilitation (receiving time loss benefits or undergoing repeat programs). Machine learning, concerned with the design of algorithms to discriminate between classes based on empirical data, was the foundation of our approach to build a classification system with multiple independent and dependent variables. The population included 8,611 unique claimants. Subjects were predominantly employed (85 %) males (64 %) with diagnoses of sprain/strain (44 %). Baseline clinician classification accuracy was high (ROC = 0.86) for selecting programs that lead to successful return-to-work. Classification performance for machine learning techniques outperformed the clinician baseline classification (ROC = 0.94). The final classifiers were multifactorial and included the variables: injury duration, occupation, job attachment status, work status, modified work availability, pain intensity rating, self-rated occupational disability, and 9 items from the SF-36 Health Survey. The use of machine learning classification techniques appears to have resulted in classification performance better than clinician decision-making. The final algorithm has been integrated into a computer-based clinical decision support tool that requires additional validation in a clinical sample.

Utility of Vital Signs, Heart-rate Variability and Complexity, and Machine Learning for Identifying the Need for Life-saving Interventions in Trauma Patients

DTIC Science & Technology

2014-08-01

thumb-mounted pulse oximeter to the WVSM were recorded at rates of 230 and 75 Hz, respectively. For intubated patients, respiration waveform data were...also recorded at a rate of 10 Hz using a handheld capnograph/ oximeter (Microcap; Covidien, Mansfield, Mass). Standard vital signs used during trauma...SI = HR/SBP) and pulse pressure (PP = SBP j DBP). All nonelectronic data were manually recorded on an electronic run sheet (RescueNet ePCR; Zoll

The role of personal self-regulation and regulatory teaching to predict motivational-affective variables, achievement, and satisfaction: a structural model

PubMed Central

De la Fuente, Jesus; Zapata, Lucía; Martínez-Vicente, Jose M.; Sander, Paul; Cardelle-Elawar, María

2014-01-01

The present investigation examines how personal self-regulation (presage variable) and regulatory teaching (process variable of teaching) relate to learning approaches, strategies for coping with stress, and self-regulated learning (process variables of learning) and, finally, how they relate to performance and satisfaction with the learning process (product variables). The objective was to clarify the associative and predictive relations between these variables, as contextualized in two different models that use the presage-process-product paradigm (the Biggs and DEDEPRO models). A total of 1101 university students participated in the study. The design was cross-sectional and retrospective with attributional (or selection) variables, using correlations and structural analysis. The results provide consistent and significant empirical evidence for the relationships hypothesized, incorporating variables that are part of and influence the teaching–learning process in Higher Education. Findings confirm the importance of interactive relationships within the teaching–learning process, where personal self-regulation is assumed to take place in connection with regulatory teaching. Variables that are involved in the relationships validated here reinforce the idea that both personal factors and teaching and learning factors should be taken into consideration when dealing with a formal teaching–learning context at university. PMID:25964764

Big Data Toolsets to Pharmacometrics: Application of Machine Learning for Time‐to‐Event Analysis

PubMed Central

Gong, Xiajing; Hu, Meng

2018-01-01

Abstract Additional value can be potentially created by applying big data tools to address pharmacometric problems. The performances of machine learning (ML) methods and the Cox regression model were evaluated based on simulated time‐to‐event data synthesized under various preset scenarios, i.e., with linear vs. nonlinear and dependent vs. independent predictors in the proportional hazard function, or with high‐dimensional data featured by a large number of predictor variables. Our results showed that ML‐based methods outperformed the Cox model in prediction performance as assessed by concordance index and in identifying the preset influential variables for high‐dimensional data. The prediction performances of ML‐based methods are also less sensitive to data size and censoring rates than the Cox regression model. In conclusion, ML‐based methods provide a powerful tool for time‐to‐event analysis, with a built‐in capacity for high‐dimensional data and better performance when the predictor variables assume nonlinear relationships in the hazard function. PMID:29536640

Effects of visual feedback-induced variability on motor learning of handrim wheelchair propulsion.

PubMed

Leving, Marika T; Vegter, Riemer J K; Hartog, Johanneke; Lamoth, Claudine J C; de Groot, Sonja; van der Woude, Lucas H V

2015-01-01

It has been suggested that a higher intra-individual variability benefits the motor learning of wheelchair propulsion. The present study evaluated whether feedback-induced variability on wheelchair propulsion technique variables would also enhance the motor learning process. Learning was operationalized as an improvement in mechanical efficiency and propulsion technique, which are thought to be closely related during the learning process. 17 Participants received visual feedback-based practice (feedback group) and 15 participants received regular practice (natural learning group). Both groups received equal practice dose of 80 min, over 3 weeks, at 0.24 W/kg at a treadmill speed of 1.11 m/s. To compare both groups the pre- and post-test were performed without feedback. The feedback group received real-time visual feedback on seven propulsion variables with instruction to manipulate the presented variable to achieve the highest possible variability (1st 4-min block) and optimize it in the prescribed direction (2nd 4-min block). To increase motor exploration the participants were unaware of the exact variable they received feedback on. Energy consumption and the propulsion technique variables with their respective coefficient of variation were calculated to evaluate the amount of intra-individual variability. The feedback group, which practiced with higher intra-individual variability, improved the propulsion technique between pre- and post-test to the same extent as the natural learning group. Mechanical efficiency improved between pre- and post-test in the natural learning group but remained unchanged in the feedback group. These results suggest that feedback-induced variability inhibited the improvement in mechanical efficiency. Moreover, since both groups improved propulsion technique but only the natural learning group improved mechanical efficiency, it can be concluded that the improvement in mechanical efficiency and propulsion technique do not always appear simultaneously during the motor learning process. Their relationship is most likely modified by other factors such as the amount of the intra-individual variability.

Effects of Visual Feedback-Induced Variability on Motor Learning of Handrim Wheelchair Propulsion

PubMed Central

Leving, Marika T.; Vegter, Riemer J. K.; Hartog, Johanneke; Lamoth, Claudine J. C.; de Groot, Sonja; van der Woude, Lucas H. V.

2015-01-01

Background It has been suggested that a higher intra-individual variability benefits the motor learning of wheelchair propulsion. The present study evaluated whether feedback-induced variability on wheelchair propulsion technique variables would also enhance the motor learning process. Learning was operationalized as an improvement in mechanical efficiency and propulsion technique, which are thought to be closely related during the learning process. Methods 17 Participants received visual feedback-based practice (feedback group) and 15 participants received regular practice (natural learning group). Both groups received equal practice dose of 80 min, over 3 weeks, at 0.24 W/kg at a treadmill speed of 1.11 m/s. To compare both groups the pre- and post-test were performed without feedback. The feedback group received real-time visual feedback on seven propulsion variables with instruction to manipulate the presented variable to achieve the highest possible variability (1st 4-min block) and optimize it in the prescribed direction (2nd 4-min block). To increase motor exploration the participants were unaware of the exact variable they received feedback on. Energy consumption and the propulsion technique variables with their respective coefficient of variation were calculated to evaluate the amount of intra-individual variability. Results The feedback group, which practiced with higher intra-individual variability, improved the propulsion technique between pre- and post-test to the same extent as the natural learning group. Mechanical efficiency improved between pre- and post-test in the natural learning group but remained unchanged in the feedback group. Conclusion These results suggest that feedback-induced variability inhibited the improvement in mechanical efficiency. Moreover, since both groups improved propulsion technique but only the natural learning group improved mechanical efficiency, it can be concluded that the improvement in mechanical efficiency and propulsion technique do not always appear simultaneously during the motor learning process. Their relationship is most likely modified by other factors such as the amount of the intra-individual variability. PMID:25992626

Extreme learning machine: a new alternative for measuring heat collection rate and heat loss coefficient of water-in-glass evacuated tube solar water heaters.

PubMed

Liu, Zhijian; Li, Hao; Tang, Xindong; Zhang, Xinyu; Lin, Fan; Cheng, Kewei

2016-01-01

Heat collection rate and heat loss coefficient are crucial indicators for the evaluation of in service water-in-glass evacuated tube solar water heaters. However, the direct determination requires complex detection devices and a series of standard experiments, wasting too much time and manpower. To address this problem, we previously used artificial neural networks and support vector machine to develop precise knowledge-based models for predicting the heat collection rates and heat loss coefficients of water-in-glass evacuated tube solar water heaters, setting the properties measured by "portable test instruments" as the independent variables. A robust software for determination was also developed. However, in previous results, the prediction accuracy of heat loss coefficients can still be improved compared to those of heat collection rates. Also, in practical applications, even a small reduction in root mean square errors (RMSEs) can sometimes significantly improve the evaluation and business processes. As a further study, in this short report, we show that using a novel and fast machine learning algorithm-extreme learning machine can generate better predicted results for heat loss coefficient, which reduces the average RMSEs to 0.67 in testing.

Temporal structure of motor variability is dynamically regulated and predicts motor learning ability.

PubMed

Wu, Howard G; Miyamoto, Yohsuke R; Gonzalez Castro, Luis Nicolas; Ölveczky, Bence P; Smith, Maurice A

2014-02-01

Individual differences in motor learning ability are widely acknowledged, yet little is known about the factors that underlie them. Here we explore whether movement-to-movement variability in motor output, a ubiquitous if often unwanted characteristic of motor performance, predicts motor learning ability. Surprisingly, we found that higher levels of task-relevant motor variability predicted faster learning both across individuals and across tasks in two different paradigms, one relying on reward-based learning to shape specific arm movement trajectories and the other relying on error-based learning to adapt movements in novel physical environments. We proceeded to show that training can reshape the temporal structure of motor variability, aligning it with the trained task to improve learning. These results provide experimental support for the importance of action exploration, a key idea from reinforcement learning theory, showing that motor variability facilitates motor learning in humans and that our nervous systems actively regulate it to improve learning.

Temporal structure of motor variability is dynamically regulated and predicts motor learning ability

PubMed Central

Wu, Howard G; Miyamoto, Yohsuke R; Castro, Luis Nicolas Gonzalez; Ölveczky, Bence P; Smith, Maurice A

2015-01-01

Individual differences in motor learning ability are widely acknowledged, yet little is known about the factors that underlie them. Here we explore whether movement-to-movement variability in motor output, a ubiquitous if often unwanted characteristic of motor performance, predicts motor learning ability. Surprisingly, we found that higher levels of task-relevant motor variability predicted faster learning both across individuals and across tasks in two different paradigms, one relying on reward-based learning to shape specific arm movement trajectories and the other relying on error-based learning to adapt movements in novel physical environments. We proceeded to show that training can reshape the temporal structure of motor variability, aligning it with the trained task to improve learning. These results provide experimental support for the importance of action exploration, a key idea from reinforcement learning theory, showing that motor variability facilitates motor learning in humans and that our nervous systems actively regulate it to improve learning. PMID:24413700

Valence-Dependent Belief Updating: Computational Validation

PubMed Central

Kuzmanovic, Bojana; Rigoux, Lionel

2017-01-01

People tend to update beliefs about their future outcomes in a valence-dependent way: they are likely to incorporate good news and to neglect bad news. However, belief formation is a complex process which depends not only on motivational factors such as the desire for favorable conclusions, but also on multiple cognitive variables such as prior beliefs, knowledge about personal vulnerabilities and resources, and the size of the probabilities and estimation errors. Thus, we applied computational modeling in order to test for valence-induced biases in updating while formally controlling for relevant cognitive factors. We compared biased and unbiased Bayesian models of belief updating, and specified alternative models based on reinforcement learning. The experiment consisted of 80 trials with 80 different adverse future life events. In each trial, participants estimated the base rate of one of these events and estimated their own risk of experiencing the event before and after being confronted with the actual base rate. Belief updates corresponded to the difference between the two self-risk estimates. Valence-dependent updating was assessed by comparing trials with good news (better-than-expected base rates) with trials with bad news (worse-than-expected base rates). After receiving bad relative to good news, participants' updates were smaller and deviated more strongly from rational Bayesian predictions, indicating a valence-induced bias. Model comparison revealed that the biased (i.e., optimistic) Bayesian model of belief updating better accounted for data than the unbiased (i.e., rational) Bayesian model, confirming that the valence of the new information influenced the amount of updating. Moreover, alternative computational modeling based on reinforcement learning demonstrated higher learning rates for good than for bad news, as well as a moderating role of personal knowledge. Finally, in this specific experimental context, the approach based on reinforcement learning was superior to the Bayesian approach. The computational validation of valence-dependent belief updating represents a novel support for a genuine optimism bias in human belief formation. Moreover, the precise control of relevant cognitive variables justifies the conclusion that the motivation to adopt the most favorable self-referential conclusions biases human judgments. PMID:28706499

Valence-Dependent Belief Updating: Computational Validation.

PubMed

Kuzmanovic, Bojana; Rigoux, Lionel

2017-01-01

People tend to update beliefs about their future outcomes in a valence-dependent way: they are likely to incorporate good news and to neglect bad news. However, belief formation is a complex process which depends not only on motivational factors such as the desire for favorable conclusions, but also on multiple cognitive variables such as prior beliefs, knowledge about personal vulnerabilities and resources, and the size of the probabilities and estimation errors. Thus, we applied computational modeling in order to test for valence-induced biases in updating while formally controlling for relevant cognitive factors. We compared biased and unbiased Bayesian models of belief updating, and specified alternative models based on reinforcement learning. The experiment consisted of 80 trials with 80 different adverse future life events. In each trial, participants estimated the base rate of one of these events and estimated their own risk of experiencing the event before and after being confronted with the actual base rate. Belief updates corresponded to the difference between the two self-risk estimates. Valence-dependent updating was assessed by comparing trials with good news (better-than-expected base rates) with trials with bad news (worse-than-expected base rates). After receiving bad relative to good news, participants' updates were smaller and deviated more strongly from rational Bayesian predictions, indicating a valence-induced bias. Model comparison revealed that the biased (i.e., optimistic) Bayesian model of belief updating better accounted for data than the unbiased (i.e., rational) Bayesian model, confirming that the valence of the new information influenced the amount of updating. Moreover, alternative computational modeling based on reinforcement learning demonstrated higher learning rates for good than for bad news, as well as a moderating role of personal knowledge. Finally, in this specific experimental context, the approach based on reinforcement learning was superior to the Bayesian approach. The computational validation of valence-dependent belief updating represents a novel support for a genuine optimism bias in human belief formation. Moreover, the precise control of relevant cognitive variables justifies the conclusion that the motivation to adopt the most favorable self-referential conclusions biases human judgments.

Revisiting speech rate and utterance length manipulations in stuttering speakers.

PubMed

Blomgren, Michael; Goberman, Alexander M

2008-01-01

The goal of this study was to evaluate stuttering frequency across a multidimensional (2x2) hierarchy of speech performance tasks. Specifically, this study examined the interaction between changes in length of utterance and levels of speech rate stability. Forty-four adult male speakers participated in the study (22 stuttering speakers and 22 non-stuttering speakers). Participants were audio and video recorded while producing a spontaneous speech task and four different experimental speaking tasks. The four experimental speaking tasks involved reading a list of 45 words and a list 45 phrases two times each. One reading of each list involved speaking at a steady habitual rate (habitual rate tasks) and another reading involved producing each list at a variable speaking rate (variable rate tasks). For the variable rate tasks, participants were directed to produce words or phrases at randomly ordered slow, habitual, and fast rates. The stuttering speakers exhibited significantly more stuttering on the variable rate tasks than on the habitual rate tasks. In addition, the stuttering speakers exhibited significantly more stuttering on the first word of the phrase length tasks compared to the single word tasks. Overall, the results indicated that varying levels of both utterance length and temporal complexity function to modulate stuttering frequency in adult stuttering speakers. Discussion focuses on issues of speech performance according to stuttering severity and possible clinical implications. The reader will learn about and be able to: (1) describe the mediating effects of length of utterance and speech rate on the frequency of stuttering in stuttering speakers; (2) understand the rationale behind multidimensional skill performance matrices; and (3) describe possible applications of motor skill performance matrices to stuttering therapy.

Sustainable management for rangelands in a variable climate: evidence and insights from northern Australia.

PubMed

O'Reagain, P J; Scanlan, J C

2013-03-01

Inter-annual rainfall variability is a major challenge to sustainable and productive grazing management on rangelands. In Australia, rainfall variability is particularly pronounced and failure to manage appropriately leads to major economic loss and environmental degradation. Recommended strategies to manage sustainably include stocking at long-term carrying capacity (LTCC) or varying stock numbers with forage availability. These strategies are conceptually simple but difficult to implement, given the scale and spatial heterogeneity of grazing properties and the uncertainty of the climate. This paper presents learnings and insights from northern Australia gained from research and modelling on managing for rainfall variability. A method to objectively estimate LTCC in large, heterogeneous paddocks is discussed, and guidelines and tools to tactically adjust stocking rates are presented. The possible use of seasonal climate forecasts (SCF) in management is also considered. Results from a 13-year grazing trial in Queensland show that constant stocking at LTCC was far more profitable and largely maintained land condition compared with heavy stocking (HSR). Variable stocking (VAR) with or without the use of SCF was marginally more profitable, but income variability was greater and land condition poorer than constant stocking at LTCC. Two commercial scale trials in the Northern Territory with breeder cows highlighted the practical difficulties of variable stocking and provided evidence that heavier pasture utilisation rates depress reproductive performance. Simulation modelling across a range of regions in northern Australia also showed a decline in resource condition and profitability under heavy stocking rates. Modelling further suggested that the relative value of variable v. constant stocking depends on stocking rate and land condition. Importantly, variable stocking may possibly allow slightly higher stocking rates without pasture degradation. Enterprise-level simulations run for breeder herds nevertheless show that poor economic performance can occur under constant stocking and even under variable stocking in some circumstances. Modelling and research results both suggest that a form of constrained flexible stocking should be applied to manage for climate variability. Active adaptive management and research will be required as future climate changes make managing for rainfall variability increasingly challenging.

Variables affecting learning in a simulation experience: a mixed methods study.

PubMed

Beischel, Kelly P

2013-02-01

The primary purpose of this study was to test a hypothesized model describing the direct effects of learning variables on anxiety and cognitive learning outcomes in a high-fidelity simulation (HFS) experience. The secondary purpose was to explain and explore student perceptions concerning the qualities and context of HFS affecting anxiety and learning. This study used a mixed methods quantitative-dominant explanatory design with concurrent qualitative data collection to examine variables affecting learning in undergraduate, beginning nursing students (N = 124). Being ready to learn, having a strong auditory-verbal learning style, and being prepared for simulation directly affected anxiety, whereas learning outcomes were directly affected by having strong auditory-verbal and hands-on learning styles. Anxiety did not quantitatively mediate cognitive learning outcomes as theorized, although students qualitatively reported debilitating levels of anxiety. This study advances nursing education science by providing evidence concerning variables affecting learning outcomes in HFS.

Comparison of success rates, learning curves, and inter-subject performance variability of robot-assisted and manual ultrasound-guided nerve block needle guidance in simulation.

PubMed

Morse, J; Terrasini, N; Wehbe, M; Philippona, C; Zaouter, C; Cyr, S; Hemmerling, T M

2014-06-01

This study focuses on a recently developed robotic nerve block system and its impact on learning regional anaesthesia skills. We compared success rates, learning curves, performance times, and inter-subject performance variability of robot-assisted vs manual ultrasound (US)-guided nerve block needle guidance. The hypothesis of this study is that robot assistance will result in faster skill acquisition than manual needle guidance. Five co-authors with different experience with nerve blocks and the robotic system performed both manual and robot-assisted, US-guided nerve blocks on two different nerves of a nerve phantom. Ten trials were performed for each of the four procedures. Time taken to move from a shared starting position till the needle was inserted into the target nerve was defined as the performance time. A successful block was defined as the insertion of the needle into the target nerve. Average performance times were compared using analysis of variance. P<0.05 was considered significant. Data presented as mean (standard deviation). All blocks were successful. There were significant differences in performance times between co-authors to perform the manual blocks, either superficial (P=0.001) or profound (P=0.0001); no statistical difference between co-authors was noted for the robot-assisted blocks. Linear regression indicated that the average decrease in time between consecutive trials for robot-assisted blocks of 1.8 (1.6) s was significantly (P=0.007) greater than the decrease for manual blocks of 0.3 (0.3) s. Robot assistance of nerve blocks allows for faster learning of needle guidance over manual positioning and reduces inter-subject performance variability. © The Author [2014]. Published by Oxford University Press on behalf of the British Journal of Anaesthesia. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

A neural circuit mechanism for regulating vocal variability during song learning in zebra finches.

PubMed

Garst-Orozco, Jonathan; Babadi, Baktash; Ölveczky, Bence P

2014-12-15

Motor skill learning is characterized by improved performance and reduced motor variability. The neural mechanisms that couple skill level and variability, however, are not known. The zebra finch, a songbird, presents a unique opportunity to address this question because production of learned song and induction of vocal variability are instantiated in distinct circuits that converge on a motor cortex analogue controlling vocal output. To probe the interplay between learning and variability, we made intracellular recordings from neurons in this area, characterizing how their inputs from the functionally distinct pathways change throughout song development. We found that inputs that drive stereotyped song-patterns are strengthened and pruned, while inputs that induce variability remain unchanged. A simple network model showed that strengthening and pruning of action-specific connections reduces the sensitivity of motor control circuits to variable input and neural 'noise'. This identifies a simple and general mechanism for learning-related regulation of motor variability.

The employment of Support Vector Machine to classify high and low performance archers based on bio-physiological variables

NASA Astrophysics Data System (ADS)

Taha, Zahari; Muazu Musa, Rabiu; Majeed, Anwar P. P. Abdul; Razali Abdullah, Mohamad; Amirul Abdullah, Muhammad; Hasnun Arif Hassan, Mohd; Khalil, Zubair

2018-04-01

The present study employs a machine learning algorithm namely support vector machine (SVM) to classify high and low potential archers from a collection of bio-physiological variables trained on different SVMs. 50 youth archers with the average age and standard deviation of (17.0 ±.056) gathered from various archery programmes completed a one end shooting score test. The bio-physiological variables namely resting heart rate, resting respiratory rate, resting diastolic blood pressure, resting systolic blood pressure, as well as calories intake, were measured prior to their shooting tests. k-means cluster analysis was applied to cluster the archers based on their scores on variables assessed. SVM models i.e. linear, quadratic and cubic kernel functions, were trained on the aforementioned variables. The k-means clustered the archers into high (HPA) and low potential archers (LPA), respectively. It was demonstrated that the linear SVM exhibited good accuracy with a classification accuracy of 94% in comparison the other tested models. The findings of this investigation can be valuable to coaches and sports managers to recognise high potential athletes from the selected bio-physiological variables examined.

Striatal volume predicts level of video game skill acquisition.

PubMed

Erickson, Kirk I; Boot, Walter R; Basak, Chandramallika; Neider, Mark B; Prakash, Ruchika S; Voss, Michelle W; Graybiel, Ann M; Simons, Daniel J; Fabiani, Monica; Gratton, Gabriele; Kramer, Arthur F

2010-11-01

Video game skills transfer to other tasks, but individual differences in performance and in learning and transfer rates make it difficult to identify the source of transfer benefits. We asked whether variability in initial acquisition and of improvement in performance on a demanding video game, the Space Fortress game, could be predicted by variations in the pretraining volume of either of 2 key brain regions implicated in learning and memory: the striatum, implicated in procedural learning and cognitive flexibility, and the hippocampus, implicated in declarative memory. We found that hippocampal volumes did not predict learning improvement but that striatal volumes did. Moreover, for the striatum, the volumes of the dorsal striatum predicted improvement in performance but the volumes of the ventral striatum did not. Both ventral and dorsal striatal volumes predicted early acquisition rates. Furthermore, this early-stage correlation between striatal volumes and learning held regardless of the cognitive flexibility demands of the game versions, whereas the predictive power of the dorsal striatal volumes held selectively for performance improvements in a game version emphasizing cognitive flexibility. These findings suggest a neuroanatomical basis for the superiority of training strategies that promote cognitive flexibility and transfer to untrained tasks.

The effect of subjective awareness measures on performance in artificial grammar learning task.

PubMed

Ivanchei, Ivan I; Moroshkina, Nadezhda V

2018-01-01

Systematic research into implicit learning requires well-developed awareness-measurement techniques. Recently, trial-by-trial measures have been widely used. However, they can increase complexity of a study because they are an additional experimental variable. We tested the effects of these measures on performance in artificial grammar learning study. Four groups of participants were assigned to different awareness measures conditions: confidence ratings, post-decision wagering, decision strategy attribution or none. Decision-strategy-attribution participants demonstrated better grammar learning and longer response times compared to controls. They also exhibited a conservative bias. Grammaticality by itself was a stronger predictor of strings endorsement in decision-strategy-attribution group compared to other groups. Confidence ratings and post-decision wagering only affected the response times. These results were supported by an additional experiment that used a balanced chunk strength design. We conclude that a decision-strategy-attribution procedure may force participants to adopt an analytical decision-making strategy and rely mostly on conscious knowledge of artificial grammar. Copyright © 2017 Elsevier Inc. All rights reserved.

Five years of lesson modification to implement non-traditional learning sessions in a traditional-delivery curriculum: A retrospective assessment using applied implementation variables.

PubMed

Gleason, Shaun E; McNair, Bryan; Kiser, Tyree H; Franson, Kari L

Non-traditional learning (NTL), including aspects of self-directed learning (SDL), may address self-awareness development needs. Many factors can impact successful implementation of NTL. To share our multi-year experience with modifications that aim to improve NTL sessions in a traditional curriculum. To improve understanding of applied implementation variables (some of which were based on successful SDL implementation components) that impact NTL. We delivered a single lesson in a traditional-delivery curriculum once annually for five years, varying delivery annually in response to student learning and reaction-to-learning results. At year 5, we compared student learning and reaction-to-learning to applied implementation factors using logistic regression. Higher instructor involvement and overall NTL levels predicted correct exam responses (p=0.0007 and p<0.0001, respectively). Exam responses were statistically equivalent between the most traditional and highest overall NTL deliveries. Students rated instructor presentation skills and teaching methods higher when greater instructor involvement (p<0.0001, both) and lower overall NTL levels (P<0.0001, both) were used. Students perceived that teaching methods were most effective when lower student involvement and higher technology levels (p<0.0001, both) were used. When implementing NTL sessions as a single lesson in a traditional-delivery curriculum, instructor involvement appears essential, while the impact of student involvement and educational technology levels varies. Copyright © 2017 Elsevier Inc. All rights reserved.

Variability in Second Language Learning: The Roles of Individual Differences, Learning Conditions, and Linguistic Complexity

ERIC Educational Resources Information Center

Tagarelli, Kaitlyn M.; Ruiz, Simón; Vega, José Luis Moreno; Rebuschat, Patrick

2016-01-01

Second language learning outcomes are highly variable, due to a variety of factors, including individual differences, exposure conditions, and linguistic complexity. However, exactly how these factors interact to influence language learning is unknown. This article examines the relationship between these three variables in language learners.…

Examining parents' ratings of middle-school students' academic self-regulation using principal axis factoring analysis.

PubMed

Chen, Peggy P; Cleary, Timothy J; Lui, Angela M

2015-09-01

This study examined the reliability and validity of a parent rating scale, the Self-Regulation Strategy Inventory: Parent Rating Scale (SRSI-PRS), using a sample of 451 parents of sixth- and seventh-grade middle-school students. Principal axis factoring (PAF) analysis revealed a 3-factor structure for the 23-item SRSI-PRS: (a) Managing Behavior and Learning (α = .92), (b) Maladaptive Regulatory Behaviors (α = .76), and (c) Managing Environment (α = .84). The majority of the observed relations between these 3 subscales, and the SRSI-SR, student motivation beliefs, and student mathematics grades were statistically significant and in the small to medium range. After controlling for various student variables and motivation indices of parental involvement, 2 SRSI-PRS factors (Managing Behavior and Learning, Maladaptive Regulatory Behaviors) reliably predicted students' achievement in their mathematics course. This study provides initial support for the validity and reliability of the SRSI-PRS and underscores the advantages of obtaining parental ratings of students' SRL behaviors. (c) 2015 APA, all rights reserved).

Five-year growth trajectories of kindergarten children with learning difficulties in mathematics.

PubMed

Morgan, Paul L; Farkas, George; Qiong Wu

2009-01-01

The investigators used data from the Early Childhood Longitudinal Study-Kindergarten Cohort (ECLS-K) to estimate whether and to what extent the timing and persistence of mathematics difficulties (MD) in kindergarten predicted children's first through fifth grade math growth trajectories. Results indicated that children persistently displaying MD (i.e., those experiencing MD in both fall and spring of kindergarten) had the lowest subsequent growth rates, children with MD in spring only had the second-lowest growth rates, and children with MD in the fall only (and who had thus recovered from their MD by the spring of kindergarten) had the next-lowest growth rates. The children who did not have MD in either fall or spring of kindergarten had the highest growth rates. These results were observed prior to and after statistical control for additional variables. They indicate that measuring the timing and persistence of kindergarten children's mathematics learning difficulties may help identify those most at risk for failing to become mathematically proficient during elementary school.

Comparison of teaching about breast cancer via mobile or traditional learning methods in gynecology residents.

PubMed

Alipour, Sadaf; Moini, Ashraf; Jafari-Adli, Shahrzad; Gharaie, Nooshin; Mansouri, Khorshid

2012-01-01

Mobile learning enables users to interact with educational resources while in variable locations. Medical students in residency positions need to assimilate considerable knowledge besides their practical training and we therefore aimed to evaluate the impact of using short message service via cell phone as a learning tool in residents of Obstetrics and Gynecology in our hospital. We sent short messages including data about breast cancer to the cell phones of 25 residents of gynecology and obstetrics and asked them to study a well-designed booklet containing another set of information about the disease in the same period. The rate of learning derived from the two methods was compared by pre- and post-tests and self-satisfaction assessed by a relevant questionnaire at the end of the program. The mobile learning method had a significantly better effect on learning and created more interest in the subject. Learning via receiving SMS can be an effective and appealing method of knowledge acquisition in higher levels of education.

Comparison of the Effects of Typical and Atypical Anxiolytics on Learning in Monkeys and Rats,

DTIC Science & Technology

kg) and alprazolam (0.032-0.32 mg/kg) produced dose-dependent decreases in overall response rate in all subjects. However, with buspirone and 8-OH-DPAT...monkeys were variable across drugs and drug classes. Both 8-OH-DPAT and alprazolam produced large increases in percent errors in acquisition at doses

Competency-Based Education: Helping All Kentucky Students Succeed. Final Report

ERIC Educational Resources Information Center

Kentucky Department of Education, 2013

2013-01-01

The Commonwealth of Kentucky is exploring competency-based education as a way to better prepare students for success in college and their careers. Some individual schools and districts are moving ahead with this innovative approach where achievement is the constant and time is the variable. Not all students learn at the same rate, or in the same…

Within-Students Variability in Learning Experiences, and Teachers' Perceptions of Students' Task-Focus

ERIC Educational Resources Information Center

Malmberg, Lars-Erik; Lim, Wee H. T.; Tolvanen, Asko; Nurmi, Jari-Erik

2016-01-01

In order to advance our understanding of educational processes, we present a tutorial of intraindividual variability. An adaptive educational process is characterised by stable (less variability), and a maladaptive process is characterised by instable (more variability) learning experiences from one learning situation to the next. We outline step…

Modeling cascading diffusion of new energy technologies: case study of residential solid oxide fuel cells in the US and internationally.

PubMed

Herron, Seth; Williams, Eric

2013-08-06

Subsidy programs for new energy technologies are motivated by the experience curve: increased adoption of a technology leads to learning and economies of scale that lower costs. Geographic differences in fuel prices and climate lead to large variability in the economic performance of energy technologies. The notion of cascading diffusion is that regions with favorable economic conditions serve as the basis to build scale and reduce costs so that the technology becomes attractive in new regions. We develop a model of cascading diffusion and implement via a case study of residential solid oxide fuel cells (SOFCs) for combined heating and power. We consider diffusion paths within the U.S. and internationally. We construct market willingness-to-pay curves and estimate future manufacturing costs via an experience curve. Combining market and cost results, we find that for rapid cost reductions (learning rate = 25%), a modest public subsidy can make SOFC investment profitable for 20-160 million households. If cost reductions are slow however (learning rate = 15%), residential SOFCs may not become economically competitive. Due to higher energy prices in some countries, international diffusion is more favorable than domestic, mitigating much of the uncertainty in the learning rate.

Analysis of the rate of wildcat drilling and deposit discovery

USGS Publications Warehouse

Drew, L.J.

1975-01-01

The rate at which petroleum deposits were discovered during a 16-yr period (1957-72) was examined in relation to changes in a suite of economic and physical variables. The study area encompasses 11,000 mi2 and is located on the eastern flank of the Powder River Basin. A two-stage multiple-regression model was used as a basis for this analysis. The variables employed in this model were: (1) the yearly wildcat drilling rate, (2) a measure of the extent of the physical exhaustion of the resource base of the region, (3) a proxy for the discovery expectation of the exploration operators active in the region, (4) an exploration price/cost ratio, and (5) the expected depths of the exploration targets sought. The rate at which wildcat wells were drilled was strongly correlated with the discovery expectation of the exploration operators. Small additional variations in the wildcat drilling rate were explained by the price/cost ratio and target-depth variables. The number of deposits discovered each year was highly dependent on the wildcat drilling rate, but the aggregate quantity of petroleum discovered each year was independent of the wildcat drilling rate. The independence between these last two variables is a consequence of the cyclical behavior of the exploration play mechanism. Although the discovery success ratio declined sharply during the initial phases of the two exploration plays which developed in the study area, a learning effect occurred whereby the discovery success ratio improved steadily with the passage of time during both exploration plays. ?? 1975 Plenum Publishing Corporation.

A theory of local learning, the learning channel, and the optimality of backpropagation.

PubMed

Baldi, Pierre; Sadowski, Peter

2016-11-01

In a physical neural system, where storage and processing are intimately intertwined, the rules for adjusting the synaptic weights can only depend on variables that are available locally, such as the activity of the pre- and post-synaptic neurons, resulting in local learning rules. A systematic framework for studying the space of local learning rules is obtained by first specifying the nature of the local variables, and then the functional form that ties them together into each learning rule. Such a framework enables also the systematic discovery of new learning rules and exploration of relationships between learning rules and group symmetries. We study polynomial local learning rules stratified by their degree and analyze their behavior and capabilities in both linear and non-linear units and networks. Stacking local learning rules in deep feedforward networks leads to deep local learning. While deep local learning can learn interesting representations, it cannot learn complex input-output functions, even when targets are available for the top layer. Learning complex input-output functions requires local deep learning where target information is communicated to the deep layers through a backward learning channel. The nature of the communicated information about the targets and the structure of the learning channel partition the space of learning algorithms. For any learning algorithm, the capacity of the learning channel can be defined as the number of bits provided about the error gradient per weight, divided by the number of required operations per weight. We estimate the capacity associated with several learning algorithms and show that backpropagation outperforms them by simultaneously maximizing the information rate and minimizing the computational cost. This result is also shown to be true for recurrent networks, by unfolding them in time. The theory clarifies the concept of Hebbian learning, establishes the power and limitations of local learning rules, introduces the learning channel which enables a formal analysis of the optimality of backpropagation, and explains the sparsity of the space of learning rules discovered so far. Copyright © 2016 Elsevier Ltd. All rights reserved.

Incremental learning of concept drift in nonstationary environments.

PubMed

Elwell, Ryan; Polikar, Robi

2011-10-01

We introduce an ensemble of classifiers-based approach for incremental learning of concept drift, characterized by nonstationary environments (NSEs), where the underlying data distributions change over time. The proposed algorithm, named Learn(++). NSE, learns from consecutive batches of data without making any assumptions on the nature or rate of drift; it can learn from such environments that experience constant or variable rate of drift, addition or deletion of concept classes, as well as cyclical drift. The algorithm learns incrementally, as other members of the Learn(++) family of algorithms, that is, without requiring access to previously seen data. Learn(++). NSE trains one new classifier for each batch of data it receives, and combines these classifiers using a dynamically weighted majority voting. The novelty of the approach is in determining the voting weights, based on each classifier's time-adjusted accuracy on current and past environments. This approach allows the algorithm to recognize, and act accordingly, to the changes in underlying data distributions, as well as to a possible reoccurrence of an earlier distribution. We evaluate the algorithm on several synthetic datasets designed to simulate a variety of nonstationary environments, as well as a real-world weather prediction dataset. Comparisons with several other approaches are also included. Results indicate that Learn(++). NSE can track the changing environments very closely, regardless of the type of concept drift. To allow future use, comparison and benchmarking by interested researchers, we also release our data used in this paper. © 2011 IEEE

Complementary roles for amygdala and periaqueductal gray in temporal-difference fear learning.

PubMed

Cole, Sindy; McNally, Gavan P

2009-01-01

Pavlovian fear conditioning is not a unitary process. At the neurobiological level multiple brain regions and neurotransmitters contribute to fear learning. At the behavioral level many variables contribute to fear learning including the physical salience of the events being learned about, the direction and magnitude of predictive error, and the rate at which these are learned about. These experiments used a serial compound conditioning design to determine the roles of basolateral amygdala (BLA) NMDA receptors and ventrolateral midbrain periaqueductal gray (vlPAG) mu-opioid receptors (MOR) in predictive fear learning. Rats received a three-stage design, which arranged for both positive and negative prediction errors producing bidirectional changes in fear learning within the same subjects during the test stage. Intra-BLA infusion of the NR2B receptor antagonist Ifenprodil prevented all learning. In contrast, intra-vlPAG infusion of the MOR antagonist CTAP enhanced learning in response to positive predictive error but impaired learning in response to negative predictive error--a pattern similar to Hebbian learning and an indication that fear learning had been divorced from predictive error. These findings identify complementary but dissociable roles for amygdala NMDA receptors and vlPAG MOR in temporal-difference predictive fear learning.

Fuzzy support vector machines for adaptive Morse code recognition.

PubMed

Yang, Cheng-Hong; Jin, Li-Cheng; Chuang, Li-Yeh

2006-11-01

Morse code is now being harnessed for use in rehabilitation applications of augmentative-alternative communication and assistive technology, facilitating mobility, environmental control and adapted worksite access. In this paper, Morse code is selected as a communication adaptive device for persons who suffer from muscle atrophy, cerebral palsy or other severe handicaps. A stable typing rate is strictly required for Morse code to be effective as a communication tool. Therefore, an adaptive automatic recognition method with a high recognition rate is needed. The proposed system uses both fuzzy support vector machines and the variable-degree variable-step-size least-mean-square algorithm to achieve these objectives. We apply fuzzy memberships to each point, and provide different contributions to the decision learning function for support vector machines. Statistical analyses demonstrated that the proposed method elicited a higher recognition rate than other algorithms in the literature.

Using variability to guide dimensional weighting: Associative mechanisms in early word learning

PubMed Central

Apfelbaum, Keith S.; McMurray, Bob

2013-01-01

At 14 months, children appear to struggle to apply their fairly well developed speech perception abilities to learning similar sounding words (e.g. bih/dih; Stager & Werker, 1997). However, variability in non-phonetic aspects of the training stimuli seems to aid word learning at this age. Extant theories of early word learning cannot account for this benefit of variability. We offer a simple explanation for this range of effects based on associative learning. Simulations suggest that if infants encode both non-contrastive information (e.g. cues to speaker voice) and meaningful linguistic cues (e.g. place of articulation or voicing), then associative learning mechanisms predict these variability effects in early word learning. Crucially, this means that despite the importance of task variables in predicting performance, this body of work shows that phonological categories are still developing in this age, and that the structure of non-informative cues has critical influences on word learning abilities. PMID:21609356

Leveraging knowledge engineering and machine learning for microbial bio-manufacturing.

PubMed

Oyetunde, Tolutola; Bao, Forrest Sheng; Chen, Jiung-Wen; Martin, Hector Garcia; Tang, Yinjie J

2018-05-03

Genome scale modeling (GSM) predicts the performance of microbial workhorses and helps identify beneficial gene targets. GSM integrated with intracellular flux dynamics, omics, and thermodynamics have shown remarkable progress in both elucidating complex cellular phenomena and computational strain design (CSD). Nonetheless, these models still show high uncertainty due to a poor understanding of innate pathway regulations, metabolic burdens, and other factors (such as stress tolerance and metabolite channeling). Besides, the engineered hosts may have genetic mutations or non-genetic variations in bioreactor conditions and thus CSD rarely foresees fermentation rate and titer. Metabolic models play important role in design-build-test-learn cycles for strain improvement, and machine learning (ML) may provide a viable complementary approach for driving strain design and deciphering cellular processes. In order to develop quality ML models, knowledge engineering leverages and standardizes the wealth of information in literature (e.g., genomic/phenomic data, synthetic biology strategies, and bioprocess variables). Data driven frameworks can offer new constraints for mechanistic models to describe cellular regulations, to design pathways, to search gene targets, and to estimate fermentation titer/rate/yield under specified growth conditions (e.g., mixing, nutrients, and O 2 ). This review highlights the scope of information collections, database constructions, and machine learning techniques (such as deep learning and transfer learning), which may facilitate "Learn and Design" for strain development. Copyright © 2018. Published by Elsevier Inc.

Learning a novel phonological contrast depends on interactions between individual differences and training paradigm design

PubMed Central

Perrachione, Tyler K.; Lee, Jiyeon; Ha, Louisa Y. Y.; Wong, Patrick C. M.

2011-01-01

Studies evaluating phonological contrast learning typically investigate either the predictiveness of specific pretraining aptitude measures or the efficacy of different instructional paradigms. However, little research considers how these factors interact—whether different students learn better from different types of instruction—and what the psychological basis for any interaction might be. The present study demonstrates that successfully learning a foreign-language phonological contrast for pitch depends on an interaction between individual differences in perceptual abilities and the design of the training paradigm. Training from stimuli with high acoustic-phonetic variability is generally thought to improve learning; however, we found high-variability training enhanced learning only for individuals with strong perceptual abilities. Learners with weaker perceptual abilities were actually impaired by high-variability training relative to a low-variability condition. A second experiment assessing variations on the high-variability training design determined that the property of this learning environment most detrimental to perceptually weak learners is the amount of trial-by-trial variability. Learners’ perceptual limitations can thus override the benefits of high-variability training where trial-by-trial variability in other irrelevant acoustic-phonetic features obfuscates access to the target feature. These results demonstrate the importance of considering individual differences in pretraining aptitudes when evaluating the efficacy of any speech training paradigm. PMID:21786912

Machine learning for real time remote detection

NASA Astrophysics Data System (ADS)

Labbé, Benjamin; Fournier, Jérôme; Henaff, Gilles; Bascle, Bénédicte; Canu, Stéphane

2010-10-01

Infrared systems are key to providing enhanced capability to military forces such as automatic control of threats and prevention from air, naval and ground attacks. Key requirements for such a system to produce operational benefits are real-time processing as well as high efficiency in terms of detection and false alarm rate. These are serious issues since the system must deal with a large number of objects and categories to be recognized (small vehicles, armored vehicles, planes, buildings, etc.). Statistical learning based algorithms are promising candidates to meet these requirements when using selected discriminant features and real-time implementation. This paper proposes a new decision architecture benefiting from recent advances in machine learning by using an effective method for level set estimation. While building decision function, the proposed approach performs variable selection based on a discriminative criterion. Moreover, the use of level set makes it possible to manage rejection of unknown or ambiguous objects thus preserving the false alarm rate. Experimental evidences reported on real world infrared images demonstrate the validity of our approach.

The Effect of Cooperative Learning Model and Kolb Learning Styles on Learning Result of the Basics of Politics

ERIC Educational Resources Information Center

Sugiharto

2015-01-01

The aims of this research were to determine the effect of cooperative learning model and learning styles on learning result. This quasi-experimental study employed a 2x2 treatment by level, involved independent variables, i.e. cooperative learning model and learning styles, and learning result as the dependent variable. Findings signify that: (1)…

e-Learning in Advanced Life Support-What factors influence assessment outcome?

PubMed

Thorne, C J; Lockey, A S; Kimani, P K; Bullock, I; Hampshire, S; Begum-Ali, S; Perkins, G D

2017-05-01

To establish variables which are associated with favourable Advanced Life Support (ALS) course assessment outcomes, maximising learning effect. Between 1 January 2013 and 30 June 2014, 8218 individuals participated in a Resuscitation Council (UK) e-learning Advanced Life Support (e-ALS) course. Participants completed 5-8h of online e-learning prior to attending a one day face-to-face course. e-Learning access data were collected through the Learning Management System (LMS). All participants were assessed by a multiple choice questionnaire (MCQ) before and after the face-to-face aspect alongside a practical cardiac arrest simulation (CAS-Test). Participant demographics and assessment outcomes were analysed. The mean post e-learning MCQ score was 83.7 (SD 7.3) and the mean post-course MCQ score was 87.7 (SD 7.9). The first attempt CAS-Test pass rate was 84.6% and overall pass rate 96.6%. Participants with previous ALS experience, ILS experience, or who were a core member of the resuscitation team performed better in the post-course MCQ, CAS-Test and overall assessment. Median time spent on the e-learning was 5.2h (IQR 3.7-7.1). There was a large range in the degree of access to e-learning content. Increased time spent accessing e-learning had no effect on the overall result (OR 0.98, P=0.367) on simulated learning outcome. Clinical experience through membership of cardiac arrest teams and previous ILS or ALS training were independent predictors of performance on the ALS course whilst time spent accessing e-learning materials did not affect course outcomes. This supports the blended approach to e-ALS which allows participants to tailor their e-learning experience to their specific needs. Copyright © 2017 Elsevier B.V. All rights reserved.

The effect of modeled absolute timing variability and relative timing variability on observational learning.

PubMed

Grierson, Lawrence E M; Roberts, James W; Welsher, Arthur M

2017-05-01

There is much evidence to suggest that skill learning is enhanced by skill observation. Recent research on this phenomenon indicates a benefit of observing variable/erred demonstrations. In this study, we explore whether it is variability within the relative organization or absolute parameterization of a movement that facilitates skill learning through observation. To do so, participants were randomly allocated into groups that observed a model with no variability, absolute timing variability, relative timing variability, or variability in both absolute and relative timing. All participants performed a four-segment movement pattern with specific absolute and relative timing goals prior to and following the observational intervention, as well as in a 24h retention test and transfers tests that featured new relative and absolute timing goals. Absolute timing error indicated that all groups initially acquired the absolute timing, maintained their performance at 24h retention, and exhibited performance deterioration in both transfer tests. Relative timing error revealed that the observation of no variability and relative timing variability produced greater performance at the post-test, 24h retention and relative timing transfer tests, but for the no variability group, deteriorated at absolute timing transfer test. The results suggest that the learning of absolute timing following observation unfolds irrespective of model variability. However, the learning of relative timing benefits from holding the absolute features constant, while the observation of no variability partially fails in transfer. We suggest learning by observing no variability and variable/erred models unfolds via similar neural mechanisms, although the latter benefits from the additional coding of information pertaining to movements that require a correction. Copyright © 2017 Elsevier B.V. All rights reserved.

Early language processing efficiency predicts later receptive vocabulary outcomes in children born preterm.

PubMed

Marchman, Virginia A; Adams, Katherine A; Loi, Elizabeth C; Fernald, Anne; Feldman, Heidi M

2016-01-01

As rates of prematurity continue to rise, identifying which preterm children are at increased risk for learning disabilities is a public health imperative. Identifying continuities between early and later skills in this vulnerable population can also illuminate fundamental neuropsychological processes that support learning in all children. At 18 months adjusted age, we used socioeconomic status (SES), medical variables, parent-reported vocabulary, scores on the Bayley Scales of Infant and Toddler Development (third edition) language composite, and children's lexical processing speed in the looking-while-listening (LWL) task as predictor variables in a sample of 30 preterm children. Receptive vocabulary as measured by the Peabody Picture Vocabulary Test (fourth edition) at 36 months was the outcome. Receptive vocabulary was correlated with SES, but uncorrelated with degree of prematurity or a composite of medical risk. Importantly, lexical processing speed was the strongest predictor of receptive vocabulary (r = -.81), accounting for 30% unique variance. Individual differences in lexical processing efficiency may be able to serve as a marker for information processing skills that are critical for language learning.

Profiling medical school learning environments in Malaysia: a validation study of the Johns Hopkins Learning Environment Scale.

PubMed

Tackett, Sean; Bakar, Hamidah Abu; Shilkofski, Nicole A; Coady, Niamh; Rampal, Krishna; Wright, Scott

2015-01-01

While a strong learning environment is critical to medical student education, the assessment of medical school learning environments has confounded researchers. Our goal was to assess the validity and utility of the Johns Hopkins Learning Environment Scale (JHLES) for preclinical students at three Malaysian medical schools with distinct educational and institutional models. Two schools were new international partnerships, and the third was school leaver program established without international partnership. First- and second-year students responded anonymously to surveys at the end of the academic year. The surveys included the JHLES, a 28-item survey using five-point Likert scale response options, the Dundee Ready Educational Environment Measure (DREEM), the most widely used method to assess learning environments internationally, a personal growth scale, and single-item global learning environment assessment variables. The overall response rate was 369/429 (86%). After adjusting for the medical school year, gender, and ethnicity of the respondents, the JHLES detected differences across institutions in four out of seven domains (57%), with each school having a unique domain profile. The DREEM detected differences in one out of five categories (20%). The JHLES was more strongly correlated than the DREEM to two thirds of the single-item variables and the personal growth scale. The JHLES showed high internal reliability for the total score (α=0.92) and the seven domains (α, 0.56-0.85). The JHLES detected variation between learning environment domains across three educational settings, thereby creating unique learning environment profiles. Interpretation of these profiles may allow schools to understand how they are currently supporting trainees and identify areas needing attention.

Profiling medical school learning environments in Malaysia: a validation study of the Johns Hopkins Learning Environment Scale

PubMed Central

Tackett, Sean; Bakar, Hamidah Abu; Shilkofski, Nicole A.; Coady, Niamh; Rampal, Krishna; Wright, Scott

2015-01-01

Purpose: While a strong learning environment is critical to medical student education, the assessment of medical school learning environments has confounded researchers. Our goal was to assess the validity and utility of the Johns Hopkins Learning Environment Scale (JHLES) for preclinical students at three Malaysian medical schools with distinct educational and institutional models. Two schools were new international partnerships, and the third was school leaver program established without international partnership. Methods: First- and second-year students responded anonymously to surveys at the end of the academic year. The surveys included the JHLES, a 28-item survey using five-point Likert scale response options, the Dundee Ready Educational Environment Measure (DREEM), the most widely used method to assess learning environments internationally, a personal growth scale, and single-item global learning environment assessment variables. Results: The overall response rate was 369/429 (86%). After adjusting for the medical school year, gender, and ethnicity of the respondents, the JHLES detected differences across institutions in four out of seven domains (57%), with each school having a unique domain profile. The DREEM detected differences in one out of five categories (20%). The JHLES was more strongly correlated than the DREEM to two thirds of the single-item variables and the personal growth scale. The JHLES showed high internal reliability for the total score (α=0.92) and the seven domains (α, 0.56-0.85). Conclusion: The JHLES detected variation between learning environment domains across three educational settings, thereby creating unique learning environment profiles. Interpretation of these profiles may allow schools to understand how they are currently supporting trainees and identify areas needing attention. PMID:26165949

Examining Postsecondary Education Predictors and Participation for Students With Learning Disabilities.

PubMed

Joshi, Gauri S; Bouck, Emily C

2017-01-01

Given the history of poor postschool outcomes for students with disabilities, researchers repeatedly sought to demonstrate the links between predictor variables and postschool outcomes for students with disabilities. This secondary data analysis used the National Longitudinal Transition Study-2 to examine the relationship between postsecondary education-related transition services and postsecondary education participation for students with learning disabilities. Logistic regression analyses indicated receiving core content area instruction in the general education classroom was positively related to postsecondary education participation. Frequency distributions indicated students with learning disabilities attended 2-year college at higher rates than other postsecondary education programs. The results suggest educators should consider inclusion in general education classroom for core content area instruction for students with learning disabilities with postsecondary education goals to the extent permitted by their least restrictive environment. © Hammill Institute on Disabilities 2015.

Medical school clinical placements - the optimal method for assessing the clinical educational environment from a graduate entry perspective.

PubMed

Hyde, Sarah; Hannigan, Ailish; Dornan, Tim; McGrath, Deirdre

2018-01-05

Educational environment is a strong determinant of student satisfaction and achievement. The learning environments of medical students on clinical placements are busy workplaces, composed of many variables. There is no universally accepted method of evaluating the clinical learning environment, nor is there consensus on what concepts or aspects should be measured. The aims of this study were to compare the Dundee ready educational environment measure (DREEM - the current de facto standard) and the more recently developed Manchester clinical placement index (MCPI) for the assessment of the clinical learning environment in a graduate entry medical student cohort by correlating the scores of each and analysing free text comments. This study also explored student perceptionof how the clinical educational environment is assessed. An online, anonymous survey comprising of both the DREEM and MCPI instruments was delivered to students on clinical placement in a graduate entry medical school. Additional questions explored students' perceptions of instruments for giving feedback. Numeric variables (DREEM score, MCPI score, ratings) were tested for normality and summarised. Pearson's correlation coefficient was used to measure the strength of the association between total DREEM score and total MCPI scores. Thematic analysis was used to analyse the free text comments. The overall response rate to the questionnaire was 67% (n = 180), with a completed response rate for the MCPI of 60% (n = 161) and for the DREEM of 58% (n = 154). There was a strong, positive correlation between total DREEM and MCPI scores (r = 0.71, p < 0.001). On a scale of 0 to 7, the mean rating for how worthwhile students found completing the DREEM was 3.27 (SD 1.41) and for the MCPI was 3.49 (SD 1.57). 'Finding balance' and 'learning at work' were among the themes to emerge from analysis of free text comments. The present study confirms that DREEM and MCPI total scores are strongly correlated. Graduate entry students tended to favour this method of evaluation over the DREEM with the MCPI prompting rich description of the clinical learning environment. Further study is warranted to determine if this finding is transferable to all clinical medical student cohorts.

