How to Assess Creative Thinking Skill in Making Products of Liquid Pressure?

NASA Astrophysics Data System (ADS)

Chasanah, L.; Kaniawati, I.; Hernani, H.

2017-09-01

The primary skills that must be possessed in the 21st century curriculum are learning and innovation skills. One of the learning strategies that can train students to innovate and improve creative thinking skills is by applying Science, Technology, Engineering and Mathematics (STEM) in learning. Based on an interview to one of the science teachers that learning that aims to train learning and innovation skills has not been applied to learning in the classroom because there is not enough time, learning materials and assessment instruments used. This study aims to determine the results of the implementation of performance assessment of creative thinking skills on STEM-based learning in junior high school for the material of liquid pressure. This research uses descriptive method. Participants in this study were junior high school students 8th in Kudus area. The research instrument consists of observation sheet, performance assessment and documentation. The result showed that creative thinking skills performance assessment can assess student’s creativity in making products of STEM-based learning for junior high school.

The dark and bright sides of self-efficacy in predicting learning, innovative and risky performances.

PubMed

Salanova, Marisa; Lorente, Laura; Martínez, Isabel M

2012-11-01

The objective of this study is to analyze the different role that efficacy beliefs play in the prediction of learning, innovative and risky performances. We hypothesize that high levels of efficacy beliefs in learning and innovative performances have positive consequences (i.e., better academic and innovative performance, respectively), whereas in risky performances they have negative consequences (i.e., less safety performance). To achieve this objective, three studies were conducted, 1) a two-wave longitudinal field study among 527 undergraduate students (learning setting), 2) a three-wave longitudinal lab study among 165 participants performing innovative group tasks (innovative setting), and 3) a field study among 228 construction workers (risky setting). As expected, high levels of efficacy beliefs have positive or negative consequences on performance depending on the specific settings. Unexpectedly, however, we found no time x self-efficacy interaction effect over time in learning and innovative settings. Theoretical and practical implications within the social cognitive theory of A. Bandura framework are discussed.

The Preliminary Investigation of the Factors that Influence the E-Learning Adoption in Higher Education Institutes: Jordan Case Study

ERIC Educational Resources Information Center

Al-hawari, Maen; Al-halabi, Sanaa

2010-01-01

Creativity and high performance in learning processes are the main concerns of educational institutions. E-learning contributes to the creativity and performance of these institutions and reproduces a traditional learning model based primarily on knowledge transfer into more innovative models based on collaborative learning. In this paper, the…

Analysis of the ability of junior high school students’ performance in science in STEM project-based learning

NASA Astrophysics Data System (ADS)

Suryana, A.; Sinaga, P.; Suwarma, I. R.

2018-05-01

The challenges in 21st century demands the high competitiveness. The way of thinking ability, determine how it work ability and choose instrument be part of the skills will need in the 21st century. The competence it can be supported by learning involving the student performance skills. Based on the preliminary studies at one junior high school in Bandung found that the learning involving of performance skill is low.This is supported by data from respondent in received the opportunity to make devise a sketch in of learning especially based on practices or projects, the results are 75 % students said rarely and 18,75 % students said never. In addition seen also how the student activities in project based learning in class the results stated that 68,75 % of students said less, and 6.25 % of students said never. Therefore, we did a result to uncover profile performance on the design process and the performance process of junior high school student performances to the matter optical by using STEM project based learning. From this result. From the research obtained the average score classes in the activities of the design process is as much as 2,49 or dipersentasikan become 62,41 % are in the good category and the average score classes in the process of the performance of activities receive is 3,13 or 78,28 % are in the good category.

Involvement of Working Memory in College Students' Sequential Pattern Learning and Performance

ERIC Educational Resources Information Center

Kundey, Shannon M. A.; De Los Reyes, Andres; Rowan, James D.; Lee, Bern; Delise, Justin; Molina, Sabrina; Cogdill, Lindsay

2013-01-01

When learning highly organized sequential patterns of information, humans and nonhuman animals learn rules regarding the hierarchical structures of these sequences. In three experiments, we explored the role of working memory in college students' sequential pattern learning and performance in a computerized task involving a sequential…

High Bar Swing Performance in Novice Adults: Effects of Practice and Talent

ERIC Educational Resources Information Center

Busquets, Albert; Marina, Michel; Irurtia, Alfredo; Ranz, Daniel; Angulo-Barroso, Rosa M.

2011-01-01

An individual's a priori talent can affect movement performance during learning. Also, task requirements and motor-perceptual factors are critical to the learning process. This study describes changes in high bar swing performance after a 2-month practice period. Twenty-five novice participants were divided by a priori talent level…

Stressors, academic performance, and learned resourcefulness in baccalaureate nursing students.

PubMed

Goff, Anne-Marie

2011-01-01

High stress levels in nursing students may affect memory, concentration, and problem-solving ability, and may lead to decreased learning, coping, academic performance, and retention. College students with higher levels of learned resourcefulness develop greater self-confidence, motivation, and academic persistence, and are less likely to become anxious, depressed, and frustrated, but no studies specifically involve nursing students. This explanatory correlational study used Gadzella's Student-life Stress Inventory (SSI) and Rosenbaum's Self Control Scale (SCS) to explore learned resourcefulness, stressors, and academic performance in 53 baccalaureate nursing students. High levels of personal and academic stressors were evident, but not significant predictors of academic performance (p = .90). Age was a significant predictor of academic performance (p = < .01) and males and African-American/Black participants had higher learned resourcefulness scores than females and Caucasians. Studies in larger, more diverse samples are necessary to validate these findings.

Examining Middle School Science Student Self-Regulated Learning in a Hypermedia Learning Environment through Microanalysis

NASA Astrophysics Data System (ADS)

Mandell, Brian E.

The purpose of the present embedded mixed method study was to examine the self-regulatory processes used by high, average, and low achieving seventh grade students as they learned about a complex science topic from a hypermedia learning environment. Thirty participants were sampled. Participants were administered a number of measures to assess their achievement and self-efficacy. In addition, a microanalytic methodology, grounded in Zimmerman's cyclical model of self-regulated learning, was used to assess student self-regulated learning. It was hypothesized that there would be modest positive correlations between Zimmerman's three phases of self-regulated learning, that high achieving science students would deploy more self-regulatory subprocesses than average and low achieving science students, that high achieving science students would have higher self-efficacy beliefs to engage in self-regulated learning than average and low achieving science students, and that low achieving science students would over-estimate their self-efficacy for performance beliefs, average achieving science students would slightly overestimate their self-efficacy for performance beliefs, and high achieving science students would under-estimate their self-efficacy for performance beliefs. All hypotheses were supported except for the high achieving science students who under-estimated their self-efficacy for performance beliefs on the Declarative Knowledge Measure and slightly overestimated their self-efficacy for performance beliefs on the Conceptual Knowledge Measure. Finally, all measures of self-regulated learning were combined and entered into a regression formula to predict the students' scores on the two science tests, and it was revealed that the combined measure predicted 91% of the variance on the Declarative Knowledge Measure and 92% of the variance on the Conceptual Knowledge Measure. This study adds hypermedia learning environments to the contexts that the microanalytic methodology has been successfully administered. Educational implications and limitations to the study are also discussed.

Benefits of computer screen-based simulation in learning cardiac arrest procedures.

PubMed

Bonnetain, Elodie; Boucheix, Jean-Michel; Hamet, Maël; Freysz, Marc

2010-07-01

What is the best way to train medical students early so that they acquire basic skills in cardiopulmonary resuscitation as effectively as possible? Studies have shown the benefits of high-fidelity patient simulators, but have also demonstrated their limits. New computer screen-based multimedia simulators have fewer constraints than high-fidelity patient simulators. In this area, as yet, there has been no research on the effectiveness of transfer of learning from a computer screen-based simulator to more realistic situations such as those encountered with high-fidelity patient simulators. We tested the benefits of learning cardiac arrest procedures using a multimedia computer screen-based simulator in 28 Year 2 medical students. Just before the end of the traditional resuscitation course, we compared two groups. An experiment group (EG) was first asked to learn to perform the appropriate procedures in a cardiac arrest scenario (CA1) in the computer screen-based learning environment and was then tested on a high-fidelity patient simulator in another cardiac arrest simulation (CA2). While the EG was learning to perform CA1 procedures in the computer screen-based learning environment, a control group (CG) actively continued to learn cardiac arrest procedures using practical exercises in a traditional class environment. Both groups were given the same amount of practice, exercises and trials. The CG was then also tested on the high-fidelity patient simulator for CA2, after which it was asked to perform CA1 using the computer screen-based simulator. Performances with both simulators were scored on a precise 23-point scale. On the test on a high-fidelity patient simulator, the EG trained with a multimedia computer screen-based simulator performed significantly better than the CG trained with traditional exercises and practice (16.21 versus 11.13 of 23 possible points, respectively; p<0.001). Computer screen-based simulation appears to be effective in preparing learners to use high-fidelity patient simulators, which present simulations that are closer to real-life situations.

The development of automaticity in short-term memory search: Item-response learning and category learning.

PubMed

Cao, Rui; Nosofsky, Robert M; Shiffrin, Richard M

2017-05-01

In short-term-memory (STM)-search tasks, observers judge whether a test probe was present in a short list of study items. Here we investigated the long-term learning mechanisms that lead to the highly efficient STM-search performance observed under conditions of consistent-mapping (CM) training, in which targets and foils never switch roles across trials. In item-response learning, subjects learn long-term mappings between individual items and target versus foil responses. In category learning, subjects learn high-level codes corresponding to separate sets of items and learn to attach old versus new responses to these category codes. To distinguish between these 2 forms of learning, we tested subjects in categorized varied mapping (CV) conditions: There were 2 distinct categories of items, but the assignment of categories to target versus foil responses varied across trials. In cases involving arbitrary categories, CV performance closely resembled standard varied-mapping performance without categories and departed dramatically from CM performance, supporting the item-response-learning hypothesis. In cases involving prelearned categories, CV performance resembled CM performance, as long as there was sufficient practice or steps taken to reduce trial-to-trial category-switching costs. This pattern of results supports the category-coding hypothesis for sufficiently well-learned categories. Thus, item-response learning occurs rapidly and is used early in CM training; category learning is much slower but is eventually adopted and is used to increase the efficiency of search beyond that available from item-response learning. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Competency and an active learning program in undergraduate nursing education.

PubMed

Shin, Hyunsook; Sok, Sohyune; Hyun, Kyung Sun; Kim, Mi Ja

2015-03-01

To evaluate the effect of an active learning program on competency of senior students. Active learning strategies have been used to help students achieve desired nursing competency, but their effectiveness has not been systematically examined. A descriptive, cross-sectional comparative design was used. Two cohort group comparisons using t-test were made: one in an active learning group and the other in a traditional learning group. A total of 147 senior nursing students near graduation participated in this study: 73 in 2010 and 74 in 2013. The active learning program incorporated high-fidelity simulation, situation-based case studies, standardized patients, audio-video playback, reflective activities and technology such as a SmartPad-based program. The overall scores of the nursing competency in the active group were significantly higher than those in the traditional group. Of five overall subdomains, the scores of the special and general clinical performance competency, critical thinking and human understanding were significantly higher in the active group than in the traditional group. Importance-performance analysis showed that all five subdomains of the active group clustered in the high importance and high performance quadrant, indicating significantly better achievements. In contrast, the students in the traditional group showed scattered patterns in three quadrants, excluding the low importance and low performance quadrants. This pattern indicates that the traditional learning method did not yield the high performance in most important areas. The findings of this study suggest that an active learning strategy is useful for helping undergraduate students to gain competency. © 2014 John Wiley & Sons Ltd.

Instructional Leadership in Centralised Systems: Evidence from Greek High-Performing Secondary Schools

ERIC Educational Resources Information Center

Kaparou, Maria; Bush, Tony

2015-01-01

This paper examines the enactment of instructional leadership (IL) in high-performing secondary schools (HPSS), and the relationship between leadership and learning in raising student outcomes and encouraging teachers' professional learning in the highly centralised context of Greece. It reports part of a comparative research study focused on…

Interactive laboratory classes enhance neurophysiological knowledge in Thai medical students.

PubMed

Wongjarupong, Nicha; Niyomnaitham, Danai; Vilaisaktipakorn, Pitchamol; Suksiriworaboot, Tanawin; Qureshi, Shaun Peter; Bongsebandhu-Phubhakdi, Saknan

2018-03-01

Interactive laboratory class (ILC) is a two-way communication teaching method that encourages students to correlate laboratory findings with materials from lectures. In Thai medical education, active learning methods are uncommon. This paper aims to establish 1) if ILCs would effectively promote physiology learning; 2) if effectiveness would be found in both previously academically high-performing and low-performing students; and 3) the acceptability of ILCs to Thai medical students as a novel learning method. Two hundred seventy-eight second-year medical students were recruited to this study. We conducted three ILC sessions, which followed corresponding lectures. We carried out multiple-choice pre- and post-ILC assessments of knowledge and compared by repeated-measures ANOVA and unpaired t-test. Subgroup analysis was performed to compare high-performance (HighP) and low-performance (LowP) students. After the ILCs, participants self-rated their knowledge and satisfaction. Post-ILC test scores increased significantly compared with pre-ILC test scores in all three sessions. Mean scores of each post-ILC test increased significantly from pre-ILC test in both LowP and HighP groups. More students self-reported a "very high" and "high" level of knowledge after ILCs. Most students agreed that ILCs provided more discussion opportunity, motivated their learning, and made lessons more enjoyable. As an adjunct to lectures, ILCs can enhance knowledge in medical students, regardless of previous academic performance. Students perceived ILC as useful and acceptable. This study supports the active learning methods in physiology education, regardless of cultural context.

Success in introductory college physics: The role of gender, high school preparation, and student learning perceptions

NASA Astrophysics Data System (ADS)

Chen, Jean Chi-Jen

Physics is fundamental for science, engineering, medicine, and for understanding many phenomena encountered in people's daily lives. The purpose of this study was to investigate the relationships between student success in college-level introductory physics courses and various educational and background characteristics. The primary variables of this study were gender, high school mathematics and science preparation, preference and perceptions of learning physics, and performance in introductory physics courses. Demographic characteristics considered were age, student grade level, parents' occupation and level of education, high school senior grade point average, and educational goals. A Survey of Learning Preference and Perceptions was developed to collect the information for this study. A total of 267 subjects enrolled in six introductory physics courses, four algebra-based and two calculus-based, participated in the study conducted during Spring Semester 2002. The findings from the algebra-based physics courses indicated that participant's educational goal, high school senior GPA, father's educational level, mother's educational level, and mother's occupation in the area of science, engineering, or computer technology were positively related to performance while participant age was negatively related. Biology preparation, mathematics preparation, and additional mathematics and science preparation in high school were also positively related to performance. The relationships between the primary variables and performance in calculus-based physics courses were limited to high school senior year GPA and high school physics preparation. Findings from all six courses indicated that participant's educational goal, high school senior GPA, father's educational level, and mother's occupation in the area of science, engineering, or computer technology, high school preparation in mathematics, biology, and the completion of additional mathematics and science courses were positively related to performance. No significant performance differences were found between male and female students. However, there were significant gender differences in physics learning perceptions. Female participants tended to try to understand physics materials and relate the physics problems to real world situations while their male counterparts tended to rely on rote learning and equation application. This study found that participants performed better by trying to understand the physics material and relate physics problems to real world situations. Participants who relied on rote learning did not perform well.

Active Learning Strategies for Phenotypic Profiling of High-Content Screens.

PubMed

Smith, Kevin; Horvath, Peter

2014-06-01

High-content screening is a powerful method to discover new drugs and carry out basic biological research. Increasingly, high-content screens have come to rely on supervised machine learning (SML) to perform automatic phenotypic classification as an essential step of the analysis. However, this comes at a cost, namely, the labeled examples required to train the predictive model. Classification performance increases with the number of labeled examples, and because labeling examples demands time from an expert, the training process represents a significant time investment. Active learning strategies attempt to overcome this bottleneck by presenting the most relevant examples to the annotator, thereby achieving high accuracy while minimizing the cost of obtaining labeled data. In this article, we investigate the impact of active learning on single-cell-based phenotype recognition, using data from three large-scale RNA interference high-content screens representing diverse phenotypic profiling problems. We consider several combinations of active learning strategies and popular SML methods. Our results show that active learning significantly reduces the time cost and can be used to reveal the same phenotypic targets identified using SML. We also identify combinations of active learning strategies and SML methods which perform better than others on the phenotypic profiling problems we studied. © 2014 Society for Laboratory Automation and Screening.

Self-Regulated Mobile Learning and Assessment: An Evaluation of Assessment Interfaces

ERIC Educational Resources Information Center

Koorsse, Melisa; Olivier, Werner; Greyling, Jean

2014-01-01

Assessment for learning has an important role to play in self-regulated learning but the assessment interface can impact learner motivation and performance. Learners are able to assess their knowledge of learning content and, through repeated assessment and high-quality feedback, close the gap between their current performance and the performance…

The Neural Correlates of Implicit Sequence Learning in Schizophrenia

PubMed Central

Marvel, Cherie L.; Turner, Beth M.; O’Leary, Daniel S.; Johnson, Hans J.; Pierson, Ronald K.; Boles Ponto, Laura L.; Andreasen, Nancy C.

2009-01-01

Twenty-seven schizophrenia spectrum patients and 25 healthy controls performed a probabilistic version of the serial reaction time task (SRT) that included sequence trials embedded within random trials. Patients showed diminished, yet measurable, sequence learning. Postexperimental analyses revealed that a group of patients performed above chance when generating short spans of the sequence. This high-generation group showed SRT learning that was similar in magnitude to that of controls. Their learning was evident from the very 1st block; however, unlike controls, learning did not develop further with continued testing. A subset of 12 patients and 11 controls performed the SRT in conjunction with positron emission tomography. High-generation performance, which corresponded to SRT learning in patients, correlated to activity in the premotor cortex and parahippocampus. These areas have been associated with stimulus-driven visuospatial processing. Taken together, these results suggest that a subset of patients who showed moderate success on the SRT used an explicit stimulus-driven strategy to process the sequential stimuli. This adaptive strategy facilitated sequence learning but may have interfered with conventional implicit learning of the overall stimulus pattern. PMID:17983290

Impairments in learning by monetary rewards and alcohol-associated rewards in detoxified alcoholic patients.

PubMed

Jokisch, Daniel; Roser, Patrik; Juckel, Georg; Daum, Irene; Bellebaum, Christian

2014-07-01

Excessive alcohol consumption has been linked to structural and functional brain changes associated with cognitive, emotional, and behavioral impairments. It has been suggested that neural processing in the reward system is also affected by alcoholism. The present study aimed at further investigating reward-based associative learning and reversal learning in detoxified alcohol-dependent patients. Twenty-one detoxified alcohol-dependent patients and 26 healthy control subjects participated in a probabilistic learning task using monetary and alcohol-associated rewards as feedback stimuli indicating correct responses. Performance during acquisition and reversal learning in the different feedback conditions was analyzed. Alcohol-dependent patients and healthy control subjects showed an increase in learning performance over learning blocks during acquisition, with learning performance being significantly lower in alcohol-dependent patients. After changing the contingencies, alcohol-dependent patients exhibited impaired reversal learning and showed, in contrast to healthy controls, different learning curves for different types of rewards with no increase in performance for high monetary and alcohol-associated feedback. The present findings provide evidence that dysfunctional processing in the reward system in alcohol-dependent patients leads to alterations in reward-based learning resulting in a generally reduced performance. In addition, the results suggest that alcohol-dependent patients are, in particular, more impaired in changing an established behavior originally reinforced by high rewards. Copyright © 2014 by the Research Society on Alcoholism.

Effects of Concept-Mapping-Based Interactive E-Books on Active and Reflective-Style Students' Learning Performances in Junior High School Law Courses

ERIC Educational Resources Information Center

Hwang, Gwo-Jen; Sung, Han-Yu; Chang, Hsuan

2017-01-01

Researchers have pointed out that interactive e-books have rich content and interactive features which can promote students' learning interest. However, researchers have also indicated the need to integrate effective learning supports or tools to help students organize what they have learned so as to increase their learning performance, in…

Use of Visual Metaphors for Navigation in Educational Hypermedia: Effects on the Navigational Performance

ERIC Educational Resources Information Center

Firat, Mehmet; Kabakci, Isil

2010-01-01

The interactional feature of hypermedia that allows high-level student-control is considered as one of the most important advantages that hypermedia provides for learning and teaching. However, high-level student control in hypermedia might not always lead to high-level learning performance. The learner is likely to experience navigation problems…

Auditory-motor learning influences auditory memory for music.

PubMed

Brown, Rachel M; Palmer, Caroline

2012-05-01

In two experiments, we investigated how auditory-motor learning influences performers' memory for music. Skilled pianists learned novel melodies in four conditions: auditory only (listening), motor only (performing without sound), strongly coupled auditory-motor (normal performance), and weakly coupled auditory-motor (performing along with auditory recordings). Pianists' recognition of the learned melodies was better following auditory-only or auditory-motor (weakly coupled and strongly coupled) learning than following motor-only learning, and better following strongly coupled auditory-motor learning than following auditory-only learning. Auditory and motor imagery abilities modulated the learning effects: Pianists with high auditory imagery scores had better recognition following motor-only learning, suggesting that auditory imagery compensated for missing auditory feedback at the learning stage. Experiment 2 replicated the findings of Experiment 1 with melodies that contained greater variation in acoustic features. Melodies that were slower and less variable in tempo and intensity were remembered better following weakly coupled auditory-motor learning. These findings suggest that motor learning can aid performers' auditory recognition of music beyond auditory learning alone, and that motor learning is influenced by individual abilities in mental imagery and by variation in acoustic features.

Dribble Files: Methodologies to Evaluate Learning and Performance in Complex Environments

ERIC Educational Resources Information Center

Schrader, P. G.; Lawless, Kimberly A.

2007-01-01

Research in the area of technology learning environments is tremendously complex. Tasks performed in these contexts are highly cognitive and mostly invisible to the observer. The nature of performance in these contexts is explained not only by the outcome but also by the process. However, evaluating the learning process with respect to tasks…

Metacognition and High Intellectual Ability: Insights from the Study of Learning-Disabled Gifted Students.

ERIC Educational Resources Information Center

Hannah, C. Lynne; Shore, Bruce M.

1995-01-01

This study compared metacognitive performance of gifted, gifted learning-disabled, learning-disabled, and average males in grades 5 and 6 and grades 11 and 12. For metacognitive knowledge, skill on think-aloud error detection reading, and comprehension, the performance of gifted learning-disabled students resembled that of gifted students more…

Learning from Scientific Texts: Personalizing the Text Increases Transfer Performance and Task Involvement

ERIC Educational Resources Information Center

Dutke, Stephan; Grefe, Anna Christina; Leopold, Claudia

2016-01-01

In an experiment with 65 high-school students, we tested the hypothesis that personalizing learning materials would increase students' learning performance and motivation to study the learning materials. Students studied either a 915-word standard text on the anatomy and functionality of the human eye or a personalized version of the same text in…

Physical fitness modulates incidental but not intentional statistical learning of simultaneous auditory sequences during concurrent physical exercise.

PubMed

Daikoku, Tatsuya; Takahashi, Yuji; Futagami, Hiroko; Tarumoto, Nagayoshi; Yasuda, Hideki

2017-02-01

In real-world auditory environments, humans are exposed to overlapping auditory information such as those made by human voices and musical instruments even during routine physical activities such as walking and cycling. The present study investigated how concurrent physical exercise affects performance of incidental and intentional learning of overlapping auditory streams, and whether physical fitness modulates the performances of learning. Participants were grouped with 11 participants with lower and higher fitness each, based on their Vo 2 max value. They were presented simultaneous auditory sequences with a distinct statistical regularity each other (i.e. statistical learning), while they were pedaling on the bike and seating on a bike at rest. In experiment 1, they were instructed to attend to one of the two sequences and ignore to the other sequence. In experiment 2, they were instructed to attend to both of the two sequences. After exposure to the sequences, learning effects were evaluated by familiarity test. In the experiment 1, performance of statistical learning of ignored sequences during concurrent pedaling could be higher in the participants with high than low physical fitness, whereas in attended sequence, there was no significant difference in performance of statistical learning between high than low physical fitness. Furthermore, there was no significant effect of physical fitness on learning while resting. In the experiment 2, the both participants with high and low physical fitness could perform intentional statistical learning of two simultaneous sequences in the both exercise and rest sessions. The improvement in physical fitness might facilitate incidental but not intentional statistical learning of simultaneous auditory sequences during concurrent physical exercise.

Motivating learning, performance, and persistence: the synergistic effects of intrinsic goal contents and autonomy-supportive contexts.

PubMed

Vansteenkiste, Maarten; Simons, Joke; Lens, Willy; Sheldon, Kennon M; Deci, Edward L

2004-08-01

Three field experiments with high school and college students tested the self-determination theory hypotheses that intrinsic (vs. extrinsic) goals and autonomy-supportive (vs. controlling) learning climates would improve students' learning, performance, and persistence. The learning of text material or physical exercises was framed in terms of intrinsic (community, personal growth, health) versus extrinsic (money, image) goals, which were presented in an autonomy-supportive versus controlling manner. Analyses of variance confirmed that both experimentally manipulated variables yielded main effects on depth of processing, test performance, and persistence (all ps <.001), and an interaction resulted in synergistically high deep processing and test performance (but not persistence) when both intrinsic goals and autonomy support were present. Effects were significantly mediated by autonomous motivation.

Learning Style and Ability Grouping in the High School System: Some Caribbean Findings.

ERIC Educational Resources Information Center

Richardson, Arthur G.; Fergus, Eudora E.

1993-01-01

The Inventory of Learning Processes assessed the learning styles of Caribbean ninth graders (47 boys, 67 girls) in 2 ability groups. The higher ability group performed better in deep processing, fact retention, and methodical study. Girls performed better in methodical study. (SK)

Can We Train Machine Learning Methods to Outperform the High-dimensional Propensity Score Algorithm?

PubMed

Karim, Mohammad Ehsanul; Pang, Menglan; Platt, Robert W

2018-03-01

The use of retrospective health care claims datasets is frequently criticized for the lack of complete information on potential confounders. Utilizing patient's health status-related information from claims datasets as surrogates or proxies for mismeasured and unobserved confounders, the high-dimensional propensity score algorithm enables us to reduce bias. Using a previously published cohort study of postmyocardial infarction statin use (1998-2012), we compare the performance of the algorithm with a number of popular machine learning approaches for confounder selection in high-dimensional covariate spaces: random forest, least absolute shrinkage and selection operator, and elastic net. Our results suggest that, when the data analysis is done with epidemiologic principles in mind, machine learning methods perform as well as the high-dimensional propensity score algorithm. Using a plasmode framework that mimicked the empirical data, we also showed that a hybrid of machine learning and high-dimensional propensity score algorithms generally perform slightly better than both in terms of mean squared error, when a bias-based analysis is used.

Rapid learning in visual cortical networks.

PubMed

Wang, Ye; Dragoi, Valentin

2015-08-26

Although changes in brain activity during learning have been extensively examined at the single neuron level, the coding strategies employed by cell populations remain mysterious. We examined cell populations in macaque area V4 during a rapid form of perceptual learning that emerges within tens of minutes. Multiple single units and LFP responses were recorded as monkeys improved their performance in an image discrimination task. We show that the increase in behavioral performance during learning is predicted by a tight coordination of spike timing with local population activity. More spike-LFP theta synchronization is correlated with higher learning performance, while high-frequency synchronization is unrelated with changes in performance, but these changes were absent once learning had stabilized and stimuli became familiar, or in the absence of learning. These findings reveal a novel mechanism of plasticity in visual cortex by which elevated low-frequency synchronization between individual neurons and local population activity accompanies the improvement in performance during learning.

Evaluating the Effects of Executive Learning and Development on Organisational Performance: Implications for Developing Senior Manager and Executive Capabilities

ERIC Educational Resources Information Center

Akrofi, Solomon

2016-01-01

In spite of decades of research into high-performance work systems, very few studies have examined the relationship between executive learning and development and organisational performance. In an attempt to close this gap, this study explores the effects of a validated four-dimensional executive learning and development measure on a composite…

The neural correlates of implicit sequence learning in schizophrenia.

PubMed

Marvel, Cherie L; Turner, Beth M; O'Leary, Daniel S; Johnson, Hans J; Pierson, Ronald K; Ponto, Laura L Boles; Andreasen, Nancy C

2007-11-01

Twenty-seven schizophrenia spectrum patients and 25 healthy controls performed a probabilistic version of the serial reaction time task (SRT) that included sequence trials embedded within random trials. Patients showed diminished, yet measurable, sequence learning. Postexperimental analyses revealed that a group of patients performed above chance when generating short spans of the sequence. This high-generation group showed SRT learning that was similar in magnitude to that of controls. Their learning was evident from the very 1st block; however, unlike controls, learning did not develop further with continued testing. A subset of 12 patients and 11 controls performed the SRT in conjunction with positron emission tomography. High-generation performance, which corresponded to SRT learning in patients, correlated to activity in the premotor cortex and parahippocampus. These areas have been associated with stimulus-driven visuospatial processing. Taken together, these results suggest that a subset of patients who showed moderate success on the SRT used an explicit stimulus-driven strategy to process the sequential stimuli. This adaptive strategy facilitated sequence learning but may have interfered with conventional implicit learning of the overall stimulus pattern. PsycINFO Database Record (c) 2007 APA, all rights reserved.

The surprisingly high human efficiency at learning to recognize faces

PubMed Central

Peterson, Matthew F.; Abbey, Craig K.; Eckstein, Miguel P.

2009-01-01

We investigated the ability of humans to optimize face recognition performance through rapid learning of individual relevant features. We created artificial faces with discriminating visual information heavily concentrated in single features (nose, eyes, chin or mouth). In each of 2500 learning blocks a feature was randomly selected and retained over the course of four trials, during which observers identified randomly sampled, noisy face images. Observers learned the discriminating feature through indirect feedback, leading to large performance gains. Performance was compared to a learning Bayesian ideal observer, resulting in unexpectedly high learning compared to previous studies with simpler stimuli. We explore various explanations and conclude that the higher learning measured with faces cannot be driven by adaptive eye movement strategies but can be mostly accounted for by suboptimalities in human face discrimination when observers are uncertain about the discriminating feature. We show that an initial bias of humans to use specific features to perform the task even though they are informed that each of four features is equally likely to be the discriminatory feature would lead to seemingly supra-optimal learning. We also examine the possibility of inefficient human integration of visual information across the spatially distributed facial features. Together, the results suggest that humans can show large performance improvement effects in discriminating faces as they learn to identify the feature containing the discriminatory information. PMID:19000918

Model-based reinforcement learning with dimension reduction.

PubMed

Tangkaratt, Voot; Morimoto, Jun; Sugiyama, Masashi

2016-12-01

The goal of reinforcement learning is to learn an optimal policy which controls an agent to acquire the maximum cumulative reward. The model-based reinforcement learning approach learns a transition model of the environment from data, and then derives the optimal policy using the transition model. However, learning an accurate transition model in high-dimensional environments requires a large amount of data which is difficult to obtain. To overcome this difficulty, in this paper, we propose to combine model-based reinforcement learning with the recently developed least-squares conditional entropy (LSCE) method, which simultaneously performs transition model estimation and dimension reduction. We also further extend the proposed method to imitation learning scenarios. The experimental results show that policy search combined with LSCE performs well for high-dimensional control tasks including real humanoid robot control. Copyright © 2016 Elsevier Ltd. All rights reserved.

The Design and Management of an Organisation's Lifelong Learning Curriculum

ERIC Educational Resources Information Center

Dealtry, Richard

2009-01-01

Purpose: The purpose of this paper is to examine the successful design and management of high performance work-based lifelong learning processes. Design: The paper summarises the process management practices and contextual parameters that are being applied in the successful design and management of high performance work based lifelong learning…

Moving into and out of High-Performance Sport: The Cultural Learning of an Artistic Gymnast

ERIC Educational Resources Information Center

Barker-Ruchti, Natalie; Schubring, Astrid

2016-01-01

Background: High-performance sport has been described as a formative environment through which athletes learn sporting skills but also develop athletic selves. Within this process, career movements related to selection for and de-selection from representative teams constitute critical moments. Further, retirement from sport can be problematic as…

Valued Learning Experiences of Early Career and Experienced High-Performance Coaches

ERIC Educational Resources Information Center

Mallett, Clifford J.; Rynne, Steven B.; Billett, Stephen

2016-01-01

Background and purpose: This paper attempts to move the discussion of high-performance coach development from an examination of coaches' volume of experiences towards a consideration of the contribution of the learning experiences that coaches have reported throughout their careers. Furthermore, a discussion of proximal and distal guidance in the…

The benefit of deep processing and high educational level for verbal learning in young and middle-aged adults.

PubMed

Meijer, Willemien A; Van Gerven, Pascal W; de Groot, Renate H; Van Boxtel, Martin P; Jolles, Jelle

2007-10-01

The aim of the present study was to examine whether deeper processing of words during encoding in middle-aged adults leads to a smaller increase in word-learning performance and a smaller decrease in retrieval effort than in young adults. It was also assessed whether high education attenuates age-related differences in performance. Accuracy of recall and recognition, and reaction times of recognition, after performing incidental and intentional learning tasks were compared between 40 young (25-35) and 40 middle-aged (50-60) adults with low and high educational levels. Age differences in recall increased with depth of processing, whereas age differences in accuracy and reaction times of recognition did not differ across levels. High education does not moderate age-related differences in performance. These findings suggest a smaller benefit of deep processing in middle age, when no retrieval cues are available.

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.

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…

Indoor Air Quality in High Performance Schools

EPA Pesticide Factsheets

High performance schools are facilities that improve the learning environment while saving energy, resources, and money. The key is understanding the lifetime value of high performance schools and effectively managing priorities, time, and budget.

Indoor Air Quality in High Performance Schools

EPA Pesticide Factsheets

2017-02-14

High performance schools are facilities that improve the learning environment while saving energy, resources, and money. The key is understanding the lifetime value of high performance schools and effectively managing priorities, time, and budget.

Building High Performance Learning: A Focus on Career Results and the Bottom Line.

ERIC Educational Resources Information Center

Ingram, Hadyn; Sandelands, Eric; Teare, Richard

2001-01-01

Discusses how action learning can be targeted to business objectives and how electronically enabled action learning can increase productivity. Provides examples of personal learning aligned with organizational goals, including a certificate of management studies course, prior learning experiences, and an advanced diploma in virtual learning. (SK)

Reading Comprehension Performance of Adolescents with Learning Disabilities.

ERIC Educational Resources Information Center

Snider, Vicki E.

1989-01-01

The study found that instructing 13 learning-disabled junior high students in the necessary prior knowledge (information and vocabulary concepts) led to superior reading comprehension performance. Textually explicit text structure also improved reading comprehension. (DB)

Gender-specific effects of physical activity on children's academic performance: The Active Smarter Kids cluster randomized controlled trial.

PubMed

Resaland, G K; Moe, V F; Bartholomew, J B; Andersen, L B; McKay, H A; Anderssen, S A; Aadland, E

2018-01-01

Active learning combines academic content with physical activity (PA) to increase child PA and academic performance, but the impact of active learning is mixed. It may be that this is a moderated relationship in which active learning is beneficial for only some children. This paper examine the impact of baseline academic performance and gender as moderators for the effects of active learning on children's academic performance. In the ASK-study, 1129 fifth-graders from 57 Norwegian elementary schools were randomized by school to intervention or control in a physical activity intervention between November 2014 and June 2015. Academic performance in numeracy, reading, and English was measured and a composite score was calculated. Children were split into low, middle and high academic performing tertiles. 3-way-interactions for group (intervention, control)∗gender (boys, girls)∗academic performance (tertiles) were investigated using mixed model regression. There was a significant, 3-way-interaction (p=0.044). Both boys (ES=0.11) and girls (ES=0.18) in the low performing tertile had a similar beneficial trend. In contrast, middle (ES=0.03) and high performing boys (ES=0.09) responded with small beneficial trends, while middle (ES=-0.11) and high performing girls (ES=-0.06) responded with negative trends. ASK was associated with a significant increase in academic performance for low performing children. It is likely that active learning benefited children most in need of adapted education but it may have a null or negative effect for those girls who are already performing well in the sedentary classroom. Differences in gendered responses are discussed as a possible explanation for these results. Clinicaltrials.gov registry, trial registration number: NCT02132494. Copyright © 2017 Elsevier Inc. All rights reserved.

Using Mobile Communication Technology in High School Education: Motivation, Pressure, and Learning Performance

ERIC Educational Resources Information Center

Rau, Pei-Luen Patrick; Gao, Qin; Wu, Li-Mei

2008-01-01

Motivation and pressure are considered two factors impacting vocational senior high school student learning. New communication technology, especially mobile communication technology, is supposed to be effective in encouraging interaction between the student and the instructor and improving learning efficiency. Social presence and information…

Going the Distance: Are There Common Factors in High Performance Distance Learning? Research Report.

ERIC Educational Resources Information Center

Hawksley, Rosemary; Owen, Jane

Common factors among high-performing distance learning (DL) programs were examined through case studies at 9 further education colleges and 2 nonsector organizations in the United Kingdom and a backup survey of a sample of 50 distance learners at 5 of the colleges. The study methodology incorporated numerous principles of process benchmarking. The…

Perceptual learning in Williams syndrome: looking beyond averages.

PubMed

Gervan, Patricia; Gombos, Ferenc; Kovacs, Ilona

2012-01-01

Williams Syndrome is a genetically determined neurodevelopmental disorder characterized by an uneven cognitive profile and surprisingly large neurobehavioral differences among individuals. Previous studies have already shown different forms of memory deficiencies and learning difficulties in WS. Here we studied the capacity of WS subjects to improve their performance in a basic visual task. We employed a contour integration paradigm that addresses occipital visual function, and analyzed the initial (i.e. baseline) and after-learning performance of WS individuals. Instead of pooling the very inhomogeneous results of WS subjects together, we evaluated individual performance by expressing it in terms of the deviation from the average performance of the group of typically developing subjects of similar age. This approach helped us to reveal information about the possible origins of poor performance of WS subjects in contour integration. Although the majority of WS individuals showed both reduced baseline and reduced learning performance, individual analysis also revealed a dissociation between baseline and learning capacity in several WS subjects. In spite of impaired initial contour integration performance, some WS individuals presented learning capacity comparable to learning in the typically developing population, and vice versa, poor learning was also observed in subjects with high initial performance levels. These data indicate a dissociation between factors determining initial performance and perceptual learning.

[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.

Qualitative Assessment of Learning Strategies among Medical Students Using Focus Group Discussions and In-depth Interviews.

PubMed

Joshi, Anuradha Sujai; Ganjiwale, Jaishree Deepak; Varma, Jagdish; Singh, Praveen; Modi, Jyoti Nath; Singh, Tejinder

2017-12-01

Globally, students with top academic performance and high intellectual capacity usually opt to study medicine. However, once students get enrolled, their academic performance varies widely. Such variations appear to be determined by various factors, one of them being types of learning strategies adopted by students. The learning strategies utilized by the students with better academic performance are likely to be more effective learning strategies. The objective is to identify effective learning strategies used by medical students. This study was carried out among the MBBS students of Final Professional Part I. Students were categorized into three groups namely: high, average, and low rankers based on overall academic performance in second Professional University examination. First, a questionnaire consisting of closed- and open-ended questions was administered to students, to find their learning strategies. Subsequently, focus group discussion and in-depth interviews were conducted for high- and low-rankers. Discussions were audio-recorded, transcribed, and analyzed. Key statements were highlighted, collated, and categorized into general themes and sub-themes. Evident themes which emerged as effective strategies were hard work in the form of regularity of studies, meticulous preparation of notes, constructive use of time, utilization of e-learning, learning styles and deep learning approach and regular ward visits. Intrinsic motivation, family support, balancing physical activities and studies, guidance by seniors, teachers, dealing with nonacademic issues such as language barriers and stress were also identified as important strategies. Disseminating effective learning strategies in a systematic manner may be helpful to students in achieving better academic outcomes. Furthermore, educationists need to modulate their teaching strategies based on students' feedback.

Jet-images — deep learning edition

DOE PAGES

de Oliveira, Luke; Kagan, Michael; Mackey, Lester; ...

2016-07-13

Building on the notion of a particle physics detector as a camera and the collimated streams of high energy particles, or jets, it measures as an image, we investigate the potential of machine learning techniques based on deep learning architectures to identify highly boosted W bosons. Modern deep learning algorithms trained on jet images can out-perform standard physically-motivated feature driven approaches to jet tagging. We develop techniques for visualizing how these features are learned by the network and what additional information is used to improve performance. Finally, this interplay between physically-motivated feature driven tools and supervised learning algorithms is generalmore » and can be used to significantly increase the sensitivity to discover new particles and new forces, and gain a deeper understanding of the physics within jets.« less

Jet-images — deep learning edition

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

de Oliveira, Luke; Kagan, Michael; Mackey, Lester

Building on the notion of a particle physics detector as a camera and the collimated streams of high energy particles, or jets, it measures as an image, we investigate the potential of machine learning techniques based on deep learning architectures to identify highly boosted W bosons. Modern deep learning algorithms trained on jet images can out-perform standard physically-motivated feature driven approaches to jet tagging. We develop techniques for visualizing how these features are learned by the network and what additional information is used to improve performance. Finally, this interplay between physically-motivated feature driven tools and supervised learning algorithms is generalmore » and can be used to significantly increase the sensitivity to discover new particles and new forces, and gain a deeper understanding of the physics within jets.« less

Feature saliency and feedback information interactively impact visual category learning

PubMed Central

Hammer, Rubi; Sloutsky, Vladimir; Grill-Spector, Kalanit

2015-01-01

Visual category learning (VCL) involves detecting which features are most relevant for categorization. VCL relies on attentional learning, which enables effectively redirecting attention to object’s features most relevant for categorization, while ‘filtering out’ irrelevant features. When features relevant for categorization are not salient, VCL relies also on perceptual learning, which enables becoming more sensitive to subtle yet important differences between objects. Little is known about how attentional learning and perceptual learning interact when VCL relies on both processes at the same time. Here we tested this interaction. Participants performed VCL tasks in which they learned to categorize novel stimuli by detecting the feature dimension relevant for categorization. Tasks varied both in feature saliency (low-saliency tasks that required perceptual learning vs. high-saliency tasks), and in feedback information (tasks with mid-information, moderately ambiguous feedback that increased attentional load, vs. tasks with high-information non-ambiguous feedback). We found that mid-information and high-information feedback were similarly effective for VCL in high-saliency tasks. This suggests that an increased attentional load, associated with the processing of moderately ambiguous feedback, has little effect on VCL when features are salient. In low-saliency tasks, VCL relied on slower perceptual learning; but when the feedback was highly informative participants were able to ultimately attain the same performance as during the high-saliency VCL tasks. However, VCL was significantly compromised in the low-saliency mid-information feedback task. We suggest that such low-saliency mid-information learning scenarios are characterized by a ‘cognitive loop paradox’ where two interdependent learning processes have to take place simultaneously. PMID:25745404

An analysis of mathematical connection ability based on student learning style on visualization auditory kinesthetic (VAK) learning model with self-assessment

NASA Astrophysics Data System (ADS)

Apipah, S.; Kartono; Isnarto

2018-03-01

This research aims to analyze the quality of VAK learning with self-assessment toward the ability of mathematical connection performed by students and to analyze students’ mathematical connection ability based on learning styles in VAK learning model with self-assessment. This research applies mixed method type with concurrent embedded design. The subject of this research consists of VIII grade students from State Junior High School 9 Semarang who apply visual learning style, auditory learning style, and kinesthetic learning style. The data of learning style is collected by using questionnaires, the data of mathematical connection ability is collected by performing tests, and the data of self-assessment is collected by using assessment sheets. The quality of learning is qualitatively valued from planning stage, realization stage, and valuation stage. The result of mathematical connection ability test is analyzed quantitatively by mean test, conducting completeness test, mean differentiation test, and mean proportional differentiation test. The result of the research shows that VAK learning model results in well-qualified learning regarded from qualitative and quantitative sides. Students with visual learning style perform the highest mathematical connection ability, students with kinesthetic learning style perform average mathematical connection ability, and students with auditory learning style perform the lowest mathematical connection ability.

The effects of contextual learning instruction on science achievement of male and female tenth-grade students

NASA Astrophysics Data System (ADS)

Ingram, Samantha Jones

The purpose of this study was to investigate the effects of the contextual learning method on science performance, attitudes toward science, and motivational factors that influence high school students to learn science. Gender differences in science performance and attitudes toward science were also investigated. The sample included four tenth-grade classes of African-American students enrolled in Chemistry I. All students were required to review for the Alabama High School Graduation Exam in Science. Students were administered a science pretest and posttest to measure science performance. A two-way analysis of covariance was performed on the test data. The results showed a main effect of contextual learning instruction on science achievement and no significant differences between females' and males' performance in science. The Science Attitude and the Alabama High School Graduation Exam (AHSGE) Review Class Surveys were administered to assess students' beliefs and attitudes toward science. The Science Attitude Survey results indicated a control effect in three subscales: perception of guardian's attitude, attitude toward success in science, and perception of teacher's attitude. No significant differences resulted between males and females in their beliefs about science from the attitude survey. However, students' attitudes toward science were more favorable in the contextual learning classes based on the results of the Review Class Survey. The survey data revealed that both males and females in the contextual classes had positive attitudes toward science and toward being active participants in the learning process. Qualitative data on student motivation were collected to examine the meaningfulness of the contextual learning content and materials. The majority of the students in the treatment (96%) and the control groups (86%) reported high interest in the lesson on Newton's three laws of motion. Both the treatment and the control groups indicated their interest ratings were a result of their prior experiences. This study shows that contextual learning instruction positively influences student motivation, interest, and achievement in science. Student achievement in science improved in the contextual learning classes as a result of increased interest due to learning that emphasized relevancy and purposeful meaning.

Performance, Performance System, and High Performance System

ERIC Educational Resources Information Center

Jang, Hwan Young

2009-01-01

This article proposes needed transitions in the field of human performance technology. The following three transitions are discussed: transitioning from training to performance, transitioning from performance to performance system, and transitioning from learning organization to high performance system. A proposed framework that comprises…

Learning in tele-autonomous systems using Soar

NASA Technical Reports Server (NTRS)

Laird, John E.; Yager, Eric S.; Tuck, Christopher M.; Hucka, Michael

1989-01-01

Robo-Soar is a high-level robot arm control system implemented in Soar. Robo-Soar learns to perform simple block manipulation tasks using advice from a human. Following learning, the system is able to perform similar tasks without external guidance. It can also learn to correct its knowledge, using its own problem solving in addition to outside guidance. Robo-Soar corrects its knowledge by accepting advice about relevance of features in its domain, using a unique integration of analytic and empirical learning techniques.

Work-Based Learning: Learning To Work; Working To Learn; Learning To Learn.

ERIC Educational Resources Information Center

Strumpf, Lori; Mains, Kristine

This document describes a work-based learning approach designed to integrate work and learning at the workplace and thereby help young people develop the skills required for changing workplaces. The following considerations in designing work-based programs are discussed: the trend toward high performance workplaces and changes in the way work is…

Performance of Children with Developmental Dyslexia on High and Low Topological Entropy Artificial Grammar Learning Task

ERIC Educational Resources Information Center

Katan, Pesia; Kahta, Shani; Sasson, Ayelet; Schiff, Rachel

2017-01-01

Graph complexity as measured by topological entropy has been previously shown to affect performance on artificial grammar learning tasks among typically developing children. The aim of this study was to examine the effect of graph complexity on implicit sequential learning among children with developmental dyslexia. Our goal was to determine…

Curriculum Guide for Fashion Merchandising (Fashion Salesperson).

ERIC Educational Resources Information Center

Gregory, Margaret R.

This curriculum guide is designed to help teachers teach a course in fashion merchandising to high school students. The guide contains eight performance-based learning modules, each consisting of one to seven units. Each unit teaches a job-relevant task, and includes performance objectives, performance guides, resources, learning activities,…

Assessment of Clicker Training for Shelter Cats

PubMed Central

Kogan, Lori

2017-01-01

Simple Summary Living conditions in animal shelters can be stressful for cats. Clicker training might be able to alleviate this stress, by giving cats an opportunity to learn new behaviors and interact with humans. In this study, we assessed the initial ability of 100 shelter cats to perform four cued behaviors: touching a target, sitting, spinning, and giving a high-five. Each cat completed 15, five-min training sessions over a two-week span. At the end of the program, we assessed the cats’ ability to perform the same behaviors. On average, the cats performed better on all four behaviors after clicker training, suggesting that the cats could learn to perform specific behaviors on cue. Individual cats with a higher level of interest in food showed greater gains in learning for two of the behaviors (high-five and touching a target). Cats with a bolder temperament at post-assessment demonstrated greater gains in learning than those classified as shy. We suggest that clicker training can be used to enhance cats’ well-being while they are housed in shelters, and that the learned behaviors might make them more desirable to adopters. Abstract Clicker training has the potential to mitigate stress among shelter cats by providing environmental enrichment and human interaction. This study assessed the ability of cats housed in a shelter-like setting to learn new behaviors via clicker training in a limited amount of time. One hundred shelter cats were enrolled in the study. Their baseline ability to perform four specific behaviors touching a target, sitting, spinning, and giving a high-five was assessed, before exposing them to 15, five-min clicker training sessions, followed by a post-training assessment. Significant gains in performance scores were found for all four cued behaviors after training (p = 0.001). A cat’s age and sex did not have any effect on successful learning, but increased food motivation was correlated with greater gains in learning for two of the cued behaviors: high-five and targeting. Temperament also correlated with learning, as bolder cats at post assessment demonstrated greater gains in performance scores than shyer ones. Over the course of this study, 79% of cats mastered the ability to touch a target, 27% mastered sitting, 60% mastered spinning, and 31% mastered high-fiving. Aside from the ability to influence the cats’ well-being, clicker training also has the potential to make cats more desirable to adopters. PMID:28937608

Carpet Aids Learning in High Performance Schools

ERIC Educational Resources Information Center

Hurd, Frank

2009-01-01

The Healthy and High Performance Schools Act of 2002 has set specific federal guidelines for school design, and developed a federal/state partnership program to assist local districts in their school planning. According to the Collaborative for High Performance Schools (CHPS), high-performance schools are, among other things, healthy, comfortable,…

Preceptors’ Self-Assessment of Their Ability to Perform the Learning Objectives of an Experiential Program

PubMed Central

2012-01-01

Objective. To evaluate preceptors’ perception of their ability to perform the Structured Practical Experiences in Pharmacy (SPEP) learning objectives through a self-assessment activity. Methods. A self-assessment instrument consisting of 28 learning objectives associated with clinic, community, and hospital pharmacy practice experiences were developed. Preceptors rated their performance ability for each of the learning objectives using a 3-point Likert scale. Results. Of the 116 preceptors, 89 (77%) completed the self-assessment survey instrument. The overall preceptor responses to the items on performance of the 28 SPEP learning objectives ranged from good to excellent. Years of experience, practice experience setting, and involvement as a SPEP or SPEP and PharmD preceptor had no influence on their self-reported capabilities. Conclusion. Most preceptors rated their ability to perform the learning objectives for the structured practical experiences in pharmacy as high. Competency areas requiring further preceptor development were identified. PMID:23193333

High Performance Work and Learning Systems: Crafting a Worker-Centered Approach. Proceedings of a Conference (Washington, D.C., September 1991).

ERIC Educational Resources Information Center

Marschall, Daniel, Ed.

A consensus that unions must develop coherent and comprehensive policies on new work systems and continuous learning in order to guide local activities, was the central theme of this conference on the interrelated issues of the high performance work organization. These proceedings include the following presentations: "Labor's Stake in High…

Relationships of Cognitive and Metacognitive Learning Strategies to Mathematics Achievement in Four High-Performing East Asian Education Systems

ERIC Educational Resources Information Center

Areepattamannil, Shaljan; Caleon, Imelda S.

2013-01-01

The authors examined the relationships of cognitive (i.e., memorization and elaboration) and metacognitive learning strategies (i.e., control strategies) to mathematics achievement among 15-year-old students in 4 high-performing East Asian education systems: Shanghai-China, Hong Kong-China, Korea, and Singapore. In all 4 East Asian education…

Group Investigation as a Cooperative Learning Strategy: An Integrated Analysis of the Literature

ERIC Educational Resources Information Center

Mitchell, Mitzi G.; Montgomery, Hilary; Holder, Michelle; Stuart, Dan

2008-01-01

The cooperative learning strategy of group investigation has been used extensively in elementary and high school classrooms. Whereas this learning strategy seems to benefit low- and middle-achieving students, the performance of high-achieving students seems to change little. This article examines the literature on group investigation as a…

Cultures of Learning in Effective High Schools

ERIC Educational Resources Information Center

Tichnor-Wagner, Ariel; Harrison, Christopher; Cohen-Vogel, Lora

2016-01-01

Purpose: Research indicates that a culture of learning is a key factor in building high schools that foster academic achievement in all students. Yet less is known about which elements of a culture of learning differentiate schools with higher levels of academic performance. To fill this gap, this comparative case study examined the cultures of…

Teachers' Views of School-Based Professional Learning in Six High-Performing, High-Poverty, Urban Schools

ERIC Educational Resources Information Center

Reinhorn, Stefanie Karchmer

2015-01-01

Policy makers, practitioners and scholars agree that teachers need sustained job-embedded professional learning experiences to help students meet the demands of new accountability systems, higher education, and the workforce (Smylie, Miretzky, & Konkol, 2004; Valli & Buese, 2007). Research shows that job-embedded learning for teachers can…

Heterogeneity in perceptual category learning by high functioning children with autism spectrum disorder

PubMed Central

Mercado, Eduardo; Church, Barbara A.; Coutinho, Mariana V. C.; Dovgopoly, Alexander; Lopata, Christopher J.; Toomey, Jennifer A.; Thomeer, Marcus L.

2015-01-01

Previous research suggests that high functioning (HF) children with autism spectrum disorder (ASD) sometimes have problems learning categories, but often appear to perform normally in categorization tasks. The deficits that individuals with ASD show when learning categories have been attributed to executive dysfunction, general deficits in implicit learning, atypical cognitive strategies, or abnormal perceptual biases and abilities. Several of these psychological explanations for category learning deficits have been associated with neural abnormalities such as cortical underconnectivity. The present study evaluated how well existing neurally based theories account for atypical perceptual category learning shown by HF children with ASD across multiple category learning tasks involving novel, abstract shapes. Consistent with earlier results, children’s performances revealed two distinct patterns of learning and generalization associated with ASD: one was indistinguishable from performance in typically developing children; the other revealed dramatic impairments. These two patterns were evident regardless of training regimen or stimulus set. Surprisingly, some children with ASD showed both patterns. Simulations of perceptual category learning could account for the two observed patterns in terms of differences in neural plasticity. However, no current psychological or neural theory adequately explains why a child with ASD might show such large fluctuations in category learning ability across training conditions or stimulus sets. PMID:26157368

Is all motivation good for learning? Dissociable influences of approach and avoidance motivation in declarative memory.

