E-Learning in Australia and Korea: Learning from Practice

ERIC Educational Resources Information Center

Misko, Josie; Choi, Jihee; Hong, Sun Yee; Lee, In Sook

2004-01-01

This project investigates the uptake of e-learning in two countries (Australia and Korea) which have experienced a rapid expansion of the use of information technology in education and training. A key finding is that although e-learning can increase flexibility and cost efficiencies in the delivery of training, it cannot on its own guarantee…

AZOrange - High performance open source machine learning for QSAR modeling in a graphical programming environment

PubMed Central

2011-01-01

Background Machine learning has a vast range of applications. In particular, advanced machine learning methods are routinely and increasingly used in quantitative structure activity relationship (QSAR) modeling. QSAR data sets often encompass tens of thousands of compounds and the size of proprietary, as well as public data sets, is rapidly growing. Hence, there is a demand for computationally efficient machine learning algorithms, easily available to researchers without extensive machine learning knowledge. In granting the scientific principles of transparency and reproducibility, Open Source solutions are increasingly acknowledged by regulatory authorities. Thus, an Open Source state-of-the-art high performance machine learning platform, interfacing multiple, customized machine learning algorithms for both graphical programming and scripting, to be used for large scale development of QSAR models of regulatory quality, is of great value to the QSAR community. Results This paper describes the implementation of the Open Source machine learning package AZOrange. AZOrange is specially developed to support batch generation of QSAR models in providing the full work flow of QSAR modeling, from descriptor calculation to automated model building, validation and selection. The automated work flow relies upon the customization of the machine learning algorithms and a generalized, automated model hyper-parameter selection process. Several high performance machine learning algorithms are interfaced for efficient data set specific selection of the statistical method, promoting model accuracy. Using the high performance machine learning algorithms of AZOrange does not require programming knowledge as flexible applications can be created, not only at a scripting level, but also in a graphical programming environment. Conclusions AZOrange is a step towards meeting the needs for an Open Source high performance machine learning platform, supporting the efficient development of highly accurate QSAR models fulfilling regulatory requirements. PMID:21798025

AZOrange - High performance open source machine learning for QSAR modeling in a graphical programming environment.

PubMed

Stålring, Jonna C; Carlsson, Lars A; Almeida, Pedro; Boyer, Scott

2011-07-28

Machine learning has a vast range of applications. In particular, advanced machine learning methods are routinely and increasingly used in quantitative structure activity relationship (QSAR) modeling. QSAR data sets often encompass tens of thousands of compounds and the size of proprietary, as well as public data sets, is rapidly growing. Hence, there is a demand for computationally efficient machine learning algorithms, easily available to researchers without extensive machine learning knowledge. In granting the scientific principles of transparency and reproducibility, Open Source solutions are increasingly acknowledged by regulatory authorities. Thus, an Open Source state-of-the-art high performance machine learning platform, interfacing multiple, customized machine learning algorithms for both graphical programming and scripting, to be used for large scale development of QSAR models of regulatory quality, is of great value to the QSAR community. This paper describes the implementation of the Open Source machine learning package AZOrange. AZOrange is specially developed to support batch generation of QSAR models in providing the full work flow of QSAR modeling, from descriptor calculation to automated model building, validation and selection. The automated work flow relies upon the customization of the machine learning algorithms and a generalized, automated model hyper-parameter selection process. Several high performance machine learning algorithms are interfaced for efficient data set specific selection of the statistical method, promoting model accuracy. Using the high performance machine learning algorithms of AZOrange does not require programming knowledge as flexible applications can be created, not only at a scripting level, but also in a graphical programming environment. AZOrange is a step towards meeting the needs for an Open Source high performance machine learning platform, supporting the efficient development of highly accurate QSAR models fulfilling regulatory requirements.

Do emergency medicine residents and faculty have similar learning styles when assessed with the Kolb learning style assessment tool?

PubMed

Fredette, Jenna; O'Brien, Corinne; Poole, Christy; Nomura, Jason

2015-04-01

Experiential learning theory and the Kolb Learning Style Inventory (Kolb LSI) have influenced educators worldwide for decades. Knowledge of learning styles can create efficient learning environments, increase information retention, and improve learner satisfaction. Learning styles have been examined in medicine previously, but not specifically with Emergency Medicine (EM) residents and attendings. Using the Kolb LSI, the learning styles of Emergency Medicine residents and attendings were assessed. The findings showed that the majority of EM residents and attendings shared the accommodating learning style. This result was different than prior studies that found the majority of medical professionals had a converging learning style and other studies that found attendings often have different learning styles than residents. The issue of learning styles among emergency medical residents and attendings is important because learning style knowledge may have an impact on how a residency program structures curriculum and how EM residents are successfully, efficiently, and creatively educated.

Does Proactive Personality Matter in Mobile Learning?

ERIC Educational Resources Information Center

Huang, Rui-Ting; Tang, Tzy-Wen; Lee, Yi Ping; Yang, Fang-Ying

2017-01-01

Increasing attention has been paid to mobile learning studies. However, there is still a dearth of studies investigating the moderating effect of proactive personality on mobile learning achievements. Accordingly, the primary purpose of this study is not only to investigate the key elements that could improve the effectiveness and efficiency of…

How to Create Shared Symbols.

PubMed

Fay, Nicolas; Walker, Bradley; Swoboda, Nik; Garrod, Simon

2018-05-01

Human cognition and behavior are dominated by symbol use. This paper examines the social learning strategies that give rise to symbolic communication. Experiment 1 contrasts an individual-level account, based on observational learning and cognitive bias, with an inter-individual account, based on social coordinative learning. Participants played a referential communication game in which they tried to communicate a range of recurring meanings to a partner by drawing, but without using their conventional language. Individual-level learning, via observation and cognitive bias, was sufficient to produce signs that became increasingly effective, efficient, and shared over games. However, breaking a referential precedent eliminated these benefits. The most effective, most efficient, and most shared signs arose when participants could directly interact with their partner, indicating that social coordinative learning is important to the creation of shared symbols. Experiment 2 investigated the contribution of two distinct aspects of social interaction: behavior alignment and concurrent partner feedback. Each played a complementary role in the creation of shared symbols: Behavior alignment primarily drove communication effectiveness, and partner feedback primarily drove the efficiency of the evolved signs. In conclusion, inter-individual social coordinative learning is important to the evolution of effective, efficient, and shared symbols. Copyright © 2018 Cognitive Science Society, Inc.