Learning Rate Updating Methods Applied to Adaptive Fuzzy Equalizers for Broadband Power Line Communications

NASA Astrophysics Data System (ADS)

Ribeiro, Moisés V.

2004-12-01

This paper introduces adaptive fuzzy equalizers with variable step size for broadband power line (PL) communications. Based on delta-bar-delta and local Lipschitz estimation updating rules, feedforward, and decision feedback approaches, we propose singleton and nonsingleton fuzzy equalizers with variable step size to cope with the intersymbol interference (ISI) effects of PL channels and the hardness of the impulse noises generated by appliances and nonlinear loads connected to low-voltage power grids. The computed results show that the convergence rates of the proposed equalizers are higher than the ones attained by the traditional adaptive fuzzy equalizers introduced by J. M. Mendel and his students. Additionally, some interesting BER curves reveal that the proposed techniques are efficient for mitigating the above-mentioned impairments.

Rethinking the learning of belief network probabilities

DOE Office of Scientific and Technical Information (OSTI.GOV)

Musick, R.

Belief networks are a powerful tool for knowledge discovery that provide concise, understandable probabilistic models of data. There are methods grounded in probability theory to incrementally update the relationships described by the belief network when new information is seen, to perform complex inferences over any set of variables in the data, to incorporate domain expertise and prior knowledge into the model, and to automatically learn the model from data. This paper concentrates on part of the belief network induction problem, that of learning the quantitative structure (the conditional probabilities), given the qualitative structure. In particular, the current practice of rotemore » learning the probabilities in belief networks can be significantly improved upon. We advance the idea of applying any learning algorithm to the task of conditional probability learning in belief networks, discuss potential benefits, and show results of applying neutral networks and other algorithms to a medium sized car insurance belief network. The results demonstrate from 10 to 100% improvements in model error rates over the current approaches.« less

Forgetting of Foreign-Language Skills: A Corpus-Based Analysis of Online Tutoring Software

ERIC Educational Resources Information Center

Ridgeway, Karl; Mozer, Michael C.; Bowles, Anita R.

2017-01-01

We explore the nature of forgetting in a corpus of 125,000 students learning Spanish using the Rosetta Stone® foreign-language instruction software across 48 lessons. Students are tested on a lesson after its initial study and are then retested after a variable time lag. We observe forgetting consistent with power function decay at a rate that…

Big Data Toolsets to Pharmacometrics: Application of Machine Learning for Time-to-Event Analysis.

PubMed

Gong, Xiajing; Hu, Meng; Zhao, Liang

2018-05-01

Additional value can be potentially created by applying big data tools to address pharmacometric problems. The performances of machine learning (ML) methods and the Cox regression model were evaluated based on simulated time-to-event data synthesized under various preset scenarios, i.e., with linear vs. nonlinear and dependent vs. independent predictors in the proportional hazard function, or with high-dimensional data featured by a large number of predictor variables. Our results showed that ML-based methods outperformed the Cox model in prediction performance as assessed by concordance index and in identifying the preset influential variables for high-dimensional data. The prediction performances of ML-based methods are also less sensitive to data size and censoring rates than the Cox regression model. In conclusion, ML-based methods provide a powerful tool for time-to-event analysis, with a built-in capacity for high-dimensional data and better performance when the predictor variables assume nonlinear relationships in the hazard function. © 2018 The Authors. Clinical and Translational Science published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.

The Impact of Individual Differences on E-Learning System Behavioral Intention

NASA Astrophysics Data System (ADS)

Liao, Peiwen; Yu, Chien; Yi, Chincheh

This study investigated the impact of contingent variables on the relationship between four predictors and employees' behavioral intention with e-learning. Seven hundred and twenty-two employees in online training and education were asked to answer questionnaires about their learning styles, perceptions of the quality of the proposed predictors and behavioral intention with e-learning systems. The results of analysis showed that three contingent variables, gender, job title and industry, significantly influenced the perceptions of predictors and employees' behavioral intention with the e-learning system. This study also found a statistically significant moderating effect of two contingent variables, gender, job title and industry, on the relationship between predictors and e-learning system behavioral intention. The results suggest that a serious consideration of contingent variables is crucial for improving e-learning system behavioral intention. The implications of these results for the management of e-learning systems are discussed.

Efficient Prediction of Low-Visibility Events at Airports Using Machine-Learning Regression

NASA Astrophysics Data System (ADS)

Cornejo-Bueno, L.; Casanova-Mateo, C.; Sanz-Justo, J.; Cerro-Prada, E.; Salcedo-Sanz, S.

2017-11-01

We address the prediction of low-visibility events at airports using machine-learning regression. The proposed model successfully forecasts low-visibility events in terms of the runway visual range at the airport, with the use of support-vector regression, neural networks (multi-layer perceptrons and extreme-learning machines) and Gaussian-process algorithms. We assess the performance of these algorithms based on real data collected at the Valladolid airport, Spain. We also propose a study of the atmospheric variables measured at a nearby tower related to low-visibility atmospheric conditions, since they are considered as the inputs of the different regressors. A pre-processing procedure of these input variables with wavelet transforms is also described. The results show that the proposed machine-learning algorithms are able to predict low-visibility events well. The Gaussian process is the best algorithm among those analyzed, obtaining over 98% of the correct classification rate in low-visibility events when the runway visual range is {>}1000 m, and about 80% under this threshold. The performance of all the machine-learning algorithms tested is clearly affected in extreme low-visibility conditions ({<}500 m). However, we show improved results of all the methods when data from a neighbouring meteorological tower are included, and also with a pre-processing scheme using a wavelet transform. Also presented are results of the algorithm performance in daytime and nighttime conditions, and for different prediction time horizons.

Learning a novel phonological contrast depends on interactions between individual differences and training paradigm design.

PubMed

Perrachione, Tyler K; Lee, Jiyeon; Ha, Louisa Y Y; Wong, Patrick C M

2011-07-01

Studies evaluating phonological contrast learning typically investigate either the predictiveness of specific pretraining aptitude measures or the efficacy of different instructional paradigms. However, little research considers how these factors interact--whether different students learn better from different types of instruction--and what the psychological basis for any interaction might be. The present study demonstrates that successfully learning a foreign-language phonological contrast for pitch depends on an interaction between individual differences in perceptual abilities and the design of the training paradigm. Training from stimuli with high acoustic-phonetic variability is generally thought to improve learning; however, we found high-variability training enhanced learning only for individuals with strong perceptual abilities. Learners with weaker perceptual abilities were actually impaired by high-variability training relative to a low-variability condition. A second experiment assessing variations on the high-variability training design determined that the property of this learning environment most detrimental to perceptually weak learners is the amount of trial-by-trial variability. Learners' perceptual limitations can thus override the benefits of high-variability training where trial-by-trial variability in other irrelevant acoustic-phonetic features obfuscates access to the target feature. These results demonstrate the importance of considering individual differences in pretraining aptitudes when evaluating the efficacy of any speech training paradigm. © 2011 Acoustical Society of America

Learning environment assessments of a single curriculum being taught at two medical schools 10,000 miles apart.

PubMed

Tackett, Sean; Shochet, Robert; Shilkofski, Nicole A; Colbert-Getz, Jorie; Rampal, Krishna; Abu Bakar, Hamidah; Wright, Scott

2015-06-17

Perdana University Graduate School of Medicine (PUGSOM), the first graduate-entry medical school in Malaysia, was established in 2011 in collaboration with Johns Hopkins University School of Medicine (JHUSOM), an American medical school. This study compared learning environments (LE) at these two schools, which shared the same overarching curriculum, along with a comparator Malaysian medical school, Cyberjaya University College of Medical Sciences (CUCMS). As a secondary aim, we compared 2 LE assessment tools - the widely-used Dundee Ready Educational Environment Measure (DREEM) and the newer Johns Hopkins Learning Environment Scale (JHLES). Students responded anonymously at the end of their first year of medical school to surveys which included DREEM, JHLES, single-item global LE assessment variables, and demographics questions. Respondents included 24/24 (100 %) students at PUGSOM, 100/120 (83 %) at JHUSOM, and 79/83 (95 %) at CUCMS. PUGSOM had the highest overall LE ratings (p < 0.05) [DREEM 155.3 (SD 21.3); JHLES 116.5 (SD 12.2)], followed by JHUSOM [DREEM 143.3 (SD 22.5); JHLES 111.7 (SD 12.0)] and CUCMS [DREEM 138.5 (SD 22.4); JHLES 106.4 (SD 14.5)]. PUGSOM's overall high LE ratings were driven by responses in "perception of teaching," "meaningful engagement," and "acceptance and safety" domains. JHLES detected significant differences across schools in 5/7 domains and had stronger correlations than DREEM to each global LE assessment variable. The inaugural class of medical students at PUGSOM rated their LE exceptionally highly, providing evidence that transporting a medical school curriculum may be successful. The JHLES showed promise as a LE assessment tool for use in international settings.

Post-task Effects on EEG Brain Activity Differ for Various Differential Learning and Contextual Interference Protocols

PubMed Central

Henz, Diana; John, Alexander; Merz, Christian; Schöllhorn, Wolfgang I.

2018-01-01

A large body of research has shown superior learning rates in variable practice compared to repetitive practice. More specifically, this has been demonstrated in the contextual interference (CI) and in the differential learning (DL) approach that are both representatives of variable practice. Behavioral studies have indicate different learning processes in CI and DL. Aim of the present study was to examine immediate post-task effects on electroencephalographic (EEG) brain activation patterns after CI and DL protocols that reveal underlying neural processes at the early stage of motor consolidation. Additionally, we tested two DL protocols (gradual DL, chaotic DL) to examine the effect of different degrees of stochastic fluctuations within the DL approach with a low degree of fluctuations in gradual DL and a high degree of fluctuations in chaotic DL. Twenty-two subjects performed badminton serves according to three variable practice protocols (CI, gradual DL, chaotic DL), and a repetitive learning protocol in a within-subjects design. Spontaneous EEG activity was measured before, and immediately after each 20-min practice session from 19 electrodes. Results showed distinguishable neural processes after CI, DL, and repetitive learning. Increases in EEG theta and alpha power were obtained in somatosensory regions (electrodes P3, P7, Pz, P4, P8) in both DL conditions compared to CI, and repetitive learning. Increases in theta and alpha activity in motor areas (electrodes C3, Cz, C4) were found after chaotic DL compared to gradual DL, and CI. Anterior areas (electrodes F3, F7, Fz, F4, F8) showed increased activity in the beta and gamma bands after CI. Alpha activity was increased in occipital areas (electrodes O1, O2) after repetitive learning. Post-task EEG brain activation patterns suggest that DL stimulates the somatosensory and motor system, and engages more regions of the cortex than repetitive learning due to a tighter stimulation of the motor and somatosensory system during DL practice. CI seems to activate specifically executively controlled processing in anterior brain areas. We discuss the obtained patterns of post-training EEG traces as evidence for different underlying neural processes in CI, DL, and repetitive learning at the early stage of motor learning. PMID:29445334

Beatquency domain and machine learning improve prediction of cardiovascular death after acute coronary syndrome.

PubMed

Liu, Yun; Scirica, Benjamin M; Stultz, Collin M; Guttag, John V

2016-10-06

Frequency domain measures of heart rate variability (HRV) are associated with adverse events after a myocardial infarction. However, patterns in the traditional frequency domain (measured in Hz, or cycles per second) may capture different cardiac phenomena at different heart rates. An alternative is to consider frequency with respect to heartbeats, or beatquency. We compared the use of frequency and beatquency domains to predict patient risk after an acute coronary syndrome. We then determined whether machine learning could further improve the predictive performance. We first evaluated the use of pre-defined frequency and beatquency bands in a clinical trial dataset (N = 2302) for the HRV risk measure LF/HF (the ratio of low frequency to high frequency power). Relative to frequency, beatquency improved the ability of LF/HF to predict cardiovascular death within one year (Area Under the Curve, or AUC, of 0.730 vs. 0.704, p < 0.001). Next, we used machine learning to learn frequency and beatquency bands with optimal predictive power, which further improved the AUC for beatquency to 0.753 (p < 0.001), but not for frequency. Results in additional validation datasets (N = 2255 and N = 765) were similar. Our results suggest that beatquency and machine learning provide valuable tools in physiological studies of HRV.

Autocorrelation structure at rest predicts value correlates of single neurons during reward-guided choice

PubMed Central

Cavanagh, Sean E; Wallis, Joni D; Kennerley, Steven W; Hunt, Laurence T

2016-01-01

Correlates of value are routinely observed in the prefrontal cortex (PFC) during reward-guided decision making. In previous work (Hunt et al., 2015), we argued that PFC correlates of chosen value are a consequence of varying rates of a dynamical evidence accumulation process. Yet within PFC, there is substantial variability in chosen value correlates across individual neurons. Here we show that this variability is explained by neurons having different temporal receptive fields of integration, indexed by examining neuronal spike rate autocorrelation structure whilst at rest. We find that neurons with protracted resting temporal receptive fields exhibit stronger chosen value correlates during choice. Within orbitofrontal cortex, these neurons also sustain coding of chosen value from choice through the delivery of reward, providing a potential neural mechanism for maintaining predictions and updating stored values during learning. These findings reveal that within PFC, variability in temporal specialisation across neurons predicts involvement in specific decision-making computations. DOI: http://dx.doi.org/10.7554/eLife.18937.001 PMID:27705742

Human-robot cooperative movement training: learning a novel sensory motor transformation during walking with robotic assistance-as-needed.

PubMed

Emken, Jeremy L; Benitez, Raul; Reinkensmeyer, David J

2007-03-28

A prevailing paradigm of physical rehabilitation following neurologic injury is to "assist-as-needed" in completing desired movements. Several research groups are attempting to automate this principle with robotic movement training devices and patient cooperative algorithms that encourage voluntary participation. These attempts are currently not based on computational models of motor learning. Here we assume that motor recovery from a neurologic injury can be modelled as a process of learning a novel sensory motor transformation, which allows us to study a simplified experimental protocol amenable to mathematical description. Specifically, we use a robotic force field paradigm to impose a virtual impairment on the left leg of unimpaired subjects walking on a treadmill. We then derive an "assist-as-needed" robotic training algorithm to help subjects overcome the virtual impairment and walk normally. The problem is posed as an optimization of performance error and robotic assistance. The optimal robotic movement trainer becomes an error-based controller with a forgetting factor that bounds kinematic errors while systematically reducing its assistance when those errors are small. As humans have a natural range of movement variability, we introduce an error weighting function that causes the robotic trainer to disregard this variability. We experimentally validated the controller with ten unimpaired subjects by demonstrating how it helped the subjects learn the novel sensory motor transformation necessary to counteract the virtual impairment, while also preventing them from experiencing large kinematic errors. The addition of the error weighting function allowed the robot assistance to fade to zero even though the subjects' movements were variable. We also show that in order to assist-as-needed, the robot must relax its assistance at a rate faster than that of the learning human. The assist-as-needed algorithm proposed here can limit error during the learning of a dynamic motor task. The algorithm encourages learning by decreasing its assistance as a function of the ongoing progression of movement error. This type of algorithm is well suited for helping people learn dynamic tasks for which large kinematic errors are dangerous or discouraging, and thus may prove useful for robot-assisted movement training of walking or reaching following neurologic injury.

Psychological and Organizational Variables Associated with Workplace Learning in Small and Medium Manufacturing Businesses in Korea

ERIC Educational Resources Information Center

Moon, Se-Yeon; Na, Seung-Il

2009-01-01

The purpose of this study was to determine the relationship between workplace learning and psychological variables, such as learning competency, motivation, curiosity, self-esteem and locus of control, and organizational variables, such as centralization of power, formality, merit system and communication. The studied population consisted entirely…

The neural correlates of learned motor acuity

PubMed Central

Yang, Juemin; Caffo, Brian; Mazzoni, Pietro; Krakauer, John W.

2014-01-01

We recently defined a component of motor skill learning as “motor acuity,” quantified as a shift in the speed-accuracy trade-off function for a task. These shifts are primarily driven by reductions in movement variability. To determine the neural correlates of improvement in motor acuity, we devised a motor task compatible with magnetic resonance brain imaging that required subjects to make finely controlled wrist movements under visual guidance. Subjects were imaged on day 1 and day 5 while they performed this task and were trained outside the scanner on intervening days 2, 3, and 4. The potential confound of performance changes between days 1 and 5 was avoided by constraining movement time to a fixed duration. After training, subjects showed a marked increase in success rate and a reduction in trial-by-trial variability for the trained task but not for an untrained control task, without changes in mean trajectory. The decrease in variability for the trained task was associated with increased activation in contralateral primary motor and premotor cortical areas and in ipsilateral cerebellum. A global nonlocalizing multivariate analysis confirmed that learning was associated with increased overall brain activation. We suggest that motor acuity is acquired through increases in the number of neurons recruited in contralateral motor cortical areas and in ipsilateral cerebellum, which could reflect increased signal-to-noise ratio in motor output and improved state estimation for feedback corrections, respectively. PMID:24848466

Management practices associated with conception rate and service rate of lactating Holstein cows in large, commercial dairy herds.

PubMed

Schefers, J M; Weigel, K A; Rawson, C L; Zwald, N R; Cook, N B

2010-04-01

Data from lactating Holstein cows in herds that participate in a commercial progeny testing program were analyzed to explain management factors associated with herd-average conception and service rates on large commercial dairies. On-farm herd management software was used as the source of data related to production, reproduction, culling, and milk quality for 108 herds. Also, a survey regarding management, facilities, nutrition, and labor was completed on 86 farms. A total of 41 explanatory variables related to management factors and conditions that could affect conception and service rate were considered in this study. Models explaining conception and service rates were developed using a machine learning algorithm for constructing model trees. The most important explanatory variables associated with conception rate were the percentage of repeated inseminations between 4 and 17 d post-artificial insemination, stocking density in the breeding pen, length of the voluntary waiting period, days at pregnancy examination, and somatic cell score. The most important explanatory variables associated with service rate were the number of lactating cows per breeding technician, use of a resynchronization program, utilization of soakers in the holding area during the summer, and bunk space per cow in the breeding pen. The aforementioned models explained 35% and 40% of the observed variation in conception rate and service rate, respectively, and underline the association of herd-level management factors not strictly related to reproduction with herd reproductive performance. Copyright (c) 2010 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Learning curve for intracranial angioplasty and stenting in single center.

PubMed

Cai, Qiankun; Li, Yongkun; Xu, Gelin; Sun, Wen; Xiong, Yunyun; Sun, Wenshan; Bao, Yuanfei; Huang, Xianjun; Zhang, Yao; Zhou, Lulu; Zhu, Wusheng; Liu, Xinfeng

2014-01-01

To identify the specific caseload to overcome learning curve effect based on data from consecutive patients treated with Intracranial Angioplasty and Stenting (IAS) in our center. The Stenting and Aggressive Medical Management for Preventing Recurrent Stroke and Intracranial Stenosis trial was prematurely terminated owing to the high rate of periprocedural complications in the endovascular arm. To date, there are no data available for determining the essential caseload sufficient to overcome the learning effect and perform IAS with an acceptable level of complications. Between March 2004 and May 2012, 188 consecutive patients with 194 lesions who underwent IAS were analyzed retrospectively. The outcome variables used to assess the learning curve were periprocedural complications (included transient ischemic attack, ischemic stroke, vessel rupture, cerebral hyperperfusion syndrome, and vessel perforation). Multivariable logistic regression analysis was employed to illustrate the existence of learning curve effect on IAS. A risk-adjusted cumulative sum chart was performed to identify the specific caseload to overcome learning curve effect. The overall rate of 30-days periprocedural complications was 12.4% (24/194). After adjusting for case-mix, multivariate logistic regression analysis showed that operator experience was an independent predictor for periprocedural complications. The learning curve of IAS to overcome complications in a risk-adjusted manner was 21 cases. Operator's level of experience significantly affected the outcome of IAS. Moreover, we observed that the amount of experience sufficient for performing IAS in our center was 21 cases. Copyright © 2013 Wiley Periodicals, Inc.

Draft genome assembly of the Bengalese finch, Lonchura striata domestica, a model for motor skill variability and learning

PubMed Central

Mets, David G; Brainard, Michael S

2018-01-01

Abstract Background Vocal learning in songbirds has emerged as a powerful model for sensorimotor learning. Neurobehavioral studies of Bengalese finch (Lonchura striata domestica) song, naturally more variable and plastic than songs of other finch species, have demonstrated the importance of behavioral variability for initial learning, maintenance, and plasticity of vocalizations. However, the molecular and genetic underpinnings of this variability and the learning it supports are poorly understood. Findings To establish a platform for the molecular analysis of behavioral variability and plasticity, we generated an initial draft assembly of the Bengalese finch genome from a single male animal to 151× coverage and an N50 of 3.0 MB. Furthermore, we developed an initial set of gene models using RNA-seq data from 8 samples that comprise liver, muscle, cerebellum, brainstem/midbrain, and forebrain tissue from juvenile and adult Bengalese finches of both sexes. Conclusions We provide a draft Bengalese finch genome and gene annotation to facilitate the study of the molecular-genetic influences on behavioral variability and the process of vocal learning. These data will directly support many avenues for the identification of genes involved in learning, including differential expression analysis, comparative genomic analysis (through comparison to existing avian genome assemblies), and derivation of genetic maps for linkage analysis. Bengalese finch gene models and sequences will be essential for subsequent manipulation (molecular or genetic) of genes and gene products, enabling novel mechanistic investigations into the role of variability in learned behavior. PMID:29618046

Draft genome assembly of the Bengalese finch, Lonchura striata domestica, a model for motor skill variability and learning.

PubMed

Colquitt, Bradley M; Mets, David G; Brainard, Michael S

2018-03-01

Vocal learning in songbirds has emerged as a powerful model for sensorimotor learning. Neurobehavioral studies of Bengalese finch (Lonchura striata domestica) song, naturally more variable and plastic than songs of other finch species, have demonstrated the importance of behavioral variability for initial learning, maintenance, and plasticity of vocalizations. However, the molecular and genetic underpinnings of this variability and the learning it supports are poorly understood. To establish a platform for the molecular analysis of behavioral variability and plasticity, we generated an initial draft assembly of the Bengalese finch genome from a single male animal to 151× coverage and an N50 of 3.0 MB. Furthermore, we developed an initial set of gene models using RNA-seq data from 8 samples that comprise liver, muscle, cerebellum, brainstem/midbrain, and forebrain tissue from juvenile and adult Bengalese finches of both sexes. We provide a draft Bengalese finch genome and gene annotation to facilitate the study of the molecular-genetic influences on behavioral variability and the process of vocal learning. These data will directly support many avenues for the identification of genes involved in learning, including differential expression analysis, comparative genomic analysis (through comparison to existing avian genome assemblies), and derivation of genetic maps for linkage analysis. Bengalese finch gene models and sequences will be essential for subsequent manipulation (molecular or genetic) of genes and gene products, enabling novel mechanistic investigations into the role of variability in learned behavior.

Academic achievement in children with epilepsy: a review.

PubMed

Reilly, Colin; Neville, Brian G R

2011-11-01

To examine published studies which have focussed on academic achievement in children with epilepsy with respect to prevalence rates of academic difficulties and possible correlates of academic achievement. This review examines studies which have focussed on prevalence rates of academic difficulties and correlates of academic achievement in children with epilepsy from 1990 to 2010. Prevalence rates of low academic achievement and academic underachievement are reported and correlates of academic achievement including seizure/epilepsy variables, demographic variables, and child/family variables are examined with respect to published studies. Published studies suggest that low academic achievement is more common than academic underachievement (achievement below that expected on basis of IQ scores) and it is not clear from published studies if rates of academic underachievement are significantly higher than in the general population. Clear patterns with regard to the identification of correlates of academic underachievement have not emerged although low achievement may be influenced in many cases by lower than average levels of cognitive functioning. Most studies have not focussed on the IQ-achievement discrepancy definitions of (specific) learning disability. Children with epilepsy who are experiencing academic difficulties may not qualify for formal educational supports to address these difficulties if eligibility criteria for such supports stress an IQ-achievement discrepancy. Copyright © 2011 Elsevier B.V. All rights reserved.

Machine learning and linear regression models to predict catchment-level base cation weathering rates across the southern Appalachian Mountain region, USA

Treesearch

Nicholas A. Povak; Paul F. Hessburg; Todd C. McDonnell; Keith M. Reynolds; Timothy J. Sullivan; R. Brion Salter; Bernard J. Crosby

2014-01-01

Accurate estimates of soil mineral weathering are required for regional critical load (CL) modeling to identify ecosystems at risk of the deleterious effects from acidification. Within a correlative modeling framework, we used modeled catchment-level base cation weathering (BCw) as the response variable to identify key environmental correlates and predict a continuous...

Analysis of Quality and Output of Entrepreneurship in the Field of Refractionist Optician

NASA Astrophysics Data System (ADS)

Wesnita, A.; Dewi, M.

2018-02-01

The launching of the Asean Economic Community (AEC) caused a rivalry on the exchange of the work, especially workers who are involved in the sector of specific expertise that increased sharply. The solution offered is through the implementation of entrepreneurship learning, but despite entrepreneurship courses have been given to students since 2007, data from the last three years states only 21% of graduates of Refractionist Optician Academy in Padang, Indonesia, which became entrepreneurs in the fields of optics and eye health. Therefore, there needs to be an assessment whether the 5 variables of learning process that has been done three variables affect the output quality of learning entrepreneurship. This study has a quantitative method to determine the effect on output quality of the learning process of learning through regression analysis, test t, R2 and the percentage of contribution. The population consist of 121 students and the sample is 54 people. The study states that the academic skills of entrepreneurship is influenced by four variables of learning process, personnel skills of entrepreneurship is not influenced by any learning variable process, and social skills of entrepreneurship is influenced by three variables learning process.

Level of recall, retrieval speed, and variability on the Cued-Recall Retrieval Speed Task (CRRST) in individuals with amnestic mild cognitive impairment.

PubMed

Ramratan, Wendy S; Rabin, Laura A; Wang, Cuiling; Zimmerman, Molly E; Katz, Mindy J; Lipton, Richard B; Buschke, Herman

2012-03-01

Individuals with amnestic mild cognitive impairment (aMCI) show deficits on traditional episodic memory tasks and reductions in speed of performance on reaction time tasks. We present results on a novel task, the Cued-Recall Retrieval Speed Task (CRRST), designed to simultaneously measure level and speed of retrieval. A total of 390 older adults (mean age, 80.2 years), learned 16 words based on corresponding categorical cues. In the retrieval phase, we measured accuracy (% correct) and retrieval speed/reaction time (RT; time from cue presentation to voice onset of a correct response) across 6 trials. Compared to healthy elderly adults (HEA, n = 303), those with aMCI (n = 87) exhibited poorer performance in retrieval speed (difference = -0.13; p < .0001) and accuracy on the first trial (difference = -0.19; p < .0001), and their rate of improvement in retrieval speed was slower over subsequent trials. Those with aMCI also had greater within-person variability in processing speed (variance ratio = 1.22; p = .0098) and greater between-person variability in accuracy (variance ratio = 2.08; p = .0001) relative to HEA. Results are discussed in relation to the possibility that computer-based measures of cued-learning and processing speed variability may facilitate early detection of dementia in at-risk older adults.

Assessing learning styles of Saudi dental students using Kolb's Learning Style Inventory.

PubMed

ALQahtani, Dalal A; Al-Gahtani, Sara M

2014-06-01

Experiential learning theory (ELT), a theory developed by David Kolb that considers experience to be very important for learning, classifies learners into four categories: Divergers, Assimilators, Convergers, and Accommodators. Kolb used his Learning Style Inventory (LSI) to validate ELT. Knowing the learning styles of students facilitates their understanding of themselves and thereby increases teaching efficiency. Few studies have been conducted that investigate learning preferences of students in the field of dentistry. This study was designed to distinguish learning styles among Saudi dental students and interns utilizing Kolb's LSI. The survey had a response rate of 62 percent (424 of 685 dental students), but surveys with incomplete answers or errors were excluded, resulting in 291 usable surveys (42 percent of the student population). The independent variables of this study were gender, clinical experience level, academic achievement as measured by grade point average (GPA), and specialty interest. The Diverging learning style was the dominant style among those in the sample. While the students preferred the Assimilating style during their early preclinical years, they preferred the Diverging style during their later clinical years. No associations were found between students' learning style and their gender, GPA, or specialty interest. Further research is needed to support these findings and demonstrate the impact of learning styles on dental students' learning.

Using the Contextual Model of Learning to Understand Visitor Learning from a Science Center Exhibition

ERIC Educational Resources Information Center

Falk, John; Storksdieck, Martin

2005-01-01

Falk and Dierking's Contextual Model of Learning was used as a theoretical construct for investigating learning within a free-choice setting. A review of previous research identified key variables fundamental to free-choice science learning. The study sought to answer two questions: (1) How do specific independent variables individually contribute…

Effects of Self-Explanation and Game-Reward on Sixth Graders' Algebra Variable Learning

ERIC Educational Resources Information Center

Sun-Lin, Hong-Zheng; Chiou, Guey-Fa

2017-01-01

This study examined the interaction effects of self-explanation and game-reward strategies on sixth graders' algebra variable learning achievement, learning attitude, and meta-cognitive awareness. A learning system was developed to support the learning activity, and a 2×2 quasi-experiment was conducted. Ninety-seven students were invited to…

How does informational heterogeneity affect the quality of forecasts?

NASA Astrophysics Data System (ADS)

Gualdi, S.; De Martino, A.

2010-01-01

We investigate a toy model of inductive interacting agents aiming to forecast a continuous, exogenous random variable E. Private information on E is spread heterogeneously across agents. Herding turns out to be the preferred forecasting mechanism when heterogeneity is maximal. However in such conditions aggregating information efficiently is hard even in the presence of learning, as the herding ratio rises significantly above the efficient market expectation of 1 and remarkably close to the empirically observed values. We also study how different parameters (interaction range, learning rate, cost of information and score memory) may affect this scenario and improve efficiency in the hard phase.

Effects of Example Variability and Prior Knowledge in How Students Learn to Solve Equations

ERIC Educational Resources Information Center

Guo, Jian-Peng; Yang, Ling-Yan; Ding, Yi

2014-01-01

Researchers have consistently demonstrated that multiple examples are better than one example in facilitating learning because the comparison evoked by multiple examples supports learning and transfer. However, research outcomes are unclear regarding the effects of example variability and prior knowledge on learning from comparing multiple…

Effects of Group Awareness and Self-Regulation Level on Online Learning Behaviors

ERIC Educational Resources Information Center

Lin, Jian-Wei; Szu, Yu-Chin; Lai, Ching-Neng

2016-01-01

Group awareness can affect student online learning while self-regulation also can substantially influence student online learning. Although some studies identify that these two variables may partially determine learning behavior, few empirical studies or thorough analyses elucidate the simultaneous impact of these two variables (group awareness…

Testing communication strategies to convey genomic concepts using virtual reality technology.

PubMed

Kaphingst, Kimberly A; Persky, Susan; McCall, Cade; Lachance, Christina; Beall, Andrew C; Blascovich, Jim

2009-06-01

Health professionals need to be able to communicate information about genomic susceptibility in understandable and usable ways, but substantial challenges are involved. We developed four learning modules that varied along two factors: (1) learning mode (active learning vs. didactic learning) and (2) metaphor (risk elevator vs. bridge) and tested them using a 2 x 2 between-subjects, repeated measures design. The study used an innovative virtual reality technology experimental platform; four virtual worlds were designed to convey the concept that genetic and behavioral factors interact to affect common disease risk. The primary outcome was comprehension (recall, transfer). Study participants were 42 undergraduates aged 19-23. The results indicated that the elevator metaphor better supported learning of the concept than the bridge metaphor. Mean transfer score was significantly higher for the elevator metaphor (p < 0.05). Mean change in recall was significantly higher for didactic learning than active learning (p < 0.05). Mean ratings for variables posited to be associated with better learning (e.g., motivation), however, were generally higher for the active learning worlds. The results suggested that active learning might not always be more effective than didactic learning in increasing comprehension of health information. The findings also indicated that less complex metaphors might convey abstract concepts more effectively.

Testing Communication Strategies to Convey Genomic Concepts Using Virtual Reality Technology

PubMed Central

Kaphingst, Kimberly A.; Persky, Susan; McCall, Cade; Lachance, Christina; Beall, Andrew C.; Blascovich, Jim

2009-01-01

Health professionals need to be able to communicate information about genomic susceptibility in understandable and usable ways, but substantial challenges are involved. We developed four learning modules that varied along two factors: (1) learning mode (active learning vs. didactic learning) and (2) metaphor (risk elevator vs. bridge) and tested them using a 2×2 between-subjects, repeated measures design. The study used an innovative virtual reality technology experimental platform; four virtual worlds were designed to convey the concept that genetic and behavioral factors interact to affect common disease risk. The primary outcome was comprehension (recall, transfer). Study participants were 42 undergraduates aged 19–23. The results indicated that the elevator metaphor better supported learning of the concept than the bridge metaphor. Mean transfer score was significantly higher for the elevator metaphor (p<0.05). Mean change in recall was significantly higher for didactic learning than active learning (p<0.05). However, mean ratings for variables posited to be associated with better learning (e.g., motivation) were generally higher for the active learning worlds. The results suggested that active learning might not always be more effective than didactic learning in increasing comprehension of health information. The findings also indicated that less complex metaphors might convey abstract concepts more effectively. PMID:19466649

Exemplar Variability Facilitates Retention of Word Learning by Children with Specific Language Impairment

ERIC Educational Resources Information Center

Aguilar, Jessica M.; Plante, Elena; Sandoval, Michelle

2018-01-01

Purpose: Variability in the input plays an important role in language learning. The current study examined the role of object variability for new word learning by preschoolers with specific language impairment (SLI). Method: Eighteen 4- and 5-year-old children with SLI were taught 8 new words in 3 short activities over the course of 3 sessions.…

Input Variability Facilitates Unguided Subcategory Learning in Adults

PubMed Central

Eidsvåg, Sunniva Sørhus; Austad, Margit; Asbjørnsen, Arve E.

2015-01-01

Purpose This experiment investigated whether input variability would affect initial learning of noun gender subcategories in an unfamiliar, natural language (Russian), as it is known to assist learning of other grammatical forms. Method Forty adults (20 men, 20 women) were familiarized with examples of masculine and feminine Russian words. Half of the participants were familiarized with 32 different root words in a high-variability condition. The other half were familiarized with 16 different root words, each repeated twice for a total of 32 presentations in a high-repetition condition. Participants were tested on untrained members of the category to assess generalization. Familiarization and testing was completed 2 additional times. Results Only participants in the high-variability group showed evidence of learning after an initial period of familiarization. Participants in the high-repetition group were able to learn after additional input. Both groups benefited when words included 2 cues to gender compared to a single cue. Conclusions The results demonstrate that the degree of input variability can influence learners' ability to generalize a grammatical subcategory (noun gender) from a natural language. In addition, the presence of multiple cues to linguistic subcategory facilitated learning independent of variability condition. PMID:25680081

Input Variability Facilitates Unguided Subcategory Learning in Adults.

PubMed

Eidsvåg, Sunniva Sørhus; Austad, Margit; Plante, Elena; Asbjørnsen, Arve E

2015-06-01

This experiment investigated whether input variability would affect initial learning of noun gender subcategories in an unfamiliar, natural language (Russian), as it is known to assist learning of other grammatical forms. Forty adults (20 men, 20 women) were familiarized with examples of masculine and feminine Russian words. Half of the participants were familiarized with 32 different root words in a high-variability condition. The other half were familiarized with 16 different root words, each repeated twice for a total of 32 presentations in a high-repetition condition. Participants were tested on untrained members of the category to assess generalization. Familiarization and testing was completed 2 additional times. Only participants in the high-variability group showed evidence of learning after an initial period of familiarization. Participants in the high-repetition group were able to learn after additional input. Both groups benefited when words included 2 cues to gender compared to a single cue. The results demonstrate that the degree of input variability can influence learners' ability to generalize a grammatical subcategory (noun gender) from a natural language. In addition, the presence of multiple cues to linguistic subcategory facilitated learning independent of variability condition.

Accuracy of latent-variable estimation in Bayesian semi-supervised learning.

PubMed

Yamazaki, Keisuke

2015-09-01

Hierarchical probabilistic models, such as Gaussian mixture models, are widely used for unsupervised learning tasks. These models consist of observable and latent variables, which represent the observable data and the underlying data-generation process, respectively. Unsupervised learning tasks, such as cluster analysis, are regarded as estimations of latent variables based on the observable ones. The estimation of latent variables in semi-supervised learning, where some labels are observed, will be more precise than that in unsupervised, and one of the concerns is to clarify the effect of the labeled data. However, there has not been sufficient theoretical analysis of the accuracy of the estimation of latent variables. In a previous study, a distribution-based error function was formulated, and its asymptotic form was calculated for unsupervised learning with generative models. It has been shown that, for the estimation of latent variables, the Bayes method is more accurate than the maximum-likelihood method. The present paper reveals the asymptotic forms of the error function in Bayesian semi-supervised learning for both discriminative and generative models. The results show that the generative model, which uses all of the given data, performs better when the model is well specified. Copyright © 2015 Elsevier Ltd. All rights reserved.

Machine learning approaches to the social determinants of health in the health and retirement study.

PubMed

Seligman, Benjamin; Tuljapurkar, Shripad; Rehkopf, David

2018-04-01

Social and economic factors are important predictors of health and of recognized importance for health systems. However, machine learning, used elsewhere in the biomedical literature, has not been extensively applied to study relationships between society and health. We investigate how machine learning may add to our understanding of social determinants of health using data from the Health and Retirement Study. A linear regression of age and gender, and a parsimonious theory-based regression additionally incorporating income, wealth, and education, were used to predict systolic blood pressure, body mass index, waist circumference, and telomere length. Prediction, fit, and interpretability were compared across four machine learning methods: linear regression, penalized regressions, random forests, and neural networks. All models had poor out-of-sample prediction. Most machine learning models performed similarly to the simpler models. However, neural networks greatly outperformed the three other methods. Neural networks also had good fit to the data ( R 2 between 0.4-0.6, versus <0.3 for all others). Across machine learning models, nine variables were frequently selected or highly weighted as predictors: dental visits, current smoking, self-rated health, serial-seven subtractions, probability of receiving an inheritance, probability of leaving an inheritance of at least $10,000, number of children ever born, African-American race, and gender. Some of the machine learning methods do not improve prediction or fit beyond simpler models, however, neural networks performed well. The predictors identified across models suggest underlying social factors that are important predictors of biological indicators of chronic disease, and that the non-linear and interactive relationships between variables fundamental to the neural network approach may be important to consider.

Observed differences in learning ability of heart rate self-regulation as a function of hypnotic susceptibility

NASA Technical Reports Server (NTRS)

Cowings, P. S.

1977-01-01

Three groups of eight male and female subjects (aged 20-27 yr) categorized by low and high hypnotic susceptibility were taught to control their heart rates by means of an appropriate autogenic therapy/biofeedback technique. The experimental groups were trained by autogenic therapy and biofeedback, while the control group received only biofeedback. Significant differences are observed in all psychological test scores between subjects of high and low hypnotic susceptibility. The results confirm that (1) there are qualitative and quantitative differences between the performance of individuals with high and low hypnotic susceptibility; (2) interindividual-variability tests yield data relevant to individual performance in visceral learning tasks; (3) the combined autogenic therapy/biofeedback/verbal feedback technique is suitable for conditioning large stable autonomic responses in humans; and (4) this kind of conditioning is effective in eliminating or alleviating physiological reactions to some environmental stressors.

Learning-related skills and academic achievement in academically at-risk first graders

PubMed Central

Cerda, Carissa A.; Im, Myung Hee; Hughes, Jan N.

2015-01-01

Using an academically at-risk, ethnically diverse sample of 744 first-grade children, this study tested a multi-method (i.e., child performance measures, teacher ratings, and peer ratings) measurement model of learning-related skills (i.e., effortful control [EC], behavioral self-regulation [BSR], and social competence [SC]), and their shared and unique contributions to children's reading and math achievement, above the effect of demographic variables. The hypothesized correlated factor measurement model demonstrated relatively good fit, with BSR and SC correlated highly with one another and moderately with EC. When entered in separate regression equations, EC and BSR each predicted children's reading and math achievement; SC only predicted reading achievement. When considered simultaneously, neither EC, BSR, nor SC contributed independently to reading achievement; however, EC had a direct effect on math achievement and an indirect effect on reading achievement via both BSR and SC. Implications for research and early intervention efforts are discussed. PMID:25908886

Dataset on Investigating the role of onsite learning in the optimisation of craft gang's productivity in the construction industry.

PubMed

Ugulu, Rex Asibuodu; Allen, Stephen

2017-12-01

The data presented in this article is an original data on "Investigating the role of onsite learning in the optimisation of craft gang's productivity in the construction industry". This article describes the constraints influencing craft gang's productivity and the influence of onsite learning on the blockwork craft gang's productivity. It also presented the method of data collection, using a semi-structured interview and an observation method to collect data from construction organisations. We provided statistics on the top most important constraints affecting the craft gang's productivity using 3-D Bar charts. In addition, we computed the correlation coefficients and the regression model on the influence of onsite learning on craft gang's productivity using the man-hour as the dependent variable. The relationship between blockwork inputs and cycle numbers was determined at 5% significance level. Finally, we presented data information on the application of the learning curve theory using the unit straight-line model equations and computed the learning rate of the observed craft gang's blockwork repetitive work.

Study preferences for exemplar variability in self-regulated category learning.

PubMed

Wahlheim, Christopher N; DeSoto, K Andrew

2017-02-01

Increasing exemplar variability during category learning can enhance classification of novel exemplars from studied categories. Four experiments examined whether participants preferred variability when making study choices with the goal of later classifying novel exemplars. In Experiments 1-3, participants were familiarised with exemplars of birds from multiple categories prior to making category-level assessments of learning and subsequent choices about whether to receive more variability or repetitions of exemplars during study. After study, participants classified novel exemplars from studied categories. The majority of participants showed a consistent preference for variability in their study, but choices were not related to category-level assessments of learning. Experiment 4 provided evidence that study preferences were based primarily on theoretical beliefs in that most participants indicated a preference for variability on questionnaires that did not include prior experience with exemplars. Potential directions for theoretical development and applications to education are discussed.

Testing the effects of educational strategies on comprehension of a genomic concept using virtual reality technology.

PubMed

Kaphingst, Kimberly A; Persky, Susan; McCall, Cade; Lachance, Christina; Loewenstein, Johanna; Beall, Andrew C; Blascovich, Jim

2009-11-01

Applying genetic susceptibility information to improve health will likely require educating patients about abstract concepts, for which there is little existing research. This experimental study examined the effect of learning mode on comprehension of a genomic concept. 156 individuals aged 18-40 without specialized knowledge were randomly assigned to either a virtual reality active learning or didactic learning condition. The outcome was comprehension (recall, transfer, mental models). Change in recall was greater for didactic learning than for active learning (p<0.001). Mean transfer and change in mental models were also higher for didactic learning (p<0.0001 and p<0.05, respectively). Believability was higher for didactic learning (p<0.05), while ratings for motivation (p<0.05), interest (p<0.0001), and enjoyment (p<0.0001) were higher for active learning, but these variables did not mediate the association between learning mode and comprehension. These results show that learning mode affects comprehension, but additional research is needed regarding how and in what contexts different approaches are best for educating patients about abstract concepts. Didactic, interpersonal health education approaches may be more effective than interactive games in educating patients about abstract, unfamiliar concepts. These findings indicate the importance of traditional health education approaches in emerging areas like genomics.

[Association between self-directed learning behaviors, socio-demographic and academic variables among medical students].

PubMed

Fasce H, Eduardo; Ortega B, Javiera; Pérez V, Cristhian; Márquez U, Carolina; Parra P, Paula; Ortiz M, Liliana; Matus, Olga

2013-09-01

Medical education must encourage autonomous learning behaviors among students. However the great income profile disparity among university students may influence their capacity to acquire such skills. To assess the association between self-directed learning, socio-demographic and academic variables. The self-directed learning readiness scale was applied to 202 medical students aged between 17 and 25 years (64% males). Simultaneously information about each surveyed participant was obtained from the databases of the medical school. There is an association between socio-demographic and academic variables with the general scale of self-directed learning and the subscales learning planning and willingness to learn. Participants coming from municipal schools have a greater willingness to learn than their counterparts coming from subsidized and private schools. High school grades are related to self-directed learning and the subscales learning planning and self-assessment. Among the surveyed medical students, there is a relationship between self-directed learning behaviors, the type of school where they come from and the grades that they obtained during high school.

Improving primary care in British Columbia, Canada: evaluation of a peer-to-peer continuing education program for family physicians.

PubMed

MacCarthy, Dan; Kallstrom, Liza; Kadlec, Helena; Hollander, Marcus

2012-11-09

An innovative program, the Practice Support Program (PSP), for full-service family physicians and their medical office assistants in primary care practices was recently introduced in British Columbia, Canada. The PSP was jointly approved by both government and physician groups, and is a dynamic, interactive, educational and supportive program that offers peer-to-peer training to physicians and their office staff. Topic areas range from clinical tools/skills to office management relevant to General Practitioner (GP) practices and "doable in real GP time". PSP learning modules consist of three half-day learning sessions interspersed with 6-8 week action periods. At the end of the third learning session, all participants were asked to complete a pen-and-paper survey that asked them to rate (a) their satisfaction with the learning module components, including the content and (b) the perceived impact the learning has had on their practices and patients. A total of 887 GPs (response rates ranging from 26.0% to 60.2% across three years) and 405 MOAs (response rates from 21.3% to 49.8%) provided responses on a pen-and-paper survey administered at the last learning session of the learning module. The survey asked respondents to rate (a) their satisfaction with the learning module components, including the content and (b) the perceived impact the learning has had on their practices and patients. The psychometric properties (Chronbach's alphas) of the satisfaction and impact scales ranged from .82 to .94. Evaluation findings from the first three years of the PSP indicated consistently high satisfaction ratings and perceived impact on GP practices and patients, regardless of physician characteristics (gender, age group) or work-related variables (e.g., time worked in family practice). The Advanced Access Learning Module, which offers tools to improve office efficiencies, decreased wait times for urgent, regular and third next available appointments by an average of 1.2, 3.3, and by 3.4 days across all physicians. For the Chronic Disease Management module, over 87% of all GP respondents developed a CDM patient registry and reported being able to take better care of their patients. After attending the Adult Mental Health module: 94.1% of GPs agreed that they felt more comfortable helping patients who required mental health care; over 82% agreed that their skills and their confidence in diagnosing and treating mental health conditions had improved; and 41.0% agreed that their frequency of prescribing medications, if appropriate, had decreased. Additionally for the Adult Mental Health module, a 3-6 month follow-up survey of the GPs indicated that the implemented changes were sustained over time. GP and medical office assistant participant ratings show that the PSP learning modules were consistently successful in providing GPs and their staff with new learning that was relevant and could be implemented and used in "real-GP-time".

Dynamic functional connectivity shapes individual differences in associative learning.

PubMed

Fatima, Zainab; Kovacevic, Natasha; Misic, Bratislav; McIntosh, Anthony Randal

2016-11-01

Current neuroscientific research has shown that the brain reconfigures its functional interactions at multiple timescales. Here, we sought to link transient changes in functional brain networks to individual differences in behavioral and cognitive performance by using an active learning paradigm. Participants learned associations between pairs of unrelated visual stimuli by using feedback. Interindividual behavioral variability was quantified with a learning rate measure. By using a multivariate statistical framework (partial least squares), we identified patterns of network organization across multiple temporal scales (within a trial, millisecond; across a learning session, minute) and linked these to the rate of change in behavioral performance (fast and slow). Results indicated that posterior network connectivity was present early in the trial for fast, and later in the trial for slow performers. In contrast, connectivity in an associative memory network (frontal, striatal, and medial temporal regions) occurred later in the trial for fast, and earlier for slow performers. Time-dependent changes in the posterior network were correlated with visual/spatial scores obtained from independent neuropsychological assessments, with fast learners performing better on visual/spatial subtests. No relationship was found between functional connectivity dynamics in the memory network and visual/spatial test scores indicative of cognitive skill. By using a comprehensive set of measures (behavioral, cognitive, and neurophysiological), we report that individual variations in learning-related performance change are supported by differences in cognitive ability and time-sensitive connectivity in functional neural networks. Hum Brain Mapp 37:3911-3928, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

A particle swarm optimization variant with an inner variable learning strategy.

PubMed

Wu, Guohua; Pedrycz, Witold; Ma, Manhao; Qiu, Dishan; Li, Haifeng; Liu, Jin

2014-01-01

Although Particle Swarm Optimization (PSO) has demonstrated competitive performance in solving global optimization problems, it exhibits some limitations when dealing with optimization problems with high dimensionality and complex landscape. In this paper, we integrate some problem-oriented knowledge into the design of a certain PSO variant. The resulting novel PSO algorithm with an inner variable learning strategy (PSO-IVL) is particularly efficient for optimizing functions with symmetric variables. Symmetric variables of the optimized function have to satisfy a certain quantitative relation. Based on this knowledge, the inner variable learning (IVL) strategy helps the particle to inspect the relation among its inner variables, determine the exemplar variable for all other variables, and then make each variable learn from the exemplar variable in terms of their quantitative relations. In addition, we design a new trap detection and jumping out strategy to help particles escape from local optima. The trap detection operation is employed at the level of individual particles whereas the trap jumping out strategy is adaptive in its nature. Experimental simulations completed for some representative optimization functions demonstrate the excellent performance of PSO-IVL. The effectiveness of the PSO-IVL stresses a usefulness of augmenting evolutionary algorithms by problem-oriented domain knowledge.

High stimulus variability in nonnative speech learning supports formation of abstract categories: evidence from Japanese geminates.

PubMed

Sadakata, Makiko; McQueen, James M

2013-08-01

This study reports effects of a high-variability training procedure on nonnative learning of a Japanese geminate-singleton fricative contrast. Thirty native speakers of Dutch took part in a 5-day training procedure in which they identified geminate and singleton variants of the Japanese fricative /s/. Participants were trained with either many repetitions of a limited set of words recorded by a single speaker (low-variability training) or with fewer repetitions of a more variable set of words recorded by multiple speakers (high-variability training). Both types of training enhanced identification of speech but not of nonspeech materials, indicating that learning was domain specific. High-variability training led to superior performance in identification but not in discrimination tests, and supported better generalization of learning as shown by transfer from the trained fricatives to the identification of untrained stops and affricates. Variability thus helps nonnative listeners to form abstract categories rather than to enhance early acoustic analysis.

How do task characteristics affect learning and performance? The roles of variably mapped and dynamic tasks.

PubMed

Macnamara, Brooke N; Frank, David J

2018-05-01

For well over a century, scientists have investigated individual differences in performance. The majority of studies have focused on either differences in practice, or differences in cognitive resources. However, the predictive ability of either practice or cognitive resources varies considerably across tasks. We are the first to examine task characteristics' impact on learning and performance in a complex task while controlling for other task characteristics. In 2 experiments we test key theoretical task characteristic thought to moderate the relationship between practice, cognitive resources, and performance. We devised a task where each of several key task characteristics can be manipulated independently. Participants played 5 rounds of a game similar to the popular tower defense videogame Plants vs. Zombies where both cognitive load and game characteristics were manipulated. In Experiment 1, participants either played a consistently mapped version-the stimuli and the associated meaning of their properties were constant across the 5 rounds-or played a variably mapped version-the stimuli and the associated meaning of their properties changed every few minutes. In Experiment 2, participants either played a static version-that is, turn taking with no time pressure-or played a dynamic version-that is, the stimuli moved regardless of participants' response rates. In Experiment 1, participants' accuracy and efficiency were substantially hindered in the variably mapped conditions. In Experiment 2, learning and performance accuracy were hindered in the dynamic conditions, especially when under cognitive load. Our results suggest that task characteristics impact the relative importance of cognitive resources and practice on predicting learning and performance. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

The Perceived Success of Tutoring Students with Learning Disabilities: Relations to Tutee and Tutoring Variables

ERIC Educational Resources Information Center

Michael, Rinat

2016-01-01

The current study examined the contribution of two types of variables to the perceived success of a tutoring project for college students with learning disabilities (LD): tutoring-related variables (the degree of engagement in different tutoring activities and difficulties encountered during tutoring), and tutee-related variables (learning…

Age group differences in positive and negative affect among oldest-old adults: findings from the Georgia Centenarian Study.

PubMed

Cho, Jinmyoung; Martin, Peter; Poon, Leonard W; MacDonald, M; Jazwinski, S M; Green, R C; Gearing, M; Johnson, M A; Markesbery, W R; Woodard, J L; Tenover, J S; Siegler, L C; Rott, C; Rodgers, W L; Hausman, D; Arnold, J; Davey, A

2013-01-01

The developmental adaptation model (Martin & Martin, 2002) provides insights into how current experiences and resources (proximal variables) and past experiences (distal variables) are correlated with outcomes (e.g., well-being) in later life. Applying this model, the current study examined proximal and distal variables associated with positive and negative affect in oldest-old adults, investigating age differences. Data from 306 octogenarians and centenarians who participated in Phase III of the Georgia Centenarian Study were used. Proximal variables included physical functioning, cognitive functioning, self-rated health, number of chronic conditions, social resources, and perceived economic status; distal variables included education, social productive activities, management of personal assets, and other learning experiences. Analysis of variance and block-wise regression analyses were conducted. Octogenarians showed significantly higher levels of positive emotion than centenarians. Cognitive functioning was significantly associated with positive affect, and number of health problems was significantly associated with negative affect after controlling for gender, ethnicity, residence, and marital status. Furthermore, four significant interaction effects suggested that positive affect significantly depended on the levels of cognitive and physical functioning among centenarians, whereas positive affect was dependent on the levels of physical health problems and learning experiences among octogenarians. Findings of this study addressed the importance of current and past experiences and resources in subjective well-being among oldest-old adults as a life-long process. Mechanisms connecting aging processes at the end of a long life to subjective well-being should be explored in future studies.