PubMed

Murty, Vishnu P; LaBar, Kevin S; Hamilton, Derek A; Adcock, R Alison

2011-01-01

The present study investigated the effects of approach versus avoidance motivation on declarative learning. Human participants navigated a virtual reality version of the Morris water task, a classic spatial memory paradigm, adapted to permit the experimental manipulation of motivation during learning. During this task, participants were instructed to navigate to correct platforms while avoiding incorrect platforms. To manipulate motivational states participants were either rewarded for navigating to correct locations (approach) or punished for navigating to incorrect platforms (avoidance). Participants' skin conductance levels (SCLs) were recorded during navigation to investigate the role of physiological arousal in motivated learning. Behavioral results revealed that, overall, approach motivation enhanced and avoidance motivation impaired memory performance compared to nonmotivated spatial learning. This advantage was evident across several performance indices, including accuracy, learning rate, path length, and proximity to platform locations during probe trials. SCL analysis revealed three key findings. First, within subjects, arousal interacted with approach motivation, such that high arousal on a given trial was associated with performance deficits. In addition, across subjects, high arousal negated or reversed the benefits of approach motivation. Finally, low-performing, highly aroused participants showed SCL responses similar to those of avoidance-motivation participants, suggesting that for these individuals, opportunities for reward may evoke states of learning similar to those typically evoked by threats of punishment. These results provide a novel characterization of how approach and avoidance motivation influence declarative memory and indicate a critical and selective role for arousal in determining how reinforcement influences goal-oriented learning.

Is all motivation good for learning? Dissociable influences of approach and avoidance motivation in declarative memory

PubMed Central

Murty, Vishnu P.; LaBar, Kevin S.; Hamilton, Derek A.; Adcock, R. Alison

2011-01-01

The present study investigated the effects of approach versus avoidance motivation on declarative learning. Human participants navigated a virtual reality version of the Morris water task, a classic spatial memory paradigm, adapted to permit the experimental manipulation of motivation during learning. During this task, participants were instructed to navigate to correct platforms while avoiding incorrect platforms. To manipulate motivational states participants were either rewarded for navigating to correct locations (approach) or punished for navigating to incorrect platforms (avoidance). Participants’ skin conductance levels (SCLs) were recorded during navigation to investigate the role of physiological arousal in motivated learning. Behavioral results revealed that, overall, approach motivation enhanced and avoidance motivation impaired memory performance compared to nonmotivated spatial learning. This advantage was evident across several performance indices, including accuracy, learning rate, path length, and proximity to platform locations during probe trials. SCL analysis revealed three key findings. First, within subjects, arousal interacted with approach motivation, such that high arousal on a given trial was associated with performance deficits. In addition, across subjects, high arousal negated or reversed the benefits of approach motivation. Finally, low-performing, highly aroused participants showed SCL responses similar to those of avoidance–motivation participants, suggesting that for these individuals, opportunities for reward may evoke states of learning similar to those typically evoked by threats of punishment. These results provide a novel characterization of how approach and avoidance motivation influence declarative memory and indicate a critical and selective role for arousal in determining how reinforcement influences goal-oriented learning. PMID:22021253

The difference in learning culture and learning performance between a traditional clinical placement, a dedicated education unit and work-based learning.

PubMed

Claeys, Maureen; Deplaecie, Monique; Vanderplancke, Tine; Delbaere, Ilse; Myny, Dries; Beeckman, Dimitri; Verhaeghe, Sofie

2015-09-01

An experiment was carried out on the bachelor's degree course in nursing with two new clinical placement concepts: workplace learning and the dedicated education centre. The aim was to establish a learning culture that creates a sufficiently high learning performance for students. The objectives of this study are threefold: (1) to look for a difference in the "learning culture" and "learning performance" in traditional clinical placement departments and the new clinical placement concepts, the "dedicated education centre" and "workplace learning"; (2) to assess factors influencing the learning culture and learning performance; and (3) to investigate whether there is a link between the learning culture and the learning performance. A non-randomised control study was carried out. The experimental group consisted of 33 final-year nursing undergraduates who were following clinical placements at dedicated education centres and 70 nursing undergraduates who undertook workplace learning. The control group consisted of 106 students who followed a traditional clinical placement. The "learning culture" outcome was measured using the Clinical Learning Environment, Supervision and Nurse Teacher scale. The "learning performance" outcome consisting of three competencies was measured using the Nursing Competence Questionnaire. The traditional clinical placement concept achieved the highest score for learning culture (p<0.001). The new concepts scored higher for learning performance of which the dedicated education centres achieved the highest scores. The 3 clinical placement concepts showed marked differences in learning performance for the "assessment" competency (p<0.05) and for the "interventions" competency (p<0.05). Traditional clinical placement, a dedicated education centre and workplace learning can be seen as complementary clinical placement concepts. The organisation of clinical placements under the dedicated education centre concept and workplace learning is recommended for final-year undergraduate nursing students. Copyright © 2015 Elsevier Ltd. All rights reserved.

Conditional High-Order Boltzmann Machines for Supervised Relation Learning.

PubMed

Huang, Yan; Wang, Wei; Wang, Liang; Tan, Tieniu

2017-09-01

Relation learning is a fundamental problem in many vision tasks. Recently, high-order Boltzmann machine and its variants have shown their great potentials in learning various types of data relation in a range of tasks. But most of these models are learned in an unsupervised way, i.e., without using relation class labels, which are not very discriminative for some challenging tasks, e.g., face verification. In this paper, with the goal to perform supervised relation learning, we introduce relation class labels into conventional high-order multiplicative interactions with pairwise input samples, and propose a conditional high-order Boltzmann Machine (CHBM), which can learn to classify the data relation in a binary classification way. To be able to deal with more complex data relation, we develop two improved variants of CHBM: 1) latent CHBM, which jointly performs relation feature learning and classification, by using a set of latent variables to block the pathway from pairwise input samples to output relation labels and 2) gated CHBM, which untangles factors of variation in data relation, by exploiting a set of latent variables to multiplicatively gate the classification of CHBM. To reduce the large number of model parameters generated by the multiplicative interactions, we approximately factorize high-order parameter tensors into multiple matrices. Then, we develop efficient supervised learning algorithms, by first pretraining the models using joint likelihood to provide good parameter initialization, and then finetuning them using conditional likelihood to enhance the discriminant ability. We apply the proposed models to a series of tasks including invariant recognition, face verification, and action similarity labeling. Experimental results demonstrate that by exploiting supervised relation labels, our models can greatly improve the performance.

Effects of Learning Style and Training Method on Computer Attitude and Performance in World Wide Web Page Design Training.

ERIC Educational Resources Information Center

Chou, Huey-Wen; Wang, Yu-Fang

1999-01-01

Compares the effects of two training methods on computer attitude and performance in a World Wide Web page design program in a field experiment with high school students in Taiwan. Discusses individual differences, Kolb's Experiential Learning Theory and Learning Style Inventory, Computer Attitude Scale, and results of statistical analyses.…

Effectiveness of simulation-based learning on student nurses' self-efficacy and performance while learning fundamental nursing skills.

PubMed

Lin, Hsin-Hsin

2015-01-01

It was noted worldwide while learning fundamental skills and facing skills assessments, nursing students seemed to experience low confidence and high anxiety levels. Could simulation-based learning help to enhance students' self-efficacy and performance? Its effectiveness is mostly unidentified. This study was conducted to provide a shared experience to give nurse educators confidence and an insight into how simulation-based teaching can fit into nursing skills learning. A pilot study was completed with 50 second-year undergraduate nursing students, and the main study included 98 students where a pretest-posttest design was adopted. Data were gathered through four questionnaires and a performance assessment under scrutinized controls such as previous experiences, lecturers' teaching skills, duration of teaching, procedure of skills performance assessment and the inter-rater reliability. The results showed that simulation-based learning significantly improved students' self-efficacy regarding skills learning and the skills performance that nurse educators wish students to acquire. However, technology anxiety, examiners' critical attitudes towards students' performance and their unpredicted verbal and non-verbal expressions, have been found as possible confounding factors. The simulation-based learning proved to have a powerful positive effect on students' achievement outcomes. Nursing skills learning is one area that can benefit greatly from this kind of teaching and learning method.

High-Performance Computing User Facility | Computational Science | NREL

Science.gov Websites

User Facility High-Performance Computing User Facility The High-Performance Computing User Facility technologies. Photo of the Peregrine supercomputer The High Performance Computing (HPC) User Facility provides Gyrfalcon Mass Storage System. Access Our HPC User Facility Learn more about these systems and how to access

The Effect of Teacher Performance in Implementation of The 2013 Curriculum Toward Chemistry Learning Achievement

NASA Astrophysics Data System (ADS)

Dewi, L. P.; Djohar, A.

2018-04-01

This research is a study about implementation of the 2013 Curriculum on Chemistry subject. This study aims to determine the effect of teacher performance toward chemistry learning achievement. The research design involves the independent variable, namely the performance of Chemistry teacher, and the dependent variable that is Chemistry learning achievement which includes the achievement in knowledge and skill domain. The subject of this research are Chemistry teachers and High School students in Bandung City. The research data is obtained from questionnaire about teacher performance assessed by student and Chemistry learning achievement from the students’ report. Data were analyzed by using MANOVA test. The result of multivariate significance test shows that there is a significant effect of teacher performance toward Chemistry learning achievement in knowledge and skill domain with medium effect size.

Similar Comparative Low and High Doses of Deltamethrin and Acetamiprid Differently Impair the Retrieval of the Proboscis Extension Reflex in the Forager Honey Bee (Apis mellifera).

PubMed

Thany, Steeve H; Bourdin, Céline M; Graton, Jérôme; Laurent, Adèle D; Mathé-Allainmat, Monique; Lebreton, Jacques; Questel, Jean-Yves le

2015-09-28

In the present study, the effects of low (10 ng/bee) and high (100 ng/bee) doses of acetamiprid and deltamethrin insecticides on multi-trial learning and retrieval were evaluated in the honey bee Apis mellifera. After oral application, acetamiprid and deltamethrin at the concentrations used were not able to impair learning sessions. When the retention tests were performed 1 h, 6 h, and 24 h after learning, we found a significant difference between bees after learning sessions when drugs were applied 24 h before learning. Deltamethrin-treated bees were found to be more sensitive at 10 ng/bee and 100 ng/bee doses compared to acetamiprid-treated bees, only with amounts of 100 ng/bee and at 6 h and 24 h delays. When insecticides were applied during learning sessions, none of the tested insecticides was able to impair learning performance at 10 ng/bee or 100 ng/bee but retention performance was altered 24 h after learning sessions. Acetamiprid was the only one to impair retrieval at 10 ng/bee, whereas at 100 ng/bee an impairment of retrieval was found with both insecticides. The present results therefore suggest that acetamiprid and deltamethrin are able to impair retrieval performance in the honey bee Apis mellifera.

E-Learning as an Emerging Technology in India

ERIC Educational Resources Information Center

Grover, Pooja; Gupta, Nehta

2010-01-01

E-learning is a combination of learning services and technology that allow us to provide high value integrated learning any time, any place. It is about a new blend of resources, interactivity, performance support and structured learning activities. This methodology makes use of various types of technologies in order to enhance or transform the…

Pupil Science Learning in Resource-Based e-Learning Environments

ERIC Educational Resources Information Center

So, Wing-mui Winnie; Ching, Ngai-ying Fiona

2011-01-01

With the rapid expansion of broadband Internet connection and availability of high performance yet low priced computers, many countries around the world are advocating the adoption of e-learning, the use of computer technology to improve learning and teaching. The trend of e-learning has urged many teachers to incorporate online resources in their…

Pygmalion in Media-Based Learning: Effects of Quality Expectancies on Learning Outcomes

ERIC Educational Resources Information Center

Fries, Stefan; Horz, Holger; Haimerl, Charlotte

2006-01-01

Two studies investigated how quality expectations affect students' outcomes of media-based learning. Experiment 1 (N=62) demonstrated that students expecting a high-end computer-based training programme learned most, whereas students expecting a programme of ambiguous quality learned least and students having no expectations performed in between.…

Interaction between motor ability and skill learning in children: Application of implicit and explicit approaches.

PubMed

Maxwell, Jon P; Capio, Catherine M; Masters, Rich S W

2017-05-01

The benefits of implicit and explicit motor learning approaches in young adults have been studied extensively, but much less in children. This study investigated the relationship between fundamental motor ability and implicit/explicit learning in children using the errorless learning paradigm. First, the motor ability of 261 children (142 boys, 119 girls) aged 9-12 years (M = 9.74, SD = 0.67) was measured. Second, children with motor ability scores in the upper and lower quartile learned a golf-putting skill in either an errorless (implicit) or errorful (explicit) learning condition. Four groups were formed: Errorless High-Ability (n = 13), Errorless Low-Ability (n = 11), Errorful High-Ability (n = 10), and Errorful Low-Ability (n = 11). Learning consisted of 300 practice trials, while testing included a 50-trial retention test, followed by a 50-trial secondary task transfer test, and another 50-trial retention test. The results showed that for high- and low-ability errorless learners, motor performance was unaffected by the secondary task, as was the case for high-ability errorful learners. Low-ability errorful learners performed worse with a secondary task and were significantly poorer than the corresponding high-ability group. These results suggest that implicit motor learning (errorless) may be beneficial for children with low motor ability. The findings also show a trend that children of high motor ability might benefit from learning explicitly (errorful). Further research is recommended to examine the compatibility of implicit and explicit approaches for children of different abilities.

A randomised controlled trial of blended learning to improve the newborn examination skills of medical students.

PubMed

Stewart, Alice; Inglis, Garry; Jardine, Luke; Koorts, Pieter; Davies, Mark William

2013-03-01

To evaluate the hypotheses that a blended learning approach would improve the newborn examination skills of medical students and yield a higher level of satisfaction with learning newborn examination. Undergraduate medical students at a tertiary teaching hospital were individually randomised to receive either a standard neonatology teaching programme (control group), or additional online access to the PENSKE Baby Check Learning Module (blended learning group). The primary outcome was performance of newborn examination on standardised assessment by blinded investigators. The secondary outcomes were performance of all 'essential' items of the examination, and participant satisfaction. The recruitment rate was 88% (71/81). The blended learning group achieved a significantly higher mean score than the control group (p=0.02) for newborn examination. There was no difference for performance of essential items, or satisfaction with learning newborn examination. The blended learning group rated the module highly for effective use of learning time and ability to meet specific learning needs. A blended learning approach resulted in a higher level of performance of newborn examination on standardised assessment. This is consistent with published literature on blended learning and has implications for all neonatal clinicians including junior doctors, midwifes and nurse practitioners.

Verbal learning in schizopsychotic outpatients and healthy volunteers as a function of cognitive performance levels.

PubMed

Karilampi, Ulla; Helldin, Lars; Hjärthag, Fredrik; Norlander, Torsten; Archer, Trevor

2007-02-01

The aim was to analyze and compare neurocognitive test profiles related to different levels of verbal learning performance among schizopsychotic patients and healthy volunteers. A single-center patient cohort of 196 participants was compared with an equal-sized volunteer group to form three cognitive subgroups based on the shared verbal learning performance. 43.9% of the patients had normal learning ability. Despite this, all patients underperformed the volunteers on all subtests with the exception of working memory, and, for those with high learning ability, even verbal facility. All patients also presented equally poor visuomotor processing speed/efficacy. A global neurocognitive retardation of speed-related processing in schizophrenia is suggested.

Computer-based learning in neuroanatomy: A longitudinal study of learning, transfer, and retention

NASA Astrophysics Data System (ADS)

Chariker, Julia H.

A longitudinal experiment was conducted to explore computer-based learning of neuroanatomy. Using a realistic 3D graphical model of neuroanatomy, and sections derived from the model, exploratory graphical tools were integrated into interactive computer programs so as to allow adaptive exploration. 72 participants learned either sectional anatomy alone or learned whole anatomy followed by sectional anatomy. Sectional anatomy was explored either in perceptually continuous animation or discretely, as in the use of an anatomical atlas. Learning was measured longitudinally to a high performance criterion. After learning, transfer to biomedical images and long-term retention was tested. Learning whole anatomy prior to learning sectional anatomy led to a more efficient learning experience. Learners demonstrated high levels of transfer from whole anatomy to sectional anatomy and from sectional anatomy to complex biomedical images. All learning groups demonstrated high levels of retention at 2--3 weeks.

Learning Styles of Sophomore Students of PUP Laboratory High School (SY 2006-2007)

ERIC Educational Resources Information Center

Castolo, Carmencita L.; Rebusquillo, Lizyl R.

2008-01-01

Learning styles have a big contribution to the academic performance of a student. Awareness of one's learning styles will help a person maximize his potential in accumulating learning to the best of his ability with the use of his preferred learning styles. The teacher's awareness of the student's learning styles will help him/her select teaching…

The effect of image quality, repeated study, and assessment method on anatomy learning.

PubMed

Fenesi, Barbara; Mackinnon, Chelsea; Cheng, Lucia; Kim, Joseph A; Wainman, Bruce C

2017-06-01

The use of two-dimensional (2D) images is consistently used to prepare anatomy students for handling real specimen. This study examined whether the quality of 2D images is a critical component in anatomy learning. The visual clarity and consistency of 2D anatomical images was systematically manipulated to produce low-quality and high-quality images of the human hand and human eye. On day 0, participants learned about each anatomical specimen from paper booklets using either low-quality or high-quality images, and then completed a comprehension test using either 2D images or three-dimensional (3D) cadaveric specimens. On day 1, participants relearned each booklet, and on day 2 participants completed a final comprehension test using either 2D images or 3D cadaveric specimens. The effect of image quality on learning varied according to anatomical content, with high-quality images having a greater effect on improving learning of hand anatomy than eye anatomy (high-quality vs. low-quality for hand anatomy P = 0.018; high-quality vs. low-quality for eye anatomy P = 0.247). Also, the benefit of high-quality images on hand anatomy learning was restricted to performance on short-answer (SA) questions immediately after learning (high-quality vs. low-quality on SA questions P = 0.018), but did not apply to performance on multiple-choice (MC) questions (high-quality vs. low-quality on MC questions P = 0.109) or after participants had an additional learning opportunity (24 hours later) with anatomy content (high vs. low on SA questions P = 0.643). This study underscores the limited impact of image quality on anatomy learning, and questions whether investment in enhancing image quality of learning aids significantly promotes knowledge development. Anat Sci Educ 10: 249-261. © 2016 American Association of Anatomists. © 2016 American Association of Anatomists.

A qualitative inquiry into the effects of visualization on high school chemistry students' learning process of molecular structure

NASA Astrophysics Data System (ADS)

Deratzou, Susan

This research studies the process of high school chemistry students visualizing chemical structures and its role in learning chemical bonding and molecular structure. Minimal research exists with high school chemistry students and more research is necessary (Gabel & Sherwood, 1980; Seddon & Moore, 1986; Seddon, Tariq, & Dos Santos Veiga, 1984). Using visualization tests (Ekstrom, French, Harman, & Dermen, 1990a), a learning style inventory (Brown & Cooper, 1999), and observations through a case study design, this study found visual learners performed better, but needed more practice and training. Statistically, all five pre- and post-test visualization test comparisons were highly significant in the two-tailed t-test (p > .01). The research findings are: (1) Students who tested high in the Visual (Language and/or Numerical) and Tactile Learning Styles (and Social Learning) had an advantage. Students who learned the chemistry concepts more effectively were better at visualizing structures and using molecular models to enhance their knowledge. (2) Students showed improvement in learning after visualization practice. Training in visualization would improve students' visualization abilities and provide them with a way to think about these concepts. (3) Conceptualization of concepts indicated that visualizing ability was critical and that it could be acquired. Support for this finding was provided by pre- and post-Visualization Test data with a highly significant t-test. (4) Various molecular animation programs and websites were found to be effective. (5) Visualization and modeling of structures encompassed both two- and three-dimensional space. The Visualization Test findings suggested that the students performed better with basic rotation of structures as compared to two- and three-dimensional objects. (6) Data from observations suggest that teaching style was an important factor in student learning of molecular structure. (7) Students did learn the chemistry concepts. Based on the Visualization Test results, which showed that most of the students performed better on the post-test, the visualization experience and the abstract nature of the content allowed them to transfer some of their chemical understanding and practice to non-chemical structures. Finally, implications for teaching of chemistry, students learning chemistry, curriculum, and research for the field of chemical education were discussed.

Development of simulation-based learning programme for improving adherence to time-out protocol on high-risk invasive procedures outside of operating room.

PubMed

Jeong, Eun Ju; Chung, Hyun Soo; Choi, Jeong Yun; Kim, In Sook; Hong, Seong Hee; Yoo, Kyung Sook; Kim, Mi Kyoung; Won, Mi Yeol; Eum, So Yeon; Cho, Young Soon

2017-06-01

The aim of this study was to develop a simulation-based time-out learning programme targeted to nurses participating in high-risk invasive procedures and to figure out the effects of application of the new programme on acceptance of nurses. This study was performed using a simulation-based learning predesign and postdesign to figure out the effects of implementation of this programme. It was targeted to 48 registered nurses working in the general ward and the emergency department in a tertiary teaching hospital. Difference between acceptance and performance rates has been figured out by using mean, standard deviation, and Wilcoxon-signed rank test. The perception survey and score sheet have been validated through content validation index, and the reliability of evaluator has been verified by using intraclass correlation coefficient. Results showed high level of acceptance of high-risk invasive procedure (P<.01). Further, improvement was consistent regardless of clinical experience, workplace, or experience in simulation-based learning. The face validity of the programme showed over 4.0 out of 5.0. This simulation-based learning programme was effective in improving the recognition of time-out protocol and has given the participants the opportunity to become proactive in cases of high-risk invasive procedures performed outside of operating room. © 2017 John Wiley & Sons Australia, Ltd.

Distributed deep learning networks among institutions for medical imaging.

PubMed

Chang, Ken; Balachandar, Niranjan; Lam, Carson; Yi, Darvin; Brown, James; Beers, Andrew; Rosen, Bruce; Rubin, Daniel L; Kalpathy-Cramer, Jayashree

2018-03-29

Deep learning has become a promising approach for automated support for clinical diagnosis. When medical data samples are limited, collaboration among multiple institutions is necessary to achieve high algorithm performance. However, sharing patient data often has limitations due to technical, legal, or ethical concerns. In this study, we propose methods of distributing deep learning models as an attractive alternative to sharing patient data. We simulate the distribution of deep learning models across 4 institutions using various training heuristics and compare the results with a deep learning model trained on centrally hosted patient data. The training heuristics investigated include ensembling single institution models, single weight transfer, and cyclical weight transfer. We evaluated these approaches for image classification in 3 independent image collections (retinal fundus photos, mammography, and ImageNet). We find that cyclical weight transfer resulted in a performance that was comparable to that of centrally hosted patient data. We also found that there is an improvement in the performance of cyclical weight transfer heuristic with a high frequency of weight transfer. We show that distributing deep learning models is an effective alternative to sharing patient data. This finding has implications for any collaborative deep learning study.

The active learning hypothesis of the job-demand-control model: an experimental examination.

PubMed

Häusser, Jan Alexander; Schulz-Hardt, Stefan; Mojzisch, Andreas

2014-01-01

The active learning hypothesis of the job-demand-control model [Karasek, R. A. 1979. "Job Demands, Job Decision Latitude, and Mental Strain: Implications for Job Redesign." Administration Science Quarterly 24: 285-307] proposes positive effects of high job demands and high job control on performance. We conducted a 2 (demands: high vs. low) × 2 (control: high vs. low) experimental office workplace simulation to examine this hypothesis. Since performance during a work simulation is confounded by the boundaries of the demands and control manipulations (e.g. time limits), we used a post-test, in which participants continued working at their task, but without any manipulation of demands and control. This post-test allowed for examining active learning (transfer) effects in an unconfounded fashion. Our results revealed that high demands had a positive effect on quantitative performance, without affecting task accuracy. In contrast, high control resulted in a speed-accuracy tradeoff, that is participants in the high control conditions worked slower but with greater accuracy than participants in the low control conditions.

Boys' and Girls' Attribution of Performance in Learning English as a Foreign Language: The Case of Adama High Schools in Ethiopia

ERIC Educational Resources Information Center

Tulu, Geberew

2013-01-01

The main aim of the study was to examine students' attribution of performance in learning English as a foreign language at Adama town government high schools and see into its pedagogical implications. It aimed at investigating the perceived causes of success and failure of boys and girls. In order to meet the objectives of the study, data were…

Relationships between milk consumption and academic performance, learning motivation and strategy, and personality in Korean adolescents.

PubMed

Kim, Sun Hyo; Kim, Woo Kyoung; Kang, Myung-Hee

2016-04-01

A healthy diet has been reported to be associated with physical development, cognition and academic performance, and personality during adolescence. This study was performed to investigate the relationships among milk consumption and academic performance, learning motivation and strategies, and personality among Korean adolescents. The study was divided into two parts. The first part was a survey on the relationship between milk consumption and academic performance, in which intakes of milk and milk products and academic scores were examined in percentiles among 630 middle and high school students residing in small and medium-sized cities in 2009. The second part was a survey on the relationships between milk consumption and learning motivation and strategy as well as personality, in which milk consumption habits were collected and Learning Motivation and Strategy Test (L-MOST) for adolescents and Total Personality Inventory for Adolescents (TPI-A) were conducted in 262 high school students in 2011. In the 2009 survey, milk and milk product intakes of subjects were divided into a low intake group (LM: ≤ 60.2 g/day), medium intake group (MM: 60.3-150.9 g/day), and high intake group (HM: ≥ 151.0 g/day). Academic performance of each group was expressed as a percentile, and performance in Korean, social science, and mathematics was significantly higher in the HM group (P < 0.05). In the 2011 survey, the group with a higher frequency of everyday milk consumption showed significantly higher "learning strategy total," "testing technique," and "resources management technique" scores (P < 0.05) in all subjects. However, when subjects were divided by gender, milk intake frequency, learning strategy total, class participation technique, and testing technique showed significantly positive correlations (P < 0.05) in boys, whereas no correlation was observed in girls. Correlations between milk intake frequency and each item of the personality test were only detected in boys, and milk intake frequency showed positive correlations with "total agreeability", "organization", "responsibility", "conscientiousness", and "intellectual curiosity" (P < 0.05). Intakes of milk and milk products were correlated with academic performance (Korean, social science, and mathematics) in Korean adolescents. In male high school students, particularly, higher milk intake frequency was positively correlated with learning motivation and strategy as well as some items of the personality inventory.

Relationships between milk consumption and academic performance, learning motivation and strategy, and personality in Korean adolescents

PubMed Central

Kim, Sun Hyo; Kim, Woo Kyoung

2016-01-01

BACKGROUND/OBJECTIVES A healthy diet has been reported to be associated with physical development, cognition and academic performance, and personality during adolescence. This study was performed to investigate the relationships among milk consumption and academic performance, learning motivation and strategies, and personality among Korean adolescents. SUBJECTS/METHODS The study was divided into two parts. The first part was a survey on the relationship between milk consumption and academic performance, in which intakes of milk and milk products and academic scores were examined in percentiles among 630 middle and high school students residing in small and medium-sized cities in 2009. The second part was a survey on the relationships between milk consumption and learning motivation and strategy as well as personality, in which milk consumption habits were collected and Learning Motivation and Strategy Test (L-MOST) for adolescents and Total Personality Inventory for Adolescents (TPI-A) were conducted in 262 high school students in 2011. RESULTS In the 2009 survey, milk and milk product intakes of subjects were divided into a low intake group (LM: ≤ 60.2 g/day), medium intake group (MM: 60.3-150.9 g/day), and high intake group (HM: ≥ 151.0 g/day). Academic performance of each group was expressed as a percentile, and performance in Korean, social science, and mathematics was significantly higher in the HM group (P < 0.05). In the 2011 survey, the group with a higher frequency of everyday milk consumption showed significantly higher "learning strategy total," "testing technique," and "resources management technique" scores (P < 0.05) in all subjects. However, when subjects were divided by gender, milk intake frequency, learning strategy total, class participation technique, and testing technique showed significantly positive correlations (P < 0.05) in boys, whereas no correlation was observed in girls. Correlations between milk intake frequency and each item of the personality test were only detected in boys, and milk intake frequency showed positive correlations with "total agreeability", "organization", "responsibility", "conscientiousness", and "intellectual curiosity" (P < 0.05). CONCLUSION Intakes of milk and milk products were correlated with academic performance (Korean, social science, and mathematics) in Korean adolescents. In male high school students, particularly, higher milk intake frequency was positively correlated with learning motivation and strategy as well as some items of the personality inventory. PMID:27087904

Virtual reality simulation training of mastoidectomy - studies on novice performance.

PubMed

Andersen, Steven Arild Wuyts

2016-08-01

Virtual reality (VR) simulation-based training is increasingly used in surgical technical skills training including in temporal bone surgery. The potential of VR simulation in enabling high-quality surgical training is great and VR simulation allows high-stakes and complex procedures such as mastoidectomy to be trained repeatedly, independent of patients and surgical tutors, outside traditional learning environments such as the OR or the temporal bone lab, and with fewer of the constraints of traditional training. This thesis aims to increase the evidence-base of VR simulation training of mastoidectomy and, by studying the final-product performances of novices, investigates the transfer of skills to the current gold-standard training modality of cadaveric dissection, the effect of different practice conditions and simulator-integrated tutoring on performance and retention of skills, and the role of directed, self-regulated learning. Technical skills in mastoidectomy were transferable from the VR simulation environment to cadaveric dissection with significant improvement in performance after directed, self-regulated training in the VR temporal bone simulator. Distributed practice led to a better learning outcome and more consolidated skills than massed practice and also resulted in a more consistent performance after three months of non-practice. Simulator-integrated tutoring accelerated the initial learning curve but also caused over-reliance on tutoring, which resulted in a drop in performance when the simulator-integrated tutor-function was discontinued. The learning curves were highly individual but often plateaued early and at an inadequate level, which related to issues concerning both the procedure and the VR simulator, over-reliance on the tutor function and poor self-assessment skills. Future simulator-integrated automated assessment could potentially resolve some of these issues and provide trainees with both feedback during the procedure and immediate assessment following each procedure. Standard setting by establishing a proficiency level that can be used for mastery learning with deliberate practice could also further sophisticate directed, self-regulated learning in VR simulation-based training. VR simulation-based training should be embedded in a systematic and competency-based training curriculum for high-quality surgical skills training, ultimately leading to improved safety and patient care.

Category Learning Strategies in Younger and Older Adults: Rule Abstraction and Memorization

PubMed Central

Wahlheim, Christopher N.; McDaniel, Mark A.; Little, Jeri L.

2016-01-01

Despite the fundamental role of category learning in cognition, few studies have examined how this ability differs between younger and older adults. The present experiment examined possible age differences in category learning strategies and their effects on learning. Participants were trained on a category determined by a disjunctive rule applied to relational features. The utilization of rule- and exemplar-based strategies was indexed by self-reports and transfer performance. Based on self-reported strategies, both age groups had comparable frequencies of rule- and exemplar-based learners, but older adults had a higher frequency of intermediate learners (i.e., learners not identifying with a reliance on either rule- or exemplar-based strategies). Training performance was higher for younger than older adults regardless of the strategy utilized, showing that older adults were impaired in their ability to learn the correct rule or to remember exemplar-label associations. Transfer performance converged with strategy reports in showing higher fidelity category representations for younger adults. Younger adults with high working memory capacity were more likely to use an exemplar-based strategy, and older adults with high working memory capacity showed better training performance. Age groups did not differ in their self-reported memory beliefs, and these beliefs did not predict training strategies or performance. Overall, the present results contradict earlier findings that older adults prefer rule- to exemplar-based learning strategies, presumably to compensate for memory deficits. PMID:26950225

Evaluation of Semi-supervised Learning for Classification of Protein Crystallization Imagery.

PubMed

Sigdel, Madhav; Dinç, İmren; Dinç, Semih; Sigdel, Madhu S; Pusey, Marc L; Aygün, Ramazan S

2014-03-01

In this paper, we investigate the performance of two wrapper methods for semi-supervised learning algorithms for classification of protein crystallization images with limited labeled images. Firstly, we evaluate the performance of semi-supervised approach using self-training with naïve Bayesian (NB) and sequential minimum optimization (SMO) as the base classifiers. The confidence values returned by these classifiers are used to select high confident predictions to be used for self-training. Secondly, we analyze the performance of Yet Another Two Stage Idea (YATSI) semi-supervised learning using NB, SMO, multilayer perceptron (MLP), J48 and random forest (RF) classifiers. These results are compared with the basic supervised learning using the same training sets. We perform our experiments on a dataset consisting of 2250 protein crystallization images for different proportions of training and test data. Our results indicate that NB and SMO using both self-training and YATSI semi-supervised approaches improve accuracies with respect to supervised learning. On the other hand, MLP, J48 and RF perform better using basic supervised learning. Overall, random forest classifier yields the best accuracy with supervised learning for our dataset.

Using learning automata to determine proper subset size in high-dimensional spaces

NASA Astrophysics Data System (ADS)

Seyyedi, Seyyed Hossein; Minaei-Bidgoli, Behrouz

2017-03-01

In this paper, we offer a new method called FSLA (Finding the best candidate Subset using Learning Automata), which combines the filter and wrapper approaches for feature selection in high-dimensional spaces. Considering the difficulties of dimension reduction in high-dimensional spaces, FSLA's multi-objective functionality is to determine, in an efficient manner, a feature subset that leads to an appropriate tradeoff between the learning algorithm's accuracy and efficiency. First, using an existing weighting function, the feature list is sorted and selected subsets of the list of different sizes are considered. Then, a learning automaton verifies the performance of each subset when it is used as the input space of the learning algorithm and estimates its fitness upon the algorithm's accuracy and the subset size, which determines the algorithm's efficiency. Finally, FSLA introduces the fittest subset as the best choice. We tested FSLA in the framework of text classification. The results confirm its promising performance of attaining the identified goal.

High Performers in Marketing and Advertising Majors: Do Their Perceptions of Business Programs Differ from Their Peers?

ERIC Educational Resources Information Center

Walsh, Ann D.; Woosley, Sherry A.

2013-01-01

This study examined the perceptions of high performing undergraduate students in marketing and advertising majors. Specifically, it examined the relationships among three levels of student performance and student satisfaction with their business programs and learning outcomes. High performing students were more satisfied with their programs and…

eLearning techniques supporting problem based learning in clinical simulation.

PubMed

Docherty, Charles; Hoy, Derek; Topp, Helena; Trinder, Kathryn

2005-08-01

This paper details the results of the first phase of a project using eLearning to support students' learning within a simulated environment. The locus was a purpose built clinical simulation laboratory (CSL) where the School's philosophy of problem based learning (PBL) was challenged through lecturers using traditional teaching methods. a student-centred, problem based approach to the acquisition of clinical skills that used high quality learning objects embedded within web pages, substituting for lecturers providing instruction and demonstration. This encouraged student nurses to explore, analyse and make decisions within the safety of a clinical simulation. Learning was facilitated through network communications and reflection on video performances of self and others. Evaluations were positive, students demonstrating increased satisfaction with PBL, improved performance in exams, and increased self-efficacy in the performance of nursing activities. These results indicate that eLearning techniques can help students acquire clinical skills in the safety of a simulated environment within the context of a problem based learning curriculum.

High Performance Schools--It's a No-Brainer.

ERIC Educational Resources Information Center

Nicklas, Mike

2002-01-01

A North Carolina middle school demonstrates that high performance, sustainable school buildings cost no more to build and are more comfortable and productive learning environments than conventional buildings. (Author)

How do highly proficient bilinguals control their lexicalization process? Inhibitory and language-specific selection mechanisms are both functional.

PubMed

Costa, Albert; Santesteban, Mikel; Ivanova, Iva

2006-09-01

The authors report 4 experiments exploring the language-switching performance of highly proficient bilinguals in a picture-naming task. In Experiment 1, they tested the impact of language similarity and age of 2nd language acquisition on the language-switching performance of highly proficient bilinguals. Experiments 2, 3, and 4 assessed the performance of highly proficient bilinguals in language-switching contexts involving (a) the 2nd language (L2) and the L3 of the bilinguals, (b) the L3 and the L4, and (c) the L1 and a recently learned new language. Highly proficient bilinguals showed symmetrical switching costs regardless of the age at which the L2 was learned and of the similarities of the 2 languages and asymmetrical switching costs when 1 of the languages involved in the switching task was very weak (an L4 or a recently learned language). The theoretical implications of these results for the attentional mechanisms used by highly proficient bilinguals to control their lexicalization process are discussed. Copyright 2006 APA

Academic performance and comparative effectiveness of computer- and textbook-based self-instruction.

PubMed

Kurihara, Yukio; Kuramoto, Shu; Matsuura, Kimio; Miki, Yoichiro; Oda, Katsushi; Seo, Hiromi; Watabe, Teruaki; Qayumi, A Karim

2004-01-01

We intended to clarify the influence of student academic ability on the effectiveness of CAI, using data of a study to assess the effectiveness of a new type of CAI software, cyberPatient (CP), at Kochi Medical School (KMS). A total of 59 third-year students were randomly assigned to four groups: Group-1 used a textbook for self-instruction, Group-2 used CP, Group-3 used both types of learning materials, and Group-4 did not learn. Learning performance was evaluated by multiple-choice examination and OSCE. In order to clarify the influence of students' academic ability on the effectiveness of CAI, statistical analyses were conducted, assigning students as either high or medium or low performance students. High performance students from Group-1, -2 and -3 did not differ significantly in test performance after self-instruction. However, low performance students in Group-1 scored significantly lower than those in Group-2 and -3. All students in Group-2 and -3 reported that CP stimulated willingness to learn and assisted understanding. The present analysis suggested that effectiveness of CAI might be associated with the academic ability of students.

The moderating role of team resources in translating nursing teams' accountability into learning and performance: a cross-sectional study.

PubMed

Rashkovits, Sarit; Drach-Zahavy, Anat

2017-05-01

The aim of this study was to test the moderated-mediation model suggesting that nursing teams' accountability affects team effectiveness by enhancing team learning when relevant resources are available to the team. Disappointing evidence regarding improvement in nurses' safe and quality care elevate the need in broadening our knowledge regarding the factors that enhance constant learning in nursing teams. Accountability is considered as crucial for team learning and quality of care but empirical findings have shown mixed evidence. A cross-sectional design. Forty-four nursing teams participated in the study. Data were collected in 2013-2014: Head nurses completed validated questionnaires, regarding team resources for learning (time availability, team autonomy and team performance feedback), and nursing teams' effectiveness; and nurses answered questionnaires regarding teams' accountability and learning (answers were aggregated to the team level). The model was tested using a moderated-mediation analysis with resources as moderating variables, and team learning as the mediator in the team accountability-team effectiveness link. The results of a mixed linear regression show that, as expected, nursing teams' accountability was positively linked to nursing teams' learning, when time availability, and team autonomy were high rather than low, and team performance feedback was low rather than high. Nurturing team accountability is not enough for achieving team learning and subsequent team effectiveness. Rather there is a need to provide nursing teams with adequate time, autonomy, and be cautious with performance feedback, as the latter may motivate nurses to repeat routine work strategies rather than explore improved ones. © 2016 John Wiley & Sons Ltd.

We've Got High Hopes: Using Hope to Improve Higher Education

ERIC Educational Resources Information Center

Acosta, Alan

2017-01-01

In recent years, higher education's focus has changed from curricula-focused on student learning to curricula-intended to meet performance metrics and secure institutional resources. Shifting attention from students' learning to students' performance has left many students, faculty, and staff within higher education feeling isolated and…

Science Rocks! A Performance Assessment for Earth Science

ERIC Educational Resources Information Center

Waters, Melia; Straits, William

2008-01-01

This article presents an activity in which students pool their knowledge and creativity to make a song presenting what they have learned in a unit on the rock cycle. This highly motivating, integrated performance assessment incorporates multiple intelligences, reinforces learning, and is a student favorite in the elementary and middle grades.

Is Blended e-Learning as Measured by an Achievement Test and Self-Assessment Better than Traditional Classroom Learning for Vocational High School Students?

ERIC Educational Resources Information Center

Chang, Chi-Cheng; Shu, Kuen-Ming; Liang, Chaoyun; Tseng, Ju-Shih; Hsu, Yu-Sheng

2014-01-01

The purpose of this study is to examine the effects of blended e-learning on electrical machinery performance (achievement test and self-assessment). Participants were two classes of 11th graders majoring in electrical engineering and taking the electrical machinery class at a vocational high school in Taiwan. The participants were randomly…

Comparative Analysis of Rote Learning on High and Low Achievers in Graduate and Undergraduate Programs

ERIC Educational Resources Information Center

Ahmed, Ambreen; Ahmed, Nawaz

2017-01-01

A survey was conducted to study the preferred learning strategies; that is, surface learning or deep learning of undergraduate and graduate male and female students and the impact of the preferred strategy on their academic performance. Both learning strategies help university students to get good scores in their examinations to meet the demands…

When Collaborative Learning Meets Nature: Collaborative Learning as a Meaningful Learning Tool in the Ecology Inquiry Based Project

ERIC Educational Resources Information Center

Rozenszayn, Ronit; Assaraf, Orit Ben-Zvi

2011-01-01

This research suggests utilizing collaborative learning among high school students for better performance on ecology inquiry-based projects. A case study of nine 12th grade students who participated in collaborative learning sessions in the open field and in class is examined. The results show that the students concentrated on discussing the…

Assessing the Effectiveness of Learning Solid Geometry by Using an Augmented Reality-Assisted Learning System

ERIC Educational Resources Information Center

Lin, Hao-Chiang Koong; Chen, Mei-Chi; Chang, Chih-Kai

2015-01-01

This study integrates augmented reality (AR) technology into teaching activities to design a learning system that assists junior high-school students in learning solid geometry. The following issues are addressed: (1) the relationship between achievements in mathematics and performance in spatial perception; (2) whether system-assisted learning…

An Analysis of a High Performing School District's Culture

ERIC Educational Resources Information Center

Corum, Kenneth D.; Schuetz, Todd B.

2012-01-01

This report describes a problem based learning project focusing on the cultural elements of a high performing school district. Current literature on school district culture provides numerous cultural elements that are present in high performing school districts. With the current climate in education placing pressure on school districts to perform…

Effective Science Instruction: Impact on High-Stakes Assessment Performance

ERIC Educational Resources Information Center

Johnson, Carla C.; Zhang, Danhui; Kahle, Jane Butler

2012-01-01

This longitudinal prospective cohort study was conducted to determine the impact of effective science instruction on performance on high-stakes high school graduation assessments in science. This study provides powerful findings to support authentic science teaching to enhance long-term retention of learning and performance on state-mandated…

The dread factor: how hazards and safety training influence learning and performance.

PubMed

Burke, Michael J; Salvador, Rommel O; Smith-Crowe, Kristin; Chan-Serafin, Suzanne; Smith, Alexis; Sonesh, Shirley

2011-01-01

On the basis of hypotheses derived from social and experiential learning theories, we meta-analytically investigated how safety training and workplace hazards impact the development of safety knowledge and safety performance. The results were consistent with an expected interaction between the level of engagement of safety training and hazardous event/exposure severity in the promotion of safety knowledge and performance. For safety knowledge and safety performance, highly engaging training was considerably more effective than less engaging training when hazardous event/exposure severity was high, whereas highly and less engaging training had comparable levels of effectiveness when hazardous event/exposure severity was low. Implications of these findings for theory testing and incorporating information on objective risk into workplace safety research and practice are discussed.

Mapping the Proxies of Memory and Learning Function in Senior Adults with High-performing, Normal Aging and Neurocognitive Disorders.

PubMed

Lu, Hanna; Xi, Ni; Fung, Ada W T; Lam, Linda C W

2018-06-09

Memory and learning, as the core brain function, shows controversial results across studies focusing on aging and dementia. One of the reasons is because of the multi-faceted nature of memory and learning. However, there is still a dearth of comparable proxies with psychometric and morphometric portrait in clinical and non-clinical populations. We aim to investigate the proxies of memory and learning function with direct and derived measures and examine their associations with morphometric features in senior adults with different cognitive status. Based on two modality-driven tests, we assessed the component-specific memory and learning in the individuals with high performing (HP), normal aging, and neurocognitive disorders (NCD) (n = 488). Structural magnetic resonance imaging was used to measure the regional cortical thickness with surface-based morphometry analysis in a subsample (n = 52). Compared with HP elderly, the ones with normal aging and minor NCD showed declined recognition memory and working memory, whereas had better learning performance (derived scores). Meanwhile, major NCD patients showed more breakdowns of memory and learning function. The correlation between proxies of memory and learning and cortical thickness exhibited the overlapped and unique neural underpinnings. The proxies of memory and learning could be characterized by component-specific constructs with psychometric and morphometric bases. Overall, the constructs of memory are more likely related to the pathological changes, and the constructs of learning tend to reflect the cognitive abilities of compensation.

Stimulus discriminability may bias value-based probabilistic learning.

PubMed

Schutte, Iris; Slagter, Heleen A; Collins, Anne G E; Frank, Michael J; Kenemans, J Leon

2017-01-01

Reinforcement learning tasks are often used to assess participants' tendency to learn more from the positive or more from the negative consequences of one's action. However, this assessment often requires comparison in learning performance across different task conditions, which may differ in the relative salience or discriminability of the stimuli associated with more and less rewarding outcomes, respectively. To address this issue, in a first set of studies, participants were subjected to two versions of a common probabilistic learning task. The two versions differed with respect to the stimulus (Hiragana) characters associated with reward probability. The assignment of character to reward probability was fixed within version but reversed between versions. We found that performance was highly influenced by task version, which could be explained by the relative perceptual discriminability of characters assigned to high or low reward probabilities, as assessed by a separate discrimination experiment. Participants were more reliable in selecting rewarding characters that were more discriminable, leading to differences in learning curves and their sensitivity to reward probability. This difference in experienced reinforcement history was accompanied by performance biases in a test phase assessing ability to learn from positive vs. negative outcomes. In a subsequent large-scale web-based experiment, this impact of task version on learning and test measures was replicated and extended. Collectively, these findings imply a key role for perceptual factors in guiding reward learning and underscore the need to control stimulus discriminability when making inferences about individual differences in reinforcement learning.

Sleep stages, memory and learning.

PubMed Central

Dotto, L

1996-01-01

Learning and memory can be impaired by sleep loss during specific vulnerable "windows" for several days after new tasks have been learned. Different types of tasks are differentially vulnerable to the loss of different stages of sleep. Memory required to perform cognitive procedural tasks is affected by the loss of rapid-eye-movement (REM) sleep on the first night after learning occurs and again on the third night after learning. REM-sleep deprivation on the second night after learning does not produce memory deficits. Declarative memory, which is used for the recall of specific facts, is not similarly affected by REM-sleep loss. The learning of procedural motor tasks, including those required in many sports, is impaired by the loss of stage 2 sleep, which occurs primarily in the early hours of the morning. These findings have implications for the academic and athletic performance of students and for anyone whose work involves ongoing learning and demands high standards of performance. Images p1194-a PMID:8612256

Presenting Science in a Video-Delivered, Web-based Format: Comparing Learning Settings To Get the Most Out of Teaching.

ERIC Educational Resources Information Center

Urven, Lance E.; Yin, L. Roger; Eshelman, Bruce D.; Bak, John D.

2000-01-01

Describes a high school course entitled "Science Technology in Society". High school students use live video presentations and world wide web courseware. Concludes that distance learning students performed as well as traditionally instructed students. (SAH)

Effect of Chunk Strength on the Performance of Children with Developmental Dyslexia on Artificial Grammar Learning Task May Be Related to Complexity

ERIC Educational Resources Information Center

Schiff, Rachel; Katan, Pesia; Sasson, Ayelet; Kahta, Shani

2017-01-01

There is a long held view that chunks play a crucial role in artificial grammar learning performance. We compared chunk strength influences on performance, in high and low topological entropy (a measure of complexity) grammar systems, with dyslexic children, age-matched and reading-level-matched control participants. Findings show that age-matched…

The Role of Independent Activities in Development of Strategic Learning Competences and Increase of School Performance Level, within the Study of High School Pedagogy

ERIC Educational Resources Information Center

Anca, Monica-Iuliana; Bocos, Musata

2017-01-01

The experimental research performed by us with the purpose of exploring the possibilities of development of strategic learning competences and improvement of school performance of 11th grade students, pedagogical profile, specialisation in primary school-kindergarten teacher, falls in the category of researches aiming to make efficient certain…

Learned Helplessness and "Fear of Success" in College Women.

ERIC Educational Resources Information Center

Ris, Martin D.; Woods, Donald J.

1983-01-01

Examines anagram performance of 90 high, medium, and low fear-of-success (FOS) women, after the subjects had experienced conditions within the traditional triadic learned helplessness design. Concluded that increased attention should be given to personality variables within the learned helplessness paradigm. (CMG)

A Study of Complex Deep Learning Networks on High Performance, Neuromorphic, and Quantum Computers

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

Potok, Thomas E; Schuman, Catherine D; Young, Steven R

Current Deep Learning models use highly optimized convolutional neural networks (CNN) trained on large graphical processing units (GPU)-based computers with a fairly simple layered network topology, i.e., highly connected layers, without intra-layer connections. Complex topologies have been proposed, but are intractable to train on current systems. Building the topologies of the deep learning network requires hand tuning, and implementing the network in hardware is expensive in both cost and power. In this paper, we evaluate deep learning models using three different computing architectures to address these problems: quantum computing to train complex topologies, high performance computing (HPC) to automatically determinemore » network topology, and neuromorphic computing for a low-power hardware implementation. Due to input size limitations of current quantum computers we use the MNIST dataset for our evaluation. The results show the possibility of using the three architectures in tandem to explore complex deep learning networks that are untrainable using a von Neumann architecture. We show that a quantum computer can find high quality values of intra-layer connections and weights, while yielding a tractable time result as the complexity of the network increases; a high performance computer can find optimal layer-based topologies; and a neuromorphic computer can represent the complex topology and weights derived from the other architectures in low power memristive hardware. This represents a new capability that is not feasible with current von Neumann architecture. It potentially enables the ability to solve very complicated problems unsolvable with current computing technologies.« less

Evaluating the Security of Machine Learning Algorithms

DTIC Science & Technology

2008-05-20

Two far-reaching trends in computing have grown in significance in recent years. First, statistical machine learning has entered the mainstream as a...computing applications. The growing intersection of these trends compels us to investigate how well machine learning performs under adversarial conditions... machine learning has a structure that we can use to build secure learning systems. This thesis makes three high-level contributions. First, we develop a

A Short History of Performance Assessment: Lessons Learned.

ERIC Educational Resources Information Center

Madaus, George F.; O'Dwyer, Laura M.

1999-01-01

Places performance assessment in the context of high-stakes uses, describes underlying technologies, and outlines the history of performance testing from 210 B.C.E. to the present. Historical issues of fairness, efficiency, cost, and infrastructure influence contemporary efforts to use performance assessments in large-scale, high-stakes testing…

Research and Teaching: "It's Not You, It's the Room"--Are the High-Tech, Active Learning Classrooms Worth It?

ERIC Educational Resources Information Center

Cotner, Sehoya; Loper, Jessica; Walker, J. D.; Brooks, D. Christopher

2013-01-01

Several institutions have redesigned traditional learning spaces to better realize the potential of active, experiential learning. We compare student performance in traditional and active learning classrooms in a large, introductory biology course using the same syllabus, course goals, exams, and instructor. Using ACT scores as predictive, we…

Study of the Influence of Social Relationships among Students on Knowledge Building Using a Moderately Constructivist Learning Model

ERIC Educational Resources Information Center

Alonso, Fernando; Manrique, Daniel; Martínez, Loïc; Viñes, José M.

2015-01-01

The main objective of higher education institutions is to educate students to high standards to proficiently perform their role in society. Elsewhere we presented empirical evidence illustrating that the use of a blended learning approach to the learning process that applies a moderate constructivist e-learning instructional model improves…

The Impact of the Supplemental Instruction Leader on Student Performance in Introductory Accounting

ERIC Educational Resources Information Center

Jones, Jefferson P.

2013-01-01

This study explores the association between a supplemental instruction (SI) program and student performance in an introductory accounting course. SI is a proactive academic support program that is aimed at improving student learning in traditionally "high-risk" college courses by integrating learning and critical thinking strategies with…

The Effects of Test Anxiety on Learning at Superficial and Deep Levels of Processing.

ERIC Educational Resources Information Center

Weinstein, Claire E.; And Others

1982-01-01

Using a deep-level processing strategy, low test-anxious college students performed significantly better than high test-anxious students in learning a paired-associate word list. Using a superficial-level processing strategy resulted in no significant difference in performance. A cognitive-attentional theory and test anxiety mechanisms are…

Learning Motivation and Performance Excellence in Adolescents with High Intellectual Potential: What Really Matters?

ERIC Educational Resources Information Center

Schick, Hella; Phillipson, Shane N.

2009-01-01

In the development of performance excellence, the relative roles played by intellectual ability and motivation remain speculative. This study investigates the role played by general intelligence, school environment, self-efficacy, and aspects of personal identity in the formation of learning motivation in German students attending the Gymnasium…

Promoting Single-Parent Family Children's Attitudes toward Science and Science Performance through Extracurricular Science Intervention in Taiwan

ERIC Educational Resources Information Center

Hong, Zuway-R.; Lin, Huann-shyang; Lawrenz, Frances

2008-01-01

This study investigated the efficacy of extracurricular science intervention in promoting students' science learning performance and attitudes toward science. The Junior High School Student Questionnaire (JSSQ) was used to measure attitudes toward science, sexist attitudes and perceptions of the classroom learning environment. Twenty-eight eighth…

Pursuing Their Own Learning Agenda: How Mastery-Oriented Students Jeopardize Their Class Performance

ERIC Educational Resources Information Center

Senko, Corwin; Miles, Kenneth M.

2008-01-01

This study explored why mastery-based achievement goals often are unrelated to class grades despite promoting deep learning strategies and high course interest. We hypothesized that mastery-oriented students jeopardize their exam performance by allowing their individual interests to dictate their study efforts such that they neglect boring topics…

DeepX: Deep Learning Accelerator for Restricted Boltzmann Machine Artificial Neural Networks.

PubMed

Kim, Lok-Won

2018-05-01

Although there have been many decades of research and commercial presence on high performance general purpose processors, there are still many applications that require fully customized hardware architectures for further computational acceleration. Recently, deep learning has been successfully used to learn in a wide variety of applications, but their heavy computation demand has considerably limited their practical applications. This paper proposes a fully pipelined acceleration architecture to alleviate high computational demand of an artificial neural network (ANN) which is restricted Boltzmann machine (RBM) ANNs. The implemented RBM ANN accelerator (integrating network size, using 128 input cases per batch, and running at a 303-MHz clock frequency) integrated in a state-of-the art field-programmable gate array (FPGA) (Xilinx Virtex 7 XC7V-2000T) provides a computational performance of 301-billion connection-updates-per-second and about 193 times higher performance than a software solution running on general purpose processors. Most importantly, the architecture enables over 4 times (12 times in batch learning) higher performance compared with a previous work when both are implemented in an FPGA device (XC2VP70).