[E-Learning--an important contribution to general medical training and continuing education?].

PubMed

Ruf, D; Berner, M M; Kriston, L; Härter, M

2008-09-01

There is increasing activity in the development of e-learning modules for general medical training and continuing education. One of the central advantages of e-learning is flexibility regarding time and place of its use. The quality of the available e-learning opportunities varies quite considerably. For users it is often not easy to assess the quality of e-learning modules or to find offers of high quality. This could be a reason for the fact that despite the huge number of e-learning modules still only few students and physicians are using them. This is although e-learning has proven to be as effective as and even more efficient than learning in the classroom or with paper-based materials. This article summarizes the different models of e-learning, how and where to find offers of high quality, advantages of using e-learning, and the effectiveness and efficiency of such offers. In addition problems of e-learning and possibilities to overcome these problems are shown.

Neuronal boost to evolutionary dynamics.

PubMed

de Vladar, Harold P; Szathmáry, Eörs

2015-12-06

Standard evolutionary dynamics is limited by the constraints of the genetic system. A central message of evolutionary neurodynamics is that evolutionary dynamics in the brain can happen in a neuronal niche in real time, despite the fact that neurons do not reproduce. We show that Hebbian learning and structural synaptic plasticity broaden the capacity for informational replication and guided variability provided a neuronally plausible mechanism of replication is in place. The synergy between learning and selection is more efficient than the equivalent search by mutation selection. We also consider asymmetric landscapes and show that the learning weights become correlated with the fitness gradient. That is, the neuronal complexes learn the local properties of the fitness landscape, resulting in the generation of variability directed towards the direction of fitness increase, as if mutations in a genetic pool were drawn such that they would increase reproductive success. Evolution might thus be more efficient within evolved brains than among organisms out in the wild.

Marginal Space Deep Learning: Efficient Architecture for Volumetric Image Parsing.

PubMed

Ghesu, Florin C; Krubasik, Edward; Georgescu, Bogdan; Singh, Vivek; Yefeng Zheng; Hornegger, Joachim; Comaniciu, Dorin

2016-05-01

Robust and fast solutions for anatomical object detection and segmentation support the entire clinical workflow from diagnosis, patient stratification, therapy planning, intervention and follow-up. Current state-of-the-art techniques for parsing volumetric medical image data are typically based on machine learning methods that exploit large annotated image databases. Two main challenges need to be addressed, these are the efficiency in scanning high-dimensional parametric spaces and the need for representative image features which require significant efforts of manual engineering. We propose a pipeline for object detection and segmentation in the context of volumetric image parsing, solving a two-step learning problem: anatomical pose estimation and boundary delineation. For this task we introduce Marginal Space Deep Learning (MSDL), a novel framework exploiting both the strengths of efficient object parametrization in hierarchical marginal spaces and the automated feature design of Deep Learning (DL) network architectures. In the 3D context, the application of deep learning systems is limited by the very high complexity of the parametrization. More specifically 9 parameters are necessary to describe a restricted affine transformation in 3D, resulting in a prohibitive amount of billions of scanning hypotheses. The mechanism of marginal space learning provides excellent run-time performance by learning classifiers in clustered, high-probability regions in spaces of gradually increasing dimensionality. To further increase computational efficiency and robustness, in our system we learn sparse adaptive data sampling patterns that automatically capture the structure of the input. Given the object localization, we propose a DL-based active shape model to estimate the non-rigid object boundary. Experimental results are presented on the aortic valve in ultrasound using an extensive dataset of 2891 volumes from 869 patients, showing significant improvements of up to 45.2% over the state-of-the-art. To our knowledge, this is the first successful demonstration of the DL potential to detection and segmentation in full 3D data with parametrized representations.

Inter-Individual Differences in the Initial 80 Minutes of Motor Learning of Handrim Wheelchair Propulsion

PubMed Central

Vegter, Riemer J. K.; Lamoth, Claudine J.; de Groot, Sonja; Veeger, Dirkjan H. E. J.; van der Woude, Lucas H. V.

2014-01-01

Handrim wheelchair propulsion is a cyclic skill that needs to be learned during rehabilitation. Yet it is unclear how inter-individual differences in motor learning impact wheelchair propulsion practice. Therefore we studied how early-identified motor learning styles in novice able-bodied participants impact the outcome of a low-intensity wheelchair-practice intervention. Over a 12-minute pre-test, 39 participants were split in two groups based on a relative 10% increase in mechanical efficiency. Following the pretest the participants continued one of four different low-intensity wheelchair practice interventions, yet all performed in the same trial-setup with a total 80-minute dose at 1.11 m/s at 0.20 W/kg. Instead of focusing on the effect of the different interventions, we focused on differences in motor learning between participants over the intervention. Twenty-six participants started the pretest with a lower mechanical efficiency and a less optimal propulsion technique, but showed a fast improvement during the first 12 minutes and this effect continued over the 80 minutes of practice. Eventually these initially fast improvers benefitted more from the given practice indicated by a better propulsion technique (like reduced frequency and increased stroke angle) and a higher mechanical efficiency. The initially fast improvers also had a higher intra-individual variability in the pre and posttest, which possibly relates to the increased motor learning of the initially fast improvers. Further exploration of the common characteristics of different types of learners will help to better tailor rehabilitation to the needs of wheelchair-dependent persons and improve our understanding of cyclic motor learning processes. PMID:24586992

Making Online Learning Accessible for Students with Disabilities

ERIC Educational Resources Information Center

Hashey, Andrew I.; Stahl, Skip

2014-01-01

The growing presence of K-12 online education programs is a trend that promises to increase flexibility, improve efficiency, and foster engagement in learning. Students with disabilities can benefit from dynamic online educational environments, but only to the extent that they can access and participate in the learning process. As students with…

Evaluation Criterion for Quality Assessment of E-Learning Content

ERIC Educational Resources Information Center

Al-Alwani, Abdulkareem

2014-01-01

Research trends related to e-learning systems are oriented towards increasing the efficiency and capacity of the systems, thus they reflect a large variance in performance when considering content conformity and quality standards. The Framework related to standardisation of digital content for e-learning systems is likely to play a significant…

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

PubMed

Berger, Anna M; Davelaar, Eddy J

2018-05-15

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

Initial Skill Acquisition of Handrim Wheelchair Propulsion: A New Perspective.