Who wants feedback? An investigation of the variables influencing residents' feedback-seeking behavior in relation to night shifts.

PubMed

Teunissen, Pim W; Stapel, Diederik A; van der Vleuten, Cees; Scherpbier, Albert; Boor, Klarke; Scheele, Fedde

2009-07-01

The literature on feedback in clinical medical education has predominantly treated trainees as passive recipients. Past research has focused on how clinical supervisors can use feedback to improve a trainee's performance. On the basis of research in social and organizational psychology, the authors reconceptualized residents as active seekers of feedback. They investigated what individual and situational variables influence residents' feedback-seeking behavior on night shifts. Early in 2008, the authors sent obstetrics-gynecology residents in the Netherlands--both those in their first two years of graduate training and those gaining experience between undergraduate and graduate training--a questionnaire that assessed four predictor variables (learning and performance goal orientation, and instrumental and supportive leadership), two mediator variables (perceived feedback benefits and costs), and two outcome variables (frequency of feedback inquiry and monitoring). They used structural equation modeling software to test a hypothesized model of relationships between variables. The response rate was 76.5%. Results showed that residents who perceive more feedback benefits report a higher frequency of feedback inquiry and monitoring. More perceived feedback costs result mainly in more feedback monitoring. Residents with a higher learning goal orientation perceive more feedback benefits and fewer costs. Residents with a higher performance goal orientation perceive more feedback costs. Supportive physicians lead residents to perceive more feedback benefits and fewer costs. This study showed that some residents actively seek feedback. Residents' feedback-seeking behavior partially depends on attending physicians' supervisory style. Residents' goal orientations influence their perceptions of the benefits and costs of feedback-seeking.

Comparison of Machine Learning Methods for the Arterial Hypertension Diagnostics

PubMed Central

Belo, David; Gamboa, Hugo

2017-01-01

The paper presents results of machine learning approach accuracy applied analysis of cardiac activity. The study evaluates the diagnostics possibilities of the arterial hypertension by means of the short-term heart rate variability signals. Two groups were studied: 30 relatively healthy volunteers and 40 patients suffering from the arterial hypertension of II-III degree. The following machine learning approaches were studied: linear and quadratic discriminant analysis, k-nearest neighbors, support vector machine with radial basis, decision trees, and naive Bayes classifier. Moreover, in the study, different methods of feature extraction are analyzed: statistical, spectral, wavelet, and multifractal. All in all, 53 features were investigated. Investigation results show that discriminant analysis achieves the highest classification accuracy. The suggested approach of noncorrelated feature set search achieved higher results than data set based on the principal components. PMID:28831239

Effects of ICT Assisted Real and Virtual Learning on the Performance of Secondary School Students

ERIC Educational Resources Information Center

Deka, Monisha; Jena, Ananta Kumar

2017-01-01

The study aimed to assess the effect of ICT assisted real and virtual learning performance over the traditional approach of secondary school students. Non-Equivalent Pretest-Posttest Quasi Experimental Design used to assess and relate the effects of independent variables virtual learning on dependent variables (i.e. learning performance).…

[Why are some high achievers on the course final exam unsuccessful on the proficiency exam in English?].

PubMed

Matsunuma, Mitsuyasu

2009-04-01

This study examined why some high achievers on the course final exam were unsuccessful on the proficiency exam in English. We hypothesized that the learning motives and learning behaviors (learning strategy, learning time) had different effects on the outcomes of the exams. First, the relation between the variables was investigated using structural equation modeling. Second, the learning behaviors of students who got good marks on both exams were compared with students who did well only on the course final exam. The results were as follows. (a) Learning motives influenced test performance via learning behaviors. (b) Content-attached motives influenced all variables concerning learning behaviors. (c) Content-detached motives influenced all variables concerning learning behaviors that were related only to the course final exam. (d) The students who got good marks on both exams performed the learning behaviors that were useful on the proficiency exam more frequently than the students who did well only on the course final exam.

An Examination of Strategy Implementation During Abstract Nonlinguistic Category Learning in Aphasia.

PubMed

Vallila-Rohter, Sofia; Kiran, Swathi

2015-08-01

Our purpose was to study strategy use during nonlinguistic category learning in aphasia. Twelve control participants without aphasia and 53 participants with aphasia (PWA) completed a computerized feedback-based category learning task consisting of training and testing phases. Accuracy rates of categorization in testing phases were calculated. To evaluate strategy use, strategy analyses were conducted over training and testing phases. Participant data were compared with model data that simulated complex multi-cue, single feature, and random pattern strategies. Learning success and strategy use were evaluated within the context of standardized cognitive-linguistic assessments. Categorization accuracy was higher among control participants than among PWA. The majority of control participants implemented suboptimal or optimal multi-cue and single-feature strategies by testing phases of the experiment. In contrast, a large subgroup of PWA implemented random patterns, or no strategy, during both training and testing phases of the experiment. Person-to-person variability arises not only in category learning ability but also in the strategies implemented to complete category learning tasks. PWA less frequently developed effective strategies during category learning tasks than control participants. Certain PWA may have impairments of strategy development or feedback processing not captured by language and currently probed cognitive abilities.

Level of Recall, Retrieval Speed, and Variability on the Cued-Recall Retrieval Speed Task (CRRST) in Individuals with Amnestic Mild Cognitive Impairment

PubMed Central

Ramratan, Wendy S.; Rabin, Laura A.; Wang, Cuiling; Zimmerman, Molly E.; Katz, Mindy J.; Lipton, Richard B.; Buschke, Herman

2013-01-01

Individuals with amnestic mild cognitive impairment (aMCI) show deficits on traditional episodic memory tasks and reductions in speed of performance on reaction time tasks. We present results on a novel task, the Cued-Recall Retrieval Speed Test (CRRST), designed to simultaneously measure level and speed of retrieval. 390 older adults (mean age of 80.2 years), learned 16 words based on corresponding categorical cues. In the retrieval phase, we measured accuracy (% correct) and retrieval speed/reaction time (RT; time from cue presentation to voice onset of a correct response) across 6 trials. Compared to healthy elderly adults (HEA, n = 303), those with aMCI (n = 87) exhibited poorer performance in retrieval speed (difference = −0.13, p<.0001) and accuracy on the first trial (difference = −0.19, p<.0001), and their rate of improvement in retrieval speed was slower over subsequent trials. Those with aMCI also had greater within-person variability in processing speed (variance ratio = 1.22, p = 0.0098) and greater between-person variability in accuracy (variance ratio = 2.08, p = 0.0001) relative to HEA. Results are discussed in relation to the possibility that computer-based measures of cued-learning and processing speed variability may facilitate early detection of dementia in at-risk older adults. PMID:22265423

Human-robot cooperative movement training: Learning a novel sensory motor transformation during walking with robotic assistance-as-needed

PubMed Central

Emken, Jeremy L; Benitez, Raul; Reinkensmeyer, David J

2007-01-01

Background A prevailing paradigm of physical rehabilitation following neurologic injury is to "assist-as-needed" in completing desired movements. Several research groups are attempting to automate this principle with robotic movement training devices and patient cooperative algorithms that encourage voluntary participation. These attempts are currently not based on computational models of motor learning. Methods Here we assume that motor recovery from a neurologic injury can be modelled as a process of learning a novel sensory motor transformation, which allows us to study a simplified experimental protocol amenable to mathematical description. Specifically, we use a robotic force field paradigm to impose a virtual impairment on the left leg of unimpaired subjects walking on a treadmill. We then derive an "assist-as-needed" robotic training algorithm to help subjects overcome the virtual impairment and walk normally. The problem is posed as an optimization of performance error and robotic assistance. The optimal robotic movement trainer becomes an error-based controller with a forgetting factor that bounds kinematic errors while systematically reducing its assistance when those errors are small. As humans have a natural range of movement variability, we introduce an error weighting function that causes the robotic trainer to disregard this variability. Results We experimentally validated the controller with ten unimpaired subjects by demonstrating how it helped the subjects learn the novel sensory motor transformation necessary to counteract the virtual impairment, while also preventing them from experiencing large kinematic errors. The addition of the error weighting function allowed the robot assistance to fade to zero even though the subjects' movements were variable. We also show that in order to assist-as-needed, the robot must relax its assistance at a rate faster than that of the learning human. Conclusion The assist-as-needed algorithm proposed here can limit error during the learning of a dynamic motor task. The algorithm encourages learning by decreasing its assistance as a function of the ongoing progression of movement error. This type of algorithm is well suited for helping people learn dynamic tasks for which large kinematic errors are dangerous or discouraging, and thus may prove useful for robot-assisted movement training of walking or reaching following neurologic injury. PMID:17391527

How does a newly encountered face become familiar? The effect of within-person variability on adults' and children's perception of identity.

PubMed

Baker, Kristen A; Laurence, Sarah; Mondloch, Catherine J

2017-04-01

Adults and children aged 6years and older easily recognize multiple images of a familiar face, but often perceive two images of an unfamiliar face as belonging to different identities. Here we examined the process by which a newly encountered face becomes familiar, defined as accurate recognition of multiple images that capture natural within-person variability in appearance. In Experiment 1 we examined whether exposure to within-person variability in appearance helps children learn a new face. Children aged 6-13years watched a 10-min video of a woman reading a story; she was filmed on a single day (low variability) or over three days, across which her appearance and filming conditions (e.g., camera, lighting) varied (high variability). After familiarization, participants sorted a set of images comprising novel images of the target identity intermixed with distractors. Compared to participants who received no familiarization, children showed evidence of learning only in the high-variability condition, in contrast to adults who showed evidence of learning in both the low- and high-variability conditions. Experiment 2 highlighted the efficiency with which adults learn a new face; their accuracy was comparable across training conditions despite variability in duration (1 vs. 10min) and type (video vs. static images) of training. Collectively, our findings show that exposure to variability leads to the formation of a robust representation of facial identity, consistent with perceptual learning in other domains (e.g., language), and that the development of face learning is protracted throughout childhood. We discuss possible underlying mechanisms. Copyright © 2016. Published by Elsevier B.V.

The Use of a Virtual Learning Centre in the Context of a University Lecture: Factors Influencing Satisfaction and Performance

ERIC Educational Resources Information Center

Weibel, David; Stricker, Daniel; Wissmath, Bartholomaus

2012-01-01

We provided a virtual learning tool to undergraduate psychology students (n = 72) and investigated how different variables influence the learning outcome in terms of performance in an exam and satisfaction with the e-learning tool. These variables were: perceived usefulness, perceived ease of use, attitude towards computers, attitude towards the…

The Effect of Feedback Delay and Feedback Type on Perceptual Category Learning: The Limits of Multiple Systems

ERIC Educational Resources Information Center

Dunn, John C.; Newell, Ben R.; Kalish, Michael L.

2012-01-01

Evidence that learning rule-based (RB) and information-integration (II) category structures can be dissociated across different experimental variables has been used to support the view that such learning is supported by multiple learning systems. Across 4 experiments, we examined the effects of 2 variables, the delay between response and feedback…

Variable Behavior and Repeated Learning in Two Mouse Strains: Developmental and Genetic Contributions.

PubMed

Arnold, Megan A; Newland, M Christopher

2018-06-16

Behavioral inflexibility is often assessed using reversal learning tasks, which require a relatively low degree of response variability. No studies have assessed sensitivity to reinforcement contingencies that specifically select highly variable response patterns in mice, let alone in models of neurodevelopmental disorders involving limited response variation. Operant variability and incremental repeated acquisition (IRA) were used to assess unique aspects of behavioral variability of two mouse strains: BALB/c, a model of some deficits in ASD, and C57Bl/6. On the operant variability task, BALB/c mice responded more repetitively during adolescence than C57Bl/6 mice when reinforcement did not require variability but responded more variably when reinforcement required variability. During IRA testing in adulthood, both strains acquired an unchanging, performance sequence equally well. Strain differences emerged, however, after novel learning sequences began alternating with the performance sequence: BALB/c mice substantially outperformed C57Bl/6 mice. Using litter-mate controls, it was found that adolescent experience with variability did not affect either learning or performance on the IRA task in adulthood. These findings constrain the use of BALB/c mice as a model of ASD, but once again reveal this strain is highly sensitive to reinforcement contingencies and they are fast and robust learners. Copyright © 2018. Published by Elsevier B.V.

The learning curve to achieve satisfactory completion rates in upper GI endoscopy: an analysis of a national training database.

PubMed

Ward, S T; Hancox, A; Mohammed, M A; Ismail, T; Griffiths, E A; Valori, R; Dunckley, P

2017-06-01

The aim of this study was to determine the number of OGDs (oesophago-gastro-duodenoscopies) trainees need to perform to acquire competency in terms of successful unassisted completion to the second part of the duodenum 95% of the time. OGD data were retrieved from the trainee e-portfolio developed by the Joint Advisory Group on GI Endoscopy (JAG) in the UK. All trainees were included unless they were known to have a baseline experience of >20 procedures or had submitted data for <20 procedures. The primary outcome measure was OGD completion, defined as passage of the endoscope to the second part of the duodenum without physical assistance. The number of OGDs required to achieve a 95% completion rate was calculated by the moving average method and learning curve cumulative summation (LC-Cusum) analysis. To determine which factors were independently associated with OGD completion, a mixed effects logistic regression model was constructed with OGD completion as the outcome variable. Data were analysed for 1255 trainees over 288 centres, representing 243 555 OGDs. By moving average method, trainees attained a 95% completion rate at 187 procedures. By LC-Cusum analysis, after 200 procedures, >90% trainees had attained a 95% completion rate. Total number of OGDs performed, trainee age and experience in lower GI endoscopy were factors independently associated with OGD completion. There are limited published data on the OGD learning curve. This is the largest study to date analysing the learning curve for competency acquisition. The JAG competency requirement for 200 procedures appears appropriate. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Adaptive Control Based Harvesting Strategy for a Predator-Prey Dynamical System.

PubMed

Sen, Moitri; Simha, Ashutosh; Raha, Soumyendu

2018-04-23

This paper deals with designing a harvesting control strategy for a predator-prey dynamical system, with parametric uncertainties and exogenous disturbances. A feedback control law for the harvesting rate of the predator is formulated such that the population dynamics is asymptotically stabilized at a positive operating point, while maintaining a positive, steady state harvesting rate. The hierarchical block strict feedback structure of the dynamics is exploited in designing a backstepping control law, based on Lyapunov theory. In order to account for unknown parameters, an adaptive control strategy has been proposed in which the control law depends on an adaptive variable which tracks the unknown parameter. Further, a switching component has been incorporated to robustify the control performance against bounded disturbances. Proofs have been provided to show that the proposed adaptive control strategy ensures asymptotic stability of the dynamics at a desired operating point, as well as exact parameter learning in the disturbance-free case and learning with bounded error in the disturbance prone case. The dynamics, with uncertainty in the death rate of the predator, subjected to a bounded disturbance has been simulated with the proposed control strategy.

Differences in stress and coping for mothers and fathers of children with Asperger's syndrome and nonverbal learning disorders.

PubMed

Little, Liza

2002-01-01

Research conducted on families of children with disabilities shows that family cohesion and positive family outcomes are influenced by how mothers and fathers cope with raising their child with disabilities. This study was designed to examine stress and coping differences between mothers and fathers (n = 103) of children with Asperger's syndrome (AS) and nonverbal learning disorders (NLD). A repeated measure design was used to compare how mothers and fathers cope with caring for a particular child to control for differences in the severity and nature of the disability across children. Few studies that compare mothers and fathers do so at the couple level. Responses indicated that mothers had higher rates of stress related to family problems and pessimism about their child's future, higher rates of antidepressant use, and higher rates of therapy use than did fathers. Mothers found some coping strategies more helpful than fathers did. Maternal education and child's age also were related to some stress and coping variables. Implications for nurses and future research are discussed.

Aptitude for Learning a Foreign Language.

ERIC Educational Resources Information Center

Sparks, Richard; Ganschow, Leonore

2001-01-01

Review research on foreign language aptitude and its measurement prior to 1990. Describes research areas in the 1990s, including affective variables, language learning strategies, learning styles as contributors to aptitude and aptitude as a cognitive construct affected by language variables. Reviews research on individual differences and the…

Family context variables and the development of self-regulation in college students.

PubMed

Strage, A A

1998-01-01

While researchers have begun to specify how features of students' immediate learning environments affect the development and use of self-regulation skills, relatively little attention has been paid to the role of the family context in fostering or impeding the development of these skills. This paper proposes a conceptual framework based on attachment theory (Ainsworth et al., 1978; Bowlby, 1982) and Baumrind's parenting styles typology (Baumrind, 1967, 1991) for examining the relationship between family context variables and the development of self-regulation skills. It also presents initial findings from a study of the parental practices and values associated with academic self-regulation in college students. A sample of 465 students completed the 104-item Student Attitudes and Perceptions Survey, which consists of 4 personal profile scales, 7 family background scales, 2 course characteristics scales, and 2 study habits scales. Perceptions of parents as authoritative and of family as emotionally close were found to be predictive of (1) general confidence and positive sense of self, (2) positive goal-orientation at school, (3) general concern about preparation for the future, and (4) positive adjustment to college. These family profiles were also predictive of (1) students' rating their introductory psychology course as interesting and supportive, (2) favorable ratings of their time and effort management and note-taking skills, and (3) strong agreement with a series of items reflecting components of self-regulated learning. Perceptions of parents as authoritarian and of family as nagging or enmeshed were also predictive of concern about preparation for the future. These family profiles were generally predictive of students' rating their introductory psychology course as difficult, and of time and effort management difficulties. The patterns linking family background profiles with course perceptions, study habits, and individual indices of self-regulated learning persisted even when students' sense of confidence was factored out, and were strong for students living with their parents as well as for those living on their own.

Perceptual learning in a non-human primate model of artificial vision

PubMed Central

Killian, Nathaniel J.; Vurro, Milena; Keith, Sarah B.; Kyada, Margee J.; Pezaris, John S.

2016-01-01

Visual perceptual grouping, the process of forming global percepts from discrete elements, is experience-dependent. Here we show that the learning time course in an animal model of artificial vision is predicted primarily from the density of visual elements. Three naïve adult non-human primates were tasked with recognizing the letters of the Roman alphabet presented at variable size and visualized through patterns of discrete visual elements, specifically, simulated phosphenes mimicking a thalamic visual prosthesis. The animals viewed a spatially static letter using a gaze-contingent pattern and then chose, by gaze fixation, between a matching letter and a non-matching distractor. Months of learning were required for the animals to recognize letters using simulated phosphene vision. Learning rates increased in proportion to the mean density of the phosphenes in each pattern. Furthermore, skill acquisition transferred from trained to untrained patterns, not depending on the precise retinal layout of the simulated phosphenes. Taken together, the findings suggest that learning of perceptual grouping in a gaze-contingent visual prosthesis can be described simply by the density of visual activation. PMID:27874058

Integrative Motivation: Changes during a Year-Long Intermediate-Level Language Course

ERIC Educational Resources Information Center

Gardner, R. C.; Masgoret, A. M.; Tennant, J.; Mihic, L.

2004-01-01

The socioeducational model of second language acquisition postulates that language learning is a dynamic process in which affective variables influence language achievement and achievement and experiences in language learning can influence some affective variables. Five classes of variable are emphasized: integrativeness, attitudes toward the…

Online Pairwise Learning Algorithms.

PubMed

Ying, Yiming; Zhou, Ding-Xuan

2016-04-01

Pairwise learning usually refers to a learning task that involves a loss function depending on pairs of examples, among which the most notable ones are bipartite ranking, metric learning, and AUC maximization. In this letter we study an online algorithm for pairwise learning with a least-square loss function in an unconstrained setting of a reproducing kernel Hilbert space (RKHS) that we refer to as the Online Pairwise lEaRning Algorithm (OPERA). In contrast to existing works (Kar, Sriperumbudur, Jain, & Karnick, 2013 ; Wang, Khardon, Pechyony, & Jones, 2012 ), which require that the iterates are restricted to a bounded domain or the loss function is strongly convex, OPERA is associated with a non-strongly convex objective function and learns the target function in an unconstrained RKHS. Specifically, we establish a general theorem that guarantees the almost sure convergence for the last iterate of OPERA without any assumptions on the underlying distribution. Explicit convergence rates are derived under the condition of polynomially decaying step sizes. We also establish an interesting property for a family of widely used kernels in the setting of pairwise learning and illustrate the convergence results using such kernels. Our methodology mainly depends on the characterization of RKHSs using its associated integral operators and probability inequalities for random variables with values in a Hilbert space.

Effects of Acoustic Variability on Second Language Vocabulary Learning

ERIC Educational Resources Information Center

Barcroft, Joe; Sommers, Mitchell S.

2005-01-01

This study examined the effects of acoustic variability on second language vocabulary learning. English native speakers learned new words in Spanish. Exposure frequency to the words was constant. Dependent measures were accuracy and latency of picture-to-Spanish and Spanish-to-English recall. Experiment 1 compared presentation formats of neutral…

Input Variability Facilitates Unguided Subcategory Learning in Adults

ERIC Educational Resources Information Center

Eidsvåg, Sunniva Sørhus; Austad, Margit; Plante, Elena; Asbjørnsen, Arve E.

2015-01-01

Purpose: This experiment investigated whether input variability would affect initial learning of noun gender subcategories in an unfamiliar, natural language (Russian), as it is known to assist learning of other grammatical forms. Method: Forty adults (20 men, 20 women) were familiarized with examples of masculine and feminine Russian words. Half…

The Impact of Workplace Learning on Job Satisfaction in Small US Commercial Banks

ERIC Educational Resources Information Center

Rowden, Robert W.; Conine, Clyde T., Jr.

2005-01-01

Purpose: This study aims to examine workplace learning and job satisfaction in small, commercial US banks. Design/methodology/approach: Survey data collection with correlational procedure. Findings: The study found a statistically significant relationship between the workplace learning variables and the job satisfaction variables. Research…

Students' Evaluation of Teaching, Approaches to Learning, and Academic Achievement

ERIC Educational Resources Information Center

Diseth, Age

2007-01-01

Students' evaluation and perception of the learning environment are considered to be important predictors of students' approaches to learning. These variables may also account for variance in academic outcome, such as in examination grades, but previous research has rarely included a comparison between all of these variables. This article…

Interaction Effects of Hypervideo Navigation Variables in College Students' Self-Regulated Learning

ERIC Educational Resources Information Center

Azmy, Nabil

2013-01-01

The purpose of this study is to investigate the question of whether the interaction effects of hypervideo navigation variables (navigation control and navigation links) would affect college students' self-regulated learning just after their learning from instructional hypervideo programs. Navigation control (free navigation or free navigation with…

Influence of Strategy of Learning and Achievement Motivation of Learning Achievement Class VIII Students of State Junior High School in District Blitar

ERIC Educational Resources Information Center

Ayundawati, Dyah; Setyosari, Punaji; Susilo, Herawati; Sihkabuden

2016-01-01

This study aims for know influence of problem-based learning strategies and achievement motivation on learning achievement. The method used in this research is quantitative method. The instrument used in this study is two fold instruments to measure moderator variable (achievement motivation) and instruments to measure the dependent variable (the…

Testing the Effects of Educational Strategies on Comprehension of a Genomic Concept Using Virtual Reality Technology

PubMed Central

Kaphingst, Kimberly A.; Persky, Susan; McCall, Cade; Lachance, Christina; Loewenstein, Johanna; Beall, Andrew C.; Blascovich, Jim

2009-01-01

Objective Applying genetic susceptibility information to improve health will likely require educating patients about abstract concepts, for which there is little existing research. This experimental study examined the effect of learning mode on comprehension of a genomic concept. Methods 156 individuals aged 18–40 without specialized knowledge were randomly assigned to either a virtual reality active learning or didactic learning condition. The outcome was comprehension (recall, transfer, mental models). Results Change in recall was greater for didactic learning than active learning (p<0.001). Mean transfer and change in mental models were also higher for didactic learning (p<0.0001 and p<0.05, respectively). Believability was higher for didactic learning (p<0.05), while ratings for motivation (p<0.05), interest (p<0.0001), and enjoyment (p<0.0001) were higher for active learning, but these variables did not mediate the association between learning mode and comprehension. Conclusion These results show that learning mode affects comprehension, but additional research is needed regarding how and in what contexts different approaches are best for educating patients about abstract concepts. Practice implications Didactic, interpersonal health education approaches may be more effective than interactive games in educating patients about abstract, unfamiliar concepts. These findings indicate the importance of traditional health education approaches in emerging areas like genomics. PMID:19409749

Some Variables in Relation to Students' Anxiety in Learning Statistics.

ERIC Educational Resources Information Center

Sutarso, Toto

The purpose of this study was to investigate some variables that relate to students' anxiety in learning statistics. The variables included sex, class level, students' achievement, school, mathematical background, previous statistics courses, and race. The instrument used was the 24-item Students' Attitudes Toward Statistics (STATS), which was…

Learning Supervised Topic Models for Classification and Regression from Crowds.

PubMed

Rodrigues, Filipe; Lourenco, Mariana; Ribeiro, Bernardete; Pereira, Francisco C

2017-12-01

The growing need to analyze large collections of documents has led to great developments in topic modeling. Since documents are frequently associated with other related variables, such as labels or ratings, much interest has been placed on supervised topic models. However, the nature of most annotation tasks, prone to ambiguity and noise, often with high volumes of documents, deem learning under a single-annotator assumption unrealistic or unpractical for most real-world applications. In this article, we propose two supervised topic models, one for classification and another for regression problems, which account for the heterogeneity and biases among different annotators that are encountered in practice when learning from crowds. We develop an efficient stochastic variational inference algorithm that is able to scale to very large datasets, and we empirically demonstrate the advantages of the proposed model over state-of-the-art approaches.

A cortical motor nucleus drives the basal ganglia-recipient thalamus in singing birds

PubMed Central

Goldberg, Jesse H.

2012-01-01

The pallido-recipient thalamus transmits information from the basal ganglia (BG) to the cortex and plays a critical role motor initiation and learning. Thalamic activity is strongly inhibited by pallidal inputs from the BG, but the role of non-pallidal inputs, such as excitatory inputs from cortex, is unclear. We have recorded simultaneously from presynaptic pallidal axon terminals and postsynaptic thalamocortical neurons in a BG-recipient thalamic nucleus necessary for vocal variability and learning in zebra finches. We found that song-locked rate modulations in the thalamus could not be explained by pallidal inputs alone, and persisted following pallidal lesion. Instead, thalamic activity was likely driven by inputs from a motor ‘cortical’ nucleus also necessary for singing. These findings suggest a role for cortical inputs to the pallido-recipient thalamus in driving premotor signals important for exploratory behavior and learning. PMID:22327474

Relationship between clinical fieldwork educator performance and health professional students' perceptions of their practice education learning environments.

PubMed

Brown, Ted; Williams, Brett; Lynch, Marty

2013-12-01

The Dundee Ready Education Environment Measure, Clinical Teaching Effectiveness Instrument, and Clinical Learning Environment Inventory were completed by 548 undergraduate students (54.5% response rate) enrolled in eight health professional bachelor degree courses. Regression analysis was used to investigate the significant predictors of the Clinical Teaching Effectiveness Instrument with the Dundee Ready Education Environment Measure and Clinical Learning Environment Inventory subscales as independent variables. The results indicated that the Dundee Ready Education Environment Measure and Clinical Learning Environment Inventory Actual version subscale scores explained 44% of the total variance in the Clinical Teaching Effectiveness Instrument score. The Dundee Ready Education Environment Measure subscale Academic Self-Perception explained 1.1% of the variance in the Clinical Teaching Effectiveness Instrument score. The Clinical Learning Environment Inventory Actual subscales accounted for the following variance percentages in the Clinical Teaching Effectiveness Instrument score: personalization, 1.1%; satisfaction, 1.7%; task orientation, 5.1%; and innovation, 6.2%. Aspects of the clinical learning environment appear to be predictive of the effectiveness of the clinical teaching that students experience. Fieldwork educator performance might be a significant contributing factor toward student skill development and practitioner success. © 2013 Wiley Publishing Asia Pty Ltd.

Learning in later life: participation in formal, non-formal and informal activities in a nationally representative Spanish sample.

PubMed

Villar, Feliciano; Celdrán, Montserrat

2013-06-01

This article examines the participation of Spanish older people in formal, non-formal and informal learning activities and presents a profile of participants in each kind of learning activity. We used data from a nationally representative sample of Spanish people between 60 and 75 years old ( n  = 4,703). The data were extracted from the 2007 Encuesta sobre la Participación de la Población Adulta en Actividades de Aprendizaje (EADA, Survey on Adult Population Involvement in Learning Activities). Overall, only 22.8 % of the sample participated in a learning activity. However, there was wide variation in the participation rates for the different types of activity. Informal activities were far more common than formal ones. Multivariate logistic regression indicated that education level and involvement in social and cultural activities were associated with likelihood of participating, regardless of the type of learning activity. When these variables were taken into account, age did not predict decreasing participation, at least in non-formal and informal activities. Implications for further research, future trends and policies to promote older adult education are discussed.

Collaborative mining and transfer learning for relational data

NASA Astrophysics Data System (ADS)

Levchuk, Georgiy; Eslami, Mohammed

2015-06-01

Many of the real-world problems, - including human knowledge, communication, biological, and cyber network analysis, - deal with data entities for which the essential information is contained in the relations among those entities. Such data must be modeled and analyzed as graphs, with attributes on both objects and relations encode and differentiate their semantics. Traditional data mining algorithms were originally designed for analyzing discrete objects for which a set of features can be defined, and thus cannot be easily adapted to deal with graph data. This gave rise to the relational data mining field of research, of which graph pattern learning is a key sub-domain [11]. In this paper, we describe a model for learning graph patterns in collaborative distributed manner. Distributed pattern learning is challenging due to dependencies between the nodes and relations in the graph, and variability across graph instances. We present three algorithms that trade-off benefits of parallelization and data aggregation, compare their performance to centralized graph learning, and discuss individual benefits and weaknesses of each model. Presented algorithms are designed for linear speedup in distributed computing environments, and learn graph patterns that are both closer to ground truth and provide higher detection rates than centralized mining algorithm.

Determining next steps in a hand hygiene improvement initiative by examining variation in hand hygiene compliance rates.

PubMed

Homa, Karen; Kirkland, Kathryn B

2011-01-01

Health care worker hand hygiene (HH) is a major quality and safety concern since poor hand hygiene has been linked with hospital associated infections. Dartmouth-Hitchcock Medical Center has been involved in a 4-year initiative to improve hand hygiene. In 2006, HH compliance occurred 41% of the time and by 2009, it had improved to 91%. We wanted to understand some of the unexplained variability in HH to help determine where to target more specific strategies. To help determine where some of the variability in HH compliance rates occurred, an analysis of means chart was used to determine whether role type of the health care worker and hospital areas had significantly different HH rates compared with the overall HH rate. The overall HH rate between March 2008 and December 2009 was 87%. There was a wide and significant variation between the 16 groups of 2 types of health care workers in 8 hospital areas from the lowest rate of 64% to a high of 96%. Analysis of means revealed significant differences in HH rates relative to the type of worker and hospital areas. Although the method does not inform the organization of what type of intervention will work where and why, it allows high and low performing groups to be identified, so that organizations can learn from them to generate and test theories.

Metacognitive judgments of repetition and variability effects in natural concept learning: evidence for variability neglect.

PubMed

Wahlheim, Christopher N; Finn, Bridgid; Jacoby, Larry L

2012-07-01

In four experiments, we examined the effects of repetitions and variability on the learning of bird families and metacognitive awareness of such effects. Of particular interest was the accuracy of, and bases for, predictions regarding classification of novel bird species, referred to as category learning judgments (CLJs). Participants studied birds in high repetitions and high variability conditions. These conditions differed in the number of presentations of each bird (repetitions) and the number of unique species from each family (variability). After study, participants made CLJs for each family and were then tested. Results from a classification test revealed repetition benefits for studied species and variability benefits for novel species. In contrast with performance, CLJs did not reflect the benefits of variability. Results showed that CLJs were susceptible to accessibility-based metacognitive illusions produced by additional repetitions of studied items.

BN-FLEMOps pluvial - A probabilistic multi-variable loss estimation model for pluvial floods

NASA Astrophysics Data System (ADS)

Roezer, V.; Kreibich, H.; Schroeter, K.; Doss-Gollin, J.; Lall, U.; Merz, B.

2017-12-01

Pluvial flood events, such as in Copenhagen (Denmark) in 2011, Beijing (China) in 2012 or Houston (USA) in 2016, have caused severe losses to urban dwellings in recent years. These floods are caused by storm events with high rainfall rates well above the design levels of urban drainage systems, which lead to inundation of streets and buildings. A projected increase in frequency and intensity of heavy rainfall events in many areas and an ongoing urbanization may increase pluvial flood losses in the future. For an efficient risk assessment and adaptation to pluvial floods, a quantification of the flood risk is needed. Few loss models have been developed particularly for pluvial floods. These models usually use simple waterlevel- or rainfall-loss functions and come with very high uncertainties. To account for these uncertainties and improve the loss estimation, we present a probabilistic multi-variable loss estimation model for pluvial floods based on empirical data. The model was developed in a two-step process using a machine learning approach and a comprehensive database comprising 783 records of direct building and content damage of private households. The data was gathered through surveys after four different pluvial flood events in Germany between 2005 and 2014. In a first step, linear and non-linear machine learning algorithms, such as tree-based and penalized regression models were used to identify the most important loss influencing factors among a set of 55 candidate variables. These variables comprise hydrological and hydraulic aspects, early warning, precaution, building characteristics and the socio-economic status of the household. In a second step, the most important loss influencing variables were used to derive a probabilistic multi-variable pluvial flood loss estimation model based on Bayesian Networks. Two different networks were tested: a score-based network learned from the data and a network based on expert knowledge. Loss predictions are made through Bayesian inference using Markov chain Monte Carlo (MCMC) sampling. With the ability to cope with incomplete information and use expert knowledge, as well as inherently providing quantitative uncertainty information, it is shown that loss models based on BNs are superior to deterministic approaches for pluvial flood risk assessment.

Supporting second grade lower secondary school students’ understanding of linear equation system in two variables using ethnomathematics

NASA Astrophysics Data System (ADS)

Nursyahidah, F.; Saputro, B. A.; Rubowo, M. R.

2018-03-01

The aim of this research is to know the students’ understanding of linear equation system in two variables using Ethnomathematics and to acquire learning trajectory of linear equation system in two variables for the second grade of lower secondary school students. This research used methodology of design research that consists of three phases, there are preliminary design, teaching experiment, and retrospective analysis. Subject of this study is 28 second grade students of Sekolah Menengah Pertama (SMP) 37 Semarang. The result of this research shows that the students’ understanding in linear equation system in two variables can be stimulated by using Ethnomathematics in selling buying tradition in Peterongan traditional market in Central Java as a context. All of strategies and model that was applied by students and also their result discussion shows how construction and contribution of students can help them to understand concept of linear equation system in two variables. All the activities that were done by students produce learning trajectory to gain the goal of learning. Each steps of learning trajectory of students have an important role in understanding the concept from informal to the formal level. Learning trajectory using Ethnomathematics that is produced consist of watching video of selling buying activity in Peterongan traditional market to construct linear equation in two variables, determine the solution of linear equation in two variables, construct model of linear equation system in two variables from contextual problem, and solving a contextual problem related to linear equation system in two variables.

The Role of Learner and Input Variables in Learning Inflectional Morphology

ERIC Educational Resources Information Center

Brooks, Patricia J.; Kempe, Vera; Sionov, Ariel

2006-01-01

To examine effects of input and learner characteristics on morphology acquisition, 60 adult English speakers learned to inflect masculine and feminine Russian nouns in nominative, dative, and genitive cases. By varying training vocabulary size (i.e., type variability), holding constant the number of learning trials, we tested whether learners…

High School Students' Motivation to Learn Mathematics: The Role of Multiple Goals

ERIC Educational Resources Information Center

Ng, Chi-hung Clarence

2018-01-01

Using a sample of 310 Year 10 Chinese students from Hong Kong, this survey study examined the effects of multiple goals in learning mathematics. Independent variables were mastery, performance-approach, performance-avoidance, and pro-social goals. Dependent variables included perceived classroom goal structures, teacher's support, learning motives…

The Role of Transformative Leadership, ICT-Infrastructure and Learning Climate in Teachers' Use of Digital Learning Materials during Their Classes

ERIC Educational Resources Information Center

Vermeulen, Marjan; Kreijns, Karel; van Buuren, Hans; Van Acker, Frederik

2017-01-01

This study investigated whether school organizational variables (ie, transformative leadership (TL), ICT-infrastructure (technical and social) and organizational learning climate were related to teachers' dispositional variables (ie, attitude, perceived norm and perceived behavior control [PBC]). The direct and indirect influences of the…

Causal Structure Learning over Time: Observations and Interventions

ERIC Educational Resources Information Center

Rottman, Benjamin M.; Keil, Frank C.

2012-01-01

Seven studies examined how people learn causal relationships in scenarios when the variables are temporally dependent--the states of variables are stable over time. When people intervene on X, and Y subsequently changes state compared to before the intervention, people infer that X influences Y. This strategy allows people to learn causal…

Evaluating the Effectiveness Roles of Variables in the Novice Programmers Learning

ERIC Educational Resources Information Center

Shi, Nianfeng; Cui, Wen; Zhang, Ping; Sun, Ximing

2018-01-01

This research applies the roles of variables to the novice programmers in the C language programming. The results are evaluated using the Structure of Observed Learning Outcomes (SOLO) taxonomy. The participants were divided into an experimental group and a control group. The students from the control group learned programming in the traditional…

Perceptual Learning Style Preferences among Iranian Graduate Students

ERIC Educational Resources Information Center

Naserieh, Farid; Sarab, Mohammad Reza Anani

2013-01-01

Research suggests that a host of cognitive, affective, and perceptual variables are at work when individuals go about the task of second or foreign language learning. Among these variables are learning styles that are habitual ways of perceiving, processing, and storing information. This study was conducted as a response to Isemonger and…

An evaluation of consensus techniques for diagnostic interpretation

NASA Astrophysics Data System (ADS)

Sauter, Jake N.; LaBarre, Victoria M.; Furst, Jacob D.; Raicu, Daniela S.

2018-02-01

Learning diagnostic labels from image content has been the standard in computer-aided diagnosis. Most computer-aided diagnosis systems use low-level image features extracted directly from image content to train and test machine learning classifiers for diagnostic label prediction. When the ground truth for the diagnostic labels is not available, reference truth is generated from the experts diagnostic interpretations of the image/region of interest. More specifically, when the label is uncertain, e.g. when multiple experts label an image and their interpretations are different, techniques to handle the label variability are necessary. In this paper, we compare three consensus techniques that are typically used to encode the variability in the experts labeling of the medical data: mean, median and mode, and their effects on simple classifiers that can handle deterministic labels (decision trees) and probabilistic vectors of labels (belief decision trees). Given that the NIH/NCI Lung Image Database Consortium (LIDC) data provides interpretations for lung nodules by up to four radiologists, we leverage the LIDC data to evaluate and compare these consensus approaches when creating computer-aided diagnosis systems for lung nodules. First, low-level image features of nodules are extracted and paired with their radiologists semantic ratings (1= most likely benign, , 5 = most likely malignant); second, machine learning multi-class classifiers that handle deterministic labels (decision trees) and probabilistic vectors of labels (belief decision trees) are built to predict the lung nodules semantic ratings. We show that the mean-based consensus generates the most robust classi- fier overall when compared to the median- and mode-based consensus. Lastly, the results of this study show that, when building CAD systems with uncertain diagnostic interpretation, it is important to evaluate different strategies for encoding and predicting the diagnostic label.

THE SLOAN DIGITAL SKY SURVEY REVERBERATION MAPPING PROJECT: RAPID C iv BROAD ABSORPTION LINE VARIABILITY

DOE Office of Scientific and Technical Information (OSTI.GOV)

Grier, C. J.; Brandt, W. N.; Trump, J. R.

2015-06-10

We report the discovery of rapid variations of a high-velocity C iv broad absorption line trough in the quasar SDSS J141007.74+541203.3. This object was intensively observed in 2014 as a part of the Sloan Digital Sky Survey Reverberation Mapping Project, during which 32 epochs of spectroscopy were obtained with the Baryon Oscillation Spectroscopic Survey spectrograph. We observe significant (>4σ) variability in the equivalent width (EW) of the broad (∼4000 km s{sup −1} wide) C iv trough on rest-frame timescales as short as 1.20 days (∼29 hr), the shortest broad absorption line variability timescale yet reported. The EW varied by ∼10%more » on these short timescales, and by about a factor of two over the duration of the campaign. We evaluate several potential causes of the variability, concluding that the most likely cause is a rapid response to changes in the incident ionizing continuum. If the outflow is at a radius where the recombination rate is higher than the ionization rate, the timescale of variability places a lower limit on the density of the absorbing gas of n{sub e} ≳ 3.9 × 10{sup 5} cm{sup −3}. The broad absorption line variability characteristics of this quasar are consistent with those observed in previous studies of quasars, indicating that such short-term variability may in fact be common and thus can be used to learn about outflow characteristics and contributions to quasar/host-galaxy feedback scenarios.« less

Machine-learning-based Brokers for Real-time Classification of the LSST Alert Stream

NASA Astrophysics Data System (ADS)

Narayan, Gautham; Zaidi, Tayeb; Soraisam, Monika D.; Wang, Zhe; Lochner, Michelle; Matheson, Thomas; Saha, Abhijit; Yang, Shuo; Zhao, Zhenge; Kececioglu, John; Scheidegger, Carlos; Snodgrass, Richard T.; Axelrod, Tim; Jenness, Tim; Maier, Robert S.; Ridgway, Stephen T.; Seaman, Robert L.; Evans, Eric Michael; Singh, Navdeep; Taylor, Clark; Toeniskoetter, Jackson; Welch, Eric; Zhu, Songzhe; The ANTARES Collaboration

2018-05-01

The unprecedented volume and rate of transient events that will be discovered by the Large Synoptic Survey Telescope (LSST) demand that the astronomical community update its follow-up paradigm. Alert-brokers—automated software system to sift through, characterize, annotate, and prioritize events for follow-up—will be critical tools for managing alert streams in the LSST era. The Arizona-NOAO Temporal Analysis and Response to Events System (ANTARES) is one such broker. In this work, we develop a machine learning pipeline to characterize and classify variable and transient sources only using the available multiband optical photometry. We describe three illustrative stages of the pipeline, serving the three goals of early, intermediate, and retrospective classification of alerts. The first takes the form of variable versus transient categorization, the second a multiclass typing of the combined variable and transient data set, and the third a purity-driven subtyping of a transient class. Although several similar algorithms have proven themselves in simulations, we validate their performance on real observations for the first time. We quantitatively evaluate our pipeline on sparse, unevenly sampled, heteroskedastic data from various existing observational campaigns, and demonstrate very competitive classification performance. We describe our progress toward adapting the pipeline developed in this work into a real-time broker working on live alert streams from time-domain surveys.

Classification of caesarean section and normal vaginal deliveries using foetal heart rate signals and advanced machine learning algorithms.

PubMed

Fergus, Paul; Hussain, Abir; Al-Jumeily, Dhiya; Huang, De-Shuang; Bouguila, Nizar

2017-07-06

Visual inspection of cardiotocography traces by obstetricians and midwives is the gold standard for monitoring the wellbeing of the foetus during antenatal care. However, inter- and intra-observer variability is high with only a 30% positive predictive value for the classification of pathological outcomes. This has a significant negative impact on the perinatal foetus and often results in cardio-pulmonary arrest, brain and vital organ damage, cerebral palsy, hearing, visual and cognitive defects and in severe cases, death. This paper shows that using machine learning and foetal heart rate signals provides direct information about the foetal state and helps to filter the subjective opinions of medical practitioners when used as a decision support tool. The primary aim is to provide a proof-of-concept that demonstrates how machine learning can be used to objectively determine when medical intervention, such as caesarean section, is required and help avoid preventable perinatal deaths. This is evidenced using an open dataset that comprises 506 controls (normal virginal deliveries) and 46 cases (caesarean due to pH ≤ 7.20-acidosis, n = 18; pH > 7.20 and pH < 7.25-foetal deterioration, n = 4; or clinical decision without evidence of pathological outcome measures, n = 24). Several machine-learning algorithms are trained, and validated, using binary classifier performance measures. The findings show that deep learning classification achieves sensitivity = 94%, specificity = 91%, Area under the curve = 99%, F-score = 100%, and mean square error = 1%. The results demonstrate that machine learning significantly improves the efficiency for the detection of caesarean section and normal vaginal deliveries using foetal heart rate signals compared with obstetrician and midwife predictions and systems reported in previous studies.

Dispositional hope and life satisfaction among older adults attending lifelong learning programs.

PubMed

Oliver, A; Tomás, J M; Montoro-Rodriguez, J

2017-09-01

The aim of this study is to explore the indirect effects of dispositional hope in the life satisfaction of older adults attending a lifelong learning program at the University of Valencia, Spain. We examine the mediating impact of dispositional hope regarding its ability to impact life satisfaction while considering affective and confidant social support, perceived health and leisure activities, consciousness and spirituality as predictors. Analysis were based on survey data (response rate 77.4%) provided by 737 adults 55 years old or more (Mean age=65.41, SD=6.60; 69% woman). A structural model with latent variables was specified and estimated in Mplus. The results show the ability of just a few variables to sum up a reasonable model to apply to successful aging population. All these variables are correlated and significantly predict hope with the exception of health. The model additionally includes significant positive indirect effects from spirituality, affective support and consciousness on satisfaction. The model has a good fit in terms of both the measurement and structural model. Regarding predictive power, these comprehensive four main areas of successful aging account for 42% of hope and finally for one third of the life satisfaction variance. Results support the mediating role of dispositional hope on the life satisfaction among older adults attending lifelong learning programs. These findings also support the MacArthur model of successful aging adapted to older adults with high levels of functional, social and cognitive ability. Dispositional hope, perceived health, and social support were the strongest predictors of satisfaction with life. Copyright © 2017 Elsevier B.V. All rights reserved.

Visual variability affects early verb learning.

PubMed

Twomey, Katherine E; Lush, Lauren; Pearce, Ruth; Horst, Jessica S

2014-09-01

Research demonstrates that within-category visual variability facilitates noun learning; however, the effect of visual variability on verb learning is unknown. We habituated 24-month-old children to a novel verb paired with an animated star-shaped actor. Across multiple trials, children saw either a single action from an action category (identical actions condition, for example, travelling while repeatedly changing into a circle shape) or multiple actions from that action category (variable actions condition, for example, travelling while changing into a circle shape, then a square shape, then a triangle shape). Four test trials followed habituation. One paired the habituated verb with a new action from the habituated category (e.g., 'dacking' + pentagon shape) and one with a completely novel action (e.g., 'dacking' + leg movement). The others paired a new verb with a new same-category action (e.g., 'keefing' + pentagon shape), or a completely novel category action (e.g., 'keefing' + leg movement). Although all children discriminated novel verb/action pairs, children in the identical actions condition discriminated trials that included the completely novel verb, while children in the variable actions condition discriminated the out-of-category action. These data suggest that - as in noun learning - visual variability affects verb learning and children's ability to form action categories. © 2014 The British Psychological Society.

Variables Predicting Foreign Language Reading Comprehension and Vocabulary Acquisition in a Linear Hypermedia Environment

ERIC Educational Resources Information Center

Akbulut, Yavuz

2007-01-01

Factors predicting vocabulary learning and reading comprehension of advanced language learners of English in a linear multimedia text were investigated in the current study. Predictor variables of interest were multimedia type, reading proficiency, learning styles, topic interest and background knowledge about the topic. The outcome variables of…

Culture, Organizational Learning and Selected Employee Background Variables in Small-Size Business Enterprises

ERIC Educational Resources Information Center

Graham, Carroll M.; Nafukho, Fredrick Muyia

2007-01-01

Purpose: The purpose of this study is to determine the relationship between four independent variables educational level, longevity, type of enterprise, and gender and the dependent variable culture, as a dimension that explains organizational learning readiness in seven small-size business enterprises. Design/methodology/approach: An exploratory…

Reinforcement Learning Trees

PubMed Central

Zhu, Ruoqing; Zeng, Donglin; Kosorok, Michael R.

2015-01-01

In this paper, we introduce a new type of tree-based method, reinforcement learning trees (RLT), which exhibits significantly improved performance over traditional methods such as random forests (Breiman, 2001) under high-dimensional settings. The innovations are three-fold. First, the new method implements reinforcement learning at each selection of a splitting variable during the tree construction processes. By splitting on the variable that brings the greatest future improvement in later splits, rather than choosing the one with largest marginal effect from the immediate split, the constructed tree utilizes the available samples in a more efficient way. Moreover, such an approach enables linear combination cuts at little extra computational cost. Second, we propose a variable muting procedure that progressively eliminates noise variables during the construction of each individual tree. The muting procedure also takes advantage of reinforcement learning and prevents noise variables from being considered in the search for splitting rules, so that towards terminal nodes, where the sample size is small, the splitting rules are still constructed from only strong variables. Last, we investigate asymptotic properties of the proposed method under basic assumptions and discuss rationale in general settings. PMID:26903687

Improving Students' Learning With Effective Learning Techniques: Promising Directions From Cognitive and Educational Psychology.

PubMed

Dunlosky, John; Rawson, Katherine A; Marsh, Elizabeth J; Nathan, Mitchell J; Willingham, Daniel T

2013-01-01

Many students are being left behind by an educational system that some people believe is in crisis. Improving educational outcomes will require efforts on many fronts, but a central premise of this monograph is that one part of a solution involves helping students to better regulate their learning through the use of effective learning techniques. Fortunately, cognitive and educational psychologists have been developing and evaluating easy-to-use learning techniques that could help students achieve their learning goals. In this monograph, we discuss 10 learning techniques in detail and offer recommendations about their relative utility. We selected techniques that were expected to be relatively easy to use and hence could be adopted by many students. Also, some techniques (e.g., highlighting and rereading) were selected because students report relying heavily on them, which makes it especially important to examine how well they work. The techniques include elaborative interrogation, self-explanation, summarization, highlighting (or underlining), the keyword mnemonic, imagery use for text learning, rereading, practice testing, distributed practice, and interleaved practice. To offer recommendations about the relative utility of these techniques, we evaluated whether their benefits generalize across four categories of variables: learning conditions, student characteristics, materials, and criterion tasks. Learning conditions include aspects of the learning environment in which the technique is implemented, such as whether a student studies alone or with a group. Student characteristics include variables such as age, ability, and level of prior knowledge. Materials vary from simple concepts to mathematical problems to complicated science texts. Criterion tasks include different outcome measures that are relevant to student achievement, such as those tapping memory, problem solving, and comprehension. We attempted to provide thorough reviews for each technique, so this monograph is rather lengthy. However, we also wrote the monograph in a modular fashion, so it is easy to use. In particular, each review is divided into the following sections: General description of the technique and why it should work How general are the effects of this technique?  2a. Learning conditions  2b. Student characteristics  2c. Materials  2d. Criterion tasks Effects in representative educational contexts Issues for implementation Overall assessment The review for each technique can be read independently of the others, and particular variables of interest can be easily compared across techniques. To foreshadow our final recommendations, the techniques vary widely with respect to their generalizability and promise for improving student learning. Practice testing and distributed practice received high utility assessments because they benefit learners of different ages and abilities and have been shown to boost students' performance across many criterion tasks and even in educational contexts. Elaborative interrogation, self-explanation, and interleaved practice received moderate utility assessments. The benefits of these techniques do generalize across some variables, yet despite their promise, they fell short of a high utility assessment because the evidence for their efficacy is limited. For instance, elaborative interrogation and self-explanation have not been adequately evaluated in educational contexts, and the benefits of interleaving have just begun to be systematically explored, so the ultimate effectiveness of these techniques is currently unknown. Nevertheless, the techniques that received moderate-utility ratings show enough promise for us to recommend their use in appropriate situations, which we describe in detail within the review of each technique. Five techniques received a low utility assessment: summarization, highlighting, the keyword mnemonic, imagery use for text learning, and rereading. These techniques were rated as low utility for numerous reasons. Summarization and imagery use for text learning have been shown to help some students on some criterion tasks, yet the conditions under which these techniques produce benefits are limited, and much research is still needed to fully explore their overall effectiveness. The keyword mnemonic is difficult to implement in some contexts, and it appears to benefit students for a limited number of materials and for short retention intervals. Most students report rereading and highlighting, yet these techniques do not consistently boost students' performance, so other techniques should be used in their place (e.g., practice testing instead of rereading). Our hope is that this monograph will foster improvements in student learning, not only by showcasing which learning techniques are likely to have the most generalizable effects but also by encouraging researchers to continue investigating the most promising techniques. Accordingly, in our closing remarks, we discuss some issues for how these techniques could be implemented by teachers and students, and we highlight directions for future research. © The Author(s) 2013.