Evaluation of Semi-supervised Learning for Classification of Protein Crystallization Imagery

PubMed Central

Sigdel, Madhav; Dinç, İmren; Dinç, Semih; Sigdel, Madhu S.; Pusey, Marc L.; Aygün, Ramazan S.

2015-01-01

In this paper, we investigate the performance of two wrapper methods for semi-supervised learning algorithms for classification of protein crystallization images with limited labeled images. Firstly, we evaluate the performance of semi-supervised approach using self-training with naïve Bayesian (NB) and sequential minimum optimization (SMO) as the base classifiers. The confidence values returned by these classifiers are used to select high confident predictions to be used for self-training. Secondly, we analyze the performance of Yet Another Two Stage Idea (YATSI) semi-supervised learning using NB, SMO, multilayer perceptron (MLP), J48 and random forest (RF) classifiers. These results are compared with the basic supervised learning using the same training sets. We perform our experiments on a dataset consisting of 2250 protein crystallization images for different proportions of training and test data. Our results indicate that NB and SMO using both self-training and YATSI semi-supervised approaches improve accuracies with respect to supervised learning. On the other hand, MLP, J48 and RF perform better using basic supervised learning. Overall, random forest classifier yields the best accuracy with supervised learning for our dataset. PMID:25914518

Putting the Focus on Student Engagement: The Benefits of Performance-Based Assessment

ERIC Educational Resources Information Center

Barlowe, Avram; Cook, Ann

2016-01-01

For more than two decades, the New York Performance Standards Consortium, a coalition of 38 public high schools, has steered clear of high-stakes testing, which superficially assess student learning. Instead, the consortium's approach relies on performance-based assessments--essays, research papers, science experiments, and high-level mathematical…

Factors Related to Competency Test Performance for High School Learning Disabled Students.

ERIC Educational Resources Information Center

Hall, Julia; And Others

1985-01-01

This study explored some factors associated with learning disabled high school students who passed the North Carolina Minimum Competency Test on the second administration. Factors examined include reading score on the first competency test, intelligence quotient, locus of control, mother's education, teacher support, student/teacher ratio, and…

School Restructuring: What Works When? A Guide for Education Leaders

ERIC Educational Resources Information Center

Hassel, Emily Ayscue; Hassel, Bryan C.; Arkin, Matthew D.; Kowal, Julie M.; Steiner, Lucy M.

2009-01-01

Studies of high-performing schools, where all students learn more than similar students in other schools, show common design elements. These elements are comprehensive, affecting the whole school, and include: (1) Clear mission guiding daily activities; (2) High, unyielding expectations that all students will learn; (3) Frequent monitoring of…

The Coach's Learning Community: Standards-Based Program Develops School Wide Capacity

ERIC Educational Resources Information Center

Reitz, Diane; Hall, Gene E.

2017-01-01

Challenges inherent to increasing student literacy are well-documented particularly in under performing schools. Those challenges increase in schools experiencing high staff turnover, high populations of English language learners, and greater poverty. In order to improve student learning in these communities there needs to be a comprehensive…

Student performance in computing education: an empirical analysis of online learning in programming education environments

NASA Astrophysics Data System (ADS)

Xia, Belle Selene; Liitiäinen, Elia

2017-11-01

The benefits of using online exercises have been analysed in terms of distance learning, automatic assessment and self-regulated learning. In this study, we have not found a direct proportional relationship between student performance in the course exercises that use online technologies and the exam grades. We see that the average submission rate to these online exercises is not positively correlated with the exercise points. Yet, our results confirm that doing exercises along supports student learning and skill accumulation equipping them with the knowledge of programming. While the student performance in programming courses is affected by factors such as prior background in programming, cognitive skills and the quality of teaching, completing the course exercises via learning-by-doing is an indispensable part of teaching. Based on the student feedback from the course survey, the students are highly satisfied with using online technologies as part of learning.

Training monitoring skills in helicopter pilots.

PubMed

Potter, Brian A; Blickensderfer, Elizabeth L; Boquet, Albert J

2014-05-01

Prior research has indicated that ineffective pilot monitoring has been associated with aircraft accidents. Despite this finding, empirical research concerning pilot monitoring skill training programs is nearly nonexistent. E-learning may prove to be an effective method to foster nontechnical flight skills, including monitoring. This study examined the effect of using e-learning to enhance helicopter aircrew monitoring skill performance. The design was a posttest only field study. Forty-four helicopter pilots completed either an e-learning training module or a control activity and then flew two scenarios in a high-fidelity flight simulator. Learner reactions and knowledge gained were assessed immediately following the e-learning module. Two observer raters assessed behaviors and performance outcomes using recordings of the simulation flights. Subjects who completed the e-learning training module scored almost twice as high as did the control group on the administered knowledge test (experimental group, mean = 92.8%; control group, mean = 47.7%) and demonstrated up to 150% more monitoring behaviors during the simulated flights than the control subjects. In addition, the participating pilots rated the course highly. The results supported the hypothesis that a relatively inexpensive and brief training course implemented through e-learning can foster monitoring skill development among helicopter pilots.

The Effects of Prior-knowledge and Online Learning Approaches on Students' Inquiry and Argumentation Abilities

NASA Astrophysics Data System (ADS)

Yang, Wen-Tsung; Lin, Yu-Ren; She, Hsiao-Ching; Huang, Kai-Yi

2015-07-01

This study investigated the effects of students' prior science knowledge and online learning approaches (social and individual) on their learning with regard to three topics: science concepts, inquiry, and argumentation. Two science teachers and 118 students from 4 eighth-grade science classes were invited to participate in this research. Students in each class were divided into three groups according to their level of prior science knowledge; they then took either our social- or individual-based online science learning program. The results show that students in the social online argumentation group performed better in argumentation and online argumentation learning. Qualitative analysis indicated that the students' social interactions benefited the co-construction of sound arguments and the accurate understanding of science concepts. In constructing arguments, students in the individual online argumentation group were limited to knowledge recall and self-reflection. High prior-knowledge students significantly outperformed low prior-knowledge students in all three aspects of science learning. However, the difference in inquiry and argumentation performance between low and high prior-knowledge students decreased with the progression of online learning topics.

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.

Using Verbatim Text Recordings to Enhance Reading Comprehension in Learning Disabled Adolescents.

ERIC Educational Resources Information Center

Torgesen, Joseph K.; And Others

1987-01-01

Three studies evaluated the effectiveness of using verbatim text recordings to increase learning disabled high school students' reading comprehension and learning ability. Use of the recordings did produce performance gains, especially when used in conjunction with completion of a related worksheet. (DB)

Case Studies of Factors Affecting the Motivation of Musical High Achievers to Learn Music in Hong Kong

ERIC Educational Resources Information Center

Leung, Bo Wah; McPherson, Gary E.

2011-01-01

This article reports on the personal beliefs and attitudes of highly motivated Hong Kong school-age subjects who studied music. A total of 24 participants who demonstrated high achievement and interest in learning music were interviewed. Content analysis of the interview data was performed to elucidate four topics: background information about the…

Effects of reflex-based self-defence training on police performance in simulated high-pressure arrest situations.

PubMed

Renden, Peter G; Savelsbergh, Geert J P; Oudejans, Raôul R D

2017-05-01

We investigated the effects of reflex-based self-defence training on police performance in simulated high-pressure arrest situations. Police officers received this training as well as a regular police arrest and self-defence skills training (control training) in a crossover design. Officers' performance was tested on several variables in six reality-based scenarios before and after each training intervention. Results showed improved performance after the reflex-based training, while there was no such effect of the regular police training. Improved performance could be attributed to better communication, situational awareness (scanning area, alertness), assertiveness, resolution, proportionality, control and converting primary responses into tactical movements. As officers trained complete violent situations (and not just physical skills), they learned to use their actions before physical contact for de-escalation but also for anticipation on possible attacks. Furthermore, they learned to respond against attacks with skills based on their primary reflexes. The results of this study seem to suggest that reflex-based self-defence training better prepares officers for performing in high-pressure arrest situations than the current form of police arrest and self-defence skills training. Practitioner Summary: Police officers' performance in high-pressure arrest situations improved after a reflex-based self-defence training, while there was no such effect of a regular police training. As officers learned to anticipate on possible attacks and to respond with skills based on their primary reflexes, they were better able to perform effectively.

[The influence of phychological features and learning styles on the academic performance of medical students].

PubMed

Bitran, Marcela; Lafuente, Montserrat; Zúñiga, Denisse; Viviani, Paola; Mena, Beltrán

2004-09-01

The degree of difficulty we experience while learning different concepts and skills depends, among other things, on our psychological features and learning style. This may be particularly true for medical students, whose formation involves the acquisition of multiple cognitive, affective and psychomotor skills. To assess whether the psychological features and learning styles of medical students are associated with their academic performance. The psychological preferences and learning styles of 66 students of the 2001-graduating cohort were determined with the Myers Briggs Type Inventory (MBTI) and the Kolb Learning Style Inventory (LSI), respectively. The academic performance was assessed by the Calificación Médica Nacional (CMN), Chile and by the marks obtained during the Basic (1st to 3rd), Preclinical (4th and 5th) and Clinical (6th and 7th) years of undergraduate training. The psychological features, together with the sex of students were found to be associated with the performance in the Preclinical and Clinical years, and to the CMN. In men, the interest and ability to communicate with people and the concern for harmony, and in women the tendency to function in a systematic and orderly way are the features associated to high academic performance. No associations were found between learning styles and academic performance. The finding that the psychological preferences of medical students are relevent to their academic performance opens a new perspective to analyze the medical education and to design programs aimed at improving learning.

Cause or effect? The relationship between student perception of the medical school learning environment and academic performance on USMLE Step 1.

PubMed

Wayne, Sharon J; Fortner, Sally A; Kitzes, Judith A; Timm, Craig; Kalishman, Summers

2013-05-01

A school's learning environment is believed to influence academic performance yet few studies have evaluated this association controlling for prior academic ability, an important factor since students who do well in school tend to rate their school's environment more highly than students who are less academically strong. To evaluate the effect of student perception of the learning environment on their performance on a standardized licensing test while controlling for prior academic ability. We measured perception of the learning environment after the first year of medical school in 267 students from five consecutive classes and related that measure to performance on United States Medical Licensing Examination (USMLE) Step 1, taken approximately six months later. We controlled for prior academic performance by including Medical College Admission Test score and undergraduate grade point average in linear regression models. Three of the five learning environment subscales were statistically associated with Step 1 performance (p < 0.05): meaningful learning environment, emotional climate, and student-student interaction. A one-point increase in the rating of the subscales (scale of 1-4) was associated with increases of 6.8, 6.6, and 4.8 points on the Step 1 exam. Our findings provide some evidence for the widely held assumption that a positively perceived learning environment contributes to better academic performance.

Policies | High-Performance Computing | NREL

Science.gov Websites

Use Learn about policy governing user accountability, resource use, use by foreign nationals states. Data Security Learn about the data security policy, including data protection, data security retention policy, including project-centric and user-centric data. Shared Storage Usage Learn about a policy

Investigating the Value of Personalization in a Mobile Learning System

ERIC Educational Resources Information Center

Kalloo, Vani; Mohan, Permanand

2015-01-01

This paper investigates the potential benefits of personalization in a mobile learning environment for high school students learning mathematics. Personalization was expected to benefit the students in two main ways. These are improving their performance in mathematics and making navigation of the application easier. Personalization was…

The Ideology of Performative Pedagogies: A Cultural Symptomology

ERIC Educational Resources Information Center

Hides, Shaun

2005-01-01

This article examines the interplay of power, identity and culture within online learning in higher education. Specifically it addresses the relation between online learning, or e-learning, and the apparent disappearance of ideology within postmodernity, in the context of teaching highly diverse cohorts of students. This conjunction is theorised…

Learning in practice: experiences and perceptions of high-scoring physicians.

PubMed

Sargeant, Joan; Mann, Karen; Sinclair, Douglas; Ferrier, Suzanne; Muirhead, Philip; van der Vleuten, Cees; Metsemakers, Job

2006-07-01

To increase understanding of informal learning in practice (e.g., consulting with colleagues, reading journals) through exploring the experiences and perceptions of physicians perceived to be performing well. Objectives were to find out how physicians learned in practice and maintained their competence, and how they learned about the communication skills domain specifically. Of 142 family physicians participating in a formal multisource feedback (360-degree) formative assessment, 25 receiving high scores were invited to participate in interviews conducted in 2003 at Dalhousie University Faculty of Medicine. Twelve responded. Interviews were 1.5 hours each, recorded, transcribed, and analyzed by the research team using accepted qualitative procedures. While formal learning appeared important to most, informal learning, especially through patients and colleagues, appeared to be fundamental. The physicians appeared to learn intentionally from practice and work experiences, and reflection appeared integral to learning and monitoring the impact of learning. Two findings were surprising: participants' conceptions of competence and perceptions that communication skills were innate rather than learned. These physicians' ways of intentional learning from practice concur with current models of informal learning. However, informal learning is largely unrecognized by formal institutions. Additionally, the physicians did not in general share notions of professional competence held by educators and others in authority. These findings suggest the need to make implicit content and learning processes more explicit. Additional research areas include exploring whether physicians across the range of performance levels demonstrate similar processes of reflective learning.

21st Century Community Learning Centers: Providing Afterschool and Summer Learning Support to Communities Nationwide

ERIC Educational Resources Information Center

Afterschool Alliance, 2014

2014-01-01

The 21st Century Community Learning Centers (21st CCLC) initiative is the only federal funding source dedicated exclusively to before-school, afterschool, and summer learning programs. Each state education agency receives funds based on its share of Title I funding for low-income students at high-poverty, low performing schools. Funds are also…

Workforce and Leader Development: Learning From the Baldrige Winners in Health Care.

PubMed

Arnold, Edwin W; Goodson, Jane R; Duarte, Neville T

2015-01-01

It is ironic that perhaps the only constant in health care organizations today is change. To compete successfully in health care and position an organization for high performance amid continuous change, it is very important for managers to have knowledge of the best learning and development practices of high-performing organizations in their industry. The rapid increases in the rate of technological change and geometric increases in knowledge make it virtually imperative that human resources are developed effectively. This article discusses the best learning and development practices among the Malcolm Baldrige National Quality Award winners in the health care industry since 2002 when the industry had its first award-winning organization.

When Is Ignorance Bliss? The Effects of Inaccurate Self-Assessments of Knowledge on Learning and Attrition

ERIC Educational Resources Information Center

Sitzmann, Traci; Johnson, Stefanie K.

2012-01-01

Two studies were conducted to examine the implications of inaccurate self-appraisals in online training. Self-assessment of knowledge moderated the effects of trainees' performance on subsequent performance and attrition. Performance was highest after uniformly positive ratings (i.e., high self-assessment and high performance), followed by…

Procedural instructions, principles, and examples: how to structure instructions for procedural tasks to enhance performance, learning, and transfer.

PubMed

Eiriksdottir, Elsa; Catrambone, Richard

2011-12-01

The goal of this article is to investigate how instructions can be constructed to enhance performance and learning of procedural tasks. Important determinants of the effectiveness of instructions are type of instructions (procedural information, principles, and examples) and pedagogical goal (initial performance, learning, and transfer). Procedural instructions describe how to complete tasks in a stepwise manner, principles describe rules governing the tasks, and examples demonstrate how instances of the task are carried out. The authors review the research literature associated with each type of instruction to identify factors determining effectiveness for different pedagogical goals. The results suggest a trade-off between usability and learnability. Specific instructions help initial performance, whereas more general instructions, requiring problem solving, help learning and transfer. Learning from instructions takes cognitive effort, and research suggests that learners typically opt for low effort. However, it is possible to meet both goals of good initial performance and learning with methods such as fading and by combining different types of instructions. How instructions are constructed influences their effectiveness for the goals of good initial performance, learning, and transfer, and it is therefore important for researchers and practitioners alike to define the pedagogical goal of instructions. If the goal is good initial performance, then instructions should highly resemble the task at hand (e.g., in the form of detailed procedural instructions and examples), but if the goal is good learning and transfer, then instructions should be more abstract, inducing learners to expend the necessary cognitive effort for learning.

Learning Arithmetic Outdoors in Junior High School--Influence on Performance and Self-Regulating Skills

ERIC Educational Resources Information Center

Fägerstam, Emilia; Samuelsson, Joakim

2014-01-01

This study aims to explore the influence of outdoor teaching among students, aged 13, on arithmetic performance and self-regulation skills as previous research concerning outdoor mathematics learning is limited. This study had a quasi-experimental design. An outdoor and a traditional group answered a test and a self-regulation skills questionnaire…

Green Schools as High Performance Learning Facilities

ERIC Educational Resources Information Center

Gordon, Douglas E.

2010-01-01

In practice, a green school is the physical result of a consensus process of planning, design, and construction that takes into account a building's performance over its entire 50- to 60-year life cycle. The main focus of the process is to reinforce optimal learning, a goal very much in keeping with the parallel goals of resource efficiency and…

Measuring the Impact of Language-Learning Software on Test Performance of Chinese Learners of English

ERIC Educational Resources Information Center

Nicholes, Justin

2016-01-01

This classroom quasi-experiment aimed to learn if and to what degree supplementing classroom instruction with Rosetta Stone (RS), Tell Me More (TMM), Memrise (MEM), or ESL WOW (WOW) impacted high-stakes English test performance in areas of university-level writing, reading, speaking, listening, and grammar. Seventy-eight (N = 78) Chinese learners…

Automated condition-invariable neurite segmentation and synapse classification using textural analysis-based machine-learning algorithms

PubMed Central

Kandaswamy, Umasankar; Rotman, Ziv; Watt, Dana; Schillebeeckx, Ian; Cavalli, Valeria; Klyachko, Vitaly

2013-01-01

High-resolution live-cell imaging studies of neuronal structure and function are characterized by large variability in image acquisition conditions due to background and sample variations as well as low signal-to-noise ratio. The lack of automated image analysis tools that can be generalized for varying image acquisition conditions represents one of the main challenges in the field of biomedical image analysis. Specifically, segmentation of the axonal/dendritic arborizations in brightfield or fluorescence imaging studies is extremely labor-intensive and still performed mostly manually. Here we describe a fully automated machine-learning approach based on textural analysis algorithms for segmenting neuronal arborizations in high-resolution brightfield images of live cultured neurons. We compare performance of our algorithm to manual segmentation and show that it combines 90% accuracy, with similarly high levels of specificity and sensitivity. Moreover, the algorithm maintains high performance levels under a wide range of image acquisition conditions indicating that it is largely condition-invariable. We further describe an application of this algorithm to fully automated synapse localization and classification in fluorescence imaging studies based on synaptic activity. Textural analysis-based machine-learning approach thus offers a high performance condition-invariable tool for automated neurite segmentation. PMID:23261652

High and Dry

ERIC Educational Resources Information Center

Johnson, Robert L.

2005-01-01

High-performance schools are facilities that improve the learning environment while saving energy, resources and money. Creating a high-performance school requires an integrated design approach. Key systems--including lighting, HVAC, electrical and plumbing--must be considered from the beginning of the design process. According to William H.…

Learning about learning: Mining human brain sub-network biomarkers from fMRI data

PubMed Central

Dereli, Nazli; Dang, Xuan-Hong; Bassett, Danielle S.; Wymbs, Nicholas F.; Grafton, Scott T.; Singh, Ambuj K.

2017-01-01

Modeling the brain as a functional network can reveal the relationship between distributed neurophysiological processes and functional interactions between brain structures. Existing literature on functional brain networks focuses mainly on a battery of network properties in “resting state” employing, for example, modularity, clustering, or path length among regions. In contrast, we seek to uncover functionally connected subnetworks that predict or correlate with cohort differences and are conserved within the subjects within a cohort. We focus on differences in both the rate of learning as well as overall performance in a sensorimotor task across subjects and develop a principled approach for the discovery of discriminative subgraphs of functional connectivity based on imaging acquired during practice. We discover two statistically significant subgraph regions: one involving multiple regions in the visual cortex and another involving the parietal operculum and planum temporale. High functional coherence in the former characterizes sessions in which subjects take longer to perform the task, while high coherence in the latter is associated with high learning rate (performance improvement across trials). Our proposed methodology is general, in that it can be applied to other cognitive tasks, to study learning or to differentiate between healthy patients and patients with neurological disorders, by revealing the salient interactions among brain regions associated with the observed global state. The discovery of such significant discriminative subgraphs promises a better data-driven understanding of the dynamic brain processes associated with high-level cognitive functions. PMID:29016686

Learning about learning: Mining human brain sub-network biomarkers from fMRI data.

PubMed

Bogdanov, Petko; Dereli, Nazli; Dang, Xuan-Hong; Bassett, Danielle S; Wymbs, Nicholas F; Grafton, Scott T; Singh, Ambuj K

2017-01-01

Modeling the brain as a functional network can reveal the relationship between distributed neurophysiological processes and functional interactions between brain structures. Existing literature on functional brain networks focuses mainly on a battery of network properties in "resting state" employing, for example, modularity, clustering, or path length among regions. In contrast, we seek to uncover functionally connected subnetworks that predict or correlate with cohort differences and are conserved within the subjects within a cohort. We focus on differences in both the rate of learning as well as overall performance in a sensorimotor task across subjects and develop a principled approach for the discovery of discriminative subgraphs of functional connectivity based on imaging acquired during practice. We discover two statistically significant subgraph regions: one involving multiple regions in the visual cortex and another involving the parietal operculum and planum temporale. High functional coherence in the former characterizes sessions in which subjects take longer to perform the task, while high coherence in the latter is associated with high learning rate (performance improvement across trials). Our proposed methodology is general, in that it can be applied to other cognitive tasks, to study learning or to differentiate between healthy patients and patients with neurological disorders, by revealing the salient interactions among brain regions associated with the observed global state. The discovery of such significant discriminative subgraphs promises a better data-driven understanding of the dynamic brain processes associated with high-level cognitive functions.

Personalized Opportunities To Learn (POTL): Achieving to High Standards for All Students.

ERIC Educational Resources Information Center

Gervais, J. Donna; Baker, Mona

This paper describes Maine's high standards for all students and a model for personalizing instruction and assessment to fit student needs, thus providing fair opportunities for all children to achieve the standards. Maine's academic standards, the Learning Results, are structured in three levels: broad performance goals for all students (guiding…

Impact of a Freshman Academy on Student Performance at a Comprehensive Public High School

ERIC Educational Resources Information Center

Hernandez, Jose Angel, Jr.

2012-01-01

Previous high school research has highlighted the importance of students' freshman year. Limited research has supported the implementation of a smaller learning community, also known as a freshman academy. The theoretical framework of the study was based on stage environment fit, adolescence development and smaller learning community theories. The…

Scaling Up Graph-Based Semisupervised Learning via Prototype Vector Machines

PubMed Central

Zhang, Kai; Lan, Liang; Kwok, James T.; Vucetic, Slobodan; Parvin, Bahram

2014-01-01

When the amount of labeled data are limited, semi-supervised learning can improve the learner's performance by also using the often easily available unlabeled data. In particular, a popular approach requires the learned function to be smooth on the underlying data manifold. By approximating this manifold as a weighted graph, such graph-based techniques can often achieve state-of-the-art performance. However, their high time and space complexities make them less attractive on large data sets. In this paper, we propose to scale up graph-based semisupervised learning using a set of sparse prototypes derived from the data. These prototypes serve as a small set of data representatives, which can be used to approximate the graph-based regularizer and to control model complexity. Consequently, both training and testing become much more efficient. Moreover, when the Gaussian kernel is used to define the graph affinity, a simple and principled method to select the prototypes can be obtained. Experiments on a number of real-world data sets demonstrate encouraging performance and scaling properties of the proposed approach. It also compares favorably with models learned via ℓ1-regularization at the same level of model sparsity. These results demonstrate the efficacy of the proposed approach in producing highly parsimonious and accurate models for semisupervised learning. PMID:25720002

Impaired Statistical Learning in Developmental Dyslexia

PubMed Central

Thiessen, Erik D.; Holt, Lori L.

2015-01-01

Purpose Developmental dyslexia (DD) is commonly thought to arise from phonological impairments. However, an emerging perspective is that a more general procedural learning deficit, not specific to phonological processing, may underlie DD. The current study examined if individuals with DD are capable of extracting statistical regularities across sequences of passively experienced speech and nonspeech sounds. Such statistical learning is believed to be domain-general, to draw upon procedural learning systems, and to relate to language outcomes. Method DD and control groups were familiarized with a continuous stream of syllables or sine-wave tones, the ordering of which was defined by high or low transitional probabilities across adjacent stimulus pairs. Participants subsequently judged two 3-stimulus test items with either high or low statistical coherence as being the most similar to the sounds heard during familiarization. Results As with control participants, the DD group was sensitive to the transitional probability structure of the familiarization materials as evidenced by above-chance performance. However, the performance of participants with DD was significantly poorer than controls across linguistic and nonlinguistic stimuli. In addition, reading-related measures were significantly correlated with statistical learning performance of both speech and nonspeech material. Conclusion Results are discussed in light of procedural learning impairments among participants with DD. PMID:25860795

Category learning strategies in younger and older adults: Rule abstraction and memorization.

PubMed

Wahlheim, Christopher N; McDaniel, Mark A; Little, Jeri L

2016-06-01

Despite the fundamental role of category learning in cognition, few studies have examined how this ability differs between younger and older adults. The present experiment examined possible age differences in category learning strategies and their effects on learning. Participants were trained on a category determined by a disjunctive rule applied to relational features. The utilization of rule- and exemplar-based strategies was indexed by self-reports and transfer performance. Based on self-reported strategies, the frequencies of rule- and exemplar-based learners were not significantly different between age groups, but there was a significantly higher frequency of intermediate learners (i.e., learners not identifying with a reliance on either rule- or exemplar-based strategies) in the older than younger adult group. Training performance was higher for younger than older adults regardless of the strategy utilized, showing that older adults were impaired in their ability to learn the correct rule or to remember exemplar-label associations. Transfer performance converged with strategy reports in showing higher fidelity category representations for younger adults. Younger adults with high working memory capacity were more likely to use an exemplar-based strategy, and older adults with high working memory capacity showed better training performance. Age groups did not differ in their self-reported memory beliefs, and these beliefs did not predict training strategies or performance. Overall, the present results contradict earlier findings that older adults prefer rule- to exemplar-based learning strategies, presumably to compensate for memory deficits. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Talent development of high performance coaches in team sports in Ireland.

PubMed

Sherwin, Ian; Campbell, Mark J; Macintyre, Tadhg Eoghan

2017-04-01

Coaches are central to the development of the expert performer and similarly to continued lifelong participation in sport. Coaches are uniquely positioned to deliver specific technical and tactical instruction and mentoring programmes that support the psychological and social development of athletes in a challenging, goal-oriented and motivational environment. The current study aimed to qualitatively investigate current coach learning sources and coaches' educational backgrounds in team sports in Ireland. Coaches from five team sports in Ireland were asked to complete an online questionnaire. Subsequently male coaches (n = 19) from five team sports who completed the questionnaire and met the inclusion criteria were invited to attend a follow-up semi-structured interview. Inclusion criteria for coaches were that they possess at least 10 years' experience coaching their sport and were coaching more than 4 hours per week. Formal coach education does not meet the needs of high performance coaches who rely more on self-directed learning and coaching experience as their main sources of CPD. Although prior playing experience at a high level is both valuable and desirable, there are concerns about fast-tracking of ex-players into high performance coaching roles. Preferred sources of education and the best learning environment for coaches of team sports in Ireland are more informal than formal. Further research is needed to examine how this learning is applied in a practical manner by examining coaching behaviours and the impact it has on the athlete development process.

Deep Unsupervised Learning on a Desktop PC: A Primer for Cognitive Scientists.

PubMed

Testolin, Alberto; Stoianov, Ivilin; De Filippo De Grazia, Michele; Zorzi, Marco

2013-01-01

Deep belief networks hold great promise for the simulation of human cognition because they show how structured and abstract representations may emerge from probabilistic unsupervised learning. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. However, learning in deep networks typically requires big datasets and it can involve millions of connection weights, which implies that simulations on standard computers are unfeasible. Developing realistic, medium-to-large-scale learning models of cognition would therefore seem to require expertise in programing parallel-computing hardware, and this might explain why the use of this promising approach is still largely confined to the machine learning community. Here we show how simulations of deep unsupervised learning can be easily performed on a desktop PC by exploiting the processors of low cost graphic cards (graphic processor units) without any specific programing effort, thanks to the use of high-level programming routines (available in MATLAB or Python). We also show that even an entry-level graphic card can outperform a small high-performance computing cluster in terms of learning time and with no loss of learning quality. We therefore conclude that graphic card implementations pave the way for a widespread use of deep learning among cognitive scientists for modeling cognition and behavior.

Deep Unsupervised Learning on a Desktop PC: A Primer for Cognitive Scientists

PubMed Central

Testolin, Alberto; Stoianov, Ivilin; De Filippo De Grazia, Michele; Zorzi, Marco

2013-01-01

Deep belief networks hold great promise for the simulation of human cognition because they show how structured and abstract representations may emerge from probabilistic unsupervised learning. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. However, learning in deep networks typically requires big datasets and it can involve millions of connection weights, which implies that simulations on standard computers are unfeasible. Developing realistic, medium-to-large-scale learning models of cognition would therefore seem to require expertise in programing parallel-computing hardware, and this might explain why the use of this promising approach is still largely confined to the machine learning community. Here we show how simulations of deep unsupervised learning can be easily performed on a desktop PC by exploiting the processors of low cost graphic cards (graphic processor units) without any specific programing effort, thanks to the use of high-level programming routines (available in MATLAB or Python). We also show that even an entry-level graphic card can outperform a small high-performance computing cluster in terms of learning time and with no loss of learning quality. We therefore conclude that graphic card implementations pave the way for a widespread use of deep learning among cognitive scientists for modeling cognition and behavior. PMID:23653617

Doodling Effects on Junior High School Students' Learning

ERIC Educational Resources Information Center

Tadayon, Mariam; Afhami, Reza

2017-01-01

The main purpose of this study was to assess the effects of doodling on the learning performance of high school female students in Tehran. The design of this research was a pre-test-post-test with a control group. A group of 169 junior high school 12-13 year-old students was chosen for this study. After being taught a section of the Natural…

Doodling Effects on Junior High School Students' Learning

ERIC Educational Resources Information Center

Tadayon, Mariam; Afhami, Reza

2017-01-01

The main purpose of this study was to assess the effects of doodling on the learning performance of high school female students in Tehran. The design of this research was a pre-test-post-test with a control group. A group of 169 junior high school 12-13 year-old students was chosen for this study. After being taught a section of the Natural…

Perceptual Learning Improves Adult Amblyopic Vision Through Rule-Based Cognitive Compensation

PubMed Central

Zhang, Jun-Yun; Cong, Lin-Juan; Klein, Stanley A.; Levi, Dennis M.; Yu, Cong

2014-01-01

Purpose. We investigated whether perceptual learning in adults with amblyopia could be enabled to transfer completely to an orthogonal orientation, which would suggest that amblyopic perceptual learning results mainly from high-level cognitive compensation, rather than plasticity in the amblyopic early visual brain. Methods. Nineteen adults (mean age = 22.5 years) with anisometropic and/or strabismic amblyopia were trained following a training-plus-exposure (TPE) protocol. The amblyopic eyes practiced contrast, orientation, or Vernier discrimination at one orientation for six to eight sessions. Then the amblyopic or nonamblyopic eyes were exposed to an orthogonal orientation via practicing an irrelevant task. Training was first performed at a lower spatial frequency (SF), then at a higher SF near the cutoff frequency of the amblyopic eye. Results. Perceptual learning was initially orientation specific. However, after exposure to the orthogonal orientation, learning transferred to an orthogonal orientation completely. Reversing the exposure and training order failed to produce transfer. Initial lower SF training led to broad improvement of contrast sensitivity, and later higher SF training led to more specific improvement at high SFs. Training improved visual acuity by 1.5 to 1.6 lines (P < 0.001) in the amblyopic eyes with computerized tests and a clinical E acuity chart. It also improved stereoacuity by 53% (P < 0.001). Conclusions. The complete transfer of learning suggests that perceptual learning in amblyopia may reflect high-level learning of rules for performing a visual discrimination task. These rules are applicable to new orientations to enable learning transfer. Therefore, perceptual learning may improve amblyopic vision mainly through rule-based cognitive compensation. PMID:24550359

Perceptual learning improves adult amblyopic vision through rule-based cognitive compensation.

PubMed

Zhang, Jun-Yun; Cong, Lin-Juan; Klein, Stanley A; Levi, Dennis M; Yu, Cong

2014-04-01

We investigated whether perceptual learning in adults with amblyopia could be enabled to transfer completely to an orthogonal orientation, which would suggest that amblyopic perceptual learning results mainly from high-level cognitive compensation, rather than plasticity in the amblyopic early visual brain. Nineteen adults (mean age = 22.5 years) with anisometropic and/or strabismic amblyopia were trained following a training-plus-exposure (TPE) protocol. The amblyopic eyes practiced contrast, orientation, or Vernier discrimination at one orientation for six to eight sessions. Then the amblyopic or nonamblyopic eyes were exposed to an orthogonal orientation via practicing an irrelevant task. Training was first performed at a lower spatial frequency (SF), then at a higher SF near the cutoff frequency of the amblyopic eye. Perceptual learning was initially orientation specific. However, after exposure to the orthogonal orientation, learning transferred to an orthogonal orientation completely. Reversing the exposure and training order failed to produce transfer. Initial lower SF training led to broad improvement of contrast sensitivity, and later higher SF training led to more specific improvement at high SFs. Training improved visual acuity by 1.5 to 1.6 lines (P < 0.001) in the amblyopic eyes with computerized tests and a clinical E acuity chart. It also improved stereoacuity by 53% (P < 0.001). The complete transfer of learning suggests that perceptual learning in amblyopia may reflect high-level learning of rules for performing a visual discrimination task. These rules are applicable to new orientations to enable learning transfer. Therefore, perceptual learning may improve amblyopic vision mainly through rule-based cognitive compensation.

Developing Web-Based Assessment Strategies for Facilitating Junior High School Students to Perform Self-Regulated Learning in an E-Learning Environment

ERIC Educational Resources Information Center

Wang, Tzu-Hua

2011-01-01

This research refers to the self-regulated learning strategies proposed by Pintrich (1999) in developing a multiple-choice Web-based assessment system, the Peer-Driven Assessment Module of the Web-based Assessment and Test Analysis system (PDA-WATA). The major purpose of PDA-WATA is to facilitate learner use of self-regulatory learning behaviors…

Learning at Every Age? Life Cycle Dynamics of Adult Education in Europe

ERIC Educational Resources Information Center

Beblavy, Miroslav; Thum, Anna-Elisabeth; Potjagailo, Galina

2014-01-01

Adult learning is seen as a key factor for enhancing employment, innovation and growth. The aim of this paper is to understand the points in the life cycle at which adult learning takes place and whether it leads to reaching a medium or high level of educational attainment. We perform a synthetic panel analysis of adult learning for cohorts aged…

Intrinsic Motivation, Learning Goals, Engagement, and Achievement in a Diverse High School

ERIC Educational Resources Information Center

Froiland, John Mark; Worrell, Frank C.

2016-01-01

Using structural equation models, with gender, parent education, and prior grade point average (GPA) as control variables, we examined the relationships among intrinsic motivation to learn, learning goals, behavioral engagement at school, and academic performance (measured by GPA) in 1,575 students in an ethnically and racially diverse high…

New York: Expanding Time, Increasing Opportunities for Achievement

ERIC Educational Resources Information Center

Miller, Tiffany D.

2014-01-01

New York is poised to take an important step to improve student achievement by expanding learning time for students attending high-poverty, low-performing schools. Recent district- and state-level investments in expanded learning time--a promising strategy to close achievement and opportunity gaps--will give students more time to learn core…

Perspectives on Technology Assessment

DTIC Science & Technology

1991-01-01

middle and high school students . They typically have strong social needs which are not always met in distance learning ...given distance learning program impacted student achievement and subsequent ability to use (transfer) the knowledge acquired outside of the instructional ...1980). An analysis of the effects of learning to program on student math performance and attitude toward school . Dissertation Abstract

Ontario District Embraces an Evolving Approach to Learning

ERIC Educational Resources Information Center

Belchetz, Denese; Witherow, Kathy

2014-01-01

The York Region District School Board is recognized as a high-performing district in Ontario, Canada, and has also garnered international attention. Visitors from across Canada, as well as Singapore, Finland, England, Scotland, Holland, the Bahamas, Korea, China, and Taiwan, have come to learn about its system and observe the teaching, learning,…

It's Time for Summer: An Analysis of Recent Policy and Funding Opportunities

ERIC Educational Resources Information Center

Fairchild, Ron; Smink, Jeff; Stewart, Ashley B.

2009-01-01

The National Summer Learning Association is the only national organization that focuses exclusively on learning during the summer months. It works to ensure that children and youth in high-poverty communities have access to quality summer learning opportunities that support their academic performance and healthy development through hands-on…

Increased Learning Time under Stimulus-Funded School Improvement Grants: High Hopes, Varied Implementation

ERIC Educational Resources Information Center

McMurrer, Jennifer

2012-01-01

Research has long suggested that significantly increasing quality time in school for teaching and learning can have a positive impact on student achievement. Recognizing this connection, federal guidance requires low-performing schools to increase student learning time if they are implementing two popular reform models using school improvement…

Effect of Blended Learning Environment Model on High School Students' Academic Achievement

ERIC Educational Resources Information Center

Kazu, Ibrahim Yasar; Demirkol, Mehmet

2014-01-01

This study analyzes the students' academic performance by comparing the blended learning environment and traditional learning environment. It has been observed whether there is a significant difference between the academic achievement grade dispersions and the male-female students' grades. The study has been carried out in Diyarbakir Anatolian…

Learning nursing procedures: the influence of simulator fidelity and student gender on teaching effectiveness.

PubMed

Grady, Janet L; Kehrer, Rosemary G; Trusty, Carole E; Entin, Eileen B; Entin, Elliot E; Brunye, Tad T

2008-09-01

Simulation technologies are gaining widespread acceptance across a variety of educational domains and applications. The current research examines whether basic nursing procedure training with high-fidelity versus low-fidelity mannequins results in differential skill acquisition and perceptions of simulator utility. Fifty-two first-year students were taught nasogastric tube and indwelling urinary catheter insertion in one of two ways. The first group learned nasogastric tube and urinary catheter insertion using high-fidelity and low-fidelity mannequins, respectively, and the second group learned nasogastric tube and urinary catheter insertion using low-fidelity and high-fidelity mannequins, respectively. The dependent measures included student performance on nasogastric tube and urinary catheter insertion testing, as measured by observer-based instruments, and self-report questionnaires probing student attitudes about the use of simulation in nursing education. Results demonstrated higher performance with high-fidelity than with low-fidelity mannequin training. In response to a self-report posttraining questionnaire, participants expressed a more positive attitude toward the high-fidelity mannequin, especially regarding its responsiveness and realism.

The Learning Environment Counts: Longitudinal Qualitative Analysis of Study Strategies Adopted by First-Year Medical Students in a Competency-Based Educational Program.

PubMed

Bierer, S Beth; Dannefer, Elaine F

2016-11-01

The move toward competency-based education will require medical schools and postgraduate training programs to restructure learning environments to motivate trainees to take personal ownership for learning. This qualitative study explores how medical students select and implement study strategies while enrolled in a unique, nontraditional program that emphasizes reflection on performance and competence rather than relying on high-stakes examinations or grades to motivate students to learn and excel. Fourteen first-year medical students volunteered to participate in three, 45-minute interviews (42 overall) scheduled three months apart during 2013-2014. Two medical educators used structured interview guides to solicit students' previous assessment experiences, preferred learning strategies, and performance monitoring processes. Interviews were digitally recorded and transcribed verbatim. Participants confirmed accuracy of transcripts. Researchers independently read transcripts and met regularly to discuss transcripts and judge when themes achieved saturation. Medical students can adopt an assessment for learning mind-set with faculty guidance and implement appropriate study strategies for mastery-learning demands. Though students developed new strategies at different rates during the year, they all eventually identified study and performance monitoring strategies to meet learning needs. Students who had diverse learning experiences in college embraced mastery-based study strategies sooner than peers after recognizing that the learning environment did not reward performance-based strategies. Medical students can take ownership for their learning and implement specific strategies to regulate behavior when learning environments contain building blocks emphasized in self-determination theory. Findings should generalize to educational programs seeking strategies to design learning environments that promote self-regulated learning.

Paired-Associate Learning in Young and Old Adults as Related to Stimulus Concreteness and Presentation Method

ERIC Educational Resources Information Center

Witte, Kenneth L.; Freund, Joel S.

1976-01-01

Investigated the learning of young and old adults as related to two variables, stimulus concreteness (low vs. high) and presentation method (recall vs. multiple choice vs. associate matching). Main findings were: (a) the elderly did not perform as well as young adults, (b) for both groups, performance was better for the pairs with concrete…

A service based adaptive U-learning system using UX.

PubMed

Jeong, Hwa-Young; Yi, Gangman

2014-01-01

In recent years, traditional development techniques for e-learning systems have been changing to become more convenient and efficient. One new technology in the development of application systems includes both cloud and ubiquitous computing. Cloud computing can support learning system processes by using services while ubiquitous computing can provide system operation and management via a high performance technical process and network. In the cloud computing environment, a learning service application can provide a business module or process to the user via the internet. This research focuses on providing the learning material and processes of courses by learning units using the services in a ubiquitous computing environment. And we also investigate functions that support users' tailored materials according to their learning style. That is, we analyzed the user's data and their characteristics in accordance with their user experience. We subsequently applied the learning process to fit on their learning performance and preferences. Finally, we demonstrate how the proposed system outperforms learning effects to learners better than existing techniques.

A Service Based Adaptive U-Learning System Using UX

PubMed Central

Jeong, Hwa-Young

2014-01-01

In recent years, traditional development techniques for e-learning systems have been changing to become more convenient and efficient. One new technology in the development of application systems includes both cloud and ubiquitous computing. Cloud computing can support learning system processes by using services while ubiquitous computing can provide system operation and management via a high performance technical process and network. In the cloud computing environment, a learning service application can provide a business module or process to the user via the internet. This research focuses on providing the learning material and processes of courses by learning units using the services in a ubiquitous computing environment. And we also investigate functions that support users' tailored materials according to their learning style. That is, we analyzed the user's data and their characteristics in accordance with their user experience. We subsequently applied the learning process to fit on their learning performance and preferences. Finally, we demonstrate how the proposed system outperforms learning effects to learners better than existing techniques. PMID:25147832

School physics teacher class management, laboratory practice, student engagement, critical thinking, cooperative learning and use of simulations effects on student performance

NASA Astrophysics Data System (ADS)

Riaz, Muhammad

The purpose of this study was to examine how simulations in physics class, class management, laboratory practice, student engagement, critical thinking, cooperative learning, and use of simulations predicted the percentage of students achieving a grade point average of B or higher and their academic performance as reported by teachers in secondary school physics classes. The target population consisted of secondary school physics teachers who were members of Science Technology, Engineeering and,Mathematics Teachers of New York City (STEMteachersNYC) and American Modeling Teachers Association (AMTA). They used simulations in their physics classes in the 2013 and 2014 school years. Subjects for this study were volunteers. A survey was constructed based on a literature review. Eighty-two physics teachers completed the survey about instructional practice in physics. All respondents were anonymous. Classroom management was the only predictor of the percent of students achieving a grade point average of B or higher in high school physics class. Cooperative learning, use of simulations, and student engagement were predictors of teacher's views of student academic performance in high school physics class. All other variables -- class management, laboratory practice, critical thinking, and teacher self-efficacy -- were not predictors of teacher's views of student academic performance in high school physics class. The implications of these findings were discussed and recommendations for physics teachers to improve student learning were presented.

Effects of the High School Economics Curriculum on Learning in the College Principles Class.

ERIC Educational Resources Information Center

Lopus, Jane S.

1997-01-01

Asserts that most research into the effects of high school economics curriculum upon performance in college level economics courses fails to consider that secondary economics education rarely teaches micro- and macroeconomic concepts. Discovers that any gap in learning between students in economics is narrowed by the first semester in college.…

Effects of Collaborative Preteaching on Science Performance of High School Students with Specific Learning Disabilities

ERIC Educational Resources Information Center

Thornton, Amanda; McKissick, Bethany R.; Spooner, Fred; Lo, Ya-yu; Anderson, Adrienne L.

2015-01-01

Investigating the effectiveness of inclusive practices in science instruction and determining how to best support high school students with specific learning disabilities (SLD) in the general education classroom is a topic of increasing research attention in the field. In this study, the researchers conducted a single-subject multiple probe across…

Creating a Powerful Learning Environment with Networked Mobile Learning Devices

ERIC Educational Resources Information Center

Crawford, Valerie M.

2007-01-01

Highly mobile devices can make important information available to teachers in real-time, anywhere in the classroom, and in the form of easy-to-read graphical displays that support classroom decision making. By supporting such important teaching activities, we can create a high-performance classroom that supports teachers and the art of teaching,…

The Perceptions of Temporal Path Analysis of Learners' Self-Regulation on Learning Stress and Social Relationships in Junior High School

ERIC Educational Resources Information Center

Chang, Hsiu-Ju

2016-01-01

This research focus on the temporal path analysis of learning stress, test anxiety, peer stress (classmate relatedness), teacher relatedness, autonomy, and self-regulative performance in junior high school. Owing to the processes of self-determination always combines several negotiations with the interactive perceptions of personal experiences and…

A Systematic Approach to Improving E-Learning Implementations in High Schools

ERIC Educational Resources Information Center

Pardamean, Bens; Suparyanto, Teddy

2014-01-01

This study was based on the current growing trend of implementing e-learning in high schools. Most endeavors have been inefficient, rendering an objective of determining the initial steps that could be taken to improve these efforts by assessing a student population's computer skill levels and performances in an IT course. Demographic factors were…

Becoming a High-Fidelity--"Super"--Imitator: What Are the Contributions of Social and Individual Learning?

ERIC Educational Resources Information Center

Subiaul, Francys; Patterson, Eric M.; Schilder, Brian; Renner, Elizabeth; Barr, Rachel

2015-01-01

In contrast to other primates, human children's imitation performance goes from low to high fidelity soon after infancy. Are such changes associated with the development of other forms of learning? We addressed this question by testing 215 children (26-59 months) on two social conditions (imitation, emulation)--involving a demonstration--and two…

Effects of Peer-Tutor Competences on Learner Cognitive Load and Learning Performance during Knowledge Sharing

ERIC Educational Resources Information Center

Hsiao, Ya-Ping; Brouns, Francis; van Bruggen, Jan; Sloep, Peter B.

2012-01-01

In Learning Networks, learners need to share knowledge with others to build knowledge. In particular, when working on complex tasks, they often need to acquire extra cognitive resources from others to process a high task load. However, without support high task load and organizing knowledge sharing themselves might easily overload learners'…

Implementation of a Learning Assistant Program Improves Student Performance on Higher-Order Assessments

PubMed Central

Sellami, Nadia; Shaked, Shanna; Laski, Frank A.; Eagan, Kevin M.; Sanders, Erin R.

2017-01-01

Learning assistant (LA) programs have been implemented at a range of institutions, usually as part of a comprehensive curricular transformation accompanied by a pedagogical switch to active learning. While this shift in pedagogy has led to increased student learning gains, the positive effect of LAs has not yet been distinguished from that of active learning. To determine the effect that LAs would have beyond a student-centered instructional modality that integrated active learning, we introduced an LA program into a large-enrollment introductory molecular biology course that had already undergone a pedagogical transformation to a highly structured, flipped (HSF) format. We used questions from a concept test (CT) and exams to compare student performance in LA-supported HSF courses with student performance in courses without LAs. Students in the LA-supported course did perform better on exam questions common to both HSF course modalities but not on the CT. In particular, LA-supported students’ scores were higher on common exam questions requiring higher-order cognitive skills, which LAs were trained to foster. Additionally, underrepresented minority (URM) students particularly benefited from LA implementation. These findings suggest that LAs may provide additional learning benefits to students beyond the use of active learning, especially for URM students. PMID:29167224

The effect of the use of android-based application in learning together to improve students' academic performance

NASA Astrophysics Data System (ADS)

Ulfa, Andi Maria; Sugiyarto, Kristian H.; Ikhsan, Jaslin

2017-05-01

Poor achievement of students' performance on Chemistry may result from unfavourable learning processes. Therefore, innovation on learning process must be created. Regarding fast development of mobile technology, learning process cannot ignore the crucial role of the technology. This research and development (R&D) studies was done to develop android based application and to study the effect of its integration in Learning together (LT) into the improvement of students' learning creativity and cognitive achievement. The development of the application was carried out by adapting Borg & Gall and Dick & Carey model. The developed-product was reviewed by chemist, learning media practitioners, peer reviewers, and educators. After the revision based on the reviews, the application was used in the LT model on the topic of Stoichiometry in a senior high school. The instruments were questionnaires to get comments and suggestion from the reviewers about the application, and the another questionnaire was to collect the data of learning creativity. Another instrument used was a set of test by which data of students' achievement was collected. The results showed that the use of the mobile based application on Learning Together can bring about significant improvement of students' performance including creativity and cognitive achievement.

A deep learning framework for financial time series using stacked autoencoders and long-short term memory.

PubMed

Bao, Wei; Yue, Jun; Rao, Yulei

2017-01-01

The application of deep learning approaches to finance has received a great deal of attention from both investors and researchers. This study presents a novel deep learning framework where wavelet transforms (WT), stacked autoencoders (SAEs) and long-short term memory (LSTM) are combined for stock price forecasting. The SAEs for hierarchically extracted deep features is introduced into stock price forecasting for the first time. The deep learning framework comprises three stages. First, the stock price time series is decomposed by WT to eliminate noise. Second, SAEs is applied to generate deep high-level features for predicting the stock price. Third, high-level denoising features are fed into LSTM to forecast the next day's closing price. Six market indices and their corresponding index futures are chosen to examine the performance of the proposed model. Results show that the proposed model outperforms other similar models in both predictive accuracy and profitability performance.

From see one do one, to see a good one do a better one: learning physical examination skills through peer observation.

PubMed

St-Onge, Christina; Martineau, Bernard; Harvey, Anne; Bergeron, Linda; Mamede, Silvia; Rikers, Remy

2013-01-01

Learning and mastering the skills required to execute physical exams is of great importance and should be fostered early during medical training. Observing peers has been shown to positively influence the acquisition of psychomotor skills. The current study investigated the influence of peer observation on the acquisition of psychomotor skills required to execute a physical examination. Second-year medical students (N=194) learned the neurological physical examination for low back pain in groups of three. Each student learned and performed the physical examination while the other students observed. Analyses compared the impact of the quantity and the quality of observed performances on students' learning of the physical examination skills. Students benefited from observing peers while they executed their examination. Moreover, observing a high-performing peer increased the acquisition of physical examination skills. Results suggest that group learning activities that allow students to observe their peers during physical examination should be favored.

Multi-Objective Reinforcement Learning for Cognitive Radio-Based Satellite Communications

NASA Technical Reports Server (NTRS)

Ferreira, Paulo Victor R.; Paffenroth, Randy; Wyglinski, Alexander M.; Hackett, Timothy M.; Bilen, Sven G.; Reinhart, Richard C.; Mortensen, Dale J.

2016-01-01

Previous research on cognitive radios has addressed the performance of various machine-learning and optimization techniques for decision making of terrestrial link properties. In this paper, we present our recent investigations with respect to reinforcement learning that potentially can be employed by future cognitive radios installed onboard satellite communications systems specifically tasked with radio resource management. This work analyzes the performance of learning, reasoning, and decision making while considering multiple objectives for time-varying communications channels, as well as different cross-layer requirements. Based on the urgent demand for increased bandwidth, which is being addressed by the next generation of high-throughput satellites, the performance of cognitive radio is assessed considering links between a geostationary satellite and a fixed ground station operating at Ka-band (26 GHz). Simulation results show multiple objective performance improvements of more than 3.5 times for clear sky conditions and 6.8 times for rain conditions.

Multi-Objective Reinforcement Learning for Cognitive Radio Based Satellite Communications

NASA Technical Reports Server (NTRS)

Ferreira, Paulo; Paffenroth, Randy; Wyglinski, Alexander; Hackett, Timothy; Bilen, Sven; Reinhart, Richard; Mortensen, Dale John

2016-01-01

Previous research on cognitive radios has addressed the performance of various machine learning and optimization techniques for decision making of terrestrial link properties. In this paper, we present our recent investigations with respect to reinforcement learning that potentially can be employed by future cognitive radios installed onboard satellite communications systems specifically tasked with radio resource management. This work analyzes the performance of learning, reasoning, and decision making while considering multiple objectives for time-varying communications channels, as well as different crosslayer requirements. Based on the urgent demand for increased bandwidth, which is being addressed by the next generation of high-throughput satellites, the performance of cognitive radio is assessed considering links between a geostationary satellite and a fixed ground station operating at Ka-band (26 GHz). Simulation results show multiple objective performance improvements of more than 3:5 times for clear sky conditions and 6:8 times for rain conditions.

Cognitive Fatigue Facilitates Procedural Sequence Learning.

PubMed

Borragán, Guillermo; Slama, Hichem; Destrebecqz, Arnaud; Peigneux, Philippe

2016-01-01

Enhanced procedural learning has been evidenced in conditions where cognitive control is diminished, including hypnosis, disruption of prefrontal activity and non-optimal time of the day. Another condition depleting the availability of controlled resources is cognitive fatigue (CF). We tested the hypothesis that CF, eventually leading to diminished cognitive control, facilitates procedural sequence learning. In a two-day experiment, 23 young healthy adults were administered a serial reaction time task (SRTT) following the induction of high or low levels of CF, in a counterbalanced order. CF was induced using the Time load Dual-back (TloadDback) paradigm, a dual working memory task that allows tailoring cognitive load levels to the individual's optimal performance capacity. In line with our hypothesis, reaction times (RT) in the SRTT were faster in the high- than in the low-level fatigue condition, and performance improvement was higher for the sequential than the motor components. Altogether, our results suggest a paradoxical, facilitating impact of CF on procedural motor sequence learning. We propose that facilitated learning in the high-level fatigue condition stems from a reduction in the cognitive resources devoted to cognitive control processes that normally oppose automatic procedural acquisition mechanisms.