PubMed

Vegter, Riemer J K; de Groot, Sonja; Lamoth, Claudine J; Veeger, Dirkjan Hej; van der Woude, Lucas H V

2014-01-01

To gain insight into cyclic motor learning processes, hand rim wheelchair propulsion is a suitable cyclic task, to be learned during early rehabilitation and novel to almost every individual. To propel in an energy efficient manner, wheelchair users must learn to control bimanually applied forces onto the rims, preserving both speed and direction of locomotion. The purpose of this study was to evaluate mechanical efficiency and propulsion technique during the initial stage of motor learning. Therefore, 70 naive able-bodied men received 12-min uninstructed wheelchair practice, consisting of three 4-min blocks separated by 2 min rest. Practice was performed on a motor-driven treadmill at a fixed belt speed and constant power output relative to body mass. Energy consumption and the kinetics of propulsion technique were continuously measured. Participants significantly increased their mechanical efficiency and changed their propulsion technique from a high frequency mode with a lot of negative work to a longer-slower movement pattern with less power losses. Furthermore a multi-level model showed propulsion technique to relate to mechanical efficiency. Finally improvers and non-improvers were identified. The non-improving group was already more efficient and had a better propulsion technique in the first block of practice (i.e., the fourth minute). These findings link propulsion technique to mechanical efficiency, support the importance of a correct propulsion technique for wheelchair users and show motor learning differences.

Impact of Introduction of Blended Learning in Gross Anatomy on Student Outcomes

ERIC Educational Resources Information Center

Green, Rodney A.; Whitburn, Laura Y.

2016-01-01

Blended learning has become increasingly common, in a variety of disciplines, to take advantage of new technology and potentially increase the efficiency and flexibility of delivery. This study aimed to describe blended delivery of a gross anatomy course and to evaluate the effectiveness of the delivery in terms of student outcomes. A gross…

The Sweet Spot in Professional Learning: When Student Learning Goals and Educator Performance Standards Align, Everything Is Possible

ERIC Educational Resources Information Center

Killion, Joellen; Kennedy, Jacqueline

2012-01-01

A sweet spot is a place where a combination of factors comes together to produce the best results with greatest efficiency. As school systems around the world are increasing expectations for what students learn and what educators do to support their learning, they must aim for the sweet spot to achieve maximum results for their efforts. When…

Learning to Ignore: A Modeling Study of a Decremental Cholinergic Pathway and Its Influence on Attention and Learning

ERIC Educational Resources Information Center

Oros, Nicolas; Chiba, Andrea A.; Nitz, Douglas A.; Krichmar, Jeffrey L.

2014-01-01

Learning to ignore irrelevant stimuli is essential to achieving efficient and fluid attention, and serves as the complement to increasing attention to relevant stimuli. The different cholinergic (ACh) subsystems within the basal forebrain regulate attention in distinct but complementary ways. ACh projections from the substantia innominata/nucleus…

A Study Module in the Logical Structure of Cognitive Process in the Context of Variable-Based Blended Learning

ERIC Educational Resources Information Center

Smirnova, Galina I.; Katashev, Valery G.

2017-01-01

Blended learning is increasingly gaining importance in all levels of educational system, particularly in tertiary education. In engineering profiles the core blended learning activity is students' independent work, the efficiency of which is defined by the degree of students' active involvement into the educational process, their ability to absorb…

Improving the Science Excursion: An Educational Technologist's View

ERIC Educational Resources Information Center

Balson, M.

1973-01-01

Analyzes the nature of the learning process and attempts to show how the three components of a reinforcement contingency, the stimulus, the response and the reinforcement can be utilized to increase the efficiency of a typical science learning experience, the excursion. (JR)

Perceptual learning improves visual performance in juvenile amblyopia.

PubMed

Li, Roger W; Young, Karen G; Hoenig, Pia; Levi, Dennis M

2005-09-01

To determine whether practicing a position-discrimination task improves visual performance in children with amblyopia and to determine the mechanism(s) of improvement. Five children (age range, 7-10 years) with amblyopia practiced a positional acuity task in which they had to judge which of three pairs of lines was misaligned. Positional noise was produced by distributing the individual patches of each line segment according to a Gaussian probability function. Observers were trained at three noise levels (including 0), with each observer performing between 3000 and 4000 responses in 7 to 10 sessions. Trial-by-trial feedback was provided. Four of the five observers showed significant improvement in positional acuity. In those four observers, on average, positional acuity with no noise improved by approximately 32% and with high noise by approximately 26%. A position-averaging model was used to parse the improvement into an increase in efficiency or a decrease in equivalent input noise. Two observers showed increased efficiency (51% and 117% improvements) with no significant change in equivalent input noise across sessions. The other two observers showed both a decrease in equivalent input noise (18% and 29%) and an increase in efficiency (17% and 71%). All five observers showed substantial improvement in Snellen acuity (approximately 26%) after practice. Perceptual learning can improve visual performance in amblyopic children. The improvement can be parsed into two important factors: decreased equivalent input noise and increased efficiency. Perceptual learning techniques may add an effective new method to the armamentarium of amblyopia treatments.