An Examination of Strategy Implementation During Abstract Nonlinguistic Category Learning in Aphasia

PubMed Central

Kiran, Swathi

2015-01-01

Purpose Our purpose was to study strategy use during nonlinguistic category learning in aphasia. Method Twelve control participants without aphasia and 53 participants with aphasia (PWA) completed a computerized feedback-based category learning task consisting of training and testing phases. Accuracy rates of categorization in testing phases were calculated. To evaluate strategy use, strategy analyses were conducted over training and testing phases. Participant data were compared with model data that simulated complex multi-cue, single feature, and random pattern strategies. Learning success and strategy use were evaluated within the context of standardized cognitive–linguistic assessments. Results Categorization accuracy was higher among control participants than among PWA. The majority of control participants implemented suboptimal or optimal multi-cue and single-feature strategies by testing phases of the experiment. In contrast, a large subgroup of PWA implemented random patterns, or no strategy, during both training and testing phases of the experiment. Conclusions Person-to-person variability arises not only in category learning ability but also in the strategies implemented to complete category learning tasks. PWA less frequently developed effective strategies during category learning tasks than control participants. Certain PWA may have impairments of strategy development or feedback processing not captured by language and currently probed cognitive abilities. PMID:25908438

Children's Learning in Scientific Thinking: Instructional Approaches and Roles of Variable Identification and Executive Function

NASA Astrophysics Data System (ADS)

Blums, Angela

The present study examines instructional approaches and cognitive factors involved in elementary school children's thinking and learning the Control of Variables Strategy (CVS), a critical aspect of scientific reasoning. Previous research has identified several features related to effective instruction of CVS, including using a guided learning approach, the use of self-reflective questions, and learning in individual and group contexts. The current study examined the roles of procedural and conceptual instruction in learning CVS and investigated the role of executive function in the learning process. Additionally, this study examined how learning to identify variables is a part of the CVS process. In two studies (individual and classroom experiments), 139 third, fourth, and fifth grade students participated in hands-on and paper and pencil CVS learning activities and, in each study, were assigned to either a procedural instruction, conceptual instruction, or control (no instruction) group. Participants also completed a series of executive function tasks. The study was carried out with two parts--Study 1 used an individual context and Study 2 was carried out in a group setting. Results indicated that procedural and conceptual instruction were more effective than no instruction, and the ability to identify variables was identified as a key piece to the CVS process. Executive function predicted ability to identify variables and predicted success on CVS tasks. Developmental differences were present, in that older children outperformed younger children on CVS tasks, and that conceptual instruction was slightly more effective for older children. Some differences between individual and group instruction were found, with those in the individual context showing some advantage over the those in the group setting in learning CVS concepts. Conceptual implications about scientific thinking and practical implications in science education are discussed.

Policy learning for flood mitigation: a longitudinal assessment of the community rating system in Florida.

PubMed

Brody, Samuel D; Zahran, Sammy; Highfield, Wesley E; Bernhardt, Sarah P; Vedlitz, Arnold

2009-06-01

Floods continue to inflict the most damage upon human communities among all natural hazards in the United States. Because localized flooding tends to be spatially repetitive over time, local decisionmakers often have an opportunity to learn from previous events and make proactive policy adjustments to reduce the adverse effects of a subsequent storm. Despite the importance of understanding the degree to which local jurisdictions learn from flood risks and under what circumstances, little if any empirical, longitudinal research has been conducted along these lines. This article addresses the research gap by examining the change in local flood mitigation policies in Florida from 1999 to 2005. We track 18 different mitigation activities organized into four series of activities under the Federal Emergency Management Agency's (FEMA) Community Rating System (CRS) for every local jurisdiction in Florida participating in the FEMA program on a yearly time step. We then identify the major factors contributing to policy changes based on CRS scores over the seven-year study period. Using multivariate statistical models to analyze both natural and social science data, we isolate the effects of several variables categorized into the following groups: hydrologic conditions, flood disaster history, socioeconomic and human capital controls. Results indicate that local jurisdictions do in fact learn from histories of flood risk and this process is expedited under specific conditions.

Person Re-Identification via Distance Metric Learning With Latent Variables.

PubMed

Sun, Chong; Wang, Dong; Lu, Huchuan

2017-01-01

In this paper, we propose an effective person re-identification method with latent variables, which represents a pedestrian as the mixture of a holistic model and a number of flexible models. Three types of latent variables are introduced to model uncertain factors in the re-identification problem, including vertical misalignments, horizontal misalignments and leg posture variations. The distance between two pedestrians can be determined by minimizing a given distance function with respect to latent variables, and then be used to conduct the re-identification task. In addition, we develop a latent metric learning method for learning the effective metric matrix, which can be solved via an iterative manner: once latent information is specified, the metric matrix can be obtained based on some typical metric learning methods; with the computed metric matrix, the latent variables can be determined by searching the state space exhaustively. Finally, extensive experiments are conducted on seven databases to evaluate the proposed method. The experimental results demonstrate that our method achieves better performance than other competing algorithms.

Reorganization of finger coordination patterns during adaptation to rotation and scaling of a newly learned sensorimotor transformation.

PubMed

Liu, Xiaolin; Mosier, Kristine M; Mussa-Ivaldi, Ferdinando A; Casadio, Maura; Scheidt, Robert A

2011-01-01

We examined how people organize redundant kinematic control variables (finger joint configurations) while learning to make goal-directed movements of a virtual object (a cursor) within a low-dimensional task space (a computer screen). Subjects participated in three experiments performed on separate days. Learning progressed rapidly on day 1, resulting in reduced target capture error and increased cursor trajectory linearity. On days 2 and 3, one group of subjects adapted to a rotation of the nominal map, imposed either stepwise or randomly over trials. Another group experienced a scaling distortion. We report two findings. First, adaptation rates and memory-dependent motor command updating depended on distortion type. Stepwise application and removal of the rotation induced a marked increase in finger motion variability but scaling did not, suggesting that the rotation initiated a more exhaustive search through the space of viable finger motions to resolve the target capture task than did scaling. Indeed, subjects formed new coordination patterns in compensating the rotation but relied on patterns established during baseline practice to compensate the scaling. These findings support the idea that the brain compensates direction and extent errors separately and in computationally distinct ways, but are inconsistent with the idea that once a task is learned, command updating is limited to those degrees of freedom contributing to performance (thereby minimizing energetic or similar costs of control). Second, we report that subjects who learned a scaling while moving to just one target generalized more narrowly across directions than those who learned a rotation. This contrasts with results from whole-arm reaching studies, where a learned scaling generalizes more broadly across direction than rotation. Based on inverse- and forward-dynamics analyses of reaching with the arm, we propose the difference in results derives from extensive exposure in reaching with familiar arm dynamics versus the novelty of the manual task.

A Learning Theory for Reward-Modulated Spike-Timing-Dependent Plasticity with Application to Biofeedback

PubMed Central

Maass, Wolfgang

2008-01-01

Reward-modulated spike-timing-dependent plasticity (STDP) has recently emerged as a candidate for a learning rule that could explain how behaviorally relevant adaptive changes in complex networks of spiking neurons could be achieved in a self-organizing manner through local synaptic plasticity. However, the capabilities and limitations of this learning rule could so far only be tested through computer simulations. This article provides tools for an analytic treatment of reward-modulated STDP, which allows us to predict under which conditions reward-modulated STDP will achieve a desired learning effect. These analytical results imply that neurons can learn through reward-modulated STDP to classify not only spatial but also temporal firing patterns of presynaptic neurons. They also can learn to respond to specific presynaptic firing patterns with particular spike patterns. Finally, the resulting learning theory predicts that even difficult credit-assignment problems, where it is very hard to tell which synaptic weights should be modified in order to increase the global reward for the system, can be solved in a self-organizing manner through reward-modulated STDP. This yields an explanation for a fundamental experimental result on biofeedback in monkeys by Fetz and Baker. In this experiment monkeys were rewarded for increasing the firing rate of a particular neuron in the cortex and were able to solve this extremely difficult credit assignment problem. Our model for this experiment relies on a combination of reward-modulated STDP with variable spontaneous firing activity. Hence it also provides a possible functional explanation for trial-to-trial variability, which is characteristic for cortical networks of neurons but has no analogue in currently existing artificial computing systems. In addition our model demonstrates that reward-modulated STDP can be applied to all synapses in a large recurrent neural network without endangering the stability of the network dynamics. PMID:18846203

Language Learning Strategies, Course Grades, and Age in EFL Secondary School Learners

ERIC Educational Resources Information Center

Tragant, Elsa; Victori, Mia

2012-01-01

In studies dealing with language learning strategies in the school context, the variables of proficiency and age are often difficult to isolate since students accumulate more hours of foreign language instruction as they move up from grade to grade. This study aimed to deal with these two variables independently by analysing learning strategy use…

Child Predictors of Learning to Control Variables via Instruction or Self-Discovery

ERIC Educational Resources Information Center

Wagensveld, Barbara; Segers, Eliane; Kleemans, Tijs; Verhoeven, Ludo

2015-01-01

We examined the role child factors on the acquisition and transfer of learning the control of variables strategy (CVS) via instruction or self-discovery. Seventy-six fourth graders and 43 sixth graders were randomly assigned to a group receiving direct CVS instruction or a discovery learning group. Prior to the intervention, cognitive, scientific,…

Analyzing the Classroom Teachers' Levels of Creating a Constructivist Learning Environments in Terms of Various Variables: A Mersin Case

ERIC Educational Resources Information Center

Üredi, Lütfi

2014-01-01

In this research, it was aimed to analyze the classroom teachers' level of creating a constructivist learning environment in terms of various variables. For that purpose, relational screening model was used in the research. Classroom teachers' level of creating a constructivist learning environment was determined using the "constructivist…

The Relationship between Iranian EFL Instructors' Understanding of Learning Styles and Their Students' Success in Reading Comprehension

ERIC Educational Resources Information Center

Khademi, Marzieh; Motallebzadeh, Khalil; Ashraf, Hamid

2013-01-01

Many variables reasonably influence teachers' education. One of these considering variables is being aware of the students' learning styles. Dörnyei (2005) maintains that individual differences correlate strongly with L2 achievements. Keefe (1979) believes that learning styles might be thought of as cognitive, affective, and physiological traits…

Statistical learning in reading: variability in irrelevant letters helps children learn phonics skills.

PubMed

Apfelbaum, Keith S; Hazeltine, Eliot; McMurray, Bob

2013-07-01

Early reading abilities are widely considered to derive in part from statistical learning of regularities between letters and sounds. Although there is substantial evidence from laboratory work to support this, how it occurs in the classroom setting has not been extensively explored; there are few investigations of how statistics among letters and sounds influence how children actually learn to read or what principles of statistical learning may improve learning. We examined 2 conflicting principles that may apply to learning grapheme-phoneme-correspondence (GPC) regularities for vowels: (a) variability in irrelevant units may help children derive invariant relationships and (b) similarity between words may force children to use a deeper analysis of lexical structure. We trained 224 first-grade students on a small set of GPC regularities for vowels, embedded in words with either high or low consonant similarity, and tested their generalization to novel tasks and words. Variability offered a consistent benefit over similarity for trained and new words in both trained and new tasks.

Effects of Talker Variability on Perceptual Learning of Dialects

PubMed Central

Clopper, Cynthia G.; Pisoni, David B.

2012-01-01

Two groups of listeners learned to categorize a set of unfamiliar talkers by dialect region using sentences selected from the TIMIT speech corpus. One group learned to categorize a single talker from each of six American English dialect regions. A second group learned to categorize three talkers from each dialect region. Following training, both groups were asked to categorize new talkers using the same categorization task. While the single-talker group was more accurate during initial training and test phases when familiar talkers produced the sentences, the three-talker group performed better on the generalization task with unfamiliar talkers. This cross-over effect in dialect categorization suggests that while talker variation during initial perceptual learning leads to more difficult learning of specific exemplars, exposure to intertalker variability facilitates robust perceptual learning and promotes better categorization performance of unfamiliar talkers. The results suggest that listeners encode and use acoustic-phonetic variability in speech to reliably perceive the dialect of unfamiliar talkers. PMID:15697151

Success in baccalaureate nursing programs: a matter of accommodation?

PubMed

Haislett, J; Hughes, R B; Atkinson, G; Williams, C L

1993-02-01

This article explores student learning styles as an important variable in four-year baccalaureate nursing programs. Student learning styles were assessed by Kolb's Learning Style Inventory-1985 (LSI-1985), which identifies the accommodator, diverger, assimilator, and converger learning styles. The authors examined the relationship between learning style and academic performance as measured by grade-point ratio (GPR) and studied behaviors and attitudes as measured by Brown and Holtzman's (1964) Survey of Study Habits and Attitudes. Analysis indicated that this sample (N = 100) included mainly assimilators and divergers, making reflective observation the most common mode of learning. Compared to the accommodator/converger group, the assimilator/diverger group earned a significantly higher GPR, significantly better scores on the study habits variable of Work Methods (WM), and moderately better scores on the study attitude variable of Educational Acceptance (EA). Accommodators were identified as the most at-risk learning style group, and specific interventions were suggested to assist accommodators in adapting to the academic rigors of a nursing curriculum.

Using student motivation to design groups in a non-majors biology course for team-based collaborative learning: Impacts on knowledge, views, attitudes, and perceptions

NASA Astrophysics Data System (ADS)

Walters, Kristi L.

The importance of student motivation and its connection to other learning variables (i.e., attitudes, knowledge, persistence, attendance) is well established. Collaborative work at the undergraduate level has been recognized as a valuable tool in large courses. However, motivation and collaborative group work have rarely been combined. This project utilized student motivation to learn biology to place non-major biology undergraduates in collaborative learning groups at East Carolina University, a mid-sized southeastern American university, to determine the effects of this construct on student learning. A pre-test measuring motivation to learn biology, attitudes toward biology, perceptions of biology and biologists, views of science, and content knowledge was administered. A similar post-test followed as part of the final exam. Two sections of the same introductory biology course (n = 312) were used and students were divided into homogeneous and heterogeneous groups (based on their motivation score). The heterogeneous groups (n = 32) consisted of a mixture of different motivation levels, while the homogeneous groups (n = 32) were organized into teams with similar motivation scores using tiers of high-, middle-, and low-level participants. Data analysis determined mixed perceptions of biology and biologists. These include the perceptions biology was less intriguing, less relevant, less practical, less ethical, and less understandable. Biologists were perceived as being neat and slightly intelligent, but not very altruistic, humane, ethical, logical, honest, or moral. Content knowledge scores more than doubled from pre- to post-test. Half of the items measuring views of science were not statistically significantly different from pre- to post-test. Many of the factors for attitudes toward biology became more agreeable from pre- to post-test. Correlations between motivation scores, participation levels, attendance rates, and final course grades were examined at both the individual and group level. Motivation had low correlations with the other variables. Changes in group membership (i.e., attrition) were evaluated at the group level and showed the highest rates with the heterogeneous groups and the lowest with the homogeneous middle groups. Group gender ratios were examined, but showed no correlation with final course grade. Linear regression was utilized to identify any variables that might be useful in predicting the final course grade of each student. Only participation, attendance, and final exam grade were predictive, but as they were components of the final course grade, they were not useful for the model. Differences between the groups were also examined to determine if the group type was predictive of final course grade, but no significant difference was found. Results of the study are discussed in the context of the literature on student motivation to learn science. Implications of the study are discussed through the lens of the Millennial generation's perspectives on teaching and learning. Millennials often consider an education to be a commodity and may expect results with less effort. Millennials may be expressing a pseudo-intrinsic motivation in order to impress peers and instructors, while they may actually be more extrinsically motivated to succeed

Learning (Not) to Talk about Race: Investigating What Doctoral Students Learn about Race Variables and Statistical Modeling

ERIC Educational Resources Information Center

Armijo, Michael; Lundy-Wagner, Valerie; Merrill, Elizabeth

2012-01-01

This paper asks how doctoral students understand the use of race variables in statistical modeling. More specifically, it examines how doctoral students at two universities are trained to define, operationalize, and analyze race variables. The authors interviewed students and instructors in addition to conducting a document analysis of their texts…

Does Variability across Events Affect Verb Learning in English, Mandarin, and Korean?

ERIC Educational Resources Information Center

Childers, Jane B.; Paik, Jae H.; Flores, Melissa; Lai, Gabrielle; Dolan, Megan

2017-01-01

Extending new verbs is important in becoming a productive speaker of a language. Prior results show children have difficulty extending verbs when they have seen events with varied agents. This study further examines the impact of variability on verb learning and asks whether variability interacts with event complexity or differs by language.…

A look at Behaviourism and Perceptual Control Theory in Interface Design

DTIC Science & Technology

1998-02-01

behaviours such as response variability, instinctive drift, autoshaping , etc. Perceptual Control Theory (PCT) postulates that behaviours result from the...internal variables. Behaviourism, on the other hand, can not account for variability in responses, instinctive drift, autoshaping , etc. Researchers... Autoshaping . Animals appear to learn without reinforcement. However, conditioning theory speculates that learning results only when reinforcement

Bilingualism: A Pearl to Overcome Certain Perils of Cochlear Implants

PubMed Central

Humphries, Tom; Kushalnagar, Poorna; Mathur, Gaurav; Napoli, Donna Jo; Padden, Carol; Rathmann, Christian; Smith, Scott

2014-01-01

Cochlear implants (CI) have demonstrated success in improving young deaf children’s speech and low-level speech awareness across a range of auditory functions, but this success is highly variable, and how this success correlates to high-level language development is even more variable. Prevalence on the success rate of CI as an outcome for language development is difficult to obtain because studies vary widely in methodology and variables of interest, and because not all cochlear implant technology (which continues to evolve) is the same. Still, even if the notion of treatment failure is limited narrowly to those who gain no auditory benefit from CI in that they cannot discriminate among ambient noises, the reported treatment failure rate is high enough to call into question the current lack of consideration of alternative approaches to ensure young deaf children’s language development. Recent research has highlighted the risks of delaying language input during critical periods of brain development with concomitant consequences for cognitive and social skills. As a result, we propose that before, during, and after implantation deaf children learn a sign language along with a spoken language to ensure their maximal language development and optimal long-term developmental outcomes. PMID:25419095

Perceptions of control in adults with epilepsy.

PubMed

Gehlert, S

1994-01-01

That psychosocial problems are extant in epilepsy is evidenced by a suicide rate among epileptic persons five times that of the general population and an unemployment rate estimated to be more than twice that of the population as a whole. External perceptions of control secondary to repeated episodes of seizure activity that generalize to the social sphere have been implicated as causes of these problems. The hypothesis that individuals who continue to have seizures become more and more external in perceptions of control was tested by a survey mailed to a sample of individuals with epilepsy in a metropolitan area of the Midwest. Dependent variables were, scores on instruments measuring locus of control and attributional style. The independent variable was a measure of seizure control based on present age, age at onset, and length of time since last seizure. Gender, socioeconomic status, and certain parenting characteristics were included as control variables, as they are also known to affect perceptions of control. Analysis by multiple regression techniques supported the study's hypothesis when perceptions of control was conceptualized as learned helplessness for bad, but not for good, events. The hypothesis was not confirmed when perceptions of control was conceptualized as either general or health locus of control.

The implications of renewable energy research and development: Policy scenario analysis with experience and learning effects

NASA Astrophysics Data System (ADS)

Kobos, Peter Holmes

This dissertation analyzes the current and potential future costs of renewable energy technology from an institutional perspective. The central hypothesis is that reliable technology cost forecasting can be achieved through standard and modified experience curves implemented in a dynamic simulation model. Additionally, drawing upon region-specific institutional lessons highlights the role of market, social, and political institutions throughout an economy. Socio-political influences and government policy pathways drive resource allocation decisions that may be predominately influenced by factors other than those considered in a traditional market-driven, mechanistic approach. Learning in economic systems as a research topic is an attractive complement to the notion of institutional pathways. The economic implications of learning by doing, as first outlined by Arrow (1962), highlight decreasing production costs as individuals, or more generally the firm, become more familiar with a production process. The standard approach in the literature has been to employ a common experience curve where cumulative production is the only independent variable affecting costs. This dissertation develops a two factor experience curve, adding research, development and demonstration (RD&D) expenditures as a second variable. To illustrate the concept in the context of energy planning, two factor experience curves are developed for wind energy technology and solar photovoltaic (PV) modules under different assumptions on learning rates for cumulative capacity and the knowledge stock (a function of past RD&D efforts). Additionally, a one factor experience curve and cost trajectory scenarios are developed for concentrated solar power and geothermal energy technology, respectively. Cost forecasts are then developed for all four of these technologies in a dynamic simulation model. Combining the theoretical framework of learning by doing with the fields of organizational learning and institutional economics, this dissertation argues that the current state of renewable energy technology costs is largely due to the past production efforts (learning by doing) and RD&D efforts (learning by searching) in these global industries. This cost pathway, however, may be altered through several policy process feedback mechanisms including targeted RD&D expenditures, maintenance of RD&D to promote learning effects, and financial incentive programs that support energy production from renewable energy technologies.

Is there a learning curve for the TVT-O procedure? A prospective single-surgeon study of 372 consecutive cases.

PubMed

Serati, Maurizio; Bogani, Giorgio; Braga, Andrea; Sorice, Paola; Salvatore, Stefano; Uccella, Stefano; Ghezzi, Fabio

2015-03-01

To evaluate for the first time in the literature the learning curve of Inside-out transobturator tape (TVT-O™). A prospective observational study was conducted in a tertiary reference center. Consecutive women treated by TVT-O™ performed by one surgeon were included. Data regarding subjective, objective cure rates, and adverse events were collected. Trends, over the number of procedures, were estimated using assay analyses. Number of procedures and variables were interpolating in standard curves using linear lines. Three hundred and seventy two procedures were included. Postoperative pain levels decreased with the increase in the level of expertise (pain levels: 1-day: from 6.6 (±3.3) to 4.3 (±3.1); 95%CI: -0.01603 to 0.001235, p=0.04; 2-day: from 5.6 (±4.1) to 3.6 (±3.7); 95%CI: -0.02092 to -0.002497, p=0.01; 12-month: from 0.1 (±0.7) to 0 (±0); 95%CI: -0.001814 to 0.05019, p=0.07). Overall, objective cure rate was achieved in 93.5% of patients. Additionally, 88.2% and 88.7% patients reported "much better" feeling at PGI-I scale and 80% reduction in UDI score, respectively. We observed, that delta ICIQ-sf (from 12 (±8.7) to 14 (±6.0); p=0.04) and delta-UDI (from 91% to 97%; p=0.04) improved over the time. TVT-O procedure offers excellent outcomes with high objective and subjective cure rates and low complications rate, even at the beginning of the surgeon's learning curve. However, a high experience of the surgeon could significantly improve the subjective cure rate and could reduce postoperative the groin pain. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Tracking a changing environment: optimal sampling, adaptive memory and overnight effects.

PubMed

Dunlap, Aimee S; Stephens, David W

2012-02-01

Foraging in a variable environment presents a classic problem of decision making with incomplete information. Animals must track the changing environment, remember the best options and make choices accordingly. While several experimental studies have explored the idea that sampling behavior reflects the amount of environmental change, we take the next logical step in asking how change influences memory. We explore the hypothesis that memory length should be tied to the ecological relevance and the value of the information learned, and that environmental change is a key determinant of the value of memory. We use a dynamic programming model to confirm our predictions and then test memory length in a factorial experiment. In our experimental situation we manipulate rates of change in a simple foraging task for blue jays over a 36 h period. After jays experienced an experimentally determined change regime, we tested them at a range of retention intervals, from 1 to 72 h. Manipulated rates of change influenced learning and sampling rates: subjects sampled more and learned more quickly in the high change condition. Tests of retention revealed significant interactions between retention interval and the experienced rate of change. We observed a striking and surprising difference between the high and low change treatments at the 24h retention interval. In agreement with earlier work we find that a circadian retention interval is special, but we find that the extent of this 'specialness' depends on the subject's prior experience of environmental change. Specifically, experienced rates of change seem to influence how subjects balance recent information against past experience in a way that interacts with the passage of time. Copyright © 2011 Elsevier B.V. All rights reserved.

Evaluating the learning curve for robot-assisted laparoscopic radical cystectomy.

PubMed

Pruthi, Raj S; Smith, Angela; Wallen, Eric M

2008-11-01

We seek to describe the learning curve of robot-assisted laparoscopic radical cystectomy by evaluating some of the surgical, oncologic, and clinical outcomes in our initial experience with 50 consecutive patients undergoing this novel procedure. Fifty consecutive patients (representing our initial experience with robot-assisted cystectomy) underwent radical cystectomy and urinary diversion from January 2006 to December 2007. Several different metrics were used to evaluate the learning curve of this procedure, including estimated blood loss (EBL), operative (OR) time, pathologic outcomes, and complication rate. We evaluated patients as a continuous variable, divided into five distinct time periods (quintiles), and stratified by first half and second half of robotic experience. EBL was not significantly lower until the third quintile (patients 21-30), after which further significant reductions were not observed. Mean OR time declined between each quintile for the first 30 patients (1-10 v 11-20 v 21-30). No significant declines occurred after the third quintile (21-30). When evaluated as a continuous variable, the statistical cut point at which no further significant reductions were observed was after patient 20 for OR time. No differences were observed with regard to time to flatus, bowel movement, or hospital discharge. Furthermore, complications were not different between the initial 25 patients and the most recent patients. There has been no case of a positive margin, and there was only one inadvertent bladder entry. Lymph node yield has also not significantly changed over time. This report helps to define the learning curve associated with robot-assisted laparoscopic radical cystectomy for bladder cancer. Despite the higher OR times and blood loss that is observed early in the learning curve, no such compromises are observed with regard to these oncologic parameters even early in the experience.

Socio-cognitive profiles for visual learning in young and older adults

PubMed Central

Christian, Julie; Goldstone, Aimee; Kuai, Shu-Guang; Chin, Wynne; Abrams, Dominic; Kourtzi, Zoe

2015-01-01

It is common wisdom that practice makes perfect; but why do some adults learn better than others? Here, we investigate individuals’ cognitive and social profiles to test which variables account for variability in learning ability across the lifespan. In particular, we focused on visual learning using tasks that test the ability to inhibit distractors and select task-relevant features. We tested the ability of young and older adults to improve through training in the discrimination of visual global forms embedded in a cluttered background. Further, we used a battery of cognitive tasks and psycho-social measures to examine which of these variables predict training-induced improvement in perceptual tasks and may account for individual variability in learning ability. Using partial least squares regression modeling, we show that visual learning is influenced by cognitive (i.e., cognitive inhibition, attention) and social (strategic and deep learning) factors rather than an individual’s age alone. Further, our results show that independent of age, strong learners rely on cognitive factors such as attention, while weaker learners use more general cognitive strategies. Our findings suggest an important role for higher-cognitive circuits involving executive functions that contribute to our ability to improve in perceptual tasks after training across the lifespan. PMID:26113820

Application of alpha/theta neurofeedback and heart rate variability training to young contemporary dancers: state anxiety and creativity.

PubMed

Gruzelier, J H; Thompson, T; Redding, E; Brandt, R; Steffert, T

2014-07-01

As one in a series on the impact of EEG-neurofeedback in the performing arts, we set out to replicate a previous dance study in which alpha/theta (A/T) neurofeedback and heart rate variability (HRV) biofeedback enhanced performance in competitive ballroom dancers compared with controls. First year contemporary dance conservatoire students were randomised to the same two psychophysiological interventions or a choreology instruction comparison group or a no-training control group. While there was demonstrable neurofeedback learning, there was no impact of the three interventions on dance performance as assessed by four experts. However, HRV training reduced anxiety and the reduction correlated with improved technique and artistry in performance; the anxiety scale items focussed on autonomic functions, especially cardiovascular activity. In line with the putative impact of hypnogogic training on creativity A/T training increased cognitive creativity with the test of unusual uses, but not insight problems. Methodological and theoretical implications are considered. Copyright © 2013 Elsevier B.V. All rights reserved.

[Carotid Stenting in France after the EVA 3S and SPACE publications].

PubMed

Beyssen, B; Rousseau, H; Bracard, S; Sapoval, M; Gaux, J-C

2007-01-01

Angioplasty of stenoses of the carotid bifurcation is a revascularization procedure that is used successfully in many patients. With more than 10 years of experience now, the feasibility of carotid stenting has been demonstrated. Its distribution is highly variable depending on the country, with a mean penetration rate in Europe of 15% of the number of carotid revascularizations. However, the complication rate is highly variable from one series to another and depends on the type of patient treated and the operator's learning curve. The results of the first two randomized studies comparing endarterectomy and carotid stenting, EVA 3S in France and SPACE in Germany, have just been published. The conclusions of these studies only relate to symptomatic patients, who make up a small proportion of revascularized patients. At 30 days, the French study concluded that surgery was better, and the German study showed no advantage to stenting. The analysis of these results compared to other publications should make it possible to best define the current indications for carotid stenting.

Help Seeking Among Victims of Crime: A Review of the Empirical Literature

PubMed Central

McCart, Michael R.; Smith, Daniel W.; Sawyer, Genelle K.

2013-01-01

This paper reviews the literature on help-seeking behavior among adult victims of crime. Specifically, the paper summarizes prevalence rates for formal and informal help seeking and reviews predictors of and barriers to service use following victimization. Research suggests that only a small fraction of crime victims seek help from formal support networks; however, many seek support from informal sources. Several variables are associated with increased likelihood of formal help seeking, although the manner in which these variables affect reporting behavior is not clear. From this review, it is concluded that much remains to be learned regarding patterns of help seeking among victims of crime. Gaps in the literature and directions for future research are discussed. PMID:20336674

Predicting Protein-protein Association Rates using Coarse-grained Simulation and Machine Learning

NASA Astrophysics Data System (ADS)

Xie, Zhong-Ru; Chen, Jiawen; Wu, Yinghao

2017-04-01

Protein-protein interactions dominate all major biological processes in living cells. We have developed a new Monte Carlo-based simulation algorithm to study the kinetic process of protein association. We tested our method on a previously used large benchmark set of 49 protein complexes. The predicted rate was overestimated in the benchmark test compared to the experimental results for a group of protein complexes. We hypothesized that this resulted from molecular flexibility at the interface regions of the interacting proteins. After applying a machine learning algorithm with input variables that accounted for both the conformational flexibility and the energetic factor of binding, we successfully identified most of the protein complexes with overestimated association rates and improved our final prediction by using a cross-validation test. This method was then applied to a new independent test set and resulted in a similar prediction accuracy to that obtained using the training set. It has been thought that diffusion-limited protein association is dominated by long-range interactions. Our results provide strong evidence that the conformational flexibility also plays an important role in regulating protein association. Our studies provide new insights into the mechanism of protein association and offer a computationally efficient tool for predicting its rate.

Predicting Protein–protein Association Rates using Coarse-grained Simulation and Machine Learning

PubMed Central

Xie, Zhong-Ru; Chen, Jiawen; Wu, Yinghao

2017-01-01

Protein–protein interactions dominate all major biological processes in living cells. We have developed a new Monte Carlo-based simulation algorithm to study the kinetic process of protein association. We tested our method on a previously used large benchmark set of 49 protein complexes. The predicted rate was overestimated in the benchmark test compared to the experimental results for a group of protein complexes. We hypothesized that this resulted from molecular flexibility at the interface regions of the interacting proteins. After applying a machine learning algorithm with input variables that accounted for both the conformational flexibility and the energetic factor of binding, we successfully identified most of the protein complexes with overestimated association rates and improved our final prediction by using a cross-validation test. This method was then applied to a new independent test set and resulted in a similar prediction accuracy to that obtained using the training set. It has been thought that diffusion-limited protein association is dominated by long-range interactions. Our results provide strong evidence that the conformational flexibility also plays an important role in regulating protein association. Our studies provide new insights into the mechanism of protein association and offer a computationally efficient tool for predicting its rate. PMID:28418043

Predicting Protein-protein Association Rates using Coarse-grained Simulation and Machine Learning.

PubMed

Xie, Zhong-Ru; Chen, Jiawen; Wu, Yinghao

2017-04-18

Protein-protein interactions dominate all major biological processes in living cells. We have developed a new Monte Carlo-based simulation algorithm to study the kinetic process of protein association. We tested our method on a previously used large benchmark set of 49 protein complexes. The predicted rate was overestimated in the benchmark test compared to the experimental results for a group of protein complexes. We hypothesized that this resulted from molecular flexibility at the interface regions of the interacting proteins. After applying a machine learning algorithm with input variables that accounted for both the conformational flexibility and the energetic factor of binding, we successfully identified most of the protein complexes with overestimated association rates and improved our final prediction by using a cross-validation test. This method was then applied to a new independent test set and resulted in a similar prediction accuracy to that obtained using the training set. It has been thought that diffusion-limited protein association is dominated by long-range interactions. Our results provide strong evidence that the conformational flexibility also plays an important role in regulating protein association. Our studies provide new insights into the mechanism of protein association and offer a computationally efficient tool for predicting its rate.

Grouped fuzzy SVM with EM-based partition of sample space for clustered microcalcification detection.

PubMed

Wang, Huiya; Feng, Jun; Wang, Hongyu

2017-07-20

Detection of clustered microcalcification (MC) from mammograms plays essential roles in computer-aided diagnosis for early stage breast cancer. To tackle problems associated with the diversity of data structures of MC lesions and the variability of normal breast tissues, multi-pattern sample space learning is required. In this paper, a novel grouped fuzzy Support Vector Machine (SVM) algorithm with sample space partition based on Expectation-Maximization (EM) (called G-FSVM) is proposed for clustered MC detection. The diversified pattern of training data is partitioned into several groups based on EM algorithm. Then a series of fuzzy SVM are integrated for classification with each group of samples from the MC lesions and normal breast tissues. From DDSM database, a total of 1,064 suspicious regions are selected from 239 mammography, and the measurement of Accuracy, True Positive Rate (TPR), False Positive Rate (FPR) and EVL = TPR* 1-FPR are 0.82, 0.78, 0.14 and 0.72, respectively. The proposed method incorporates the merits of fuzzy SVM and multi-pattern sample space learning, decomposing the MC detection problem into serial simple two-class classification. Experimental results from synthetic data and DDSM database demonstrate that our integrated classification framework reduces the false positive rate significantly while maintaining the true positive rate.

Effective reinforcement learning following cerebellar damage requires a balance between exploration and motor noise.

PubMed

Therrien, Amanda S; Wolpert, Daniel M; Bastian, Amy J

2016-01-01

Reinforcement and error-based processes are essential for motor learning, with the cerebellum thought to be required only for the error-based mechanism. Here we examined learning and retention of a reaching skill under both processes. Control subjects learned similarly from reinforcement and error-based feedback, but showed much better retention under reinforcement. To apply reinforcement to cerebellar patients, we developed a closed-loop reinforcement schedule in which task difficulty was controlled based on recent performance. This schedule produced substantial learning in cerebellar patients and controls. Cerebellar patients varied in their learning under reinforcement but fully retained what was learned. In contrast, they showed complete lack of retention in error-based learning. We developed a mechanistic model of the reinforcement task and found that learning depended on a balance between exploration variability and motor noise. While the cerebellar and control groups had similar exploration variability, the patients had greater motor noise and hence learned less. Our results suggest that cerebellar damage indirectly impairs reinforcement learning by increasing motor noise, but does not interfere with the reinforcement mechanism itself. Therefore, reinforcement can be used to learn and retain novel skills, but optimal reinforcement learning requires a balance between exploration variability and motor noise. © The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain.

Effective reinforcement learning following cerebellar damage requires a balance between exploration and motor noise

PubMed Central

Therrien, Amanda S.; Wolpert, Daniel M.

2016-01-01

Abstract See Miall and Galea (doi: 10.1093/awv343 ) for a scientific commentary on this article. Reinforcement and error-based processes are essential for motor learning, with the cerebellum thought to be required only for the error-based mechanism. Here we examined learning and retention of a reaching skill under both processes. Control subjects learned similarly from reinforcement and error-based feedback, but showed much better retention under reinforcement. To apply reinforcement to cerebellar patients, we developed a closed-loop reinforcement schedule in which task difficulty was controlled based on recent performance. This schedule produced substantial learning in cerebellar patients and controls. Cerebellar patients varied in their learning under reinforcement but fully retained what was learned. In contrast, they showed complete lack of retention in error-based learning. We developed a mechanistic model of the reinforcement task and found that learning depended on a balance between exploration variability and motor noise. While the cerebellar and control groups had similar exploration variability, the patients had greater motor noise and hence learned less. Our results suggest that cerebellar damage indirectly impairs reinforcement learning by increasing motor noise, but does not interfere with the reinforcement mechanism itself. Therefore, reinforcement can be used to learn and retain novel skills, but optimal reinforcement learning requires a balance between exploration variability and motor noise. PMID:26626368

Determinants of Intention to Use eLearning Based on the Technology Acceptance Model

ERIC Educational Resources Information Center

Punnoose, Alfie Chacko

2012-01-01

The purpose of this study was to find some of the predominant factors that determine the intention of students to use eLearning in the future. Since eLearning is not just a technology acceptance decision but also involves cognition, this study extended its search beyond the normal technology acceptance variables into variables that could affect…

Identifying Key Features of Effective Active Learning: The Effects of Writing and Peer Discussion

PubMed Central

Pangle, Wiline M.; Wyatt, Kevin H.; Powell, Karli N.; Sherwood, Rachel E.

2014-01-01

We investigated some of the key features of effective active learning by comparing the outcomes of three different methods of implementing active-learning exercises in a majors introductory biology course. Students completed activities in one of three treatments: discussion, writing, and discussion + writing. Treatments were rotated weekly between three sections taught by three different instructors in a full factorial design. The data set was analyzed by generalized linear mixed-effect models with three independent variables: student aptitude, treatment, and instructor, and three dependent (assessment) variables: change in score on pre- and postactivity clicker questions, and coding scores on in-class writing and exam essays. All independent variables had significant effects on student performance for at least one of the dependent variables. Students with higher aptitude scored higher on all assessments. Student scores were higher on exam essay questions when the activity was implemented with a writing component compared with peer discussion only. There was a significant effect of instructor, with instructors showing different degrees of effectiveness with active-learning techniques. We suggest that individual writing should be implemented as part of active learning whenever possible and that instructors may need training and practice to become effective with active learning. PMID:25185230

Variable training does not lead to better motor learning compared to repetitive training in children with and without DCD when exposed to active video games.

PubMed

Bonney, Emmanuel; Jelsma, Dorothee; Ferguson, Gillian; Smits-Engelsman, Bouwien

2017-03-01

Little is known about the influence of practice schedules on motor learning and skills transfer in children with and without developmental coordination disorder (DCD). Understanding how practice schedules affect motor learning is necessary for motor skills development and rehabilitation. The study investigated whether active video games (exergames) training delivered under variable practice led to better learning and transfer than repetitive practice. 111 children aged 6-10 years (M=8.0, SD=1.0) with no active exergaming experience were randomized to receive exergames training delivered under variable (Variable Game Group (VGG), n=56) or repetitive practice schedule (Repetitive Game Group (RGG), n=55). Half the participants were identified as DCD using the DSM-5 criteria, while the rest were typically developing (TD), age-matched children. Both groups participated in two 20min sessions per week for 5 weeks. Both participant groups (TD and DCD) improved equally well on game performance. There was no significant difference in positive transfer to balance tasks between practice schedules (Repetitive and Variable) and participant groups (TD and DCD). Children with and without DCD learn balance skills quite well when exposed to exergames. Gains in learning and transfer are similar regardless of the form of practice schedule employed. This is the first paper to compare the effect of practice schedules on learning in children with DCD and those with typical development. No differences in motor learning were found between repetitive and variable practice schedules. When children with and without DCD spend the same amount of time on exergames, they do not show any differences in acquisition of motor skills. Transfer of motor skills is similar in children with and without DCD regardless of differences in practice schedules. Copyright © 2017 Elsevier Ltd. All rights reserved.

The Internet as an informal learning environment: Assessing knowledge acquisition of science and engineering students using constructivist and objectivist formats

NASA Astrophysics Data System (ADS)

Hargis, Jace

This study examined the effects of two different instructional formats on Internet WebPages in an informal learning environment. The purpose of this study is to (a) identify optimal instructional formats for on-line learning; (b) identify the relationship between post-assessment scores and the student's gender, age or racial identity; (c) examine the effects of verbal aptitudes on learning in different formats; (d) identify relationships between computer attitudes and achievement; and (e) identify the potential power for self-regulated learning and self-efficacy on Internet WebPages. Two learning strategy modules were developed; a constructivist and an objectivist instruction module. The study program consisted of an on-line consent form; a computer attitude survey; a Motivated Strategies for Learning Questionnaire; a verbal aptitude test; a pre-assessment; instructional directions followed by the instructional module and a post-assessment. The study tested 145 post-secondary science and engineering participants from the University of Florida. Participants were randomly assigned to one of two treatment groups or a control in a pretest/posttest design. An analysis of covariance with general linear models was used to account for effects of individual difference variables and aptitude treatment interaction (ATI). This statistical procedure was used to determine the relationships among the dependent variable, the achievement on each of the formats and the independent variables, attitudes, gender, racial identity, verbal aptitudes, and self-regulated learning/self-efficacy. Significant results at alpha = .05 were found for none of these variables. However, a linear prediction of age shows that older participants scored higher on the post-assessment after completing the objectivist module. Although there were no significant differences between the learning format and the variables, there was a difference between the modules and the control. Therefore, it is possible that regardless of characteristics, science and engineering students can learn on-line technical material.

Improving precision of glomerular filtration rate estimating model by ensemble learning.

PubMed

Liu, Xun; Li, Ningshan; Lv, Linsheng; Fu, Yongmei; Cheng, Cailian; Wang, Caixia; Ye, Yuqiu; Li, Shaomin; Lou, Tanqi

2017-11-09

Accurate assessment of kidney function is clinically important, but estimates of glomerular filtration rate (GFR) by regression are imprecise. We hypothesized that ensemble learning could improve precision. A total of 1419 participants were enrolled, with 1002 in the development dataset and 417 in the external validation dataset. GFR was independently estimated from age, sex and serum creatinine using an artificial neural network (ANN), support vector machine (SVM), regression, and ensemble learning. GFR was measured by 99mTc-DTPA renal dynamic imaging calibrated with dual plasma sample 99mTc-DTPA GFR. Mean measured GFRs were 70.0 ml/min/1.73 m 2 in the developmental and 53.4 ml/min/1.73 m 2 in the external validation cohorts. In the external validation cohort, precision was better in the ensemble model of the ANN, SVM and regression equation (IQR = 13.5 ml/min/1.73 m 2 ) than in the new regression model (IQR = 14.0 ml/min/1.73 m 2 , P < 0.001). The precision of ensemble learning was the best of the three models, but the models had similar bias and accuracy. The median difference ranged from 2.3 to 3.7 ml/min/1.73 m 2 , 30% accuracy ranged from 73.1 to 76.0%, and P was > 0.05 for all comparisons of the new regression equation and the other new models. An ensemble learning model including three variables, the average ANN, SVM, and regression equation values, was more precise than the new regression model. A more complex ensemble learning strategy may further improve GFR estimates.

Time, rate, and conditioning.

PubMed

Gallistel, C R; Gibbon, J

2000-04-01

The authors draw together and develop previous timing models for a broad range of conditioning phenomena to reveal their common conceptual foundations: First, conditioning depends on the learning of the temporal intervals between events and the reciprocals of these intervals, the rates of event occurrence. Second, remembered intervals and rates translate into observed behavior through decision processes whose structure is adapted to noise in the decision variables. The noise and the uncertainties consequent on it have both subjective and objective origins. A third feature of these models is their timescale invariance, which the authors argue is a very important property evident in the available experimental data. This conceptual framework is similar to the psychophysical conceptual framework in which contemporary models of sensory processing are rooted. The authors contrast it with the associative conceptual framework.

Measuring the surgical 'learning curve': methods, variables and competency.

PubMed

Khan, Nuzhath; Abboudi, Hamid; Khan, Mohammed Shamim; Dasgupta, Prokar; Ahmed, Kamran

2014-03-01

To describe how learning curves are measured and what procedural variables are used to establish a 'learning curve' (LC). To assess whether LCs are a valuable measure of competency. A review of the surgical literature pertaining to LCs was conducted using the Medline and OVID databases. Variables should be fully defined and when possible, patient-specific variables should be used. Trainee's prior experience and level of supervision should be quantified; the case mix and complexity should ideally be constant. Logistic regression may be used to control for confounding variables. Ideally, a learning plateau should reach a predefined/expert-derived competency level, which should be fully defined. When the group splitting method is used, smaller cohorts should be used in order to narrow the range of the LC. Simulation technology and competence-based objective assessments may be used in training and assessment in LC studies. Measuring the surgical LC has potential benefits for patient safety and surgical education. However, standardisation in the methods and variables used to measure LCs is required. Confounding variables, such as participant's prior experience, case mix, difficulty of procedures and level of supervision, should be controlled. Competency and expert performance should be fully defined. © 2013 The Authors. BJU International © 2013 BJU International.

Development of a neural-based forecasting tool to classify recreational water quality using fecal indicator organisms.

PubMed

Motamarri, Srinivas; Boccelli, Dominic L

2012-09-15

Users of recreational waters may be exposed to elevated pathogen levels through various point/non-point sources. Typical daily notifications rely on microbial analysis of indicator organisms (e.g., Escherichia coli) that require 18, or more, hours to provide an adequate response. Modeling approaches, such as multivariate linear regression (MLR) and artificial neural networks (ANN), have been utilized to provide quick predictions of microbial concentrations for classification purposes, but generally suffer from high false negative rates. This study introduces the use of learning vector quantization (LVQ)--a direct classification approach--for comparison with MLR and ANN approaches and integrates input selection for model development with respect to primary and secondary water quality standards within the Charles River Basin (Massachusetts, USA) using meteorologic, hydrologic, and microbial explanatory variables. Integrating input selection into model development showed that discharge variables were the most important explanatory variables while antecedent rainfall and time since previous events were also important. With respect to classification, all three models adequately represented the non-violated samples (>90%). The MLR approach had the highest false negative rates associated with classifying violated samples (41-62% vs 13-43% (ANN) and <16% (LVQ)) when using five or more explanatory variables. The ANN performance was more similar to LVQ when a larger number of explanatory variables were utilized, but the ANN performance degraded toward MLR performance as explanatory variables were removed. Overall, the use of LVQ as a direct classifier provided the best overall classification ability with respect to violated/non-violated samples for both standards. Copyright © 2012 Elsevier Ltd. All rights reserved.

Evaluation of Online Log Variables That Estimate Learners' Time Management in a Korean Online Learning Context

ERIC Educational Resources Information Center

Jo, Il-Hyun; Park, Yeonjeong; Yoon, Meehyun; Sung, Hanall

2016-01-01

The purpose of this study was to identify the relationship between the psychological variables and online behavioral patterns of students, collected through a learning management system (LMS). As the psychological variable, time and study environment management (TSEM), one of the sub-constructs of MSLQ, was chosen to verify a set of time-related…

Refining a learning progression of energy

NASA Astrophysics Data System (ADS)

Yao, Jian-Xin; Guo, Yu-Ying; Neumann, Knut

2017-11-01

This paper presents a revised learning progression for the energy concept and initial findings on diverse progressions among subgroups of sample students. The revised learning progression describes how students progress towards an understanding of the energy concept along two progress variables identified from previous studies - key ideas about energy and levels of conceptual development. To assess students understanding with respect to the revised learning progression, we created a specific instrument, the Energy Concept Progression Assessment (ECPA) based on previous work on assessing students' understanding of energy. After iteratively refining the instrument in two pilot studies, the ECPA was administered to a total of 4550 students (Grades 8-12) from schools in two districts in a major city in Mainland China. Rasch analysis was used to examine the validity of the revised learning progression and explore factors explaining different progressions. Our results confirm the validity of the four conceptual development levels. In addition, we found that although following a similar progression pattern, students' progression rate was significantly influenced by environmental factors such as school type. In the discussion of our findings, we address the non-linear and complex nature of students' progression in understanding energy. We conclude with illuminating our research's implication for curriculum design and energy teaching.

Evoked prior learning experience and approach to learning as predictors of academic achievement.

PubMed

Trigwell, Keith; Ashwin, Paul; Millan, Elena S

2013-09-01

In separate studies and research from different perspectives, five factors are found to be among those related to higher quality outcomes of student learning (academic achievement). Those factors are higher self-efficacy, deeper approaches to learning, higher quality teaching, students' perceptions that their workload is appropriate, and greater learning motivation. University learning improvement strategies have been built on these research results. To investigate how students' evoked prior experience, perceptions of their learning environment, and their approaches to learning collectively contribute to academic achievement. This is the first study to investigate motivation and self-efficacy in the same educational context as conceptions of learning, approaches to learning and perceptions of the learning environment. Undergraduate students (773) from the full range of disciplines were part of a group of over 2,300 students who volunteered to complete a survey of their learning experience. On completing their degrees 6 and 18 months later, their academic achievement was matched with their learning experience survey data. A 77-item questionnaire was used to gather students' self-report of their evoked prior experience (self-efficacy, learning motivation, and conceptions of learning), perceptions of learning context (teaching quality and appropriate workload), and approaches to learning (deep and surface). Academic achievement was measured using the English honours degree classification system. Analyses were conducted using correlational and multi-variable (structural equation modelling) methods. The results from the correlation methods confirmed those found in numerous earlier studies. The results from the multi-variable analyses indicated that surface approach to learning was the strongest predictor of academic achievement, with self-efficacy and motivation also found to be directly related. In contrast to the correlation results, a deep approach to learning was not related to academic achievement, and teaching quality and conceptions of learning were only indirectly related to achievement. Research aimed at understanding how students experience their learning environment and how that experience relates to the quality of their learning needs to be conducted using a wider range of variables and more sophisticated analytical methods. In this study of one context, some of the relations found in earlier bivariate studies, and on which learning intervention strategies have been built, are not confirmed when more holistic teaching-learning contexts are analysed using multi-variable methods. © 2012 The British Psychological Society.

Structure learning in action

PubMed Central

Braun, Daniel A.; Mehring, Carsten; Wolpert, Daniel M.

2010-01-01

‘Learning to learn’ phenomena have been widely investigated in cognition, perception and more recently also in action. During concept learning tasks, for example, it has been suggested that characteristic features are abstracted from a set of examples with the consequence that learning of similar tasks is facilitated—a process termed ‘learning to learn’. From a computational point of view such an extraction of invariants can be regarded as learning of an underlying structure. Here we review the evidence for structure learning as a ‘learning to learn’ mechanism, especially in sensorimotor control where the motor system has to adapt to variable environments. We review studies demonstrating that common features of variable environments are extracted during sensorimotor learning and exploited for efficient adaptation in novel tasks. We conclude that structure learning plays a fundamental role in skill learning and may underlie the unsurpassed flexibility and adaptability of the motor system. PMID:19720086

Learning style and concept acquisition of community college students in introductory biology

NASA Astrophysics Data System (ADS)

Bobick, Sandra Burin

This study investigated the influence of learning style on concept acquisition within a sample of community college students in a general biology course. There are two subproblems within the larger problem: (1) the influence of demographic variables (age, gender, number of college credits, prior exposure to scientific information) on learning style, and (2) the correlations between prior scientific knowledge, learning style and student understanding of the concept of the gene. The sample included all students enrolled in an introductory general biology course during two consecutive semesters at an urban community college. Initial data was gathered during the first week of the semester, at which time students filled in a short questionnaire (age, gender, number of college credits, prior exposure to science information either through reading/visual sources or a prior biology course). Subjects were then given the Inventory of Learning Processes-Revised (ILP-R) which measures general preferences in five learning styles; Deep Learning; Elaborative Learning, Agentic Learning, Methodical Learning and Literal Memorization. Subjects were then given the Gene Conceptual Knowledge pretest: a 15 question objective section and an essay section. Subjects were exposed to specific concepts during lecture and laboratory exercises. At the last lab, students were given the Genetics Conceptual Knowledge Posttest. Pretest/posttest gains were correlated with demographic variables and learning styles were analyzed for significant correlations. Learning styles, as the independent variable in a simultaneous multiple regression, were significant predictors of results on the gene assessment tests, including pretest, posttest and gain. Of the learning styles, Deep Learning accounted for the greatest positive predictive value of pretest essay and pretest objective results. Literal Memorization was a significant negative predictor for posttest essay, essay gain and objective gain. Simultaneous multiple regression indicated that demographic variables were significant positive predictors for Methodical, Deep and Elaborative Learning Styles. Stepwise multiple regression resulted in number of credits, Read Science and gender (female) as significant predictors of learning styles. The findings of this study emphasize the importance of learning styles in conceptual understanding of the gene and the correlation of nonformal exposure to science information with learning style and conceptual understanding.

Analysis of Machine Learning Techniques for Heart Failure Readmissions.

PubMed

Mortazavi, Bobak J; Downing, Nicholas S; Bucholz, Emily M; Dharmarajan, Kumar; Manhapra, Ajay; Li, Shu-Xia; Negahban, Sahand N; Krumholz, Harlan M

2016-11-01

The current ability to predict readmissions in patients with heart failure is modest at best. It is unclear whether machine learning techniques that address higher dimensional, nonlinear relationships among variables would enhance prediction. We sought to compare the effectiveness of several machine learning algorithms for predicting readmissions. Using data from the Telemonitoring to Improve Heart Failure Outcomes trial, we compared the effectiveness of random forests, boosting, random forests combined hierarchically with support vector machines or logistic regression (LR), and Poisson regression against traditional LR to predict 30- and 180-day all-cause readmissions and readmissions because of heart failure. We randomly selected 50% of patients for a derivation set, and a validation set comprised the remaining patients, validated using 100 bootstrapped iterations. We compared C statistics for discrimination and distributions of observed outcomes in risk deciles for predictive range. In 30-day all-cause readmission prediction, the best performing machine learning model, random forests, provided a 17.8% improvement over LR (mean C statistics, 0.628 and 0.533, respectively). For readmissions because of heart failure, boosting improved the C statistic by 24.9% over LR (mean C statistic 0.678 and 0.543, respectively). For 30-day all-cause readmission, the observed readmission rates in the lowest and highest deciles of predicted risk with random forests (7.8% and 26.2%, respectively) showed a much wider separation than LR (14.2% and 16.4%, respectively). Machine learning methods improved the prediction of readmission after hospitalization for heart failure compared with LR and provided the greatest predictive range in observed readmission rates. © 2016 American Heart Association, Inc.