Mathematical learning instruction and teacher motivation factors affecting science technology engineering and math (STEM) major choices in 4-year colleges and universities: Multilevel structural equation modeling

NASA Astrophysics Data System (ADS)

Lee, Ahlam

2011-12-01

Using the Educational Longitudinal Study of 2002/06, this study examined the effects of the selected mathematical learning and teacher motivation factors on graduates' science, technology, engineering, and math (STEM) related major choices in 4-year colleges and universities, as mediated by math performance and math self-efficacy. Using multilevel structural equation modeling, I analyzed: (1) the association between mathematical learning instruction factors (i.e., computer, individual, and lecture-based learning activities in mathematics) and students' STEM major choices in 4-year colleges and universities as mediated by math performance and math self-efficacy and (2) the association between school factor, teacher motivation and students' STEM major choices in 4-year colleges and universities via mediators of math performance and math self-efficacy. The results revealed that among the selected learning experience factors, computer-based learning activities in math classrooms yielded the most positive effects on math self-efficacy, which significantly predicted the increase in the proportion of students' STEM major choice as mediated by math self-efficacy. Further, when controlling for base-year math Item Response Theory (IRT) scores, a positive relationship between individual-based learning activities in math classrooms and the first follow-up math IRT scores emerged, which related to the high proportion of students' STEM major choices. The results also indicated that individual and lecture-based learning activities in math yielded positive effects on math self-efficacy, which related to STEM major choice. Concerning between-school levels, teacher motivation yielded positive effects on the first follow up math IRT score, when controlling for base year IRT score. The results from this study inform educators, parents, and policy makers on how mathematics instruction can improve student math performance and encourage more students to prepare for STEM careers. Students should receive all possible opportunities to use computers to enhance their math self-efficacy, be encouraged to review math materials, and concentrate on listening to math teachers' lectures. While all selected math-learning activities should be embraced in math instruction, computer and individual-based learning activities, which reflect student-driven learning, should be emphasized in the high school instruction. Likewise, students should be encouraged to frequently engage in individual-based learning activities to improve their math performance.

The Development of Cooperative Learning Model Based on Local Wisdom of Bali for Physical Education, Sport and Health Subject in Junior High School

NASA Astrophysics Data System (ADS)

Yoda, I. K.

2017-03-01

The purpose of this research is to develop a cooperative learning model based on local wisdom (PKBKL) of Bali (Tri Pramana’s concept), for physical education, sport, and health learning in VII grade of Junior High School in Singaraja-Buleleng Bali. This research is the development research of the development design chosen refers to the development proposed by Dick and Carey. The development of model and learning devices was conducted through four stages, namely: (1) identification and needs analysis stage (2) the development of design and draft of PKBKL and RPP models, (3) testing stage (expert review, try out, and implementation). Small group try out was conducted on VII-3 grade of Undiksha Laboratory Junior High School in the academic year 2013/2014, large group try out was conducted on VIIb of Santo Paulus Junior High School Singaraja in the academic year 2014/2015, and the implementation of the model was conducted on three (3) schools namely SMPN 2 Singaraja, SMPN 3 Singaraja, and Undiksha laboratory Junior High School in the academic year 2014/2015. Data were collected using documentation, testing, non-testing, questionnaire, and observation. The data were analyzed descriptively. The findings of this research indicate that: (1) PKBKL model has met the criteria of the operation of a learning model namely: syntax, social system, principles of reaction, support system, as well as instructional and nurturing effects, (2) PKBKL model is a valid, practical, and effective model, (3) the practicality of the learning devices (RPP), is at the high category. Based on the research results, there are two things recommended: (1) in order that learning stages (syntax) of PKBKL model can be performed well, then teachers need to have an understanding of the cooperative learning model of Student Team Achievement Division (STAD) type and the concepts of scientifically approach well, (2) PKBKL model can be performed well on physical education, sport and health learning, if the teachers understand the concept of Tri Pramana, therefore if the physical education, sport and health teachers want to apply this PKBKL model, they must first learn and master the concept of Tri Pramana well.

A comparative study of two prediction models for brain tumor progression

NASA Astrophysics Data System (ADS)

Zhou, Deqi; Tran, Loc; Wang, Jihong; Li, Jiang

2015-03-01

MR diffusion tensor imaging (DTI) technique together with traditional T1 or T2 weighted MRI scans supplies rich information sources for brain cancer diagnoses. These images form large-scale, high-dimensional data sets. Due to the fact that significant correlations exist among these images, we assume low-dimensional geometry data structures (manifolds) are embedded in the high-dimensional space. Those manifolds might be hidden from radiologists because it is challenging for human experts to interpret high-dimensional data. Identification of the manifold is a critical step for successfully analyzing multimodal MR images. We have developed various manifold learning algorithms (Tran et al. 2011; Tran et al. 2013) for medical image analysis. This paper presents a comparative study of an incremental manifold learning scheme (Tran. et al. 2013) versus the deep learning model (Hinton et al. 2006) in the application of brain tumor progression prediction. The incremental manifold learning is a variant of manifold learning algorithm to handle large-scale datasets in which a representative subset of original data is sampled first to construct a manifold skeleton and remaining data points are then inserted into the skeleton by following their local geometry. The incremental manifold learning algorithm aims at mitigating the computational burden associated with traditional manifold learning methods for large-scale datasets. Deep learning is a recently developed multilayer perceptron model that has achieved start-of-the-art performances in many applications. A recent technique named "Dropout" can further boost the deep model by preventing weight coadaptation to avoid over-fitting (Hinton et al. 2012). We applied the two models on multiple MRI scans from four brain tumor patients to predict tumor progression and compared the performances of the two models in terms of average prediction accuracy, sensitivity, specificity and precision. The quantitative performance metrics were calculated as average over the four patients. Experimental results show that both the manifold learning and deep neural network models produced better results compared to using raw data and principle component analysis (PCA), and the deep learning model is a better method than manifold learning on this data set. The averaged sensitivity and specificity by deep learning are comparable with these by the manifold learning approach while its precision is considerably higher. This means that the predicted abnormal points by deep learning are more likely to correspond to the actual progression region.

Teaching childbirth with high-fidelity simulation. Is it better observing the scenario during the briefing session?

PubMed

Cuerva, Marcos J; Piñel, Carlos S; Martin, Lourdes; Espinosa, Jose A; Corral, Octavio J; Mendoza, Nicolás

2018-02-12

The design of optimal courses for obstetric undergraduate teaching is a relevant question. This study evaluates two different designs of simulator-based learning activity on childbirth with regard to respect to the patient, obstetric manoeuvres, interpretation of cardiotocography tracings (CTG) and infection prevention. This randomised experimental study which differs in the content of their briefing sessions consisted of two groups of undergraduate students, who performed two simulator-based learning activities on childbirth. The first briefing session included the observations of a properly performed scenario according to Spanish clinical practice guidelines on care in normal childbirth by the teachers whereas the second group did not include the observations of a properly performed scenario, and the students observed it only after the simulation process. The group that observed a properly performed scenario after the simulation obtained worse grades during the simulation, but better grades during the debriefing and evaluation. Simulator use in childbirth may be more fruitful when the medical students observe correct performance at the completion of the scenario compared to that at the start of the scenario. Impact statement What is already known on this subject? There is a scarcity of literature about the design of optimal high-fidelity simulation training in childbirth. It is known that preparing simulator-based learning activities is a complex process. Simulator-based learning includes the following steps: briefing, simulation, debriefing and evaluation. The most important part of high-fidelity simulations is the debriefing. A good briefing and simulation are of high relevance in order to have a fruitful debriefing session. What do the results of this study add? Our study describes a full simulator-based learning activity on childbirth that can be reproduced in similar facilities. The findings of this study add that high-fidelity simulation training in childbirth is favoured by a short briefing session and an abrupt start to the scenario, rather than a long briefing session that includes direct instruction in the scenario. What are the implications of these findings for clinical practice and/or further research? The findings of this study reveal what to include in the briefing of simulator-based learning activities on childbirth. These findings have implications in medical teaching and in medical practice.

The effect of learning climate on snack consumption and ego depletion among undergraduate students.

PubMed

Magaraggia, Christian; Dimmock, James A; Jackson, Ben

2013-10-01

We explored the effect of controlled and autonomous learning choices on the consumption of a high-energy snack food, and also examined whether snack consumption during a controlled choice learning activity could 'up-regulate' subsequent performance on a self-regulation task. Participants were randomly assigned to a controlled choice learning condition in which food was provided, a controlled choice learning condition in which food was not provided, or an autonomous choice learning condition in which food was provided. Results indicated that the autonomous choice group consumed significantly less snack food than the controlled-choice-and-food group. Participants in the autonomous choice condition also performed better on the subsequent self-regulation task than the controlled-choice-and-food group, even after controlling for the amount of food consumed. Furthermore, within the controlled-choice-and-food condition, there was no association between food consumption and subsequent self-regulation task performance. Discussion focuses on the potential impact of a controlled learning climate on snack food consumption and on the degradation of self-regulation capacities. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

Bayesian nonparametric dictionary learning for compressed sensing MRI.

PubMed

Huang, Yue; Paisley, John; Lin, Qin; Ding, Xinghao; Fu, Xueyang; Zhang, Xiao-Ping

2014-12-01

We develop a Bayesian nonparametric model for reconstructing magnetic resonance images (MRIs) from highly undersampled k -space data. We perform dictionary learning as part of the image reconstruction process. To this end, we use the beta process as a nonparametric dictionary learning prior for representing an image patch as a sparse combination of dictionary elements. The size of the dictionary and patch-specific sparsity pattern are inferred from the data, in addition to other dictionary learning variables. Dictionary learning is performed directly on the compressed image, and so is tailored to the MRI being considered. In addition, we investigate a total variation penalty term in combination with the dictionary learning model, and show how the denoising property of dictionary learning removes dependence on regularization parameters in the noisy setting. We derive a stochastic optimization algorithm based on Markov chain Monte Carlo for the Bayesian model, and use the alternating direction method of multipliers for efficiently performing total variation minimization. We present empirical results on several MRI, which show that the proposed regularization framework can improve reconstruction accuracy over other methods.

Investing in Development: Six High-Performing, High-Poverty Schools Implement the Massachusetts Teacher Evaluation Policy

ERIC Educational Resources Information Center

Reinhorn, Stefanie K.; Johnson, Susan Moore; Simon, Nicole S.

2017-01-01

We studied how six high-performing, high-poverty schools in one large Massachusetts city implemented the state's new teacher evaluation policy. The sample includes traditional, turnaround, restart, and charter schools, each of which had received the state's highest accountability rating. We sought to learn how these successful schools approached…

A machine learning model with human cognitive biases capable of learning from small and biased datasets.

PubMed

Taniguchi, Hidetaka; Sato, Hiroshi; Shirakawa, Tomohiro

2018-05-09

Human learners can generalize a new concept from a small number of samples. In contrast, conventional machine learning methods require large amounts of data to address the same types of problems. Humans have cognitive biases that promote fast learning. Here, we developed a method to reduce the gap between human beings and machines in this type of inference by utilizing cognitive biases. We implemented a human cognitive model into machine learning algorithms and compared their performance with the currently most popular methods, naïve Bayes, support vector machine, neural networks, logistic regression and random forests. We focused on the task of spam classification, which has been studied for a long time in the field of machine learning and often requires a large amount of data to obtain high accuracy. Our models achieved superior performance with small and biased samples in comparison with other representative machine learning methods.

Enhancing the Performance of Learning-Disabled Children by Dint of Theatre Education

ERIC Educational Resources Information Center

Kumar, S. Praveen; Raja, B. William Dharma

2009-01-01

In schools, teachers come across pupils who have diverse abilities and special needs. Some of the learners achieve high and some may lag behind in their learning. They may face learning problems such as difficulties in listening, speaking, thinking, reading, writing, spelling, reasoning, calculating or social skills. It is a great challenge on the…

Decreasing Cognitive Load for Learners: Strategy of Web-Based Foreign Language Learning

ERIC Educational Resources Information Center

Zhang, Jianfeng

2013-01-01

Cognitive load is one of the important factors that influence the effectiveness and efficiency of web-based foreign language learning. Cognitive load theory assumes that human's cognitive capacity in working memory is limited and if it overloads, learning will be hampered, so that high level of cognitive load can affect the performance of learning…

An Electronic Library-Based Learning Environment for Supporting Web-Based Problem-Solving Activities

ERIC Educational Resources Information Center

Tsai, Pei-Shan; Hwang, Gwo-Jen; Tsai, Chin-Chung; Hung, Chun-Ming; Huang, Iwen

2012-01-01

This study aims to develop an electronic library-based learning environment to support teachers in developing web-based problem-solving activities and analyzing the online problem-solving behaviors of students. Two experiments were performed in this study. In study 1, an experiment on 103 elementary and high school teachers (the learning activity…

Employee Retention and Performance Improvement in High-Tech Companies.

ERIC Educational Resources Information Center

Ware, B. Lynn

2001-01-01

Considers the benefits of employee retention and performance improvement in high technology, new economy companies. Discusses attracting and retaining top talent in information technology companies; targeted recruiting and hiring; employee achievement; learning and professional growth; recognition; nurturing careers; team collaboration; the TALENT…

Performance and Costs of Ductless Heat Pumps in Marine-Climate High-Performance Homes -- Habitat for Humanity The Woods

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

Michael Lubliner; Howard, Luke; Hales, David

2016-02-23

This final Building America Partnership report focuses on the results of field testing, modeling, and monitoring of ductless mini-split heat pump hybrid heating systems in seven homes built and first occupied at various times between September 2013 and October 2014. The report also provides WSU documentation of high-performance home observations, lessons learned, and stakeholder recommendations for builders of affordable high-performance housing.

Assessing the Effectiveness of a Learning Community Course Design to Improve the Math Performance of First-Year Students

ERIC Educational Resources Information Center

Hansen, Michele J.; Meshulam, Susan; Parker, Brooke

2013-01-01

National attention is focused on the persistent high failure rates for students enrolled in math courses, and the search for strategies to change these outcomes is on. This study used a mixed-method research design to assess the effectiveness of a learning community course designed to improve the math performance levels of firstyear students.…

Age-related declines of stability in visual perceptual learning.

PubMed

Chang, Li-Hung; Shibata, Kazuhisa; Andersen, George J; Sasaki, Yuka; Watanabe, Takeo

2014-12-15

One of the biggest questions in learning is how a system can resolve the plasticity and stability dilemma. Specifically, the learning system needs to have not only a high capability of learning new items (plasticity) but also a high stability to retain important items or processing in the system by preventing unimportant or irrelevant information from being learned. This dilemma should hold true for visual perceptual learning (VPL), which is defined as a long-term increase in performance on a visual task as a result of visual experience. Although it is well known that aging influences learning, the effect of aging on the stability and plasticity of the visual system is unclear. To address the question, we asked older and younger adults to perform a task while a task-irrelevant feature was merely exposed. We found that older individuals learned the task-irrelevant features that younger individuals did not learn, both the features that were sufficiently strong for younger individuals to suppress and the features that were too weak for younger individuals to learn. At the same time, there was no plasticity reduction in older individuals within the task tested. These results suggest that the older visual system is less stable to unimportant information than the younger visual system. A learning problem with older individuals may be due to a decrease in stability rather than a decrease in plasticity, at least in VPL. Copyright © 2014 Elsevier Ltd. All rights reserved.

Hello World Deep Learning in Medical Imaging.

PubMed

Lakhani, Paras; Gray, Daniel L; Pett, Carl R; Nagy, Paul; Shih, George

2018-05-03

There is recent popularity in applying machine learning to medical imaging, notably deep learning, which has achieved state-of-the-art performance in image analysis and processing. The rapid adoption of deep learning may be attributed to the availability of machine learning frameworks and libraries to simplify their use. In this tutorial, we provide a high-level overview of how to build a deep neural network for medical image classification, and provide code that can help those new to the field begin their informatics projects.

Intelligible machine learning with malibu.

PubMed

Langlois, Robert E; Lu, Hui

2008-01-01

malibu is an open-source machine learning work-bench developed in C/C++ for high-performance real-world applications, namely bioinformatics and medical informatics. It leverages third-party machine learning implementations for more robust bug-free software. This workbench handles several well-studied supervised machine learning problems including classification, regression, importance-weighted classification and multiple-instance learning. The malibu interface was designed to create reproducible experiments ideally run in a remote and/or command line environment. The software can be found at: http://proteomics.bioengr. uic.edu/malibu/index.html.

Critical Thinking Skills of Students through Mathematics Learning with ASSURE Model Assisted by Software Autograph

NASA Astrophysics Data System (ADS)

Kristianti, Y.; Prabawanto, S.; Suhendra, S.

2017-09-01

This study aims to examine the ability of critical thinking and students who attain learning mathematics with learning model ASSURE assisted Autograph software. The design of this study was experimental group with pre-test and post-test control group. The experimental group obtained a mathematics learning with ASSURE-assisted model Autograph software and the control group acquired the mathematics learning with the conventional model. The data are obtained from the research results through critical thinking skills tests. This research was conducted at junior high school level with research population in one of junior high school student in Subang Regency of Lesson Year 2016/2017 and research sample of class VIII student in one of junior high school in Subang Regency for 2 classes. Analysis of research data is administered quantitatively. Quantitative data analysis was performed on the normalized gain level between the two sample groups using a one-way anova test. The results show that mathematics learning with ASSURE assisted model Autograph software can improve the critical thinking ability of junior high school students. Mathematical learning using ASSURE-assisted model Autograph software is significantly better in improving the critical thinking skills of junior high school students compared with conventional models.

High Performance Work Systems for Online Education

ERIC Educational Resources Information Center

Contacos-Sawyer, Jonna; Revels, Mark; Ciampa, Mark

2010-01-01

The purpose of this paper is to identify the key elements of a High Performance Work System (HPWS) and explore the possibility of implementation in an online institution of higher learning. With the projected rapid growth of the demand for online education and its importance in post-secondary education, providing high quality curriculum, excellent…

Estimating learning outcomes from pre- and posttest student self-assessments: a longitudinal study.

PubMed

Schiekirka, Sarah; Reinhardt, Deborah; Beißbarth, Tim; Anders, Sven; Pukrop, Tobias; Raupach, Tobias

2013-03-01

Learning outcome is an important measure for overall teaching quality and should be addressed by comprehensive evaluation tools. The authors evaluated the validity of a novel evaluation tool based on student self-assessments, which may help identify specific strengths and weaknesses of a particular course. In 2011, the authors asked 145 fourth-year students at Göttingen Medical School to self-assess their knowledge on 33 specific learning objectives in a pretest and posttest as part of a cardiorespiratory module. The authors compared performance gain calculated from self-assessments with performance gain derived from formative examinations that were closely matched to these 33 learning objectives. Eighty-three students (57.2%) completed the assessment. There was good agreement between performance gain derived from subjective data and performance gain derived from objective examinations (Pearson r=0.78; P<.0001) on the group level. The association between the two measures was much weaker when data were analyzed on the individual level. Further analysis determined a quality cutoff for performance gain derived from aggregated student self-assessments. When using this cutoff, the evaluation tool was highly sensitive in identifying specific learning objectives with favorable or suboptimal objective performance gains. The tool is easy to implement, takes initial performance levels into account, and does not require extensive pre-post testing. By providing valid estimates of actual performance gain obtained during a teaching module, it may assist medical teachers in identifying strengths and weaknesses of a particular course on the level of specific learning objectives.

Exploring the role of task performance and learning style on prefrontal hemodynamics during a working memory task.

PubMed

Anderson, Afrouz A; Parsa, Kian; Geiger, Sydney; Zaragoza, Rachel; Kermanian, Riley; Miguel, Helga; Dashtestani, Hadis; Chowdhry, Fatima A; Smith, Elizabeth; Aram, Siamak; Gandjbakhche, Amir H

2018-01-01

Existing literature outlines the quality and location of activation in the prefrontal cortex (PFC) during working memory (WM) tasks. However, the effects of individual differences on the underlying neural process of WM tasks are still unclear. In this functional near infrared spectroscopy study, we administered a visual and auditory n-back task to examine activation in the PFC while considering the influences of task performance, and preferred learning strategy (VARK score). While controlling for age, results indicated that high performance (HP) subjects (accuracy > 90%) showed task dependent lower activation compared to normal performance subjects in PFC region Specifically HP groups showed lower activation in left dorsolateral PFC (DLPFC) region during performance of auditory task whereas during visual task they showed lower activation in the right DLPFC. After accounting for learning style, we found a correlation between visual and aural VARK score and level of activation in the PFC. Subjects with higher visual VARK scores displayed lower activation during auditory task in left DLPFC, while those with higher visual scores exhibited higher activation during visual task in bilateral DLPFC. During performance of auditory task, HP subjects had higher visual VARK scores compared to NP subjects indicating an effect of learning style on the task performance and activation. The results of this study show that learning style and task performance can influence PFC activation, with applications toward neurological implications of learning style and populations with deficits in auditory or visual processing.

Exploring the role of task performance and learning style on prefrontal hemodynamics during a working memory task

PubMed Central

Anderson, Afrouz A.; Parsa, Kian; Geiger, Sydney; Zaragoza, Rachel; Kermanian, Riley; Miguel, Helga; Chowdhry, Fatima A.; Smith, Elizabeth; Aram, Siamak; Gandjbakhche, Amir H.

2018-01-01

Existing literature outlines the quality and location of activation in the prefrontal cortex (PFC) during working memory (WM) tasks. However, the effects of individual differences on the underlying neural process of WM tasks are still unclear. In this functional near infrared spectroscopy study, we administered a visual and auditory n-back task to examine activation in the PFC while considering the influences of task performance, and preferred learning strategy (VARK score). While controlling for age, results indicated that high performance (HP) subjects (accuracy > 90%) showed task dependent lower activation compared to normal performance subjects in PFC region Specifically HP groups showed lower activation in left dorsolateral PFC (DLPFC) region during performance of auditory task whereas during visual task they showed lower activation in the right DLPFC. After accounting for learning style, we found a correlation between visual and aural VARK score and level of activation in the PFC. Subjects with higher visual VARK scores displayed lower activation during auditory task in left DLPFC, while those with higher visual scores exhibited higher activation during visual task in bilateral DLPFC. During performance of auditory task, HP subjects had higher visual VARK scores compared to NP subjects indicating an effect of learning style on the task performance and activation. The results of this study show that learning style and task performance can influence PFC activation, with applications toward neurological implications of learning style and populations with deficits in auditory or visual processing. PMID:29870536

Does Instructional Format Really Matter? Cognitive Load Theory, Multimedia and Teaching English Literature

ERIC Educational Resources Information Center

Martin, Stewart

2012-01-01

This article reports a quasi-experimental study on the effects of multimedia teaching and learning in English Literature--a subject which places high cognitive load on students. A large-scale study was conducted in 4 high-achieving secondary schools to examine the differences made to students' learning and performance by the use of multimedia and…

Mathematical Problem Posing as a Measure of Curricular Effect on Students' Learning

ERIC Educational Resources Information Center

Cai, Jinfa; Moyer, John C.; Wang, Ning; Hwang, Stephen; Nie, Bikai; Garber, Tammy

2013-01-01

In this study, we used problem posing as a measure of the effect of middle-school curriculum on students' learning in high school. Students who had used a standards-based curriculum in middle school performed equally well or better in high school than students who had used more traditional curricula. The findings from this study not only show…

Crafting a New Vision for High School: How States Can Join Academic and Technical Studies to Promote More Powerful Learning

ERIC Educational Resources Information Center

Bottoms, Gene; Young, Marna

2008-01-01

The authors advocate harnessing the applied teaching strategies of career/technical education (CTE) and infusing them into college-preparatory academics to transform secondary schools into high-performing centers of learning where students are both challenged and engaged. By pursuing this strategy, say the authors, states can help many more…

Impaired associative learning with food rewards in obese women.

PubMed

Zhang, Zhihao; Manson, Kirk F; Schiller, Daniela; Levy, Ifat

2014-08-04

Obesity is a major epidemic in many parts of the world. One of the main factors contributing to obesity is overconsumption of high-fat and high-calorie food, which is driven by the rewarding properties of these types of food. Previous studies have suggested that dysfunction in reward circuits may be associated with overeating and obesity. The nature of this dysfunction, however, is still unknown. Here, we demonstrate impairment in reward-based associative learning specific to food in obese women. Normal-weight and obese participants performed an appetitive reversal learning task in which they had to learn and modify cue-reward associations. To test whether any learning deficits were specific to food reward or were more general, we used a between-subject design in which half of the participants received food reward and the other half received money reward. Our results reveal a marked difference in associative learning between normal-weight and obese women when food was used as reward. Importantly, no learning deficits were observed with money reward. Multiple regression analyses also established a robust negative association between body mass index and learning performance in the food domain in female participants. Interestingly, such impairment was not observed in obese men. These findings suggest that obesity may be linked to impaired reward-based associative learning and that this impairment may be specific to the food domain. Copyright © 2014 Elsevier Ltd. All rights reserved.

Coupled dictionary learning for joint MR image restoration and segmentation

NASA Astrophysics Data System (ADS)

Yang, Xuesong; Fan, Yong

2018-03-01

To achieve better segmentation of MR images, image restoration is typically used as a preprocessing step, especially for low-quality MR images. Recent studies have demonstrated that dictionary learning methods could achieve promising performance for both image restoration and image segmentation. These methods typically learn paired dictionaries of image patches from different sources and use a common sparse representation to characterize paired image patches, such as low-quality image patches and their corresponding high quality counterparts for the image restoration, and image patches and their corresponding segmentation labels for the image segmentation. Since learning these dictionaries jointly in a unified framework may improve the image restoration and segmentation simultaneously, we propose a coupled dictionary learning method to concurrently learn dictionaries for joint image restoration and image segmentation based on sparse representations in a multi-atlas image segmentation framework. Particularly, three dictionaries, including a dictionary of low quality image patches, a dictionary of high quality image patches, and a dictionary of segmentation label patches, are learned in a unified framework so that the learned dictionaries of image restoration and segmentation can benefit each other. Our method has been evaluated for segmenting the hippocampus in MR T1 images collected with scanners of different magnetic field strengths. The experimental results have demonstrated that our method achieved better image restoration and segmentation performance than state of the art dictionary learning and sparse representation based image restoration and image segmentation methods.

An Overview and Evaluation of Recent Machine Learning Imputation Methods Using Cardiac Imaging Data.

PubMed

Liu, Yuzhe; Gopalakrishnan, Vanathi

2017-03-01

Many clinical research datasets have a large percentage of missing values that directly impacts their usefulness in yielding high accuracy classifiers when used for training in supervised machine learning. While missing value imputation methods have been shown to work well with smaller percentages of missing values, their ability to impute sparse clinical research data can be problem specific. We previously attempted to learn quantitative guidelines for ordering cardiac magnetic resonance imaging during the evaluation for pediatric cardiomyopathy, but missing data significantly reduced our usable sample size. In this work, we sought to determine if increasing the usable sample size through imputation would allow us to learn better guidelines. We first review several machine learning methods for estimating missing data. Then, we apply four popular methods (mean imputation, decision tree, k-nearest neighbors, and self-organizing maps) to a clinical research dataset of pediatric patients undergoing evaluation for cardiomyopathy. Using Bayesian Rule Learning (BRL) to learn ruleset models, we compared the performance of imputation-augmented models versus unaugmented models. We found that all four imputation-augmented models performed similarly to unaugmented models. While imputation did not improve performance, it did provide evidence for the robustness of our learned models.

Data-driven methods towards learning the highly nonlinear inverse kinematics of tendon-driven surgical manipulators.

PubMed

Xu, Wenjun; Chen, Jie; Lau, Henry Y K; Ren, Hongliang

2017-09-01

Accurate motion control of flexible surgical manipulators is crucial in tissue manipulation tasks. The tendon-driven serpentine manipulator (TSM) is one of the most widely adopted flexible mechanisms in minimally invasive surgery because of its enhanced maneuverability in torturous environments. TSM, however, exhibits high nonlinearities and conventional analytical kinematics model is insufficient to achieve high accuracy. To account for the system nonlinearities, we applied a data driven approach to encode the system inverse kinematics. Three regression methods: extreme learning machine (ELM), Gaussian mixture regression (GMR) and K-nearest neighbors regression (KNNR) were implemented to learn a nonlinear mapping from the robot 3D position states to the control inputs. The performance of the three algorithms was evaluated both in simulation and physical trajectory tracking experiments. KNNR performed the best in the tracking experiments, with the lowest RMSE of 2.1275 mm. The proposed inverse kinematics learning methods provide an alternative and efficient way to accurately model the tendon driven flexible manipulator. Copyright © 2016 John Wiley & Sons, Ltd.

Position Paper: Applying Machine Learning to Software Analysis to Achieve Trusted, Repeatable Scientific Computing

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

Prowell, Stacy J; Symons, Christopher T

2015-01-01

Producing trusted results from high-performance codes is essential for policy and has significant economic impact. We propose combining rigorous analytical methods with machine learning techniques to achieve the goal of repeatable, trustworthy scientific computing.

The effects of error augmentation on learning to walk on a narrow balance beam.

PubMed

Domingo, Antoinette; Ferris, Daniel P

2010-10-01

Error augmentation during training has been proposed as a means to facilitate motor learning due to the human nervous system's reliance on performance errors to shape motor commands. We studied the effects of error augmentation on short-term learning of walking on a balance beam to determine whether it had beneficial effects on motor performance. Four groups of able-bodied subjects walked on a treadmill-mounted balance beam (2.5-cm wide) before and after 30 min of training. During training, two groups walked on the beam with a destabilization device that augmented error (Medium and High Destabilization groups). A third group walked on a narrower beam (1.27-cm) to augment error (Narrow). The fourth group practiced walking on the 2.5-cm balance beam (Wide). Subjects in the Wide group had significantly greater improvements after training than the error augmentation groups. The High Destabilization group had significantly less performance gains than the Narrow group in spite of similar failures per minute during training. In a follow-up experiment, a fifth group of subjects (Assisted) practiced with a device that greatly reduced catastrophic errors (i.e., stepping off the beam) but maintained similar pelvic movement variability. Performance gains were significantly greater in the Wide group than the Assisted group, indicating that catastrophic errors were important for short-term learning. We conclude that increasing errors during practice via destabilization and a narrower balance beam did not improve short-term learning of beam walking. In addition, the presence of qualitatively catastrophic errors seems to improve short-term learning of walking balance.

DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics

NASA Astrophysics Data System (ADS)

Wang, Han; Zhang, Linfeng; Han, Jiequn; E, Weinan

2018-07-01

Recent developments in many-body potential energy representation via deep learning have brought new hopes to addressing the accuracy-versus-efficiency dilemma in molecular simulations. Here we describe DeePMD-kit, a package written in Python/C++ that has been designed to minimize the effort required to build deep learning based representation of potential energy and force field and to perform molecular dynamics. Potential applications of DeePMD-kit span from finite molecules to extended systems and from metallic systems to chemically bonded systems. DeePMD-kit is interfaced with TensorFlow, one of the most popular deep learning frameworks, making the training process highly automatic and efficient. On the other end, DeePMD-kit is interfaced with high-performance classical molecular dynamics and quantum (path-integral) molecular dynamics packages, i.e., LAMMPS and the i-PI, respectively. Thus, upon training, the potential energy and force field models can be used to perform efficient molecular simulations for different purposes. As an example of the many potential applications of the package, we use DeePMD-kit to learn the interatomic potential energy and forces of a water model using data obtained from density functional theory. We demonstrate that the resulted molecular dynamics model reproduces accurately the structural information contained in the original model.

Becoming a high-fidelity - super - imitator: what are the contributions of social and individual learning?

PubMed

Subiaul, Francys; Patterson, Eric M; Schilder, Brian; Renner, Elizabeth; Barr, Rachel

2015-11-01

In contrast to other primates, human children's imitation performance goes from low to high fidelity soon after infancy. Are such changes associated with the development of other forms of learning? We addressed this question by testing 215 children (26-59 months) on two social conditions (imitation, emulation) - involving a demonstration - and two asocial conditions (trial-and-error, recall) - involving individual learning - using two touchscreen tasks. The tasks required responding to either three different pictures in a specific picture order (Cognitive: Airplane→Ball→Cow) or three identical pictures in a specific spatial order (Motor-Spatial: Up→Down→Right). There were age-related improvements across all conditions and imitation, emulation and recall performance were significantly better than trial-and-error learning. Generalized linear models demonstrated that motor-spatial imitation fidelity was associated with age and motor-spatial emulation performance, but cognitive imitation fidelity was only associated with age. While this study provides evidence for multiple imitation mechanisms, the development of one of those mechanisms - motor-spatial imitation - may be bootstrapped by the development of another social learning skill - motor-spatial emulation. Together, these findings provide important clues about the development of imitation, which is arguably a distinctive feature of the human species. © 2014 John Wiley & Sons Ltd.

[Effect of emotional content and self reference of learning materials on recall performance].

PubMed

Spies, K

1994-01-01

It is assumed that high affective value and high self-reference of learning material help to improve memory performance as these factors allow better memory consolidation (activation hypothesis) or better integration of the new material into existing knowledge structures (extent-of-processing hypothesis). To test this assumption, 60 subjects were shown 16 short advertising films characterized by low vs. high affective value and low vs. high self-reference. Both factors were varied within subjects. After the films had each been presented twice, subjects had to recall the product names and answer two questions to each film. Results showed for both dependent variables that films with high affective values were better remembered than films with low affective values. The same held true--though to a lower extent--with respect to self-reference. According to the expected linear trend, performance was best for material scoring high on affective value as well as on self-reference, while it was worst for material scoring low on both factors.

Deep Learning for Automated Extraction of Primary Sites From Cancer Pathology Reports.

PubMed

Qiu, John X; Yoon, Hong-Jun; Fearn, Paul A; Tourassi, Georgia D

2018-01-01

Pathology reports are a primary source of information for cancer registries which process high volumes of free-text reports annually. Information extraction and coding is a manual, labor-intensive process. In this study, we investigated deep learning and a convolutional neural network (CNN), for extracting ICD-O-3 topographic codes from a corpus of breast and lung cancer pathology reports. We performed two experiments, using a CNN and a more conventional term frequency vector approach, to assess the effects of class prevalence and inter-class transfer learning. The experiments were based on a set of 942 pathology reports with human expert annotations as the gold standard. CNN performance was compared against a more conventional term frequency vector space approach. We observed that the deep learning models consistently outperformed the conventional approaches in the class prevalence experiment, resulting in micro- and macro-F score increases of up to 0.132 and 0.226, respectively, when class labels were well populated. Specifically, the best performing CNN achieved a micro-F score of 0.722 over 12 ICD-O-3 topography codes. Transfer learning provided a consistent but modest performance boost for the deep learning methods but trends were contingent on the CNN method and cancer site. These encouraging results demonstrate the potential of deep learning for automated abstraction of pathology reports.

Among friends: the role of academic-preparedness diversity in individual performance within a small-group STEM learning environment

NASA Astrophysics Data System (ADS)

Micari, Marina; Van Winkle, Zachary; Pazos, Pilar

2016-08-01

In this study, we investigate the relationship between academic-preparedness diversity within small learning groups and individual academic performance in science, technology, engineering, and mathematics (STEM) university courses. We further examine whether academic-preparedness diversity impacts academically more- and less-prepared students differently. We use data from 5367 university students nested within 1141 science, engineering, and mathematics learning groups and use a regression analysis to estimate the effect of group diversity, measured in two ways, on course performance. Our results indicate that academic-preparedness diversity is generally associated with positive learning outcomes, that academically less-prepared students derive greater benefit, and that less-prepared students fare best when they are not alone in a group of highly prepared students. Implications for teaching and small-group facilitation are addressed.

Imbalanced Learning for Functional State Assessment

NASA Technical Reports Server (NTRS)

Li, Feng; McKenzie, Frederick; Li, Jiang; Zhang, Guangfan; Xu, Roger; Richey, Carl; Schnell, Tom

2011-01-01

This paper presents results of several imbalanced learning techniques applied to operator functional state assessment where the data is highly imbalanced, i.e., some function states (majority classes) have much more training samples than other states (minority classes). Conventional machine learning techniques usually tend to classify all data samples into majority classes and perform poorly for minority classes. In this study, we implemented five imbalanced learning techniques, including random undersampling, random over-sampling, synthetic minority over-sampling technique (SMOTE), borderline-SMOTE and adaptive synthetic sampling (ADASYN) to solve this problem. Experimental results on a benchmark driving lest dataset show thai accuracies for minority classes could be improved dramatically with a cost of slight performance degradations for majority classes,

Weakly supervised classification in high energy physics

DOE PAGES

Dery, Lucio Mwinmaarong; Nachman, Benjamin; Rubbo, Francesco; ...

2017-05-01

As machine learning algorithms become increasingly sophisticated to exploit subtle features of the data, they often become more dependent on simulations. Here, this paper presents a new approach called weakly supervised classification in which class proportions are the only input into the machine learning algorithm. Using one of the most challenging binary classification tasks in high energy physics $-$ quark versus gluon tagging $-$ we show that weakly supervised classification can match the performance of fully supervised algorithms. Furthermore, by design, the new algorithm is insensitive to any mis-modeling of discriminating features in the data by the simulation. Weakly supervisedmore » classification is a general procedure that can be applied to a wide variety of learning problems to boost performance and robustness when detailed simulations are not reliable or not available.« less

Weakly supervised classification in high energy physics

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

Dery, Lucio Mwinmaarong; Nachman, Benjamin; Rubbo, Francesco

As machine learning algorithms become increasingly sophisticated to exploit subtle features of the data, they often become more dependent on simulations. Here, this paper presents a new approach called weakly supervised classification in which class proportions are the only input into the machine learning algorithm. Using one of the most challenging binary classification tasks in high energy physics $-$ quark versus gluon tagging $-$ we show that weakly supervised classification can match the performance of fully supervised algorithms. Furthermore, by design, the new algorithm is insensitive to any mis-modeling of discriminating features in the data by the simulation. Weakly supervisedmore » classification is a general procedure that can be applied to a wide variety of learning problems to boost performance and robustness when detailed simulations are not reliable or not available.« less

How Methodology Decisions Affect the Variability of Schools Identified as Beating the Odds. REL 2015-071.rev

ERIC Educational Resources Information Center

Abe, Yasuyo; Weinstock, Phyllis; Chan, Vincent; Meyers, Coby; Gerdeman, R. Dean; Brandt, W. Christopher

2015-01-01

A number of states and school districts have identified schools that perform better than expected, given the populations they serve, in order to recognize school performance or to learn from local school practices and policies. These schools have been labeled "beating the odds," "high-performing/high-poverty,"…

Sensorimotor Learning in a Computerized Athletic Training Battery.

PubMed

Krasich, Kristina; Ramger, Ben; Holton, Laura; Wang, Lingling; Mitroff, Stephen R; Gregory Appelbaum, L

2016-01-01

Sensorimotor abilities are crucial for performance in athletic, military, and other occupational activities, and there is great interest in understanding learning in these skills. Here, behavioral performance was measured over three days as twenty-seven participants practiced multiple sessions on the Nike SPARQ Sensory Station (Nike, Inc., Beaverton, Oregon), a computerized visual and motor assessment battery. Wrist-worn actigraphy was recorded to monitor sleep-wake cycles. Significant learning was observed in tasks with high visuomotor control demands but not in tasks of visual sensitivity. Learning was primarily linear, with up to 60% improvement, but did not relate to sleep quality in this normal-sleeping population. These results demonstrate differences in the rate and capacity for learning across perceptual and motor domains, indicating potential targets for sensorimotor training interventions.

Automatic Earthquake Detection by Active Learning

NASA Astrophysics Data System (ADS)

Bergen, K.; Beroza, G. C.

2017-12-01

In recent years, advances in machine learning have transformed fields such as image recognition, natural language processing and recommender systems. Many of these performance gains have relied on the availability of large, labeled data sets to train high-accuracy models; labeled data sets are those for which each sample includes a target class label, such as waveforms tagged as either earthquakes or noise. Earthquake seismologists are increasingly leveraging machine learning and data mining techniques to detect and analyze weak earthquake signals in large seismic data sets. One of the challenges in applying machine learning to seismic data sets is the limited labeled data problem; learning algorithms need to be given examples of earthquake waveforms, but the number of known events, taken from earthquake catalogs, may be insufficient to build an accurate detector. Furthermore, earthquake catalogs are known to be incomplete, resulting in training data that may be biased towards larger events and contain inaccurate labels. This challenge is compounded by the class imbalance problem; the events of interest, earthquakes, are infrequent relative to noise in continuous data sets, and many learning algorithms perform poorly on rare classes. In this work, we investigate the use of active learning for automatic earthquake detection. Active learning is a type of semi-supervised machine learning that uses a human-in-the-loop approach to strategically supplement a small initial training set. The learning algorithm incorporates domain expertise through interaction between a human expert and the algorithm, with the algorithm actively posing queries to the user to improve detection performance. We demonstrate the potential of active machine learning to improve earthquake detection performance with limited available training data.

Taking the brakes off the learning curve.

PubMed

Gheysen, Freja; Lasne, Gabriel; Pélégrini-Issac, Mélanie; Albouy, Genevieve; Meunier, Sabine; Benali, Habib; Doyon, Julien; Popa, Traian

2017-03-01

Motor learning is characterized by patterns of cerebello-striato-cortical activations shifting in time, yet the early dynamic and function of these activations remains unclear. Five groups of subjects underwent either continuous or intermittent theta-burst stimulation of one cerebellar hemisphere, or no stimulation just before learning a new motor sequence during fMRI scanning. We identified three phases during initial learning: one rapid, one slow, and one quasi-asymptotic performance phase. These phases were not changed by left cerebellar stimulation. Right cerebellar inhibition, however, accelerated learning and enhanced brain activation in critical motor learning-related areas during the first phase, continuing with reduced brain activation but high-performance in late phase. Right cerebellar excitation did not affect the early learning process, but slowed learning significantly in late phase, along with increased brain activation. We conclude that the right cerebellum is a key factor coordinating other neuronal loops in the early acquisition of an explicit motor sequential skill. Hum Brain Mapp 38:1676-1691, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Attentional Bias in Human Category Learning: The Case of Deep Learning.

PubMed

Hanson, Catherine; Caglar, Leyla Roskan; Hanson, Stephen José

2018-01-01

Category learning performance is influenced by both the nature of the category's structure and the way category features are processed during learning. Shepard (1964, 1987) showed that stimuli can have structures with features that are statistically uncorrelated (separable) or statistically correlated (integral) within categories. Humans find it much easier to learn categories having separable features, especially when attention to only a subset of relevant features is required, and harder to learn categories having integral features, which require consideration of all of the available features and integration of all the relevant category features satisfying the category rule (Garner, 1974). In contrast to humans, a single hidden layer backpropagation (BP) neural network has been shown to learn both separable and integral categories equally easily, independent of the category rule (Kruschke, 1993). This "failure" to replicate human category performance appeared to be strong evidence that connectionist networks were incapable of modeling human attentional bias. We tested the presumed limitations of attentional bias in networks in two ways: (1) by having networks learn categories with exemplars that have high feature complexity in contrast to the low dimensional stimuli previously used, and (2) by investigating whether a Deep Learning (DL) network, which has demonstrated humanlike performance in many different kinds of tasks (language translation, autonomous driving, etc.), would display human-like attentional bias during category learning. We were able to show a number of interesting results. First, we replicated the failure of BP to differentially process integral and separable category structures when low dimensional stimuli are used (Garner, 1974; Kruschke, 1993). Second, we show that using the same low dimensional stimuli, Deep Learning (DL), unlike BP but similar to humans, learns separable category structures more quickly than integral category structures. Third, we show that even BP can exhibit human like learning differences between integral and separable category structures when high dimensional stimuli (face exemplars) are used. We conclude, after visualizing the hidden unit representations, that DL appears to extend initial learning due to feature development thereby reducing destructive feature competition by incrementally refining feature detectors throughout later layers until a tipping point (in terms of error) is reached resulting in rapid asymptotic learning.

Role of beliefs and emotions in numerical problem solving in university physics education

NASA Astrophysics Data System (ADS)

Bodin, Madelen; Winberg, Mikael

2012-06-01

Numerical problem solving in classical mechanics in university physics education offers a learning situation where students have many possibilities of control and creativity. In this study, expertlike beliefs about physics and learning physics together with prior knowledge were the most important predictors of the quality of performance of a task with many degrees of freedom. Feelings corresponding to control and concentration, i.e., emotions that are expected to trigger students’ intrinsic motivation, were also important in predicting performance. Unexpectedly, intrinsic motivation, as indicated by enjoyment and interest, together with students’ personal interest and utility value beliefs did not predict performance. This indicates that although a certain degree of enjoyment is probably necessary, motivated behavior is rather regulated by integration and identification of expertlike beliefs about learning and are more strongly associated with concentration and control during learning and, ultimately, with high performance. The results suggest that the development of students’ epistemological beliefs is important for students’ ability to learn from realistic problem-solving situations with many degrees of freedom in physics education.

The Effects of Game-Based Learning on Mathematical Confidence and Performance: High Ability vs. Low Ability

ERIC Educational Resources Information Center

Ku, Oskar; Chen, Sherry Y.; Wu, Denise H.; Lao, Andrew C. C.; Chan, Tak-Wai

2014-01-01

Many students possess low confidence toward learning mathematics, which, in turn, may lead them to give up pursuing more mathematics knowledge. Recently, game-based learning (GBL) is regarded as a potential means in improving students' confidence. Thus, this study tried to promote students' confidence toward mathematics by using GBL. In addition,…

How Do Tests and Summary Writing Tasks Enhance Long-Term Retention of Students with Different Levels of Test Anxiety?

ERIC Educational Resources Information Center

Mok, Wilson Shun; Chan, Winnie Wai

2016-01-01

Testing has been found to facilitate students' long-term retention of information. However, the learning performance of highly test-anxious students can be impaired by tests. Thus, these students may learn ineffectively in a testing context. By contrast, summary writing may not trigger test anxiety and is therefore another learning strategy to…

The Development of Automaticity in Short-Term Memory Search: Item-Response Learning and Category Learning

ERIC Educational Resources Information Center

Cao, Rui; Nosofsky, Robert M.; Shiffrin, Richard M.

2017-01-01

In short-term-memory (STM)-search tasks, observers judge whether a test probe was present in a short list of study items. Here we investigated the long-term learning mechanisms that lead to the highly efficient STM-search performance observed under conditions of consistent-mapping (CM) training, in which targets and foils never switch roles across…

How the World's Best Schools Stay on Top: Study's Key Findings Pinpoint Practices That Align with Learning Forward

ERIC Educational Resources Information Center

Killion, Joellen

2016-01-01

Key findings from a new study highlight how Learning Forward's long-standing position on professional learning correlates with practices in high-performing systems in Singapore, Shanghai, Hong Kong, and British Columbia. The purpose of this article is to share key findings from the study so that educators might apply them to strengthening…

Effects of noise frequency on performance and annoyance for women and men

NASA Technical Reports Server (NTRS)

Key, K. F.; Payne, M. C., Jr.

1981-01-01

Effects of noise frequencies on both performance on a complex psychomotor task and annoyance were investigated for men (n = 30) and women (n = 30). Each subject performed a complex psychomotor task for 50 min in the presence of low-frequency noise, high-frequency noise, or ambient noise. Women and men learned the task at different rates. Little effect of noise was shown. Annoyance ratings were subsequently obtained from each subject for noises of various frequencies by the method of magnitude estimation. High-frequency noises were more annoying than low-frequency noises regardless of sex and immediate prior exposure to noise. Sex differences in annoyance did not occur. No direct relationship between learning to perform a complex task while exposed to noise and annoyance by that noise was demonstrated.

Age difference in dual-task interference effects on procedural learning in children.

PubMed

Lejeune, Caroline; Desmottes, Lise; Catale, Corinne; Meulemans, Thierry

2015-01-01

The current study aimed to investigate the role played by explicit mechanisms during procedural learning in two age groups of children (7 and 10 years) using a dual-task paradigm. To do this, we explored the effect of an interference task during the early and late phases of a mirror tracing learning task. The results showed a differential impact of the secondary task on the two age groups, but only during the first learning phase; the performance of 10-year-olds was affected by the second task, whereas in 7-year-olds no performance difference was found between the single- and dual-task conditions. Overall, our study suggests that there are differences in the amount of effortful processing in which 7- and 10-year-olds engage at the beginning of the learning process; procedural learning in young children is mainly implicit, as attested by its lesser sensitivity to an interference task, whereas high-level explicit mechanisms seem to contribute to the procedural performance of 10-year-olds. However, these explicit mechanisms, even if they have an effect on performance, might not have an impact on the learning curve given that no difference in rate of acquisition was found between age groups. These findings are discussed in the light of classical conceptions of procedural learning. Copyright © 2014 Elsevier Inc. All rights reserved.

Debugging and Performance Analysis Software Tools for Peregrine System |

Science.gov Websites

High-Performance Computing | NREL Debugging and Performance Analysis Software Tools for Peregrine System Debugging and Performance Analysis Software Tools for Peregrine System Learn about debugging and performance analysis software tools available to use with the Peregrine system. Allinea

Posttraining transcranial magnetic stimulation of striate cortex disrupts consolidation early in visual skill learning.

PubMed

De Weerd, Peter; Reithler, Joel; van de Ven, Vincent; Been, Marin; Jacobs, Christianne; Sack, Alexander T

2012-02-08

Practice-induced improvements in skilled performance reflect "offline " consolidation processes extending beyond daily training sessions. According to visual learning theories, an early, fast learning phase driven by high-level areas is followed by a late, asymptotic learning phase driven by low-level, retinotopic areas when higher resolution is required. Thus, low-level areas would not contribute to learning and offline consolidation until late learning. Recent studies have challenged this notion, demonstrating modified responses to trained stimuli in primary visual cortex (V1) and offline activity after very limited training. However, the behavioral relevance of modified V1 activity for offline consolidation of visual skill memory in V1 after early training sessions remains unclear. Here, we used neuronavigated transcranial magnetic stimulation (TMS) directed to a trained retinotopic V1 location to test for behaviorally relevant consolidation in human low-level visual cortex. Applying TMS to the trained V1 location within 45 min of the first or second training session strongly interfered with learning, as measured by impaired performance the next day. The interference was conditional on task context and occurred only when training in the location targeted by TMS was followed by training in a second location before TMS. In this condition, high-level areas may become coupled to the second location and uncoupled from the previously trained low-level representation, thereby rendering consolidation vulnerable to interference. Our data show that, during the earliest phases of skill learning in the lowest-level visual areas, a behaviorally relevant form of consolidation exists of which the robustness is controlled by high-level, contextual factors.

Effect of chunk strength on the performance of children with developmental dyslexia on artificial grammar learning task may be related to complexity.

PubMed

Schiff, Rachel; Katan, Pesia; Sasson, Ayelet; Kahta, Shani

2017-07-01

There's a long held view that chunks play a crucial role in artificial grammar learning performance. We compared chunk strength influences on performance, in high and low topological entropy (a measure of complexity) grammar systems, with dyslexic children, age-matched and reading-level-matched control participants. Findings show that age-matched control participants' performance reflected equivalent influence of chunk strength in the two topological entropy conditions, as typically found in artificial grammar learning experiments. By contrast, dyslexic children and reading-level-matched controls' performance reflected knowledge of chunk strength only under the low topological entropy condition. In the low topological entropy grammar system, they appeared completely unable to utilize chunk strength to make appropriate test item selections. In line with previous research, this study suggests that for typically developing children, it is the chunks that are attended during artificial grammar learning and create a foundation on which implicit associative learning mechanisms operate, and these chunks are unitized to different strengths. However, for children with dyslexia, it is complexity that may influence the subsequent memorability of chunks, independently of their strength.

Impact of Expert Teaching Quality on Novice Academic Performance in the Jigsaw Cooperative Learning Method

NASA Astrophysics Data System (ADS)

Berger, Roland; Hänze, Martin

2015-01-01

We assessed the impact of expert students' instructional quality on the academic performance of novice students in 12th-grade physics classes organized in an expert model of cooperative learning ('jigsaw classroom'). The instructional quality of 129 expert students was measured by a newly developed rating system. As expected, when aggregating across all four subtopics taught, regression analysis revealed that academic performance of novice students increases with the quality of expert students' instruction. The difficulty of subtopics, however, moderates this effect: higher instructional quality of more difficult subtopics did not lead to better academic performance of novice students. We interpret this finding in the light of Cognitive Load Theory. Demanding tasks cause high intrinsic cognitive load and hindered the novice students' learning.

Study on Practical Application of Turboprop Engine Condition Monitoring and Fault Diagnostic System Using Fuzzy-Neuro Algorithms

NASA Astrophysics Data System (ADS)

Kong, Changduk; Lim, Semyeong; Kim, Keunwoo

2013-03-01

The Neural Networks is mostly used to engine fault diagnostic system due to its good learning performance, but it has a drawback due to low accuracy and long learning time to build learning data base. This work builds inversely a base performance model of a turboprop engine to be used for a high altitude operation UAV using measuring performance data, and proposes a fault diagnostic system using the base performance model and artificial intelligent methods such as Fuzzy and Neural Networks. Each real engine performance model, which is named as the base performance model that can simulate a new engine performance, is inversely made using its performance test data. Therefore the condition monitoring of each engine can be more precisely carried out through comparison with measuring performance data. The proposed diagnostic system identifies firstly the faulted components using Fuzzy Logic, and then quantifies faults of the identified components using Neural Networks leaned by fault learning data base obtained from the developed base performance model. In leaning the measuring performance data of the faulted components, the FFBP (Feed Forward Back Propagation) is used. In order to user's friendly purpose, the proposed diagnostic program is coded by the GUI type using MATLAB.

Digital Learning As Enhanced Learning Processing? Cognitive Evidence for New insight of Smart Learning.

PubMed

Di Giacomo, Dina; Ranieri, Jessica; Lacasa, Pilar

2017-01-01

Large use of technology improved quality of life across aging and favoring the development of digital skills. Digital skills can be considered an enhancing to human cognitive activities. New research trend is about the impact of the technology in the elaboration information processing of the children. We wanted to analyze the influence of technology in early age evaluating the impact on cognition. We investigated the performance of a sample composed of n. 191 children in school age distributed in two groups as users: high digital users and low digital users. We measured the verbal and visuoperceptual cognitive performance of children by n. 8 standardized psychological tests and ad hoc self-report questionnaire. Results have evidenced the influence of digital exposition on cognitive development: the cognitive performance is looked enhanced and better developed: high digital users performed better in naming, semantic, visual memory and logical reasoning tasks. Our finding confirms the data present in literature and suggests the strong impact of the technology using not only in the social, educational and quality of life of the people, but also it outlines the functionality and the effect of the digital exposition in early age; increased cognitive abilities of the children tailor digital skilled generation with enhanced cognitive processing toward to smart learning.

Predicting explorative motor learning using decision-making and motor noise.