Learning to assign binary weights to binary descriptor

NASA Astrophysics Data System (ADS)

Huang, Zhoudi; Wei, Zhenzhong; Zhang, Guangjun

2016-10-01

Constructing robust binary local feature descriptors are receiving increasing interest due to their binary nature, which can enable fast processing while requiring significantly less memory than their floating-point competitors. To bridge the performance gap between the binary and floating-point descriptors without increasing the computational cost of computing and matching, optimal binary weights are learning to assign to binary descriptor for considering each bit might contribute differently to the distinctiveness and robustness. Technically, a large-scale regularized optimization method is applied to learn float weights for each bit of the binary descriptor. Furthermore, binary approximation for the float weights is performed by utilizing an efficient alternatively greedy strategy, which can significantly improve the discriminative power while preserve fast matching advantage. Extensive experimental results on two challenging datasets (Brown dataset and Oxford dataset) demonstrate the effectiveness and efficiency of the proposed method.

Neuronal boost to evolutionary dynamics

PubMed Central

de Vladar, Harold P.; Szathmáry, Eörs

2015-01-01

Standard evolutionary dynamics is limited by the constraints of the genetic system. A central message of evolutionary neurodynamics is that evolutionary dynamics in the brain can happen in a neuronal niche in real time, despite the fact that neurons do not reproduce. We show that Hebbian learning and structural synaptic plasticity broaden the capacity for informational replication and guided variability provided a neuronally plausible mechanism of replication is in place. The synergy between learning and selection is more efficient than the equivalent search by mutation selection. We also consider asymmetric landscapes and show that the learning weights become correlated with the fitness gradient. That is, the neuronal complexes learn the local properties of the fitness landscape, resulting in the generation of variability directed towards the direction of fitness increase, as if mutations in a genetic pool were drawn such that they would increase reproductive success. Evolution might thus be more efficient within evolved brains than among organisms out in the wild. PMID:26640653

Design of Open Content Social Learning That Increases Learning Efficiency and Engagement Based on Open Pedagogy

ERIC Educational Resources Information Center

John, Benneaser; Thavavel, V.; Jayaraj, Jayakumar; Muthukumar, A.; Jeevanandam, Poornaselvan Kittu

2016-01-01

Academic writing skills are crucial when students, e.g., in teacher education programs, write their undergraduate theses. A multi-modal web-based and self-regulated learning resource on academic writing was developed, using texts, hypertext, moving images, podcasts and templates. A study, using surveys and a focus group, showed that students used…

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

PubMed

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

2015-01-01

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

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

PubMed Central

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

2015-01-01

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

Data Mining for Efficient and Accurate Large Scale Retrieval of Geophysical Parameters

NASA Astrophysics Data System (ADS)

Obradovic, Z.; Vucetic, S.; Peng, K.; Han, B.

2004-12-01

Our effort is devoted to developing data mining technology for improving efficiency and accuracy of the geophysical parameter retrievals by learning a mapping from observation attributes to the corresponding parameters within the framework of classification and regression. We will describe a method for efficient learning of neural network-based classification and regression models from high-volume data streams. The proposed procedure automatically learns a series of neural networks of different complexities on smaller data stream chunks and then properly combines them into an ensemble predictor through averaging. Based on the idea of progressive sampling the proposed approach starts with a very simple network trained on a very small chunk and then gradually increases the model complexity and the chunk size until the learning performance no longer improves. Our empirical study on aerosol retrievals from data obtained with the MISR instrument mounted at Terra satellite suggests that the proposed method is successful in learning complex concepts from large data streams with near-optimal computational effort. We will also report on a method that complements deterministic retrievals by constructing accurate predictive algorithms and applying them on appropriately selected subsets of observed data. The method is based on developing more accurate predictors aimed to catch global and local properties synthesized in a region. The procedure starts by learning the global properties of data sampled over the entire space, and continues by constructing specialized models on selected localized regions. The global and local models are integrated through an automated procedure that determines the optimal trade-off between the two components with the objective of minimizing the overall mean square errors over a specific region. Our experimental results on MISR data showed that the combined model can increase the retrieval accuracy significantly. The preliminary results on various large heterogeneous spatial-temporal datasets provide evidence that the benefits of the proposed methodology for efficient and accurate learning exist beyond the area of retrieval of geophysical parameters.

Efficient Testing Combining Design of Experiment and Learn-to-Fly Strategies

NASA Technical Reports Server (NTRS)

Murphy, Patrick C.; Brandon, Jay M.

2017-01-01

Rapid modeling and efficient testing methods are important in a number of aerospace applications. In this study efficient testing strategies were evaluated in a wind tunnel test environment and combined to suggest a promising approach for both ground-based and flight-based experiments. Benefits of using Design of Experiment techniques, well established in scientific, military, and manufacturing applications are evaluated in combination with newly developing methods for global nonlinear modeling. The nonlinear modeling methods, referred to as Learn-to-Fly methods, utilize fuzzy logic and multivariate orthogonal function techniques that have been successfully demonstrated in flight test. The blended approach presented has a focus on experiment design and identifies a sequential testing process with clearly defined completion metrics that produce increased testing efficiency.

Effect of E-learning on primigravida women's satisfaction and awareness concerning prenatal care.

PubMed

Mohamadirizi, Soheila; Bahadoran, Parvin; Fahami, Fariba

2014-01-01

E-learning, in addition to promotion of patients' level of awareness, causes a more efficient way to increase patient-personnel interaction and provision of patients' educational content. In a quasi-experimental study, 100 primigravida women, referring to Navab Safavi health care center affiliated to Isfahan University of Medical Sciences, were selected through convenient sampling. The subjects received education via E-learning or booklet education methods for four weeks. Questionnaire of satisfaction with the awareness of prenatal care was completed by both groups before and 4-6 weeks after education. Data were analyzed by student t-test and paired t-test through SPSS with a significance level of P < 0.05. No significant difference was noted between scores of satisfaction and awareness in both groups before education, while a significant difference was observed four weeks after intervention (P = 0.004). There was a significant difference between scores of satisfaction and awareness after intervention in both groups (P = 0.001, P = 0.034). Satisfaction and awareness scores increased by 169% and 123%, and 61% and 37% in the E-learning and control groups, respectively (P = 0.034). E-learning can cause an increase in the level of primigravida women's satisfaction and awareness. Therefore, conducting such education, as an efficient learning method, is recommended as it needs less time, has lower costs, and does not need any special equipment.