Variability of Neuronal Responses: Types and Functional Significance in Neuroplasticity and Neural Darwinism

PubMed Central

Chervyakov, Alexander V.; Sinitsyn, Dmitry O.; Piradov, Michael A.

2016-01-01

HIGHLIGHTS We suggest classifying variability of neuronal responses as follows: false (associated with a lack of knowledge about the influential factors), “genuine harmful” (noise), “genuine neutral” (synonyms, repeats), and “genuine useful” (the basis of neuroplasticity and learning).The genuine neutral variability is considered in terms of the phenomenon of degeneracy.Of particular importance is the genuine useful variability that is considered as a potential basis for neuroplasticity and learning. This type of variability is considered in terms of the neural Darwinism theory. In many cases, neural signals detected under the same external experimental conditions significantly change from trial to trial. The variability phenomenon, which complicates extraction of reproducible results and is ignored in many studies by averaging, has attracted attention of researchers in recent years. In this paper, we classify possible types of variability based on its functional significance and describe features of each type. We describe the key adaptive significance of variability at the neural network level and the degeneracy phenomenon that may be important for learning processes in connection with the principle of neuronal group selection. PMID:27932969

Variability of Neuronal Responses: Types and Functional Significance in Neuroplasticity and Neural Darwinism.

PubMed

Chervyakov, Alexander V; Sinitsyn, Dmitry O; Piradov, Michael A

2016-01-01

HIGHLIGHTS We suggest classifying variability of neuronal responses as follows: false (associated with a lack of knowledge about the influential factors), "genuine harmful" (noise), "genuine neutral" (synonyms, repeats), and "genuine useful" (the basis of neuroplasticity and learning).The genuine neutral variability is considered in terms of the phenomenon of degeneracy.Of particular importance is the genuine useful variability that is considered as a potential basis for neuroplasticity and learning. This type of variability is considered in terms of the neural Darwinism theory. In many cases, neural signals detected under the same external experimental conditions significantly change from trial to trial. The variability phenomenon, which complicates extraction of reproducible results and is ignored in many studies by averaging, has attracted attention of researchers in recent years. In this paper, we classify possible types of variability based on its functional significance and describe features of each type. We describe the key adaptive significance of variability at the neural network level and the degeneracy phenomenon that may be important for learning processes in connection with the principle of neuronal group selection.

Prediction of insemination outcomes in Holstein dairy cattle using alternative machine learning algorithms.

PubMed

Shahinfar, Saleh; Page, David; Guenther, Jerry; Cabrera, Victor; Fricke, Paul; Weigel, Kent

2014-02-01

When making the decision about whether or not to breed a given cow, knowledge about the expected outcome would have an economic impact on profitability of the breeding program and net income of the farm. The outcome of each breeding can be affected by many management and physiological features that vary between farms and interact with each other. Hence, the ability of machine learning algorithms to accommodate complex relationships in the data and missing values for explanatory variables makes these algorithms well suited for investigation of reproduction performance in dairy cattle. The objective of this study was to develop a user-friendly and intuitive on-farm tool to help farmers make reproduction management decisions. Several different machine learning algorithms were applied to predict the insemination outcomes of individual cows based on phenotypic and genotypic data. Data from 26 dairy farms in the Alta Genetics (Watertown, WI) Advantage Progeny Testing Program were used, representing a 10-yr period from 2000 to 2010. Health, reproduction, and production data were extracted from on-farm dairy management software, and estimated breeding values were downloaded from the US Department of Agriculture Agricultural Research Service Animal Improvement Programs Laboratory (Beltsville, MD) database. The edited data set consisted of 129,245 breeding records from primiparous Holstein cows and 195,128 breeding records from multiparous Holstein cows. Each data point in the final data set included 23 and 25 explanatory variables and 1 binary outcome for of 0.756 ± 0.005 and 0.736 ± 0.005 for primiparous and multiparous cows, respectively. The naïve Bayes algorithm, Bayesian network, and decision tree algorithms showed somewhat poorer classification performance. An information-based variable selection procedure identified herd average conception rate, incidence of ketosis, number of previous (failed) inseminations, days in milk at breeding, and mastitis as the most effective explanatory variables in predicting pregnancy outcome. Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Notion de temps d'apprentissage et son evaluation en situation d'enseignement (The Idea of Learning Time and Its Evaluation in Teaching Situations).

ERIC Educational Resources Information Center

Brunelle, Jean; And Others

1983-01-01

The article explains how the time that students devote to learning was identified as a variable in instruction effectiveness studies and shows how the variable was integrated into research on the effectiveness of physical education instruction. The article describes a French version of the "ALT-PE" system on estimating learning time. (SB)

Development of an Efficient Identifier for Nuclear Power Plant Transients Based on Latest Advances of Error Back-Propagation Learning Algorithm

NASA Astrophysics Data System (ADS)

Moshkbar-Bakhshayesh, Khalil; Ghofrani, Mohammad B.

2014-02-01

This study aims to improve the performance of nuclear power plants (NPPs) transients training and identification using the latest advances of error back-propagation (EBP) learning algorithm. To this end, elements of EBP, including input data, initial weights, learning rate, cost function, activation function, and weights updating procedure are investigated and an efficient neural network is developed. Usefulness of modular networks is also examined and appropriate identifiers, one for each transient, are employed. Furthermore, the effect of transient type on transient identifier performance is illustrated. Subsequently, the developed transient identifier is applied to Bushehr nuclear power plant (BNPP). Seven types of the plant events are probed to analyze the ability of the proposed identifier. The results reveal that identification occurs very early with only five plant variables, whilst in the previous studies a larger number of variables (typically 15 to 20) were required. Modular networks facilitated identification due to its sole dependency on the sign of each network output signal. Fast training of input patterns, extendibility for identification of more transients and reduction of false identification are other advantageous of the proposed identifier. Finally, the balance between the correct answer to the trained transients (memorization) and reasonable response to the test transients (generalization) is improved, meeting one of the primary design criteria of identifiers.

Using "big data" to guide implementation of a web and mobile adaptive learning platform for medical students.

PubMed

Menon, Ashwin; Gaglani, Shiv; Haynes, M Ryan; Tackett, Sean

2017-09-01

Adaptive learning platforms (ALPs) can revolutionize medical education by making learning more efficient, but their potential has not been realized because students do not use them persistently. We applied educational data mining methods to study United States medical students who used an ALP called Osmosis ( www.osmosis.org ) from 1 August 2014 to 31 July 2015. Multivariate logistic regressions modeled persistence on Osmosis as the dependent variable and Osmosis-collected variables as predictors. The 6787 students included in our analysis responded to a total of 887,193 items, with 2138 (31.5%) using Osmosis persistently. Number of items per student, mobile device use, subscription payment, and group membership were independently associated with persisting (p < 0.001 in all models). Persistent users rated quality more favorably (p < 0.01) but were not more confident in answer selections (p = 0.80). While persisters were more accurate than non-persisters (55% (SD 18%) vs 52% (SD 22%), p < 0.001), after adjusting for number of items, lower accuracy was associated with persistent use (OR 0.93 [95% CI 0.90-0.97], p < 0.01). Our study of a large sample of U.S. medical students illustrates big data medical education research and provides guidance for improving implementation of ALPs and further investigation.

Individual Differences in Discriminatory Fear Learning under Conditions of Ambiguity: A Vulnerability Factor for Anxiety Disorders?

PubMed Central

Arnaudova, Inna; Krypotos, Angelos-Miltiadis; Effting, Marieke; Boddez, Yannick; Kindt, Merel; Beckers, Tom

2013-01-01

Complex fear learning procedures might be better suited than the common differential fear-conditioning paradigm for detecting individual differences related to vulnerability for anxiety disorders. Two such procedures are the blocking procedure and the protection-from-overshadowing procedure. Their comparison allows for the examination of discriminatory fear learning under conditions of ambiguity. The present study examined the role of individual differences in such discriminatory fear learning. We hypothesized that heightened trait anxiety would be related to a deficit in discriminatory fear learning. Participants gave US-expectancy ratings as an index for the threat value of individual CSs following blocking and protection-from-overshadowing training. The difference in threat value at test between the protected-from-overshadowing conditioned stimulus (CS) and the blocked CS was negatively correlated with scores on a self-report tension-stress scale that approximates facets of generalized anxiety disorder (GAD), the Depression Anxiety Stress Scale-Stress (DASS-S), but not with other individual difference variables. In addition, a behavioral test showed that only participants scoring high on the DASS-S avoided the protected-from-overshadowing CS. This observed deficit in discriminatory fear learning for participants with high levels of tension-stress might be an underlying mechanism for fear overgeneralization in diffuse anxiety disorders such as GAD. PMID:23755030

Scenarios for Motivating the Learning of Variability: An Example in Finances

ERIC Educational Resources Information Center

Cordani, Lisbeth K.

2013-01-01

This article explores an example in finances in order to motivate the random variable learning to the very beginners in statistics. In addition, it offers a relationship between standard deviation and range in a very specific situation.

Using a Six Sigma Fishbone Analysis Approach To Evaluate the Effect of Extreme Weather Events on Salmonella Positives in Young Chicken Slaughter Establishments.

PubMed

Linville, John W; Schumann, Douglas; Aston, Christopher; Defibaugh-Chavez, Stephanie; Seebohm, Scott; Touhey, Lucy

2016-12-01

A six sigma fishbone analysis approach was used to develop a machine learning model in SAS, Version 9.4, by using stepwise linear regression. The model evaluated the effect of a wide variety of variables, including slaughter establishment operational measures, normal (30-year average) weather, and extreme weather events on the rate of Salmonella -positive carcasses in young chicken slaughter establishments. Food Safety and Inspection Service (FSIS) verification carcass sampling data, as well as corresponding data from the National Oceanographic and Atmospheric Administration and the Federal Emergency Management Agency, from September 2011 through April 2015, were included in the model. The results of the modeling show that in addition to basic establishment operations, normal weather patterns, differences from normal and disaster events, including time lag weather and disaster variables, played a role in explaining the Salmonella percent positive that varied by slaughter volume quartile. Findings show that weather and disaster events should be considered as explanatory variables when assessing pathogen-related prevalence analysis or research and slaughter operational controls. The apparent significance of time lag weather variables suggested that at least some of the impact on Salmonella rates occurred after the weather events, which may offer opportunities for FSIS or the poultry industry to implement interventions to mitigate those effects.

Learning by Demonstration for Motion Planning of Upper-Limb Exoskeletons

PubMed Central

Lauretti, Clemente; Cordella, Francesca; Ciancio, Anna Lisa; Trigili, Emilio; Catalan, Jose Maria; Badesa, Francisco Javier; Crea, Simona; Pagliara, Silvio Marcello; Sterzi, Silvia; Vitiello, Nicola; Garcia Aracil, Nicolas; Zollo, Loredana

2018-01-01

The reference joint position of upper-limb exoskeletons is typically obtained by means of Cartesian motion planners and inverse kinematics algorithms with the inverse Jacobian; this approach allows exploiting the available Degrees of Freedom (i.e. DoFs) of the robot kinematic chain to achieve the desired end-effector pose; however, if used to operate non-redundant exoskeletons, it does not ensure that anthropomorphic criteria are satisfied in the whole human-robot workspace. This paper proposes a motion planning system, based on Learning by Demonstration, for upper-limb exoskeletons that allow successfully assisting patients during Activities of Daily Living (ADLs) in unstructured environment, while ensuring that anthropomorphic criteria are satisfied in the whole human-robot workspace. The motion planning system combines Learning by Demonstration with the computation of Dynamic Motion Primitives and machine learning techniques to construct task- and patient-specific joint trajectories based on the learnt trajectories. System validation was carried out in simulation and in a real setting with a 4-DoF upper-limb exoskeleton, a 5-DoF wrist-hand exoskeleton and four patients with Limb Girdle Muscular Dystrophy. Validation was addressed to (i) compare the performance of the proposed motion planning with traditional methods; (ii) assess the generalization capabilities of the proposed method with respect to the environment variability. Three ADLs were chosen to validate the system: drinking, pouring and lifting a light sphere. The achieved results showed a 100% success rate in the task fulfillment, with a high level of generalization with respect to the environment variability. Moreover, an anthropomorphic configuration of the exoskeleton is always ensured. PMID:29527161

Learning by Demonstration for Motion Planning of Upper-Limb Exoskeletons.

PubMed

Lauretti, Clemente; Cordella, Francesca; Ciancio, Anna Lisa; Trigili, Emilio; Catalan, Jose Maria; Badesa, Francisco Javier; Crea, Simona; Pagliara, Silvio Marcello; Sterzi, Silvia; Vitiello, Nicola; Garcia Aracil, Nicolas; Zollo, Loredana

2018-01-01

The reference joint position of upper-limb exoskeletons is typically obtained by means of Cartesian motion planners and inverse kinematics algorithms with the inverse Jacobian; this approach allows exploiting the available Degrees of Freedom (i.e. DoFs) of the robot kinematic chain to achieve the desired end-effector pose; however, if used to operate non-redundant exoskeletons, it does not ensure that anthropomorphic criteria are satisfied in the whole human-robot workspace. This paper proposes a motion planning system, based on Learning by Demonstration, for upper-limb exoskeletons that allow successfully assisting patients during Activities of Daily Living (ADLs) in unstructured environment, while ensuring that anthropomorphic criteria are satisfied in the whole human-robot workspace. The motion planning system combines Learning by Demonstration with the computation of Dynamic Motion Primitives and machine learning techniques to construct task- and patient-specific joint trajectories based on the learnt trajectories. System validation was carried out in simulation and in a real setting with a 4-DoF upper-limb exoskeleton, a 5-DoF wrist-hand exoskeleton and four patients with Limb Girdle Muscular Dystrophy. Validation was addressed to (i) compare the performance of the proposed motion planning with traditional methods; (ii) assess the generalization capabilities of the proposed method with respect to the environment variability. Three ADLs were chosen to validate the system: drinking, pouring and lifting a light sphere. The achieved results showed a 100% success rate in the task fulfillment, with a high level of generalization with respect to the environment variability. Moreover, an anthropomorphic configuration of the exoskeleton is always ensured.

An evaluation of pediatric dental patient education materials using contemporary health literacy measures.

PubMed

Kang, Edith; Fields, Henry W; Cornett, Sandy; Beck, F Michael

2005-01-01

The purpose of this study was to determine the appropriateness of nationally available dental information materials according to the suitability assessment of materials (SAM) method. Clinically related, professionally produced patient dental health education materials (N=22) provided by the American Academy of Pediatric Dentistry (AAPD) were evaluated using the SAM method that had previously been judged valid and reliable. A rater was trained by an experienced health literacy evaluator to establish validity. The rater then rated all materials for 5 categories of assessment (content, literacy demand, graphics, layout and typography, and learning stimulation/motivation) and an overall assessment, and repeated 5 materials to establish intrarater reliability. When compared to the experienced rater, the validity was K=0.43. The reliability was established for all ratings as K=0.52. The consistently weakest categories were content, graphics, and learning stimulation, while reading level as part of literacy demand was often not suitable. The overall suitability of the AAPD materials was generally classified as superior. Reliable and valid evaluation of available dental patient information materials can be accomplished. The materials were largely superior. There is great variability within the categories of evaluation. The categories of content, graphics, and learning stimulation require attention and could raise the overall quality of the materials.

Modification Of Learning Rate With Lvq Model Improvement In Learning Backpropagation

NASA Astrophysics Data System (ADS)

Tata Hardinata, Jaya; Zarlis, Muhammad; Budhiarti Nababan, Erna; Hartama, Dedy; Sembiring, Rahmat W.

2017-12-01

One type of artificial neural network is a backpropagation, This algorithm trained with the network architecture used during the training as well as providing the correct output to insert a similar but not the same with the architecture in use at training.The selection of appropriate parameters also affects the outcome, value of learning rate is one of the parameters which influence the process of training, Learning rate affects the speed of learning process on the network architecture.If the learning rate is set too large, then the algorithm will become unstable and otherwise the algorithm will converge in a very long period of time.So this study was made to determine the value of learning rate on the backpropagation algorithm. LVQ models of learning rate is one of the models used in the determination of the value of the learning rate of the algorithm LVQ.By modifying this LVQ model to be applied to the backpropagation algorithm. From the experimental results known to modify the learning rate LVQ models were applied to the backpropagation algorithm learning process becomes faster (epoch less).

Pediatricians', obstetricians', gynecologists', and family medicine physicians' experiences with and attitudes about breast-feeding.

PubMed

Anchondo, Inés; Berkeley, Lizabeth; Mulla, Zuber D; Byrd, Theresa; Nuwayhid, Bahij; Handal, Gilbert; Akins, Ralitsa

2012-05-01

Investigate physicians' breast-feeding experiences and attitudes using a survey based on two behavioral theories: theory of reasoned action (TRA) and the health belief model (HBM). There were 73 participants included in the investigation. These participants were resident and faculty physicians from pediatrics, obstetrics/gynecology, and family medicine at a university campus, located on the US-Mexico border. The sample was reduced to 53 and 56 records for the attitude and confidence variables, respectively. Physicians answered a survey about their breast-feeding experiences and attitudes to learn about intention and ability applying constructs from TRA and HBM. An attitude scale, confidence variable (from self-efficacy items), and a lactation training index were created for the analysis. Analysis of the association between physicians' breastfeeding experiences and their attitudes revealed physicians are knowledgeable about breast-feeding and have positive attitudes towards breast-feeding. They did not seem to remember how long they breast-fed their children or whether they enjoyed breast-feeding, but they wanted to continue breast-feeding. Physicians cite work as a main reason for not continuing to breast-feed. Physicians' attitudes toward breast-feeding are positive. They are expected to practice health-promotion behavior including breast-feeding; however, physicians' breast-feeding rates are low and although they are knowledgeable about breast-feeding their training lacks on didactic depth and hands-on experience. If physicians learn more about breast-feeding and breast-feed exclusively and successfully, the rates in the United States would increase naturally.

Simulation fails to replicate stress in trainees performing a technical procedure in the clinical environment.

PubMed

Baker, B G; Bhalla, A; Doleman, B; Yarnold, E; Simons, S; Lund, J N; Williams, J P

2017-01-01

Simulation-based training (SBT) has become an increasingly important method by which doctors learn. Stress has an impact upon learning, performance, technical, and non-technical skills. However, there are currently no studies that compare stress in the clinical and simulated environment. We aimed to compare objective (heart rate variability, HRV) and subjective (state trait anxiety inventory, STAI) measures of stress theatre with a simulated environment. HRV recordings were obtained from eight anesthetic trainees performing an uncomplicated rapid sequence induction at pre-determined procedural steps using a wireless Polar RS800CX monitor © in an emergency theatre setting. This was repeated in the simulated environment. Participants completed an STAI before and after the procedure. Eight trainees completed the study. The theatre environment caused an increase in objective stress vs baseline (p = .004). There was no significant difference between average objective stress levels across all time points (p = .20) between environments. However, there was a significant interaction between the variables of objective stress and environment (p = .045). There was no significant difference in subjective stress (p = .27) between environments. Simulation was unable to accurately replicate the stress of the technical procedure. This is the first study that compares the stress during SBT with the theatre environment and has implications for the assessment of simulated environments for use in examinations, rating of technical and non-technical skills, and stress management training.

A learning controller for nonrepetitive robotic operation

NASA Technical Reports Server (NTRS)

Miller, W. T., III

1987-01-01

A practical learning control system is described which is applicable to complex robotic and telerobotic systems involving multiple feedback sensors and multiple command variables. In the controller, the learning algorithm is used to learn to reproduce the nonlinear relationship between the sensor outputs and the system command variables over particular regions of the system state space, rather than learning the actuator commands required to perform a specific task. The learned information is used to predict the command signals required to produce desired changes in the sensor outputs. The desired sensor output changes may result from automatic trajectory planning or may be derived from interactive input from a human operator. The learning controller requires no a priori knowledge of the relationships between the sensor outputs and the command variables. The algorithm is well suited for real time implementation, requiring only fixed point addition and logical operations. The results of learning experiments using a General Electric P-5 manipulator interfaced to a VAX-11/730 computer are presented. These experiments involved interactive operator control, via joysticks, of the position and orientation of an object in the field of view of a video camera mounted on the end of the robot arm.

The influence of two cognitive-linguistic variables on incidental word learning in 5-year-olds.

PubMed

Abel, Alyson D; Schuele, C Melanie

2014-08-01

The relation between incidental word learning and two cognitive-linguistic variables--phonological memory and phonological awareness--is not fully understood. Thirty-five typically developing, 5-year-old, preschool children participated in a study examining the association between phonological memory, phonological awareness, and incidental word learning. Children were exposed to target words in a read-aloud story that accompanied a wordless picture book. Target word comprehension was assessed before and after two readings of the story. Phonological awareness predicted incidental word learning but phonological memory did not. The influence of phonological awareness and phonological memory on word learning may be dependent on the demands of the word learning task.

Multi-centre diagnostic classification of individual structural neuroimaging scans from patients with major depressive disorder.

PubMed

Mwangi, Benson; Ebmeier, Klaus P; Matthews, Keith; Steele, J Douglas

2012-05-01

Quantitative abnormalities of brain structure in patients with major depressive disorder have been reported at a group level for decades. However, these structural differences appear subtle in comparison with conventional radiologically defined abnormalities, with considerable inter-subject variability. Consequently, it has not been possible to readily identify scans from patients with major depressive disorder at an individual level. Recently, machine learning techniques such as relevance vector machines and support vector machines have been applied to predictive classification of individual scans with variable success. Here we describe a novel hybrid method, which combines machine learning with feature selection and characterization, with the latter aimed at maximizing the accuracy of machine learning prediction. The method was tested using a multi-centre dataset of T(1)-weighted 'structural' scans. A total of 62 patients with major depressive disorder and matched controls were recruited from referred secondary care clinical populations in Aberdeen and Edinburgh, UK. The generalization ability and predictive accuracy of the classifiers was tested using data left out of the training process. High prediction accuracy was achieved (~90%). While feature selection was important for maximizing high predictive accuracy with machine learning, feature characterization contributed only a modest improvement to relevance vector machine-based prediction (~5%). Notably, while the only information provided for training the classifiers was T(1)-weighted scans plus a categorical label (major depressive disorder versus controls), both relevance vector machine and support vector machine 'weighting factors' (used for making predictions) correlated strongly with subjective ratings of illness severity. These results indicate that machine learning techniques have the potential to inform clinical practice and research, as they can make accurate predictions about brain scan data from individual subjects. Furthermore, machine learning weighting factors may reflect an objective biomarker of major depressive disorder illness severity, based on abnormalities of brain structure.

Human instrumental performance in ratio and interval contingencies: A challenge for associative theory.

PubMed

Pérez, Omar D; Aitken, Michael R F; Zhukovsky, Peter; Soto, Fabián A; Urcelay, Gonzalo P; Dickinson, Anthony

2016-12-15

Associative learning theories regard the probability of reinforcement as the critical factor determining responding. However, the role of this factor in instrumental conditioning is not completely clear. In fact, free-operant experiments show that participants respond at a higher rate on variable ratio than on variable interval schedules even though the reinforcement probability is matched between the schedules. This difference has been attributed to the differential reinforcement of long inter-response times (IRTs) by interval schedules, which acts to slow responding. In the present study, we used a novel experimental design to investigate human responding under random ratio (RR) and regulated probability interval (RPI) schedules, a type of interval schedule that sets a reinforcement probability independently of the IRT duration. Participants responded on each type of schedule before a final choice test in which they distributed responding between two schedules similar to those experienced during training. Although response rates did not differ during training, the participants responded at a lower rate on the RPI schedule than on the matched RR schedule during the choice test. This preference cannot be attributed to a higher probability of reinforcement for long IRTs and questions the idea that similar associative processes underlie classical and instrumental conditioning.

Achieving enlightenment: what do we know about the implicit learning system and its interaction with explicit knowledge?

PubMed

Vidoni, Eric D; Boyd, Lara A

2007-09-01

Two major memory and learning systems operate in the brain: one for facts and ideas (ie, the declarative or explicit system), one for habits and behaviors (ie, the procedural or implicit system). Broadly speaking these two memory systems can operate either in concert or entirely independently of one another during the performance and learning of skilled motor behaviors. This Special Issue article has two parts. In the first, we present a review of implicit motor skill learning that is largely centered on the interactions between declarative and procedural learning and memory. Because distinct neuroanatomical substrates support unique aspects of learning and memory and thus focal injury can cause impairments that are dependent on lesion location, we also broadly consider which brain regions mediate implicit and explicit learning and memory. In the second part of this article, the interactive nature of these two memory systems is illustrated by the presentation of new data that reveal that both learning implicitly and acquiring explicit knowledge through physical practice lead to motor sequence learning. In our new data, we discovered that for healthy individuals use of the implicit versus explicit memory system differently affected variability of performance during acquisition practice; variability was higher early in practice for the implicit group and later in practice for the acquired explicit group. Despite the difference in performance variability, by retention both groups demonstrated comparable change in tracking accuracy and thus, motor sequence learning. Clinicians should be aware of the potential effects of implicit and explicit interactions when designing rehabilitation interventions, particularly when delivering explicit instructions before task practice, working with individuals with focal brain damage, and/or adjusting therapeutic parameters based on acquisition performance variability.

Prevalence and risk factors for irritable bowel syndrome in recovered and non-recovered borderline patients over 10 years of prospective follow-up.

PubMed

Niesten, Isabella J M; Karan, Esen; Frankenburg, Frances R; Fitzmaurice, Garrett M; Zanarini, Mary C

2014-02-01

This study examined rates of irritable bowel syndrome (IBS) over 10 years of prospective follow-up among recovered and non-recovered patients with borderline personality disorder (BPD). Subsequently, risk factors for IBS were examined in female BPD patients. As part of the McLean Study of Adult Development, 264 BPD patients were assessed at baseline, and their medical conditions and time-varying predictors of IBS were assessed over five waves of follow-up (from 6-year follow-up to 16-year follow-up). Semi-structured interviews were used to assess both our IBS outcome variable and our baseline and time-varying predictor variables. Rates of IBS were not significantly different between recovered and non-recovered borderline patients when men and women were considered together and when men were considered alone. However, a significant difference in IBS rates was found between recovered and non-recovered female BPD patients, with the latter reporting significantly higher rates. The rates of IBS in women with BPD were found to be significantly predicted by a family history of IBS and a childhood history of verbal, emotional and/or physical abuse. Taken together, the results of this study suggest that both biological/social learning factors and childhood adversity may be risk factors for IBS in women with BPD. Copyright © 2013 John Wiley & Sons, Ltd.

The Impacts of Demographic Variables on Technological and Contextual Challenges of E-learning Implementation

NASA Astrophysics Data System (ADS)

Aldowah, Hanan; Ghazal, Samar; Naufal Umar, Irfan; Muniandy, Balakrishnan

2017-09-01

Information technology has achieved robust growth which has made it possible for learning to occur quickly. The rapid development of information, communication and technologies (ICT) has initiated an unparalleled transformation in universities all over the world. This development of technology and learning is offering new techniques to represent knowledge, new practices, and new global communities of learners. As a result, today’s economic and social changes force universities to try to find new learning approaches and systems. E-learning seems to be an appropriate approach in this aspect. However, the implementation of e-learning systems in universities is not an easy task because of some challenges related to context, technology, and other challenges. This paper studied the impacts of demographic data and reported the critical points for the decision makers to consider when planning and implementing e-learning in universities. A quantitative approach was used to study the effects of technological and contextual challenges on e-learning implementation in which a questionnaire was used for the data collection. According to the findings of the study, the most important challenges of the implementation of e-learning are related either to organizational (Contextual) and technological (technical) issues. The demographic variables have been found to play a direct and indirect role with the technological and contextual challenges of implementing e-learning. This paper showed that there are some significant differences in the two challenges faced by instructors in terms of the demographic variables. The result revealed that some significant differences exist between demographic variables and the two challenges of e-learning in terms of gender, age, teaching experience, ICT experience and e-learning experience. However, there is no significant difference in terms of e-learning experience. The obtained data, from such study, can provide information about what academic institutions can do before implementing e-learning to reduce and overcome the challenges in implementing e-learning in universities. So, university administrators interested in implementing e-learning should recognize the challenges that their instructors are facing and to provide the necessary policy and support to help overcome these challenges.

A Q-Learning Approach to Flocking With UAVs in a Stochastic Environment.

PubMed

Hung, Shao-Ming; Givigi, Sidney N

2017-01-01

In the past two decades, unmanned aerial vehicles (UAVs) have demonstrated their efficacy in supporting both military and civilian applications, where tasks can be dull, dirty, dangerous, or simply too costly with conventional methods. Many of the applications contain tasks that can be executed in parallel, hence the natural progression is to deploy multiple UAVs working together as a force multiplier. However, to do so requires autonomous coordination among the UAVs, similar to swarming behaviors seen in animals and insects. This paper looks at flocking with small fixed-wing UAVs in the context of a model-free reinforcement learning problem. In particular, Peng's Q(λ) with a variable learning rate is employed by the followers to learn a control policy that facilitates flocking in a leader-follower topology. The problem is structured as a Markov decision process, where the agents are modeled as small fixed-wing UAVs that experience stochasticity due to disturbances such as winds and control noises, as well as weight and balance issues. Learned policies are compared to ones solved using stochastic optimal control (i.e., dynamic programming) by evaluating the average cost incurred during flight according to a cost function. Simulation results demonstrate the feasibility of the proposed learning approach at enabling agents to learn how to flock in a leader-follower topology, while operating in a nonstationary stochastic environment.

Adolescent-specific patterns of behavior and neural activity during social reinforcement learning

PubMed Central

Jones, Rebecca M.; Somerville, Leah H.; Li, Jian; Ruberry, Erika J.; Powers, Alisa; Mehta, Natasha; Dyke, Jonathan; Casey, BJ

2014-01-01

Humans are sophisticated social beings. Social cues from others are exceptionally salient, particularly during adolescence. Understanding how adolescents interpret and learn from variable social signals can provide insight into the observed shift in social sensitivity during this period. The current study tested 120 participants between the ages of 8 and 25 years on a social reinforcement learning task where the probability of receiving positive social feedback was parametrically manipulated. Seventy-eight of these participants completed the task during fMRI scanning. Modeling trial-by-trial learning, children and adults showed higher positive learning rates than adolescents, suggesting that adolescents demonstrated less differentiation in their reaction times for peers who provided more positive feedback. Forming expectations about receiving positive social reinforcement correlated with neural activity within the medial prefrontal cortex and ventral striatum across age. Adolescents, unlike children and adults, showed greater insular activity during positive prediction error learning and increased activity in the supplementary motor cortex and the putamen when receiving positive social feedback regardless of the expected outcome, suggesting that peer approval may motivate adolescents towards action. While different amounts of positive social reinforcement enhanced learning in children and adults, all positive social reinforcement equally motivated adolescents. Together, these findings indicate that sensitivity to peer approval during adolescence goes beyond simple reinforcement theory accounts and suggests possible explanations for how peers may motivate adolescent behavior. PMID:24550063

Adolescent-specific patterns of behavior and neural activity during social reinforcement learning.

PubMed

Jones, Rebecca M; Somerville, Leah H; Li, Jian; Ruberry, Erika J; Powers, Alisa; Mehta, Natasha; Dyke, Jonathan; Casey, B J

2014-06-01

Humans are sophisticated social beings. Social cues from others are exceptionally salient, particularly during adolescence. Understanding how adolescents interpret and learn from variable social signals can provide insight into the observed shift in social sensitivity during this period. The present study tested 120 participants between the ages of 8 and 25 years on a social reinforcement learning task where the probability of receiving positive social feedback was parametrically manipulated. Seventy-eight of these participants completed the task during fMRI scanning. Modeling trial-by-trial learning, children and adults showed higher positive learning rates than did adolescents, suggesting that adolescents demonstrated less differentiation in their reaction times for peers who provided more positive feedback. Forming expectations about receiving positive social reinforcement correlated with neural activity within the medial prefrontal cortex and ventral striatum across age. Adolescents, unlike children and adults, showed greater insular activity during positive prediction error learning and increased activity in the supplementary motor cortex and the putamen when receiving positive social feedback regardless of the expected outcome, suggesting that peer approval may motivate adolescents toward action. While different amounts of positive social reinforcement enhanced learning in children and adults, all positive social reinforcement equally motivated adolescents. Together, these findings indicate that sensitivity to peer approval during adolescence goes beyond simple reinforcement theory accounts and suggest possible explanations for how peers may motivate adolescent behavior.

Linear Relationship between Resilience, Learning Approaches, and Coping Strategies to Predict Achievement in Undergraduate Students

PubMed Central

de la Fuente, Jesús; Fernández-Cabezas, María; Cambil, Matilde; Vera, Manuel M.; González-Torres, Maria Carmen; Artuch-Garde, Raquel

2017-01-01

The aim of the present research was to analyze the linear relationship between resilience (meta-motivational variable), learning approaches (meta-cognitive variables), strategies for coping with academic stress (meta-emotional variable) and academic achievement, necessary in the context of university academic stress. A total of 656 students from a southern university in Spain completed different questionnaires: a resiliency scale, a coping strategies scale, and a study process questionnaire. Correlations and structural modeling were used for data analyses. There was a positive and significant linear association showing a relationship of association and prediction of resilience to the deep learning approach, and problem-centered coping strategies. In a complementary way, these variables positively and significantly predicted the academic achievement of university students. These results enabled a linear relationship of association and consistent and differential prediction to be established among the variables studied. Implications for future research are set out. PMID:28713298

Variability, constraints, and creativity. Shedding light on Claude Monet.

PubMed

Stokes, P D

2001-04-01

Recent experimental research suggests 2 things. The first is that along with learning how to do something, people also learn how variably or differently to continue doing it. The second is that high variability is maintained by constraining, precluding a currently successful, often repetitive solution to a problem. In this view, Claude Monet's habitually high level of variability in painting was acquired during his childhood and early apprenticeship and was maintained throughout his adult career by a continuous series of task constraints imposed by the artist on his own work. For Monet, variability was rewarded and rewarding.

Learning.

ERIC Educational Resources Information Center

Glaser, Robert

A report on learning psychology and its relationship to the study of school learning emphasizes the increasing interaction between theorists and educational practitioners, particularly in attempting to learn which variables influence the instructional process and to find an appropriate methodology to measure and evaluate learning. "Learning…

Learning by heart-the relationship between resting vagal tone and metacognitive judgments: a pilot study.

PubMed

Meessen, Judith; Sütterlin, Stefan; Gauggel, Siegfried; Forkmann, Thomas

2018-05-23

Metacognitive awareness and resting vagally mediated heart rate variability (HRV) as a physiological trait marker of cognitive inhibitory control capacities are both associated with better well-being and seem to share a common neural basis. Executive functioning which is considered a prerequisite for delivering prospective metacognitive judgments has been found to be correlated with HRV. This pilot study addresses the question, whether metacognitive awareness and resting vagally mediated HRV are positively associated. A sample of 20 healthy participants was analyzed that completed a typical Judgment of Learning task after an electrocardiogram had been recorded. The root-mean-squares of successive differences were used to calculate vagally mediated HRV. Metacognitive awareness was measured by comparing the judgments of learning with the actual memory performance, yielding a deviation score. HRV was found to be positively correlated with metacognitive awareness. Results suggest that metacognitive abilities might relate to physiological trait markers of cognitive inhibitory control capacities. Further experimental studies are needed to investigate causal relations.

Infinite hidden conditional random fields for human behavior analysis.

PubMed

Bousmalis, Konstantinos; Zafeiriou, Stefanos; Morency, Louis-Philippe; Pantic, Maja

2013-01-01

Hidden conditional random fields (HCRFs) are discriminative latent variable models that have been shown to successfully learn the hidden structure of a given classification problem (provided an appropriate validation of the number of hidden states). In this brief, we present the infinite HCRF (iHCRF), which is a nonparametric model based on hierarchical Dirichlet processes and is capable of automatically learning the optimal number of hidden states for a classification task. We show how we learn the model hyperparameters with an effective Markov-chain Monte Carlo sampling technique, and we explain the process that underlines our iHCRF model with the Restaurant Franchise Rating Agencies analogy. We show that the iHCRF is able to converge to a correct number of represented hidden states, and outperforms the best finite HCRFs--chosen via cross-validation--for the difficult tasks of recognizing instances of agreement, disagreement, and pain. Moreover, the iHCRF manages to achieve this performance in significantly less total training, validation, and testing time.

A visual tracking method based on improved online multiple instance learning

NASA Astrophysics Data System (ADS)

He, Xianhui; Wei, Yuxing

2016-09-01

Visual tracking is an active research topic in the field of computer vision and has been well studied in the last decades. The method based on multiple instance learning (MIL) was recently introduced into the tracking task, which can solve the problem that template drift well. However, MIL method has relatively poor performance in running efficiency and accuracy, due to its strong classifiers updating strategy is complicated, and the speed of the classifiers update is not always same with the change of the targets' appearance. In this paper, we present a novel online effective MIL (EMIL) tracker. A new update strategy for strong classifier was proposed to improve the running efficiency of MIL method. In addition, to improve the t racking accuracy and stability of the MIL method, a new dynamic mechanism for learning rate renewal of the classifier and variable search window were proposed. Experimental results show that our method performs good performance under the complex scenes, with strong stability and high efficiency.

Mesolimbic Dopamine Signals the Value of Work

PubMed Central

Hamid, Arif A.; Pettibone, Jeffrey R.; Mabrouk, Omar S.; Hetrick, Vaughn L.; Schmidt, Robert; Vander Weele, Caitlin M.; Kennedy, Robert T.; Aragona, Brandon J.; Berke, Joshua D.

2015-01-01

Dopamine cell firing can encode errors in reward prediction, providing a learning signal to guide future behavior. Yet dopamine is also a key modulator of motivation, invigorating current behavior. Existing theories propose that fast (“phasic”) dopamine fluctuations support learning, while much slower (“tonic”) dopamine changes are involved in motivation. We examined dopamine release in the nucleus accumbens across multiple time scales, using complementary microdialysis and voltammetric methods during adaptive decision-making. We first show that minute-by-minute dopamine levels covary with reward rate and motivational vigor. We then show that second-by-second dopamine release encodes an estimate of temporally-discounted future reward (a value function). We demonstrate that changing dopamine immediately alters willingness to work, and reinforces preceding action choices by encoding temporal-difference reward prediction errors. Our results indicate that dopamine conveys a single, rapidly-evolving decision variable, the available reward for investment of effort, that is employed for both learning and motivational functions. PMID:26595651

Effects of variable practice on the motor learning outcomes in manual wheelchair propulsion.

PubMed

Leving, Marika T; Vegter, Riemer J K; de Groot, Sonja; van der Woude, Lucas H V

2016-11-23

Handrim wheelchair propulsion is a cyclic skill that needs to be learned during rehabilitation. It has been suggested that more variability in propulsion technique benefits the motor learning process of wheelchair propulsion. The purpose of this study was to determine the influence of variable practice on the motor learning outcomes of wheelchair propulsion in able-bodied participants. Variable practice was introduced in the form of wheelchair basketball practice and wheelchair-skill practice. Motor learning was operationalized as improvements in mechanical efficiency and propulsion technique. Eleven Participants in the variable practice group and 12 participants in the control group performed an identical pre-test and a post-test. Pre- and post-test were performed in a wheelchair on a motor-driven treadmill (1.11 m/s) at a relative power output of 0.23 W/kg. Energy consumption and the propulsion technique variables with their respective coefficient of variation were calculated. Between the pre- and the post-test the variable practice group received 7 practice sessions. During the practice sessions participants performed one-hour of variable practice, consisting of five wheelchair-skill tasks and a 30 min wheelchair basketball game. The control group did not receive any practice between the pre- and the post-test. Comparison of the pre- and the post-test showed that the variable practice group significantly improved the mechanical efficiency (4.5 ± 0.6% → 5.7 ± 0.7%) in contrast to the control group (4.5 ± 0.6% → 4.4 ± 0.5%) (group x time interaction effect p < 0.001).With regard to propulsion technique, both groups significantly reduced the push frequency and increased the contact angle of the hand with the handrim (within group, time effect). No significant group × time interaction effects were found for propulsion technique. With regard to propulsion variability, the variable practice group increased variability when compared to the control group (interaction effect p < 0.001). Compared to a control, variable practice, resulted in an increase in mechanical efficiency and increased variability. Interestingly, the large relative improvement in mechanical efficiency was concomitant with only moderate improvements in the propulsion technique, which were similar in the control group, suggesting that other factors besides propulsion technique contributed to the lower energy expenditure.

Team Based Work. Symposium.

ERIC Educational Resources Information Center

2002

This document contains three papers from a symposium on team-based work in human resource development (HRD). "Toward Transformational Learning in Organizations: Effects of Model-II Governing Variables on Perceived Learning in Teams" (Blair K. Carruth) summarizes a study that indicated that, regardless of which Model-II variable (valid…

Factors Influencing the Learning of Classical Mechanics.

ERIC Educational Resources Information Center

Champagne, Audrey B.; And Others

1980-01-01

Describes a study investigating the combined effect of certain variables on student achievement in classical mechanics. The purpose was to (1) describe preinstructional knowledge and skills; (2) correlate these variables with the student's success in learning classical mechanics; and (3) develop hypothesis about relationships between these…

An explanatory model of academic achievement based on aptitudes, goal orientations, self-concept and learning strategies.

PubMed

Miñano Pérez, Pablo; Castejón Costa, Juan-Luis; Gilar Corbí, Raquel

2012-03-01

As a result of studies examining factors involved in the learning process, various structural models have been developed to explain the direct and indirect effects that occur between the variables in these models. The objective was to evaluate a structural model of cognitive and motivational variables predicting academic achievement, including general intelligence, academic self-concept, goal orientations, effort and learning strategies. The sample comprised of 341 Spanish students in the first year of compulsory secondary education. Different tests and questionnaires were used to evaluate each variable, and Structural Equation Modelling (SEM) was applied to contrast the relationships of the initial model. The model proposed had a satisfactory fit, and all the hypothesised relationships were significant. General intelligence was the variable most able to explain academic achievement. Also important was the direct influence of academic self-concept on achievement, goal orientations and effort, as well as the mediating ability of effort and learning strategies between academic goals and final achievement.

Construction of a Calibrated Probabilistic Classification Catalog: Application to 50k Variable Sources in the All-Sky Automated Survey

NASA Astrophysics Data System (ADS)

Richards, Joseph W.; Starr, Dan L.; Miller, Adam A.; Bloom, Joshua S.; Butler, Nathaniel R.; Brink, Henrik; Crellin-Quick, Arien

2012-12-01

With growing data volumes from synoptic surveys, astronomers necessarily must become more abstracted from the discovery and introspection processes. Given the scarcity of follow-up resources, there is a particularly sharp onus on the frameworks that replace these human roles to provide accurate and well-calibrated probabilistic classification catalogs. Such catalogs inform the subsequent follow-up, allowing consumers to optimize the selection of specific sources for further study and permitting rigorous treatment of classification purities and efficiencies for population studies. Here, we describe a process to produce a probabilistic classification catalog of variability with machine learning from a multi-epoch photometric survey. In addition to producing accurate classifications, we show how to estimate calibrated class probabilities and motivate the importance of probability calibration. We also introduce a methodology for feature-based anomaly detection, which allows discovery of objects in the survey that do not fit within the predefined class taxonomy. Finally, we apply these methods to sources observed by the All-Sky Automated Survey (ASAS), and release the Machine-learned ASAS Classification Catalog (MACC), a 28 class probabilistic classification catalog of 50,124 ASAS sources in the ASAS Catalog of Variable Stars. We estimate that MACC achieves a sub-20% classification error rate and demonstrate that the class posterior probabilities are reasonably calibrated. MACC classifications compare favorably to the classifications of several previous domain-specific ASAS papers and to the ASAS Catalog of Variable Stars, which had classified only 24% of those sources into one of 12 science classes.

CONSTRUCTION OF A CALIBRATED PROBABILISTIC CLASSIFICATION CATALOG: APPLICATION TO 50k VARIABLE SOURCES IN THE ALL-SKY AUTOMATED SURVEY

DOE Office of Scientific and Technical Information (OSTI.GOV)

Richards, Joseph W.; Starr, Dan L.; Miller, Adam A.

2012-12-15

With growing data volumes from synoptic surveys, astronomers necessarily must become more abstracted from the discovery and introspection processes. Given the scarcity of follow-up resources, there is a particularly sharp onus on the frameworks that replace these human roles to provide accurate and well-calibrated probabilistic classification catalogs. Such catalogs inform the subsequent follow-up, allowing consumers to optimize the selection of specific sources for further study and permitting rigorous treatment of classification purities and efficiencies for population studies. Here, we describe a process to produce a probabilistic classification catalog of variability with machine learning from a multi-epoch photometric survey. In additionmore » to producing accurate classifications, we show how to estimate calibrated class probabilities and motivate the importance of probability calibration. We also introduce a methodology for feature-based anomaly detection, which allows discovery of objects in the survey that do not fit within the predefined class taxonomy. Finally, we apply these methods to sources observed by the All-Sky Automated Survey (ASAS), and release the Machine-learned ASAS Classification Catalog (MACC), a 28 class probabilistic classification catalog of 50,124 ASAS sources in the ASAS Catalog of Variable Stars. We estimate that MACC achieves a sub-20% classification error rate and demonstrate that the class posterior probabilities are reasonably calibrated. MACC classifications compare favorably to the classifications of several previous domain-specific ASAS papers and to the ASAS Catalog of Variable Stars, which had classified only 24% of those sources into one of 12 science classes.« less

High pressure air compressor valve fault diagnosis using feedforward neural networks

NASA Astrophysics Data System (ADS)

James Li, C.; Yu, Xueli

1995-09-01

Feedforward neural networks (FNNs) are developed and implemented to classify a four-stage high pressure air compressor into one of the following conditions: baseline, suction or exhaust valve faults. These FNNs are used for the compressor's automatic condition monitoring and fault diagnosis. Measurements of 39 variables are obtained under different baseline conditions and third-stage suction and exhaust valve faults. These variables include pressures and temperatures at all stages, voltage between phase aand phase b, voltage between phase band phase c, total three-phase real power, cooling water flow rate, etc. To reduce the number of variables, the amount of their discriminatory information is quantified by scattering matrices to identify statistical significant ones. Measurements of the selected variables are then used by a fully automatic structural and weight learning algorithm to construct three-layer FNNs to classify the compressor's condition. This learning algorithm requires neither guesses of initial weight values nor number of neurons in the hidden layer of an FNN. It takes an incremental approach in which a hidden neuron is trained by exemplars and then augmented to the existing network. These exemplars are then made orthogonal to the newly identified hidden neuron. They are subsequently used for the training of the next hidden neuron. The betterment continues until a desired accuracy is reached. After the neural networks are established, novel measurements from various conditions that haven't been previously seen by the FNNs are then used to evaluate their ability in fault diagnosis. The trained neural networks provide very accurate diagnosis for suction and discharge valve defects.

Personalized Learning: From Neurogenetics of Behaviors to Designing Optimal Language Training

PubMed Central

Wong, Patrick C. M.; Vuong, Loan; Liu, Kevin

2016-01-01

Variability in drug responsivity has prompted the development of Personalized Medicine, which has shown great promise in utilizing genotypic information to develop safer and more effective drug regimens for patients. Similarly, individual variability in learning outcomes has puzzled researchers who seek to create optimal learning environments for students. “Personalized Learning” seeks to identify genetic, neural and behavioral predictors of individual differences in learning and aims to use predictors to help create optimal teaching paradigms. Evidence for Personalized Learning can be observed by connecting research in pharmacogenomics, cognitive genetics and behavioral experiments across domains of learning, which provides a framework for conducting empirical studies from the laboratory to the classroom and holds promise for addressing learning effectiveness in the individual learners. Evidence can also be seen in the subdomain of speech learning, thus providing initial support for the applicability of Personalized Learning to language. PMID:27720749

When money is not enough: awareness, success, and variability in motor learning.

PubMed

Manley, Harry; Dayan, Peter; Diedrichsen, Jörn

2014-01-01

When performing a skill such as throwing a dart, many different combinations of joint motions suffice to hit the target. The motor system adapts rapidly to reduce bias in the desired outcome (i.e., the first-order moment of the error); however, the essence of skill is to produce movements with less variability (i.e., to reduce the second-order moment). It is easy to see how feedback about success or failure could sculpt performance to achieve this aim. However, it is unclear whether the dimensions responsible for success or failure need to be known explicitly by the subjects, or whether learning can proceed without explicit awareness of the movement parameters that need to change. Here, we designed a redundant, two-dimensional reaching task in which we could selectively manipulate task success and the variability of action outcomes, whilst also manipulating awareness of the dimension along which performance could be improved. Variability was manipulated either by amplifying natural errors, leaving the correlation between the executed movement and the visual feedback intact, or by adding extrinsic noise, decorrelating movement and feedback. We found that explicit, binary, feedback about success or failure was only sufficient for learning when participants were aware of the dimension along which motor behavior had to change. Without such awareness, learning was only present when extrinsic noise was added to the feedback, but not when task success or variability was manipulated in isolation; learning was also much slower. Our results highlight the importance of conscious awareness of the relevant dimension during motor learning, and suggest that higher-order moments of outcome signals are likely to play a significant role in skill learning in complex tasks.

[Prediction of mathematics achievement: effect of personal, socioeducational and contextual variables].

PubMed

Rosário, Pedro; Lourenço, Abílio; Paiva, Olímpia; Rodrigues, Adriana; Valle, Antonio; Tuero-Herrero, Ellián

2012-05-01

Based upon the self-regulated learning theoretical framework this study examined to what extent students' Math school achievement (fifth to ninth graders from compulsory education) can be explained by different cognitive-motivational, social, educational, and contextual variables. A sample of 571 students (10 to 15 year old) enrolled in the study. Findings suggest that Math achievement can be predicted by self-efficacy in Math, school success and self-regulated learning and that these same variables can be explained by other motivational (ej., achievement goals) and contextual variables (school disruption) stressing this way the main importance of self-regulated learning processes and the role context can play in the promotion of school success. The educational implications of the results to the school levels taken are also discussed in the present paper.

Geographic distribution of habitat, development, and population growth rates of the Asian citrus psyllid, Diaphorina citri, in Mexico.

PubMed

López-Collado, José; Isabel López-Arroyo, J; Robles-García, Pedro L; Márquez-Santos, Magdalena

2013-01-01

The Asian citrus psyllid, Diaphorina citri Kuwayama (Hemiptera: Psyllidae), is an introduced pest in Mexico and a vector of huanglongbing, a lethal citrus disease. Estimations of the habitat distribution and population growth rates of D. citri are required to establish regional and areawide management strategies and can be used as a pest risk analysis tools. In this study, the habitat distribution of D. citri in Mexico was computed with MaxEnt, an inductive, machine-learning program that uses bioclimatic layers and point location data. Geographic distributions of development and population growth rates were determined by fitting a temperature-dependent, nonlinear model and projecting the rates over the target area, using the annual mean temperature as the predictor variable. The results showed that the most suitable regions for habitat of D. citri comprise the Gulf of Mexico states, Yucatán Peninsula, and areas scattered throughout the Pacific coastal states. Less suitable areas occurred in northern and central states. The most important predictor variables were related to temperature. Development and growth rates had a distribution wider than habitat, reaching some of the northern states of México. Habitat, development, and population growth rates were correlated to each other and with the citrus producing area. These relationships indicated that citrus producing states are within the most suitable regions for the occurrence, development, and population growth of D. citri, therefore increasing the risk of huanglongbing dispersion.

Geographic Distribution of Habitat, Development, and Population Growth Rates of the Asian Citrus Psyllid, Diaphorina citri, in Mexico

PubMed Central

López-Collado, José; Isabel López-Arroyo, J.; Robles-García, Pedro L.; Márquez-Santos, Magdalena

2013-01-01

The Asian citrus psyllid, Diaphorina citri Kuwayama (Hemiptera: Psyllidae), is an introduced pest in Mexico and a vector of huanglongbing, a lethal citrus disease. Estimations of the habitat distribution and population growth rates of D. citri are required to establish regional and areawide management strategies and can be used as a pest risk analysis tools. In this study, the habitat distribution of D. citri in Mexico was computed with MaxEnt, an inductive, machine-learning program that uses bioclimatic layers and point location data. Geographic distributions of development and population growth rates were determined by fitting a temperature-dependent, nonlinear model and projecting the rates over the target area, using the annual mean temperature as the predictor variable. The results showed that the most suitable regions for habitat of D. citri comprise the Gulf of Mexico states, Yucatán Peninsula, and areas scattered throughout the Pacific coastal states. Less suitable areas occurred in northern and central states. The most important predictor variables were related to temperature. Development and growth rates had a distribution wider than habitat, reaching some of the northern states of México. Habitat, development, and population growth rates were correlated to each other and with the citrus producing area. These relationships indicated that citrus producing states are within the most suitable regions for the occurrence, development, and population growth of D. citri, therefore increasing the risk of huanglongbing dispersion. PMID:24735280

Combined use of autogenic therapy and biofeedback in training effective control of heart rate by humans

NASA Technical Reports Server (NTRS)

Cowings, P. S.

1977-01-01

Experiments were performed on 24 men and women (aged 20-27 yr) in three equal groups who were taught to control their own heart rates by autogenic training and biofeedback under dark and sound-isolated conditions. Group I was parasympathetic dominant, group II was sympathetic dominant, and group III consisted of parasympathetic-dominant subjects and controls who received only biofeedback of their own heart rates. The results corroborate three hypotheses: (1) subjects with para-sympathetic-dominant autonomic profiles perform in a way that is both qualitatively and quantitatively different from subjects with sympathetic-dominant autonomic profiles; (2) tests of interindividual variability yield data relevant to individual performance in visceral learning tasks; and (3) the combined use of autogenic training, biofeedback, and verbal feedback is suitable for conditioning large stable autonomic responses in humans.