PubMed

Chen, Xiuli; Mohr, Kieran; Galea, Joseph M

2017-04-01

A fundamental problem faced by humans is learning to select motor actions based on noisy sensory information and incomplete knowledge of the world. Recently, a number of authors have asked whether this type of motor learning problem might be very similar to a range of higher-level decision-making problems. If so, participant behaviour on a high-level decision-making task could be predictive of their performance during a motor learning task. To investigate this question, we studied performance during an explorative motor learning task and a decision-making task which had a similar underlying structure with the exception that it was not subject to motor (execution) noise. We also collected an independent measurement of each participant's level of motor noise. Our analysis showed that explorative motor learning and decision-making could be modelled as the (approximately) optimal solution to a Partially Observable Markov Decision Process bounded by noisy neural information processing. The model was able to predict participant performance in motor learning by using parameters estimated from the decision-making task and the separate motor noise measurement. This suggests that explorative motor learning can be formalised as a sequential decision-making process that is adjusted for motor noise, and raises interesting questions regarding the neural origin of explorative motor learning.

Predicting explorative motor learning using decision-making and motor noise

PubMed Central

Galea, Joseph M.

2017-01-01

A fundamental problem faced by humans is learning to select motor actions based on noisy sensory information and incomplete knowledge of the world. Recently, a number of authors have asked whether this type of motor learning problem might be very similar to a range of higher-level decision-making problems. If so, participant behaviour on a high-level decision-making task could be predictive of their performance during a motor learning task. To investigate this question, we studied performance during an explorative motor learning task and a decision-making task which had a similar underlying structure with the exception that it was not subject to motor (execution) noise. We also collected an independent measurement of each participant’s level of motor noise. Our analysis showed that explorative motor learning and decision-making could be modelled as the (approximately) optimal solution to a Partially Observable Markov Decision Process bounded by noisy neural information processing. The model was able to predict participant performance in motor learning by using parameters estimated from the decision-making task and the separate motor noise measurement. This suggests that explorative motor learning can be formalised as a sequential decision-making process that is adjusted for motor noise, and raises interesting questions regarding the neural origin of explorative motor learning. PMID:28437451

Role of dopamine D2 receptors in human reinforcement learning.

PubMed

Eisenegger, Christoph; Naef, Michael; Linssen, Anke; Clark, Luke; Gandamaneni, Praveen K; Müller, Ulrich; Robbins, Trevor W

2014-09-01

Influential neurocomputational models emphasize dopamine (DA) as an electrophysiological and neurochemical correlate of reinforcement learning. However, evidence of a specific causal role of DA receptors in learning has been less forthcoming, especially in humans. Here we combine, in a between-subjects design, administration of a high dose of the selective DA D2/3-receptor antagonist sulpiride with genetic analysis of the DA D2 receptor in a behavioral study of reinforcement learning in a sample of 78 healthy male volunteers. In contrast to predictions of prevailing models emphasizing DA's pivotal role in learning via prediction errors, we found that sulpiride did not disrupt learning, but rather induced profound impairments in choice performance. The disruption was selective for stimuli indicating reward, whereas loss avoidance performance was unaffected. Effects were driven by volunteers with higher serum levels of the drug, and in those with genetically determined lower density of striatal DA D2 receptors. This is the clearest demonstration to date for a causal modulatory role of the DA D2 receptor in choice performance that might be distinct from learning. Our findings challenge current reward prediction error models of reinforcement learning, and suggest that classical animal models emphasizing a role of postsynaptic DA D2 receptors in motivational aspects of reinforcement learning may apply to humans as well.

Role of Dopamine D2 Receptors in Human Reinforcement Learning

PubMed Central

Eisenegger, Christoph; Naef, Michael; Linssen, Anke; Clark, Luke; Gandamaneni, Praveen K; Müller, Ulrich; Robbins, Trevor W

2014-01-01

Influential neurocomputational models emphasize dopamine (DA) as an electrophysiological and neurochemical correlate of reinforcement learning. However, evidence of a specific causal role of DA receptors in learning has been less forthcoming, especially in humans. Here we combine, in a between-subjects design, administration of a high dose of the selective DA D2/3-receptor antagonist sulpiride with genetic analysis of the DA D2 receptor in a behavioral study of reinforcement learning in a sample of 78 healthy male volunteers. In contrast to predictions of prevailing models emphasizing DA's pivotal role in learning via prediction errors, we found that sulpiride did not disrupt learning, but rather induced profound impairments in choice performance. The disruption was selective for stimuli indicating reward, whereas loss avoidance performance was unaffected. Effects were driven by volunteers with higher serum levels of the drug, and in those with genetically determined lower density of striatal DA D2 receptors. This is the clearest demonstration to date for a causal modulatory role of the DA D2 receptor in choice performance that might be distinct from learning. Our findings challenge current reward prediction error models of reinforcement learning, and suggest that classical animal models emphasizing a role of postsynaptic DA D2 receptors in motivational aspects of reinforcement learning may apply to humans as well. PMID:24713613

Stereotype Threat Effects on Learning From a Cognitively Demanding Mathematics Lesson.

PubMed

Lyons, Emily McLaughlin; Simms, Nina; Begolli, Kreshnik N; Richland, Lindsey E

2018-03-01

Stereotype threat-a situational context in which individuals are concerned about confirming a negative stereotype-is often shown to impact test performance, with one hypothesized mechanism being that cognitive resources are temporarily co-opted by intrusive thoughts and worries, leading individuals to underperform despite high content knowledge and ability (see Schmader & Beilock, ). We test here whether stereotype threat may also impact initial student learning and knowledge formation when experienced prior to instruction. Predominantly African American fifth-grade students provided either their race or the date before a videotaped, conceptually demanding mathematics lesson. Students who gave their race retained less learning over time, enjoyed the lesson less, reported a diminished desire to learn more, and were less likely to choose to engage in an optional math activity. The detrimental impact was greatest among students with high baseline cognitive resources. While stereotype threat has been well documented to harm test performance, the finding that effects extend to initial learning suggests that stereotype threat's contribution to achievement gaps may be greatly underestimated. Copyright © 2017 Cognitive Science Society, Inc.

Self-directed learning readiness of Asian students: students perspective on a hybrid problem based learning curriculum.

PubMed

Leatemia, Lukas D; Susilo, Astrid P; van Berkel, Henk

2016-12-03

To identify the student's readiness to perform self-directed learning and the underlying factors influencing it on the hybrid problem based learning curriculum. A combination of quantitative and qualitative studies was conducted in five medical schools in Indonesia. In the quantitative study, the Self Directed Learning Readiness Scale was distributed to all students in all batches, who had experience with the hybrid problem based curriculum. They were categorized into low- and high -level based on the score of the questionnaire. Three focus group discussions (low-, high-, and mixed level) were conducted in the qualitative study with six to twelve students chosen randomly from each group to find the factors influencing their self-directed learning readiness. Two researchers analysed the qualitative data as a measure of triangulation. The quantitative study showed only half of the students had a high-level of self-directed learning readiness, and a similar trend also occurred in each batch. The proportion of students with a high level of self-directed learning readiness was lower in the senior students compared to more junior students. The qualitative study showed that problem based learning processes, assessments, learning environment, students' life styles, students' perceptions of the topics, and mood, were factors influencing their self-directed learning. A hybrid problem based curriculum may not fully affect the students' self-directed learning. The curriculum system, teacher's experiences, student's background and cultural factors might contribute to the difficulties for the student's in conducting self-directed learning.

Gifted Programs.

ERIC Educational Resources Information Center

Luehning, Barbara

1979-01-01

Describes programs for the gifted: visual and performing arts for secondary students, enrichment for rural elementary students, and a learning center elementary enrichment program. NOTE: includes "INTERARTS: The High School Program for the Talented in the Arts" by Barbara Luehning, "Spice" by Jane V. Salisbury, and "Learning Center Enrichment…

Comparison of computer-assisted instruction (CAI) versus traditional textbook methods for training in abdominal examination (Japanese experience).

PubMed

Qayumi, A K; Kurihara, Y; Imai, M; Pachev, G; Seo, H; Hoshino, Y; Cheifetz, R; Matsuura, K; Momoi, M; Saleem, M; Lara-Guerra, H; Miki, Y; Kariya, Y

2004-10-01

This study aimed to compare the effects of computer-assisted, text-based and computer-and-text learning conditions on the performances of 3 groups of medical students in the pre-clinical years of their programme, taking into account their academic achievement to date. A fourth group of students served as a control (no-study) group. Participants were recruited from the pre-clinical years of the training programmes in 2 medical schools in Japan, Jichi Medical School near Tokyo and Kochi Medical School near Osaka. Participants were randomly assigned to 4 learning conditions and tested before and after the study on their knowledge of and skill in performing an abdominal examination, in a multiple-choice test and an objective structured clinical examination (OSCE), respectively. Information about performance in the programme was collected from school records and students were classified as average, good or excellent. Student and faculty evaluations of their experience in the study were explored by means of a short evaluation survey. Compared to the control group, all 3 study groups exhibited significant gains in performance on knowledge and performance measures. For the knowledge measure, the gains of the computer-assisted and computer-assisted plus text-based learning groups were significantly greater than the gains of the text-based learning group. The performances of the 3 groups did not differ on the OSCE measure. Analyses of gains by performance level revealed that high achieving students' learning was independent of study method. Lower achieving students performed better after using computer-based learning methods. The results suggest that computer-assisted learning methods will be of greater help to students who do not find the traditional methods effective. Explorations of the factors behind this are a matter for future research.

The Impacts of Virtual Manipulatives and Prior Knowledge on Geometry Learning Performance in Junior High School

ERIC Educational Resources Information Center

Lee, Chun-Yi; Chen, Ming-Jang

2014-01-01

Previous studies on the effects of virtual and physical manipulatives have failed to consider the impact of prior knowledge on the efficacy of manipulatives. This study focuses on the learning of plane geometry in junior high schools, including the sum of interior angles in polygons, the sum of exterior angles in polygons, and the properties of…

Assessing Chemistry-Learning Competencies of Students in Isolated Rural Senior High Schools by Using the National Examination: A Case Study of Simeulue Island, Indonesia

ERIC Educational Resources Information Center

Adlim, M-; Soewarno, S.; Ali, Hasbi; Ibrahim, Armia; Umar, Hasmunir; Ismail, Khairil; Gani, Usman A.; Hasan, Ishak; Yasin, Burhanuddin

2014-01-01

This study explored learning competency based on the Indonesian National Examination focusing especially on chemistry performance and the circumstances of senior high school students and teachers in rural areas of Simeulue Island, Indonesia. The National Examination total score and chemistry score for students in rural areas were consistently…

Learning and Study Strategies Inventory subtests and factors as predictors of National Board of Chiropractic Examiners Part 1 examination performance.

PubMed

Schutz, Christine M; Dalton, Leanne; Tepe, Rodger E

2013-01-01

This study was designed to extend research on the relationship between chiropractic students' learning and study strategies and national board examination performance. Sixty-nine first trimester chiropractic students self-administered the Learning and Study Strategies Inventory (LASSI). Linear trends tests (for continuous variables) and Mantel-Haenszel trend tests (for categorical variables) were utilized to determine if the 10 LASSI subtests and 3 factors predicted low, medium and high levels of National Board of Chiropractic Examiners (NBCE) Part 1 scores. Multiple regression was performed to predict overall mean NBCE examination scores using the 3 LASSI factors as predictor variables. Four LASSI subtests (Anxiety, Concentration, Selecting Main Ideas, Test Strategies) and one factor (Goal Orientation) were significantly associated with NBCE examination levels. One factor (Goal Orientation) was a significant predictor of overall mean NBCE examination performance. Learning and study strategies are predictive of NBCE Part 1 examination performance in chiropractic students. The current study found LASSI subtests Anxiety, Concentration, Selecting Main Ideas, and Test Strategies, and the Goal-Orientation factor to be significant predictors of NBCE scores. The LASSI may be useful to educators in preparing students for academic success. Further research is warranted to explore the effects of learning and study strategies training on GPA and NBCE performance.

The Relationship Between Fidelity and Learning in Aviation Training and Assessment

NASA Technical Reports Server (NTRS)

Noble, Cliff

2002-01-01

Flight simulators can be designed to train pilots or assess their flight performance. Low-Fidelity simulators maximize the initial learning rate of novice pilots and minimize initial costs; whereas, expensive, high-fidelity simulators predict the realworld in-flight performance of expert pilots (Fink & Shriver, 1978 Hays & Singer 1989; Kinkade & Wheaton. 1972). Although intuitively appealing and intellectually convenient to generalize concepts of learning and assessment, what holds true for the role of fidelity in assessment may not always hold true for learning, and vice versa. To bring clarity to this issue, the author distinguishes the role of fidelity in learning from its role in assessment as a function of skill level by applying the hypothesis of Alessi (1988) and reviewing the Laughery, Ditzian, and Houtman (1982) study on simulator validity. Alessi hypothesized that there is it point beyond which one additional unit of flight-simulator fidelity results in a diminished rate of learning. The author of this current paper also suggests the existence of an optimal point beyond which one additional unit of flight-simulator fidelity results in a diminished rate of practical assessment of nonexpert pilot performance.

Functional Equivalence of Spatial Images from Touch and Vision: Evidence from Spatial Updating in Blind and Sighted Individuals

PubMed Central

Giudice, Nicholas A.; Betty, Maryann R.; Loomis, Jack M.

2012-01-01

This research examines whether visual and haptic map learning yield functionally equivalent spatial images in working memory, as evidenced by similar encoding bias and updating performance. In three experiments, participants learned four-point routes either by seeing or feeling the maps. At test, blindfolded participants made spatial judgments about the maps from imagined perspectives that were either aligned or misaligned with the maps as represented in working memory. Results from Experiments 1 and 2 revealed a highly similar pattern of latencies and errors between visual and haptic conditions. These findings extend the well known alignment biases for visual map learning to haptic map learning, provide further evidence of haptic updating, and most importantly, show that learning from the two modalities yields very similar performance across all conditions. Experiment 3 found the same encoding biases and updating performance with blind individuals, demonstrating that functional equivalence cannot be due to visual recoding and is consistent with an amodal hypothesis of spatial images. PMID:21299331

[Motivation and learning strategies in pediatric residents].

PubMed

Sepúlveda-Vildósola, Ana Carolina; Carrada-Legaria, Sol; Reyes-Lagunes, Isabel

2015-01-01

Motivation is an internal mood that moves individuals to act, points them in certain directions, and maintains them in activities, playing a very important role in self-regulated learning and academic performance. Our objective was to evaluate motivation and self-regulation of knowledge in pediatric residents in a third-level hospital, and to determine if there are differences according to the type of specialty and sociodemographic variables. All residents who agreed to participate responded to the Motivated Strategies for Learning Questionnaire. Cronbach alpha was performed to determine reliability. The mean value of each subscale was compared with Student's t test or ANOVA, correlation of subscales with Pearson test. A value of p≤0.05 was considered significant. We included 118 residents. The questionnaire was highly reliable (α=0.939). There were no significant differences in motivation or learning strategies according to sex, marital status, or age. Those residents studying a second or third specialization had significantly higher scores in elaboration, critical thinking, and peer learning. There were significant correlations between intrinsic motivation and self-efficacy with the development of knowledge strategies such as elaboration, critical thinking, and metacognitive self-regulation. Our students present average-to-high scores of motivation and knowledge strategies, with a significant difference according to type of specialization. There is a high correlation between motivation and knowledge strategies.

Imbalance aware lithography hotspot detection: a deep learning approach

NASA Astrophysics Data System (ADS)

Yang, Haoyu; Luo, Luyang; Su, Jing; Lin, Chenxi; Yu, Bei

2017-03-01

With the advancement of VLSI technology nodes, light diffraction caused lithographic hotspots have become a serious problem affecting manufacture yield. Lithography hotspot detection at the post-OPC stage is imperative to check potential circuit failures when transferring designed patterns onto silicon wafers. Although conventional lithography hotspot detection methods, such as machine learning, have gained satisfactory performance, with extreme scaling of transistor feature size and more and more complicated layout patterns, conventional methodologies may suffer from performance degradation. For example, manual or ad hoc feature extraction in a machine learning framework may lose important information when predicting potential errors in ultra-large-scale integrated circuit masks. In this paper, we present a deep convolutional neural network (CNN) targeting representative feature learning in lithography hotspot detection. We carefully analyze impact and effectiveness of different CNN hyper-parameters, through which a hotspot-detection-oriented neural network model is established. Because hotspot patterns are always minorities in VLSI mask design, the training data set is highly imbalanced. In this situation, a neural network is no longer reliable, because a trained model with high classification accuracy may still suffer from high false negative results (missing hotspots), which is fatal in hotspot detection problems. To address the imbalance problem, we further apply minority upsampling and random-mirror flipping before training the network. Experimental results show that our proposed neural network model achieves highly comparable or better performance on the ICCAD 2012 contest benchmark compared to state-of-the-art hotspot detectors based on deep or representative machine leaning.

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.

Automated analysis of high-content microscopy data with deep learning.

PubMed

Kraus, Oren Z; Grys, Ben T; Ba, Jimmy; Chong, Yolanda; Frey, Brendan J; Boone, Charles; Andrews, Brenda J

2017-04-18

Existing computational pipelines for quantitative analysis of high-content microscopy data rely on traditional machine learning approaches that fail to accurately classify more than a single dataset without substantial tuning and training, requiring extensive analysis. Here, we demonstrate that the application of deep learning to biological image data can overcome the pitfalls associated with conventional machine learning classifiers. Using a deep convolutional neural network (DeepLoc) to analyze yeast cell images, we show improved performance over traditional approaches in the automated classification of protein subcellular localization. We also demonstrate the ability of DeepLoc to classify highly divergent image sets, including images of pheromone-arrested cells with abnormal cellular morphology, as well as images generated in different genetic backgrounds and in different laboratories. We offer an open-source implementation that enables updating DeepLoc on new microscopy datasets. This study highlights deep learning as an important tool for the expedited analysis of high-content microscopy data. © 2017 The Authors. Published under the terms of the CC BY 4.0 license.

Development of instructional manual encouraging student active learning for high school teaching on fluid mechanics through Torricelli's tank experiment

NASA Astrophysics Data System (ADS)

Apiwan, Suttinee; Puttharugsa, Chokchai; Khemmani, Supitch

2018-01-01

The purposes of this research were to help students to perform Physics laboratory by themselves and to provide guidelines for high school teacher to develop active learning on fluid mechanics by using Torricelli's tank experiment. The research was conducted as follows: 1) constructed an appropriate Torricelli's tank experiment for high school teaching and investigated the condition for maximum water falling distance. As a consequence, it was found that the distance of the falling water measured from the experiment was shorter than that obtained from the theory of ideal fluid because of the energy loss during a flow, 2) developed instructional manual for high school teaching that encourages active learning by using problem based learning (PBL) approach, which is consistent with the trend of teaching and learning in 21st century. The content validity of our instructional manual using Index of Item-objective Congruence (IOC) as evaluated by three experts was over 0.67. The manual developed was therefore qualified for classroom practice.

Conventional vs. e-learning in nursing education: A systematic review and meta-analysis.

PubMed

Voutilainen, Ari; Saaranen, Terhi; Sormunen, Marjorita

2017-03-01

By and large, in health professions training, the direction of the effect of e-learning, positive or negative, strongly depends on the learning outcome in question as well as on learning methods which e-learning is compared to. In nursing education, meta-analytically generated knowledge regarding the comparisons between conventional and e-learning is scarce. The aim of this review is to discover the size of the effect of e-learning on learning outcomes in nursing education and to assess the quality of studies in which e-learning has been compared to conventional learning. A systematic search of six electronic databases, PubMed, Ovid MEDLINE®, CINAHL (EBSCOhost), Cochrane Library, PsycINFO, and ERIC, was conducted in order to identify relevant peer-reviewed English language articles published between 2011 and 2015. The quality of the studies included as well as the risk of bias in each study was assessed. A random-effects meta-analysis was performed to generate a pooled mean difference in the learning outcome. Altogether, 10 studies were eligible for the quality assessment and meta-analysis. Nine studies were evaluated as good quality studies, but not without a risk of bias. Performance bias caused a high risk in nearly all the studies. In the meta-analysis, an e-learning method resulted in test scores that were, on average, five points higher than a conventional method on a 0-100 scale. Heterogeneity between the studies was very large. The size and direction of the effect of a learning method on learning outcomes appeared to be strongly situational. We suggest that meta-regressions should be performed instead of basic meta-analyses in order to reveal factors that cause variation in the learning outcomes of nursing education. It might be necessary to perform separate meta-analyses between e-learning interventions aimed at improving nursing knowledge and those aimed at improving nursing skills. Copyright © 2016 Elsevier Ltd. All rights reserved.

Dynamic Social Networks in High Performance Football Coaching

ERIC Educational Resources Information Center

Occhino, Joseph; Mallett, Cliff; Rynne, Steven

2013-01-01

Background: Sports coaching is largely a social activity where engagement with athletes and support staff can enhance the experiences for all involved. This paper examines how high performance football coaches develop knowledge through their interactions with others within a social learning theory framework. Purpose: The key purpose of this study…

Effects of Didactic Instruction and Test-Enhanced Learning in a Nursing Review Course.

PubMed

Tu, Yu-Ching; Lin, Yi-Jung; Lee, Jonathan W; Fan, Lir-Wan

2017-11-01

Determining the most effective approach for students' successful academic performance and achievement on the national licensure examination for RNs is important to nursing education and practice. A quasi-experimental design was used to compare didactic instruction and test-enhanced learning among nursing students divided into two fundamental nursing review courses in their final semester. Students in each course were subdivided into low-, intermediate-, and high-score groups based on their first examination scores. Mixed model of repeated measure and two-way analysis of variance were applied to evaluate students' academic results and both teaching approaches. Intermediate-scoring students' performances improved more through didactic instruction, whereas low-scoring students' performances improved more through test-enhanced learning. Each method had differing effects on individual subgroups within the different performance level groups of their classes, which points to the importance of considering both the didactic and test-enhanced learning approaches. [J Nurs Educ. 2017;56(11):683-687.]. Copyright 2017, SLACK Incorporated.

Learning effect of computerized cognitive tests in older adults

PubMed Central

de Oliveira, Rafaela Sanches; Trezza, Beatriz Maria; Busse, Alexandre Leopold; Jacob-Filho, Wilson

2014-01-01

ABSTRACT Objective: To evaluate the learning effect of computerized cognitive testing in the elderly. Methods: Cross-sectional study with 20 elderly, 10 women and 10 men, with average age of 77.5 (±4.28) years. The volunteers performed two series of computerized cognitive tests in sequence and their results were compared. The applied tests were: Trail Making A and B, Spatial Recognition, Go/No Go, Memory Span, Pattern Recognition Memory and Reverse Span. Results: Based on the comparison of the results, learning effects were observed only in the Trail Making A test (p=0.019). Other tests performed presented no significant performance improvements. There was no correlation between learning effect and age (p=0.337) and education (p=0.362), as well as differences between genders (p=0.465). Conclusion: The computerized cognitive tests repeated immediately afterwards, for elderly, revealed no change in their performance, with the exception of the Trail Making test, demonstrating high clinical applicability, even in short intervals. PMID:25003917

A deep learning framework for financial time series using stacked autoencoders and long-short term memory

PubMed Central

Bao, Wei; Rao, Yulei

2017-01-01

The application of deep learning approaches to finance has received a great deal of attention from both investors and researchers. This study presents a novel deep learning framework where wavelet transforms (WT), stacked autoencoders (SAEs) and long-short term memory (LSTM) are combined for stock price forecasting. The SAEs for hierarchically extracted deep features is introduced into stock price forecasting for the first time. The deep learning framework comprises three stages. First, the stock price time series is decomposed by WT to eliminate noise. Second, SAEs is applied to generate deep high-level features for predicting the stock price. Third, high-level denoising features are fed into LSTM to forecast the next day’s closing price. Six market indices and their corresponding index futures are chosen to examine the performance of the proposed model. Results show that the proposed model outperforms other similar models in both predictive accuracy and profitability performance. PMID:28708865

Mapping Sub-Saharan African Agriculture in High-Resolution Satellite Imagery with Computer Vision & Machine Learning

NASA Astrophysics Data System (ADS)

Debats, Stephanie Renee

Smallholder farms dominate in many parts of the world, including Sub-Saharan Africa. These systems are characterized by small, heterogeneous, and often indistinct field patterns, requiring a specialized methodology to map agricultural landcover. In this thesis, we developed a benchmark labeled data set of high-resolution satellite imagery of agricultural fields in South Africa. We presented a new approach to mapping agricultural fields, based on efficient extraction of a vast set of simple, highly correlated, and interdependent features, followed by a random forest classifier. The algorithm achieved similar high performance across agricultural types, including spectrally indistinct smallholder fields, and demonstrated the ability to generalize across large geographic areas. In sensitivity analyses, we determined multi-temporal images provided greater performance gains than the addition of multi-spectral bands. We also demonstrated how active learning can be incorporated in the algorithm to create smaller, more efficient training data sets, which reduced computational resources, minimized the need for humans to hand-label data, and boosted performance. We designed a patch-based uncertainty metric to drive the active learning framework, based on the regular grid of a crowdsourcing platform, and demonstrated how subject matter experts can be replaced with fleets of crowdsourcing workers. Our active learning algorithm achieved similar performance as an algorithm trained with randomly selected data, but with 62% less data samples. This thesis furthers the goal of providing accurate agricultural landcover maps, at a scale that is relevant for the dominant smallholder class. Accurate maps are crucial for monitoring and promoting agricultural production. Furthermore, improved agricultural landcover maps will aid a host of other applications, including landcover change assessments, cadastral surveys to strengthen smallholder land rights, and constraints for crop modeling and famine prediction.

Automatic detection of hemorrhagic pericardial effusion on PMCT using deep learning - a feasibility study.

PubMed

Ebert, Lars C; Heimer, Jakob; Schweitzer, Wolf; Sieberth, Till; Leipner, Anja; Thali, Michael; Ampanozi, Garyfalia

2017-12-01

Post mortem computed tomography (PMCT) can be used as a triage tool to better identify cases with a possibly non-natural cause of death, especially when high caseloads make it impossible to perform autopsies on all cases. Substantial data can be generated by modern medical scanners, especially in a forensic setting where the entire body is documented at high resolution. A solution for the resulting issues could be the use of deep learning techniques for automatic analysis of radiological images. In this article, we wanted to test the feasibility of such methods for forensic imaging by hypothesizing that deep learning methods can detect and segment a hemopericardium in PMCT. For deep learning image analysis software, we used the ViDi Suite 2.0. We retrospectively selected 28 cases with, and 24 cases without, hemopericardium. Based on these data, we trained two separate deep learning networks. The first one classified images into hemopericardium/not hemopericardium, and the second one segmented the blood content. We randomly selected 50% of the data for training and 50% for validation. This process was repeated 20 times. The best performing classification network classified all cases of hemopericardium from the validation images correctly with only a few false positives. The best performing segmentation network would tend to underestimate the amount of blood in the pericardium, which is the case for most networks. This is the first study that shows that deep learning has potential for automated image analysis of radiological images in forensic medicine.

FUNCTION Follows FORM: Building the Foundations for Student Achievement Employing "School as a Teaching Tool" Protocol a Place-Based Learning Approach

ERIC Educational Resources Information Center

da Silva, Joseph; Alvarado, Manuel Cordero

2011-01-01

The experience of observing students actively engaged in the learning process is precious. There is no better way to celebrate "Children's Health and Energy Awareness Month" than assembling in a world class high performance green school with gifted and talented students learning how to take an integrated approach to sustainable school…

Identifying, Preparing and Evaluating Army Instructors

DTIC Science & Technology

2016-04-01

Description WB1 Monitor/observe students to ensure learning is taking place and that problems/issues (e.g., learning off track, faulty thinking ) are...improve performance. S9 Apply educational technology in ways that enhance student learning . 8 Table 4 Abilities required for an Instructor to... mathematics and science at both the elementary and high school levels. Yet many individual studies often indicate no significant differences in student

Novel Breast Imaging and Machine Learning: Predicting Breast Lesion Malignancy at Cone-Beam CT Using Machine Learning Techniques.

PubMed

Uhlig, Johannes; Uhlig, Annemarie; Kunze, Meike; Beissbarth, Tim; Fischer, Uwe; Lotz, Joachim; Wienbeck, Susanne

2018-05-24

The purpose of this study is to evaluate the diagnostic performance of machine learning techniques for malignancy prediction at breast cone-beam CT (CBCT) and to compare them to human readers. Five machine learning techniques, including random forests, back propagation neural networks (BPN), extreme learning machines, support vector machines, and K-nearest neighbors, were used to train diagnostic models on a clinical breast CBCT dataset with internal validation by repeated 10-fold cross-validation. Two independent blinded human readers with profound experience in breast imaging and breast CBCT analyzed the same CBCT dataset. Diagnostic performance was compared using AUC, sensitivity, and specificity. The clinical dataset comprised 35 patients (American College of Radiology density type C and D breasts) with 81 suspicious breast lesions examined with contrast-enhanced breast CBCT. Forty-five lesions were histopathologically proven to be malignant. Among the machine learning techniques, BPNs provided the best diagnostic performance, with AUC of 0.91, sensitivity of 0.85, and specificity of 0.82. The diagnostic performance of the human readers was AUC of 0.84, sensitivity of 0.89, and specificity of 0.72 for reader 1 and AUC of 0.72, sensitivity of 0.71, and specificity of 0.67 for reader 2. AUC was significantly higher for BPN when compared with both reader 1 (p = 0.01) and reader 2 (p < 0.001). Machine learning techniques provide a high and robust diagnostic performance in the prediction of malignancy in breast lesions identified at CBCT. BPNs showed the best diagnostic performance, surpassing human readers in terms of AUC and specificity.

Impact of Virtual Patients as Optional Learning Material in Veterinary Biochemistry Education.

PubMed

Kleinsorgen, Christin; von Köckritz-Blickwede, Maren; Naim, Hassan Y; Branitzki-Heinemann, Katja; Kankofer, Marta; Mándoki, Míra; Adler, Martin; Tipold, Andrea; Ehlers, Jan P

2018-01-01

Biochemistry and physiology teachers from veterinary faculties in Hannover, Budapest, and Lublin prepared innovative, computer-based, integrative clinical case scenarios as optional learning materials for teaching and learning in basic sciences. These learning materials were designed to enhance attention and increase interest and intrinsic motivation for learning, thus strengthening autonomous, active, and self-directed learning. We investigated learning progress and success by administering a pre-test before exposure to the virtual patients (vetVIP) cases, offered vetVIP cases alongside regular biochemistry courses, and then administered a complementary post-test. We analyzed improvement in cohort performance and level of confidence in rating questions. Results of the performance in biochemistry examinations in 2014, 2015, and 2016 were correlated with the use of and performance in vetVIP cases throughout biochemistry courses in Hannover. Surveys of students reflected that interactive cases helped them understand the relevance of basic sciences in veterinary education. Differences between identical pre- and post-tests revealed knowledge improvement (correct answers: +28% in Hannover, +9% in Lublin) and enhanced confidence in decision making ("I don't know" answers: -20% in Hannover, -7.5% in Lublin). High case usage and voluntary participation (use of vetVIP cases in Hannover and Lublin >70%, Budapest <1%; response rates in pre-test 72% and post-test 48%) indicated a good increase in motivation for the subject of biochemistry. Despite increased motivation, there was only a weak correlation between performance in final exams and performance in the vetVIP cases. Case-based e-learning could be extended and generated cases should be shared across veterinary faculties.

Improvement of Self-regulated Learning in Mathematics through a Hypermedia Application: Differences based on Academic Performance and Previous Knowledge.

PubMed

Cueli, Marisol; Rodríguez, Celestino; Areces, Débora; García, Trinidad; González-Castro, Paloma

2017-12-04

Self-regulation on behalf of the student is crucial in learning Mathematics through hypermedia applications and is an even greater challenge in these IT environments. Two aims are formulated. First, to analyze the effectiveness of a hypermedia tool in improving perceived knowledge of self-regulatory strategies and the perceived usage of the planning, executing and assessment strategy on behalf of students with low, medium and high levels of academic performance. Second, to analyze the effectiveness of the hypermedia tool in improving perceived usage of the strategy for planning, monitoring and evaluating on behalf of students with a perceived knowledge (low, medium and high). Participants were 624 students (aged 10-13), classified into a treatment group (TG; 391) and a comparative group (CG; 233). They completed a questionnaire on perceived knowledge (Perceived Knowledge of Self-Regulatory Strategies) and another one on perceived usage of the strategy for planning, performing and evaluating (Inventory of Self-regulatory Learning Processes). Univariate covariance analyses (ANCOVAs) and Student-t tests were used. ANCOVA results were not statistically significant. However, the linear contrast indicated a significant improvement in perceived knowledge of strategies among the TG with low, medium and high academic performance (p ≤ .001). Results are discussed in the light of past and future research.

The Relationship between Social Activities and School Performance for Secondary Students with Learning Disabilities. Findings from the National Longitudinal Transition Study of Special Education Students.

ERIC Educational Resources Information Center

Newman, Lynn

This study used data from the National Longitudinal Transition Study of Special Education Students to examine whether social activities had an impact on the academic performance of 832 youth with learning disabilities. More than one-third of the high-school youth were reported to see friends outside of school 6 or 7 days a week. These students had…

Exploring deliberate practice in medicine: how do physicians learn in the workplace?

PubMed

van de Wiel, Margje W J; Van den Bossche, Piet; Janssen, Sandra; Jossberger, Helen

2011-03-01

Medical professionals need to keep on learning as part of their everyday work to deliver high-quality health care. Although the importance of physicians' learning is widely recognized, few studies have investigated how they learn in the workplace. Based on insights from deliberate practice research, this study examined the activities physicians engage in during their work that might further their professional development. As deliberate practice requires a focused effort to improve performance, the study also examined the goals underlying this behaviour. Semi-structured interviews were conducted with 50 internal medicine physicians: 19 residents, 18 internists working at a university hospital, and 13 working at a non-university hospital. The results showed that learning in medical practice was very much embedded in clinical work. Most relevant learning activities were directly related to patient care rather than motivated by competence improvement goals. Advice and feedback were sought when necessary to provide this care. Performance standards were tied to patients' conditions. The patients encountered and the discussions with colleagues about patients were valued most for professional development, while teaching and updating activities were also valued in this respect. In conclusion, physicians' learning is largely guided by practical experience rather than deliberately sought. When professionals interact in diagnosing and treating patients to achieve high-quality care, their experiences contribute to expertise development. However, much could be gained from managing learning opportunities more explicitly. We offer suggestions for increasing the focus on learning in medical practice and further research.

Contingency proportion systematically influences contingency learning.

PubMed

Forrin, Noah D; MacLeod, Colin M

2018-01-01

In the color-word contingency learning paradigm, each word appears more often in one color (high contingency) than in the other colors (low contingency). Shortly after beginning the task, color identification responses become faster on the high-contingency trials than on the low-contingency trials-the contingency learning effect. Across five groups, we varied the high-contingency proportion in 10% steps, from 80% to 40%. The size of the contingency learning effect was positively related to high-contingency proportion, with the effect disappearing when high contingency was reduced to 40%. At the two highest contingency proportions, the magnitude of the effect increased over trials, the pattern suggesting that there was an increasing cost for the low-contingency trials rather than an increasing benefit for the high-contingency trials. Overall, the results fit a modified version of Schmidt's (2013, Acta Psychologica, 142, 119-126) parallel episodic processing account in which prior trial instances are routinely retrieved from memory and influence current trial performance.

Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms.

PubMed

Ozcift, Akin; Gulten, Arif

2011-12-01

Improving accuracies of machine learning algorithms is vital in designing high performance computer-aided diagnosis (CADx) systems. Researches have shown that a base classifier performance might be enhanced by ensemble classification strategies. In this study, we construct rotation forest (RF) ensemble classifiers of 30 machine learning algorithms to evaluate their classification performances using Parkinson's, diabetes and heart diseases from literature. While making experiments, first the feature dimension of three datasets is reduced using correlation based feature selection (CFS) algorithm. Second, classification performances of 30 machine learning algorithms are calculated for three datasets. Third, 30 classifier ensembles are constructed based on RF algorithm to assess performances of respective classifiers with the same disease data. All the experiments are carried out with leave-one-out validation strategy and the performances of the 60 algorithms are evaluated using three metrics; classification accuracy (ACC), kappa error (KE) and area under the receiver operating characteristic (ROC) curve (AUC). Base classifiers succeeded 72.15%, 77.52% and 84.43% average accuracies for diabetes, heart and Parkinson's datasets, respectively. As for RF classifier ensembles, they produced average accuracies of 74.47%, 80.49% and 87.13% for respective diseases. RF, a newly proposed classifier ensemble algorithm, might be used to improve accuracy of miscellaneous machine learning algorithms to design advanced CADx systems. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Brain tumor image segmentation using kernel dictionary learning.

PubMed

Jeon Lee; Seung-Jun Kim; Rong Chen; Herskovits, Edward H

2015-08-01

Automated brain tumor image segmentation with high accuracy and reproducibility holds a big potential to enhance the current clinical practice. Dictionary learning (DL) techniques have been applied successfully to various image processing tasks recently. In this work, kernel extensions of the DL approach are adopted. Both reconstructive and discriminative versions of the kernel DL technique are considered, which can efficiently incorporate multi-modal nonlinear feature mappings based on the kernel trick. Our novel discriminative kernel DL formulation allows joint learning of a task-driven kernel-based dictionary and a linear classifier using a K-SVD-type algorithm. The proposed approaches were tested using real brain magnetic resonance (MR) images of patients with high-grade glioma. The obtained preliminary performances are competitive with the state of the art. The discriminative kernel DL approach is seen to reduce computational burden without much sacrifice in performance.

Theory of mind selectively predicts preschoolers’ knowledge-based selective word learning

PubMed Central

Brosseau-Liard, Patricia; Penney, Danielle; Poulin-Dubois, Diane

2015-01-01

Children can selectively attend to various attributes of a model, such as past accuracy or physical strength, to guide their social learning. There is a debate regarding whether a relation exists between theory-of-mind skills and selective learning. We hypothesized that high performance on theory-of-mind tasks would predict preference for learning new words from accurate informants (an epistemic attribute), but not from physically strong informants (a non-epistemic attribute). Three- and 4-year-olds (N = 65) completed two selective learning tasks, and their theory of mind abilities were assessed. As expected, performance on a theory-of-mind battery predicted children’s preference to learn from more accurate informants but not from physically stronger informants. Results thus suggest that preschoolers with more advanced theory of mind have a better understanding of knowledge and apply that understanding to guide their selection of informants. This work has important implications for research on children’s developing social cognition and early learning. PMID:26211504

Theory of mind selectively predicts preschoolers' knowledge-based selective word learning.

PubMed

Brosseau-Liard, Patricia; Penney, Danielle; Poulin-Dubois, Diane

2015-11-01

Children can selectively attend to various attributes of a model, such as past accuracy or physical strength, to guide their social learning. There is a debate regarding whether a relation exists between theory-of-mind skills and selective learning. We hypothesized that high performance on theory-of-mind tasks would predict preference for learning new words from accurate informants (an epistemic attribute), but not from physically strong informants (a non-epistemic attribute). Three- and 4-year-olds (N = 65) completed two selective learning tasks, and their theory-of-mind abilities were assessed. As expected, performance on a theory-of-mind battery predicted children's preference to learn from more accurate informants but not from physically stronger informants. Results thus suggest that preschoolers with more advanced theory of mind have a better understanding of knowledge and apply that understanding to guide their selection of informants. This work has important implications for research on children's developing social cognition and early learning. © 2015 The British Psychological Society.

EEG Beta Power but Not Background Music Predicts the Recall Scores in a Foreign-Vocabulary Learning Task.

PubMed

Küssner, Mats B; de Groot, Annette M B; Hofman, Winni F; Hillen, Marij A

2016-01-01

As tantalizing as the idea that background music beneficially affects foreign vocabulary learning may seem, there is-partly due to a lack of theory-driven research-no consistent evidence to support this notion. We investigated inter-individual differences in the effects of background music on foreign vocabulary learning. Based on Eysenck's theory of personality we predicted that individuals with a high level of cortical arousal should perform worse when learning with background music compared to silence, whereas individuals with a low level of cortical arousal should be unaffected by background music or benefit from it. Participants were tested in a paired-associate learning paradigm consisting of three immediate word recall tasks, as well as a delayed recall task one week later. Baseline cortical arousal assessed with spontaneous EEG measurement in silence prior to the learning rounds was used for the analyses. Results revealed no interaction between cortical arousal and the learning condition (background music vs. silence). Instead, we found an unexpected main effect of cortical arousal in the beta band on recall, indicating that individuals with high beta power learned more vocabulary than those with low beta power. To substantiate this finding we conducted an exact replication of the experiment. Whereas the main effect of cortical arousal was only present in a subsample of participants, a beneficial main effect of background music appeared. A combined analysis of both experiments suggests that beta power predicts the performance in the word recall task, but that there is no effect of background music on foreign vocabulary learning. In light of these findings, we discuss whether searching for effects of background music on foreign vocabulary learning, independent of factors such as inter-individual differences and task complexity, might be a red herring. Importantly, our findings emphasize the need for sufficiently powered research designs and exact replications of theory-driven experiments when investigating effects of background music and inter-individual variation on task performance.

EEG Beta Power but Not Background Music Predicts the Recall Scores in a Foreign-Vocabulary Learning Task

PubMed Central

de Groot, Annette M. B.; Hofman, Winni F.; Hillen, Marij A.

2016-01-01

As tantalizing as the idea that background music beneficially affects foreign vocabulary learning may seem, there is—partly due to a lack of theory-driven research—no consistent evidence to support this notion. We investigated inter-individual differences in the effects of background music on foreign vocabulary learning. Based on Eysenck’s theory of personality we predicted that individuals with a high level of cortical arousal should perform worse when learning with background music compared to silence, whereas individuals with a low level of cortical arousal should be unaffected by background music or benefit from it. Participants were tested in a paired-associate learning paradigm consisting of three immediate word recall tasks, as well as a delayed recall task one week later. Baseline cortical arousal assessed with spontaneous EEG measurement in silence prior to the learning rounds was used for the analyses. Results revealed no interaction between cortical arousal and the learning condition (background music vs. silence). Instead, we found an unexpected main effect of cortical arousal in the beta band on recall, indicating that individuals with high beta power learned more vocabulary than those with low beta power. To substantiate this finding we conducted an exact replication of the experiment. Whereas the main effect of cortical arousal was only present in a subsample of participants, a beneficial main effect of background music appeared. A combined analysis of both experiments suggests that beta power predicts the performance in the word recall task, but that there is no effect of background music on foreign vocabulary learning. In light of these findings, we discuss whether searching for effects of background music on foreign vocabulary learning, independent of factors such as inter-individual differences and task complexity, might be a red herring. Importantly, our findings emphasize the need for sufficiently powered research designs and exact replications of theory-driven experiments when investigating effects of background music and inter-individual variation on task performance. PMID:27537520

Automated Decomposition of Model-based Learning Problems

NASA Technical Reports Server (NTRS)

Williams, Brian C.; Millar, Bill

1996-01-01

A new generation of sensor rich, massively distributed autonomous systems is being developed that has the potential for unprecedented performance, such as smart buildings, reconfigurable factories, adaptive traffic systems and remote earth ecosystem monitoring. To achieve high performance these massive systems will need to accurately model themselves and their environment from sensor information. Accomplishing this on a grand scale requires automating the art of large-scale modeling. This paper presents a formalization of [\\em decompositional model-based learning (DML)], a method developed by observing a modeler's expertise at decomposing large scale model estimation tasks. The method exploits a striking analogy between learning and consistency-based diagnosis. Moriarty, an implementation of DML, has been applied to thermal modeling of a smart building, demonstrating a significant improvement in learning rate.

Improving Accuracy and Temporal Resolution of Learning Curve Estimation for within- and across-Session Analysis

PubMed Central

Tabelow, Karsten; König, Reinhard; Polzehl, Jörg

2016-01-01

Estimation of learning curves is ubiquitously based on proportions of correct responses within moving trial windows. Thereby, it is tacitly assumed that learning performance is constant within the moving windows, which, however, is often not the case. In the present study we demonstrate that violations of this assumption lead to systematic errors in the analysis of learning curves, and we explored the dependency of these errors on window size, different statistical models, and learning phase. To reduce these errors in the analysis of single-subject data as well as on the population level, we propose adequate statistical methods for the estimation of learning curves and the construction of confidence intervals, trial by trial. Applied to data from an avoidance learning experiment with rodents, these methods revealed performance changes occurring at multiple time scales within and across training sessions which were otherwise obscured in the conventional analysis. Our work shows that the proper assessment of the behavioral dynamics of learning at high temporal resolution can shed new light on specific learning processes, and, thus, allows to refine existing learning concepts. It further disambiguates the interpretation of neurophysiological signal changes recorded during training in relation to learning. PMID:27303809

Rapid word-learning in normal-hearing and hearing-impaired children: effects of age, receptive vocabulary, and high-frequency amplification.

PubMed

Pittman, A L; Lewis, D E; Hoover, B M; Stelmachowicz, P G

2005-12-01

This study examined rapid word-learning in 5- to 14-year-old children with normal and impaired hearing. The effects of age and receptive vocabulary were examined as well as those of high-frequency amplification. Novel words were low-pass filtered at 4 kHz (typical of current amplification devices) and at 9 kHz. It was hypothesized that (1) the children with normal hearing would learn more words than the children with hearing loss, (2) word-learning would increase with age and receptive vocabulary for both groups, and (3) both groups would benefit from a broader frequency bandwidth. Sixty children with normal hearing and 37 children with moderate sensorineural hearing losses participated in this study. Each child viewed a 4-minute animated slideshow containing 8 nonsense words created using the 24 English consonant phonemes (3 consonants per word). Each word was repeated 3 times. Half of the 8 words were low-pass filtered at 4 kHz and half were filtered at 9 kHz. After viewing the story twice, each child was asked to identify the words from among pictures in the slide show. Before testing, a measure of current receptive vocabulary was obtained using the Peabody Picture Vocabulary Test (PPVT-III). The PPVT-III scores of the hearing-impaired children were consistently poorer than those of the normal-hearing children across the age range tested. A similar pattern of results was observed for word-learning in that the performance of the hearing-impaired children was significantly poorer than that of the normal-hearing children. Further analysis of the PPVT and word-learning scores suggested that although word-learning was reduced in the hearing-impaired children, their performance was consistent with their receptive vocabularies. Additionally, no correlation was found between overall performance and the age of identification, age of amplification, or years of amplification in the children with hearing loss. Results also revealed a small increase in performance for both groups in the extended bandwidth condition but the difference was not significant at the traditional p = 0.05 level. The ability to learn words rapidly appears to be poorer in children with hearing loss over a wide range of ages. These results coincide with the consistently poorer receptive vocabularies for these children. Neither the word-learning or receptive-vocabulary measures were related to the amplification histories of these children. Finally, providing an extended high-frequency bandwidth did not significantly improve rapid word-learning for either group with these stimuli.

Performance and Costs of Ductless Heat Pumps in Marine-Climate High-Performance Homes -- Habitat for Humanity The Woods

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

Lubliner, Michael; Howard, Luke; Hales, David

The Woods is a Habitat for Humanity (HFH) community of ENERGY STAR Homes Northwest (ESHNW)-certified homes located in the marine climate of Tacoma/Pierce County, Washington. This research report builds on an earlier preliminary draft 2014 BA report, and includes significant billing analysis and cost effectiveness research from a collaborative, ongoing Ductless Heat Pump (DHP)research effort for Tacoma Public Utilities (TPU) and Bonneville Power Administration (BPA). This report focuses on the results of field testing, modeling, and monitoring of ductless mini-split heat pump hybrid heating systems in seven homes built and first occupied at various times between September 2013 and Octobermore » 2014. The report also provides WSU documentation of high-performance home observations, lessons learned, and stakeholder recommendations for builders of affordable high-performance housing such as HFH. Tacoma Public Utilities (TPU) and Bonneville Power Administration (BPA). This report focuses on the results of field testing, modeling, and monitoring of ductless mini-split heat pump hybrid heating systems in seven homes built and first occupied at various times between September 2013 and October 2014. The report also provides WSU documentation of high-performance home observations, lessons learned, and stakeholder recommendations for builders of affordable high-performance housing such as HFH.« less

Not all anxious individuals get lost: Trait anxiety and mental rotation ability interact to explain performance in map-based route learning in men.

PubMed

Thoresen, John C; Francelet, Rebecca; Coltekin, Arzu; Richter, Kai-Florian; Fabrikant, Sara I; Sandi, Carmen

2016-07-01

Navigation through an environment is a fundamental human activity. Although group differences in navigational ability are documented (e.g., gender), little is known about traits that predict these abilities. Apart from a well-established link between mental rotational abilities and navigational learning abilities, recent studies point to an influence of trait anxiety on the formation of internal cognitive spatial representations. However, it is unknown whether trait anxiety affects the processing of information obtained through externalized representations such as maps. Here, we addressed this question by taking into account emerging evidence indicating impaired performance in executive tasks by high trait anxiety specifically in individuals with lower executive capacities. For this purpose, we tested 104 male participants, previously characterised on trait anxiety and mental rotation ability, on a newly-designed map-based route learning task, where participants matched routes presented dynamically on a city map to one presented immediately before (same/different judgments). We predicted an interaction between trait anxiety and mental rotation ability, specifically that performance in the route learning task would be negatively affected by anxiety in participants with low mental rotation ability. Importantly, and as predicted, an interaction between anxiety and mental rotation ability was observed: trait anxiety negatively affected participants with low-but not high-mental rotation ability. Our study reveals a detrimental role of trait anxiety in map-based route learning and specifies a disadvantage in the processing of map representations for high-anxious individuals with low mental rotation abilities. Copyright © 2016 Elsevier Inc. All rights reserved.

Supervised Machine Learning Algorithms Can Classify Open-Text Feedback of Doctor Performance With Human-Level Accuracy

PubMed Central

2017-01-01

Background Machine learning techniques may be an effective and efficient way to classify open-text reports on doctor’s activity for the purposes of quality assurance, safety, and continuing professional development. Objective The objective of the study was to evaluate the accuracy of machine learning algorithms trained to classify open-text reports of doctor performance and to assess the potential for classifications to identify significant differences in doctors’ professional performance in the United Kingdom. Methods We used 1636 open-text comments (34,283 words) relating to the performance of 548 doctors collected from a survey of clinicians’ colleagues using the General Medical Council Colleague Questionnaire (GMC-CQ). We coded 77.75% (1272/1636) of the comments into 5 global themes (innovation, interpersonal skills, popularity, professionalism, and respect) using a qualitative framework. We trained 8 machine learning algorithms to classify comments and assessed their performance using several training samples. We evaluated doctor performance using the GMC-CQ and compared scores between doctors with different classifications using t tests. Results Individual algorithm performance was high (range F score=.68 to .83). Interrater agreement between the algorithms and the human coder was highest for codes relating to “popular” (recall=.97), “innovator” (recall=.98), and “respected” (recall=.87) codes and was lower for the “interpersonal” (recall=.80) and “professional” (recall=.82) codes. A 10-fold cross-validation demonstrated similar performance in each analysis. When combined together into an ensemble of multiple algorithms, mean human-computer interrater agreement was .88. Comments that were classified as “respected,” “professional,” and “interpersonal” related to higher doctor scores on the GMC-CQ compared with comments that were not classified (P<.05). Scores did not vary between doctors who were rated as popular or innovative and those who were not rated at all (P>.05). Conclusions Machine learning algorithms can classify open-text feedback of doctor performance into multiple themes derived by human raters with high performance. Colleague open-text comments that signal respect, professionalism, and being interpersonal may be key indicators of doctor’s performance. PMID:28298265

Supervised Machine Learning Algorithms Can Classify Open-Text Feedback of Doctor Performance With Human-Level Accuracy.

PubMed

Gibbons, Chris; Richards, Suzanne; Valderas, Jose Maria; Campbell, John

2017-03-15

Machine learning techniques may be an effective and efficient way to classify open-text reports on doctor's activity for the purposes of quality assurance, safety, and continuing professional development. The objective of the study was to evaluate the accuracy of machine learning algorithms trained to classify open-text reports of doctor performance and to assess the potential for classifications to identify significant differences in doctors' professional performance in the United Kingdom. We used 1636 open-text comments (34,283 words) relating to the performance of 548 doctors collected from a survey of clinicians' colleagues using the General Medical Council Colleague Questionnaire (GMC-CQ). We coded 77.75% (1272/1636) of the comments into 5 global themes (innovation, interpersonal skills, popularity, professionalism, and respect) using a qualitative framework. We trained 8 machine learning algorithms to classify comments and assessed their performance using several training samples. We evaluated doctor performance using the GMC-CQ and compared scores between doctors with different classifications using t tests. Individual algorithm performance was high (range F score=.68 to .83). Interrater agreement between the algorithms and the human coder was highest for codes relating to "popular" (recall=.97), "innovator" (recall=.98), and "respected" (recall=.87) codes and was lower for the "interpersonal" (recall=.80) and "professional" (recall=.82) codes. A 10-fold cross-validation demonstrated similar performance in each analysis. When combined together into an ensemble of multiple algorithms, mean human-computer interrater agreement was .88. Comments that were classified as "respected," "professional," and "interpersonal" related to higher doctor scores on the GMC-CQ compared with comments that were not classified (P<.05). Scores did not vary between doctors who were rated as popular or innovative and those who were not rated at all (P>.05). Machine learning algorithms can classify open-text feedback of doctor performance into multiple themes derived by human raters with high performance. Colleague open-text comments that signal respect, professionalism, and being interpersonal may be key indicators of doctor's performance. ©Chris Gibbons, Suzanne Richards, Jose Maria Valderas, John Campbell. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 15.03.2017.

Lessons from High-Performing Hispanic Schools: Creating Learning Communities. Critical Issues in Educational Leadership Series.

ERIC Educational Resources Information Center

Reyes, Pedro, Ed.; Scribner, Jay D., Ed.; Scribner, Alicia Paredes, Ed.

The current poor condition of education for Hispanic students need not exist. This book reports on high-performing schools along the Texas-Mexico border that have achieved schoolwide success by creating communities of learners. Three elementary, three middle, and two high schools in the border region were selected for study based on the following…

Effect of a Cooperative Learning Technique on the Academic Performance of High School Students in Mathematics

ERIC Educational Resources Information Center

Idowu, Olumuyiwa Ayodeji

2013-01-01

Over the past 2 years, almost 45% of the students attending a local suburban high school failed Algebra 2. The purpose of this study was to compare the impact of a cooperative instructional technique (student teams-achievement divisions [STAD]) to traditional instructional methods on performance in high school algebra. Motivational and cognitive…

Deep Learning for Automated Extraction of Primary Sites from Cancer Pathology Reports

DOE PAGES

Qiu, John; Yoon, Hong-Jun; Fearn, Paul A.; ...