Effect of E-learning on primigravida women's satisfaction and awareness concerning prenatal care

PubMed Central

Mohamadirizi, Soheila; Bahadoran, Parvin; Fahami, Fariba

2014-01-01

Background: E-learning, in addition to promotion of patients’ level of awareness, causes a more efficient way to increase patient-personnel interaction and provision of patients’ educational content. Materials and Methods: In a quasi-experimental study, 100 primigravida women, referring to Navab Safavi health care center affiliated to Isfahan University of Medical Sciences, were selected through convenient sampling. The subjects received education via E-learning or booklet education methods for four weeks. Questionnaire of satisfaction with the awareness of prenatal care was completed by both groups before and 4-6 weeks after education. Data were analyzed by student t-test and paired t-test through SPSS with a significance level of P < 0.05. Results: No significant difference was noted between scores of satisfaction and awareness in both groups before education, while a significant difference was observed four weeks after intervention (P = 0.004). There was a significant difference between scores of satisfaction and awareness after intervention in both groups (P = 0.001, P = 0.034). Satisfaction and awareness scores increased by 169% and 123%, and 61% and 37% in the E-learning and control groups, respectively (P = 0.034). Conclusions: E-learning can cause an increase in the level of primigravida women's satisfaction and awareness. Therefore, conducting such education, as an efficient learning method, is recommended as it needs less time, has lower costs, and does not need any special equipment. PMID:24741653

Language Learning as Linguistic Entrepreneurship: Implications for Language Education

ERIC Educational Resources Information Center

De Costa, Peter; Park, Joseph; Wee, Lionel

2016-01-01

The growing emphasis on accountability, competitiveness, efficiency, and profit demonstrates how language education has been impacted by neoliberalism. To bring out the implications of neoliberalism on language education, we explore how language learning is increasingly constructed as a form of linguistic entrepreneurship, or "an act of…

Does input influence uptake? Links between maternal talk, processing speed and vocabulary size in Spanish-learning children

PubMed Central

Hurtado, Nereyda; Marchman, Virginia A.; Fernald, Anne

2010-01-01

It is well established that variation in caregivers' speech is associated with language outcomes, yet little is known about the learning principles that mediate these effects. This longitudinal study (n = 27) explores whether Spanish-learning children's early experiences with language predict efficiency in real-time comprehension and vocabulary learning. Measures of mothers' speech at 18 months were examined in relation to children's speech processing efficiency and reported vocabulary at 18 and 24 months. Children of mothers who provided more input at 18 months knew more words and were faster in word recognition at 24 months. Moreover, multiple regression analyses indicated that the influences of caregiver speech on speed of word recognition and vocabulary were largely overlapping. This study provides the first evidence that input shapes children's lexical processing efficiency and that vocabulary growth and increasing facility in spoken word comprehension work together to support the uptake of the information that rich input affords the young language learner. PMID:19046145

Technology Engineering in Science Education: Where Instructional Challenges Interface Nonconforming Productivity to Increase Retention, Enhance Transfer, and Maximize Student Learning

ERIC Educational Resources Information Center

Osler, James E.; Hollowell, Gail P.; Nichols, Stacy M.

2012-01-01

Technology Engineering is an innovative component of a much larger arena of teaching that effectively uses interactive technology as a method of enhancing learning and the learning environment. Using this method to teach science and math content empowers the teacher and enhances the curriculum as the classroom becomes more efficient and effective.…

Exploring New Potentials of Blogs for Learning: Can Children Use Blogs for Personal Information Management (PIM)?

ERIC Educational Resources Information Center

Yeo, Hwan-Ik; Lee, Yekyung Lisa

2014-01-01

This study explores the use of blogs for personal information management (PIM) as a learning tool that could bring increased efficiency and academic self-efficacy for carrying out learning tasks. In order to identify the uses and effects of using blogs for PIM by children, a control group that used personal spaces within the class website and an…

Large-scale Cortical Network Properties Predict Future Sound-to-Word Learning Success

PubMed Central

Sheppard, John Patrick; Wang, Ji-Ping; Wong, Patrick C. M.

2013-01-01

The human brain possesses a remarkable capacity to interpret and recall novel sounds as spoken language. These linguistic abilities arise from complex processing spanning a widely distributed cortical network and are characterized by marked individual variation. Recently, graph theoretical analysis has facilitated the exploration of how such aspects of large-scale brain functional organization may underlie cognitive performance. Brain functional networks are known to possess small-world topologies characterized by efficient global and local information transfer, but whether these properties relate to language learning abilities remains unknown. Here we applied graph theory to construct large-scale cortical functional networks from cerebral hemodynamic (fMRI) responses acquired during an auditory pitch discrimination task and found that such network properties were associated with participants’ future success in learning words of an artificial spoken language. Successful learners possessed networks with reduced local efficiency but increased global efficiency relative to less successful learners and had a more cost-efficient network organization. Regionally, successful and less successful learners exhibited differences in these network properties spanning bilateral prefrontal, parietal, and right temporal cortex, overlapping a core network of auditory language areas. These results suggest that efficient cortical network organization is associated with sound-to-word learning abilities among healthy, younger adults. PMID:22360625

Efficient and self-adaptive in-situ learning in multilayer memristor neural networks.