Learning curves for urological procedures: a systematic review.

PubMed

Abboudi, Hamid; Khan, Mohammed Shamim; Guru, Khurshid A; Froghi, Saied; de Win, Gunter; Van Poppel, Hendrik; Dasgupta, Prokar; Ahmed, Kamran

2014-10-01

To determine the number of cases a urological surgeon must complete to achieve proficiency for various urological procedures. The MEDLINE, EMBASE and PsycINFO databases were systematically searched for studies published up to December 2011. Studies pertaining to learning curves of urological procedures were included. Two reviewers independently identified potentially relevant articles. Procedure name, statistical analysis, procedure setting, number of participants, outcomes and learning curves were analysed. Forty-four studies described the learning curve for different urological procedures. The learning curve for open radical prostatectomy ranged from 250 to 1000 cases and for laparoscopic radical prostatectomy from 200 to 750 cases. The learning curve for robot-assisted laparoscopic prostatectomy (RALP) has been reported to be 40 procedures as a minimum number. Robot-assisted radical cystectomy has a documented learning curve of 16-30 cases, depending on which outcome variable is measured. Irrespective of previous laparoscopic experience, there is a significant reduction in operating time (P = 0.008), estimated blood loss (P = 0.008) and complication rates (P = 0.042) after 100 RALPs. The available literature can act as a guide to the learning curves of trainee urologists. Although the learning curve may vary among individual surgeons, a consensus should exist for the minimum number of cases to achieve proficiency. The complexities associated with defining procedural competence are vast. The majority of learning curve trials have focused on the latest surgical techniques and there is a paucity of data pertaining to basic urological procedures. © 2013 The Authors. BJU International © 2013 BJU International.

What is the impact of a national postgraduate medical specialist education reform on the daily clinical training 3.5 years after implementation? A questionnaire survey.

PubMed

Mortensen, Lene; Malling, Bente; Ringsted, Charlotte; Rubak, Sune

2010-06-18

Many countries have recently reformed their postgraduate medical education (PGME). New pedagogic initiatives and blueprints have been introduced to improve quality and effectiveness of the education. Yet it is unknown whether these changes improved the daily clinical training. The purpose was to examine the impact of a national PGME reform on the daily clinical training practice. The Danish reform included change of content and format of specialist education in line with outcome-based education using the CanMEDS framework. We performed a questionnaire survey among all hospital doctors in the North Denmark Region. The questionnaire included items on educational appraisal meetings, individual learning plans, incorporating training issues into work routines, supervision and feedback, and interpersonal acquaintance. Data were collected before start and 31/2 years later. Mean score values were compared, and response variables were analysed by multiple regression to explore the relation between the ratings and seniority, type of hospital, type of specialty, and effect of attendance to courses in learning and teaching among respondents. Response rates were 2105/2817 (75%) and 1888/3284 (58%), respectively. We found limited impact on clinical training practice and learning environment. Variances in ratings were hardly affected by type of hospital, whereas belonging to the laboratory specialities compared to other specialties was related to higher ratings concerning all aspects. The impact on daily clinical training practice of a national PGME reform was limited after 31/2 years. Future initiatives must focus on changing the pedagogical competences of the doctors participating in daily clinical training and on implementation strategies for changing educational culture.

Motivation and Self-Regulated Learning Influences on Middle School Mathematics Achievement

ERIC Educational Resources Information Center

Cleary, Timothy J.; Kitsantas, Anastasia

2017-01-01

The primary purpose of the current study was to use structural equation modeling to examine the relations among background variables (socioeconomic status, prior mathematics achievement), motivation variables (self-efficacy, task interest, school connectedness), self-regulated learning (SRL) behaviors, and performance in middle school mathematics…

Perception toward Organizational Learning Culture in Small-Size Business Enterprises

ERIC Educational Resources Information Center

Graham, Carroll M.; Nafukho, Fredrick M.

2007-01-01

This study sought to determine the relationship between four independent variables educational level, longevity, gender, type of enterprise, and the dependent variable respondents' perception of culture toward organizational learning readiness. An exploratory correlational research design was employed to survey 498 employees in seven small…

Problem-based learning outcomes: the glass half-full.

PubMed

Distlehorst, Linda H; Dawson, Elizabeth; Robbs, Randall S; Barrows, Howard S

2005-03-01

To compare the characteristics and outcome data of students from a single institution with a two-track, problem based learning (PBL) and standard (STND) curriculum. PBL and STND students from nine graduating classes at Southern Illinois University School of Medicine were compared using common medical school performance outcomes (USMLE Step 1, USMLE Step 2, clerkship mean ratings, number of clerkship honors and remediation designations, and the senior clinical competency exam), as well as common admission and demographic variables. PBL students were older, and the cohort had a higher proportion of women. The two tracks had similar USMLE Step 1 and 2 mean scores and pass rates. Performance differences were significant for PBL students in two clerkships as well as in the clerkship subcategories of clinical performance, knowledge and clinical reasoning, and noncognitive behaviors. In addition, the proportion of PBL students earning honors was greater. The traditional undergraduate educational outcomes for the PBL and STND students are very positive. In several of the clerkship performance measures, the PBL students performed significantly better, and in no circumstance did they perform worse than the STND students.

Informal Workplace Learning: An Exploration of Age Differences in Learning Competence

ERIC Educational Resources Information Center

Schulz, Melanie; Rosznagel, Christian Stamov

2010-01-01

Informal learning is becoming a standard format in companies' training and development (T&D) activities. It requires a specific learning competence comprising cognitive, metacognitive, and motivational dimensions. In the present study, it was investigated whether learning-competence variables predict success in informal learning. Given the…

Undergraduate research internships: veterinary students' experiences and the relation with internship quality.

PubMed

Jaarsma, Debbie A D C; Muijtjens, Arno M M; Dolmans, Diana H J M; Schuurmans, Eva M; Van Beukelen, Peter; Scherpbier, Albert J J A

2009-05-01

The learning environment of undergraduate research internships has received little attention, compared to postgraduate research training. This study investigates students' experiences with research internships, particularly the quality of supervision, development of research skills, the intellectual and social climate, infrastructure support, and the clarity of goals and the relationship between the experiences and the quality of students' research reports and their overall satisfaction with internships. A questionnaire (23 items, a 5-point Likert scale) was administered to 101 Year five veterinary students after completion of a research internship. Multiple linear regression analyses were conducted with quality of supervision, development of research skills, climate, infrastructure and clarity of goals as independent variables and the quality of students' research reports and students' overall satisfaction as dependent variables. The response rate was 79.2%. Students' experiences are generally positive. Students' experiences with the intellectual and social climate are significantly correlated with the quality of research reports whilst the quality of supervision is significantly correlated with both the quality of research reports and students' overall satisfaction with the internship. Both the quality of supervision and the climate are found to be crucial factors in students' research learning and satisfaction with the internship.

Memory Effects on Movement Behavior in Animal Foraging

PubMed Central

Bracis, Chloe; Gurarie, Eliezer; Van Moorter, Bram; Goodwin, R. Andrew

2015-01-01

An individual’s choices are shaped by its experience, a fundamental property of behavior important to understanding complex processes. Learning and memory are observed across many taxa and can drive behaviors, including foraging behavior. To explore the conditions under which memory provides an advantage, we present a continuous-space, continuous-time model of animal movement that incorporates learning and memory. Using simulation models, we evaluate the benefit memory provides across several types of landscapes with variable-quality resources and compare the memory model within a nested hierarchy of simpler models (behavioral switching and random walk). We find that memory almost always leads to improved foraging success, but that this effect is most marked in landscapes containing sparse, contiguous patches of high-value resources that regenerate relatively fast and are located in an otherwise devoid landscape. In these cases, there is a large payoff for finding a resource patch, due to size, value, or locational difficulty. While memory-informed search is difficult to differentiate from other factors using solely movement data, our results suggest that disproportionate spatial use of higher value areas, higher consumption rates, and consumption variability all point to memory influencing the movement direction of animals in certain ecosystems. PMID:26288228

Memory Effects on Movement Behavior in Animal Foraging.

PubMed

Bracis, Chloe; Gurarie, Eliezer; Van Moorter, Bram; Goodwin, R Andrew

2015-01-01

An individual's choices are shaped by its experience, a fundamental property of behavior important to understanding complex processes. Learning and memory are observed across many taxa and can drive behaviors, including foraging behavior. To explore the conditions under which memory provides an advantage, we present a continuous-space, continuous-time model of animal movement that incorporates learning and memory. Using simulation models, we evaluate the benefit memory provides across several types of landscapes with variable-quality resources and compare the memory model within a nested hierarchy of simpler models (behavioral switching and random walk). We find that memory almost always leads to improved foraging success, but that this effect is most marked in landscapes containing sparse, contiguous patches of high-value resources that regenerate relatively fast and are located in an otherwise devoid landscape. In these cases, there is a large payoff for finding a resource patch, due to size, value, or locational difficulty. While memory-informed search is difficult to differentiate from other factors using solely movement data, our results suggest that disproportionate spatial use of higher value areas, higher consumption rates, and consumption variability all point to memory influencing the movement direction of animals in certain ecosystems.

High or low? Comparing high and low-variability phonetic training in adult and child second language learners

PubMed Central

Brown, Helen; Clayards, Meghan

2017-01-01

Background High talker variability (i.e., multiple voices in the input) has been found effective in training nonnative phonetic contrasts in adults. A small number of studies suggest that children also benefit from high-variability phonetic training with some evidence that they show greater learning (more plasticity) than adults given matched input, although results are mixed. However, no study has directly compared the effectiveness of high versus low talker variability in children. Methods Native Greek-speaking eight-year-olds (N = 52), and adults (N = 41) were exposed to the English /i/-/ɪ/ contrast in 10 training sessions through a computerized word-learning game. Pre- and post-training tests examined discrimination of the contrast as well as lexical learning. Participants were randomly assigned to high (four talkers) or low (one talker) variability training conditions. Results Both age groups improved during training, and both improved more while trained with a single talker. Results of a three-interval oddity discrimination test did not show the predicted benefit of high-variability training in either age group. Instead, children showed an effect in the reverse direction—i.e., reliably greater improvements in discrimination following single talker training, even for untrained generalization items, although the result is qualified by (accidental) differences between participant groups at pre-test. Adults showed a numeric advantage for high-variability but were inconsistent with respect to voice and word novelty. In addition, no effect of variability was found for lexical learning. There was no evidence of greater plasticity for phonetic learning in child learners. Discussion This paper adds to the handful of studies demonstrating that, like adults, child learners can improve their discrimination of a phonetic contrast via computerized training. There was no evidence of a benefit of training with multiple talkers, either for discrimination or word learning. The results also do not support the findings of greater plasticity in child learners found in a previous paper (Giannakopoulou, Uther & Ylinen, 2013a). We discuss these results in terms of various differences between training and test tasks used in the current work compared with previous literature. PMID:28584698

High or low? Comparing high and low-variability phonetic training in adult and child second language learners.

PubMed

Giannakopoulou, Anastasia; Brown, Helen; Clayards, Meghan; Wonnacott, Elizabeth

2017-01-01

High talker variability (i.e., multiple voices in the input) has been found effective in training nonnative phonetic contrasts in adults. A small number of studies suggest that children also benefit from high-variability phonetic training with some evidence that they show greater learning (more plasticity) than adults given matched input, although results are mixed. However, no study has directly compared the effectiveness of high versus low talker variability in children. Native Greek-speaking eight-year-olds ( N = 52), and adults ( N = 41) were exposed to the English /i/-/ɪ/ contrast in 10 training sessions through a computerized word-learning game. Pre- and post-training tests examined discrimination of the contrast as well as lexical learning. Participants were randomly assigned to high (four talkers) or low (one talker) variability training conditions. Both age groups improved during training, and both improved more while trained with a single talker. Results of a three-interval oddity discrimination test did not show the predicted benefit of high-variability training in either age group. Instead, children showed an effect in the reverse direction-i.e., reliably greater improvements in discrimination following single talker training, even for untrained generalization items, although the result is qualified by (accidental) differences between participant groups at pre-test. Adults showed a numeric advantage for high-variability but were inconsistent with respect to voice and word novelty. In addition, no effect of variability was found for lexical learning. There was no evidence of greater plasticity for phonetic learning in child learners. This paper adds to the handful of studies demonstrating that, like adults, child learners can improve their discrimination of a phonetic contrast via computerized training. There was no evidence of a benefit of training with multiple talkers, either for discrimination or word learning. The results also do not support the findings of greater plasticity in child learners found in a previous paper (Giannakopoulou, Uther & Ylinen, 2013a). We discuss these results in terms of various differences between training and test tasks used in the current work compared with previous literature.

Foundational Principles for Large-Scale Inference: Illustrations Through Correlation Mining.

PubMed

Hero, Alfred O; Rajaratnam, Bala

2016-01-01

When can reliable inference be drawn in fue "Big Data" context? This paper presents a framework for answering this fundamental question in the context of correlation mining, wifu implications for general large scale inference. In large scale data applications like genomics, connectomics, and eco-informatics fue dataset is often variable-rich but sample-starved: a regime where the number n of acquired samples (statistical replicates) is far fewer than fue number p of observed variables (genes, neurons, voxels, or chemical constituents). Much of recent work has focused on understanding the computational complexity of proposed methods for "Big Data". Sample complexity however has received relatively less attention, especially in the setting when the sample size n is fixed, and the dimension p grows without bound. To address fuis gap, we develop a unified statistical framework that explicitly quantifies the sample complexity of various inferential tasks. Sampling regimes can be divided into several categories: 1) the classical asymptotic regime where fue variable dimension is fixed and fue sample size goes to infinity; 2) the mixed asymptotic regime where both variable dimension and sample size go to infinity at comparable rates; 3) the purely high dimensional asymptotic regime where the variable dimension goes to infinity and the sample size is fixed. Each regime has its niche but only the latter regime applies to exa cale data dimension. We illustrate this high dimensional framework for the problem of correlation mining, where it is the matrix of pairwise and partial correlations among the variables fua t are of interest. Correlation mining arises in numerous applications and subsumes the regression context as a special case. we demonstrate various regimes of correlation mining based on the unifying perspective of high dimensional learning rates and sample complexity for different structured covariance models and different inference tasks.

Gentle vs. aversive handling of pregnant ewes: II. Physiology and behavior of the lambs.

PubMed

Coulon, M; Hild, S; Schroeer, A; Janczak, A M; Zanella, A J

2011-07-06

We compared the effects of aversive and gentle handling in late pregnant ewes on fearfulness, heart rate variability and spatial learning in lambs. Twenty-four Norwegian-Dala ewes were studied. Ewes were subjected to gentle (i.e. soft talking and calm behavior) or aversive handling (i.e. swift movements and shouting) for 10 min twice a day during the last five weeks of pregnancy. Lambs from aversively (AVS) or gently (GEN) treated ewes were tested at 4 weeks of age. Lamb behavior was recorded during a) a human approach test, composed of 4 min of isolation and 4 min of exposure to an unfamiliar human, b) an umbrella startle test followed by 5-min recording, and c) two repetitions of a maze test. In addition, heart rate variability was recorded telemetrically before and after the human and startle tests. The baseline heart rate variability measures suggested a lower influence of vagal stimulation in AVS lambs. In the human approach test, AVS lambs vocalized and explored the environment less, and were slower to approach the human. They also tended to have higher flight distances during the startle test than the GEN lambs. The prenatal treatment had no significant effect in the maze test. In conclusion, we showed that aversive handling of pregnant ewes increased fearfulness and reduced vagal tone in their progeny compared to GEN lambs. These effects can have consequences for how lambs cope with rearing conditions. Copyright © 2011 Elsevier Inc. All rights reserved.

Spatial analysis of participation in the Waterloo Residential Energy Efficiency Project

NASA Astrophysics Data System (ADS)

Song, Ge Bella

Researchers are in broad agreement that energy-conserving actions produce economic as well as energy savings. Household energy rating systems (HERS) have been established in many countries to inform households of their house's current energy performance and to help reduce their energy consumption and greenhouse gas emissions. In Canada, the national EnerGuide for Houses (EGH) program is delivered by many local delivery agents, including non-profit green community organizations. Waterloo Region Green Solutions is the local non-profit that offers the EGH residential energy evaluation service to local households. The purpose of this thesis is to explore the determinants of household's participation in the residential energy efficiency program (REEP) in Waterloo Region, to explain the relationship between the explanatory variables and REEP participation, and to propose ways to improve this kind of program. A spatial (trend) analysis was conducted within a geographic information system (GIS) to determine the spatial patterns of the REEP participation in Waterloo Region from 1999 to 2006. The impact of sources of information on participation and relationships between participation rates and explanatory variables were identified. GIS proved successful in presenting a visual interpretation of spatial patterns of the REEP participation. In general, the participating households tend to be clustered in urban areas and scattered in rural areas. Different sources of information played significant roles in reaching participants in different years. Moreover, there was a relationship between each explanatory variable and the REEP participation rates. Statistical analysis was applied to obtain a quantitative assessment of relationships between hypothesized explanatory variables and participation in the REEP. The Poisson regression model was used to determine the relationship between hypothesized explanatory variables and REEP participation at the CDA level. The results show that all of the independent variables have a statistically significant positive relationship with REEP participation. These variables include level of education, average household income, employment rate, home ownership, population aged 65 and over, age of home, and number of eligible dwellings. The logistic regression model was used to assess the ability of the hypothesized explanatory variables to predict whether or not households would participate in a second follow-up evaluation after completing upgrades to their home. The results show all the explanatory variables have significant relationships with the dependent variable. The increased rating score, average household income, aged population, and age of home are positively related to the dependent variable. While the dwelling size and education has negative relationships with the dependent variable. In general, the contribution of this work provides a practical understanding of how the energy efficiency program operates, and insight into the type of variables that may be successful in bringing about changes in performance in the energy efficiency project in Waterloo Region. Secondly, with the completion of this research, future residential energy efficiency programs can use the information from this research and emulate or expand upon the efforts and lessons learned from the Residential Energy Efficiency Project in Waterloo Region case study. Thirdly, this research also contributes to practical experience on how to integrate different datasets using GIS.

Effects of training strategies implemented in a complex videogame on functional connectivity of attentional networks.

PubMed

Voss, Michelle W; Prakash, Ruchika Shaurya; Erickson, Kirk I; Boot, Walter R; Basak, Chandramallika; Neider, Mark B; Simons, Daniel J; Fabiani, Monica; Gratton, Gabriele; Kramer, Arthur F

2012-01-02

We used the Space Fortress videogame, originally developed by cognitive psychologists to study skill acquisition, as a platform to examine learning-induced plasticity of interacting brain networks. Novice videogame players learned Space Fortress using one of two training strategies: (a) focus on all aspects of the game during learning (fixed priority), or (b) focus on improving separate game components in the context of the whole game (variable priority). Participants were scanned during game play using functional magnetic resonance imaging (fMRI), both before and after 20 h of training. As expected, variable priority training enhanced learning, particularly for individuals who initially performed poorly. Functional connectivity analysis revealed changes in brain network interaction reflective of more flexible skill learning and retrieval with variable priority training, compared to procedural learning and skill implementation with fixed priority training. These results provide the first evidence for differences in the interaction of large-scale brain networks when learning with different training strategies. Our approach and findings also provide a foundation for exploring the brain plasticity involved in transfer of trained abilities to novel real-world tasks such as driving, sport, or neurorehabilitation. Copyright © 2011 Elsevier Inc. All rights reserved.

Machine learning search for variable stars

NASA Astrophysics Data System (ADS)

Pashchenko, Ilya N.; Sokolovsky, Kirill V.; Gavras, Panagiotis

2018-04-01

Photometric variability detection is often considered as a hypothesis testing problem: an object is variable if the null hypothesis that its brightness is constant can be ruled out given the measurements and their uncertainties. The practical applicability of this approach is limited by uncorrected systematic errors. We propose a new variability detection technique sensitive to a wide range of variability types while being robust to outliers and underestimated measurement uncertainties. We consider variability detection as a classification problem that can be approached with machine learning. Logistic Regression (LR), Support Vector Machines (SVM), k Nearest Neighbours (kNN), Neural Nets (NN), Random Forests (RF), and Stochastic Gradient Boosting classifier (SGB) are applied to 18 features (variability indices) quantifying scatter and/or correlation between points in a light curve. We use a subset of Optical Gravitational Lensing Experiment phase two (OGLE-II) Large Magellanic Cloud (LMC) photometry (30 265 light curves) that was searched for variability using traditional methods (168 known variable objects) as the training set and then apply the NN to a new test set of 31 798 OGLE-II LMC light curves. Among 205 candidates selected in the test set, 178 are real variables, while 13 low-amplitude variables are new discoveries. The machine learning classifiers considered are found to be more efficient (select more variables and fewer false candidates) compared to traditional techniques using individual variability indices or their linear combination. The NN, SGB, SVM, and RF show a higher efficiency compared to LR and kNN.

GWASinlps: Nonlocal prior based iterative SNP selection tool for genome-wide association studies.

PubMed

Sanyal, Nilotpal; Lo, Min-Tzu; Kauppi, Karolina; Djurovic, Srdjan; Andreassen, Ole A; Johnson, Valen E; Chen, Chi-Hua

2018-06-19

Multiple marker analysis of the genome-wide association study (GWAS) data has gained ample attention in recent years. However, because of the ultra high-dimensionality of GWAS data, such analysis is challenging. Frequently used penalized regression methods often lead to large number of false positives, whereas Bayesian methods are computationally very expensive. Motivated to ameliorate these issues simultaneously, we consider the novel approach of using nonlocal priors in an iterative variable selection framework. We develop a variable selection method, named, iterative nonlocal prior based selection for GWAS, or GWASinlps, that combines, in an iterative variable selection framework, the computational efficiency of the screen-and-select approach based on some association learning and the parsimonious uncertainty quantification provided by the use of nonlocal priors. The hallmark of our method is the introduction of 'structured screen-and-select' strategy, that considers hierarchical screening, which is not only based on response-predictor associations, but also based on response-response associations, and concatenates variable selection within that hierarchy. Extensive simulation studies with SNPs having realistic linkage disequilibrium structures demonstrate the advantages of our computationally efficient method compared to several frequentist and Bayesian variable selection methods, in terms of true positive rate, false discovery rate, mean squared error, and effect size estimation error. Further, we provide empirical power analysis useful for study design. Finally, a real GWAS data application was considered with human height as phenotype. An R-package for implementing the GWASinlps method is available at https://cran.r-project.org/web/packages/GWASinlps/index.html. Supplementary data are available at Bioinformatics online.

Learning Activity Package, Algebra 93-94, LAPs 12-22.

ERIC Educational Resources Information Center

Evans, Diane

A set of 11 teacher-prepared Learning Activity Packages (LAPs) in beginning algebra, these units cover sets, properties of operations, operations over real numbers, open expressions, solution sets of equations and inequalities, equations and inequalities with two variables, solution sets of equations with two variables, exponents, factoring and…

The Influence of Two Cognitive-Linguistic Variables on Incidental Word Learning in 5-Year-Olds

ERIC Educational Resources Information Center

Abel, Alyson D.; Schuele, C. Melanie

2014-01-01

The relation between incidental word learning and two cognitive-linguistic variables--phonological memory and phonological awareness--is not fully understood. Thirty-five typically developing, 5-year-old, preschool children participated in a study examining the association between phonological memory, phonological awareness, and incidental word…

Investigating Antecedents of Task Commitment and Task Attraction in Service Learning Team Projects

ERIC Educational Resources Information Center

Schaffer, Bryan S.; Manegold, Jennifer G.

2018-01-01

The authors investigated the antecedents of team task cohesiveness in service learning classroom environments. Focusing on task commitment and task attraction as key dependent variables representing cohesiveness, and task interdependence as the primary independent variable, the authors position three important task action phase processes as…

The Decline and Fall of the Laws of Learning

ERIC Educational Resources Information Center

McKeachie, W. J.

1974-01-01

Problems in trying to apply the laws of learning to educational situations derive both from the failure to take account of differences between humans and other animals, and from failure to take into account of important variables interacting with independent variables in natural educational settings. (Author/JM)

Effect of Response Practice Variables on Learning Spelling and Sight Vocabulary.

ERIC Educational Resources Information Center

Cuvo, Anthony J.; And Others

1995-01-01

Response practice variables for learning spelling and sight vocabulary were studied in 4 experiments involving a total of 18 rehabilitation clients and adolescents with developmental disabilities or behavior disorders. The experiments specifically examined the "cover write" method, written versus oral practice, less versus more response practice,…

Evaluating the Learning Curve for Percutaneous Nephrolithotomy under Total Ultrasound Guidance.

PubMed

Song, Yan; Ma, YaNan; Song, YongSheng; Fei, Xiang

2015-01-01

To investigate the learning curve of percutaneous nephrolithotomy under total ultrasound guidance. One hundred and twenty consecutive PCNL operations under total ultrasound guidance performed by a novice surgeon in a tertiary referral center were studied. Operations were analyzed in cohorts of 15 to determine when a plateau was reached for the variables such as operation duration, ultrasound screening time, tract dilation time, stone-free rate and complication rate. Comparison was made with the results of a surgeon who had performed more than 1000 PCNLs. Fluoroscopy was not used at all during procedure. The mean operation time dropped from 82.5 min for the first 15 patients to a mean of 64.7 min for cases 46 through 60(P = 0.047). The ultrasound screening time was a peak of 6.4 min in the first 15 cases, whereas it dropped to a mean of 3.9 min for cases 46 through 60(P = 0.01). The tract dilation time dropped from 4.9 min for the first 15 patients to a mean of 3.8 min for cases 46 through 60(P = 0.036). The senior surgeon had a mean operating time, screening time and tract dilation time equivalent to those of the novice surgeon after 60 cases. There was no significant difference in stone free rate and complication rate. The competence of ultrasound guided PCNL is reached after 60 cases with good stone free rate and without major complications.

Problem-based learning: Dental student's perception of their education environments at Qassim University.

PubMed

Alkhuwaiter, Shahad S; Aljuailan, Roqayah I; Banabilh, Saeed M

2016-01-01

The objectives of this study were to assess perceptions of the Saudi dental students of the problem-based learning (PBL) curriculum and to compare their perceptions among different sex and academic years. Data was collected through a questionnaire-based survey at Qassim College of dentistry. The questionnaire consisted of 19 questions regarding the perception of PBL curriculum and was distributed to 240 students. The chi-square test was used for statistical analysis of the data. Out of the 240 students recruited for this study, 146 returned a complete questionnaire (the response rate was 60.8%). The majority of the students perceived that PBL enhances the ability to speak in front of people (91.1%); improved the ability to find the information using the internet/library (81.5%); enhances the problem-solving skills (71.3%); increases the practice of cooperative and collaborative learning (69.2%); improves the decision-making skills (66.4%). Sixty-five percent ( n = 96) noted that some students dominate whereas others are passive during PBL discussion session. Statistically, significant differences were found in the following variables according to the academic year students assuming before responsibility for their own learning ( P < 0.037) and the role of facilitator in the process ( P < 0.034). Moreover, according to gender; there were statistically significant differences in the following variables, assuming responsibility for own learning ( P < 0.003); activating prior knowledge and learning to elaborate and organize their knowledge ( P < 0.009); enhancing the ability to find the information using the Internet/library ( P < 0.014); PBL is effective without having lecture of the same topic ( P < 0.025); helping in identifying the areas of weakness for improvement ( P < 0.031); student understanding the objectives of the PBL session better than the conventional way ( P < 0.040); and enhancing the ability to speak in front of people ( P < 0.040). Perceptions of Saudi dental students regarding their education environments at Qassim College of dentistry using PBL hybrid curriculum were more positive than negative. However, improvements are still required to provide students with stimulating favorable learning environment and to take the students recommendations into consideration.

Transformation of Cortex-wide Emergent Properties during Motor Learning.

PubMed

Makino, Hiroshi; Ren, Chi; Liu, Haixin; Kim, An Na; Kondapaneni, Neehar; Liu, Xin; Kuzum, Duygu; Komiyama, Takaki

2017-05-17

Learning involves a transformation of brain-wide operation dynamics. However, our understanding of learning-related changes in macroscopic dynamics is limited. Here, we monitored cortex-wide activity of the mouse brain using wide-field calcium imaging while the mouse learned a motor task over weeks. Over learning, the sequential activity across cortical modules became temporally more compressed, and its trial-by-trial variability decreased. Moreover, a new flow of activity emerged during learning, originating from premotor cortex (M2), and M2 became predictive of the activity of many other modules. Inactivation experiments showed that M2 is critical for the post-learning dynamics in the cortex-wide activity. Furthermore, two-photon calcium imaging revealed that M2 ensemble activity also showed earlier activity onset and reduced variability with learning, which was accompanied by changes in the activity-movement relationship. These results reveal newly emergent properties of macroscopic cortical dynamics during motor learning and highlight the importance of M2 in controlling learned movements. Copyright © 2017 Elsevier Inc. All rights reserved.

Logic Learning Machine and standard supervised methods for Hodgkin's lymphoma prognosis using gene expression data and clinical variables.

PubMed

Parodi, Stefano; Manneschi, Chiara; Verda, Damiano; Ferrari, Enrico; Muselli, Marco

2018-03-01

This study evaluates the performance of a set of machine learning techniques in predicting the prognosis of Hodgkin's lymphoma using clinical factors and gene expression data. Analysed samples from 130 Hodgkin's lymphoma patients included a small set of clinical variables and more than 54,000 gene features. Machine learning classifiers included three black-box algorithms ( k-nearest neighbour, Artificial Neural Network, and Support Vector Machine) and two methods based on intelligible rules (Decision Tree and the innovative Logic Learning Machine method). Support Vector Machine clearly outperformed any of the other methods. Among the two rule-based algorithms, Logic Learning Machine performed better and identified a set of simple intelligible rules based on a combination of clinical variables and gene expressions. Decision Tree identified a non-coding gene ( XIST) involved in the early phases of X chromosome inactivation that was overexpressed in females and in non-relapsed patients. XIST expression might be responsible for the better prognosis of female Hodgkin's lymphoma patients.

Catecholaminergic Regulation of Learning Rate in a Dynamic Environment

PubMed Central

Jepma, Marieke; Nassar, Matthew R.; Rangel-Gomez, Mauricio; Meeter, Martijn; Nieuwenhuis, Sander

2016-01-01

Adaptive behavior in a changing world requires flexibly adapting one’s rate of learning to the rate of environmental change. Recent studies have examined the computational mechanisms by which various environmental factors determine the impact of new outcomes on existing beliefs (i.e., the ‘learning rate’). However, the brain mechanisms, and in particular the neuromodulators, involved in this process are still largely unknown. The brain-wide neurophysiological effects of the catecholamines norepinephrine and dopamine on stimulus-evoked cortical responses suggest that the catecholamine systems are well positioned to regulate learning about environmental change, but more direct evidence for a role of this system is scant. Here, we report evidence from a study employing pharmacology, scalp electrophysiology and computational modeling (N = 32) that suggests an important role for catecholamines in learning rate regulation. We found that the P3 component of the EEG—an electrophysiological index of outcome-evoked phasic catecholamine release in the cortex—predicted learning rate, and formally mediated the effect of prediction-error magnitude on learning rate. P3 amplitude also mediated the effects of two computational variables—capturing the unexpectedness of an outcome and the uncertainty of a preexisting belief—on learning rate. Furthermore, a pharmacological manipulation of catecholamine activity affected learning rate following unanticipated task changes, in a way that depended on participants’ baseline learning rate. Our findings provide converging evidence for a causal role of the human catecholamine systems in learning-rate regulation as a function of environmental change. PMID:27792728

Quantum Machine Learning over Infinite Dimensions

DOE PAGES

Lau, Hoi-Kwan; Pooser, Raphael; Siopsis, George; ...

2017-02-21

Machine learning is a fascinating and exciting eld within computer science. Recently, this ex- citement has been transferred to the quantum information realm. Currently, all proposals for the quantum version of machine learning utilize the nite-dimensional substrate of discrete variables. Here we generalize quantum machine learning to the more complex, but still remarkably practi- cal, in nite-dimensional systems. We present the critical subroutines of quantum machine learning algorithms for an all-photonic continuous-variable quantum computer that achieve an exponential speedup compared to their equivalent classical counterparts. Finally, we also map out an experi- mental implementation which can be used as amore » blueprint for future photonic demonstrations.« less

Quantum Machine Learning over Infinite Dimensions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lau, Hoi-Kwan; Pooser, Raphael; Siopsis, George

Machine learning is a fascinating and exciting eld within computer science. Recently, this ex- citement has been transferred to the quantum information realm. Currently, all proposals for the quantum version of machine learning utilize the nite-dimensional substrate of discrete variables. Here we generalize quantum machine learning to the more complex, but still remarkably practi- cal, in nite-dimensional systems. We present the critical subroutines of quantum machine learning algorithms for an all-photonic continuous-variable quantum computer that achieve an exponential speedup compared to their equivalent classical counterparts. Finally, we also map out an experi- mental implementation which can be used as amore » blueprint for future photonic demonstrations.« less

The impact of managers' perceptions of learning organizations on innovation in healthcare: sample of Turkey.

PubMed

Ugurluoglu, Ozgur; Ugurluoglu Aldogan, Ece; Dilmac, Elife

2013-01-01

Organizational learning is the process of increasing effective organizational activities through knowledge and understanding. Innovation is the creation of any product, service or process, which is new to a business unit. Significant amount of research on organizational learning place a central meaning on the fact that there is a positive relationship between organizational learning and innovation. Both organizational learning and innovation are essential for organizations to prepare for change. The aim of this study is to determine to what extent the identified learning organization dimensions are associated with innovation. The study used a quantitative non-experimental design employing statistical analysis via multiple regression and correlation methods to identify the relationships between the variables examined. Because the research was conducted in a non-experimental way, learning organization dimensions are referred to as predictor variables, and innovation is referred to as the criterion variable. Watkins and Marsick's Dimensions of the Learning Organization Questionnaire was used in the study. Questionnaires were distributed to 498 hospital managers and, 243 valid responses were used in this study. Therefore, 243 hospital managers working at 250 Ministry of Health (public) hospitals across Turkey participated in the study. Results demonstrate that there are significant and positive correlations between learning organization dimensions and innovation. Intercorrelations between learning organization dimensions and correlations between learning organization dimensions and innovation were average and high, respectively. Results further indicate that the dimensions of the learning organizations explained 66.5% of the variance for the innovation. Copyright © 2012 John Wiley & Sons, Ltd.

Sex differences and the role of acute stress in the open-field tower maze.

PubMed

Lipatova, Olga; Campolattaro, Matthew M; Dixon, Dawndra C; Durak, Ayse

2018-05-15

Many studies provide evidence that differences in spatial learning exist between males and females. However, it is necessary to consider non-mnemonic factors that may influence these findings. The present experiment investigated acquisition, retention, and the effects of stress on response- and place-learning in male and female rats. Rats were trained in an open-field tower maze. Procedures were used to minimize stress in the rats, and their ability to solve place- or response-learning in the maze was determined by analyzing a response variable (i.e., first choice correct response) that was not influenced by general locomotor activity. The results revealed that male and female rats acquire place- and response-learning at the same rate even though females moved significantly faster in the maze. However, females showed better retrieval of place-, but not response-learning compared to male rats. This effect appeared to be enhanced when the rats were tested immediately following an acute restraint stress. Furthermore, both female and male rats that were exposed to acute restraint stress showed less impairment than controls when subsequently tested in a novel situation. These findings have clinical implications that a mild physiological stress response can make one more cognitively resistant to adversities later in life. Copyright © 2018 Elsevier Inc. All rights reserved.

Student Perceptions of Online Homework Use for Formative Assessment of Learning in Organic Chemistry.

PubMed

Richards-Babb, Michelle; Curtis, Reagan; Georgieva, Zomitsa; Penn, John H

2015-11-10

Use of online homework as a formative assessment tool for organic chemistry coursework was examined. Student perceptions of online homework in terms of (i) its ranking relative to other course aspects, (ii) their learning of organic chemistry, and (iii) whether it improved their study habits and how students used it as a learning tool were investigated. Our students perceived the online homework as one of the more useful course aspects for learning organic chemistry content. We found a moderate and statistically significant correlation between online homework performance and final grade. Gender as a variable was ruled out since significant gender differences in overall attitude toward online homework use and course success rates were not found. Our students expressed relatively positive attitudes toward use of online homework with a majority indicating improved study habits (e.g., study in a more consistent manner). Our students used a variety of resources to remediate incorrect responses (e.g., class materials, general online materials, and help from others). However, 39% of our students admitted to guessing at times, instead of working to remediate incorrect responses. In large enrollment organic chemistry courses, online homework may act to bridge the student-instructor gap by providing students with a supportive mechanism for regulated learning of content.

[Influence of learning styles of nursing students on teaching strategies choice].

PubMed

Vacas Pérez, Juan Crisostomo; Mérida Serrano, Rosario; Molina Recio, Guillermo; Mesa Blanco, María del Pilar

2012-12-01

The objective of this research focuses on the framework of teaching strategies, by acknowledging learning styles as first determination and, in relation to the changes that these are going through, identifying the teaching strategies best rated and preferred by the students. This is a prospective open cohort study with the students of Nursing Diploma 2007/2010 of the Universidad de Córdoba. Once the population was identified in the first year (first analysis), annual measurings were undertaken every year during their training. In order to study the learning styles, the questionnaire CHAEA was administered and a scale from 1 to 10 (1 = highest, 10 = lowest) was used to determine the preferences for learning strategies. The results show the variability of the learner (up to 11 styles). However, the dominant style is the reflective, followed by the theoretical and the pragmatic. The least developed was the active style. As the years of training go by, a tendency towards a dual style (reflective-theoretical) can be observed. In relation to teaching strategies, the preferred ones were those set in professional areas, workshops and debates. Relevant changes were also seen as they advanced in their training. The results establish a specific significant relationship between learning styles and teaching strategies.

Learning to visually perceive the relative mass of colliding balls in globally and locally constrained task ecologies.

PubMed

Jacobs, D M; Runeson, S; Michaels, C F

2001-10-01

Novice observers differ from each other in the kinematic variables they use for the perception of kinetic properties, but they converge on more useful variables after practice with feedback. The colliding-balls paradigm was used to investigate how the convergence depends on the relations between the candidate variables and the to-be-perceived property, relative mass. Experiment 1 showed that observers do not change in the variables they use if the variables with which they start allow accurate performance. Experiment 2 showed that, at least for some observers, convergence can be facilitated by reducing the correlations between commonly used nonspecifying variables and relative mass but not by keeping those variables constant. Experiments 3a and 3b further demonstrated that observers learn not to rely on a particular nonspecifying variable if the correlation between that variable and relative mass is reduced.

Monitoring for the management of disease risk in animal translocation programmes

USGS Publications Warehouse

Nichols, James D.; Hollmen, Tuula E.; Grand, James B.

2017-01-01

Monitoring is best viewed as a component of some larger programme focused on science or conservation. The value of monitoring is determined by the extent to which it informs the parent process. Animal translocation programmes are typically designed to augment or establish viable animal populations without changing the local community in any detrimental way. Such programmes seek to minimize disease risk to local wild animals, to translocated animals, and in some cases to humans. Disease monitoring can inform translocation decisions by (1) providing information for state-dependent decisions, (2) assessing progress towards programme objectives, and (3) permitting learning in order to make better decisions in the future. Here we discuss specific decisions that can be informed by both pre-release and post-release disease monitoring programmes. We specify state variables and vital rates needed to inform these decisions. We then discuss monitoring data and analytic methods that can be used to estimate these state variables and vital rates. Our discussion is necessarily general, but hopefully provides a basis for tailoring disease monitoring approaches to specific translocation programmes.

Why so GLUMM? Detecting depression clusters through graphing lifestyle-environs using machine-learning methods (GLUMM).

PubMed

Dipnall, J F; Pasco, J A; Berk, M; Williams, L J; Dodd, S; Jacka, F N; Meyer, D

2017-01-01

Key lifestyle-environ risk factors are operative for depression, but it is unclear how risk factors cluster. Machine-learning (ML) algorithms exist that learn, extract, identify and map underlying patterns to identify groupings of depressed individuals without constraints. The aim of this research was to use a large epidemiological study to identify and characterise depression clusters through "Graphing lifestyle-environs using machine-learning methods" (GLUMM). Two ML algorithms were implemented: unsupervised Self-organised mapping (SOM) to create GLUMM clusters and a supervised boosted regression algorithm to describe clusters. Ninety-six "lifestyle-environ" variables were used from the National health and nutrition examination study (2009-2010). Multivariate logistic regression validated clusters and controlled for possible sociodemographic confounders. The SOM identified two GLUMM cluster solutions. These solutions contained one dominant depressed cluster (GLUMM5-1, GLUMM7-1). Equal proportions of members in each cluster rated as highly depressed (17%). Alcohol consumption and demographics validated clusters. Boosted regression identified GLUMM5-1 as more informative than GLUMM7-1. Members were more likely to: have problems sleeping; unhealthy eating; ≤2 years in their home; an old home; perceive themselves underweight; exposed to work fumes; experienced sex at ≤14 years; not perform moderate recreational activities. A positive relationship between GLUMM5-1 (OR: 7.50, P<0.001) and GLUMM7-1 (OR: 7.88, P<0.001) with depression was found, with significant interactions with those married/living with partner (P=0.001). Using ML based GLUMM to form ordered depressive clusters from multitudinous lifestyle-environ variables enabled a deeper exploration of the heterogeneous data to uncover better understandings into relationships between the complex mental health factors. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

Extraction of temporally correlated features from dynamic vision sensors with spike-timing-dependent plasticity.

PubMed

Bichler, Olivier; Querlioz, Damien; Thorpe, Simon J; Bourgoin, Jean-Philippe; Gamrat, Christian

2012-08-01

A biologically inspired approach to learning temporally correlated patterns from a spiking silicon retina is presented. Spikes are generated from the retina in response to relative changes in illumination at the pixel level and transmitted to a feed-forward spiking neural network. Neurons become sensitive to patterns of pixels with correlated activation times, in a fully unsupervised scheme. This is achieved using a special form of Spike-Timing-Dependent Plasticity which depresses synapses that did not recently contribute to the post-synaptic spike activation, regardless of their activation time. Competitive learning is implemented with lateral inhibition. When tested with real-life data, the system is able to extract complex and overlapping temporally correlated features such as car trajectories on a freeway, after only 10 min of traffic learning. Complete trajectories can be learned with a 98% detection rate using a second layer, still with unsupervised learning, and the system may be used as a car counter. The proposed neural network is extremely robust to noise and it can tolerate a high degree of synaptic and neuronal variability with little impact on performance. Such results show that a simple biologically inspired unsupervised learning scheme is capable of generating selectivity to complex meaningful events on the basis of relatively little sensory experience. Copyright © 2012 Elsevier Ltd. All rights reserved.

Factors associated to clinical learning in nursing students in primary health care: an analytical cross-sectional study

PubMed Central

Serrano-Gallardo, Pilar; Martínez-Marcos, Mercedes; Espejo-Matorrales, Flora; Arakawa, Tiemi; Magnabosco, Gabriela Tavares; Pinto, Ione Carvalho

2016-01-01

ABSTRACT Objective: to identify the students' perception about the quality of clinical placements and asses the influence of the different tutoring processes in clinical learning. Methods: analytical cross-sectional study on second and third year nursing students (n=122) about clinical learning in primary health care. The Clinical Placement Evaluation Tool and a synthetic index of attitudes and skills were computed to give scores to the clinical learning (scale 0-10). Univariate, bivariate and multivariate (multiple linear regression) analyses were performed. Results: the response rate was 91.8%. The most commonly identified tutoring process was "preceptor-professor" (45.2%). The clinical placement was assessed as "optimal" by 55.1%, relationship with team-preceptor was considered good by 80.4% of the cases and the average grade for clinical learning was 7.89. The multiple linear regression model with more explanatory capacity included the variables "Academic year" (beta coefficient = 1.042 for third-year students), "Primary Health Care Area (PHC)" (beta coefficient = 0.308 for Area B) and "Clinical placement perception" (beta coefficient = - 0.204 for a suboptimal perception). Conclusions: timeframe within the academic program, location and clinical placement perception were associated with students' clinical learning. Students' perceptions of setting quality were positive and a good team-preceptor relationship is a matter of relevance. PMID:27627124

The development of thematic materials using project based learning for elementary school

NASA Astrophysics Data System (ADS)

Yuliana, M.; Wiryawan, S. A.; Riyadi

2018-05-01

Teaching materials is one of the important factors in supporting on learning process. This paper discussed about developing thematic materials using project based learning. Thematic materials are designed to make students to be active, creative, cooperative, easy in thinking to solve the problem. The purpose of the research was to develop thematic material using project based learning which used valid variables. The method of research which used in this research was four stages of research and development proposed by Thiagarajan consisting of 4 stages, namely: (1) definition stage, (2) design stage, (3) development stage, and (4) stage of dissemination. The first stage was research and information collection, it was in form of need analysis with questionnaire, observation, interview, and document analysis. Design stage was based on the competencies and indicator. The third was development stage, this stage was used to product validation from expert. The validity of research development involved media validator, material validator, and linguistic validator. The result from the validation of thematic material by expert showed that the overall result had a very good rating which ranged from 1 to 5 likert scale, media validation showed a mean score 4,83, the material validation showed mean score 4,68, and the mean of linguistic validation was e 4,74. It showed that the thematic material using project based learning was valid and feasible to be implemented in the context thematic learning.

Handwriting generates variable visual output to facilitate symbol learning.

PubMed

Li, Julia X; James, Karin H

2016-03-01

Recent research has demonstrated that handwriting practice facilitates letter categorization in young children. The present experiments investigated why handwriting practice facilitates visual categorization by comparing 2 hypotheses: that handwriting exerts its facilitative effect because of the visual-motor production of forms, resulting in a direct link between motor and perceptual systems, or because handwriting produces variable visual instances of a named category in the environment that then changes neural systems. We addressed these issues by measuring performance of 5-year-old children on a categorization task involving novel, Greek symbols across 6 different types of learning conditions: 3 involving visual-motor practice (copying typed symbols independently, tracing typed symbols, tracing handwritten symbols) and 3 involving visual-auditory practice (seeing and saying typed symbols of a single typed font, of variable typed fonts, and of handwritten examples). We could therefore compare visual-motor production with visual perception both of variable and similar forms. Comparisons across the 6 conditions (N = 72) demonstrated that all conditions that involved studying highly variable instances of a symbol facilitated symbol categorization relative to conditions where similar instances of a symbol were learned, regardless of visual-motor production. Therefore, learning perceptually variable instances of a category enhanced performance, suggesting that handwriting facilitates symbol understanding by virtue of its environmental output: supporting the notion of developmental change though brain-body-environment interactions. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Handwriting generates variable visual input to facilitate symbol learning

PubMed Central

Li, Julia X.; James, Karin H.

2015-01-01

Recent research has demonstrated that handwriting practice facilitates letter categorization in young children. The present experiments investigated why handwriting practice facilitates visual categorization by comparing two hypotheses: That handwriting exerts its facilitative effect because of the visual-motor production of forms, resulting in a direct link between motor and perceptual systems, or because handwriting produces variable visual instances of a named category in the environment that then changes neural systems. We addressed these issues by measuring performance of 5 year-old children on a categorization task involving novel, Greek symbols across 6 different types of learning conditions: three involving visual-motor practice (copying typed symbols independently, tracing typed symbols, tracing handwritten symbols) and three involving visual-auditory practice (seeing and saying typed symbols of a single typed font, of variable typed fonts, and of handwritten examples). We could therefore compare visual-motor production with visual perception both of variable and similar forms. Comparisons across the six conditions (N=72) demonstrated that all conditions that involved studying highly variable instances of a symbol facilitated symbol categorization relative to conditions where similar instances of a symbol were learned, regardless of visual-motor production. Therefore, learning perceptually variable instances of a category enhanced performance, suggesting that handwriting facilitates symbol understanding by virtue of its environmental output: supporting the notion of developmental change though brain-body-environment interactions. PMID:26726913

The role of beginner’s luck in learning to prefer risky patches by socially foraging house sparrows

PubMed Central

2013-01-01

Although there has been extensive research on the evolution of individual decision making under risk (when facing variable outcomes), little is known on how the evolution of such decision-making mechanisms has been shaped by social learning and exploitation. We presented socially foraging house sparrows with a choice between scattered feeding wells in which millet seeds were hidden under 2 types of colored sand: green sand offering ~80 seeds with a probability of 0.1 (high risk–high reward) and yellow sand offering 1 seed with certainty (low risk–low reward). Although the expected benefit of choosing variable wells was 8 times higher than that of choosing constant wells, only some sparrows developed a preference for variable wells, whereas others developed a significant preference for constant wells. We found that this dichotomy could be explained by stochastic individual differences in sampling success during foraging, rather than by social foraging strategies (active searching vs. joining others). Moreover, preference for variable or constant wells was related to the sparrows’ success during searching, rather than during joining others or when picking exposed seeds (i.e., they learn when actively searching in the sand). Finally, although for many sparrows learning resulted in an apparently maladaptive risk aversion, group living still allowed them to enjoy profitable variable wells by occasionally joining variable-preferring sparrows. PMID:24137046

Evaluation of Deep Learning Models for Predicting CO2 Flux

NASA Astrophysics Data System (ADS)

Halem, M.; Nguyen, P.; Frankel, D.

2017-12-01

Artificial neural networks have been employed to calculate surface flux measurements from station data because they are able to fit highly nonlinear relations between input and output variables without knowing the detail relationships between the variables. However, the accuracy in performing neural net estimates of CO2 flux from observations of CO2 and other atmospheric variables is influenced by the architecture of the neural model, the availability, and complexity of interactions between physical variables such as wind, temperature, and indirect variables like latent heat, and sensible heat, etc. We evaluate two deep learning models, feed forward and recurrent neural network models to learn how they each respond to the physical measurements, time dependency of the measurements of CO2 concentration, humidity, pressure, temperature, wind speed etc. for predicting the CO2 flux. In this paper, we focus on a) building neural network models for estimating CO2 flux based on DOE data from tower Atmospheric Radiation Measurement data; b) evaluating the impact of choosing the surface variables and model hyper-parameters on the accuracy and predictions of surface flux; c) assessing the applicability of the neural network models on estimate CO2 flux by using OCO-2 satellite data; d) studying the efficiency of using GPU-acceleration for neural network performance using IBM Power AI deep learning software and packages on IBM Minsky system.

An information processing/associative learning account of behavioral disinhibition in externalizing psychopathology.

PubMed

Endres, Michael J; Donkin, Chris; Finn, Peter R

2014-04-01

Externalizing psychopathology (EXT) is associated with low executive working memory (EWM) capacity and problems with inhibitory control and decision-making; however, the specific cognitive processes underlying these problems are not well known. This study used a linear ballistic accumulator computational model of go/no-go associative-incentive learning conducted with and without a working memory (WM) load to investigate these cognitive processes in 510 young adults varying in EXT (lifetime problems with substance use, conduct disorder, ADHD, adult antisocial behavior). High scores on an EXT factor were associated with low EWM capacity and higher scores on a latent variable reflecting the cognitive processes underlying disinhibited decision-making (more false alarms, faster evidence accumulation rates for false alarms [vFA], and lower scores on a Response Precision Index [RPI] measure of information processing efficiency). The WM load increased disinhibited decision-making, decisional uncertainty, and response caution for all subjects. Higher EWM capacity was associated with lower scores on the latent disinhibited decision-making variable (lower false alarms, lower vFAs and RPI scores) in both WM load conditions. EWM capacity partially mediated the association between EXT and disinhibited decision-making under no-WM load, and completely mediated this association under WM load. The results underline the role that EWM has in associative-incentive go/no-go learning and indicate that common to numerous types of EXT are impairments in the cognitive processes associated with the evidence accumulation-evaluation-decision process. PsycINFO Database Record (c) 2014 APA, all rights reserved.

The Effect of Neurobehavioral Test Performance on the All-Cause Mortality among US Population

PubMed Central

Wu, Li-Wei; Liaw, Fang-Yih; Wang, Gia-Chi; Wang, Chung-Ching

2016-01-01

Evidence of the association between global cognitive function and mortality is much, but whether specific cognitive function is related to mortality is unclear. To address the paucity of knowledge on younger populations in the US, we analyzed the association between specific cognitive function and mortality in young and middle-aged adults. We analyzed data from 5,144 men and women between 20 and 59 years of age in the Third National Health and Nutrition Examination Survey (1988–94) with mortality follow-up evaluation through 2006. Cognitive function tests, including assessments of executive function/processing speed (symbol digit substitution) and learning recall/short-term memory (serial digit learning), were performed. All-cause mortality was the outcome of interest. After adjusting for multiple variables, total mortality was significantly higher in males with poorer executive function/processing speed (hazard ratio (HR) 2.02; 95% confidence interval 1.36 to 2.99) and poorer recall/short-term memory (HR 1.47; 95% confidence interval 1.02 to 2.12). After adjusting for multiple variables, the mortality risk did not significantly increase among the females in these two cognitive tests groups. In this sample of the US population, poorer executive function/processing speed and poorer learning recall/short-term memory were significantly associated with increased mortality rates, especially in males. This study highlights the notion that poorer specific cognitive function predicts all-cause mortality in young and middle-aged males. PMID:27595105

An information processing/associative learning account of behavioral disinhibition in externalizing psychopathology

PubMed Central

Endres, Michael J.; Donkin, Chris; Finn, Peter R.