2017-05-03

Pathology reports are a primary source of information for cancer registries which process high volumes of free-text reports annually. Information extraction and coding is a manual, labor-intensive process. Here in this study we investigated deep learning and a convolutional neural network (CNN), for extracting ICDO- 3 topographic codes from a corpus of breast and lung cancer pathology reports. We performed two experiments, using a CNN and a more conventional term frequency vector approach, to assess the effects of class prevalence and inter-class transfer learning. The experiments were based on a set of 942 pathology reports with human expert annotations asmore » the gold standard. CNN performance was compared against a more conventional term frequency vector space approach. We observed that the deep learning models consistently outperformed the conventional approaches in the class prevalence experiment, resulting in micro and macro-F score increases of up to 0.132 and 0.226 respectively when class labels were well populated. Specifically, the best performing CNN achieved a micro-F score of 0.722 over 12 ICD-O-3 topography codes. Transfer learning provided a consistent but modest performance boost for the deep learning methods but trends were contingent on CNN method and cancer site. Finally, these encouraging results demonstrate the potential of deep learning for automated abstraction of pathology reports.« less

Deep Learning for Automated Extraction of Primary Sites from Cancer Pathology Reports

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

Qiu, John; Yoon, Hong-Jun; Fearn, Paul A.

Pathology reports are a primary source of information for cancer registries which process high volumes of free-text reports annually. Information extraction and coding is a manual, labor-intensive process. Here in this study we investigated deep learning and a convolutional neural network (CNN), for extracting ICDO- 3 topographic codes from a corpus of breast and lung cancer pathology reports. We performed two experiments, using a CNN and a more conventional term frequency vector approach, to assess the effects of class prevalence and inter-class transfer learning. The experiments were based on a set of 942 pathology reports with human expert annotations asmore » the gold standard. CNN performance was compared against a more conventional term frequency vector space approach. We observed that the deep learning models consistently outperformed the conventional approaches in the class prevalence experiment, resulting in micro and macro-F score increases of up to 0.132 and 0.226 respectively when class labels were well populated. Specifically, the best performing CNN achieved a micro-F score of 0.722 over 12 ICD-O-3 topography codes. Transfer learning provided a consistent but modest performance boost for the deep learning methods but trends were contingent on CNN method and cancer site. Finally, these encouraging results demonstrate the potential of deep learning for automated abstraction of pathology reports.« less

Tracking Plasticity: Effects of Long-Term Rehearsal in Expert Dancers Encoding Music to Movement

PubMed Central

Bar, Rachel J.; DeSouza, Joseph F. X.

2016-01-01

Our knowledge of neural plasticity suggests that neural networks show adaptation to environmental and intrinsic change. In particular, studies investigating the neuroplastic changes associated with learning and practicing motor tasks have shown that practicing such tasks results in an increase in neural activation in several specific brain regions. However, studies comparing experts and non-experts suggest that experts employ less neuronal activation than non-experts when performing a familiar motor task. Here, we aimed to determine the long-term changes in neural networks associated with learning a new dance in professional ballet dancers over 34 weeks. Subjects visualized dance movements to music while undergoing fMRI scanning at four time points over 34-weeks. Results demonstrated that initial learning and performance at seven weeks led to increases in activation in cortical regions during visualization compared to the first week. However, at 34 weeks, the cortical networks showed reduced activation compared to week seven. Specifically, motor learning and performance over the 34 weeks showed the typical inverted-U-shaped function of learning. Further, our result demonstrate that learning of a motor sequence of dance movements to music in the real world can be visualized by expert dancers using fMRI and capture highly significant modeled fits of the brain network variance of BOLD signals from early learning to expert level performance. PMID:26824475

Teaching About "Brain and Learning" in High School Biology Classes: Effects on Teachers' Knowledge and Students' Theory of Intelligence.

PubMed

Dekker, Sanne; Jolles, Jelle

2015-01-01

This study evaluated a new teaching module about "Brain and Learning" using a controlled design. The module was implemented in high school biology classes and comprised three lessons: (1) brain processes underlying learning; (2) neuropsychological development during adolescence; and (3) lifestyle factors that influence learning performance. Participants were 32 biology teachers who were interested in "Brain and Learning" and 1241 students in grades 8-9. Teachers' knowledge and students' beliefs about learning potential were examined using online questionnaires. Results indicated that before intervention, biology teachers were significantly less familiar with how the brain functions and develops than with its structure and with basic neuroscientific concepts (46 vs. 75% correct answers). After intervention, teachers' knowledge of "Brain and Learning" had significantly increased (64%), and more students believed that intelligence is malleable (incremental theory). This emphasizes the potential value of a short teaching module, both for improving biology teachers' insights into "Brain and Learning," and for changing students' beliefs about intelligence.

Self-directed learning readiness of Asian students: students perspective on a hybrid problem based learning curriculum

PubMed Central

Susilo, Astrid P.; van Berkel, Henk

2016-01-01

Objectives To identify the student’s readiness to perform self-directed learning and the underlying factors influencing it on the hybrid problem based learning curriculum. Methods A combination of quantitative and qualitative studies was conducted in five medical schools in Indonesia. In the quantitative study, the Self Directed Learning Readiness Scale was distributed to all students in all batches, who had experience with the hybrid problem based curriculum. They were categorized into low- and high -level based on the score of the questionnaire. Three focus group discussions (low-, high-, and mixed level) were conducted in the qualitative study with six to twelve students chosen randomly from each group to find the factors influencing their self-directed learning readiness. Two researchers analysed the qualitative data as a measure of triangulation. Results The quantitative study showed only half of the students had a high-level of self-directed learning readiness, and a similar trend also occurred in each batch. The proportion of students with a high level of self-directed learning readiness was lower in the senior students compared to more junior students. The qualitative study showed that problem based learning processes, assessments, learning environment, students’ life styles, students’ perceptions of the topics, and mood, were factors influencing their self-directed learning. Conclusion A hybrid problem based curriculum may not fully affect the students’ self-directed learning. The curriculum system, teacher’s experiences, student’s background and cultural factors might contribute to the difficulties for the student’s in conducting self-directed learning. PMID:27915308

Spatial learning while navigating with severely degraded viewing: The role of attention and mobility monitoring

PubMed Central

Rand, Kristina M.; Creem-Regehr, Sarah H.; Thompson, William B.

2015-01-01

The ability to navigate without getting lost is an important aspect of quality of life. In five studies, we evaluated how spatial learning is affected by the increased demands of keeping oneself safe while walking with degraded vision (mobility monitoring). We proposed that safe low-vision mobility requires attentional resources, providing competition for those needed to learn a new environment. In Experiments 1 and 2 participants navigated along paths in a real-world indoor environment with simulated degraded vision or normal vision. Memory for object locations seen along the paths was better with normal compared to degraded vision. With degraded vision, memory was better when participants were guided by an experimenter (low monitoring demands) versus unguided (high monitoring demands). In Experiments 3 and 4, participants walked while performing an auditory task. Auditory task performance was superior with normal compared to degraded vision. With degraded vision, auditory task performance was better when guided compared to unguided. In Experiment 5, participants performed both the spatial learning and auditory tasks under degraded vision. Results showed that attention mediates the relationship between mobility-monitoring demands and spatial learning. These studies suggest that more attention is required and spatial learning is impaired when navigating with degraded viewing. PMID:25706766

The effects of humor on memory for non-sensical pictures.

PubMed

Takahashi, Masanobu; Inoue, Tomoyoshi

2009-09-01

Two experiments investigated the effects of humor on memory for non-sensical pictures. Each picture was given three labels that differed in the degree of humor: high, low, and no humor labels. In Experiment 1, the humor of the picture labels was manipulated between participants. Participants were shown 30 pictures for 10s each and were asked to rate the degree of humor of each picture. After the rating task, participants were asked to draw the pictures in an unexpected memory test. Performance in the memory test was best in the high humor label group, followed by the low and the no humor label groups. In Experiment 2, intention to learn (incidental versus intentional encoding tasks) as well as humor label was manipulated between the participants. In the incidental learning condition, the high humor group performed better than the low humor group, but in the intentional learning condition, there was no humor effect. The effects of humor on picture memory were discussed in terms of appraisal processing within a distinctiveness framework.

A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load

NASA Astrophysics Data System (ADS)

Zhang, Wei; Li, Chuanhao; Peng, Gaoliang; Chen, Yuanhang; Zhang, Zhujun

2018-02-01

In recent years, intelligent fault diagnosis algorithms using machine learning technique have achieved much success. However, due to the fact that in real world industrial applications, the working load is changing all the time and noise from the working environment is inevitable, degradation of the performance of intelligent fault diagnosis methods is very serious. In this paper, a new model based on deep learning is proposed to address the problem. Our contributions of include: First, we proposed an end-to-end method that takes raw temporal signals as inputs and thus doesn't need any time consuming denoising preprocessing. The model can achieve pretty high accuracy under noisy environment. Second, the model does not rely on any domain adaptation algorithm or require information of the target domain. It can achieve high accuracy when working load is changed. To understand the proposed model, we will visualize the learned features, and try to analyze the reasons behind the high performance of the model.

Rapid and Accurate Machine Learning Recognition of High Performing Metal Organic Frameworks for CO2 Capture.

PubMed

Fernandez, Michael; Boyd, Peter G; Daff, Thomas D; Aghaji, Mohammad Zein; Woo, Tom K

2014-09-04

In this work, we have developed quantitative structure-property relationship (QSPR) models using advanced machine learning algorithms that can rapidly and accurately recognize high-performing metal organic framework (MOF) materials for CO2 capture. More specifically, QSPR classifiers have been developed that can, in a fraction of a section, identify candidate MOFs with enhanced CO2 adsorption capacity (>1 mmol/g at 0.15 bar and >4 mmol/g at 1 bar). The models were tested on a large set of 292 050 MOFs that were not part of the training set. The QSPR classifier could recover 945 of the top 1000 MOFs in the test set while flagging only 10% of the whole library for compute intensive screening. Thus, using the machine learning classifiers as part of a high-throughput screening protocol would result in an order of magnitude reduction in compute time and allow intractably large structure libraries and search spaces to be screened.

Acute stress differentially affects spatial configuration learning in high and low cortisol-responding healthy adults

PubMed Central

Meyer, Thomas; Smeets, Tom; Giesbrecht, Timo; Quaedflieg, Conny W. E. M.; Merckelbach, Harald

2013-01-01

Background Stress and stress hormones modulate memory formation in various ways that are relevant to our understanding of stress-related psychopathology, such as posttraumatic stress disorder (PTSD). Particular relevance is attributed to efficient memory formation sustained by the hippocampus and parahippocampus. This process is thought to reduce the occurrence of intrusions and flashbacks following trauma, but may be negatively affected by acute stress. Moreover, recent evidence suggests that the efficiency of visuo-spatial processing and learning based on the hippocampal area is related to PTSD symptoms. Objective The current study investigated the effect of acute stress on spatial configuration learning using a spatial contextual cueing task (SCCT) known to heavily rely on structures in the parahippocampus. Method Acute stress was induced by subjecting participants (N = 34) to the Maastricht Acute Stress Test (MAST). Following a counterbalanced within-subject approach, the effects of stress and the ensuing hormonal (i.e., cortisol) activity on subsequent SCCT performance were compared to SCCT performance following a no-stress control condition. Results Acute stress did not impact SCCT learning overall, but opposing effects emerged for high versus low cortisol responders to the MAST. Learning scores following stress were reduced in low cortisol responders, while high cortisol-responding participants showed improved learning. Conclusions The effects of stress on spatial configuration learning were moderated by the magnitude of endogenous cortisol secretion. These findings suggest a possible mechanism by which cortisol responses serve an adaptive function during stress and trauma, and this may prove to be a promising route for future research in this area. PMID:23671762

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.

Factors Contributing to Problem-Solving Performance in First-Semester Organic Chemistry

ERIC Educational Resources Information Center

Lopez, Enrique J.; Shavelson, Richard J.; Nandagopal, Kiruthiga; Szu, Evan; Penn, John

2014-01-01

Problem solving is a highly valued skill in chemistry. Courses within this discipline place a substantial emphasis on problem-solving performance and tend to weigh such performance heavily in assessments of learning. Researchers have dedicated considerable effort investigating individual factors that influence problem-solving performance. The…

Barriers and facilitators to learning and performing cardiopulmonary resuscitation in neighborhoods with low bystander cardiopulmonary resuscitation prevalence and high rates of cardiac arrest in Columbus, OH.

PubMed

Sasson, Comilla; Haukoos, Jason S; Bond, Cindy; Rabe, Marilyn; Colbert, Susan H; King, Renee; Sayre, Michael; Heisler, Michele

2013-09-01

Residents who live in neighborhoods that are primarily black, Latino, or poor are more likely to have an out-of-hospital cardiac arrest, less likely to receive cardiopulmonary resuscitation (CPR), and less likely to survive. No prior studies have been conducted to understand the contributing factors that may decrease the likelihood of residents learning and performing CPR in these neighborhoods. The goal of this study was to identify barriers and facilitators to learning and performing CPR in 3 low-income, high-risk, and predominantly black neighborhoods in Columbus, OH. Community-Based Participatory Research approaches were used to develop and conduct 6 focus groups in conjunction with community partners in 3 target high-risk neighborhoods in Columbus, OH, in January to February 2011. Snowball and purposeful sampling, done by community liaisons, was used to recruit participants. Three reviewers analyzed the data in an iterative process to identify recurrent and unifying themes. Three major barriers to learning CPR were identified and included financial, informational, and motivational factors. Four major barriers were identified for performing CPR and included fear of legal consequences, emotional issues, knowledge, and situational concerns. Participants suggested that family/self-preservation, emotional, and economic factors may serve as potential facilitators in increasing the provision of bystander CPR. The financial cost of CPR training, lack of information, and the fear of risking one's own life must be addressed when designing a community-based CPR educational program. Using data from the community can facilitate improved design and implementation of CPR programs.

Deep generative learning for automated EHR diagnosis of traditional Chinese medicine.

PubMed

Liang, Zhaohui; Liu, Jun; Ou, Aihua; Zhang, Honglai; Li, Ziping; Huang, Jimmy Xiangji

2018-05-04

Computer-aided medical decision-making (CAMDM) is the method to utilize massive EMR data as both empirical and evidence support for the decision procedure of healthcare activities. Well-developed information infrastructure, such as hospital information systems and disease surveillance systems, provides abundant data for CAMDM. However, the complexity of EMR data with abstract medical knowledge makes the conventional model incompetent for the analysis. Thus a deep belief networks (DBN) based model is proposed to simulate the information analysis and decision-making procedure in medical practice. The purpose of this paper is to evaluate a deep learning architecture as an effective solution for CAMDM. A two-step model is applied in our study. At the first step, an optimized seven-layer deep belief network (DBN) is applied as an unsupervised learning algorithm to perform model training to acquire feature representation. Then a support vector machine model is adopted to DBN at the second step of the supervised learning. There are two data sets used in the experiments. One is a plain text data set indexed by medical experts. The other is a structured dataset on primary hypertension. The data are randomly divided to generate the training set for the unsupervised learning and the testing set for the supervised learning. The model performance is evaluated by the statistics of mean and variance, the average precision and coverage on the data sets. Two conventional shallow models (support vector machine / SVM and decision tree / DT) are applied as the comparisons to show the superiority of our proposed approach. The deep learning (DBN + SVM) model outperforms simple SVM and DT on two data sets in terms of all the evaluation measures, which confirms our motivation that the deep model is good at capturing the key features with less dependence when the index is built up by manpower. Our study shows the two-step deep learning model achieves high performance for medical information retrieval over the conventional shallow models. It is able to capture the features of both plain text and the highly-structured database of EMR data. The performance of the deep model is superior to the conventional shallow learning models such as SVM and DT. It is an appropriate knowledge-learning model for information retrieval of EMR system. Therefore, deep learning provides a good solution to improve the performance of CAMDM systems. Copyright © 2018. Published by Elsevier B.V.

The effects of concept and vee mappings under three learning modes on Jamaican eighth graders' knowledge of nutrition and plant reproduction

NASA Astrophysics Data System (ADS)

Ugwu, Okechukwu; Soyibo, Kola

2004-01-01

The first objective of this study was to investigate if the experimental students' post-test knowledge of nutrition and plant reproduction would be improved more significantly than that of their control group counterparts based on their treatment, attitudes to science, self-esteem, gender and socio-economic background. Treatment involved teaching the experimental students under three learning modes--pure cooperative, cooperative-competitive and individualistic whole class interpersonal competitive condition--using concept and vee mappings and the lecture method. The control groups received the same treatment but were not exposed to concept and vee mappings. This study's second objective was to determine which of the three learning modes would produce the highest post-test mean gain in the subjects' knowledge of the two biology concepts. The study's sample comprised 932 eighth graders (12-13-year-olds) in 14 co-educational comprehensive high schools randomly selected from two Jamaican parishes. An integrated science performance test, an attitudes to science questionnaire and a self-esteem questionnaire were used to collect data. The results indicated that the experimental students (a) under the three learning modes, (b) with high, moderate, and low attitudes to science, and (c) with high, moderate, and low self-esteem, performed significantly better than their control group counterparts. The individualist whole class learning mode engendered the highest mean gain on the experimental students' knowledge, while the cooperative-competitive learning mode generated the highest mean gain for the control group students.

Correlating Student Interest and High School Preparation with Learning and Performance in an Introductory University Physics Course

ERIC Educational Resources Information Center

Harlow, Jason J.?B.; Harrison, David M.; Meyertholen, Andrew

2014-01-01

We have studied the correlation of student performance in a large first year university physics course with their reasons for taking the course and whether or not the student took a senior-level high school physics course. Performance was measured both by the Force Concept Inventory and by the grade on the final examination. Students who took the…

User Account Passwords | High-Performance Computing | NREL

Science.gov Websites

Account Passwords User Account Passwords For NREL's high-performance computing (HPC) systems, learn about user account password requirements and how to set up, log in, and change passwords. Password Logging In the First Time After you request an HPC user account, you'll receive a temporary password. Set

DCL System Using Deep Learning Approaches for Land-Based or Ship-Based Real Time Recognition and Localization of Marine Mammals

DTIC Science & Technology

2015-09-30

Clark (2014), "Using High Performance Computing to Explore Large Complex Bioacoustic Soundscapes : Case Study for Right Whale Acoustics," Procedia...34Using High Performance Computing to Explore Large Complex Bioacoustic Soundscapes : Case Study for Right Whale Acoustics," Procedia Computer Science 20

Correlation between the McREL Leadership Practices and High and Low Performing Schools

ERIC Educational Resources Information Center

Abuyen, Jocelyn Lodronio

2016-01-01

This study explored elementary school principal leadership responsibilities in one school district in San Diego County. The researcher sought to examine which of the 21 leadership responsibilities and second-order change behaviors identified by Mid-continent Research for Education and Learning (McREL) do principals of high-performing schools…

Do pre-trained deep learning models improve computer-aided classification of digital mammograms?

NASA Astrophysics Data System (ADS)

Aboutalib, Sarah S.; Mohamed, Aly A.; Zuley, Margarita L.; Berg, Wendie A.; Luo, Yahong; Wu, Shandong

2018-02-01

Digital mammography screening is an important exam for the early detection of breast cancer and reduction in mortality. False positives leading to high recall rates, however, results in unnecessary negative consequences to patients and health care systems. In order to better aid radiologists, computer-aided tools can be utilized to improve distinction between image classifications and thus potentially reduce false recalls. The emergence of deep learning has shown promising results in the area of biomedical imaging data analysis. This study aimed to investigate deep learning and transfer learning methods that can improve digital mammography classification performance. In particular, we evaluated the effect of pre-training deep learning models with other imaging datasets in order to boost classification performance on a digital mammography dataset. Two types of datasets were used for pre-training: (1) a digitized film mammography dataset, and (2) a very large non-medical imaging dataset. By using either of these datasets to pre-train the network initially, and then fine-tuning with the digital mammography dataset, we found an increase in overall classification performance in comparison to a model without pre-training, with the very large non-medical dataset performing the best in improving the classification accuracy.

Sequential Nonlinear Learning for Distributed Multiagent Systems via Extreme Learning Machines.

PubMed

Vanli, Nuri Denizcan; Sayin, Muhammed O; Delibalta, Ibrahim; Kozat, Suleyman Serdar

2017-03-01

We study online nonlinear learning over distributed multiagent systems, where each agent employs a single hidden layer feedforward neural network (SLFN) structure to sequentially minimize arbitrary loss functions. In particular, each agent trains its own SLFN using only the data that is revealed to itself. On the other hand, the aim of the multiagent system is to train the SLFN at each agent as well as the optimal centralized batch SLFN that has access to all the data, by exchanging information between neighboring agents. We address this problem by introducing a distributed subgradient-based extreme learning machine algorithm. The proposed algorithm provides guaranteed upper bounds on the performance of the SLFN at each agent and shows that each of these individual SLFNs asymptotically achieves the performance of the optimal centralized batch SLFN. Our performance guarantees explicitly distinguish the effects of data- and network-dependent parameters on the convergence rate of the proposed algorithm. The experimental results illustrate that the proposed algorithm achieves the oracle performance significantly faster than the state-of-the-art methods in the machine learning and signal processing literature. Hence, the proposed method is highly appealing for the applications involving big data.

Intrinsic Motivation: An Overlooked Component for Student Success

ERIC Educational Resources Information Center

Augustyniak, Robert A.; Ables, Adrienne Z.; Guilford, Philip; Lujan, Heidi L.; Cortright, Ronald N.; DiCarlo, Stephen E.

2016-01-01

Intrinsic motivation to learn involves engaging in learning opportunities because they are seen as enjoyable, interesting, or relevant to meeting one's core psychological needs. As a result, intrinsic motivation is associated with high levels of effort and task performance. Students with greater levels of intrinsic motivation demonstrate strong…

Social and Emotional Learning in the Classroom: Promoting Mental Health and Academic Success

ERIC Educational Resources Information Center

Merrell, Kenneth W.; Gueldner, Barbara A.

2010-01-01

This highly engaging, eminently practical book provides essential resources for implementing social and emotional learning (SEL) in any K-12 setting. Numerous vivid examples illustrate the nuts and bolts of this increasingly influential approach to supporting students' mental health, behavior, and academic performance. Helpful reproducibles are…

Pacesetter in Personalized Learning

ERIC Educational Resources Information Center

Jacobs, Joanne

2017-01-01

The Chicago International Charter School (CICS) Irving Park's middle school is one of 130 schools nationwide piloting the Summit Learning Program (SLP), developed--and offered entirely free--by Summit Public Schools, a high-performing charter network based in California. Summit's eight schools, two of them in Washington State, are known for an…

Some Psychometric and Design Implications of Game-Based Learning Analytics

ERIC Educational Resources Information Center

Gibson, David; Clarke-Midura, Jody

2013-01-01

The rise of digital game and simulation-based learning applications has led to new approaches in educational measurement that take account of patterns in time, high resolution paths of action, and clusters of virtual performance artifacts. The new approaches, which depart from traditional statistical analyses, include data mining, machine…

Partners in Learning: Teacher Leaders Drive Instructional Excellence

ERIC Educational Resources Information Center

Duff, Victoria; Islas, M. René

2013-01-01

New educator evaluation systems demand a focus on effective teaching and learning while promoting the professional growth of all teachers. By identifying and leveraging the contributions of high-performing teachers as instructional leaders, problem solvers, and decision-makers to lead improvement at the classroom level, the system builds capacity…

Discover the Possibilities

ERIC Educational Resources Information Center

Celeste, Eric

2016-01-01

The purpose of professional learning is for educators to develop the knowledge, skills, practices, and dispositions they need to help students perform at higher levels. It is not a simple matter to connect the dots between high quality professional learning and student outcomes. However, the theory of action that drives the standards, and indeed…

Correlates of Inquiry Learning in Science: Constructing Concepts of Density and Buoyancy.

ERIC Educational Resources Information Center

Mastropieri, Margo A.; Scruggs, Thomas E.; Boon, Richard; Carter, Karen Butcher

2001-01-01

A study involving 75 elementary students, 51 with high-incidence disabilities, investigated variables associated with learning in an inquiry-oriented approach to the study of density and buoyancy. Preconceptions, scientific predictions, and academic achievement measures were not predictive of task performance. However, grade level and IQ were…

Study Methods for Improving Quality Learning and Performance in Higher Education

ERIC Educational Resources Information Center

Mutsotso, S. N.; Abenga, E. S. B.

2010-01-01

Education is an investment to development and poor study methods should not compromise the mandate of higher education institutions to generate, preserve and disseminate knowledge and produce high quality graduates. Universities admit students with varying backgrounds in terms of learning/study styles, levels of preparedness and concepts of…

The Components of Good Acoustics in a High Performance School

ERIC Educational Resources Information Center

Stewart, William

2009-01-01

Acoustics has received greater importance in the learning environment in recent years. In August 2000, The Acoustical Society of America (ASA) published the study "Classroom Acoustics: A Resource for Creating Learning Environments with Desirable Listening Conditions" providing a framework for understanding the qualities, descriptors of the…

TALIS 2013 Technical Report: Teaching and Learning International Survey

ERIC Educational Resources Information Center

OECD Publishing, 2013

2013-01-01

Effective teaching and teachers are key to producing high-performing students worldwide. So how can countries prepare teachers to face the diverse challenges in today's schools? The Teaching and Learning International Survey (TALIS) helps answer this question. TALIS asks teachers and schools about their working conditions and the learning…

Effects of activation and blockade of NMDA receptors on the extinction of a conditioned passive avoidance response in mice with different levels of anxiety.

PubMed

Tomilenko, R A; Dubrovina, N I

2007-06-01

The effects of an agonist (D-cycloserine) and an antagonist (dizocilpine) of N-methyl-D-aspartate (NMDA) receptors on the learning and extinction of a conditioned passive avoidance response were studied in mice with low, intermediate, and high levels of anxiety. In intermediate-anxiety mice, D-cycloserine (30 mg/kg) had no effect on learning but accelerated extinction, while dizocilpine (0.15 mg/kg) degraded acquisition of the reflex but delayed extinction. In high-anxiety mice, with good learning and no extinction, D-cycloserine had no effect, while dizocilpine decreased learning and facilitated retention of performance of the memory trace at the ongoing level in conditions promoting extinction. In low-anxiety mice, D-cycloserine degraded learning and accelerated extinction, while dizocilpine completely blocked learning and the retention of the passive avoidance response.

Negative symptoms in schizophrenia result from a failure to represent the expected value of rewards: Behavioral and computational modeling evidence

PubMed Central

Gold, James M.; Waltz, James A.; Matveeva, Tatyana M.; Kasanova, Zuzana; Strauss, Gregory P.; Herbener, Ellen S.; Collins, Anne G.E.; Frank, Michael J.

2015-01-01

Context Negative symptoms are a core feature of schizophrenia, but their pathophysiology remains unclear. Objective Negative symptoms are defined by the absence of normal function. However, there must be a productive mechanism that leads to this absence. Here, we test a reinforcement learning account suggesting that negative symptoms result from a failure to represent the expected value of rewards coupled with preserved loss avoidance learning. Design Subjects performed a probabilistic reinforcement learning paradigm involving stimulus pairs in which choices resulted in either reward or avoidance of loss. Following training, subjects indicated their valuation of the stimuli in a transfer task. Computational modeling was used to distinguish between alternative accounts of the data. Setting A tertiary care research outpatient clinic. Patients A total of 47 clinically stable patients with a diagnosis of schizophrenia or schizoaffective disorder and 28 healthy volunteers participated. Patients were divided into high and low negative symptom groups. Main Outcome measures 1) The number of choices leading to reward or loss avoidance and 2) performance in the transfer phase. Quantitative fits from three different models were examined. Results High negative symptom patients demonstrated impaired learning from rewards but intact loss avoidance learning, and failed to distinguish rewarding stimuli from loss-avoiding stimuli in the transfer phase. Model fits revealed that high negative symptom patients were better characterized by an “actor-critic” model, learning stimulus-response associations, whereas controls and low negative symptom patients incorporated expected value of their actions (“Q-learning”) into the selection process. Conclusions Negative symptoms are associated with a specific reinforcement learning abnormality: High negative symptoms patients do not represent the expected value of rewards when making decisions but learn to avoid punishments through the use of prediction errors. This computational framework offers the potential to understand negative symptoms at a mechanistic level. PMID:22310503

Deep learning in breast cancer risk assessment: evaluation of convolutional neural networks on a clinical dataset of full-field digital mammograms.

PubMed

Li, Hui; Giger, Maryellen L; Huynh, Benjamin Q; Antropova, Natalia O

2017-10-01

To evaluate deep learning in the assessment of breast cancer risk in which convolutional neural networks (CNNs) with transfer learning are used to extract parenchymal characteristics directly from full-field digital mammographic (FFDM) images instead of using computerized radiographic texture analysis (RTA), 456 clinical FFDM cases were included: a "high-risk" BRCA1/2 gene-mutation carriers dataset (53 cases), a "high-risk" unilateral cancer patients dataset (75 cases), and a "low-risk dataset" (328 cases). Deep learning was compared to the use of features from RTA, as well as to a combination of both in the task of distinguishing between high- and low-risk subjects. Similar classification performances were obtained using CNN [area under the curve [Formula: see text]; standard error [Formula: see text

Beta Hebbian Learning as a New Method for Exploratory Projection Pursuit.

PubMed

Quintián, Héctor; Corchado, Emilio

2017-09-01

In this research, a novel family of learning rules called Beta Hebbian Learning (BHL) is thoroughly investigated to extract information from high-dimensional datasets by projecting the data onto low-dimensional (typically two dimensional) subspaces, improving the existing exploratory methods by providing a clear representation of data's internal structure. BHL applies a family of learning rules derived from the Probability Density Function (PDF) of the residual based on the beta distribution. This family of rules may be called Hebbian in that all use a simple multiplication of the output of the neural network with some function of the residuals after feedback. The derived learning rules can be linked to an adaptive form of Exploratory Projection Pursuit and with artificial distributions, the networks perform as the theory suggests they should: the use of different learning rules derived from different PDFs allows the identification of "interesting" dimensions (as far from the Gaussian distribution as possible) in high-dimensional datasets. This novel algorithm, BHL, has been tested over seven artificial datasets to study the behavior of BHL parameters, and was later applied successfully over four real datasets, comparing its results, in terms of performance, with other well-known Exploratory and projection models such as Maximum Likelihood Hebbian Learning (MLHL), Locally-Linear Embedding (LLE), Curvilinear Component Analysis (CCA), Isomap and Neural Principal Component Analysis (Neural PCA).

Dynamic neural networks based on-line identification and control of high performance motor drives

NASA Technical Reports Server (NTRS)

Rubaai, Ahmed; Kotaru, Raj

1995-01-01

In the automated and high-tech industries of the future, there wil be a need for high performance motor drives both in the low-power range and in the high-power range. To meet very straight demands of tracking and regulation in the two quadrants of operation, advanced control technologies are of a considerable interest and need to be developed. In response a dynamics learning control architecture is developed with simultaneous on-line identification and control. the feature of the proposed approach, to efficiently combine the dual task of system identification (learning) and adaptive control of nonlinear motor drives into a single operation is presented. This approach, therefore, not only adapts to uncertainties of the dynamic parameters of the motor drives but also learns about their inherent nonlinearities. In fact, most of the neural networks based adaptive control approaches in use have an identification phase entirely separate from the control phase. Because these approaches separate the identification and control modes, it is not possible to cope with dynamic changes in a controlled process. Extensive simulation studies have been conducted and good performance was observed. The robustness characteristics of neuro-controllers to perform efficiently in a noisy environment is also demonstrated. With this initial success, the principal investigator believes that the proposed approach with the suggested neural structure can be used successfully for the control of high performance motor drives. Two identification and control topologies based on the model reference adaptive control technique are used in this present analysis. No prior knowledge of load dynamics is assumed in either topology while the second topology also assumes no knowledge of the motor parameters.

Motor strategies and bilateral transfer in sensorimotor learning of patients with subacute stroke and healthy subjects. A randomized controlled trial.

PubMed

Iosa, M; Morone, G; Ragaglini, M R; Fusco, A; Paolucci, S

2013-06-01

Bilateral transfer, i.e. the capacity to transfer from one to the other hand a learned motor skill, may help the recovery of upper limb functions after stroke. To investigate the motor strategies at the basis of sensorimotor learning involved in bilateral transfer. Randomized controlled trial. Neurorehabilitation Hospital. Eighty right-handed participants (65 ± 13 years old): 40 patients with subacute stroke, 40 control healthy subjects. Subjects performed the 9 hole-peg-test twice in an order defined by random allocation: first with low and then with high skilled hand (LS-HS) or the reverse (HS-LS). Time spent to complete the test and filling sequence were recorded, together with maximum pinch force (assessed using a dynamometer), upper limb functioning (Motricity Index), spasticity (modified Ashworth Scale), limb dominance (Edinburgh Handeness Inventory). As expected, in patients, the performance was found related to the residual pinch force (P<0.001), upper limb motricity (P=0.006) and side of hemiparesis (P=0.016). The performances of all subjects improved more in HS-LS than in LS-HS subgroups (P=0.043). The strategy adopted in the first trial influenced the velocity in the second one (P=0.030). Bilateral transfer was observed from high to low skilled hand. Learning was not due to a mere sequence repetition, but on a strategy chosen on the basis of the previous performance. The affected hand of patients with subacute stroke may benefit from sensorimotor learning occurred with the un-affected hand.

Enhanced visual statistical learning in adults with autism

PubMed Central

Roser, Matthew E.; Aslin, Richard N.; McKenzie, Rebecca; Zahra, Daniel; Fiser, József

2014-01-01

Individuals with autism spectrum disorder (ASD) are often characterized as having social engagement and language deficiencies, but a sparing of visuo-spatial processing and short-term memory, with some evidence of supra-normal levels of performance in these domains. The present study expanded on this evidence by investigating the observational learning of visuospatial concepts from patterns of covariation across multiple exemplars. Child and adult participants with ASD, and age-matched control participants, viewed multi-shape arrays composed from a random combination of pairs of shapes that were each positioned in a fixed spatial arrangement. After this passive exposure phase, a post-test revealed that all participant groups could discriminate pairs of shapes with high covariation from randomly paired shapes with low covariation. Moreover, learning these shape-pairs with high covariation was superior in adults with ASD than in age-matched controls, while performance in children with ASD was no different than controls. These results extend previous observations of visuospatial enhancement in ASD into the domain of learning, and suggest that enhanced visual statistical learning may have arisen from a sustained bias to attend to local details in complex arrays of visual features. PMID:25151115

Social stress reactivity alters reward and punishment learning

PubMed Central

Frank, Michael J.; Allen, John J. B.

2011-01-01

To examine how stress affects cognitive functioning, individual differences in trait vulnerability (punishment sensitivity) and state reactivity (negative affect) to social evaluative threat were examined during concurrent reinforcement learning. Lower trait-level punishment sensitivity predicted better reward learning and poorer punishment learning; the opposite pattern was found in more punishment sensitive individuals. Increasing state-level negative affect was directly related to punishment learning accuracy in highly punishment sensitive individuals, but these measures were inversely related in less sensitive individuals. Combined electrophysiological measurement, performance accuracy and computational estimations of learning parameters suggest that trait and state vulnerability to stress alter cortico-striatal functioning during reinforcement learning, possibly mediated via medio-frontal cortical systems. PMID:20453038

Social stress reactivity alters reward and punishment learning.

PubMed

Cavanagh, James F; Frank, Michael J; Allen, John J B

2011-06-01

To examine how stress affects cognitive functioning, individual differences in trait vulnerability (punishment sensitivity) and state reactivity (negative affect) to social evaluative threat were examined during concurrent reinforcement learning. Lower trait-level punishment sensitivity predicted better reward learning and poorer punishment learning; the opposite pattern was found in more punishment sensitive individuals. Increasing state-level negative affect was directly related to punishment learning accuracy in highly punishment sensitive individuals, but these measures were inversely related in less sensitive individuals. Combined electrophysiological measurement, performance accuracy and computational estimations of learning parameters suggest that trait and state vulnerability to stress alter cortico-striatal functioning during reinforcement learning, possibly mediated via medio-frontal cortical systems.

Lewis and Fischer 344 rats as a model for genetic differences in spatial learning and memory: Cocaine effects.

PubMed

Fole, Alberto; Miguéns, Miguel; Morales, Lidia; González-Martín, Carmen; Ambrosio, Emilio; Del Olmo, Nuria

2017-06-02

Lewis (LEW) and Fischer 344 (F344) rats are considered a model of genetic vulnerability to drug addiction. We previously showed important differences in spatial learning and memory between them, but in contrast with previous experiments demonstrating cocaine-induced enhanced learning in Morris water maze (MWM) highly demanding tasks, the eight-arm radial maze (RAM) performance was not modified either in LEW or F344 rats after chronic cocaine treatment. In the present work, chronically cocaine-treated LEW and F344 adult rats have been evaluated in learning and memory performance using the Y-maze, two RAM protocols that differ in difficulty, and a reversal protocol that tests cognitive flexibility. After one of the RAM protocols, we quantified dendritic spine density in hippocampal CA1 neurons and compared it to animals treated with cocaine but not submitted to RAM. LEW cocaine treated rats showed a better performance in the Y maze than their saline counterparts, an effect that was not evident in the F344 strain. F344 rats significantly took more time to learn the RAM task and made a greater number of errors than LEW animals in both protocols tested, whereas cocaine treatment induced deleterious effects in learning and memory in the highly difficult protocol. Moreover, hippocampal spine density was cocaine-modulated in LEW animals whereas no effects were found in F344 rats. We propose that differences in addictive-like behavior between LEW and F344 rats could be related to differences in hippocampal learning and memory processes that could be on the basis of individual vulnerability to cocaine addiction. Copyright © 2017 Elsevier Inc. All rights reserved.

Punishment sensitivity modulates the processing of negative feedback but not error-induced learning.

PubMed

Unger, Kerstin; Heintz, Sonja; Kray, Jutta

2012-01-01

Accumulating evidence suggests that individual differences in punishment and reward sensitivity are associated with functional alterations in neural systems underlying error and feedback processing. In particular, individuals highly sensitive to punishment have been found to be characterized by larger mediofrontal error signals as reflected in the error negativity/error-related negativity (Ne/ERN) and the feedback-related negativity (FRN). By contrast, reward sensitivity has been shown to relate to the error positivity (Pe). Given that Ne/ERN, FRN, and Pe have been functionally linked to flexible behavioral adaptation, the aim of the present research was to examine how these electrophysiological reflections of error and feedback processing vary as a function of punishment and reward sensitivity during reinforcement learning. We applied a probabilistic learning task that involved three different conditions of feedback validity (100%, 80%, and 50%). In contrast to prior studies using response competition tasks, we did not find reliable correlations between punishment sensitivity and the Ne/ERN. Instead, higher punishment sensitivity predicted larger FRN amplitudes, irrespective of feedback validity. Moreover, higher reward sensitivity was associated with a larger Pe. However, only reward sensitivity was related to better overall learning performance and higher post-error accuracy, whereas highly punishment sensitive participants showed impaired learning performance, suggesting that larger negative feedback-related error signals were not beneficial for learning or even reflected maladaptive information processing in these individuals. Thus, although our findings indicate that individual differences in reward and punishment sensitivity are related to electrophysiological correlates of error and feedback processing, we found less evidence for influences of these personality characteristics on the relation between performance monitoring and feedback-based learning.

Omega-3 deficiency impairs honey bee learning

PubMed Central

Arien, Yael; Dag, Arnon; Zarchin, Shlomi; Masci, Tania

2015-01-01

Deficiency in essential omega-3 polyunsaturated fatty acids (PUFAs), particularly the long-chain form of docosahexaenoic acid (DHA), has been linked to health problems in mammals, including many mental disorders and reduced cognitive performance. Insects have very low long-chain PUFA concentrations, and the effect of omega-3 deficiency on cognition in insects has not been studied. We show a low omega-6:3 ratio of pollen collected by honey bee colonies in heterogenous landscapes and in many hand-collected pollens that we analyzed. We identified Eucalyptus as an important bee-forage plant particularly poor in omega-3 and high in the omega-6:3 ratio. We tested the effect of dietary omega-3 deficiency on olfactory and tactile associative learning of the economically highly valued honey bee. Bees fed either of two omega-3–poor diets, or Eucalyptus pollen, showed greatly reduced learning abilities in conditioned proboscis-extension assays compared with those fed omega-3–rich diets, or omega-3–rich pollen mixture. The effect on performance was not due to reduced sucrose sensitivity. Omega-3 deficiency also led to smaller hypopharyngeal glands. Bee brains contained high omega-3 concentrations, which were only slightly affected by diet, suggesting additional peripheral effects on learning. The shift from a low to high omega-6:3 ratio in the Western human diet is deemed a primary cause of many diseases and reduced mental health. A similar shift seems to be occurring in bee forage, possibly an important factor in colony declines. Our study shows the detrimental effect on cognitive performance of omega-3 deficiency in a nonmammal. PMID:26644556

Deep Learning to Predict Falls in Older Adults Based on Daily-Life Trunk Accelerometry.

PubMed

Nait Aicha, Ahmed; Englebienne, Gwenn; van Schooten, Kimberley S; Pijnappels, Mirjam; Kröse, Ben

2018-05-22

Early detection of high fall risk is an essential component of fall prevention in older adults. Wearable sensors can provide valuable insight into daily-life activities; biomechanical features extracted from such inertial data have been shown to be of added value for the assessment of fall risk. Body-worn sensors such as accelerometers can provide valuable insight into fall risk. Currently, biomechanical features derived from accelerometer data are used for the assessment of fall risk. Here, we studied whether deep learning methods from machine learning are suited to automatically derive features from raw accelerometer data that assess fall risk. We used an existing dataset of 296 older adults. We compared the performance of three deep learning model architectures (convolutional neural network (CNN), long short-term memory (LSTM) and a combination of these two (ConvLSTM)) to each other and to a baseline model with biomechanical features on the same dataset. The results show that the deep learning models in a single-task learning mode are strong in recognition of identity of the subject, but that these models only slightly outperform the baseline method on fall risk assessment. When using multi-task learning, with gender and age as auxiliary tasks, deep learning models perform better. We also found that preprocessing of the data resulted in the best performance (AUC = 0.75). We conclude that deep learning models, and in particular multi-task learning, effectively assess fall risk on the basis of wearable sensor data.

Deep Learning to Predict Falls in Older Adults Based on Daily-Life Trunk Accelerometry

PubMed Central

Englebienne, Gwenn; Pijnappels, Mirjam

2018-01-01

Early detection of high fall risk is an essential component of fall prevention in older adults. Wearable sensors can provide valuable insight into daily-life activities; biomechanical features extracted from such inertial data have been shown to be of added value for the assessment of fall risk. Body-worn sensors such as accelerometers can provide valuable insight into fall risk. Currently, biomechanical features derived from accelerometer data are used for the assessment of fall risk. Here, we studied whether deep learning methods from machine learning are suited to automatically derive features from raw accelerometer data that assess fall risk. We used an existing dataset of 296 older adults. We compared the performance of three deep learning model architectures (convolutional neural network (CNN), long short-term memory (LSTM) and a combination of these two (ConvLSTM)) to each other and to a baseline model with biomechanical features on the same dataset. The results show that the deep learning models in a single-task learning mode are strong in recognition of identity of the subject, but that these models only slightly outperform the baseline method on fall risk assessment. When using multi-task learning, with gender and age as auxiliary tasks, deep learning models perform better. We also found that preprocessing of the data resulted in the best performance (AUC = 0.75). We conclude that deep learning models, and in particular multi-task learning, effectively assess fall risk on the basis of wearable sensor data. PMID:29786659

Examining Opportunity-to-Learn and Success in High School Mathematics Performance in California under NCLB

ERIC Educational Resources Information Center

Gavrilovic, Daniel Miodrag

2013-01-01

The No Child Left Behind Act of 2001 has put many schools under a lot of pressure to meet its high demands. In this quantitative study, the effects that the NCLB act has had on students' opportunity to learn (OTL) and Subject Level Success (SS) from 2004 to 2012 in 9th, 10th, and 11th grade math coursework (Algebra 1, Geometry, Algebra 2, and…

A Community Health Worker "logic model": towards a theory of enhanced performance in low- and middle-income countries.

PubMed

Naimoli, Joseph F; Frymus, Diana E; Wuliji, Tana; Franco, Lynne M; Newsome, Martha H

2014-10-02

There has been a resurgence of interest in national Community Health Worker (CHW) programs in low- and middle-income countries (LMICs). A lack of strong research evidence persists, however, about the most efficient and effective strategies to ensure optimal, sustained performance of CHWs at scale. To facilitate learning and research to address this knowledge gap, the authors developed a generic CHW logic model that proposes a theoretical causal pathway to improved performance. The logic model draws upon available research and expert knowledge on CHWs in LMICs. Construction of the model entailed a multi-stage, inductive, two-year process. It began with the planning and implementation of a structured review of the existing research on community and health system support for enhanced CHW performance. It continued with a facilitated discussion of review findings with experts during a two-day consultation. The process culminated with the authors' review of consultation-generated documentation, additional analysis, and production of multiple iterations of the model. The generic CHW logic model posits that optimal CHW performance is a function of high quality CHW programming, which is reinforced, sustained, and brought to scale by robust, high-performing health and community systems, both of which mobilize inputs and put in place processes needed to fully achieve performance objectives. Multiple contextual factors can influence CHW programming, system functioning, and CHW performance. The model is a novel contribution to current thinking about CHWs. It places CHW performance at the center of the discussion about CHW programming, recognizes the strengths and limitations of discrete, targeted programs, and is comprehensive, reflecting the current state of both scientific and tacit knowledge about support for improving CHW performance. The model is also a practical tool that offers guidance for continuous learning about what works. Despite the model's limitations and several challenges in translating the potential for learning into tangible learning, the CHW generic logic model provides a solid basis for exploring and testing a causal pathway to improved performance.

Association between learning style preferences and anatomy assessment outcomes in graduate-entry and undergraduate medical students.

PubMed

O'Mahony, Siobhain M; Sbayeh, Amgad; Horgan, Mary; O'Flynn, Siun; O'Tuathaigh, Colm M P

2016-07-08

An improved understanding of the relationship between anatomy learning performance and approaches to learning can lead to the development of a more tailored approach to delivering anatomy teaching to medical students. This study investigated the relationship between learning style preferences, as measured by Visual, Aural, Read/write, and Kinesthetic (VARK) inventory style questionnaire and Honey and Mumford's learning style questionnaire (LSQ), and anatomy and clinical skills assessment performance at an Irish medical school. Additionally, mode of entry to medical school [undergraduate/direct-entry (DEM) vs. graduate-entry (GEM)], was examined in relation to individual learning style, and assessment results. The VARK and LSQ were distributed to first and second year DEM, and first year GEM students. DEM students achieved higher clinical skills marks than GEM students, but anatomy marks did not differ between each group. Several LSQ style preferences were shown to be weakly correlated with anatomy assessment performance in a program- and year-specific manner. Specifically, the "Activist" style was negatively correlated with anatomy scores in DEM Year 2 students (rs = -0.45, P = 0.002). The "Theorist" style demonstrated a weak correlation with anatomy performance in DEM Year 2 (rs = 0.18, P = 0.003). Regression analysis revealed that, among the LSQ styles, the "Activist" was associated with poorer anatomy assessment performance (P < 0.05), while improved scores were associated with students who scored highly on the VARK "Aural" modality (P < 0.05). These data support the contention that individual student learning styles contribute little to variation in academic performance in medical students. Anat Sci Educ 9: 391-399. © 2016 American Association of Anatomists. © 2016 American Association of Anatomists.

Restructuring High-Poverty Elementary Schools for Success: A Description of the Hi-Perform School Design

ERIC Educational Resources Information Center

Pogrow, Stanley

2006-01-01

In this second of a two-part series, the author outlines the basic structure of the kind of school that will help the children of poverty gain ground and so reduce the learning gap. In an attempt to establish far more effective high-poverty schools, the author proposes one approach, which is the Hi-Perform School redesign for high-poverty…

Empowerment of Metacognitive Skills through Development of Instructional Materials on the Topic of Hydrolysis and Buffer Solutions

NASA Astrophysics Data System (ADS)

Azizah, U.; Nasrudin, H.

2018-01-01

Metacognitive skills are one of the high-level thinking skills that pre-service teachers need in chemistry problem-solving. Metacognitive skills that empowered in learning focuses on how pre-service teachers participate in designing what was to be learned, monitor the progress of learning outcomes, and assess what has been learned in solving problems. The purpose of this research was (1) describe how pre-service teachers empowering metacognitive skills using developed instructional materials, and (2) describe the pre-service teacher’s response to the learning process. The research involved 22 pre-service teachers in Chemistry Education Program Universitas Negeri Surabaya, Indonesia. The design of this research was a pre-experimental research with One Group Pretest-Posttest Design. The data of the research was analyzed by quantitative descriptive. The result of the research that: (1) performance of metacognitive skills pre-service teachers have high and very high criteria in learning chemistry on each indicator includes goal setting, identify the known knowledge, determining the learning strategies, monitoring the relevance of knowledge which has been owned with learning strategies are used, monitoring the achievement of the goal in the making conclusions, and evaluating the process and outcomes of thinking, and (2) most of the pre-service teachers are willing to join to this teaching-learning activity.

Active and observational reward learning in adults with autism spectrum disorder: relationship with empathy in an atypical sample.

PubMed

Bellebaum, Christian; Brodmann, Katja; Thoma, Patrizia

2014-01-01

Autism spectrum disorders (ASDs) are characterised by disturbances in social behaviour. A prevailing hypothesis suggests that these problems are related to deficits in assigning rewarding value to social stimuli. The present study aimed to examine monetary reward processing in adults with ASDs by means of event-related potentials (ERPs). Ten individuals with mild ASDs (Asperger's syndrome and high-functioning autism) and 12 healthy control subjects performed an active and an observational probabilistic reward-learning task. Both groups showed similar overall learning performance. With respect to reward processing, subjects with ASDs exhibited a general reduction in feedback-related negativity (FRN) amplitude, irrespective of feedback valence and type of learning (active or observational). Individuals with ASDs showed lower scores for cognitive empathy, while affective empathy did not differ between groups. Correlation analyses revealed that higher empathy (both cognitive and affective) negatively affected performance in observational learning in controls and in active learning in ASDs (only cognitive empathy). No relationships were seen between empathy and ERPs. Reduced FRN amplitudes are discussed in terms of a deficit in fast reward processing in ASDs, which may indicate altered reward system functioning.

Learning algorithms for human-machine interfaces.

PubMed

Danziger, Zachary; Fishbach, Alon; Mussa-Ivaldi, Ferdinando A

2009-05-01

The goal of this study is to create and examine machine learning algorithms that adapt in a controlled and cadenced way to foster a harmonious learning environment between the user and the controlled device. To evaluate these algorithms, we have developed a simple experimental framework. Subjects wear an instrumented data glove that records finger motions. The high-dimensional glove signals remotely control the joint angles of a simulated planar two-link arm on a computer screen, which is used to acquire targets. A machine learning algorithm was applied to adaptively change the transformation between finger motion and the simulated robot arm. This algorithm was either LMS gradient descent or the Moore-Penrose (MP) pseudoinverse transformation. Both algorithms modified the glove-to-joint angle map so as to reduce the endpoint errors measured in past performance. The MP group performed worse than the control group (subjects not exposed to any machine learning), while the LMS group outperformed the control subjects. However, the LMS subjects failed to achieve better generalization than the control subjects, and after extensive training converged to the same level of performance as the control subjects. These results highlight the limitations of coadaptive learning using only endpoint error reduction.

Learning Algorithms for Human–Machine Interfaces

PubMed Central

Fishbach, Alon; Mussa-Ivaldi, Ferdinando A.

2012-01-01

The goal of this study is to create and examine machine learning algorithms that adapt in a controlled and cadenced way to foster a harmonious learning environment between the user and the controlled device. To evaluate these algorithms, we have developed a simple experimental framework. Subjects wear an instrumented data glove that records finger motions. The high-dimensional glove signals remotely control the joint angles of a simulated planar two-link arm on a computer screen, which is used to acquire targets. A machine learning algorithm was applied to adaptively change the transformation between finger motion and the simulated robot arm. This algorithm was either LMS gradient descent or the Moore–Penrose (MP) pseudoinverse transformation. Both algorithms modified the glove-to-joint angle map so as to reduce the endpoint errors measured in past performance. The MP group performed worse than the control group (subjects not exposed to any machine learning), while the LMS group outperformed the control subjects. However, the LMS subjects failed to achieve better generalization than the control subjects, and after extensive training converged to the same level of performance as the control subjects. These results highlight the limitations of coadaptive learning using only endpoint error reduction. PMID:19203886

Peer-assisted learning to train high-school students to perform basic life-support.

PubMed

Choi, Hyung Soo; Lee, Dong Hoon; Kim, Chan Woong; Kim, Sung Eun; Oh, Je Hyeok

2015-01-01

The inclusion of cardiopulmonary resuscitation (CPR) in formal education has been a useful approach to providing basic life support (BLS) services. However, because not all students have been able to learn directly from certified instructors, we studied the educational efficacy of the use of peer-assisted learning (PAL) to train high-school students to perform BLS services. This study consisted of 187 high-school students: 68 participants served as a control group and received a 1-hour BLS training from a school nurse, and 119 were included in a PAL group and received a 1-hour CPR training from a PAL leader. Participants' BLS training was preceded by the completion of questionnaires regarding their background. Three months after the training, the participants were asked to respond to questionnaires about their willingness to perform CPR on bystander CPR and their retention of knowledge of BLS. We found no statistically significant difference between the control and PAL groups in their willingness to perform CPR on bystanders (control: 55.2%, PAL: 64.7%, P=0.202). The PAL group was not significantly different from the control group (control: 60.78±39.77, PAL: 61.76±17.80, P=0.848) in retention of knowledge about BLS services. In educating high school students about BLS, there was no significant difference between PAL and traditional education in increasing the willingness to provide CPR to bystanders or the ability to retain knowledge about BLS.

Twenty-first century learning in schools: A case study of New Technology High School in Napa, California.

PubMed

Pearlman, Bob

2006-01-01

The most pertinent question concerning teaching and learning in the twenty-first century is not what knowledge and skills students need--that laundry list was identified over a decade ago--but rather how to foster twenty-first century learning. What curricula, experiences, assessments, environments, and technology best support twenty-first century learning? New Technology High School (NTHS) in Napa, California, is one example of a successful twenty-first century school. In this chapter, the author describes the components of this exemplary high school, illustrating an environment that will cultivate twenty-first century student learning. New Technology High School began by defining eight learning outcomes, aligned with the standards of the Partnership for 21st Century Skills; to graduate, students demonstrate mastery of these outcomes through an online portfolio. To help students achieve the outcomes, NTHS employs project- and problem-based learning. Whereas in traditional classrooms students work alone on short-term assignments that do not lend themselves to deep understanding, the project-based learning approach has students working in teams on long-term, in-depth, rigorous projects. Students' work is supported by the school's workplace-like environment and effectiv use of technology. Meaningful assessment is essential to project-based learning; students receive continuous feedback, helping them become self-directed learners. In fact, NTHS uses outcome-based grading through which students constantly know how they are performing on the twenty-first century outcomes. Research has shown that NTHS graduates are better prepared for postsecondary education, careers, and citizenship than their peers from other schools. To facilitate twenty-first century learning, all schools need to rethink their approach to teaching and learning. New Technology High School is one way to do so.