PubMed

Li, Can; Belkin, Daniel; Li, Yunning; Yan, Peng; Hu, Miao; Ge, Ning; Jiang, Hao; Montgomery, Eric; Lin, Peng; Wang, Zhongrui; Song, Wenhao; Strachan, John Paul; Barnell, Mark; Wu, Qing; Williams, R Stanley; Yang, J Joshua; Xia, Qiangfei

2018-06-19

Memristors with tunable resistance states are emerging building blocks of artificial neural networks. However, in situ learning on a large-scale multiple-layer memristor network has yet to be demonstrated because of challenges in device property engineering and circuit integration. Here we monolithically integrate hafnium oxide-based memristors with a foundry-made transistor array into a multiple-layer neural network. We experimentally demonstrate in situ learning capability and achieve competitive classification accuracy on a standard machine learning dataset, which further confirms that the training algorithm allows the network to adapt to hardware imperfections. Our simulation using the experimental parameters suggests that a larger network would further increase the classification accuracy. The memristor neural network is a promising hardware platform for artificial intelligence with high speed-energy efficiency.

Will E-Business Shape the Future of Open and Distance Learning?

ERIC Educational Resources Information Center

Oblinger, Diana

2001-01-01

Explores the impact that electronic business is likely to have on the growth of open and distance learning. Discusses global consortia and global virtual universities; technological developments, including Web qualities; value chains; pricing models; the importance of scale; operating efficiencies; and increasing competition. (Author/LRW)

An Instructional Media Selection Guide for Distance Learning. Fourth Edition

ERIC Educational Resources Information Center

Holden, Jolly T.; Westfall, Philip J.-L.

2007-01-01

Increasingly, educators and trainers are challenged within their respective organizations to provide for the efficient distribution of instructional content using instructional media. The appropriate selection of instructional media to support distance learning is not intuitive and does not occur as a matter of personal preference. On the…

Mobile Learning: An Analysis of Student Preferences and Perceptions Surrounding Podcasting

ERIC Educational Resources Information Center

McCombs, Shawn William

2010-01-01

Today's learner arrives on our campuses with certain expectations, among them are technology innovation and availability, and the use of modern and efficient technology solutions to communicate and coexist. Meanwhile, institutions of higher learning across the country struggle with increased operating costs, decreasing legislative funding, and…

Time and learning efficiency in Internet-based learning: a systematic review and meta-analysis.

PubMed

Cook, David A; Levinson, Anthony J; Garside, Sarah

2010-12-01

Authors have claimed that Internet-based instruction promotes greater learning efficiency than non-computer methods. determine, through a systematic synthesis of evidence in health professions education, how Internet-based instruction compares with non-computer instruction in time spent learning, and what features of Internet-based instruction are associated with improved learning efficiency. we searched databases including MEDLINE, CINAHL, EMBASE, and ERIC from 1990 through November 2008. STUDY SELECTION AND DATA ABSTRACTION we included all studies quantifying learning time for Internet-based instruction for health professionals, compared with other instruction. Reviewers worked independently, in duplicate, to abstract information on interventions, outcomes, and study design. we identified 20 eligible studies. Random effects meta-analysis of 8 studies comparing Internet-based with non-Internet instruction (positive numbers indicating Internet longer) revealed pooled effect size (ES) for time -0.10 (p = 0.63). Among comparisons of two Internet-based interventions, providing feedback adds time (ES 0.67, p =0.003, two studies), and greater interactivity generally takes longer (ES 0.25, p = 0.089, five studies). One study demonstrated that adapting to learner prior knowledge saves time without significantly affecting knowledge scores. Other studies revealed that audio narration, video clips, interactive models, and animations increase learning time but also facilitate higher knowledge and/or satisfaction. Across all studies, time correlated positively with knowledge outcomes (r = 0.53, p = 0.021). on average, Internet-based instruction and non-computer instruction require similar time. Instructional strategies to enhance feedback and interactivity typically prolong learning time, but in many cases also enhance learning outcomes. Isolated examples suggest potential for improving efficiency in Internet-based instruction.

E-Learning Development Process for Operating System Course in Vocational School

NASA Astrophysics Data System (ADS)

Tuna, J. R.; Manoppo, C. T. M.; Kaparang, D. R.; Mewengkang, A.

2018-02-01

This development research aims to produce learning media in the form of E- Learning media using Edmodo which is interesting, efficient and effective on the subjects of operating system for students of class X TKJ in SMKN 3 Manado. The development model used was developed by S. Thiagarajan et al., Often known as the Four-D model, but this research only uses (define, design, and develop). Trial of the product is done twice (limited and wide). The experimental design used was the before-after experimental design. Data collection techniques used are interview techniques, questionnaires, and tests. The analytical technique used in this development research is descriptive qualitative. These include analysis of attractiveness test, efficiency and effectiveness of E-Learning media using Edmodo. The media attractiveness test was measured using a student response questionnaire. Media efficiency test was obtained through interviews of researchers with operating system subjects teachers and students of class X TKJ 1 at SMKN 3 Manado. While the media effectiveness test obtained from student learning outcomes before and after applying E-Learning media using Edomodo. Then tested by paired sample t test formula. After the media was piloted on the subject of trials (limited and broad), and the results show that E-Learning media using Edmodo is interesting, efficient and effective. It is shown on average student response score of 88.15% with very interesting interpretation. While the average value of student learning outcomes increased from 76.33 to 82.93. The results of differential test (paired sample t-test) the value of t = 11 217 ≥ ttable = 2,045 with significant value = 0.000 <α = 0.050 showing the media E -Learning using Edmodo is effective.

Does Competition Improve Public School Efficiency? A Spatial Analysis

ERIC Educational Resources Information Center

Misra, Kaustav; Grimes, Paul W.; Rogers, Kevin E.

2012-01-01

Advocates for educational reform frequently call for policies to increase competition between schools because it is argued that market forces naturally lead to greater efficiencies, including improved student learning, when schools face competition. Researchers examining this issue are confronted with difficulties in defining reasonable measures…

Representing high-dimensional data to intelligent prostheses and other wearable assistive robots: A first comparison of tile coding and selective Kanerva coding.