2014-01-01

Externalizing psychopathology (EXT) is associated with low executive working memory (EWM) capacity and problems with inhibitory control and decision-making; however, the specific cognitive processes underlying these problems are not well known. This study used a linear ballistic accumulator computational model of go/no-go associative-incentive learning conducted with and without a working memory (WM) load to investigate these cognitive processes in 510 young adults varying in EXT (lifetime problems with substance use, conduct disorder, ADHD, adult antisocial behavior). High scores on an EXT factor were associated with low EWM capacity and higher scores on a latent variable reflecting the cognitive processes underlying disinhibited decision making (more false alarms, faster evidence accumulation rates for false alarms (vFA), and lower scores on a Response Precision Index (RPI) measure of information processing efficiency). The WM load increased disinhibited decision making, decisional uncertainty, and response caution for all subjects. Higher EWM capacity was associated with lower scores on the latent disinhibited decision making variable (lower false alarms, lower vFAs and RPI scores) in both WM load conditions. EWM capacity partially mediated the association between EXT and disinhibited decision making under no-WM load, and completely mediated this association under WM load. The results underline the role that EWM has in associative – incentive go/no-go learning and indicate that common to numerous types of EXT are impairments in the cognitive processes associated with the evidence accumulation – evaluation – decision process. PMID:24611834

Predictors of science success: The impact of motivation and learning strategies on college chemistry performance

NASA Astrophysics Data System (ADS)

Obrentz, Shari B.

As the number of college students studying science continues to grow, it is important to identify variables that predict their success. The literature indicates that motivation and learning strategy use facilitate science success. Research findings show these variables can change throughout a semester and differ by performance level, gender and ethnicity. However, significant predictors of performance vary by research study and by group. The current study looks beyond the traditional predictors of grade point averages, SAT scores and completion of advanced placement (AP) chemistry to consider a comprehensive set of variables not previously investigated within the same study. Research questions address the predictive ability of motivation constructs and learning strategies for success in introductory college chemistry, how these variables change throughout a semester, and how they differ by performance level, gender and ethnicity. Participants were 413 introductory college chemistry students at a highly selective university in the southeast. Participants completed the Chemistry Motivation Questionnaire (CMQ) and Learning Strategies section of the Motivated Strategies for Learning Questionnaire (MSLQ) three times during the semester. Self-efficacy, effort regulation, assessment anxiety and previous achievement were significant predictors of chemistry course success. Levels of motivation changed with significant decreases in self-efficacy and increases in personal relevance and assessment anxiety. Learning strategy use changed with significant increases in elaboration, critical thinking, metacognitive self-regulation skills and peer learning, and significant decreases in time and study management and effort regulation. High course performers reported the highest levels of motivation and learning strategy use. Females reported lower intrinsic motivation, personal relevance, self-efficacy and critical thinking, and higher assessment anxiety, rehearsal and organization. Self-efficacy predicted performance for males and females, while self-determination, help-seeking and time and study environment also predicted female success. Few differences in these variables were found between ethnicity groups. Self-efficacy positively predicted performance for Asians and Whites, and metacognitive self-regulation skills negatively predicted success for Other students. The results have implications for college science instructors who are encouraged to collect and utilize data on students' motivation and learning strategy use, promote both in science classes, and design interventions for specific students who need more support.

Compensation of significant parametric uncertainties using sliding mode online learning

NASA Astrophysics Data System (ADS)

Schnetter, Philipp; Kruger, Thomas

An augmented nonlinear inverse dynamics (NID) flight control strategy using sliding mode online learning for a small unmanned aircraft system (UAS) is presented. Because parameter identification for this class of aircraft often is not valid throughout the complete flight envelope, aerodynamic parameters used for model based control strategies may show significant deviations. For the concept of feedback linearization this leads to inversion errors that in combination with the distinctive susceptibility of small UAS towards atmospheric turbulence pose a demanding control task for these systems. In this work an adaptive flight control strategy using feedforward neural networks for counteracting such nonlinear effects is augmented with the concept of sliding mode control (SMC). SMC-learning is derived from variable structure theory. It considers a neural network and its training as a control problem. It is shown that by the dynamic calculation of the learning rates, stability can be guaranteed and thus increase the robustness against external disturbances and system failures. With the resulting higher speed of convergence a wide range of simultaneously occurring disturbances can be compensated. The SMC-based flight controller is tested and compared to the standard gradient descent (GD) backpropagation algorithm under the influence of significant model uncertainties and system failures.

The influence of contextual diversity on word learning.

PubMed

Johns, Brendan T; Dye, Melody; Jones, Michael N

2016-08-01

In a series of analyses over mega datasets, Jones, Johns, and Recchia (Canadian Journal of Experimental Psychology, 66(2), 115-124, 2012) and Johns et al. (Journal of the Acoustical Society of America, 132:2, EL74-EL80, 2012) found that a measure of contextual diversity that takes into account the semantic variability of a word's contexts provided a better fit to both visual and spoken word recognition data than traditional measures, such as word frequency or raw context counts. This measure was empirically validated with an artificial language experiment (Jones et al.). The present study extends the empirical results with a unique natural language learning paradigm, which allows for an examination of the semantic representations that are acquired as semantic diversity is varied. Subjects were incidentally exposed to novel words as they rated short selections from articles, books, and newspapers. When novel words were encountered across distinct discourse contexts, subjects were both faster and more accurate at recognizing them than when they were seen in redundant contexts. However, learning across redundant contexts promoted the development of more stable semantic representations. These findings are predicted by a distributional learning model trained on the same materials as our subjects.

Application of Machine-Learning Models to Predict Tacrolimus Stable Dose in Renal Transplant Recipients

NASA Astrophysics Data System (ADS)

Tang, Jie; Liu, Rong; Zhang, Yue-Li; Liu, Mou-Ze; Hu, Yong-Fang; Shao, Ming-Jie; Zhu, Li-Jun; Xin, Hua-Wen; Feng, Gui-Wen; Shang, Wen-Jun; Meng, Xiang-Guang; Zhang, Li-Rong; Ming, Ying-Zi; Zhang, Wei

2017-02-01

Tacrolimus has a narrow therapeutic window and considerable variability in clinical use. Our goal was to compare the performance of multiple linear regression (MLR) and eight machine learning techniques in pharmacogenetic algorithm-based prediction of tacrolimus stable dose (TSD) in a large Chinese cohort. A total of 1,045 renal transplant patients were recruited, 80% of which were randomly selected as the “derivation cohort” to develop dose-prediction algorithm, while the remaining 20% constituted the “validation cohort” to test the final selected algorithm. MLR, artificial neural network (ANN), regression tree (RT), multivariate adaptive regression splines (MARS), boosted regression tree (BRT), support vector regression (SVR), random forest regression (RFR), lasso regression (LAR) and Bayesian additive regression trees (BART) were applied and their performances were compared in this work. Among all the machine learning models, RT performed best in both derivation [0.71 (0.67-0.76)] and validation cohorts [0.73 (0.63-0.82)]. In addition, the ideal rate of RT was 4% higher than that of MLR. To our knowledge, this is the first study to use machine learning models to predict TSD, which will further facilitate personalized medicine in tacrolimus administration in the future.

A Spreadsheet-Based Visualized Mindtool for Improving Students' Learning Performance in Identifying Relationships between Numerical Variables

ERIC Educational Resources Information Center

Lai, Chiu-Lin; Hwang, Gwo-Jen

2015-01-01

In this study, a spreadsheet-based visualized Mindtool was developed for improving students' learning performance when finding relationships between numerical variables by engaging them in reasoning and decision-making activities. To evaluate the effectiveness of the proposed approach, an experiment was conducted on the "phenomena of climate…

Learning-Method Choices and Personal Characteristics in Solving a Physical Education Problem

ERIC Educational Resources Information Center

Vincent-Morin, Madeleine; Lafont, Lucile

2005-01-01

The goal of this study was to identify the relationships between the learning choices made by pupils and their personal characteristics, including cognitive style (field dependence--independence), a motivational variable (feeling of self-efficacy), and a cognitive variable (task representation). The participants were 64 twelve-year-old sixth…

Beyond IQ: A Latent State-Trait Analysis of General Intelligence, Dynamic Decision Making, and Implicit Learning

ERIC Educational Resources Information Center

Danner, Daniel; Hagemann, Dirk; Schankin, Andrea; Hager, Marieke; Funke, Joachim

2011-01-01

The present study investigated cognitive performance measures beyond IQ. In particular, we investigated the psychometric properties of dynamic decision making variables and implicit learning variables and their relation with general intelligence and professional success. N = 173 employees from different companies and occupational groups completed…

Individual Differences in Statistical Learning Predict Children's Comprehension of Syntax

ERIC Educational Resources Information Center

Kidd, Evan; Arciuli, Joanne

2016-01-01

Variability in children's language acquisition is likely due to a number of cognitive and social variables. The current study investigated whether individual differences in statistical learning (SL), which has been implicated in language acquisition, independently predicted 6- to 8-year-old's comprehension of syntax. Sixty-eight (N = 68)…

Language Experience Interviews: What Can They Tell Us about Individual Differences?

ERIC Educational Resources Information Center

Polat, Brittany

2013-01-01

While language learners and teachers have long known that individual differences (IDs) among students result in differential learning, we still do not know how traditional ID variables interact or the specific impact each one has on language learning. The present study proposes that instead of looking at isolated variables, researchers should…

Verification of Causal Influences of Reasoning Skills and Epistemology on Physics Conceptual Learning

ERIC Educational Resources Information Center

Ding, Lin

2014-01-01

This study seeks to test the causal influences of reasoning skills and epistemologies on student conceptual learning in physics. A causal model, integrating multiple variables that were investigated separately in the prior literature, is proposed and tested through path analysis. These variables include student preinstructional reasoning skills…

How Do Different Background Variables Predict Learning Outcomes?

ERIC Educational Resources Information Center

Kallio, Manne; Metsärinne, Mika

2017-01-01

This article is a part of a research project aimed to find out how different background variables are related to learning outcomes in school subject Sloyd as found in the national evaluation of the Finnish National Board of Education. Results from this larger research project were previously published in this journal, where pupils' readiness for…

Relationships between Students' and Instructional Variables with Satisfaction and Learning from a Web-Based Course.

ERIC Educational Resources Information Center

Hong, Kian-Sam

2002-01-01

Discusses the results of a study conducted at the Universiti Malaysia Sarawak that investigated the effects of student characteristics and instructional variables on satisfaction and achievement in a Web-based course. Considers gender, age, scholastic aptitude, learning styles, initial computer skills, time spent on the course, perceptions of…

Students' Self-Regulation for Interaction with Others in Online Learning Environments

ERIC Educational Resources Information Center

Cho, Moon-Heum; Kim, B. Joon

2013-01-01

The purpose of this study was to explore variables explaining students' self-regulation (SR) for interaction with others, specifically peers and instructors, in online learning environments. A total of 407 students participated in the study. With hierarchical regression model (HRM), several variables were regressed on students' SR for interaction…

Self-Regulated Learning and Ethnic/Racial Variables: Predicting Minority First-Generation College Students' Persistence

ERIC Educational Resources Information Center

Moore, John S., III.

2013-01-01

The purpose of this study was to investigate how self-regulated learning and ethnic/racial variables predict minority first-generation college student persistence and related constructs. Participants were drawn nationally from the U.S. Department of Education funded TRiO Student Support Services Programs. Additional participants from the Talent…

Reinforcement learning state estimator.

PubMed

Morimoto, Jun; Doya, Kenji

2007-03-01

In this study, we propose a novel use of reinforcement learning for estimating hidden variables and parameters of nonlinear dynamical systems. A critical issue in hidden-state estimation is that we cannot directly observe estimation errors. However, by defining errors of observable variables as a delayed penalty, we can apply a reinforcement learning frame-work to state estimation problems. Specifically, we derive a method to construct a nonlinear state estimator by finding an appropriate feedback input gain using the policy gradient method. We tested the proposed method on single pendulum dynamics and show that the joint angle variable could be successfully estimated by observing only the angular velocity, and vice versa. In addition, we show that we could acquire a state estimator for the pendulum swing-up task in which a swing-up controller is also acquired by reinforcement learning simultaneously. Furthermore, we demonstrate that it is possible to estimate the dynamics of the pendulum itself while the hidden variables are estimated in the pendulum swing-up task. Application of the proposed method to a two-linked biped model is also presented.

Learning Path Recommendation Based on Modified Variable Length Genetic Algorithm

ERIC Educational Resources Information Center

Dwivedi, Pragya; Kant, Vibhor; Bharadwaj, Kamal K.

2018-01-01

With the rapid advancement of information and communication technologies, e-learning has gained a considerable attention in recent years. Many researchers have attempted to develop various e-learning systems with personalized learning mechanisms for assisting learners so that they can learn more efficiently. In this context, curriculum sequencing…

Evolution of learning strategies in temporally and spatially variable environments: A review of theory

PubMed Central

Aoki, Kenichi; Feldman, Marcus W.

2013-01-01

The theoretical literature from 1985 to the present on the evolution of learning strategies in variable environments is reviewed, with the focus on deterministic dynamical models that are amenable to local stability analysis, and on deterministic models yielding evolutionarily stable strategies. Individual learning, unbiased and biased social learning, mixed learning, and learning schedules are considered. A rapidly changing environment or frequent migration in a spatially heterogeneous environment favors individual learning over unbiased social learning. However, results are not so straightforward in the context of learning schedules or when biases in social learning are introduced. The three major methods of modeling temporal environmental change – coevolutionary, two-timescale, and information decay – are compared and shown to sometimes yield contradictory results. The so-called Rogers’ paradox is inherent in the two-timescale method as originally applied to the evolution of pure strategies, but is often eliminated when the other methods are used. Moreover, Rogers’ paradox is not observed for the mixed learning strategies and learning schedules that we review. We believe that further theoretical work is necessary on learning schedules and biased social learning, based on models that are logically consistent and empirically pertinent. PMID:24211681

Evolution of learning strategies in temporally and spatially variable environments: a review of theory.

PubMed

Aoki, Kenichi; Feldman, Marcus W

2014-02-01

The theoretical literature from 1985 to the present on the evolution of learning strategies in variable environments is reviewed, with the focus on deterministic dynamical models that are amenable to local stability analysis, and on deterministic models yielding evolutionarily stable strategies. Individual learning, unbiased and biased social learning, mixed learning, and learning schedules are considered. A rapidly changing environment or frequent migration in a spatially heterogeneous environment favors individual learning over unbiased social learning. However, results are not so straightforward in the context of learning schedules or when biases in social learning are introduced. The three major methods of modeling temporal environmental change--coevolutionary, two-timescale, and information decay--are compared and shown to sometimes yield contradictory results. The so-called Rogers' paradox is inherent in the two-timescale method as originally applied to the evolution of pure strategies, but is often eliminated when the other methods are used. Moreover, Rogers' paradox is not observed for the mixed learning strategies and learning schedules that we review. We believe that further theoretical work is necessary on learning schedules and biased social learning, based on models that are logically consistent and empirically pertinent. Copyright © 2013 Elsevier Inc. All rights reserved.

Servant teaching: the power and promise for nursing education.

PubMed

Robinson, F Patrick

2009-01-01

The best theoretical or practical approaches to achieving learning outcomes in nursing likely depend on multiple variables, including instructor-related variables. This paper explores one such variable and its potential impact on learning. Application of the principles inherent in servant leadership to teaching/learning in nursing education is suggested as a way to produce professional nurses who are willing and able to transform the health care environment to achieve higher levels of quality and safety. Thus, the concept of servant teaching is introduced with discussion of the following principles and their application to teaching in nursing: judicious use of power, listening and empathy, willingness to change, reflection and contemplation, collaboration and consensus, service learning, healing, conceptualization, stewardship, building community, and commitment to the growth of people. Faculty colleagues are invited to explore the use of servant teaching and its potential for nursing education.

Learning an intrinsic-variable preserving manifold for dynamic visual tracking.

PubMed

Qiao, Hong; Zhang, Peng; Zhang, Bo; Zheng, Suiwu

2010-06-01

Manifold learning is a hot topic in the field of computer science, particularly since nonlinear dimensionality reduction based on manifold learning was proposed in Science in 2000. The work has achieved great success. The main purpose of current manifold-learning approaches is to search for independent intrinsic variables underlying high dimensional inputs which lie on a low dimensional manifold. In this paper, a new manifold is built up in the training step of the process, on which the input training samples are set to be close to each other if the values of their intrinsic variables are close to each other. Then, the process of dimensionality reduction is transformed into a procedure of preserving the continuity of the intrinsic variables. By utilizing the new manifold, the dynamic tracking of a human who can move and rotate freely is achieved. From the theoretical point of view, it is the first approach to transfer the manifold-learning framework to dynamic tracking. From the application point of view, a new and low dimensional feature for visual tracking is obtained and successfully applied to the real-time tracking of a free-moving object from a dynamic vision system. Experimental results from a dynamic tracking system which is mounted on a dynamic robot validate the effectiveness of the new algorithm.

The relationship of document and quantitative literacy with learning styles and selected personal variables for aerospace technology students at Indiana State University

NASA Astrophysics Data System (ADS)

Martin, Royce Ann

The purpose of this study was to determine the extent that student scores on a researcher-constructed quantitative and document literacy test, the Aviation Documents Delineator (ADD), were associated with (a) learning styles (imaginative, analytic, common sense, dynamic, and undetermined), as identified by the Learning Type Measure, (b) program curriculum (aerospace administration, professional pilot, both aerospace administration and professional pilot, other, or undeclared), (c) overall cumulative grade point average at Indiana State University, and (d) year in school (freshman, sophomore, junior, or senior). The Aviation Documents Delineator (ADD) was a three-part, 35 question survey that required students to interpret graphs, tables, and maps. Tasks assessed in the ADD included (a) locating, interpreting, and describing specific data displayed in the document, (b) determining data for a specified point on the table through interpolation, (c) comparing data for a string of variables representing one aspect of aircraft performance to another string of variables representing a different aspect of aircraft performance, (d) interpreting the documents to make decisions regarding emergency situations, and (e) performing single and/or sequential mathematical operations on a specified set of data. The Learning Type Measure (LTM) was a 15 item self-report survey developed by Bernice McCarthy (1995) to profile an individual's processing and perception tendencies in order to reveal different individual approaches to learning. The sample used in this study included 143 students enrolled in Aerospace Technology Department courses at Indiana State University in the fall of 1996. The ADD and the LTM were administered to each subject. Data collected in this investigation were analyzed using a stepwise multiple regression analysis technique. Results of the study revealed that the variables, year in school and GPA, were significant predictors of the criterion variables, document, quantitative, and total literacy, when utilizing the ADD. The variables learning style and program of study were found not to be significant predictors of literacy scores on the ADD instrument.

Authentic early experience in Medical Education: a socio-cultural analysis identifying important variables in learning interactions within workplaces.

PubMed

Yardley, Sarah; Brosnan, Caragh; Richardson, Jane; Hays, Richard

2013-12-01

This paper addresses the question 'what are the variables influencing social interactions and learning during Authentic Early Experience (AEE)?' AEE is a complex educational intervention for new medical students. Following critique of the existing literature, multiple qualitative methods were used to create a study framework conceptually orientated to a socio-cultural perspective. Study participants were recruited from three groups at one UK medical school: students, workplace supervisors, and medical school faculty. A series of intersecting spectra identified in the data describe dyadic variables that make explicit the parameters within which social interactions are conducted in this setting. Four of the spectra describe social processes related to being in workplaces and developing the ability to manage interactions during authentic early experiences. These are: (1) legitimacy expressed through invited participation or exclusion; (2) finding a role-a spectrum from student identity to doctor mindset; (3) personal perspectives and discomfort in transition from lay to medical; and, (4) taking responsibility for 'risk'-moving from aversion to management through graded progression of responsibility. Four further spectra describe educational consequences of social interactions. These spectra identify how the reality of learning is shaped through social interactions and are (1) generic-specific objectives, (2) parallel-integrated-learning, (3) context specific-transferable learning and (4) performing or simulating-reality. Attention to these variables is important if educators are to maximise constructive learning from AEE. Application of each of the spectra could assist workplace supervisors to maximise the positive learning potential of specific workplaces.

Applied Comparative Effectiveness Researchers Must Measure Learning Rates: A Commentary on Efficiency Articles

ERIC Educational Resources Information Center

Skinner, Christopher H.

2010-01-01

Almost all academic skills deficits can be conceptualized as learning rate problems as students are not failing to learn, but not learning rapidly enough. Thus, when selecting among various possible remedial procedures, educators need an evidence base that indicates which procedure results in the greatest increases in learning rates. Previous…

Learning curve analysis of mitral valve repair using telemanipulative technology.

PubMed

Charland, Patrick J; Robbins, Tom; Rodriguez, Evilio; Nifong, Wiley L; Chitwood, Randolph W

2011-08-01

To determine if the time required to perform mitral valve repairs using telemanipulation technology decreases with experience and how that decrease is influenced by patient and procedure variables. A single-center retrospective review was conducted using perioperative and outcomes data collected contemporaneously on 458 mitral valve repair surgeries using telemanipulative technology. A regression model was constructed to assess learning with this technology and predict total robot time using multiple predictive variables. Statistical analysis was used to determine if models were significantly useful, to rule out correlation between predictor variables, and to identify terms that did not contribute to the prediction of total robot time. We found a statistically significant learning curve (P < .01). The institutional learning percentage∗ derived from total robot times† for the first 458 recorded cases of mitral valve repair using telemanipulative technology is 95% (R(2) = .40). More than one third of the variability in total robot time can be explained through our model using the following variables: type of repair (chordal procedures, ablations, and leaflet resections), band size, use of clips alone in band implantation, and the presence of a fellow at bedside (P < .01). Learning in mitral valve repair surgery using telemanipulative technology occurs at the East Carolina Heart Institute according to a logarithmic curve, with a learning percentage of 95%. From our regression output, we can make an approximate prediction of total robot time using an additive model. These metrics can be used by programs for benchmarking to manage the implementation of this new technology, as well as for capacity planning, scheduling, and capital budget analysis. Copyright © 2011 The American Association for Thoracic Surgery. All rights reserved.

Neural networks: further insights into error function, generalized weights and others

PubMed Central

2016-01-01

The article is a continuum of a previous one providing further insights into the structure of neural network (NN). Key concepts of NN including activation function, error function, learning rate and generalized weights are introduced. NN topology can be visualized with generic plot() function by passing a “nn” class object. Generalized weights assist interpretation of NN model with respect to the independent effect of individual input variables. A large variance of generalized weights for a covariate indicates non-linearity of its independent effect. If generalized weights of a covariate are approximately zero, the covariate is considered to have no effect on outcome. Finally, prediction of new observations can be performed using compute() function. Make sure that the feature variables passed to the compute() function are in the same order to that in the training NN. PMID:27668220

Learning Style Preferences of Student Teachers: A Cross-Cultural Perspective

ERIC Educational Resources Information Center

Sywelem, Mohamed; Al-Harbi, Qassem; Fathema, Nafsaniath; Witte, James E.

2012-01-01

All students learn, but not all learn in the same way. Educational researchers postulate that everyone has a learning style. This article examines how cultural variability is reflected in the learning style of students in Egypt, Saudi Arabia and United States. In this study, the learning styles of over 300 students in Teacher Education…

The Influence of Personality and Chronotype on Distance Learning Willingness and Anxiety among Vocational High School Students in Turkey

ERIC Educational Resources Information Center

Randler, Christoph; Horzum, Mehmet Baris; Vollmer, Christian

2014-01-01

There are many studies related to distance learning. Willingness and anxiety are important variables for distance learning. Recent research has shown that anxiety and willingness towards distance learning are moderated by personality. This study sought to investigate whether distance learning willingness and distance learning anxiety are…

Multilayer Joint Gait-Pose Manifolds for Human Gait Motion Modeling.

PubMed

Ding, Meng; Fan, Guolian

2015-11-01

We present new multilayer joint gait-pose manifolds (multilayer JGPMs) for complex human gait motion modeling, where three latent variables are defined jointly in a low-dimensional manifold to represent a variety of body configurations. Specifically, the pose variable (along the pose manifold) denotes a specific stage in a walking cycle; the gait variable (along the gait manifold) represents different walking styles; and the linear scale variable characterizes the maximum stride in a walking cycle. We discuss two kinds of topological priors for coupling the pose and gait manifolds, i.e., cylindrical and toroidal, to examine their effectiveness and suitability for motion modeling. We resort to a topologically-constrained Gaussian process (GP) latent variable model to learn the multilayer JGPMs where two new techniques are introduced to facilitate model learning under limited training data. First is training data diversification that creates a set of simulated motion data with different strides. Second is the topology-aware local learning to speed up model learning by taking advantage of the local topological structure. The experimental results on the Carnegie Mellon University motion capture data demonstrate the advantages of our proposed multilayer models over several existing GP-based motion models in terms of the overall performance of human gait motion modeling.

Re-examining the effects of verbal instructional type on early stage motor learning.

PubMed

Bobrownicki, Ray; MacPherson, Alan C; Coleman, Simon G S; Collins, Dave; Sproule, John

2015-12-01

The present study investigated the differential effects of analogy and explicit instructions on early stage motor learning and movement in a modified high jump task. Participants were randomly assigned to one of three experimental conditions: analogy, explicit light (reduced informational load), or traditional explicit (large informational load). During the two-day learning phase, participants learned a novel high jump technique based on the 'scissors' style using the instructions for their respective conditions. For the single-day testing phase, participants completed both a retention test and task-relevant pressure test, the latter of which featured a rising high-jump-bar pressure manipulation. Although analogy learners demonstrated slightly more efficient technique and reported fewer technical rules on average, the differences between the conditions were not statistically significant. There were, however, significant differences in joint variability with respect to instructional type, as variability was lowest for the analogy condition during both the learning and testing phases, and as a function of block, as joint variability decreased for all conditions during the learning phase. Findings suggest that reducing the informational volume of explicit instructions may mitigate the deleterious effects on performance previously associated with explicit learning in the literature. Copyright © 2015 Elsevier B.V. All rights reserved.

Rejecting salient distractors: Generalization from experience.

PubMed

Vatterott, Daniel B; Mozer, Michael C; Vecera, Shaun P

2018-02-01

Distraction impairs performance of many important, everyday tasks. Attentional control limits distraction by preferentially selecting important items for limited-capacity cognitive operations. Research in attentional control has typically investigated the degree to which selection of items is stimulus-driven versus goal-driven. Recent work finds that when observers initially learn a task, the selection is based on stimulus-driven factors, but through experience, goal-driven factors have an increasing influence. The modulation of selection by goals has been studied within the paradigm of learned distractor rejection, in which experience over a sequence of trials enables individuals eventually to ignore a perceptually salient distractor. The experiments presented examine whether observers can generalize learned distractor rejection to novel distractors. Observers searched for a target and ignored a salient color-singleton distractor that appeared in half of the trials. In Experiment 1, observers who learned distractor rejection in a variable environment rejected a novel distractor more effectively than observers who learned distractor rejection in a less variable, homogeneous environment, demonstrating that variable, heterogeneous stimulus environments encourage generalizable learned distractor rejection. Experiments 2 and 3 investigated the time course of learned distractor rejection across the experiment and found that after experiencing four color-singleton distractors in different blocks, observers could effectively reject subsequent novel color-singleton distractors. These results suggest that the optimization of attentional control to the task environment can be interpreted as a form of learning, demonstrating experience's critical role in attentional control.

The relation between learning mathematics and students' competencies in undesrtanding texts

NASA Astrophysics Data System (ADS)

Hapipi, Azmi, Syahrul; Sripatmi, Amrullah

2017-08-01

This study was a descriptive study that aimed to gain an overview on the relation between learning mathematics and students' competencies in understanding texts. This research was classified as an ex post facto study due in part to the variable studied is the variable that was already happening. While the technique of taking the sample using stratified proportional sampling techniques. These techniques have been selected for the condition of the population, in the context of learning mathematics, diverse and also tiered. The results of this study indicate that there is a relationship between learning mathematics and students' competencies in understanding texts.

Learning Across Time Scales: Science, Policy, Management, and Communication

NASA Astrophysics Data System (ADS)

Stewart, M. M.

2002-05-01

This presentation will draw together common themes raised in the session and discuss lessons learned across time scales and their implications for managers and policy makers concerned with both climate change and variability. Session themes will be examined in the context of the upcoming World Summit on Sustainable Development (WSSD) and considered as opportunities for linking climate change policy discussions with lessons learned from the study of adaptation on seasonal to interannual time scales. The presentation will raise questions about future research directions, discuss recommendations for promoting learning across time scales, and explore options for better communicating the links between climate change and variability.

Multiple goals, motivation and academic learning.

PubMed

Valle, Antonio; Cabanach, Ramón G; Núnez, José C; González-Pienda, Julio; Rodríguez, Susana; Piñeiro, Isabel

2003-03-01

The type of academic goals pursued by students is one of the most important variables in motivational research in educational contexts. Although motivational theory and research have emphasised the somewhat exclusive nature of two types of goal orientation (learning goals versus performance goals), some studies (Meece, 1994; Seifert, 1995, 1996) have shown that the two kinds of goals are relatively complementary and that it is possible for students to have multiple goals simultaneously, which guarantees some flexibility to adapt more efficaciously to various contexts and learning situations. The principal aim of this study is to determine the academic goals pursued by university students and to analyse the differences in several very significant variables related to motivation and academic learning. Participants were 609 university students (74% women and 26% men) who filled in several questionnaires about the variables under study. We used cluster analysis ('quick cluster analysis' method) to establish the different groups or clusters of individuals as a function of the three types of goals (learning goals, performance goals, and social reinforcement goals). By means of MANOVA, we determined whether the groups or clusters identified were significantly different in the variables that are relevant to motivation and academic learning. Lastly, we performed ANOVA on the variables that revealed significant effects in the previous analysis. Using cluster analysis, three groups of students with different motivational orientations were identified: a group with predominance of performance goals (Group PG: n = 230), a group with predominance of multiple goals (Group MG: n = 238), and a group with predominance of learning goals (Group LG: n = 141). Groups MG and LG attributed their success more to ability, they had higher perceived ability, they took task characteristics into account when planning which strategies to use in the learning process, they showed higher persistence, and used more deep learning strategies than did the students with predominance of performance goals (Group PG). On the other hand, Groups MG and PG took the evaluation criteria more into account when deciding which strategies to use in order to learn, and they attributed their failures more to luck than did Group LG. Students from Group MG attributed their success more to effort than did the other two groups and they attained higher achievement than Group PG. Group LG tended to attribute their failures more to lack of effort than did the other two groups.

Influence of ECG sampling rate in fetal heart rate variability analysis.

PubMed

De Jonckheere, J; Garabedian, C; Charlier, P; Champion, C; Servan-Schreiber, E; Storme, L; Debarge, V; Jeanne, M; Logier, R

2017-07-01

Fetal hypoxia results in a fetal blood acidosis (pH<;7.10). In such a situation, the fetus develops several adaptation mechanisms regulated by the autonomic nervous system. Many studies demonstrated significant changes in heart rate variability in hypoxic fetuses. So, fetal heart rate variability analysis could be of precious help for fetal hypoxia prediction. Commonly used fetal heart rate variability analysis methods have been shown to be sensitive to the ECG signal sampling rate. Indeed, a low sampling rate could induce variability in the heart beat detection which will alter the heart rate variability estimation. In this paper, we introduce an original fetal heart rate variability analysis method. We hypothesize that this method will be less sensitive to ECG sampling frequency changes than common heart rate variability analysis methods. We then compared the results of this new heart rate variability analysis method with two different sampling frequencies (250-1000 Hz).

Learning to Estimate Dynamical State with Probabilistic Population Codes.

PubMed

Makin, Joseph G; Dichter, Benjamin K; Sabes, Philip N

2015-11-01

Tracking moving objects, including one's own body, is a fundamental ability of higher organisms, playing a central role in many perceptual and motor tasks. While it is unknown how the brain learns to follow and predict the dynamics of objects, it is known that this process of state estimation can be learned purely from the statistics of noisy observations. When the dynamics are simply linear with additive Gaussian noise, the optimal solution is the well known Kalman filter (KF), the parameters of which can be learned via latent-variable density estimation (the EM algorithm). The brain does not, however, directly manipulate matrices and vectors, but instead appears to represent probability distributions with the firing rates of population of neurons, "probabilistic population codes." We show that a recurrent neural network-a modified form of an exponential family harmonium (EFH)-that takes a linear probabilistic population code as input can learn, without supervision, to estimate the state of a linear dynamical system. After observing a series of population responses (spike counts) to the position of a moving object, the network learns to represent the velocity of the object and forms nearly optimal predictions about the position at the next time-step. This result builds on our previous work showing that a similar network can learn to perform multisensory integration and coordinate transformations for static stimuli. The receptive fields of the trained network also make qualitative predictions about the developing and learning brain: tuning gradually emerges for higher-order dynamical states not explicitly present in the inputs, appearing as delayed tuning for the lower-order states.

Learning to Estimate Dynamical State with Probabilistic Population Codes

PubMed Central

Sabes, Philip N.

2015-01-01

Tracking moving objects, including one’s own body, is a fundamental ability of higher organisms, playing a central role in many perceptual and motor tasks. While it is unknown how the brain learns to follow and predict the dynamics of objects, it is known that this process of state estimation can be learned purely from the statistics of noisy observations. When the dynamics are simply linear with additive Gaussian noise, the optimal solution is the well known Kalman filter (KF), the parameters of which can be learned via latent-variable density estimation (the EM algorithm). The brain does not, however, directly manipulate matrices and vectors, but instead appears to represent probability distributions with the firing rates of population of neurons, “probabilistic population codes.” We show that a recurrent neural network—a modified form of an exponential family harmonium (EFH)—that takes a linear probabilistic population code as input can learn, without supervision, to estimate the state of a linear dynamical system. After observing a series of population responses (spike counts) to the position of a moving object, the network learns to represent the velocity of the object and forms nearly optimal predictions about the position at the next time-step. This result builds on our previous work showing that a similar network can learn to perform multisensory integration and coordinate transformations for static stimuli. The receptive fields of the trained network also make qualitative predictions about the developing and learning brain: tuning gradually emerges for higher-order dynamical states not explicitly present in the inputs, appearing as delayed tuning for the lower-order states. PMID:26540152

Communication in mental health nursing - Bachelor Students' appraisal of a blended learning training programme - an exploratory study.

PubMed

Furnes, Merete; Kvaal, Kari Sofie; Høye, Sevald

2018-01-01

It is important that mental health nursing students at Bachelor level obtain effective communication skills. Many students dread the fact that in the mental health field they will encounter patients and relatives with various backgrounds and personalities. Large classes and limited teaching resources in nursing education are challenging. To prepare students for mental health nursing practice, a communication skills course based on the blended learning method was developed and carried out at two different campuses.The aim of the study is to explore Bachelor nursing students' appraisal of blended learning methods for enhancing communication skills in mental health nursing. This study employed an exploratory design. Teaching and information materials were available on the learning management system (LMS). Videotaped role play training was carried out in the Simulation Department. Data were collected after the course by means of a questionnaire with closed and open-ended questions. The response rate was 59.2%. Quantitative data were analysed using the Statistical package for the Social Sciences (SPSS) and the Kruskal Wallis test, while qualitative data were analysed by content analysis based on Graneheim and Lundman's approach. No impact of background variables was observed. Students appreciated teachers' participation in role play and immediate feedback was considered especially important for learning outcomes. The students perceived that their communication skills and knowledge had improved after completing the blended learning programme. According to the nursing students, blended learning is an appropriate method for improving communication skills in preparation for mental health nursing. Blended learning makes it possible to build flexible courses with limited resources.

Lecture Hall and Learning Design: A Survey of Variables, Parameters, Criteria and Interrelationships for Audio-Visual Presentation Systems and Audience Reception.

ERIC Educational Resources Information Center

Justin, J. Karl

Variables and parameters affecting architectural planning and audiovisual systems selection for lecture halls and other learning spaces are surveyed. Interrelationships of factors are discussed, including--(1) design requirements for modern educational techniques as differentiated from cinema, theater or auditorium design, (2) general hall…

The School Principal as Leader: Guiding Schools to Better Teaching and Learning. Perspective

ERIC Educational Resources Information Center

Wallace Foundation, 2012

2012-01-01

Education research shows that most school variables, considered separately, have at most small effects on learning. The real payoff comes when individual variables combine to reach critical mass. Creating the conditions under which that can occur is the job of the principal. For more than a decade, The Wallace Foundation has supported efforts to…

Perceived Learning and Timely Graduation for Business Undergraduates Taking an Online or Hybrid Course

ERIC Educational Resources Information Center

Blau, Gary; Drennan, Rob B.; Hochner, Arthur; Kapanjie, Darin

2016-01-01

An online survey tested the impact of background, technological, and course-related variables on perceived learning and timely graduation for a complete data sample of 263 business undergraduates taking at least one online or hybrid course in the fall of 2015. Hierarchical regression results showed that course-related variables (instructor…

Elementary Students' Affective Variables in a Networked Learning Environment Supported by a Blog: A Case Study

ERIC Educational Resources Information Center

Allaire, Stéphane; Thériault, Pascale; Gagnon, Vincent; Lalancette, Evelyne

2013-01-01

This study documents to what extent writing on a blog in a networked learning environment could influence the affective variables of elementary-school students' writing. The framework is grounded more specifically in theory of self-determination (Deci & Ryan, 1985), relationship to writing (Chartrand & Prince, 2009) and the transactional…

The Instructional Factors That Lead to Cheating in a Korean Cyber University Context

ERIC Educational Resources Information Center

Costley, Jamie

2017-01-01

Purpose: This paper looks at a particular type of cheating that occurs in an online university setting. That is, when students who have a connection from outside the online learning environment conspire to cheat together. It measures the correlations between student variables and cheating, instructional variables and cheating and learning outcomes…

A Structural Analysis on Korean Young Children's Mathematical Ability and Its Related Children's and Mothers' Variables

ERIC Educational Resources Information Center

Lee, Hye Jung; Kim, Jihyun

2016-01-01

The objective of this study is to examine the structural relationships among variables that predict the mathematical ability of young children, namely young children's mathematical attitude, exposure to private mathematical learning, mothers' view about their children's mathematical learning, and mothers' mathematical attitude. To this end, we…

Modeling Relationships among Learning, Attitude, Self-Perception, and Science Achievement for Grade 8 Saudi Students

ERIC Educational Resources Information Center

Tighezza, M'Hamed

2014-01-01

The purpose of the present study was to examine the validity of modeling science achievement in terms of 3 social psychological variables (school connectedness, science attitude, and active learning) and 2 self-perception variables (self-confidence and science value). Two models were tested: full mediation and partial mediation. In the…

French Nursery Schools and German Kindergartens: Effects of Individual and Contextual Variables on Early Learning

ERIC Educational Resources Information Center

Tazouti, Youssef; Viriot-Goeldel, Caroline; Matter, Cornelie; Geiger-Jaillet, Anemone; Carol, Rita; Deviterne, Dominique

2011-01-01

The present article investigates the effects of individual and contextual variables on children's early learning in French nursery schools and German kindergartens. Our study of 552 children at preschools in France (299 children from French nursery schools) and Germany (253 children from German kindergartens) measured skills that facilitate the…

Investigating Lifelong Learning Dispositions of Students Studying English Language and Literature in Terms of Different Variables

ERIC Educational Resources Information Center

Elaldi, Senel

2015-01-01

This study aims to determine lifelong learning dispositions of English Language and Literature students in terms of gender, grade levels, and age variables. Descriptive research design was used. The study group consisted of 402 students studying English Language and Literature at Cumhuriyet University in Sivas, Turkey. Research data were collected…

Predictors of Science Success: The Impact of Motivation and Learning Strategies on College Chemistry Performance

ERIC Educational Resources Information Center

Obrentz, Shari B.

2012-01-01

As the number of college students studying science continues to grow, it is important to identify variables that predict their success. The literature indicates that motivation and learning strategy use facilitate science success. Research findings show these variables can change throughout a semester and differ by performance level, gender and…

Auditory Training for Experienced and Inexperienced Second-Language Learners: Native French Speakers Learning English Vowels

ERIC Educational Resources Information Center

Iverson, Paul; Pinet, Melanie; Evans, Bronwen G.

2012-01-01

This study examined whether high-variability auditory training on natural speech can benefit experienced second-language English speakers who already are exposed to natural variability in their daily use of English. The subjects were native French speakers who had learned English in school; experienced listeners were tested in England and the less…

Measuring Student Variables Useful in the Study of Performance in an Online Learning Environment.

ERIC Educational Resources Information Center

Kennedy, Cathleen A.

This paper discusses the measurement of unobservable or latent variables of students and how they contribute to learning in an online environment. It also examines the construct validity of two questionnaires: the College Experience Survey and the Computer Experience Study, which both measure different aspects of student attitudes and behavior…

Psychosocial Variables as Predictors of School Adjustment of Gifted Students with Learning Disabilities in Nigeria

ERIC Educational Resources Information Center

Fakolade, O. A.; Oyedokun, S. O.

2015-01-01

The paper considered several psychosocial variables as predictors of school adjustment of 40 gifted students with learning disabilities in Junior Secondary School in Ikenne Local Government Council Area of Ogun State, Nigeria. Purposeful random sampling was employed to select four schools from 13 junior secondary schools in the area, six…

Secondary Teacher Attitudes toward the Inclusion of Students with Learning Disabilities in the General Mathematics Classroom

ERIC Educational Resources Information Center

Lindsey, Jacqueline

2012-01-01

The primary focus of this study was to examine the attitudes of the secondary mathematics teachers toward the inclusion of students with learning disabilities in the general mathematics classroom. Specifically, this study was concerned with the influence of selected demographic variables and school variables on the attitudes of secondary…

A Longitudinal Analysis of Torque and its Relationship to Achievement and Educational Classification among Normal, Disturbed, and Learning-Disabled Children.

ERIC Educational Resources Information Center

Alberts, Fred L.; Edwards, Ron P.

1983-01-01

Examined the effect of the presence of torque (clockwise circlings with either hand on a visual-motor task) on academic achievement variables among normal, disturbed, and learning-disabled children (N=948). Results indicated no clear relationship between torque and the various academic variables. (LLL)

Authentic Early Experience in Medical Education: A Socio-Cultural Analysis Identifying Important Variables in Learning Interactions within Workplaces

ERIC Educational Resources Information Center

Yardley, Sarah; Brosnan, Caragh; Richardson, Jane; Hays, Richard

2013-01-01

This paper addresses the question "what are the variables influencing social interactions and learning during Authentic Early Experience (AEE)?" AEE is a complex educational intervention for new medical students. Following critique of the existing literature, multiple qualitative methods were used to create a study framework conceptually…

The Effect of Visual Variability on the Learning of Academic Concepts

ERIC Educational Resources Information Center

Bourgoyne, Ashley; Alt, Mary

2017-01-01

Purpose: The purpose of this study was to identify effects of variability of visual input on development of conceptual representations of academic concepts for college-age students with normal language (NL) and those with language-learning disabilities (LLD). Method: Students with NL (n = 11) and LLD (n = 11) participated in a computer-based…

Serial killers with military experience: applying learning theory to serial murder.

PubMed

Castle, Tammy; Hensley, Christopher

2002-08-01

Scholars have endeavored to study the motivation and causality behind serial murder by researching biological, psychological, and sociological variables. Some of these studies have provided support for the relationship between these variables and serial murder. However, the study of serial murder continues to be an exploratory rather than explanatory research topic. This article examines the possible link between serial killers and military service. Citing previous research using social learning theory for the study of murder, this article explores how potential serial killers learn to reinforce violence, aggression, and murder in military boot camps. As with other variables considered in serial killer research, military experience alone cannot account for all cases of serial murder. Future research should continue to examine this possible link.

Use of Computer Technology for English Language Learning: Do Learning Styles, Gender, and Age Matter?

ERIC Educational Resources Information Center

Lee, Cynthia; Yeung, Alexander Seeshing; Ip, Tiffany

2016-01-01

Computer technology provides spaces and locales for language learning. However, learning style preference and demographic variables may affect the effectiveness of technology use for a desired goal. Adapting Reid's pioneering Perceptual Learning Style Preference Questionnaire (PLSPQ), this study investigated the relations of university students'…

Variability in University Students' Use of Technology: An "Approaches to Learning" Perspective

ERIC Educational Resources Information Center

Mimirinis, Mike

2016-01-01

This study reports the results of a cross-case study analysis of how students' approaches to learning are demonstrated in blended learning environments. It was initially propositioned that approaches to learning as key determinants of the quality of student learning outcomes are demonstrated specifically in how students utilise technology in…

Research of Water Level Prediction for a Continuous Flood due to Typhoons Based on a Machine Learning Method

NASA Astrophysics Data System (ADS)

Nakatsugawa, M.; Kobayashi, Y.; Okazaki, R.; Taniguchi, Y.

2017-12-01

This research aims to improve accuracy of water level prediction calculations for more effective river management. In August 2016, Hokkaido was visited by four typhoons, whose heavy rainfall caused severe flooding. In the Tokoro river basin of Eastern Hokkaido, the water level (WL) at the Kamikawazoe gauging station, which is at the lower reaches exceeded the design high-water level and the water rose to the highest level on record. To predict such flood conditions and mitigate disaster damage, it is necessary to improve the accuracy of prediction as well as to prolong the lead time (LT) required for disaster mitigation measures such as flood-fighting activities and evacuation actions by residents. There is the need to predict the river water level around the peak stage earlier and more accurately. Previous research dealing with WL prediction had proposed a method in which the WL at the lower reaches is estimated by the correlation with the WL at the upper reaches (hereinafter: "the water level correlation method"). Additionally, a runoff model-based method has been generally used in which the discharge is estimated by giving rainfall prediction data to a runoff model such as a storage function model and then the WL is estimated from that discharge by using a WL discharge rating curve (H-Q curve). In this research, an attempt was made to predict WL by applying the Random Forest (RF) method, which is a machine learning method that can estimate the contribution of explanatory variables. Furthermore, from the practical point of view, we investigated the prediction of WL based on a multiple correlation (MC) method involving factors using explanatory variables with high contribution in the RF method, and we examined the proper selection of explanatory variables and the extension of LT. The following results were found: 1) Based on the RF method tuned up by learning from previous floods, the WL for the abnormal flood case of August 2016 was properly predicted with a lead time of 6 h. 2) Based on the contribution of explanatory variables, factors were selected for the MC method. In this way, plausible prediction results were obtained.

Use of Balanced Indicators as a Management Tool in Nursing.

PubMed

Fugaça, Neidamar Pedrini Arias; Cubas, Marcia Regina; Carvalho, Deborah Ribeiro

2015-01-01

To develop a proposal for a nursing panel of indicators based on the guiding principles of Balanced Scorecard. A single case study that ranked 200 medical records of patients, management reports and protocols, which are capable of generating indicators. We identified 163 variables that resulted in 72 indicators; of these, 32 nursing-related: two financial indicators (patient's average revenue per day and patient's revenue per day by product used); two client indicators (overall satisfaction rate of patient with nursing care and adherence rate to the patient satisfaction survey); 23 process indicators, and five learning and growth indicators (average total hours of training, total of approved nursing professionals in the internal selection process, absenteeism rate, turnover rate and index of performance evaluation). Although there is a limit related to the amount of data generated, the methodology of Balanced Scorecard has proved to be flexible and adaptable to incorporate nursing services. It was possible to identify indicators with adherence to more than one area. Internal processes was the area with the higher number of indicators.

Artificial Neural Network System to Predict the Postoperative Outcome of Percutaneous Nephrolithotomy.

PubMed

Aminsharifi, Alireza; Irani, Dariush; Pooyesh, Shima; Parvin, Hamid; Dehghani, Sakineh; Yousofi, Khalilolah; Fazel, Ebrahim; Zibaie, Fatemeh

2017-05-01

To construct, train, and apply an artificial neural network (ANN) system for prediction of different outcome variables of percutaneous nephrolithotomy (PCNL). We calculated predictive accuracy, sensitivity, and precision for each outcome variable. During the study period, all adult patients who underwent PCNL at our institute were enrolled in the study. Preoperative and postoperative variables were recorded, and stone-free status was assessed perioperatively with computed tomography scans. MATLAB software was used to design and train the network in a feed forward back-propagation error adjustment scheme. Preoperative and postoperative data from 200 patients (training set) were used to analyze the effect and relative relevance of preoperative values on postoperative parameters. The validated adequately trained ANN was used to predict postoperative outcomes in the subsequent 254 adult patients (test set) whose preoperative values were serially fed into the system. To evaluate system accuracy in predicting each postoperative variable, predicted values were compared with actual outcomes. Two hundred fifty-four patients (155 [61%] males) were considered the test set. Mean stone burden was 6702.86 ± 381.6 mm 3 . Overall stone-free rate was 76.4%. Fifty-four out of 254 patients (21.3%) required ancillary procedures (shockwave lithotripsy 5.9%, transureteral lithotripsy 10.6%, and repeat PCNL 4.7%). The accuracy and sensitivity of the system in predicting different postoperative variables ranged from 81.0% to 98.2%. As a complex nonlinear mathematical model, our ANN system is an interconnected data mining tool, which prospectively analyzes and "learns" the relationships between variables. The accuracy and sensitivity of the system for predicting the stone-free rate, the need for blood transfusion, and post-PCNL ancillary procedures ranged from 81.0% to 98.2%.The stone burden and the stone morphometry were among the most significant preoperative characteristics that affected all postoperative outcome variables and they received the highest relative weight by the ANN system.

Timing in the Absence of Supraspinal Input I: Variable, but not Fixed, Spaced Stimulation of the Sciatic Nerve Undermines Spinally-Mediated Instrumental Learning

PubMed Central

Baumbauer, Kyle M.; Hoy, Kevin C.; Huie, John R.; Hughes, Abbey J.; Woller, Sarah A.; Puga, Denise A.; Setlow, Barry; Grau, James W.

2008-01-01

Rats with complete spinal transections are capable of acquiring a simple instrumentally trained response. If rats receive shock to one hindlimb when the limb is extended (controllable shock), the spinal cord will learn to hold the leg in a flexed position that minimizes shock exposure. If shock is delivered irrespective of leg position, subjects do not exhibit an increase in flexion duration and subsequently fail to learn when tested with controllable shock (learning deficit). Just 6 min of variable intermittent shock produces a learning deficit that lasts 24 hrs. Evidence suggests that the neural mechanisms underlying the learning deficit may be related to those involved in other instances of spinal plasticity (e.g., wind-up, long-term potentiation). The present paper begins to explore these relations by demonstrating that direct stimulation of the sciatic nerve also impairs instrumental learning. Six minutes of electrical stimulation (mono- or biphasic direct current [DC]) of the sciatic nerve in spinally transected rats produced a voltage-dependent learning deficit that persisted for 24 hr (Experiments 1–2) and was dependent on C-fiber activation (Experiment 7). Exposure to continuous stimulation did not produce a deficit, but intermittent burst or single pulse (as short as 0.1 ms) stimulation (delivered at a frequency of 0.5 Hz) did, irrespective of the pattern (fixed or variable) of stimulus delivery (Experiments 3–6, 8). When the duration of stimulation was extended from 6 to 30 min, a surprising result emerged; shocks applied in a random (variable) fashion impaired subsequent learning whereas shocks given in a regular pattern (fixed spacing) did not (Experiments 9–10). The results imply that spinal neurons are sensitive to temporal relations and that stimulation at regular intervals can have a restorative effect. PMID:18674601

Non-Gaussian Methods for Causal Structure Learning.

PubMed

Shimizu, Shohei

2018-05-22

Causal structure learning is one of the most exciting new topics in the fields of machine learning and statistics. In many empirical sciences including prevention science, the causal mechanisms underlying various phenomena need to be studied. Nevertheless, in many cases, classical methods for causal structure learning are not capable of estimating the causal structure of variables. This is because it explicitly or implicitly assumes Gaussianity of data and typically utilizes only the covariance structure. In many applications, however, non-Gaussian data are often obtained, which means that more information may be contained in the data distribution than the covariance matrix is capable of containing. Thus, many new methods have recently been proposed for using the non-Gaussian structure of data and inferring the causal structure of variables. This paper introduces prevention scientists to such causal structure learning methods, particularly those based on the linear, non-Gaussian, acyclic model known as LiNGAM. These non-Gaussian data analysis tools can fully estimate the underlying causal structures of variables under assumptions even in the presence of unobserved common causes. This feature is in contrast to other approaches. A simulated example is also provided.

More than just tapping: index finger-tapping measures procedural learning in schizophrenia.

PubMed

Da Silva, Felipe N; Irani, Farzin; Richard, Jan; Brensinger, Colleen M; Bilker, Warren B; Gur, Raquel E; Gur, Ruben C

2012-05-01

Finger-tapping has been widely studied using behavioral and neuroimaging paradigms. Evidence supports the use of finger-tapping as an endophenotype in schizophrenia, but its relationship with motor procedural learning remains unexplored. To our knowledge, this study presents the first use of index finger-tapping to study procedural learning in individuals with schizophrenia or schizoaffective disorder (SCZ/SZA) as compared to healthy controls. A computerized index finger-tapping test was administered to 1169 SCZ/SZA patients (62% male, 88% right-handed), and 689 healthy controls (40% male, 93% right-handed). Number of taps per trial and learning slopes across trials for the dominant and non-dominant hands were examined for motor speed and procedural learning, respectively. Both healthy controls and SCZ/SZA patients demonstrated procedural learning for their dominant hand but not for their non-dominant hand. In addition, patients showed a greater capacity for procedural learning even though they demonstrated more variability in procedural learning compared to healthy controls. Left-handers of both groups performed better than right-handers and had less variability in mean number of taps between non-dominant and dominant hands. Males also had less variability in mean tap count between dominant and non-dominant hands than females. As expected, patients had a lower mean number of taps than healthy controls, males outperformed females and dominant-hand trials had more mean taps than non-dominant hand trials in both groups. The index finger-tapping test can measure both motor speed and procedural learning, and motor procedural learning may be intact in SCZ/SZA patients. Copyright © 2012 Elsevier B.V. All rights reserved.