Sex and boldness explain individual differences in spatial learning in a lizard.

PubMed

Carazo, Pau; Noble, Daniel W A; Chandrasoma, Dani; Whiting, Martin J

2014-05-07

Understanding individual differences in cognitive performance is a major challenge to animal behaviour and cognition studies. We used the Eastern water skink (Eulamprus quoyii) to examine associations between exploration, boldness and individual variability in spatial learning, a dimension of lizard cognition with important bearing on fitness. We show that males perform better than females in a biologically relevant spatial learning task. This is the first evidence for sex differences in learning in a reptile, and we argue that it is probably owing to sex-specific selective pressures that may be widespread in lizards. Across the sexes, we found a clear association between boldness after a simulated predatory attack and the probability of learning the spatial task. In contrast to previous studies, we found a nonlinear association between boldness and learning: both 'bold' and 'shy' behavioural types were more successful learners than intermediate males. Our results do not fit with recent predictions suggesting that individual differences in learning may be linked with behavioural types via high-low-risk/reward trade-offs. We suggest the possibility that differences in spatial cognitive performance may arise in lizards as a consequence of the distinct environmental variability and complexity experienced by individuals as a result of their sex and social tactics.

Exploring KM Features of High-Performance Companies

NASA Astrophysics Data System (ADS)

Wu, Wei-Wen

2007-12-01

For reacting to an increasingly rival business environment, many companies emphasize the importance of knowledge management (KM). It is a favorable way to explore and learn KM features of high-performance companies. However, finding out the critical KM features of high-performance companies is a qualitative analysis problem. To handle this kind of problem, the rough set approach is suitable because it is based on data-mining techniques to discover knowledge without rigorous statistical assumptions. Thus, this paper explored KM features of high-performance companies by using the rough set approach. The results show that high-performance companies stress the importance on both tacit and explicit knowledge, and consider that incentives and evaluations are the essentials to implementing KM.

Validation and learning in the Procedicus KSA virtual reality surgical simulator.

PubMed

Ström, P; Kjellin, A; Hedman, L; Johnson, E; Wredmark, T; Felländer-Tsai, L

2003-02-01

Advanced simulator training within medicine is a rapidly growing field. Virtual reality simulators are being introduced as cost-saving educational tools, which also lead to increased patient safety. Fifteen medical students were included in the study. For 10 medical students performance was monitored, before and after 1 h of training, in two endoscopic simulators (the Procedicus KSA with haptic feedback and anatomical graphics and the established MIST simulator without this haptic feedback and graphics). Five medical students performed 50 tests in the Procedicus KSA in order to analyze learning curves. One of these five medical students performed multiple training sessions during 2 weeks and performed more than 300 tests. There was a significant improvement after 1 h of training regarding time, movement economy, and total score. The results in the two simulators were highly correlated. Our results show that the use of surgical simulators as a pedagogical tool in medical student training is encouraging. It shows rapid learning curves and our suggestion is to introduce endoscopic simulator training in undergraduate medical education during the course in surgery when motivation is high and before the development of "negative stereotypes" and incorrect practices.

Managing Conversations: The Medium for Achieving "Breakthrough" Results.

ERIC Educational Resources Information Center

Bolton, Robert

1998-01-01

Unlike traditional management development, use of conversations in coaching high-performance work teams addresses core processes of speaking and listening. Management of conversations aims to create learning that will lead to breakthroughs in team performance. (SK)

Exposure to activity-based anorexia impairs contextual learning in weight-restored rats without affecting spatial learning, taste, anxiety, or dietary-fat preference.

PubMed

Boersma, Gretha J; Treesukosol, Yada; Cordner, Zachary A; Kastelein, Anneke; Choi, Pique; Moran, Timothy H; Tamashiro, Kellie L

2016-02-01

Relapse rates are high amongst cases of anorexia nervosa (AN) suggesting that some alterations induced by AN may remain after weight restoration. To study the consequences of AN without confounds of environmental variability, a rodent model of activity-based anorexia (ABA) can be employed. We hypothesized that exposure to ABA during adolescence may have long-term consequences in taste function, cognition, and anxiety-like behavior after weight restoration. To test this hypothesis, we exposed adolescent female rats to ABA (1.5 h food access, combined with voluntary running wheel access) and compared their behavior to that of control rats after weight restoration was achieved. The rats were tested for learning/memory, anxiety, food preference, and taste in a set of behavioral tests performed during the light period. Our data show that ABA exposure leads to reduced performance during the novel object recognition task, a test for contextual learning, without altering performance in the novel place recognition task or the Barnes maze, both tasks that test spatial learning. Furthermore, we do not observe alterations in unconditioned lick responses to sucrose nor quinine (described by humans as "sweet" and "bitter," respectively). Nor Do we find alterations in anxiety-like behavior during an elevated plus maze or an open field test. Finally, preference for a diet high in fat is not altered. Overall, our data suggest that ABA exposure during adolescence impairs contextual learning in adulthood without altering spatial leaning, taste, anxiety, or fat preference. © 2015 Wiley Periodicals, Inc.

Academic goals and learning quality in higher education students.

PubMed

Valle, Antonio; Núñez, José C; Cabanach, Ramón G; González-Pienda, Julio A; Rodríguez, Susana; Rosário, Pedro; Muñoz-Cadavid, María A; Cerezo, Rebeca

2009-05-01

In this paper, the relations between academic goals and various indicators that define the quality of the learning process are analyzed. The purpose was to determine to what extent high, moderate, or low levels of academic goals were positively or negatively related to effort regulation, the value assigned to academic tasks, meta-cognitive self-regulation, self-efficacy, beliefs about learning control, and management of time and study environment. The investigation was carried out with a sample of 632 university students (70% female and 30% male) and mean age of 21.22 (SD=2.2).The results show that learning goals, or task orientation, are positively related to all the indictors of learning quality considered herein. Although for other kinds of goals-work-avoidance goals, performance-approach goals, and performance-avoidance goals-significant relations were not found with all the indicators, there was a similar tendency of significant results in all cases; the higher the levels of these goals, the lower the levels of the indicators of learning quality.

Word learning in deaf children with cochlear implants: effects of early auditory experience.

PubMed

Houston, Derek M; Stewart, Jessica; Moberly, Aaron; Hollich, George; Miyamoto, Richard T

2012-05-01

Word-learning skills were tested in normal-hearing 12- to 40-month-olds and in deaf 22- to 40-month-olds 12 to 18 months after cochlear implantation. Using the Intermodal Preferential Looking Paradigm (IPLP), children were tested for their ability to learn two novel-word/novel-object pairings. Normal-hearing children demonstrated learning on this task at approximately 18 months of age and older. For deaf children, performance on this task was significantly correlated with early auditory experience: Children whose cochlear implants were switched on by 14 months of age or who had relatively more hearing before implantation demonstrated learning in this task, but later implanted profoundly deaf children did not. Performance on this task also correlated with later measures of vocabulary size. Taken together, these findings suggest that early auditory experience facilitates word learning and that the IPLP may be useful for identifying children who may be at high risk for poor vocabulary development. © 2012 Blackwell Publishing Ltd.

Word learning in deaf children with cochlear implants: effects of early auditory experience

PubMed Central

Houston, Derek M.; Stewart, Jessica; Moberly, Aaron; Hollich, George; Miyamoto, Richard T.

2013-01-01

Word-learning skills were tested in normal-hearing 12- to 40-month-olds and in deaf 22- to 40-month-olds 12 to 18 months after cochlear implantation. Using the Intermodal Preferential Looking Paradigm (IPLP), children were tested for their ability to learn two novel-word/novel-object pairings. Normal-hearing children demonstrated learning on this task at approximately 18 months of age and older. For deaf children, performance on this task was significantly correlated with early auditory experience: Children whose cochlear implants were switched on by 14 months of age or who had relatively more hearing before implantation demonstrated learning in this task, but later implanted profoundly deaf children did not. Performance on this task also correlated with later measures of vocabulary size. Taken together, these findings suggest that early auditory experience facilitates word learning and that the IPLP may be useful for identifying children who may be at high risk for poor vocabulary development. PMID:22490184

A Failed Marriage between Standardization and Incentivism: Divergent Perspectives on Performance-Based Compensation in Shanghai

ERIC Educational Resources Information Center

La Londe, Priya G.

2017-01-01

The Chinese province of Shanghai has gained international recognition as a high performing education system with strong teaching and learning outcomes. One accountability mechanism in Shanghai's education reform strategy is statewide performance-based compensation (PBC), also known as performance- or merit pay. Providing a first time account of…

On Learning Natural-Science Categories That Violate the Family-Resemblance Principle.

PubMed

Nosofsky, Robert M; Sanders, Craig A; Gerdom, Alex; Douglas, Bruce J; McDaniel, Mark A

2017-01-01

The general view in psychological science is that natural categories obey a coherent, family-resemblance principle. In this investigation, we documented an example of an important exception to this principle: Results of a multidimensional-scaling study of igneous, metamorphic, and sedimentary rocks (Experiment 1) suggested that the structure of these categories is disorganized and dispersed. This finding motivated us to explore what might be the optimal procedures for teaching dispersed categories, a goal that is likely critical to science education in general. Subjects in Experiment 2 learned to classify pictures of rocks into compact or dispersed high-level categories. One group learned the categories through focused high-level training, whereas a second group was required to simultaneously learn classifications at a subtype level. Although high-level training led to enhanced performance when the categories were compact, subtype training was better when the categories were dispersed. We provide an interpretation of the results in terms of an exemplar-memory model of category learning.

Advanced Cell Classifier: User-Friendly Machine-Learning-Based Software for Discovering Phenotypes in High-Content Imaging Data.

PubMed

Piccinini, Filippo; Balassa, Tamas; Szkalisity, Abel; Molnar, Csaba; Paavolainen, Lassi; Kujala, Kaisa; Buzas, Krisztina; Sarazova, Marie; Pietiainen, Vilja; Kutay, Ulrike; Smith, Kevin; Horvath, Peter

2017-06-28

High-content, imaging-based screens now routinely generate data on a scale that precludes manual verification and interrogation. Software applying machine learning has become an essential tool to automate analysis, but these methods require annotated examples to learn from. Efficiently exploring large datasets to find relevant examples remains a challenging bottleneck. Here, we present Advanced Cell Classifier (ACC), a graphical software package for phenotypic analysis that addresses these difficulties. ACC applies machine-learning and image-analysis methods to high-content data generated by large-scale, cell-based experiments. It features methods to mine microscopic image data, discover new phenotypes, and improve recognition performance. We demonstrate that these features substantially expedite the training process, successfully uncover rare phenotypes, and improve the accuracy of the analysis. ACC is extensively documented, designed to be user-friendly for researchers without machine-learning expertise, and distributed as a free open-source tool at www.cellclassifier.org. Copyright © 2017 Elsevier Inc. All rights reserved.

Using human brain activity to guide machine learning.

PubMed

Fong, Ruth C; Scheirer, Walter J; Cox, David D

2018-03-29

Machine learning is a field of computer science that builds algorithms that learn. In many cases, machine learning algorithms are used to recreate a human ability like adding a caption to a photo, driving a car, or playing a game. While the human brain has long served as a source of inspiration for machine learning, little effort has been made to directly use data collected from working brains as a guide for machine learning algorithms. Here we demonstrate a new paradigm of "neurally-weighted" machine learning, which takes fMRI measurements of human brain activity from subjects viewing images, and infuses these data into the training process of an object recognition learning algorithm to make it more consistent with the human brain. After training, these neurally-weighted classifiers are able to classify images without requiring any additional neural data. We show that our neural-weighting approach can lead to large performance gains when used with traditional machine vision features, as well as to significant improvements with already high-performing convolutional neural network features. The effectiveness of this approach points to a path forward for a new class of hybrid machine learning algorithms which take both inspiration and direct constraints from neuronal data.

Reliability of a Computerized Neurocognitive Test in Baseline Concussion Testing of High School Athletes.

PubMed

MacDonald, James; Duerson, Drew

2015-07-01

Baseline assessments using computerized neurocognitive tests are frequently used in the management of sport-related concussions. Such testing is often done on an annual basis in a community setting. Reliability is a fundamental test characteristic that should be established for such tests. Our study examined the test-retest reliability of a computerized neurocognitive test in high school athletes over 1 year. Repeated measures design. Two American high schools. High school athletes (N = 117) participating in American football or soccer during the 2011-2012 and 2012-2013 academic years. All study participants completed 2 baseline computerized neurocognitive tests taken 1 year apart at their respective schools. The test measures performance on 4 cognitive tasks: identification speed (Attention), detection speed (Processing Speed), one card learning accuracy (Learning), and one back speed (Working Memory). Reliability was assessed by measuring the intraclass correlation coefficient (ICC) between the repeated measures of the 4 cognitive tasks. Pearson and Spearman correlation coefficients were calculated as a secondary outcome measure. The measure for identification speed performed best (ICC = 0.672; 95% confidence interval, 0.559-0.760) and the measure for one card learning accuracy performed worst (ICC = 0.401; 95% confidence interval, 0.237-0.542). All tests had marginal or low reliability. In a population of high school athletes, computerized neurocognitive testing performed in a community setting demonstrated low to marginal test-retest reliability on baseline assessments 1 year apart. Further investigation should focus on (1) improving the reliability of individual tasks tested, (2) controlling for external factors that might affect test performance, and (3) identifying the ideal time interval to repeat baseline testing in high school athletes. Computerized neurocognitive tests are used frequently in high school athletes, often within a model of baseline testing of asymptomatic individuals before the start of a sporting season. This study adds to the evidence that suggests in this population such testing may lack sufficient reliability to support clinical decision making.

Learning Contracts in Undergraduate Courses: Impacts on Student Behaviors and Academic Performance

DTIC Science & Technology

2013-04-01

contract. The students each set their expectations for the next exam grade and put it in writing , and the learning contract overtly empowered them to...of success (Bandura, 1977). Putting something achievable in writing , ensuring that is it clear and customized to the individual, having prior...tests of life?" Self-direction is a highly valuable skill that involves the ability to learn independently and possess metacognitive ability. Successful

Machine learning for neuroimaging with scikit-learn.

PubMed

Abraham, Alexandre; Pedregosa, Fabian; Eickenberg, Michael; Gervais, Philippe; Mueller, Andreas; Kossaifi, Jean; Gramfort, Alexandre; Thirion, Bertrand; Varoquaux, Gaël

2014-01-01

Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g., multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g., resting state functional MRI) or find sub-populations in large cohorts. By considering different functional neuroimaging applications, we illustrate how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps. Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain.

Machine learning for neuroimaging with scikit-learn

PubMed Central

Abraham, Alexandre; Pedregosa, Fabian; Eickenberg, Michael; Gervais, Philippe; Mueller, Andreas; Kossaifi, Jean; Gramfort, Alexandre; Thirion, Bertrand; Varoquaux, Gaël

2014-01-01

Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g., multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g., resting state functional MRI) or find sub-populations in large cohorts. By considering different functional neuroimaging applications, we illustrate how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps. Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain. PMID:24600388

Relevance of a neurophysiological marker of attention allocation for children's learning-related behaviors and academic performance.

PubMed

Willner, Cynthia J; Gatzke-Kopp, Lisa M; Bierman, Karen L; Greenberg, Mark T; Segalowitz, Sidney J

2015-08-01

Learning-related behaviors are important for school success. Socioeconomic disadvantage confers risk for less adaptive learning-related behaviors at school entry, yet substantial variability in school readiness exists within socioeconomically disadvantaged populations. Investigation of neurophysiological systems associated with learning-related behaviors in high-risk populations could illuminate resilience processes. This study examined the relevance of a neurophysiological measure of controlled attention allocation, amplitude of the P3b event-related potential, for learning-related behaviors and academic performance in a sample of socioeconomically disadvantaged kindergarteners. The sample consisted of 239 children from an urban, low-income community, approximately half of whom exhibited behavior problems at school entry (45% aggressive/oppositional; 64% male; 69% African American, 21% Hispanic). Results revealed that higher P3b amplitudes to target stimuli in a go/no-go task were associated with more adaptive learning-related behaviors in kindergarten. Furthermore, children's learning-related behaviors in kindergarten mediated a positive indirect effect of P3b amplitude on growth in academic performance from kindergarten to 1st grade. Given that P3b amplitude reflects attention allocation processes, these findings build on the scientific justification for interventions targeting young children's attention skills in order to promote effective learning-related behaviors and academic achievement within socioeconomically disadvantaged populations. (c) 2015 APA, all rights reserved).

Machine Learning methods for Quantitative Radiomic Biomarkers.

PubMed

Parmar, Chintan; Grossmann, Patrick; Bussink, Johan; Lambin, Philippe; Aerts, Hugo J W L

2015-08-17

Radiomics extracts and mines large number of medical imaging features quantifying tumor phenotypic characteristics. Highly accurate and reliable machine-learning approaches can drive the success of radiomic applications in clinical care. In this radiomic study, fourteen feature selection methods and twelve classification methods were examined in terms of their performance and stability for predicting overall survival. A total of 440 radiomic features were extracted from pre-treatment computed tomography (CT) images of 464 lung cancer patients. To ensure the unbiased evaluation of different machine-learning methods, publicly available implementations along with reported parameter configurations were used. Furthermore, we used two independent radiomic cohorts for training (n = 310 patients) and validation (n = 154 patients). We identified that Wilcoxon test based feature selection method WLCX (stability = 0.84 ± 0.05, AUC = 0.65 ± 0.02) and a classification method random forest RF (RSD = 3.52%, AUC = 0.66 ± 0.03) had highest prognostic performance with high stability against data perturbation. Our variability analysis indicated that the choice of classification method is the most dominant source of performance variation (34.21% of total variance). Identification of optimal machine-learning methods for radiomic applications is a crucial step towards stable and clinically relevant radiomic biomarkers, providing a non-invasive way of quantifying and monitoring tumor-phenotypic characteristics in clinical practice.

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.

A model to teach concomitant patient communication during psychomotor skill development.

PubMed

Nicholls, Delwyn; Sweet, Linda; Muller, Amanda; Hyett, Jon

2018-01-01

Many health professionals use psychomotor or task-based skills in clinical practice that require concomitant communication with a conscious patient. Verbally engaging with the patient requires highly developed verbal communication skills, enabling the delivery of patient-centred care. Historically, priority has been given to learning the psychomotor skills essential to clinical practice. However, there has been a shift towards also ensuring competent communication with the patient during skill performance. While there is literature outlining the steps to teach and learn verbal communication skills, little is known about the most appropriate instructional approach to teach how to verbally engage with the patient when also learning to perform a task. A literature review was performed and it identified that there was no model or proven approach which could be used to integrate the learning of both psychomotor and communication skills. This paper reviews the steps to teach a communication skill and provides a suggested model to guide the acquisition and development of the concomitant -communication skills required with a patient at the time a psychomotor skill is performed. Copyright © 2017 Elsevier Ltd. All rights reserved.

Is Implicit Motor Learning Preserved after Stroke? A Systematic Review with Meta-Analysis

PubMed Central

Kal, E.; Winters, M.; van der Kamp, J.; Houdijk, H.; Groet, E.; van Bennekom, C.; Scherder, E.

2016-01-01

Many stroke patients experience difficulty with performing dual-tasks. A promising intervention to target this issue is implicit motor learning, as it should enhance patients’ automaticity of movement. Yet, although it is often thought that implicit motor learning is preserved post-stroke, evidence for this claim has not been systematically analysed yet. Therefore, we systematically reviewed whether implicit motor learning is preserved post-stroke, and whether patients benefit more from implicit than from explicit motor learning. We comprehensively searched conventional (MEDLINE, Cochrane, Embase, PEDro, PsycINFO) and grey literature databases (BIOSIS, Web of Science, OpenGrey, British Library, trial registries) for relevant reports. Two independent reviewers screened reports, extracted data, and performed a risk of bias assessment. Overall, we included 20 out of the 2177 identified reports that allow for a succinct evaluation of implicit motor learning. Of these, only 1 study investigated learning on a relatively complex, whole-body (balance board) task. All 19 other studies concerned variants of the serial-reaction time paradigm, with most of these focusing on learning with the unaffected hand (N = 13) rather than the affected hand or both hands (both: N = 4). Four of the 20 studies compared explicit and implicit motor learning post-stroke. Meta-analyses suggest that patients with stroke can learn implicitly with their unaffected side (mean difference (MD) = 69 ms, 95% CI[45.1, 92.9], p < .00001), but not with their affected side (standardized MD = -.11, 95% CI[-.45, .25], p = .56). Finally, implicit motor learning seemed equally effective as explicit motor learning post-stroke (SMD = -.54, 95% CI[-1.37, .29], p = .20). However, overall, the high risk of bias, small samples, and limited clinical relevance of most studies make it impossible to draw reliable conclusions regarding the effect of implicit motor learning strategies post-stroke. High quality studies with larger samples are warranted to test implicit motor learning in clinically relevant contexts. PMID:27992442

Motivational Profiles of Medical Students of Nepalese Army Institute of Health Sciences.

PubMed

Shrestha, Lochana; Pant, Shambhu Nath

2018-01-01

Students enter the medical study with different types of motives. Given the importance of academic motivation for good academic achievement of the students, the present study was designed to reveal the possible relationship between academic motivation and achievement in medical students. In this cross-sectional study medical students (N=364) of Nepalese Army institute of Health Sciences were participated and classified to different subgroups using intrinsic and controlled motivation scores. Cluster membership was used as an independent variable to assess differences in study strategies and academic performance. Four clusters were obtained: High Intrinsic High Controlled, Low Intrinsic High Controlled, High Intrinsic Low Controlled, and Low Intrinsic Low Controlled. High Intrinsic High Controlled and High Intrinsic Low Controlled profile students constituted 36.1%, 22.6% of the population, respectively. No significant differences were observed as regards to deep strategy and surface strategy between high interest status motivated and high interest-motivated students. However, both of the clusters had significantly deeper, surface strategy and better academic performance than status-motivated and low-motivation clusters (p < 0.001). The interest status motivated and interest-motivated medical students were associated with good deep and surface study strategy and good academic performance. Low-motivation and status-motivated students were associated with the least academic performance with less interest learning behaviors. This reflected that motivation is important required component for good learning outcomes for medical students Keywords: Academic performance; controlled motivation; clusters; intrinsic motivation; motivation.

Failure Is Not an Option (TM). Six Principles That Guide Student Achievement in High-Performing Schools

ERIC Educational Resources Information Center

Blankstein, Alan M.

2004-01-01

The author builds upon a foundation which identifies courageous school leadership and the professional learning community as the center of effective school reform. The author offers six guiding principles steps for creating and sustaining a high-performing school: (1) Common mission, vision, values, and goals: (2) Systems for prevention and…

PuTTY | High-Performance Computing | NREL

Science.gov Websites

PuTTY PuTTY Learn how to use PuTTY to connect to NREL's high-performance computing (HPC) systems . Connecting When you start the PuTTY app, the program will display PuTTY's Configuration menu. When this comes . When prompted, type your password again followed by . Note: to increase

A Study of a High Performing, High Poverty Elementary School on the Texas-Mexico Border

ERIC Educational Resources Information Center

Lopez, Cynthia Iris

2012-01-01

Transforming low performing schools to ensure the academic success of Hispanic children situated in poverty remains an educational challenge. External factors impacting student learning are often targeted as the main reasons for poor academic achievement, thereby advancing the culturally deficit model. This study is about an elementary school that…

The Pedagogy of Confidence: Inspiring High Intellectual Performance in Urban Schools

ERIC Educational Resources Information Center

Jackson, Yvette

2011-01-01

In her new book, Yvette Jackson shows educators how to focus on students' strengths to inspire learning and high intellectual performance. Jackson asserts that the myth that the route to increasing achievement by focusing on weaknesses (promoted by policies such as NCLB) has blinded us to the strengths and intellectual potential of urban…

Spatial learning in the Morris water maze in mice genetically different in the predisposition to catalepsy: the effect of intraventricular treatment with brain-derived neurotrophic factor.

PubMed

Kulikov, Alexander V; Fursenko, Daria V; Khotskin, Nikita V; Bazovkina, Daria V; Kulikov, Victor A; Naumenko, Vladimir S; Bazhenova, Ekaterina Yu; Popova, Nina K

2014-07-01

Hereditary catalepsy in mice is accompanied with volume reduction of some brain structures and high vulnerability to inflammatory agents. Here an association between hereditary catalepsy and spatial learning deficit in the Morris water maze (MWM) in adult mouse males of catalepsy-resistant AKR, catalepsy-prone CBA and AKR.CBA-D13Mit76 (D13) strains was studied. Recombinant D13 strain was created by means of the transfer of the CBA-derived allele of the major gene of catalepsy to the AKR genome. D13 mice showed a low MWM performance in the acquisition test and high expression of the gene coding proinflammatory interleukin-6 (Il-6) in the hippocampus and cortex compared with mice of the parental AKR and CBA strains. An acute ivc administration of 300 ng of brain derived neurotrophic factor (BDNF) normalized the performance in the MWM, but did not decrease the high Il-6 gene expression in the brain of D13 mice. These results indicated a possible association between the hereditary catalepsy, MWM performance, BDNF and level of Il-6 mRNA in the brain, although the relation between these characteristics seems to be more complex. D13 recombinant mice with deficit of spatial learning is a promising model for study of the genetic and molecular mechanisms of learning disorders as well as for screening potential cognitive enhancers. Copyright © 2014 Elsevier Inc. All rights reserved.

Buyer Beware: Lessons Learned from EdTPA Implementation in New York State

ERIC Educational Resources Information Center

Greenblatt, Deborah; O'Hara, Kate E.

2015-01-01

As states across the country continue their implementation of the Teacher Performance Assessment Portfolio (edTPA), a complex and high-stakes certification requirement for teacher certification, there are important lessons for educators and education advocates to learn from New York State's implementation. As Linda Darling-Hammond, developer and…

Student Use of Scaffolding Software: Relationships with Motivation and Conceptual Understanding

ERIC Educational Resources Information Center

Butler, Kyle A.; Lumpe, Andrew

2008-01-01

This study was designed to theoretically articulate and empirically assess the role of computer scaffolds. In this project, several examples of educational software were developed to scaffold the learning of students performing high level cognitive activities. The software used in this study, Artemis, focused on scaffolding the learning of…

What Is Coded into Memory in the Absence of Outcome Feedback?

ERIC Educational Resources Information Center

Henriksson, Maria P.; Elwin, Ebba; Juslin, Peter

2010-01-01

Although people often have to learn from environments with scarce and highly selective outcome feedback, the question of how nonfeedback trials are represented in memory and affect later performance has received little attention in models of learning and decision making. In this article, the authors use the generalized context model (Nosofsky,…

Using MATLAB Software on the Peregrine System | High-Performance Computing

Science.gov Websites

| NREL MATLAB Software on the Peregrine System Using MATLAB Software on the Peregrine System Learn how to use MATLAB software on the Peregrine system. Running MATLAB in Batch Mode Using the node. Understanding Versions and Licenses Learn about the MATLAB software versions and licenses

Implementing Enrichment Clusters in Elementary Schools: Lessons Learned

ERIC Educational Resources Information Center

Fiddyment, Gail E.

2014-01-01

Enrichment clusters offer a way for schools to encourage a high level of learning as students and adults work together to develop a product, service, or performance by applying advanced knowledge and authentic processes to real-world problems. This study utilized a qualitative research design to examine the perceptions and experiences of two…

Updating Higher Education Expectations and Choices with Learning

ERIC Educational Resources Information Center

Milla, Joniada

2017-01-01

This paper explores how expectations and post-secondary education (PSE) path disruption decisions are affected by a learning process that students experience once enrolled in a PSE program. An unexpected change in grades, between high school and first year PSE program, serves as an informative signal on how well their academic performance and…

Assessing High School Student Learning on Science Outreach Lab Activities

ERIC Educational Resources Information Center

Thomas, Courtney L.

2012-01-01

The effect of hands-on laboratory activities on secondary student learning was examined. Assessment was conducted over a two-year period, with 262 students participating the first year and 264 students the second year. Students took a prequiz, performed a laboratory activity (gas chromatography of alcohols, or photosynthesis and respiration), and…

Contrived Collegiality versus Genuine Collegiality: Demystifying Professional Learning Communities in Chinese Schools

ERIC Educational Resources Information Center

Wang, Ting

2015-01-01

Drawing on data from a larger study on Professional Learning Communities (PLCs) and School Leadership in China, this article investigates the practices of teacher collaboration and PLCs in two urban, high-performing secondary schools in Northeast China. Qualitative data were collected from observations, documents and in-depth semi-structured…

Evaluating Serious Games through User Experience and Performance Assessment

ERIC Educational Resources Information Center

Barton, Irving Gary, Jr.

2017-01-01

Military education is held to high standards for our servicemen and women. The purpose of this quantitative, comparative study was to determine if significant differences existed in learning styles relative to military experience as determined by learning in a serious game environment. Study results are expected to advance the state of research in…

Teaching Learning Disabled Adolescents to Set Realistic Goals.

ERIC Educational Resources Information Center

Tollefson, Nona; And Others

Sixty-one learning disabled (LD) adolescents in four junior high schools were randomly assigned to experimental or control groups as part of an effort to teach LD students to set realistic goals so they might experience success and satisfaction in school. Ss in the experimental group made achievement contracts and predicted their performance in…

Using Model-Tracing to Conduct Performance Assessment of Students' Inquiry Skills within a Microworld

ERIC Educational Resources Information Center

Gobert, Janice D.; Koedinger, Kenneth R.

2011-01-01

The National frameworks for science emphasize inquiry skills (NRC, 1996), however, in typical classroom practice, science learning often focuses on rote learning in part because science process skills are difficult to assess (Fadel, Honey, & Pasnick, 2007) and rote knowledge is prioritized on high-stakes tests. Short answer assessments of…

Small Learning Communities: 2000-2003. Evaluation Brief

ERIC Educational Resources Information Center

Heath, Debra

2005-01-01

The purpose of this program evaluation was to identify the effects of Small Learning Community (SLC) reforms on school climate, student attitudes and student performance. Eight SLC programs in five Albuquerque high schools were studied for one to four years, depending on each program's date of inception. Data were collected from students,…

Illustrating performance indicators and course characteristics to support students' self-regulated learning in CS1

NASA Astrophysics Data System (ADS)

Ott, Claudia; Robins, Anthony; Haden, Patricia; Shephard, Kerry

2015-04-01

In higher education, quality feedback for students is regarded as one of the main contributors to improve student learning. Feedback to support students' development into self-regulated learners, who set their own goals, self-monitor their actual performance according to these goals, and adjust learning strategies if necessary, is seen as an important aspect of contemporary feedback practice. However, only those students who are aware of the course demands and the impact of certain study behaviors on their final achievement are in a position to self-regulate their learning on an informed basis. Learning analytics is an emerging field primarily concerned with using predictive models to inform educational instructors or learners about projected study outcomes. In a scoping study, over 200 students of an introductory programming course (CS1) were supplied with information revealing performance indicators for different stages on the course and projecting final performance for various achievement levels. The study was set out to explore the impact of this type of feedback in the confined context of a CS1 course as well as to learn about students' attitudes toward diagnostic course data in general. The results from the study suggest that students valued the information, but, despite high engagement with the information, students' study behavior and learning outcome remained rather unaffected for the aspects investigated. Given these multi-layered results, we suggest further exploration on the provision of feedback based on diagnostic course data - a vital step toward more transparency for students to foster their active role in the learning process.

Interpersonal Problem-Solving Skills, Executive Function and Learning Potential in Preadolescents with High/Low Family Risk.

PubMed

Mata, Sara; Gómez-Pérez, M Mar; Molinero, Clara; Calero, M Dolores

2017-10-30

Situations generated by high family risk have a negative effect on personal development, especially during preadolescence. Growing up in the presence of risk factors can lead to negative consequences on mental health or on school performance. The objective of this study focuses on individual factors related to this phenomenon during preadolescence. Specifically, we seek to establish whether level of family risk (high vs. low risk) is related to interpersonal problem-solving skills, executive function and learning potential in a sample of preadolescents controlling age, sex, total IQ, verbal comprehension ability and the classroom influences. The participants were 40 children, 23 boys and 17 girls between the ages of 7 and 12, twenty of which had a record on file with the Social and Childhood Protection Services of Information deleted to maintain the integrity of the review process, and therefore, a high family risk situation. The other 20 participants had a low family risk situation. Results show that the preadolescents from high family risk performed worse on interpersonal solving-problem skills and executive function (p < .05, b from -119,201.81 to 132,199.43, confidence interval from -162,589.78/-75,813.8 to 84,403.05/179,995.8). Nevertheless, they showed the same ability to learn as the participants from low family risk. These results highlight the negative effects of high family risk situation in preadolescents and give value of taking into account protective factors such as learning potential when assessing preadolescents from high family risk.

Assessing deep and shallow learning methods for quantitative prediction of acute chemical toxicity.

PubMed

Liu, Ruifeng; Madore, Michael; Glover, Kyle P; Feasel, Michael G; Wallqvist, Anders

2018-05-02

Animal-based methods for assessing chemical toxicity are struggling to meet testing demands. In silico approaches, including machine-learning methods, are promising alternatives. Recently, deep neural networks (DNNs) were evaluated and reported to outperform other machine-learning methods for quantitative structure-activity relationship modeling of molecular properties. However, most of the reported performance evaluations relied on global performance metrics, such as the root mean squared error (RMSE) between the predicted and experimental values of all samples, without considering the impact of sample distribution across the activity spectrum. Here, we carried out an in-depth analysis of DNN performance for quantitative prediction of acute chemical toxicity using several datasets. We found that the overall performance of DNN models on datasets of up to 30,000 compounds was similar to that of random forest (RF) models, as measured by the RMSE and correlation coefficients between the predicted and experimental results. However, our detailed analyses demonstrated that global performance metrics are inappropriate for datasets with a highly uneven sample distribution, because they show a strong bias for the most populous compounds along the toxicity spectrum. For highly toxic compounds, DNN and RF models trained on all samples performed much worse than the global performance metrics indicated. Surprisingly, our variable nearest neighbor method, which utilizes only structurally similar compounds to make predictions, performed reasonably well, suggesting that information of close near neighbors in the training sets is a key determinant of acute toxicity predictions.

Online Sequential Projection Vector Machine with Adaptive Data Mean Update

PubMed Central

Chen, Lin; Jia, Ji-Ting; Zhang, Qiong; Deng, Wan-Yu; Wei, Wei

2016-01-01

We propose a simple online learning algorithm especial for high-dimensional data. The algorithm is referred to as online sequential projection vector machine (OSPVM) which derives from projection vector machine and can learn from data in one-by-one or chunk-by-chunk mode. In OSPVM, data centering, dimension reduction, and neural network training are integrated seamlessly. In particular, the model parameters including (1) the projection vectors for dimension reduction, (2) the input weights, biases, and output weights, and (3) the number of hidden nodes can be updated simultaneously. Moreover, only one parameter, the number of hidden nodes, needs to be determined manually, and this makes it easy for use in real applications. Performance comparison was made on various high-dimensional classification problems for OSPVM against other fast online algorithms including budgeted stochastic gradient descent (BSGD) approach, adaptive multihyperplane machine (AMM), primal estimated subgradient solver (Pegasos), online sequential extreme learning machine (OSELM), and SVD + OSELM (feature selection based on SVD is performed before OSELM). The results obtained demonstrated the superior generalization performance and efficiency of the OSPVM. PMID:27143958

Online Sequential Projection Vector Machine with Adaptive Data Mean Update.

PubMed

Chen, Lin; Jia, Ji-Ting; Zhang, Qiong; Deng, Wan-Yu; Wei, Wei

2016-01-01

We propose a simple online learning algorithm especial for high-dimensional data. The algorithm is referred to as online sequential projection vector machine (OSPVM) which derives from projection vector machine and can learn from data in one-by-one or chunk-by-chunk mode. In OSPVM, data centering, dimension reduction, and neural network training are integrated seamlessly. In particular, the model parameters including (1) the projection vectors for dimension reduction, (2) the input weights, biases, and output weights, and (3) the number of hidden nodes can be updated simultaneously. Moreover, only one parameter, the number of hidden nodes, needs to be determined manually, and this makes it easy for use in real applications. Performance comparison was made on various high-dimensional classification problems for OSPVM against other fast online algorithms including budgeted stochastic gradient descent (BSGD) approach, adaptive multihyperplane machine (AMM), primal estimated subgradient solver (Pegasos), online sequential extreme learning machine (OSELM), and SVD + OSELM (feature selection based on SVD is performed before OSELM). The results obtained demonstrated the superior generalization performance and efficiency of the OSPVM.

Male bumblebees, Bombus terrestris, perform equally well as workers in a serial colour-learning task

PubMed Central

Wolf, Stephan; Chittka, Lars

2016-01-01

The learning capacities of males and females may differ with sex-specific behavioural requirements. Bumblebees provide a useful model system to explore how different lifestyles are reflected in learning abilities, because their (female but sterile) workers and males engage in fundamentally different behaviour routines. Bumblebee males, like workers, embark on active flower foraging but in contrast to workers they have to trade off their feeding with mate search, potentially affecting their abilities to learn and utilize floral cues efficiently during foraging. We used a serial colour-learning task with freely flying males and workers to compare their ability to flexibly learn visual floral cues with reward in a foraging scenario that changed over time. Male bumblebees did not differ from workers in both their learning speed and their ability to overcome previously acquired associations, when these ceased to predict reward. In all foraging tasks we found a significant improvement in choice accuracy in both sexes over the course of the training. In both sexes, the characteristics of the foraging performance depended largely on the colour difference of the two presented feeder types. Large colour distances entailed fast and reliable learning of the rewarding feeders whereas choice accuracy on highly similar colours improved significantly more slowly. Conversely, switching from a learned feeder type to a novel one was fastest for similar feeder colours and slow for highly different ones. Overall, we show that behavioural sex dimorphism in bumblebees did not affect their learning abilities beyond the mating context. We discuss the possible drivers and limitations shaping the foraging abilities of males and workers and implications for pollination ecology. We also suggest stingless male bumblebees as an advantageous alternative model system for the study of pollinator cognition. PMID:26877542

Comparing self-guided learning and educator-guided learning formats for simulation-based clinical training.

PubMed

Brydges, Ryan; Carnahan, Heather; Rose, Don; Dubrowski, Adam

2010-08-01

In this paper, we tested the over-arching hypothesis that progressive self-guided learning offers equivalent learning benefit vs. proficiency-based training while limiting the need to set proficiency standards. We have shown that self-guided learning is enhanced when students learn on simulators that progressively increase in fidelity during practice. Proficiency-based training, a current gold-standard training approach, requires achievement of a criterion score before students advance to the next learning level. Baccalaureate nursing students (n = 15/group) practised intravenous catheterization using simulators that differed in fidelity (i.e. students' perceived realism). Data were collected in 2008. Proficiency-based students advanced from low- to mid- to high-fidelity after achieving a proficiency criterion at each level. Progressive students self-guided their progression from low- to mid- to high-fidelity. Yoked control students followed an experimenter-defined progressive practice schedule. Open-ended students moved freely between the simulators. One week after practice, blinded experts evaluated students' skill transfer on a standardized patient simulation. Group differences were examined using analyses of variance. Proficiency-based students scored highest on the high-fidelity post-test (effect size = 1.22). An interaction effect showed that the Progressive and Open-ended groups maintained their performance from post-test to transfer test, whereas the Proficiency-based and Yoked control groups experienced a significant decrease (P < 0.05). Surprisingly, most Open-ended students (73%) chose the progressive practice schedule. Progressive training and proficiency-based training resulted in equivalent transfer test performance, suggesting that progressive students effectively self-guided when to transition between simulators. Students' preference for the progressive practice schedule indicates that educators should consider this sequence for simulation-based training.

Software on the Peregrine System | High-Performance Computing | NREL

Science.gov Websites

. Development Tools View list of tools for build automation, version control, and high-level or specialized scripting. Toolchains Learn about the available toolchains to build applications from source code

Efficacy of problem based learning in a high school science classroom

NASA Astrophysics Data System (ADS)

Rissi, James Ryan

At the high school level, the maturity of the students, as well as constraints of the traditional high school (both in terms of class time, and number of students), impedes the use of the Problem-based instruction. But with more coaching, guidance, and planning, Problem-based Learning may be an effective teaching technique with secondary students. In recent years, the State of Michigan High School Content Expectations have emphasized the importance of inquiry and problem solving in the high school science classroom. In order to help students gain inquiry and problem solving skills, a move towards a problem-based curriculum and away from the didactic approach may lead to favorable results. In this study, the problem-based-learning framework was implemented in a high school Anatomy and Physiology classroom. Using pre-tests and post-tests over the material presented using the Problem-based technique, student comprehension and long-term retention of the material was monitored. It was found that Problem-based Learning produced comparable test performance when compared to traditional lecture, note-taking, and enrichment activities. In addition, students showed evidence of gaining research and team-working skills.

Verbal learning in the context of background music: no influence of vocals and instrumentals on verbal learning

PubMed Central

2014-01-01

Background Whether listening to background music enhances verbal learning performance is still a matter of dispute. In this study we investigated the influence of vocal and instrumental background music on verbal learning. Methods 226 subjects were randomly assigned to one of five groups (one control group and 4 experimental groups). All participants were exposed to a verbal learning task. One group served as control group while the 4 further groups served as experimental groups. The control group learned without background music while the 4 experimental groups were exposed to vocal or instrumental musical pieces during learning with different subjective intensity and valence. Thus, we employed 4 music listening conditions (vocal music with high intensity: VOC_HIGH, vocal music with low intensity: VOC_LOW, instrumental music with high intensity: INST_HIGH, instrumental music with low intensity: INST_LOW) and one control condition (CONT) during which the subjects learned the word lists. Since it turned out that the high and low intensity groups did not differ in terms of the rated intensity during the main experiment these groups were lumped together. Thus, we worked with 3 groups: one control group and two groups, which were exposed to background music (vocal and instrumental) during verbal learning. As dependent variable, the number of learned words was used. Here we measured immediate recall during five learning sessions (recall 1 – recall 5) and delayed recall for 15 minutes (recall 6) and 14 days (recall 7) after the last learning session. Results Verbal learning improved during the first 5 recall sessions without any strong difference between the control and experimental groups. Also the delayed recalls were similar for the three groups. There was only a trend for attenuated verbal learning for the group passively listened to vocals. This learning attenuation diminished during the following learning sessions. Conclusions The exposure to vocal or instrumental background music during encoding did not influence verbal learning. We suggest that the participants are easily able to cope with this background stimulation by ignoring this information channel in order to focus on the verbal learning task. PMID:24670048

Validity and Practitality of Acid-Base Module Based on Guided Discovery Learning for Senior High School

NASA Astrophysics Data System (ADS)

Yerimadesi; Bayharti; Jannah, S. M.; Lufri; Festiyed; Kiram, Y.

2018-04-01

This Research and Development(R&D) aims to produce guided discovery learning based module on topic of acid-base and determine its validity and practicality in learning. Module development used Four D (4-D) model (define, design, develop and disseminate).This research was performed until development stage. Research’s instruments were validity and practicality questionnaires. Module was validated by five experts (three chemistry lecturers of Universitas Negeri Padang and two chemistry teachers of SMAN 9 Padang). Practicality test was done by two chemistry teachers and 30 students of SMAN 9 Padang. Kappa Cohen’s was used to analyze validity and practicality. The average moment kappa was 0.86 for validity and those for practicality were 0.85 by teachers and 0.76 by students revealing high category. It can be concluded that validity and practicality was proven for high school chemistry learning.

Using active learning strategies to investigate student learning and attitudes in a large enrollment, introductory geology course

NASA Astrophysics Data System (ADS)

Berry, Stacy Jane

There has been an increased emphasis for college instruction to incorporate more active and collaborative involvement of students in the learning process. These views have been asserted by The Association of American Colleges (AAC), the National Science Foundation (NSF), and The National Research Counsel (NRC), which are advocating for the modification of traditional instructional techniques to allow students the opportunity to be more cooperative (Task Group on General Education, 1988). This has guided educators and facilitators into shifting teaching paradigms from a teacher centered to a more student-centered curriculum. The present study investigated achievement outcomes and attitudes of learners in a large enrollment (n ~ 200), introductory geology course using a student centered learning cycle format of instruction versus another similar section that used a traditional lecture format. Although the course is a recruiting class for majors, over 95% of the students that enroll are non-majors. Measurements of academic evaluation were through four unit exams, classroom communication systems, weekly web-based homework, in-class activities, and a thematic collaborative poster/paper project and presentation. The qualitative methods to investigate the effectiveness of the teaching design included: direct observation, self-reporting about learning, and open-ended interviews. By disaggregating emerging data, we tried to concentrate on patterns and causal relationships between achievement performance and attitudes regarding learning geology. Statistical analyses revealed positive relationships between student engagement in supplemental activities and achievement mean scores within and between the two sections. Completing weekly online homework had the most robust relationship with overall achievement performance. Contrary to expectations, a thematic group project only led to modest gains in achievement performance, although the social and professional gains could be considered as significant as the academic merit. The qualitative data substantiated the achievement success and revealed a positive relationship between a student centered learning environment and attitudes regarding learning geology. Our findings indicated a positive trend favoring active learning instructional practices, particularly methods that emphasize independent and active thinking, and analyzing of data. Of particular interest was the correlation between the amount of student ownership in an activity and students' attitude toward authenticity and application in learning. Students' perceptions and attitudes provided depth in program evaluation and helped in identifying which components used in teaching methodologies were the most effective towards learning. Although the exigencies of high enrollment introductory courses set limits for this study, the outcomes support the positive influence that active learning has on achievement performance in a high enrollment, introductory Geology course.

Development of a water-escape motivated version of the Stone T-maze for mice

PubMed Central

Pistell, Paul J.; Ingram, Donald K.

2014-01-01

Mice provide a highly valuable resource for investigating learning and memory processes; however, many of the established tasks for evaluating learning and memory were developed for rats. Behaviors of mice in these tasks appear to be driven by different motivational factors, and as a result, they often do not perform reliably on tasks involving rewards traditionally used for rats. Because of difficulties in measuring learning and memory in mice as well as the need to have a task that can reliably measure these behavioral processes, we have developed a mouse version of the Stone T-maze utilizing what appears to be the primary motivation of mice, escape to a safe location. Specifically, we have constructed a task that requires the mouse to wade through water to reach a dark and dry goal box. To escape this aversive environment, the Stone T-maze requires learning the correct sequence of 13 left and right turns to reach the goal box. Through a series of experiments examining a variety of protocols, it was found that mice will reliably perform this task. This task can be used to assess learning and memory without the potential performance confounds that can affect performance of mice in other tasks. We believe this task offers a valuable new tool for evaluating learning and memory in mice not previously available to researchers. PMID:20026250

The Impact of Protein Structure and Sequence Similarity on the Accuracy of Machine-Learning Scoring Functions for Binding Affinity Prediction

PubMed Central

Peng, Jiangjun; Leung, Yee; Leung, Kwong-Sak; Wong, Man-Hon; Lu, Gang; Ballester, Pedro J.

2018-01-01

It has recently been claimed that the outstanding performance of machine-learning scoring functions (SFs) is exclusively due to the presence of training complexes with highly similar proteins to those in the test set. Here, we revisit this question using 24 similarity-based training sets, a widely used test set, and four SFs. Three of these SFs employ machine learning instead of the classical linear regression approach of the fourth SF (X-Score which has the best test set performance out of 16 classical SFs). We have found that random forest (RF)-based RF-Score-v3 outperforms X-Score even when 68% of the most similar proteins are removed from the training set. In addition, unlike X-Score, RF-Score-v3 is able to keep learning with an increasing training set size, becoming substantially more predictive than X-Score when the full 1105 complexes are used for training. These results show that machine-learning SFs owe a substantial part of their performance to training on complexes with dissimilar proteins to those in the test set, against what has been previously concluded using the same data. Given that a growing amount of structural and interaction data will be available from academic and industrial sources, this performance gap between machine-learning SFs and classical SFs is expected to enlarge in the future. PMID:29538331

The Impact of Protein Structure and Sequence Similarity on the Accuracy of Machine-Learning Scoring Functions for Binding Affinity Prediction.

PubMed

Li, Hongjian; Peng, Jiangjun; Leung, Yee; Leung, Kwong-Sak; Wong, Man-Hon; Lu, Gang; Ballester, Pedro J

2018-03-14

It has recently been claimed that the outstanding performance of machine-learning scoring functions (SFs) is exclusively due to the presence of training complexes with highly similar proteins to those in the test set. Here, we revisit this question using 24 similarity-based training sets, a widely used test set, and four SFs. Three of these SFs employ machine learning instead of the classical linear regression approach of the fourth SF (X-Score which has the best test set performance out of 16 classical SFs). We have found that random forest (RF)-based RF-Score-v3 outperforms X-Score even when 68% of the most similar proteins are removed from the training set. In addition, unlike X-Score, RF-Score-v3 is able to keep learning with an increasing training set size, becoming substantially more predictive than X-Score when the full 1105 complexes are used for training. These results show that machine-learning SFs owe a substantial part of their performance to training on complexes with dissimilar proteins to those in the test set, against what has been previously concluded using the same data. Given that a growing amount of structural and interaction data will be available from academic and industrial sources, this performance gap between machine-learning SFs and classical SFs is expected to enlarge in the future.

Statistical learning in social action contexts.

PubMed

Monroy, Claire; Meyer, Marlene; Gerson, Sarah; Hunnius, Sabine

2017-01-01

Sensitivity to the regularities and structure contained within sequential, goal-directed actions is an important building block for generating expectations about the actions we observe. Until now, research on statistical learning for actions has solely focused on individual action sequences, but many actions in daily life involve multiple actors in various interaction contexts. The current study is the first to investigate the role of statistical learning in tracking regularities between actions performed by different actors, and whether the social context characterizing their interaction influences learning. That is, are observers more likely to track regularities across actors if they are perceived as acting jointly as opposed to in parallel? We tested adults and toddlers to explore whether social context guides statistical learning and-if so-whether it does so from early in development. In a between-subjects eye-tracking experiment, participants were primed with a social context cue between two actors who either shared a goal of playing together ('Joint' condition) or stated the intention to act alone ('Parallel' condition). In subsequent videos, the actors performed sequential actions in which, for certain action pairs, the first actor's action reliably predicted the second actor's action. We analyzed predictive eye movements to upcoming actions as a measure of learning, and found that both adults and toddlers learned the statistical regularities across actors when their actions caused an effect. Further, adults with high statistical learning performance were sensitive to social context: those who observed actors with a shared goal were more likely to correctly predict upcoming actions. In contrast, there was no effect of social context in the toddler group, regardless of learning performance. These findings shed light on how adults and toddlers perceive statistical regularities across actors depending on the nature of the observed social situation and the resulting effects.

Statistical learning in social action contexts

PubMed Central

Meyer, Marlene; Gerson, Sarah; Hunnius, Sabine

2017-01-01

Sensitivity to the regularities and structure contained within sequential, goal-directed actions is an important building block for generating expectations about the actions we observe. Until now, research on statistical learning for actions has solely focused on individual action sequences, but many actions in daily life involve multiple actors in various interaction contexts. The current study is the first to investigate the role of statistical learning in tracking regularities between actions performed by different actors, and whether the social context characterizing their interaction influences learning. That is, are observers more likely to track regularities across actors if they are perceived as acting jointly as opposed to in parallel? We tested adults and toddlers to explore whether social context guides statistical learning and—if so—whether it does so from early in development. In a between-subjects eye-tracking experiment, participants were primed with a social context cue between two actors who either shared a goal of playing together (‘Joint’ condition) or stated the intention to act alone (‘Parallel’ condition). In subsequent videos, the actors performed sequential actions in which, for certain action pairs, the first actor’s action reliably predicted the second actor’s action. We analyzed predictive eye movements to upcoming actions as a measure of learning, and found that both adults and toddlers learned the statistical regularities across actors when their actions caused an effect. Further, adults with high statistical learning performance were sensitive to social context: those who observed actors with a shared goal were more likely to correctly predict upcoming actions. In contrast, there was no effect of social context in the toddler group, regardless of learning performance. These findings shed light on how adults and toddlers perceive statistical regularities across actors depending on the nature of the observed social situation and the resulting effects. PMID:28475619

U.S. high school curriculum: three phases of contemporary research and reform.

PubMed

Lee, Valerie E; Ready, Douglas D

2009-01-01

Valerie Lee and Douglas Ready explore the influences of the high school curriculum on student learning and the equitable distribution of that learning by race and socioeconomic status. They begin by tracing the historical development of the U.S. comprehensive high school and then examine the curricular reforms of the past three decades. During the first half of the twentieth century, the authors say, public high schools typically organized students into rigid curricular "tracks" based largely on students' past academic performance and future occupational and educational plans. During the middle of the century, however, high schools began to provide students with a choice among courses that varied in both content and academic rigor. Although the standards movement of the 1980s limited these curricular options somewhat, comprehensive curricula remained, with minority and low-income students less often completing college-prep courses. During the 1990s, say the authors, researchers who examined the associations between course-taking and student learning reported that students completing more advanced coursework learned more, regardless of their social or academic backgrounds. Based largely on this emerging research consensus favoring college-prep curriculum, in 1997 public high schools in Chicago began offering exclusively college-prep courses. To address the needs of the city's many low-performing ninth graders, schools added extra coursework in subjects in which their performance was deficient. A recent study of this reform, however, found that these approaches made little difference in student achievement. Lee and Ready hypothesize that "selection bias" may explain the divergent conclusions reached by the Chicago study and previous research. Earlier studies rarely considered the unmeasured characteristics of students who completed college-prep courses-characteristics such as motivation, access to academic supports, and better teachers-that are also positively related to student learning. Although the Chicago evaluation is only one study of one city, its findings raise the worrisome possibility that the recent push for "college-prep for all" may not generate the improvements for which researchers and policy makers had hoped.

How can surgical training benefit from theories of skilled motor development, musical skill acquisition and performance psychology?

PubMed

McCaskie, Andrew W; Kenny, Dianna T; Deshmukh, Sandeep

2011-05-02

Trainee surgeons must acquire expert status in the context of reduced hours, reduced operating room time and the need to learn complex skills involving screen-mediated techniques, computers and robotics. Ever more sophisticated surgical simulation strategies have been helpful in providing surgeons with the opportunity to practise, but not all of these strategies are widely available. Similarities in the motor skills required in skilled musical performance and surgery suggest that models of music learning, and particularly skilled motor development, may be applicable in training surgeons. More attention should be paid to factors associated with optimal arousal and optimal performance in surgical training - lessons learned from helping anxious musicians optimise performance and manage anxiety may also be transferable to trainee surgeons. The ways in which the trainee surgeon moves from novice to expert need to be better understood so that this process can be expedited using current knowledge in other disciplines requiring the performance of complex fine motor tasks with high cognitive load under pressure.