PubMed

Travnik, Jaden B; Pilarski, Patrick M

2017-07-01

Prosthetic devices have advanced in their capabilities and in the number and type of sensors included in their design. As the space of sensorimotor data available to a conventional or machine learning prosthetic control system increases in dimensionality and complexity, it becomes increasingly important that this data be represented in a useful and computationally efficient way. Well structured sensory data allows prosthetic control systems to make informed, appropriate control decisions. In this study, we explore the impact that increased sensorimotor information has on current machine learning prosthetic control approaches. Specifically, we examine the effect that high-dimensional sensory data has on the computation time and prediction performance of a true-online temporal-difference learning prediction method as embedded within a resource-limited upper-limb prosthesis control system. We present results comparing tile coding, the dominant linear representation for real-time prosthetic machine learning, with a newly proposed modification to Kanerva coding that we call selective Kanerva coding. In addition to showing promising results for selective Kanerva coding, our results confirm potential limitations to tile coding as the number of sensory input dimensions increases. To our knowledge, this study is the first to explicitly examine representations for realtime machine learning prosthetic devices in general terms. This work therefore provides an important step towards forming an efficient prosthesis-eye view of the world, wherein prompt and accurate representations of high-dimensional data may be provided to machine learning control systems within artificial limbs and other assistive rehabilitation technologies.

Learning just-in-time in medical informatics.

PubMed

Sancho, J J; Sanz, F

2000-01-01

Just-in-time learning (JITL) methodology has been applied to many areas of knowledge acquisition and dissemination. The paradigm is a challenge to the traditional classroom course-oriented approach with the aim to shorten the learning time, increasing the efficiency of the learning process, improve availability and save money. The information technology tools and platforms have been heavily involved to develop and deliver JITL. This paper discusses the main characteristics of JITL with regard to its implementation to teaching Medical Informatics.

Quantum machine learning.

PubMed

Biamonte, Jacob; Wittek, Peter; Pancotti, Nicola; Rebentrost, Patrick; Wiebe, Nathan; Lloyd, Seth

2017-09-13

Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Quantum systems produce atypical patterns that classical systems are thought not to produce efficiently, so it is reasonable to postulate that quantum computers may outperform classical computers on machine learning tasks. The field of quantum machine learning explores how to devise and implement quantum software that could enable machine learning that is faster than that of classical computers. Recent work has produced quantum algorithms that could act as the building blocks of machine learning programs, but the hardware and software challenges are still considerable.

Quantum machine learning

NASA Astrophysics Data System (ADS)

Biamonte, Jacob; Wittek, Peter; Pancotti, Nicola; Rebentrost, Patrick; Wiebe, Nathan; Lloyd, Seth

2017-09-01

Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Quantum systems produce atypical patterns that classical systems are thought not to produce efficiently, so it is reasonable to postulate that quantum computers may outperform classical computers on machine learning tasks. The field of quantum machine learning explores how to devise and implement quantum software that could enable machine learning that is faster than that of classical computers. Recent work has produced quantum algorithms that could act as the building blocks of machine learning programs, but the hardware and software challenges are still considerable.

Does Competition Improve Public School Efficiency? A Spatial Analysis

ERIC Educational Resources Information Center

Misra, Kaustav

2010-01-01

Proponents of educational reform often call for policies to increase competition between schools. It is argued that market forces naturally lead to greater efficiencies, including improved student learning, when schools face competition. In many parts of the country, public schools experience significant competition from private schools; however,…

The Effect of General Objectives Defined by Behavioral Objectives on Achievement in a College Zoology Course.

ERIC Educational Resources Information Center

Rushin, John W.; Baller, William

1981-01-01

Tests the effect of developmental level objectives on student achievement and efficiency in a zoology course. These objectives were found to have no significant effect on achievement, but they did significantly increase student efficiency in learning the content material of the module. (Author)

TU-C-218-01: Effective Medical Imaging Physics Education.

PubMed

Sprawls, P

2012-06-01

A practical and applied knowledge of physics and the associated technology is required for the clinically effective and safe use of the various medical imaging modalities. This is needed by all involved in the imaging process, including radiologists, especially residents in training, technologists, and physicists who provide consultation on optimum and safe procedures and as educators for the other imaging professionals. This area of education is undergoing considerable change and evolution for three reasons: 1. Increasing capabilities and complexity of medical imaging technology and procedures, 2.Expanding scope and availability of educational resources, especially on the internet, and 3. A significant increase in our knowledge of the mental learning process and the design of learning activities to optimize effectiveness and efficiency, especially for clinically applied physics. This course will address those three issues by providing guidance on establishing appropriate clinically focused learning outcomes, a review of the brain function for enhancing clinically applied physics, and the design and delivery of effective learning activities beginning with the classroom and continuing through learning physics during the clinical practice of radiology. Characteristics of each type of learning activity will be considered with respect to effectiveness and efficiency in achieving appropriate learning outcomes. A variety of available resources will be identified and demonstrated for use in the different phases of learning process. A major focus is on enhancing the role of the medical physicist in clinical radiology both as a resource and educator with contemporary technology being the tool, but not the teacher. 1. Develop physics learning objectives that will support effective and safe medical imaging procedures. 2. Understand specific brain functions that are involved in learning and applying physics. 3. Describe the characteristics and development of mental knowledge structures for applied clinical physics. 4. List the established levels of learning and associate each with specific functions that can be performed. 5. Analyze the different types of learning activities (classroom, individual study, clinical, etc.) with respect to effectiveness and efficiency. 6. Design and Provide a comprehensive physics education program with each activity optimized with respect to outcomes and available resources. © 2012 American Association of Physicists in Medicine.

Impact of an iDevice application on student learning in an occupational therapy kinesiology course.