The Effect of Cooperative Learning Model of Teams Games Tournament (TGT) and Students' Motivation toward Physics Learning Outcome

ERIC Educational Resources Information Center

Nadrah; Tolla, Ismail; Ali, Muhammad Sidin; Muris

2017-01-01

This research aims at describing the effect of cooperative learning model of Teams Games Tournament (TGT) and motivation toward physics learning outcome. This research was a quasi-experimental research with a factorial design conducted at SMAN 2 Makassar. Independent variables were learning models. They were cooperative learning model of TGT and…

Factors Influencing Learning Satisfaction of Migrant Workers in Korea with E-learning-Based Occupational Safety and Health Education

PubMed Central

Lee, Young Joo; Lee, Dongjoo

2015-01-01

Background E-learning-based programs have recently been introduced to the occupational safety and health (OSH) education for migrant workers in Korea. The purpose of this study was to investigate how the factors related to migrant workers' backgrounds and the instructional design affect the migrant workers' satisfaction with e-learning-based OSH education. Methods The data were collected from the surveys of 300 migrant workers who had participated in an OSH education program. Independent sample t test and one-way analysis of variance were conducted to examine differences in the degree of learning satisfaction using background variables. In addition, correlation analysis and multiple regression analysis were conducted to examine relationships between the instructional design variables and the degree of learning satisfaction. Results There was no significant difference in the degree of learning satisfaction by gender, age, level of education, number of employees, or type of occupation, except for nationality. Among the instructional design variables, “learning content” (β = 0.344, p < 0.001) affected the degree of learning satisfaction most significantly, followed by “motivation to learn” (β = 0.293, p < 0.001), “interactions with learners and instructors” (β = 0.149, p < 0.01), and “previous experience related to e-learning” (β = 0.095, p < 0.05). “Learning environment” had no significant influence on the degree of learning satisfaction. Conclusion E-learning-based OSH education for migrant workers may be an effective way to increase their safety knowledge and behavior if the accuracy, credibility, and novelty of learning content; strategies to promote learners' motivation to learn; and interactions with learners and instructors are systematically applied during the development and implementation of e-learning programs. PMID:26929830

The impacts of observing flawed and flawless demonstrations on clinical skill learning.

PubMed

Domuracki, Kurt; Wong, Arthur; Olivieri, Lori; Grierson, Lawrence E M

2015-02-01

Clinical skills expertise can be advanced through accessible and cost-effective video-based observational practice activities. Previous findings suggest that the observation of performances of skills that include flaws can be beneficial to trainees. Observing the scope of variability within a skilled movement allows learners to develop strategies to manage the potential for and consequences associated with errors. This study tests this observational learning approach on the development of the skills of central line insertion (CLI). Medical trainees with no CLI experience (n = 39) were randomised to three observational practice groups: a group which viewed and assessed videos of an expert performing a CLI without any errors (F); a group which viewed and assessed videos that contained a mix of flawless and errorful performances (E), and a group which viewed the same videos as the E group but were also given information concerning the correctness of their assessments (FA). All participants interacted with their observational videos each day for 4 days. Following this period, participants returned to the laboratory and performed a simulation-based insertion, which was assessed using a standard checklist and a global rating scale for the skill. These ratings served as the dependent measures for analysis. The checklist analysis revealed no differences between observational learning groups (grand mean ± standard error: [20.3 ± 0.7]/25). However, the global rating analysis revealed a main effect of group (d.f.2,36 = 4.51, p = 0.018), which describes better CLI performance in the FA group, compared with the F and E groups. Observational practice that includes errors improves the global performance aspects of clinical skill learning as long as learners are given confirmation that what they are observing is errorful. These findings provide a refined perspective on the optimal organisation of skill education programmes that combine physical and observational practice activities. © 2015 John Wiley & Sons Ltd.

Classification of Variable Objects in Massive Sky Monitoring Surveys

NASA Astrophysics Data System (ADS)

Woźniak, Przemek; Wyrzykowski, Łukasz; Belokurov, Vasily

2012-03-01

The era of great sky surveys is upon us. Over the past decade we have seen rapid progress toward a continuous photometric record of the optical sky. Numerous sky surveys are discovering and monitoring variable objects by hundreds of thousands. Advances in detector, computing, and networking technology are driving applications of all shapes and sizes ranging from small all sky monitors, through networks of robotic telescopes of modest size, to big glass facilities equipped with giga-pixel CCD mosaics. The Large Synoptic Survey Telescope will be the first peta-scale astronomical survey [18]. It will expand the volume of the parameter space available to us by three orders of magnitude and explore the mutable heavens down to an unprecedented level of sensitivity. Proliferation of large, multidimensional astronomical data sets is stimulating the work on new methods and tools to handle the identification and classification challenge [3]. Given exponentially growing data rates, automated classification of variability types is quickly becoming a necessity. Taking humans out of the loop not only eliminates the subjective nature of visual classification, but is also an enabling factor for time-critical applications. Full automation is especially important for studies of explosive phenomena such as γ-ray bursts that require rapid follow-up observations before the event is over. While there is a general consensus that machine learning will provide a viable solution, the available algorithmic toolbox remains underutilized in astronomy by comparison with other fields such as genomics or market research. Part of the problem is the nature of astronomical data sets that tend to be dominated by a variety of irregularities. Not all algorithms can handle gracefully uneven time sampling, missing features, or sparsely populated high-dimensional spaces. More sophisticated algorithms and better tools available in standard software packages are required to facilitate the adoption of machine learning in astronomy. The goal of this chapter is to show a number of successful applications of state-of-the-art machine learning methodology to time-resolved astronomical data, illustrate what is possible today, and help identify areas for further research and development. After a brief comparison of the utility of various machine learning classifiers, the discussion focuses on support vector machines (SVM), neural nets, and self-organizing maps. Traditionally, to detect and classify transient variability astronomers used ad hoc scan statistics. These methods will remain important as feature extractors for input into generic machine learning algorithms. Experience shows that the performance of machine learning tools on astronomical data critically depends on the definition and quality of the input features, and that a considerable amount of preprocessing is required before standard algorithms can be applied. However, with continued investments of effort by a growing number of astro-informatics savvy computer scientists and astronomers the much-needed expertise and infrastructure are growing faster than ever.

Commentary: Academic Enablers and School Learning.

ERIC Educational Resources Information Center

Keith, Timothy Z.

2002-01-01

This commentary presents academic enablers within the broader, overlapping context of school learning theory, including the theories of Carroll, Harnishfeger and Wiley, Walberg, and others. Multivariate models are needed to understand the influences of academic enabler and school learning variables on learning, as well as the influences of these…

New supervised learning theory applied to cerebellar modeling for suppression of variability of saccade end points.

PubMed

Fujita, Masahiko

2013-06-01

A new supervised learning theory is proposed for a hierarchical neural network with a single hidden layer of threshold units, which can approximate any continuous transformation, and applied to a cerebellar function to suppress the end-point variability of saccades. In motor systems, feedback control can reduce noise effects if the noise is added in a pathway from a motor center to a peripheral effector; however, it cannot reduce noise effects if the noise is generated in the motor center itself: a new control scheme is necessary for such noise. The cerebellar cortex is well known as a supervised learning system, and a novel theory of cerebellar cortical function developed in this study can explain the capability of the cerebellum to feedforwardly reduce noise effects, such as end-point variability of saccades. This theory assumes that a Golgi-granule cell system can encode the strength of a mossy fiber input as the state of neuronal activity of parallel fibers. By combining these parallel fiber signals with appropriate connection weights to produce a Purkinje cell output, an arbitrary continuous input-output relationship can be obtained. By incorporating such flexible computation and learning ability in a process of saccadic gain adaptation, a new control scheme in which the cerebellar cortex feedforwardly suppresses the end-point variability when it detects a variation in saccadic commands can be devised. Computer simulation confirmed the efficiency of such learning and showed a reduction in the variability of saccadic end points, similar to results obtained from experimental data.

Experience with compound words influences their processing: An eye movement investigation with English compound words.

PubMed

Juhasz, Barbara J

2016-11-14

Recording eye movements provides information on the time-course of word recognition during reading. Juhasz and Rayner [Juhasz, B. J., & Rayner, K. (2003). Investigating the effects of a set of intercorrelated variables on eye fixation durations in reading. Journal of Experimental Psychology: Learning, Memory and Cognition, 29, 1312-1318] examined the impact of five word recognition variables, including familiarity and age-of-acquisition (AoA), on fixation durations. All variables impacted fixation durations, but the time-course differed. However, the study focused on relatively short, morphologically simple words. Eye movements are also informative for examining the processing of morphologically complex words such as compound words. The present study further examined the time-course of lexical and semantic variables during morphological processing. A total of 120 English compound words that varied in familiarity, AoA, semantic transparency, lexeme meaning dominance, sensory experience rating (SER), and imageability were selected. The impact of these variables on fixation durations was examined when length, word frequency, and lexeme frequencies were controlled in a regression model. The most robust effects were found for familiarity and AoA, indicating that a reader's experience with compound words significantly impacts compound recognition. These results provide insight into semantic processing of morphologically complex words during reading.

A multicenter study: how do medical students perceive clinical learning climate?

PubMed

Yilmaz, Nilufer Demiral; Velipasaoglu, Serpil; Ozan, Sema; Basusta, Bilge Uzun; Midik, Ozlem; Mamakli, Sumer; Karaoglu, Nazan; Tengiz, Funda; Durak, Halil İbrahim; Sahin, Hatice

2016-01-01

The relationship between students and instructors is of crucial importance for the development of a positive learning climate. Learning climate is a multifaceted concept, and its measurement is a complicated process. The aim of this cross-sectional study was to determine medical students' perceptions about the clinical learning climate and to investigate differences in their perceptions in terms of various variables. Medical students studying at six medical schools in Turkey were recruited for the study. All students who completed clinical rotations, which lasted for 3 or more weeks, were included in the study (n=3,097). Data were collected using the Clinical Learning Climate Scale (CLCS). The CLCS (36 items) includes three subscales: clinical environment, emotion, and motivation. Each item is scored using a 5-point Likert scale (1: strongly disagree to 5: strongly agree). The response rate for the trainees was 69.67% (n=1,519), and for the interns it was 51.47% (n=917). The mean total CLCS score was 117.20±17.19. The rotation during which the clinical learning climate was perceived most favorably was the Physical Therapy and Rehabilitation rotation (mean score: 137.77). The most negatively perceived rotation was the General Internal Medicine rotation (mean score: 104.31). There were significant differences between mean total scores in terms of trainee/intern characteristics, internal medicine/surgical medicine rotations, and perception of success. The results of this study drew attention to certain aspects of the clinical learning climate in medical schools. Clinical teacher/instructor/supervisor, clinical training programs, students' interactions in clinical settings, self-realization, mood, students' intrinsic motivation, and institutional commitment are important components of the clinical learning climate. For this reason, the aforementioned components should be taken into consideration in studies aiming to improve clinical learning climate.

A multicenter study: how do medical students perceive clinical learning climate?

PubMed

Yilmaz, Nilufer Demiral; Velipasaoglu, Serpil; Ozan, Sema; Basusta, Bilge Uzun; Midik, Ozlem; Mamakli, Sumer; Karaoglu, Nazan; Tengiz, Funda; Durak, Halil İbrahim; Sahin, Hatice

2016-01-01

Background The relationship between students and instructors is of crucial importance for the development of a positive learning climate. Learning climate is a multifaceted concept, and its measurement is a complicated process. The aim of this cross-sectional study was to determine medical students' perceptions about the clinical learning climate and to investigate differences in their perceptions in terms of various variables. Methods Medical students studying at six medical schools in Turkey were recruited for the study. All students who completed clinical rotations, which lasted for 3 or more weeks, were included in the study (n=3,097). Data were collected using the Clinical Learning Climate Scale (CLCS). The CLCS (36 items) includes three subscales: clinical environment, emotion, and motivation. Each item is scored using a 5-point Likert scale (1: strongly disagree to 5: strongly agree). Results The response rate for the trainees was 69.67% (n=1,519), and for the interns it was 51.47% (n=917). The mean total CLCS score was 117.20±17.19. The rotation during which the clinical learning climate was perceived most favorably was the Physical Therapy and Rehabilitation rotation (mean score: 137.77). The most negatively perceived rotation was the General Internal Medicine rotation (mean score: 104.31). There were significant differences between mean total scores in terms of trainee/intern characteristics, internal medicine/surgical medicine rotations, and perception of success. Conclusion The results of this study drew attention to certain aspects of the clinical learning climate in medical schools. Clinical teacher/instructor/supervisor, clinical training programs, students' interactions in clinical settings, self-realization, mood, students' intrinsic motivation, and institutional commitment are important components of the clinical learning climate. For this reason, the aforementioned components should be taken into consideration in studies aiming to improve clinical learning climate.

Optimizing the learning rate for adaptive estimation of neural encoding models

PubMed Central

2018-01-01

Closed-loop neurotechnologies often need to adaptively learn an encoding model that relates the neural activity to the brain state, and is used for brain state decoding. The speed and accuracy of adaptive learning algorithms are critically affected by the learning rate, which dictates how fast model parameters are updated based on new observations. Despite the importance of the learning rate, currently an analytical approach for its selection is largely lacking and existing signal processing methods vastly tune it empirically or heuristically. Here, we develop a novel analytical calibration algorithm for optimal selection of the learning rate in adaptive Bayesian filters. We formulate the problem through a fundamental trade-off that learning rate introduces between the steady-state error and the convergence time of the estimated model parameters. We derive explicit functions that predict the effect of learning rate on error and convergence time. Using these functions, our calibration algorithm can keep the steady-state parameter error covariance smaller than a desired upper-bound while minimizing the convergence time, or keep the convergence time faster than a desired value while minimizing the error. We derive the algorithm both for discrete-valued spikes modeled as point processes nonlinearly dependent on the brain state, and for continuous-valued neural recordings modeled as Gaussian processes linearly dependent on the brain state. Using extensive closed-loop simulations, we show that the analytical solution of the calibration algorithm accurately predicts the effect of learning rate on parameter error and convergence time. Moreover, the calibration algorithm allows for fast and accurate learning of the encoding model and for fast convergence of decoding to accurate performance. Finally, larger learning rates result in inaccurate encoding models and decoders, and smaller learning rates delay their convergence. The calibration algorithm provides a novel analytical approach to predictably achieve a desired level of error and convergence time in adaptive learning, with application to closed-loop neurotechnologies and other signal processing domains. PMID:29813069

Optimizing the learning rate for adaptive estimation of neural encoding models.

PubMed

Hsieh, Han-Lin; Shanechi, Maryam M

2018-05-01

Closed-loop neurotechnologies often need to adaptively learn an encoding model that relates the neural activity to the brain state, and is used for brain state decoding. The speed and accuracy of adaptive learning algorithms are critically affected by the learning rate, which dictates how fast model parameters are updated based on new observations. Despite the importance of the learning rate, currently an analytical approach for its selection is largely lacking and existing signal processing methods vastly tune it empirically or heuristically. Here, we develop a novel analytical calibration algorithm for optimal selection of the learning rate in adaptive Bayesian filters. We formulate the problem through a fundamental trade-off that learning rate introduces between the steady-state error and the convergence time of the estimated model parameters. We derive explicit functions that predict the effect of learning rate on error and convergence time. Using these functions, our calibration algorithm can keep the steady-state parameter error covariance smaller than a desired upper-bound while minimizing the convergence time, or keep the convergence time faster than a desired value while minimizing the error. We derive the algorithm both for discrete-valued spikes modeled as point processes nonlinearly dependent on the brain state, and for continuous-valued neural recordings modeled as Gaussian processes linearly dependent on the brain state. Using extensive closed-loop simulations, we show that the analytical solution of the calibration algorithm accurately predicts the effect of learning rate on parameter error and convergence time. Moreover, the calibration algorithm allows for fast and accurate learning of the encoding model and for fast convergence of decoding to accurate performance. Finally, larger learning rates result in inaccurate encoding models and decoders, and smaller learning rates delay their convergence. The calibration algorithm provides a novel analytical approach to predictably achieve a desired level of error and convergence time in adaptive learning, with application to closed-loop neurotechnologies and other signal processing domains.

Iranian EFL Learners' Emotional Intelligence, Learning Styles, Strategy Use, and Their L2 Achievement

ERIC Educational Resources Information Center

Afshar, Hassan Soodmand; Tofighi, Somayyeh; Hamazavi, Raouf

2016-01-01

The idea that language learning is facilitated or inhibited by a multitude of factors has prompted scholars in the field to investigate variables considered to be crucial in the process of second or foreign language learning. This study investigated relationships between emotional intelligence, learning style, language learning strategy use, and…

Using Log Variables in a Learning Management System to Evaluate Learning Activity Using the Lens of Activity Theory

ERIC Educational Resources Information Center

Park, Yeonjeong; Jo, Il-Hyun

2017-01-01

As the advance of learning technologies and analytics tools continues, learning management systems (LMSs) have been required to fulfil the growing expectations for smart learning. However, the reality regarding the level of technology integration in higher education differs considerably from such expectations or the speed of advances in…

The Education of Attention as Explanation of Variability of Practice Effects : Learning the Final Approach Phase in a Flight Simulator

ERIC Educational Resources Information Center

Huet, Michael; Jacobs, David M.; Camachon, Cyril; Missenard, Olivier; Gray, Rob; Montagne, Gilles

2011-01-01

The present study reports two experiments in which a total of 20 participants without prior flight experience practiced the final approach phase in a fixed-base simulator. All participants received self-controlled concurrent feedback during 180 practice trials. Experiment 1 shows that participants learn more quickly under variable practice…

The Effects of Cognitive: Linguistic Variables and Language Experience on Behavioural and Kinematic Performances in Nonword Learning

ERIC Educational Resources Information Center

Sasisekaran, Jayanthi; Weisberg, Sanford

2013-01-01

The aim of the present study was to investigate the effects of cognitive-linguistic variables and language experience on behavioral and kinematic measures of nonword learning in young adults. Group 1 consisted of thirteen participants who spoke American English as the first and only language. Group 2 consisted of seven participants with varying…

Perceptions of Blended Learning Competencies and Obstacles among Educational Technology Students in Light of Different Anxiety Levels and Locus of Control

ERIC Educational Resources Information Center

Aldalalah, Osamah Ahmad; Gasaymeh, Al-Mothana M.

2014-01-01

The purpose of this study was to investigate the effects of locus of control and anxiety level on the Jordanian educational technology students' perceived blended learning competencies and obstacles. The independent variables were the locus of control (Internal, External) and anxiety level (Low, Moderate, High). The dependent variables were the…

The Role of Socio-Cognitive Variables in Predicting Learning Satisfaction in Smart Schools

ERIC Educational Resources Information Center

Firoozi, Mohammad Reza; Kazemi, Ali; Jokar, Maryam

2017-01-01

The present study aimed to investigate the role of Socio-Cognitive variables in predicting learning satisfaction in Smart Schools. The population was all the primary school students studying in smart schools in the city of Shiraz in the school year 2014-2015. The sample, randomly chosen through multi-stage cluster sampling, was 383 primary school…

An Investigation of the Stability and Variability in Young Children's Self-Regulated Learning Behaviors in Kindergarten

ERIC Educational Resources Information Center

Neitzel, Carin; Connor, Lisa

2018-01-01

The authors examined the relative stability and variability of self-regulated learning (SRL) in kindergartners across various contexts (teacher-directed activities, small-group work, and independent work). They assessed the role of temperament and context on children's use of SRL while seeking to identify if there are optimal contexts for…

The Relationship between Cultural Identity, Intrinsic Motivation and Pronunciation Knowledge of Iranian EFL Learners

ERIC Educational Resources Information Center

Shabani, Somayyeh; Alipoor, Iman

2017-01-01

Gardener's (1985) socio-cultural model shows that culture is among the variables that can affect learning languages. In addition, a series of studies were prompted by Dörnyie (2005) to gauge the effect of motivation on language learning. This correlational study endeavored to find out any possible interaction between these variables, i.e.,…

Analysis of Primary School Student's Science Learning Anxiety According to Some Variables

ERIC Educational Resources Information Center

Karakaya, Ferhat; Avgin, Sakine Serap; Kumperli, Ethem

2016-01-01

On this research, it is analyzed if the science learning anxiety level shows difference according to variables which are gender, grade level, science lesson grade, mother education, father education level. Scanning Design is used for this study. Research working group is consisted of 294 primary school from 6th, 7th and 8th graders on 2015-2016…

Can an Opportunity to Learn at Work Reduce Stress?: A Revisitation of the Job Demand-Control Model

ERIC Educational Resources Information Center

Panari, Chiara; Guglielmi, Dina; Simbula, Silvia; Depolo, Marco

2010-01-01

Purpose: This paper aims to extend the stress-buffering hypothesis of the demand-control model. In addition to the control variable, it seeks to analyse the role of an opportunity for learning and development (L&D) in the workplace as a moderator variable between increased demands and need for recovery. Design/methodology/approach: A…

Predicting Teachers' Use of Digital Learning Materials: Combining Self-Determination Theory and the Integrative Model of Behaviour Prediction

ERIC Educational Resources Information Center

Kreijns, Karel; Vermeulen, Marjan; Van Acker, Frederik; van Buuren, Hans

2014-01-01

In this article, we report on a study that investigated the motivational (e.g. intrinsic motivation) and dispositional variables (e.g. attitudes) that determine teachers' intention to use or not to use digital learning materials (DLMs). To understand the direct and indirect relationships between these variables, we replicated a study in which…

Help me if I can't: Social interaction effects in adult contextual word learning.

PubMed

Verga, Laura; Kotz, Sonja A

2017-11-01

A major challenge in second language acquisition is to build up new vocabulary. How is it possible to identify the meaning of a new word among several possible referents? Adult learners typically use contextual information, which reduces the number of possible referents a new word can have. Alternatively, a social partner may facilitate word learning by directing the learner's attention toward the correct new word meaning. While much is known about the role of this form of 'joint attention' in first language acquisition, little is known about its efficacy in second language acquisition. Consequently, we introduce and validate a novel visual word learning game to evaluate how joint attention affects the contextual learning of new words in a second language. Adult learners either acquired new words in a constant or variable sentence context by playing the game with a knowledgeable partner, or by playing the game alone on a computer. Results clearly show that participants who learned new words in social interaction (i) are faster in identifying a correct new word referent in variable sentence contexts, and (ii) temporally coordinate their behavior with a social partner. Testing the learned words in a post-learning recall or recognition task showed that participants, who learned interactively, better recognized words originally learned in a variable context. While this result may suggest that interactive learning facilitates the allocation of attention to a target referent, the differences in the performance during recognition and recall call for further studies investigating the effect of social interaction on learning performance. In summary, we provide first evidence on the role joint attention in second language learning. Furthermore, the new interactive learning game offers itself to further testing in complex neuroimaging research, where the lack of appropriate experimental set-ups has so far limited the investigation of the neural basis of adult word learning in social interaction. Copyright © 2017 Elsevier B.V. All rights reserved.

Absence of an association between insulin-dependent diabetes mellitus and developmental learning difficulties.

PubMed

Crawford, S G; Kaplan, B J; Field, L L

1995-01-01

For several years, investigators have been examining the relationship between learning difficulties and a variety of immunological disorders. Two recent studies by Hansen and colleagues reported a negative association between Type 1 diabetes and reading disabilities (dyslexia): subjects with Type 1 diabetes had a lower prevalence of dyslexia than their nondiabetic relatives. In order to control for the impact of environmental variables on learning, we investigated the relationship between Type 1 diabetes and learning problems in 27 sibling pairs, ranging in age from 6 to 20 years. One child in each pair had Type 1 diabetes, and the other child was the unaffected sibling closest in age. Children were assessed for cognitive skills, academic achievement in reading, mathematics, and written language, as well as for speech articulation and motor coordination. Other variables that were examined included handedness, behavioural variables, medical history, and pregnancy and birth complications. We found no significant differences between the 27 children with Type 1 diabetes and their unaffected siblings on any of the cognitive, academic achievement, or speech articulation measures. There were also no significant differences on handedness, behavioural variables, or health history.

A hybrid machine learning model to estimate nitrate contamination of production zone groundwater in the Central Valley, California

NASA Astrophysics Data System (ADS)

Ransom, K.; Nolan, B. T.; Faunt, C. C.; Bell, A.; Gronberg, J.; Traum, J.; Wheeler, D. C.; Rosecrans, C.; Belitz, K.; Eberts, S.; Harter, T.

2016-12-01

A hybrid, non-linear, machine learning statistical model was developed within a statistical learning framework to predict nitrate contamination of groundwater to depths of approximately 500 m below ground surface in the Central Valley, California. A database of 213 predictor variables representing well characteristics, historical and current field and county scale nitrogen mass balance, historical and current landuse, oxidation/reduction conditions, groundwater flow, climate, soil characteristics, depth to groundwater, and groundwater age were assigned to over 6,000 private supply and public supply wells measured previously for nitrate and located throughout the study area. The machine learning method, gradient boosting machine (GBM) was used to screen predictor variables and rank them in order of importance in relation to the groundwater nitrate measurements. The top five most important predictor variables included oxidation/reduction characteristics, historical field scale nitrogen mass balance, climate, and depth to 60 year old water. Twenty-two variables were selected for the final model and final model errors for log-transformed hold-out data were R squared of 0.45 and root mean square error (RMSE) of 1.124. Modeled mean groundwater age was tested separately for error improvement in the model and when included decreased model RMSE by 0.5% compared to the same model without age and by 0.20% compared to the model with all 213 variables. 1D and 2D partial plots were examined to determine how variables behave individually and interact in the model. Some variables behaved as expected: log nitrate decreased with increasing probability of anoxic conditions and depth to 60 year old water, generally decreased with increasing natural landuse surrounding wells and increasing mean groundwater age, generally increased with increased minimum depth to high water table and with increased base flow index value. Other variables exhibited much more erratic or noisy behavior in the model making them more difficult to interpret but highlighting the usefulness of the non-linear machine learning method. 2D interaction plots show probability of anoxic groundwater conditions largely control estimated nitrate concentrations compared to the other predictors.

Incidental Auditory Category Learning

PubMed Central

Gabay, Yafit; Dick, Frederic K.; Zevin, Jason D.; Holt, Lori L.

2015-01-01

Very little is known about how auditory categories are learned incidentally, without instructions to search for category-diagnostic dimensions, overt category decisions, or experimenter-provided feedback. This is an important gap because learning in the natural environment does not arise from explicit feedback and there is evidence that the learning systems engaged by traditional tasks are distinct from those recruited by incidental category learning. We examined incidental auditory category learning with a novel paradigm, the Systematic Multimodal Associations Reaction Time (SMART) task, in which participants rapidly detect and report the appearance of a visual target in one of four possible screen locations. Although the overt task is rapid visual detection, a brief sequence of sounds precedes each visual target. These sounds are drawn from one of four distinct sound categories that predict the location of the upcoming visual target. These many-to-one auditory-to-visuomotor correspondences support incidental auditory category learning. Participants incidentally learn categories of complex acoustic exemplars and generalize this learning to novel exemplars and tasks. Further, learning is facilitated when category exemplar variability is more tightly coupled to the visuomotor associations than when the same stimulus variability is experienced across trials. We relate these findings to phonetic category learning. PMID:26010588

Vocal exploration is locally regulated during song learning

PubMed Central

Ravbar, Primoz; Parra, Lucas C.; Lipkind, Dina; Tchernichovski, Ofer

2012-01-01

Exploratory variability is essential for sensory-motor learning, but it is not known how and at what time scales it is regulated. We manipulated song learning in zebra finches to experimentally control the requirements for vocal exploration in different parts of their song. We first trained birds to perform a one-syllable song, and once they mastered it we added a new syllable to the song model. Remarkably, when practicing the modified song, birds rapidly alternated between high and low acoustic variability to confine vocal exploration to the newly added syllable. Further, even within syllables, acoustic variability changed independently across song elements that were only milliseconds apart. Analysis of the entire vocal output during learning revealed that the variability of each song element decreased as it approached the target, correlating with momentary local distance from the target and less so with the overall distance. We conclude that vocal error is computed locally in sub-syllabic time scales and that song elements can be learned and crystalized independently. Songbirds have dedicated brain circuitry for vocal babbling in the anterior forebrain pathway (AFP), which generates exploratory song patterns that drive premotor neurons at the song nucleus RA (robust nucleus of the arcopallium). We hypothesize that either AFP adjusts the gain of vocal exploration in fine time scales, or that the sensitivity of RA premotor neurons to AFP/HVC inputs varies across song elements. PMID:22399765

Does Variability Across Events Affect Verb Learning in English, Mandarin and Korean?

PubMed Central

Childers, Jane B.; Paik, Jae H.; Flores, Melissa; Lai, Gabrielle; Dolan, Megan

2016-01-01

Extending new verbs is important to becoming a productive speaker of a language. Prior results show children have difficulty extending verbs when they have seen events with varied agents. This paper further examines the impact of variability on verb learning, and asks whether this interacts with event complexity or differs by language. Children (aged 2 ½- to 3-years) in the U.S., China, Korea and Singapore learned verbs linked to simple and complex events. Sets of events included one or three agents, and children were asked to extend the verb at test. Children learning verbs linked to simple movements performed similarly across conditions. However, children learning verbs linked to events with multiple objects were less successful if those events were enacted by multiple agents. A follow-up study rules out an influence of event order. Overall, similar patterns of results emerged across languages, suggesting common cognitive processes support children’s verb learning. PMID:27457679

TIMSS 2011 Student and Teacher Predictors for Mathematics Achievement Explored and Identified via Elastic Net.

PubMed

Yoo, Jin Eun

2018-01-01

A substantial body of research has been conducted on variables relating to students' mathematics achievement with TIMSS. However, most studies have employed conventional statistical methods, and have focused on selected few indicators instead of utilizing hundreds of variables TIMSS provides. This study aimed to find a prediction model for students' mathematics achievement using as many TIMSS student and teacher variables as possible. Elastic net, the selected machine learning technique in this study, takes advantage of both LASSO and ridge in terms of variable selection and multicollinearity, respectively. A logistic regression model was also employed to predict TIMSS 2011 Korean 4th graders' mathematics achievement. Ten-fold cross-validation with mean squared error was employed to determine the elastic net regularization parameter. Among 162 TIMSS variables explored, 12 student and 5 teacher variables were selected in the elastic net model, and the prediction accuracy, sensitivity, and specificity were 76.06, 70.23, and 80.34%, respectively. This study showed that the elastic net method can be successfully applied to educational large-scale data by selecting a subset of variables with reasonable prediction accuracy and finding new variables to predict students' mathematics achievement. Newly found variables via machine learning can shed light on the existing theories from a totally different perspective, which in turn propagates creation of a new theory or complement of existing ones. This study also examined the current scale development convention from a machine learning perspective.

TIMSS 2011 Student and Teacher Predictors for Mathematics Achievement Explored and Identified via Elastic Net

PubMed Central

Yoo, Jin Eun

2018-01-01

A substantial body of research has been conducted on variables relating to students' mathematics achievement with TIMSS. However, most studies have employed conventional statistical methods, and have focused on selected few indicators instead of utilizing hundreds of variables TIMSS provides. This study aimed to find a prediction model for students' mathematics achievement using as many TIMSS student and teacher variables as possible. Elastic net, the selected machine learning technique in this study, takes advantage of both LASSO and ridge in terms of variable selection and multicollinearity, respectively. A logistic regression model was also employed to predict TIMSS 2011 Korean 4th graders' mathematics achievement. Ten-fold cross-validation with mean squared error was employed to determine the elastic net regularization parameter. Among 162 TIMSS variables explored, 12 student and 5 teacher variables were selected in the elastic net model, and the prediction accuracy, sensitivity, and specificity were 76.06, 70.23, and 80.34%, respectively. This study showed that the elastic net method can be successfully applied to educational large-scale data by selecting a subset of variables with reasonable prediction accuracy and finding new variables to predict students' mathematics achievement. Newly found variables via machine learning can shed light on the existing theories from a totally different perspective, which in turn propagates creation of a new theory or complement of existing ones. This study also examined the current scale development convention from a machine learning perspective. PMID:29599736

A robust sound perception model suitable for neuromorphic implementation.

PubMed

Coath, Martin; Sheik, Sadique; Chicca, Elisabetta; Indiveri, Giacomo; Denham, Susan L; Wennekers, Thomas

2013-01-01

We have recently demonstrated the emergence of dynamic feature sensitivity through exposure to formative stimuli in a real-time neuromorphic system implementing a hybrid analog/digital network of spiking neurons. This network, inspired by models of auditory processing in mammals, includes several mutually connected layers with distance-dependent transmission delays and learning in the form of spike timing dependent plasticity, which effects stimulus-driven changes in the network connectivity. Here we present results that demonstrate that the network is robust to a range of variations in the stimulus pattern, such as are found in naturalistic stimuli and neural responses. This robustness is a property critical to the development of realistic, electronic neuromorphic systems. We analyze the variability of the response of the network to "noisy" stimuli which allows us to characterize the acuity in information-theoretic terms. This provides an objective basis for the quantitative comparison of networks, their connectivity patterns, and learning strategies, which can inform future design decisions. We also show, using stimuli derived from speech samples, that the principles are robust to other challenges, such as variable presentation rate, that would have to be met by systems deployed in the real world. Finally we demonstrate the potential applicability of the approach to real sounds.

An interactive tool for outdoor computer controlled cultivation of microalgae in a tubular photobioreactor system.

PubMed

Dormido, Raquel; Sánchez, José; Duro, Natividad; Dormido-Canto, Sebastián; Guinaldo, María; Dormido, Sebastián

2014-03-06

This paper describes an interactive virtual laboratory for experimenting with an outdoor tubular photobioreactor (henceforth PBR for short). This virtual laboratory it makes possible to: (a) accurately reproduce the structure of a real plant (the PBR designed and built by the Department of Chemical Engineering of the University of Almería, Spain); (b) simulate a generic tubular PBR by changing the PBR geometry; (c) simulate the effects of changing different operating parameters such as the conditions of the culture (pH, biomass concentration, dissolved O2, inyected CO2, etc.); (d) simulate the PBR in its environmental context; it is possible to change the geographic location of the system or the solar irradiation profile; (e) apply different control strategies to adjust different variables such as the CO2 injection, culture circulation rate or culture temperature in order to maximize the biomass production; (f) simulate the harvesting. In this way, users can learn in an intuitive way how productivity is affected by any change in the design. It facilitates the learning of how to manipulate essential variables for microalgae growth to design an optimal PBR. The simulator has been developed with Easy Java Simulations, a freeware open-source tool developed in Java, specifically designed for the creation of interactive dynamic simulations.

An Interactive Tool for Outdoor Computer Controlled Cultivation of Microalgae in a Tubular Photobioreactor System

PubMed Central

Dormido, Raquel; Sánchez, José; Duro, Natividad; Dormido-Canto, Sebastián; Guinaldo, María; Dormido, Sebastián

2014-01-01

This paper describes an interactive virtual laboratory for experimenting with an outdoor tubular photobioreactor (henceforth PBR for short). This virtual laboratory it makes possible to: (a) accurately reproduce the structure of a real plant (the PBR designed and built by the Department of Chemical Engineering of the University of Almería, Spain); (b) simulate a generic tubular PBR by changing the PBR geometry; (c) simulate the effects of changing different operating parameters such as the conditions of the culture (pH, biomass concentration, dissolved O2, inyected CO2, etc.); (d) simulate the PBR in its environmental context; it is possible to change the geographic location of the system or the solar irradiation profile; (e) apply different control strategies to adjust different variables such as the CO2 injection, culture circulation rate or culture temperature in order to maximize the biomass production; (f) simulate the harvesting. In this way, users can learn in an intuitive way how productivity is affected by any change in the design. It facilitates the learning of how to manipulate essential variables for microalgae growth to design an optimal PBR. The simulator has been developed with Easy Java Simulations, a freeware open-source tool developed in Java, specifically designed for the creation of interactive dynamic simulations. PMID:24662450

Regulating Approaches to Learning: Testing Learning Strategy Convergences across a Year at University

ERIC Educational Resources Information Center

Fryer, Luke K.; Vermunt, Jan D.

2018-01-01

Background: Contemporary models of student learning within higher education are often inclusive of processing and regulation strategies. Considerable research has examined their use over time and their (person-centred) convergence. The longitudinal stability/variability of learning strategy use, however, is poorly understood, but essential to…

Factors Affecting M-Learners' Course Satisfaction and Learning Persistence

ERIC Educational Resources Information Center

Joo, Young Ju; Joung, Sunyoung; Lim, Eugene; Kim, Hae Jin

2014-01-01

This study investigated whether college students' self-efficacy, level of learning strategy use, academic burnout, and school support predict course satisfaction and learning persistence. To this end, self-efficacy, level of learning strategy use, academic burnout, and school support were used as prediction variables, and course satisfaction and…

E-Learning Divides in North Cyprus

ERIC Educational Resources Information Center

Uzunboylu, Huseyin; Tuncay, Nazime

2009-01-01

The purpose of this study is to find out the differences in e-learning competences of teachers. The independent variables used were geographic location, teaching experience, Internet access, e-learning training needs, ICT teacher/non-ICT teachers, and status. A questionnaire was developed to examine the e-learning competencies of vocational…

Approaches to Learning and Study Orchestrations in High School Students

ERIC Educational Resources Information Center

Cano, Francisco

2007-01-01

In the framework of the SAL (Students' approaches to learning) position, the learning experience (approaches to learning and study orchestrations) of 572 high school students was explored, examining its interrelationships with some personal and familial variables. Three major results emerged. First, links were found between family's intellectual…

Verification of 'learning credits' by GP appraisers.

PubMed

Murie, Jill; Wakeling, Judy

2011-11-01

The RCGP CPD Learning Credits system aims to enable GPs to demonstrate knowledge and skills relevant to their daily practice. Credits are self-assessed and will form part of the 'evidence' necessary for successful revalidation. At an appraisal, GP appraisers verify the credits in terms of the time spent on the CPD activity and its impact on the GP's practice. The purpose of this study was to examine the extent to which GPs (as appraisees) are able to self-assess their own learning and, as appraisers, verify credits in a standardised way. All 17 GP appraisers in NHS Lanarkshire were invited to participate in a study, which triangulated three sources of evidence on credits: self-rating, peer-assessment and workshop discussion. The resultant data were analysed on an Excel spreadsheet. Outcomes included self-assessed credit value, peer-assessed mean score (range) and free text. Of the 17 appraisers, 15 completed the paperwork and 13 attended the workshop. GPs' self-assessed learning credits were equivalent to peer-assessed score in 5/15 cases, but considered overestimates in 4/15 and underestimates in 6/15 cases. The most extreme variance was for an oncology module, where the variance ranged from 28% to 200% of the self-assessed score. GPs have a variable understanding of how to award themselves learning credits and of how to judge the credits of potential appraisees. Without adequate resources for appraisal training, validated instruments, calibration and reliability, verification of the learning credit system will be flawed by its subjective and arbitrary nature.

Computer vision cracks the leaf code

PubMed Central

Wilf, Peter; Zhang, Shengping; Chikkerur, Sharat; Little, Stefan A.; Wing, Scott L.; Serre, Thomas

2016-01-01

Understanding the extremely variable, complex shape and venation characters of angiosperm leaves is one of the most challenging problems in botany. Machine learning offers opportunities to analyze large numbers of specimens, to discover novel leaf features of angiosperm clades that may have phylogenetic significance, and to use those characters to classify unknowns. Previous computer vision approaches have primarily focused on leaf identification at the species level. It remains an open question whether learning and classification are possible among major evolutionary groups such as families and orders, which usually contain hundreds to thousands of species each and exhibit many times the foliar variation of individual species. Here, we tested whether a computer vision algorithm could use a database of 7,597 leaf images from 2,001 genera to learn features of botanical families and orders, then classify novel images. The images are of cleared leaves, specimens that are chemically bleached, then stained to reveal venation. Machine learning was used to learn a codebook of visual elements representing leaf shape and venation patterns. The resulting automated system learned to classify images into families and orders with a success rate many times greater than chance. Of direct botanical interest, the responses of diagnostic features can be visualized on leaf images as heat maps, which are likely to prompt recognition and evolutionary interpretation of a wealth of novel morphological characters. With assistance from computer vision, leaves are poised to make numerous new contributions to systematic and paleobotanical studies. PMID:26951664

Efficient dual approach to distance metric learning.

PubMed

Shen, Chunhua; Kim, Junae; Liu, Fayao; Wang, Lei; van den Hengel, Anton

2014-02-01

Distance metric learning is of fundamental interest in machine learning because the employed distance metric can significantly affect the performance of many learning methods. Quadratic Mahalanobis metric learning is a popular approach to the problem, but typically requires solving a semidefinite programming (SDP) problem, which is computationally expensive. The worst case complexity of solving an SDP problem involving a matrix variable of size D×D with O(D) linear constraints is about O(D(6.5)) using interior-point methods, where D is the dimension of the input data. Thus, the interior-point methods only practically solve problems exhibiting less than a few thousand variables. Because the number of variables is D(D+1)/2, this implies a limit upon the size of problem that can practically be solved around a few hundred dimensions. The complexity of the popular quadratic Mahalanobis metric learning approach thus limits the size of problem to which metric learning can be applied. Here, we propose a significantly more efficient and scalable approach to the metric learning problem based on the Lagrange dual formulation of the problem. The proposed formulation is much simpler to implement, and therefore allows much larger Mahalanobis metric learning problems to be solved. The time complexity of the proposed method is roughly O(D(3)), which is significantly lower than that of the SDP approach. Experiments on a variety of data sets demonstrate that the proposed method achieves an accuracy comparable with the state of the art, but is applicable to significantly larger problems. We also show that the proposed method can be applied to solve more general Frobenius norm regularized SDP problems approximately.

Clinical learning environment and supervision: experiences of Norwegian nursing students - a questionnaire survey.

PubMed

Skaalvik, Mari Wolff; Normann, Hans Ketil; Henriksen, Nils

2011-08-01

To measure nursing students' experiences and satisfaction with their clinical learning environments. The primary interest was to compare the results between students with respect to clinical practice in nursing homes and hospital wards. Clinical learning environments are important for the learning processes of nursing students and for preferences for future workplaces. Working with older people is the least preferred area of practice among nursing students in Norway. A cross-sectional design. A validated questionnaire was distributed to all nursing students from five non-randomly selected university colleges in Norway. A total of 511 nursing students completed a Norwegian version of the questionnaire, Clinical Learning Environment, Supervision and Nurse Teacher (CLES+T) evaluation scale in 2009. Data including descriptive statistics were analysed using the Statistical Program for the Social Sciences. Factor structure was analysed by principal component analysis. Differences across sub-groups were tested with chi-square tests and Mann-Whitney U test for categorical variables and t-tests for continuous variables. Ordinal logistic regression analysis of perceptions of the ward as a good learning environment was performed with supervisory relationships and institutional contexts as independent variables, controlling for age, sex and study year. The participating nursing students with clinical placements in nursing homes assessed their clinical learning environment significantly more negatively than those with hospital placements on nearby all sub-dimensions. The evidence found in this study indicates that measures should be taken to strengthen nursing homes as learning environments for nursing students. To recruit more graduated nurses to work in nursing homes, actions to improve the learning environment are needed. © 2011 Blackwell Publishing Ltd.

Learning planar Ising models

DOE PAGES

Johnson, Jason K.; Oyen, Diane Adele; Chertkov, Michael; ...

2016-12-01

Inference and learning of graphical models are both well-studied problems in statistics and machine learning that have found many applications in science and engineering. However, exact inference is intractable in general graphical models, which suggests the problem of seeking the best approximation to a collection of random variables within some tractable family of graphical models. In this paper, we focus on the class of planar Ising models, for which exact inference is tractable using techniques of statistical physics. Based on these techniques and recent methods for planarity testing and planar embedding, we propose a greedy algorithm for learning the bestmore » planar Ising model to approximate an arbitrary collection of binary random variables (possibly from sample data). Given the set of all pairwise correlations among variables, we select a planar graph and optimal planar Ising model defined on this graph to best approximate that set of correlations. Finally, we demonstrate our method in simulations and for two applications: modeling senate voting records and identifying geo-chemical depth trends from Mars rover data.« less

Learning planar Ising models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Johnson, Jason K.; Oyen, Diane Adele; Chertkov, Michael

Inference and learning of graphical models are both well-studied problems in statistics and machine learning that have found many applications in science and engineering. However, exact inference is intractable in general graphical models, which suggests the problem of seeking the best approximation to a collection of random variables within some tractable family of graphical models. In this paper, we focus on the class of planar Ising models, for which exact inference is tractable using techniques of statistical physics. Based on these techniques and recent methods for planarity testing and planar embedding, we propose a greedy algorithm for learning the bestmore » planar Ising model to approximate an arbitrary collection of binary random variables (possibly from sample data). Given the set of all pairwise correlations among variables, we select a planar graph and optimal planar Ising model defined on this graph to best approximate that set of correlations. Finally, we demonstrate our method in simulations and for two applications: modeling senate voting records and identifying geo-chemical depth trends from Mars rover data.« less

Conative aptitudes in science learning

NASA Astrophysics Data System (ADS)

Jackson, Douglas Northrop, III

2000-09-01

The conative domain of aptitude constructs spans the domains of individual differences in motivation and volition. This research sampled a broad range of conative constructs, including achievement motivation, anxiety, goal orientations, and interest, among others. The purpose was threefold: (a) to explore relationships among conative constructs hypothesized to affect student commitment to learning and subsequent performance, (b) to determine whether or not individual differences in conative constructs were associated with the learning activities and time-on-task of students learning science, and (c) to ascertain whether or not the conative constructs and the time and activity variables were associated with performance differences in a paper-and-pencil science recall measure. This research consisted of three separate studies. Study I involved 60 U.S. college students. In Study II, 234 Canadian high school students participated. These two studies investigated the construct validity of a selection of conative constructs. A principal components analysis of the measures was undertaken and yielded seven components: Pursuit of Excellence, Evaluation Anxiety, Self-Reported Grades, Science Confidence, Science Interest vs. Science Ambivalence, Performance Orientation, and Verbal Ability. For Study III, 82 Canadian high school students completed the same conative questionnaires as were administered in Study II. A computerized environment patterned after an internet browser allowed students to learn about disease-causing microbes. The environment yielded aggregate measures of the time spent learning science, the time spent playing games, the number of games played, and the number of science-related learning activities engaged in by each student. Following administration of the computerized learning environment, students were administered a paper-and pencil science recall measure. Study III found support for the educational importance of the conative variables. Among the principal components, the strongest positive relationship was found between Science Interest vs. Science Ambivalence and performance on the recall measure. Scores on the conative variables were also correlated with both the time and activity variables from the computerized learning task. The implications of the findings are discussed with regard to the construct validation of conative constructs, the use of conative constructs for future educational research, and the design of computerized learning environments for both educational research and applied use.

HUMAN DECISIONS AND MACHINE PREDICTIONS.

PubMed

Kleinberg, Jon; Lakkaraju, Himabindu; Leskovec, Jure; Ludwig, Jens; Mullainathan, Sendhil

2018-02-01

Can machine learning improve human decision making? Bail decisions provide a good test case. Millions of times each year, judges make jail-or-release decisions that hinge on a prediction of what a defendant would do if released. The concreteness of the prediction task combined with the volume of data available makes this a promising machine-learning application. Yet comparing the algorithm to judges proves complicated. First, the available data are generated by prior judge decisions. We only observe crime outcomes for released defendants, not for those judges detained. This makes it hard to evaluate counterfactual decision rules based on algorithmic predictions. Second, judges may have a broader set of preferences than the variable the algorithm predicts; for instance, judges may care specifically about violent crimes or about racial inequities. We deal with these problems using different econometric strategies, such as quasi-random assignment of cases to judges. Even accounting for these concerns, our results suggest potentially large welfare gains: one policy simulation shows crime reductions up to 24.7% with no change in jailing rates, or jailing rate reductions up to 41.9% with no increase in crime rates. Moreover, all categories of crime, including violent crimes, show reductions; and these gains can be achieved while simultaneously reducing racial disparities. These results suggest that while machine learning can be valuable, realizing this value requires integrating these tools into an economic framework: being clear about the link between predictions and decisions; specifying the scope of payoff functions; and constructing unbiased decision counterfactuals. JEL Codes: C10 (Econometric and statistical methods and methodology), C55 (Large datasets: Modeling and analysis), K40 (Legal procedure, the legal system, and illegal behavior).

HUMAN DECISIONS AND MACHINE PREDICTIONS*

PubMed Central

Kleinberg, Jon; Lakkaraju, Himabindu; Leskovec, Jure; Ludwig, Jens; Mullainathan, Sendhil

2018-01-01

Can machine learning improve human decision making? Bail decisions provide a good test case. Millions of times each year, judges make jail-or-release decisions that hinge on a prediction of what a defendant would do if released. The concreteness of the prediction task combined with the volume of data available makes this a promising machine-learning application. Yet comparing the algorithm to judges proves complicated. First, the available data are generated by prior judge decisions. We only observe crime outcomes for released defendants, not for those judges detained. This makes it hard to evaluate counterfactual decision rules based on algorithmic predictions. Second, judges may have a broader set of preferences than the variable the algorithm predicts; for instance, judges may care specifically about violent crimes or about racial inequities. We deal with these problems using different econometric strategies, such as quasi-random assignment of cases to judges. Even accounting for these concerns, our results suggest potentially large welfare gains: one policy simulation shows crime reductions up to 24.7% with no change in jailing rates, or jailing rate reductions up to 41.9% with no increase in crime rates. Moreover, all categories of crime, including violent crimes, show reductions; and these gains can be achieved while simultaneously reducing racial disparities. These results suggest that while machine learning can be valuable, realizing this value requires integrating these tools into an economic framework: being clear about the link between predictions and decisions; specifying the scope of payoff functions; and constructing unbiased decision counterfactuals. JEL Codes: C10 (Econometric and statistical methods and methodology), C55 (Large datasets: Modeling and analysis), K40 (Legal procedure, the legal system, and illegal behavior) PMID:29755141

Variable complexity online sequential extreme learning machine, with applications to streamflow prediction

NASA Astrophysics Data System (ADS)

Lima, Aranildo R.; Hsieh, William W.; Cannon, Alex J.

2017-12-01

In situations where new data arrive continually, online learning algorithms are computationally much less costly than batch learning ones in maintaining the model up-to-date. The extreme learning machine (ELM), a single hidden layer artificial neural network with random weights in the hidden layer, is solved by linear least squares, and has an online learning version, the online sequential ELM (OSELM). As more data become available during online learning, information on the longer time scale becomes available, so ideally the model complexity should be allowed to change, but the number of hidden nodes (HN) remains fixed in OSELM. A variable complexity VC-OSELM algorithm is proposed to dynamically add or remove HN in the OSELM, allowing the model complexity to vary automatically as online learning proceeds. The performance of VC-OSELM was compared with OSELM in daily streamflow predictions at two hydrological stations in British Columbia, Canada, with VC-OSELM significantly outperforming OSELM in mean absolute error, root mean squared error and Nash-Sutcliffe efficiency at both stations.

Exploration of joint redundancy but not task space variability facilitates supervised motor learning.

PubMed

Singh, Puneet; Jana, Sumitash; Ghosal, Ashitava; Murthy, Aditya

2016-12-13

The number of joints and muscles in a human arm is more than what is required for reaching to a desired point in 3D space. Although previous studies have emphasized how such redundancy and the associated flexibility may play an important role in path planning, control of noise, and optimization of motion, whether and how redundancy might promote motor learning has not been investigated. In this work, we quantify redundancy space and investigate its significance and effect on motor learning. We propose that a larger redundancy space leads to faster learning across subjects. We observed this pattern in subjects learning novel kinematics (visuomotor adaptation) and dynamics (force-field adaptation). Interestingly, we also observed differences in the redundancy space between the dominant hand and nondominant hand that explained differences in the learning of dynamics. Taken together, these results provide support for the hypothesis that redundancy aids in motor learning and that the redundant component of motor variability is not noise.

The relationships among nurses' job characteristics and attitudes toward web-based continuing learning.

PubMed

Chiu, Yen-Lin; Tsai, Chin-Chung; Fan Chiang, Chih-Yun

2013-04-01

The purpose of this study was to explore the relationships between job characteristics (job demands, job control and social support) and nurses' attitudes toward web-based continuing learning. A total of 221 in-service nurses from hospitals in Taiwan were surveyed. The Attitudes toward Web-based Continuing Learning Survey (AWCL) was employed as the outcome variables, and the Chinese version Job Characteristic Questionnaire (C-JCQ) was administered to assess the predictors for explaining the nurses' attitudes toward web-based continuing learning. To examine the relationships among these variables, hierarchical regression was conducted. The results of the regression analysis revealed that job control and social support positively associated with nurses' attitudes toward web-based continuing learning. However, the relationship of job demands to such learning was not significant. Moreover, a significant demands×job control interaction was found, but the job demands×social support interaction had no significant relationships with attitudes toward web-based continuing learning. Copyright © 2013 Elsevier Ltd. All rights reserved.

Exploration of joint redundancy but not task space variability facilitates supervised motor learning

PubMed Central

Singh, Puneet; Jana, Sumitash; Ghosal, Ashitava; Murthy, Aditya

2016-01-01

The number of joints and muscles in a human arm is more than what is required for reaching to a desired point in 3D space. Although previous studies have emphasized how such redundancy and the associated flexibility may play an important role in path planning, control of noise, and optimization of motion, whether and how redundancy might promote motor learning has not been investigated. In this work, we quantify redundancy space and investigate its significance and effect on motor learning. We propose that a larger redundancy space leads to faster learning across subjects. We observed this pattern in subjects learning novel kinematics (visuomotor adaptation) and dynamics (force-field adaptation). Interestingly, we also observed differences in the redundancy space between the dominant hand and nondominant hand that explained differences in the learning of dynamics. Taken together, these results provide support for the hypothesis that redundancy aids in motor learning and that the redundant component of motor variability is not noise. PMID:27911808