Is Math Anxiety Always Bad for Math Learning? The Role of Math Motivation.

PubMed

Wang, Zhe; Lukowski, Sarah L; Hart, Sara A; Lyons, Ian M; Thompson, Lee A; Kovas, Yulia; Mazzocco, Michèle M M; Plomin, Robert; Petrill, Stephen A

2015-12-01

The linear relations between math anxiety and math cognition have been frequently studied. However, the relations between anxiety and performance on complex cognitive tasks have been repeatedly demonstrated to follow a curvilinear fashion. In the current studies, we aimed to address the lack of attention given to the possibility of such complex interplay between emotion and cognition in the math-learning literature by exploring the relations among math anxiety, math motivation, and math cognition. In two samples-young adolescent twins and adult college students-results showed inverted-U relations between math anxiety and math performance in participants with high intrinsic math motivation and modest negative associations between math anxiety and math performance in participants with low intrinsic math motivation. However, this pattern was not observed in tasks assessing participants' nonsymbolic and symbolic number-estimation ability. These findings may help advance the understanding of mathematics-learning processes and provide important insights for treatment programs that target improving mathematics-learning experiences and mathematical skills. © The Author(s) 2015.

VI-G, Sec. 661, P.L. 91-230. Final Performance Report.

ERIC Educational Resources Information Center

1976

Presented is the final performance report of the CSDC model which is designed to provide services for learning disabled high school students. Sections cover the following program aspects: organizational structure, inservice sessions, identification of students, materials and equipment, evaluation of student performance, evaluation of the model,…

Lessons Learned from Military Performance Assessment.

ERIC Educational Resources Information Center

Wise, Lauress L.

Lessons derived from the Job Performance Measurement (JPM) Project, which is overseen by the Office of the Assistant Secretary of Defense for Force Management and Personnel, for educational assessment are explored. The JPM Project was initiated to develop high fidelity measures of performance on the job that can be used to evaluate personnel…

Tackling the x-ray cargo inspection challenge using machine learning

NASA Astrophysics Data System (ADS)

Jaccard, Nicolas; Rogers, Thomas W.; Morton, Edward J.; Griffin, Lewis D.

2016-05-01

The current infrastructure for non-intrusive inspection of cargo containers cannot accommodate exploding com-merce volumes and increasingly stringent regulations. There is a pressing need to develop methods to automate parts of the inspection workflow, enabling expert operators to focus on a manageable number of high-risk images. To tackle this challenge, we developed a modular framework for automated X-ray cargo image inspection. Employing state-of-the-art machine learning approaches, including deep learning, we demonstrate high performance for empty container verification and specific threat detection. This work constitutes a significant step towards the partial automation of X-ray cargo image inspection.

Barriers and Facilitators to Learning and Performing Cardiopulmonary Resuscitation (CPR) in Neighborhoods with Low Bystander CPR Prevalence and High Rates of Cardiac Arrest in Columbus, Ohio

PubMed Central

Sasson, Comilla; Haukoos, Jason S.; Bond, Cindy; Rabe, Marilyn; Colbert, Susan H.; King, Renee; Sayre, Michael; Heisler, Michele

2013-01-01

Background Residents who live in neighborhoods that are primarily African-American, Latino, or poor are more likely to have an out-of-hospital cardiac arrest (OHCA), less likely to receive cardiopulmonary resuscitation (CPR), and less likely to survive. No prior studies have been conducted to understand the contributing factors that may decrease the likelihood of residents learning and performing CPR in these neighborhoods. The goal of this study was to identify barriers and facilitators to learning and performing CPR in three low-income, “high-risk” predominantly African American, neighborhoods in Columbus, Ohio. Methods and Results Community-Based Participatory Research (CBPR) approaches were used to develop and conduct six focus groups in conjunction with community partners in three target high-risk neighborhoods in Columbus, Ohio in January-February 2011. Snowball and purposeful sampling, done by community liaisons, was used to recruit participants. Three reviewers analyzed the data in an iterative process to identify recurrent and unifying themes. Three major barriers to learning CPR were identified and included financial, informational, and motivational factors. Four major barriers were identified for performing CPR and included fear of legal consequences, emotional issues, knowledge, and situational concerns. Participants suggested that family/self-preservation, emotional, and economic factors may serve as potential facilitators in increasing the provision of bystander CPR. Conclusion The financial cost of CPR training, lack of information, and the fear of risking one's own life must be addressed when designing a community-based CPR educational program. Using data from the community can facilitate improved design and implementation of CPR programs. PMID:24021699

Intellectual development is positively related to intrinsic motivation and course grades for female but not male students.

PubMed

Cortright, Ronald N; Lujan, Heidi L; Cox, Julie H; Cortright, Maria A; Langworthy, Brandon M; Petta, Lorene M; Tanner, Charles J; DiCarlo, Stephen E

2015-09-01

We hypothesized that the intellectual development of students, i.e., their beliefs about the nature of knowledge and learning, affects their intrinsic motivation and class performance. Specifically, we hypothesized that students with low intellectual development (i.e., the naive beliefs that knowledge is simple, absolute, and certain) have low intrinsic motivation and low class performance, whereas students with high intellectual development (i.e., more sophisticated beliefs that knowledge is complex, tentative, and evolving) have high intrinsic motivation and class performance. To test this hypothesis, we administered the Learning Context Questionnaire to measure intellectual development. In addition, we administered the Intrinsic Motivation Inventory to assess our students' intrinsic motivation. Furthermore, we performed regression analyses between intellectual development with both intrinsic motivation and class performance. The results document a positive relationship among intellectual development, intrinsic motivation, and class performance for female students only. In sharp contrast, there was a negative relationship between intellectual development, intrinsic motivation, and class performance for male students. The slope comparisons documented significant differences in the slopes relating intellectual development, intrinsic motivation, and class performance between female and male students. Thus, female students with more sophisticated beliefs that knowledge is personally constructed, complex, and evolving had higher intrinsic motivation and class performance. In contrast, male students with the naive beliefs that the structure of knowledge is simple, absolute, and certain had higher levels of intrinsic motivation and class performance. The results suggest that sex influences intellectual development, which has an effect on intrinsic motivation for learning a specific topic. Copyright © 2015 The American Physiological Society.

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.

A Moodle-based blended learning solution for physiology education in Montenegro: a case study.

PubMed

Popovic, Natasa; Popovic, Tomo; Rovcanin Dragovic, Isidora; Cmiljanic, Oleg

2018-03-01

This study evaluates the impact of web-based blended learning in the physiology course at the Faculty of Medicine, University of Montenegro. The two main goals of the study were: to determine the impact of e-learning on student success in mastering the course, and to assess user satisfaction after the introduction of e-learning. The study compared a group of students who attended the physiology course before, with a group of students who attended the physiology course after the Moodle platform was fully implemented as an educational tool. Formative and summative assessment scores were compared between these two groups. The impact of high vs. low Moodle use on the assessment scores was analyzed. The satisfaction among Moodle users was assessed by the survey. The study found that attendance of face-to-face lectures had a positive impact on academic performance. The introduction of Moodle in the presented model of teaching increased interest of students, attendance of face-to-face lectures, as well as formative and summative scores. High frequency of Moodle use was not always associated with better academic performance, suggesting that the introduction of a new method of teaching was most likely equally accepted by low- and high-achieving students. Most of the students agreed that Moodle was easy to use and it complemented traditional teaching very well, but it could not completely replace traditional face-to-face lectures. The study supports continuing the use of web-based learning in a form of blended learning for physiology, as well as for other courses in medical education.

Motor learning in a complex balance task and associated neuroplasticity: a comparison between endurance athletes and nonathletes.

PubMed

Seidel, Oliver; Carius, Daniel; Kenville, Rouven; Ragert, Patrick

2017-09-01

Studies suggested that motor expertise is associated with functional and structural brain alterations, which positively affect sensorimotor performance and learning capabilities. The purpose of the present study was to unravel differences in motor skill learning and associated functional neuroplasticity between endurance athletes (EA) and nonathletes (NA). For this purpose, participants had to perform a multimodal balance task (MBT) training on 2 sessions, which were separated by 1 wk. Before and after MBT training, a static balance task (SBT) had to be performed. MBT-induced functional neuroplasticity and neuromuscular alterations were assessed by means of functional near-infrared spectroscopy (fNIRS) and electromyography (EMG) during SBT performance. We hypothesized that EA would showed superior initial SBT performance and stronger MBT-induced improvements in SBT learning rates compared with NA. On a cortical level, we hypothesized that MBT training would lead to differential learning-dependent functional changes in motor-related brain regions [such as primary motor cortex (M1)] during SBT performance. In fact, EA showed superior initial SBT performance, whereas learning rates did not differ between groups. On a cortical level, fNIRS recordings (time × group interaction) revealed a stronger MBT-induced decrease in left M1 and inferior parietal lobe (IPL) for deoxygenated hemoglobin in EA. Even more interesting, learning rates were correlated with fNIRS changes in right M1/IPL. On the basis of these findings, we provide novel evidence for superior MBT training-induced functional neuroplasticity in highly trained athletes. Future studies should investigate these effects in different sports disciplines to strengthen previous work on experience-dependent neuroplasticity. NEW & NOTEWORTHY Motor expertise is associated with functional/structural brain plasticity. How such neuroplastic reorganization translates into altered motor learning processes remains elusive. We investigated endurance athletes (EA) and nonathletes (NA) in a multimodal balance task (MBT). EA showed superior static balance performance (SBT), whereas MBT-induced SBT improvements did not differ between groups. Functional near-infrared spectroscopy recordings revealed a differential MBT training-induced decrease of deoxygenated hemoglobin in left primary motor cortex and inferior parietal lobe between groups. Copyright © 2017 the American Physiological Society.

Real-Time Human Detection for Aerial Captured Video Sequences via Deep Models.

PubMed

AlDahoul, Nouar; Md Sabri, Aznul Qalid; Mansoor, Ali Mohammed

2018-01-01

Human detection in videos plays an important role in various real life applications. Most of traditional approaches depend on utilizing handcrafted features which are problem-dependent and optimal for specific tasks. Moreover, they are highly susceptible to dynamical events such as illumination changes, camera jitter, and variations in object sizes. On the other hand, the proposed feature learning approaches are cheaper and easier because highly abstract and discriminative features can be produced automatically without the need of expert knowledge. In this paper, we utilize automatic feature learning methods which combine optical flow and three different deep models (i.e., supervised convolutional neural network (S-CNN), pretrained CNN feature extractor, and hierarchical extreme learning machine) for human detection in videos captured using a nonstatic camera on an aerial platform with varying altitudes. The models are trained and tested on the publicly available and highly challenging UCF-ARG aerial dataset. The comparison between these models in terms of training, testing accuracy, and learning speed is analyzed. The performance evaluation considers five human actions (digging, waving, throwing, walking, and running). Experimental results demonstrated that the proposed methods are successful for human detection task. Pretrained CNN produces an average accuracy of 98.09%. S-CNN produces an average accuracy of 95.6% with soft-max and 91.7% with Support Vector Machines (SVM). H-ELM has an average accuracy of 95.9%. Using a normal Central Processing Unit (CPU), H-ELM's training time takes 445 seconds. Learning in S-CNN takes 770 seconds with a high performance Graphical Processing Unit (GPU).

Students' perception of the learning environment and its relation to their study year and performance in Sudan.

PubMed

Ahmed, Yasar; Taha, Mohamed H; Al-Neel, Salma; Gaffar, Abdelrahim M

2018-05-24

To evaluate students' perceptions of the learning environment and to assess any differences in perception related to students' performance and their year of study. A descriptive cross-sectional study was performed of 638 students from the second, sixth and tenth semesters at the Faculty of Medicine at Gezira University, Sudan. This study employed the Arabic-translated Dundee Ready Education Environment Measure. The main predictor variables were the study year and academic performance. Descriptive statistics and one-way analysis of variance with a post hoc Tukey-Kramer multiple comparisons test were used for data analysis. The overall score for this study was 122/200 (SD=16.6), indicating a positive perception of the learning environment. The overall mean score was 109.94/200 (SD=21.2) for Semester 2 students, 122.9/200 (SD=20.29) for Semester 6 students, and 116.53 (SD=20.12) for Semester 10 students, reflecting a significant difference in students' perceptions in different years of study (F (2,2422) = 3.21, p=0.04). There was also a significant difference between the mean overall scores with respect to academic performance. High-achieving students' mean DREEM score was 126 (SD=24.4); while low-achieving students' mean DREEM score was 102 (SD=26.25) (F (2,2453) = 3.53, p=0.029). High achievers' perceptions of the learning environment are significantly better than those of low achievers. A significant difference was observed between students in different years of study. The differences in students' academic performance should be further investigated, targeting specific domains. A large-scale study is required to differentiate between the weakness and the strength of each academic level.

Effect of task-oriented training and high-variability practice on gross motor performance and activities of daily living in children with spastic diplegia.

PubMed

Kwon, Hae-Yeon; Ahn, So-Yoon

2016-10-01

[Purpose] This study investigates how a task-oriented training and high-variability practice program can affect the gross motor performance and activities of daily living for children with spastic diplegia and provides an effective and reliable clinical database for future improvement of motor performances skills. [Subjects and Methods] This study randomly assigned seven children with spastic diplegia to each intervention group including that of a control group, task-oriented training group, and a high-variability practice group. The control group only received neurodevelopmental treatment for 40 minutes, while the other two intervention groups additionally implemented a task-oriented training and high-variability practice program for 8 weeks (twice a week, 60 min per session). To compare intra and inter-relationships of the three intervention groups, this study measured gross motor performance measure (GMPM) and functional independence measure for children (WeeFIM) before and after 8 weeks of training. [Results] There were statistically significant differences in the amount of change before and after the training among the three intervention groups for the gross motor performance measure and functional independence measure. [Conclusion] Applying high-variability practice in a task-oriented training course may be considered an efficient intervention method to improve motor performance skills that can tune to movement necessary for daily livelihood through motor experience and learning of new skills as well as change of tasks learned in a complex environment or similar situations to high-variability practice.

Novel real-time tumor-contouring method using deep learning to prevent mistracking in X-ray fluoroscopy.

PubMed

Terunuma, Toshiyuki; Tokui, Aoi; Sakae, Takeji

2018-03-01

Robustness to obstacles is the most important factor necessary to achieve accurate tumor tracking without fiducial markers. Some high-density structures, such as bone, are enhanced on X-ray fluoroscopic images, which cause tumor mistracking. Tumor tracking should be performed by controlling "importance recognition": the understanding that soft-tissue is an important tracking feature and bone structure is unimportant. We propose a new real-time tumor-contouring method that uses deep learning with importance recognition control. The novelty of the proposed method is the combination of the devised random overlay method and supervised deep learning to induce the recognition of structures in tumor contouring as important or unimportant. This method can be used for tumor contouring because it uses deep learning to perform image segmentation. Our results from a simulated fluoroscopy model showed accurate tracking of a low-visibility tumor with an error of approximately 1 mm, even if enhanced bone structure acted as an obstacle. A high similarity of approximately 0.95 on the Jaccard index was observed between the segmented and ground truth tumor regions. A short processing time of 25 ms was achieved. The results of this simulated fluoroscopy model support the feasibility of robust real-time tumor contouring with fluoroscopy. Further studies using clinical fluoroscopy are highly anticipated.

A connectionist model of category learning by individuals with high-functioning autism spectrum disorder.

PubMed

Dovgopoly, Alexander; Mercado, Eduardo

2013-06-01

Individuals with autism spectrum disorder (ASD) show atypical patterns of learning and generalization. We explored the possible impacts of autism-related neural abnormalities on perceptual category learning using a neural network model of visual cortical processing. When applied to experiments in which children or adults were trained to classify complex two-dimensional images, the model can account for atypical patterns of perceptual generalization. This is only possible, however, when individual differences in learning are taken into account. In particular, analyses performed with a self-organizing map suggested that individuals with high-functioning ASD show two distinct generalization patterns: one that is comparable to typical patterns, and a second in which there is almost no generalization. The model leads to novel predictions about how individuals will generalize when trained with simplified input sets and can explain why some researchers have failed to detect learning or generalization deficits in prior studies of category learning by individuals with autism. On the basis of these simulations, we propose that deficits in basic neural plasticity mechanisms may be sufficient to account for the atypical patterns of perceptual category learning and generalization associated with autism, but they do not account for why only a subset of individuals with autism would show such deficits. If variations in performance across subgroups reflect heterogeneous neural abnormalities, then future behavioral and neuroimaging studies of individuals with ASD will need to account for such disparities.

Comparison and combination of several MeSH indexing approaches

PubMed Central

Yepes, Antonio Jose Jimeno; Mork, James G.; Demner-Fushman, Dina; Aronson, Alan R.

2013-01-01

MeSH indexing of MEDLINE is becoming a more difficult task for the group of highly qualified indexing staff at the US National Library of Medicine, due to the large yearly growth of MEDLINE and the increasing size of MeSH. Since 2002, this task has been assisted by the Medical Text Indexer or MTI program. We extend previous machine learning analysis by adding a more diverse set of MeSH headings targeting examples where MTI has been shown to perform poorly. Machine learning algorithms exceed MTI’s performance on MeSH headings that are used very frequently and headings for which the indexing frequency is very low. We find that when we combine the MTI suggestions and the prediction of the learning algorithms, the performance improves compared to any single method for most of the evaluated MeSH headings. PMID:24551371

Comparison and combination of several MeSH indexing approaches.

PubMed

Yepes, Antonio Jose Jimeno; Mork, James G; Demner-Fushman, Dina; Aronson, Alan R

2013-01-01

MeSH indexing of MEDLINE is becoming a more difficult task for the group of highly qualified indexing staff at the US National Library of Medicine, due to the large yearly growth of MEDLINE and the increasing size of MeSH. Since 2002, this task has been assisted by the Medical Text Indexer or MTI program. We extend previous machine learning analysis by adding a more diverse set of MeSH headings targeting examples where MTI has been shown to perform poorly. Machine learning algorithms exceed MTI's performance on MeSH headings that are used very frequently and headings for which the indexing frequency is very low. We find that when we combine the MTI suggestions and the prediction of the learning algorithms, the performance improves compared to any single method for most of the evaluated MeSH headings.

Focalised stimulation using high definition transcranial direct current stimulation (HD-tDCS) to investigate declarative verbal learning and memory functioning.

PubMed

Nikolin, Stevan; Loo, Colleen K; Bai, Siwei; Dokos, Socrates; Martin, Donel M

2015-08-15

Declarative verbal learning and memory are known to be lateralised to the dominant hemisphere and to be subserved by a network of structures, including those located in frontal and temporal regions. These structures support critical components of verbal memory, including working memory, encoding, and retrieval. Their relative functional importance in facilitating declarative verbal learning and memory, however, remains unclear. To investigate the different functional roles of these structures in subserving declarative verbal learning and memory performance by applying a more focal form of transcranial direct current stimulation, "High Definition tDCS" (HD-tDCS). Additionally, we sought to examine HD-tDCS effects and electrical field intensity distributions using computer modelling. HD-tDCS was administered to the left dorsolateral prefrontal cortex (LDLPFC), planum temporale (PT), and left medial temporal lobe (LMTL) to stimulate the hippocampus, during learning on a declarative verbal memory task. Sixteen healthy participants completed a single blind, intra-individual cross-over, sham-controlled study which used a Latin Square experimental design. Cognitive effects on working memory and sustained attention were additionally examined. HD-tDCS to the LDLPFC significantly improved the rate of verbal learning (p=0.03, η(2)=0.29) and speed of responding during working memory performance (p=0.02, η(2)=0.35), but not accuracy (p=0.12, η(2)=0.16). No effect of tDCS on verbal learning, retention, or retrieval was found for stimulation targeted to the LMTL or the PT. Secondary analyses revealed that LMTL stimulation resulted in increased recency (p=0.02, η(2)=0.31) and reduced mid-list learning effects (p=0.01, η(2)=0.39), suggesting an inhibitory effect on learning. HD-tDCS to the LDLPFC facilitates the rate of verbal learning and improved efficiency of working memory may underlie performance effects. This focal method of administrating tDCS has potential for probing and enhancing cognitive functioning. Copyright © 2015 Elsevier Inc. All rights reserved.

Integrating Online and Offline Three-Dimensional Deep Learning for Automated Polyp Detection in Colonoscopy Videos.

PubMed

Lequan Yu; Hao Chen; Qi Dou; Jing Qin; Pheng Ann Heng

2017-01-01

Automated polyp detection in colonoscopy videos has been demonstrated to be a promising way for colorectal cancer prevention and diagnosis. Traditional manual screening is time consuming, operator dependent, and error prone; hence, automated detection approach is highly demanded in clinical practice. However, automated polyp detection is very challenging due to high intraclass variations in polyp size, color, shape, and texture, and low interclass variations between polyps and hard mimics. In this paper, we propose a novel offline and online three-dimensional (3-D) deep learning integration framework by leveraging the 3-D fully convolutional network (3D-FCN) to tackle this challenging problem. Compared with the previous methods employing hand-crafted features or 2-D convolutional neural network, the 3D-FCN is capable of learning more representative spatio-temporal features from colonoscopy videos, and hence has more powerful discrimination capability. More importantly, we propose a novel online learning scheme to deal with the problem of limited training data by harnessing the specific information of an input video in the learning process. We integrate offline and online learning to effectively reduce the number of false positives generated by the offline network and further improve the detection performance. Extensive experiments on the dataset of MICCAI 2015 Challenge on Polyp Detection demonstrated the better performance of our method when compared with other competitors.

Multi-fidelity machine learning models for accurate bandgap predictions of solids

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

Pilania, Ghanshyam; Gubernatis, James E.; Lookman, Turab

Here, we present a multi-fidelity co-kriging statistical learning framework that combines variable-fidelity quantum mechanical calculations of bandgaps to generate a machine-learned model that enables low-cost accurate predictions of the bandgaps at the highest fidelity level. Additionally, the adopted Gaussian process regression formulation allows us to predict the underlying uncertainties as a measure of our confidence in the predictions. In using a set of 600 elpasolite compounds as an example dataset and using semi-local and hybrid exchange correlation functionals within density functional theory as two levels of fidelities, we demonstrate the excellent learning performance of the method against actual high fidelitymore » quantum mechanical calculations of the bandgaps. The presented statistical learning method is not restricted to bandgaps or electronic structure methods and extends the utility of high throughput property predictions in a significant way.« less

High fidelity simulation effectiveness in nursing students' transfer of learning.

PubMed

Kirkman, Tera R

2013-07-13

Members of nursing faculty are utilizing interactive teaching tools to improve nursing student's clinical judgment; one method that has been found to be potentially effective is high fidelity simulation (HFS). The purpose of this time series design study was to determine whether undergraduate nursing students were able to transfer knowledge and skills learned from classroom lecture and a HFS clinical to the traditional clinical setting. Students (n=42) were observed and rated on their ability to perform a respiratory assessment. The observations and ratings took place at the bedside, prior to a respiratory lecture, following the respiratory lecture, and following simulation clinical. The findings indicated that there was a significant difference (p=0.000) in transfer of learning demonstrated over time. Transfer of learning was demonstrated and the use of HFS was found to be an effective learning and teaching method. Implications of results are discussed.

Multi-fidelity machine learning models for accurate bandgap predictions of solids

DOE PAGES

Pilania, Ghanshyam; Gubernatis, James E.; Lookman, Turab

2016-12-28

Here, we present a multi-fidelity co-kriging statistical learning framework that combines variable-fidelity quantum mechanical calculations of bandgaps to generate a machine-learned model that enables low-cost accurate predictions of the bandgaps at the highest fidelity level. Additionally, the adopted Gaussian process regression formulation allows us to predict the underlying uncertainties as a measure of our confidence in the predictions. In using a set of 600 elpasolite compounds as an example dataset and using semi-local and hybrid exchange correlation functionals within density functional theory as two levels of fidelities, we demonstrate the excellent learning performance of the method against actual high fidelitymore » quantum mechanical calculations of the bandgaps. The presented statistical learning method is not restricted to bandgaps or electronic structure methods and extends the utility of high throughput property predictions in a significant way.« less

a Fully Automated Pipeline for Classification Tasks with AN Application to Remote Sensing

NASA Astrophysics Data System (ADS)

Suzuki, K.; Claesen, M.; Takeda, H.; De Moor, B.

2016-06-01

Nowadays deep learning has been intensively in spotlight owing to its great victories at major competitions, which undeservedly pushed `shallow' machine learning methods, relatively naive/handy algorithms commonly used by industrial engineers, to the background in spite of their facilities such as small requisite amount of time/dataset for training. We, with a practical point of view, utilized shallow learning algorithms to construct a learning pipeline such that operators can utilize machine learning without any special knowledge, expensive computation environment, and a large amount of labelled data. The proposed pipeline automates a whole classification process, namely feature-selection, weighting features and the selection of the most suitable classifier with optimized hyperparameters. The configuration facilitates particle swarm optimization, one of well-known metaheuristic algorithms for the sake of generally fast and fine optimization, which enables us not only to optimize (hyper)parameters but also to determine appropriate features/classifier to the problem, which has conventionally been a priori based on domain knowledge and remained untouched or dealt with naïve algorithms such as grid search. Through experiments with the MNIST and CIFAR-10 datasets, common datasets in computer vision field for character recognition and object recognition problems respectively, our automated learning approach provides high performance considering its simple setting (i.e. non-specialized setting depending on dataset), small amount of training data, and practical learning time. Moreover, compared to deep learning the performance stays robust without almost any modification even with a remote sensing object recognition problem, which in turn indicates that there is a high possibility that our approach contributes to general classification problems.

The People Side of Performance Improvement.

ERIC Educational Resources Information Center

Gerson, Richard F.

1999-01-01

Discusses 11 keys to the personal side of performance improvement, including positive attitude, high self esteem and positive self-image, communication skills, lifelong learning, caring about other people, health and well-being, motivation, goal setting, relaxation, visualization, and personal value system. (LRW)

Hierarchical Discriminant Analysis.

PubMed

Lu, Di; Ding, Chuntao; Xu, Jinliang; Wang, Shangguang

2018-01-18

The Internet of Things (IoT) generates lots of high-dimensional sensor intelligent data. The processing of high-dimensional data (e.g., data visualization and data classification) is very difficult, so it requires excellent subspace learning algorithms to learn a latent subspace to preserve the intrinsic structure of the high-dimensional data, and abandon the least useful information in the subsequent processing. In this context, many subspace learning algorithms have been presented. However, in the process of transforming the high-dimensional data into the low-dimensional space, the huge difference between the sum of inter-class distance and the sum of intra-class distance for distinct data may cause a bias problem. That means that the impact of intra-class distance is overwhelmed. To address this problem, we propose a novel algorithm called Hierarchical Discriminant Analysis (HDA). It minimizes the sum of intra-class distance first, and then maximizes the sum of inter-class distance. This proposed method balances the bias from the inter-class and that from the intra-class to achieve better performance. Extensive experiments are conducted on several benchmark face datasets. The results reveal that HDA obtains better performance than other dimensionality reduction algorithms.

Strategies to improve learning of all students in a class

NASA Astrophysics Data System (ADS)

Suraishkumar, G. K.

2018-05-01

The statistical distribution of the student learning abilities in a typical undergraduate engineering class poses a significant challenge to simultaneously improve the learning of all the students in the class. With traditional instruction styles, the students with significantly high learning abilities are not satisfied due to a feeling of unfulfilled potential, and the students with significantly low learning abilities feel lost. To address the challenge in an undergraduate core/required course on 'transport phenomena in biological systems', a combination of learning strategies such as active learning including co-operative group learning, challenge exercises, and others were employed in a pro-advising context. The short-term and long-term impacts were evaluated through student course performances and input, respectively. The results show that it is possible to effectively address the challenge posed by the distribution of student learning abilities in a class.

Predictors of Global Self-Worth and Academic Performance among Regular Education, Learning Disabled, and Continuation High School Students.

ERIC Educational Resources Information Center

Wiest, Dudley J.; Wong, Eugene H.; Kreil, Dennis A.

1998-01-01

The ability of measures of perceived competence, control, and autonomy support to predict self-worth and academic performance was studied across groups of high school students. Stepwise regression analyses indicate these variables in model predict self-worth and grade point average. In addition, levels of school status and depression predict…

From Desktop to Teraflop: Exploiting the U.S. Lead in High Performance Computing. NSF Blue Ribbon Panel on High Performance Computing.

ERIC Educational Resources Information Center

National Science Foundation, Washington, DC.

This report addresses an opportunity to accelerate progress in virtually every branch of science and engineering concurrently, while also boosting the American economy as business firms also learn to exploit these new capabilities. The successful rapid advancement in both science and technology creates its own challenges, four of which are…

Allocation Usage Tracking and Management | High-Performance Computing |

Science.gov Websites

NREL's high-performance computing (HPC) systems, learn how to track and manage your allocations. The alloc_tracker script (/usr/local/bin/alloc_tracker) may be used to see what allocations you have access to, how much of the allocation has been used, how much remains and how many node hours will be forfeited at the

Inspiring High Student Performance through an Integrated Philosophy of Education

ERIC Educational Resources Information Center

Collins, Peter M.

2006-01-01

Despite the obvious fact that high performance can be promoted in many ways, the author of this article assumed for much of his academic life as a student and as a teacher that most students were motivated to learn by grades and not much else. Experiments with pass-fail courses and reports to students of their results seemed to confirm this…

One-Time Password Tokens | High-Performance Computing | NREL

Science.gov Websites

One-Time Password Tokens One-Time Password Tokens For connecting to NREL's high-performance computing (HPC) systems, learn how to set up a one-time password (OTP) token for remote and privileged a one-time pass code from the HPC Operations team. At the sign-in screen Enter your HPC Username in

Using High Performance Computing to Examine the Processes of Neurogenesis Underlying Pattern Separation/Completion of Episodic Information.

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

Aimone, James Bradley; Betty, Rita

Using High Performance Computing to Examine the Processes of Neurogenesis Underlying Pattern Separation/Completion of Episodic Information - Sandia researchers developed novel methods and metrics for studying the computational function of neurogenesis, thus generating substantial impact to the neuroscience and neural computing communities. This work could benefit applications in machine learning and other analysis activities.

HEPDOOP: High-Energy Physics Analysis using Hadoop

NASA Astrophysics Data System (ADS)

Bhimji, W.; Bristow, T.; Washbrook, A.

2014-06-01

We perform a LHC data analysis workflow using tools and data formats that are commonly used in the "Big Data" community outside High Energy Physics (HEP). These include Apache Avro for serialisation to binary files, Pig and Hadoop for mass data processing and Python Scikit-Learn for multi-variate analysis. Comparison is made with the same analysis performed with current HEP tools in ROOT.

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.

Long-term practice effects on a new skilled motor learning: an electrophysiological study.

PubMed

Fattapposta, F; Amabile, G; Cordischi, M V; Di Venanzio, D; Foti, A; Pierelli, F; D'Alessio, C; Pigozzi, F; Parisi, A; Morrocutti, C

1996-12-01

Cortical functions concerned with the execution of skilled movements can be studied through complex interactive tasks. Skilled performance task (SPT) offers the greatest deal of information about the electrophysiological components reflecting pre-programming, execution of the movement and control of the results. Overall, these components are indicated as "movement-related brain macropotentials' (MRBMs). Among them, Bereitschaftspotential (BP) reflects cerebral processes related to the preparation of movement and skilled performance positivity (SPP) reflects control processes on the result of performance. There is some evidence supporting a training effect on MRBMs, but less clear is whether long-term practice of a skilled activity could modify learning strategies of a new skilled task. We recorded MRBMs in subjects trained for a long time to perform a highly skillful athletic activity, i.e. gun shooting, and in a group of control subjects without any former experience in skilled motor activities. Our findings demonstrated the existence of a relationship between pre-programming and performance control, as suggested by decrease of BP amplitude and increase of SPP amplitude in presence of high levels of performance. Long-term practice seems to develop better control models on performance, that reduce the need of a high mental effort in pre-programming a skilled action.

How School Administrators and Board Members Are Improving Learning and Saving Money. Energy-Smart Building Choices.

ERIC Educational Resources Information Center

Department of Energy, Washington, DC.

This guide shows ways that school administrators and board members can contribute to energy choice decisions for educational facilities, and it discusses how reducing operating costs also can create better learning environments. The guide reveals how design guidelines help create high-performance school buildings. It explains the use of energy…

What Explains Gender Gaps in Maths Achievement in Primary Schools in Kenya?

ERIC Educational Resources Information Center

Ngware, Moses W.; Ciera, James; Abuya, Benta A.; Oketch, Moses; Mutisya, Maurice

2012-01-01

This paper aims to improve the understanding of classroom-based gender differences that may lead to differential opportunities to learn provided to girls and boys in low and high performing primary schools in Kenya. The paper uses an opportunity to learn framework and tests the hypothesis that teaching practices and classroom interactions explain…

The Perceived Impact of Unstable Gaps between Academic Ability and Performance on the Self-Image of Students with Learning Disabilities

ERIC Educational Resources Information Center

Einat, Amela

2017-01-01

Disparities among the different abilities of students with learning disabilities have attracted extensive research. The attention has largely focused on how low abilities mask good or high potential intellectual level, and the resulting frustration. Correspondingly, the literature has also concentrated on the methods of detection required to…

Cultivating an Environment that Contributes to Teaching and Learning in Schools: High School Principals' Actions

ERIC Educational Resources Information Center

Lin, Mind-Dih

2012-01-01

Improving principal leadership is a vital component to the success of educational reform initiatives that seek to improve whole-school performance, as principal leadership often exercises positive but indirect effects on student learning. Because of the importance of principals within the field of school improvement, this article focuses on…

Exploring the Extreme: High Performance Learning Activities in Mathematics, Science and Technology. An Educator's Guide. EG-2002-10-001-DFRC

ERIC Educational Resources Information Center

Dana, Judi; Kock, Meri; Lewis, Mike; Peterson, Bruce; Stowe, Steve

2010-01-01

The many activities contained in this teaching guide emphasize hands-on involvement, prediction, data collection and interpretation, teamwork, and problem solving. The guide also contains background information about aeronautical research that can help students learn how airplanes fly. Following the background sections are a series of activities…

Challenges and Strategies for E-Learning Development in the Payame Noor University of Iran

ERIC Educational Resources Information Center

Mohamadzadeh, Mahnaz; Farzaneh, Jabraeal; Mousavi, Mina; Ma'ghabl, Rouhallah; Moenikia, Mahdi

2012-01-01

Higher education in Iran is confronting with several challenges; some of them are increasing demand for education and insufficiency of current programs to meet the growing needs, emerging information age and the necessity of achieving information literacy, and extending educational justice. It is assumed that a high performance e-learning system…

Effects of Extended Time Allotments on Reading Comprehension Performance of College Students with and without Learning Disabilities

ERIC Educational Resources Information Center

Lewandowski, Lawrence; Cohen, Justin; Lovett, Benjamin J.

2013-01-01

Students with disabilities often receive test accommodations in schools and on high-stakes tests. Students with learning disabilities (LD) represent the largest disability group in schools, and extended time the most common test accommodation requested by such students. This pairing persists despite controversy over the validity of extended time…

Evaluating Students with Disabilities and Their Teachers: Use of Student Learning Objectives

ERIC Educational Resources Information Center

Joyce, Jeanette; Harrison, Judith R.; Murphy, Danielle

2016-01-01

Over the past decade, there has been a movement toward increased accountability, focusing on teacher performance, in U.S. education. The purpose of this chapter is to discuss student learning objectives (SLOs) as one component of high-stakes teacher evaluation systems, within the context of learners with special needs. We describe SLOs and their…

A Sense of Balance: District Aligns Personalized Learning with School and System Goals

ERIC Educational Resources Information Center

Donsky, Debbie; Witherow, Kathy

2015-01-01

This article addresses the challenge of personalizing learning while also ensuring alignment with system and school improvement plans. Leaders of the York Region District School Board in Ontario knew that what took their high-performing school district from good to great would not take it from great to excellent. The district's early model of…

A Study of Creativity in CaC[subscript 2] Steamship-Derived STEM Project-Based Learning

ERIC Educational Resources Information Center

Lou, Shi-Jer; Chou, Yung-Chieh; Shih, Ru-Chu; Chung, Chih-Chao

2017-01-01

This study mainly aimed to explore the effects of project-based learning (PBL) integrated into science, technology, engineering and mathematics (STEM) activities and to analyze the creativity displayed by junior high school students while performing these activities. With a quasi-experimental design, 60 ninth-grade students from a junior high…

The Impact of Video Technology on Student Performance in Physical Education

ERIC Educational Resources Information Center

Palao, Jose Manuel; Hastie, Peter Andrew; Guerrero Cruz, Prudencia; Ortega, Enrique

2015-01-01

The purpose of this study was to assess the effectiveness of the use of video feedback on student learning in physical education, while also examining the teacher's responses to the innovation. Three classes from one Spanish high school participated in different conditions for learning hurdles in a track and field unit. These conditions compared…

A Model for Stochastic Drift in Memory Strength to Account for Judgments of Learning

ERIC Educational Resources Information Center

Sikstrom, Sverker; Jonsson, Fredrik

2005-01-01

Previous research has shown that judgments of learning (JOLs) made immediately after encoding have a low correlation with actual cued-recall performance, whereas the correlation is high for delayed judgments. In this article, the authors propose a formal theory describing the stochastic drift of memory strength over the retention interval to…

Aim Higher: Lofty Goals and an Aligned System Keep a High Performer on Top

ERIC Educational Resources Information Center

McCommons, David P.

2014-01-01

Every school district is feeling the pressure to ensure higher academic achievement for all students. A focus on professional learning for an administrative team not only improves student learning and achievement, but also assists in developing a systemic approach for continued success. This is how the Fox Chapel Area School District in…

The Impact of Managerial Coaching on Learning Outcomes within the Team Context: An Analysis

ERIC Educational Resources Information Center

Hagen, Marcia; Aguilar, Mariya Gavrilova

2012-01-01

This study investigates the relationship between coaching expertise, project difficulty, and team empowerment on team learning outcomes within the context of a high-performance work team. Variables were tested using multiple regression analysis. The data were analyzed for two groups--team leaders and team members--using t-tests, factor analysis,…

Comparison of the Mathematics Expectations Related to Number and Quantity for Student Learning

ERIC Educational Resources Information Center

Chen, Jung-chih; Chen, Chia-huang

2011-01-01

In the study reported here, the authors examined the LEs (learning expectations) related to Grades 1 to 8 number and quantity in mathematics across several US states and high performing TIMSS (Third International Mathematics and Science Study) Asian countries, including Singapore, Taiwan and Japan. The general strategy used is based on the topic…

Machine learning and data science in soft materials engineering

NASA Astrophysics Data System (ADS)

Ferguson, Andrew L.

2018-01-01

In many branches of materials science it is now routine to generate data sets of such large size and dimensionality that conventional methods of analysis fail. Paradigms and tools from data science and machine learning can provide scalable approaches to identify and extract trends and patterns within voluminous data sets, perform guided traversals of high-dimensional phase spaces, and furnish data-driven strategies for inverse materials design. This topical review provides an accessible introduction to machine learning tools in the context of soft and biological materials by ‘de-jargonizing’ data science terminology, presenting a taxonomy of machine learning techniques, and surveying the mathematical underpinnings and software implementations of popular tools, including principal component analysis, independent component analysis, diffusion maps, support vector machines, and relative entropy. We present illustrative examples of machine learning applications in soft matter, including inverse design of self-assembling materials, nonlinear learning of protein folding landscapes, high-throughput antimicrobial peptide design, and data-driven materials design engines. We close with an outlook on the challenges and opportunities for the field.

Machine learning and data science in soft materials engineering.

PubMed

Ferguson, Andrew L

2018-01-31

In many branches of materials science it is now routine to generate data sets of such large size and dimensionality that conventional methods of analysis fail. Paradigms and tools from data science and machine learning can provide scalable approaches to identify and extract trends and patterns within voluminous data sets, perform guided traversals of high-dimensional phase spaces, and furnish data-driven strategies for inverse materials design. This topical review provides an accessible introduction to machine learning tools in the context of soft and biological materials by 'de-jargonizing' data science terminology, presenting a taxonomy of machine learning techniques, and surveying the mathematical underpinnings and software implementations of popular tools, including principal component analysis, independent component analysis, diffusion maps, support vector machines, and relative entropy. We present illustrative examples of machine learning applications in soft matter, including inverse design of self-assembling materials, nonlinear learning of protein folding landscapes, high-throughput antimicrobial peptide design, and data-driven materials design engines. We close with an outlook on the challenges and opportunities for the field.

Rapid and accurate intraoperative pathological diagnosis by artificial intelligence with deep learning technology.

PubMed

Zhang, Jing; Song, Yanlin; Xia, Fan; Zhu, Chenjing; Zhang, Yingying; Song, Wenpeng; Xu, Jianguo; Ma, Xuelei

2017-09-01

Frozen section is widely used for intraoperative pathological diagnosis (IOPD), which is essential for intraoperative decision making. However, frozen section suffers from some drawbacks, such as time consuming and high misdiagnosis rate. Recently, artificial intelligence (AI) with deep learning technology has shown bright future in medicine. We hypothesize that AI with deep learning technology could help IOPD, with a computer trained by a dataset of intraoperative lesion images. Evidences supporting our hypothesis included the successful use of AI with deep learning technology in diagnosing skin cancer, and the developed method of deep-learning algorithm. Large size of the training dataset is critical to increase the diagnostic accuracy. The performance of the trained machine could be tested by new images before clinical use. Real-time diagnosis, easy to use and potential high accuracy were the advantages of AI for IOPD. In sum, AI with deep learning technology is a promising method to help rapid and accurate IOPD. Copyright © 2017 Elsevier Ltd. All rights reserved.

Use of interactive live digital imaging to enhance histology learning in introductory level anatomy and physiology classes.

PubMed

Higazi, Tarig B

2011-01-01

Histology is one of the main subjects in introductory college-level Human Anatomy and Physiology classes. Institutions are moving toward the replacement of traditional microscope-based histology learning with virtual microscopy learning amid concerns of losing the valuable learning experience of traditional microscopy. This study used live digital imaging (LDI) of microscopic slides on a SMART board to enhance Histology laboratory teaching. The interactive LDI system consists of a digital camera-equipped microscope that projects live images on a wall-mounted SMART board via a computer. This set-up allows real-time illustration of microscopic slides with highlighted key structural components, as well as the ability to provide the students with relevant study and review material. The impact of interactive LDI on student learning of Histology was then measured based on performance in subsequent laboratory tests before and after its implementation. Student grades increased from a mean of 76% (70.3-82.0, 95% CI) before to 92% (88.8-95.3, 95% CI) after integration of LDI indicating highly significant (P < 0.001) enhancement in students' Histology laboratory performance. In addition, student ratings of the impact of the interactive LDI on their Histology learning were strongly positive, suggesting that a majority of students who valued this learning approach also improved learning and understanding of the material as a result. The interactive LDI technique is an innovative, highly efficient and affordable tool to enhance student Histology learning, which is likely to expand knowledge and student perception of the subject and in turn enrich future science careers. Copyright © 2011 American Association of Anatomists.

Differential effects of wakeful rest, music and video game playing on working memory performance in the n-back task.

PubMed

Kuschpel, Maxim S; Liu, Shuyan; Schad, Daniel J; Heinzel, Stephan; Heinz, Andreas; Rapp, Michael A

2015-01-01

The interruption of learning processes by breaks filled with diverse activities is common in everyday life. We investigated the effects of active computer gaming and passive relaxation (rest and music) breaks on working memory performance. Young adults were exposed to breaks involving (i) eyes-open resting, (ii) listening to music and (iii) playing the video game "Angry Birds" before performing the n-back working memory task. Based on linear mixed-effects modeling, we found that playing the "Angry Birds" video game during a short learning break led to a decline in task performance over the course of the task as compared to eyes-open resting and listening to music, although overall task performance was not impaired. This effect was associated with high levels of daily mind wandering and low self-reported ability to concentrate. These findings indicate that video games can negatively affect working memory performance over time when played in between learning tasks. We suggest further investigation of these effects because of their relevance to everyday activity.

Differential effects of wakeful rest, music and video game playing on working memory performance in the n-back task

PubMed Central

Kuschpel, Maxim S.; Liu, Shuyan; Schad, Daniel J.; Heinzel, Stephan; Heinz, Andreas; Rapp, Michael A.

2015-01-01

The interruption of learning processes by breaks filled with diverse activities is common in everyday life. We investigated the effects of active computer gaming and passive relaxation (rest and music) breaks on working memory performance. Young adults were exposed to breaks involving (i) eyes-open resting, (ii) listening to music and (iii) playing the video game “Angry Birds” before performing the n-back working memory task. Based on linear mixed-effects modeling, we found that playing the “Angry Birds” video game during a short learning break led to a decline in task performance over the course of the task as compared to eyes-open resting and listening to music, although overall task performance was not impaired. This effect was associated with high levels of daily mind wandering and low self-reported ability to concentrate. These findings indicate that video games can negatively affect working memory performance over time when played in between learning tasks. We suggest further investigation of these effects because of their relevance to everyday activity. PMID:26579055

Reforming High School Science for Low-Performing Students Using Inquiry Methods and Communities of Practice

NASA Astrophysics Data System (ADS)

Bolden, Marsha Gail

Some schools fall short of the high demand to increase science scores on state exams because low-performing students enter high school unprepared for high school science. Low-performing students are not successful in high school for many reasons. However, using inquiry methods have improved students' understanding of science concepts. The purpose of this qualitative research study was to investigate the teachers' lived experiences with using inquiry methods to motivate low-performing high school science students in an inquiry-based program called Xtreem Science. Fifteen teachers were selected from the Xtreem Science program, a program designed to assist teachers in motivating struggling science students. The research questions involved understanding (a) teachers' experiences in using inquiry methods, (b) challenges teachers face in using inquiry methods, and (c) how teachers describe student's response to inquiry methods. Strategy of data collection and analysis included capturing and understanding the teachers' feelings, perceptions, and attitudes in their lived experience of teaching using inquiry method and their experience in motivating struggling students. Analysis of interview responses revealed teachers had some good experiences with inquiry and expressed that inquiry impacted their teaching style and approach to topics, and students felt that using inquiry methods impacted student learning for the better. Inquiry gave low-performing students opportunities to catch up and learn information that moved them to the next level of science courses. Implications for positive social change include providing teachers and school district leaders with information to help improve performance of the low performing science students.

Laparoscopic recurrent inguinal hernia repair during the learning curve: it can be done?

PubMed

Bracale, Umberto; Sciuto, Antonio; Andreuccetti, Jacopo; Merola, Giovanni; Pecchia, Leandro; Melillo, Paolo; Pignata, Giusto

2017-01-01

Trans-Abdominal Preperitoneal Patch (TAPP) repairs for Recurrent Hernia (RH) is a technically demanding procedure. It has to be performed only by surgeons with extensive experience in the laparoscopic approach. The purpose of this study is to evaluate the surgical safety and the efficacy of TAPP for RH performed in a tutoring program by surgeons in practice (SP). All TAPP repairs for RH performed by the same surgical team have been included in the study. We have evaluated the results of three SP during their learning curve in a tutoring program. Then these results have been compared to those of a highly experienced laparoscopic surgeon (Benchmark). A total of 530 TAPP repairs have been performed. Among these, 83 TAPP have been executed for RH, of which 43 by the Benchmark and 40 by the SP. When we have compared the outcomes of the Benchmark with those of SP, no significant difference has been observed about morbidity and recurrence while the operative time has been significantly longer for the SP. No intraoperative complications have occurred. International guidelines urge that TAPP repair for RH has to be performed only by surgeons with extensive experience in the laparoscopic approach. The results of the present study demonstrate that TAPP for RH could be performed also by surgeons in training during a learning program. We retain that an adequate tutoring program could lead a surgeon in practice to perform more complex hernia procedures without jeopardizing patient safety throughout the learning curve period. Laparoscopy, Learning Curve, Recurrent Hernia.

[Learned helplessness, generalized self-efficacy, and immune function].

PubMed

Kuno, Mayumi; Yazawa, Hisashi; Ohira, Hideki

2003-02-01

Generalized self-efficacy is considered one of important personality traits that determine psychological and physiological stress responses. The present study examined the interaction effects of generalized self-efficacy and controllability of acute stress on salivary secretory immunoglobulin A (s-IgA), task performance, and psychological stress responses in a typical learned helplessness paradigm. Twenty low and 19 high self-efficacy undergraduate women performed two response selection tasks one after another. In the first task, they were exposed to controllable or uncontrollable aversive noise. The second task was identical for all, but perceived controllability was higher for the high self-efficacy group than the low. Performance under uncontrollable condition was lower than controllable condition. The interaction of self-efficacy and controllability was observed only on the s-IgA variable; increase of secretion of s-IgA secretion under stressor uncontrollability was more prominent in the low self-efficacy group than the high. These results suggested that generalized self-efficacy was a moderator of the stressor controllability effect on secretory immunity.

Semi-Supervised Multi-View Learning for Gene Network Reconstruction

PubMed Central

Ceci, Michelangelo; Pio, Gianvito; Kuzmanovski, Vladimir; Džeroski, Sašo

2015-01-01

The task of gene regulatory network reconstruction from high-throughput data is receiving increasing attention in recent years. As a consequence, many inference methods for solving this task have been proposed in the literature. It has been recently observed, however, that no single inference method performs optimally across all datasets. It has also been shown that the integration of predictions from multiple inference methods is more robust and shows high performance across diverse datasets. Inspired by this research, in this paper, we propose a machine learning solution which learns to combine predictions from multiple inference methods. While this approach adds additional complexity to the inference process, we expect it would also carry substantial benefits. These would come from the automatic adaptation to patterns on the outputs of individual inference methods, so that it is possible to identify regulatory interactions more reliably when these patterns occur. This article demonstrates the benefits (in terms of accuracy of the reconstructed networks) of the proposed method, which exploits an iterative, semi-supervised ensemble-based algorithm. The algorithm learns to combine the interactions predicted by many different inference methods in the multi-view learning setting. The empirical evaluation of the proposed algorithm on a prokaryotic model organism (E. coli) and on a eukaryotic model organism (S. cerevisiae) clearly shows improved performance over the state of the art methods. The results indicate that gene regulatory network reconstruction for the real datasets is more difficult for S. cerevisiae than for E. coli. The software, all the datasets used in the experiments and all the results are available for download at the following link: http://figshare.com/articles/Semi_supervised_Multi_View_Learning_for_Gene_Network_Reconstruction/1604827. PMID:26641091

A comparison of progestins within three classes: Differential effects on learning and memory in the aging surgically menopausal rat.

PubMed

Braden, B Blair; Andrews, Madeline G; Acosta, Jazmin I; Mennenga, Sarah E; Lavery, Courtney; Bimonte-Nelson, Heather A

2017-03-30

For decades, progestins have been included in hormone therapies (HT) prescribed to women to offset the risk of unopposed estrogen-induced endometrial hyperplasia. However, the potential effects on cognition of subcategories of clinically used progestins have been largely unexplored. In two studies, the present investigation evaluated the cognitive effects of norethindrone acetate (NETA), levonorgestrel (LEVO), and medroxyprogesterone acetate (MPA) on the water radial-arm maze (WRAM) and Morris water maze (MM) in middle-aged ovariectomized rats. In Study 1, six-weeks of a high-dose NETA treatment impaired learning and delayed retention on the WRAM, and impaired reference memory on the MM. Low-dose NETA treatment impaired delayed retention on the WRAM. In Study 2, high-dose NETA treatment was reduced to four-weeks and compared to MPA and LEVO. As previously shown, MPA impaired working memory performance during the lattermost portion of testing, at the highest working memory load, impaired delayed retention on the WRAM, and impaired reference memory on the MM. NETA also impaired performance on these WRAM and MM measures. Interestingly, LEVO did not impair performance, but instead enhanced learning on the WRAM. The current study corroborates previous evidence that the most commonly prescribed FDA-approved progestin for HT, MPA, impairs learning and memory in the ovariectomized middle-aged rat. When progestins from two different additional subcategories were investigated, NETA impaired learning and memory similarly to MPA, but LEVO enhanced learning. Future research is warranted to determine LEVO's potential as an ideal progestin for optimal health in women, including for cognition. Copyright © 2016 Elsevier B.V. All rights reserved.

Incremental online learning in high dimensions.

PubMed

Vijayakumar, Sethu; D'Souza, Aaron; Schaal, Stefan

2005-12-01

Locally weighted projection regression (LWPR) is a new algorithm for incremental nonlinear function approximation in high-dimensional spaces with redundant and irrelevant input dimensions. At its core, it employs nonparametric regression with locally linear models. In order to stay computationally efficient and numerically robust, each local model performs the regression analysis with a small number of univariate regressions in selected directions in input space in the spirit of partial least squares regression. We discuss when and how local learning techniques can successfully work in high-dimensional spaces and review the various techniques for local dimensionality reduction before finally deriving the LWPR algorithm. The properties of LWPR are that it (1) learns rapidly with second-order learning methods based on incremental training, (2) uses statistically sound stochastic leave-one-out cross validation for learning without the need to memorize training data, (3) adjusts its weighting kernels based on only local information in order to minimize the danger of negative interference of incremental learning, (4) has a computational complexity that is linear in the number of inputs, and (5) can deal with a large number of-possibly redundant-inputs, as shown in various empirical evaluations with up to 90 dimensional data sets. For a probabilistic interpretation, predictive variance and confidence intervals are derived. To our knowledge, LWPR is the first truly incremental spatially localized learning method that can successfully and efficiently operate in very high-dimensional spaces.

Radar HRRP Target Recognition Based on Stacked Autoencoder and Extreme Learning Machine

PubMed Central

Liu, Yongxiang; Huo, Kai; Zhang, Zhongshuai

2018-01-01

A novel radar high-resolution range profile (HRRP) target recognition method based on a stacked autoencoder (SAE) and extreme learning machine (ELM) is presented in this paper. As a key component of deep structure, the SAE does not only learn features by making use of data, it also obtains feature expressions at different levels of data. However, with the deep structure, it is hard to achieve good generalization performance with a fast learning speed. ELM, as a new learning algorithm for single hidden layer feedforward neural networks (SLFNs), has attracted great interest from various fields for its fast learning speed and good generalization performance. However, ELM needs more hidden nodes than conventional tuning-based learning algorithms due to the random set of input weights and hidden biases. In addition, the existing ELM methods cannot utilize the class information of targets well. To solve this problem, a regularized ELM method based on the class information of the target is proposed. In this paper, SAE and the regularized ELM are combined to make full use of their advantages and make up for each of their shortcomings. The effectiveness of the proposed method is demonstrated by experiments with measured radar HRRP data. The experimental results show that the proposed method can achieve good performance in the two aspects of real-time and accuracy, especially when only a few training samples are available. PMID:29320453