PubMed

Hughes, Jason K; Kearney, Pamalyn

2017-01-01

As technology continues to evolve, and information is increasingly accessed through smartphones and tablets, it is essential for university faculty to reassess teaching methodologies. This study explored how use of an iDevice application (app) by participants enrolled in an entry-level occupational therapy kinesiology course affected student learning in the course. This iDevice app was developed through a collaboration between the lead author and the Department of Technology Enhanced Learning and Innovation at Augusta University. The iDevice app was released to the public via the Apple ® App Store at the midpoint of the kinesiology course. All students were invited to use the app. Focus groups were conducted with 19 students recruited from the first year cohort of occupational therapy graduate students. These focus groups were conducted at the end of the semester once grades had been submitted. Thematic analysis of focus group transcripts revealed three themes reflecting how participants perceived app use impacting their learning. Participants report the app facilitated learning through provision of visual content, serving as a reliable source of information, and generally supporting the learning process. The Kinesiology Pro Consult App provided on demand learning, allowing students to be more autonomous with their learning and take advantage of opportunities to learn anywhere and anytime. Finally, participants reported the app allowed them to be more efficient in their learning, possibly allowing more time for other courses. Mobile device apps that support student learning in specific content areas may provide positive benefits to student learning both in the specific course related to the app but also in other courses as a result of increased efficiency in learning.

Impact of an iDevice application on student learning in an occupational therapy kinesiology course

PubMed Central

Kearney, Pamalyn

2017-01-01

Background As technology continues to evolve, and information is increasingly accessed through smartphones and tablets, it is essential for university faculty to reassess teaching methodologies. This study explored how use of an iDevice application (app) by participants enrolled in an entry-level occupational therapy kinesiology course affected student learning in the course. This iDevice app was developed through a collaboration between the lead author and the Department of Technology Enhanced Learning and Innovation at Augusta University. Methods The iDevice app was released to the public via the Apple® App Store at the midpoint of the kinesiology course. All students were invited to use the app. Focus groups were conducted with 19 students recruited from the first year cohort of occupational therapy graduate students. These focus groups were conducted at the end of the semester once grades had been submitted. Results Thematic analysis of focus group transcripts revealed three themes reflecting how participants perceived app use impacting their learning. Participants report the app facilitated learning through provision of visual content, serving as a reliable source of information, and generally supporting the learning process. The Kinesiology Pro Consult App provided on demand learning, allowing students to be more autonomous with their learning and take advantage of opportunities to learn anywhere and anytime. Finally, participants reported the app allowed them to be more efficient in their learning, possibly allowing more time for other courses. Conclusions Mobile device apps that support student learning in specific content areas may provide positive benefits to student learning both in the specific course related to the app but also in other courses as a result of increased efficiency in learning. PMID:29184895

Key Competences for the Development of Lifelong Learning in the European Union

ERIC Educational Resources Information Center

Hozjan, Dejan

2009-01-01

This paper discusses certain developments in education policy in the European Union since the implementation of the Lisbon strategy. Greater focus on lifelong learning as a means of increasing the competitiveness of the European Union, and establishment of several new, efficient policy tools (above all the "open method of coordination")…

Learning Technology: Enhancing Learning in New Designs for the Comprehensive High School.

ERIC Educational Resources Information Center

Damyanovich, Mike; And Others

Technology, directed to each of the parts that collectively give shape and direction to the school, should provide the critical mass necessary to realize the specifications for the New Designs for the Comprehensive High School project. Learners should have access to personal productivity tools that increase effectiveness and efficiency in the…

Connecting Schools in Ways that Strengthen Learning Supports. A Center Policy Brief

ERIC Educational Resources Information Center

Center for Mental Health in Schools at UCLA, 2011

2011-01-01

Given dwindling budgets, collaborations that can enhance effective and efficient use of resources increase in importance. This is particularly important with respect to efforts at schools to provide student and learning supports. Schools that formally connect to work together can be more effective, realize economies of scale, and enhance the way…

Use of Distance Education by Christian Religion to Train, Edify and Educate Adherents

ERIC Educational Resources Information Center

Satyanarayana, P.; DK Meduri, Emmanuel

2013-01-01

Distance Education has been growing fast, in a marvelously diverse fashion. The efficiency, effectiveness, validity and utility of distance teaching-learning are on increase. All communities and religious groups are making use of distance learning methodology to upgrade their knowledge, skills and attitudes. Christian educational institutions in…

The First Step in Educational Problem Solving---A Systematic Assessment of Student Benefits.

ERIC Educational Resources Information Center

Sweigert, Ray L., Jr.

The limitations on educational resources and the increasing complexity in all phases of social organization demand that the educational process become more efficient and effective. A strong opinion among educators suggests that students can learn faster if the educational forces are applied systematically. To achieve this goal, learning objectives…

Assessing, Recognising and Certifying Informal and Non-Formal Learning (ARCNIL): Evolution and Challenges

ERIC Educational Resources Information Center

Svetlik, Ivan

2009-01-01

Certifying non-formal and informal knowledge may be a consequence of separating education and training from other social and economic activities. Specialisation and formalisation of education and training both aim to increase learning efficiency. In the emerging knowledge society, this has attracted particular attention among researchers and…

Incorporating the Hybrid Learning Model into Minority Education at a Historically Black University

ERIC Educational Resources Information Center

Buzzetto-More, Nicole A.; Sweat-Guy, Retta

2006-01-01

Proponents of hybrid learning proclaim it to be an effective and efficient way of expanding course content that supports in-depth delivery and analysis of knowledge (Young, 2002) and increases students satisfaction (Campos & Harasim, 1999; Dziuban & Moskal, 2001; Rivera, McAlister, & Rice, 2002; Wu & Hiltz, 2004). In the years to…

An Analysis of Learning To Plan as a Search Problem.

ERIC Educational Resources Information Center

Gratch, Jonathan; DeJong, Gerald

Increasingly, machine learning is entertained as a mechanism for improving the efficiency of planning systems. Research in this area has generated an impressive battery of techniques and a growing body of empirical successes. Unfortunately the formal properties of these systems are not well understood. This is highlighted by a growing corpus of…

Challenges Facing the Semantic Web and Social Software as Communication Technology Agents in E-Learning Environments

ERIC Educational Resources Information Center

Olaniran, Bolanle A.

2010-01-01

The semantic web describes the process whereby information content is made available for machine consumption. With increased reliance on information communication technologies, the semantic web promises effective and efficient information acquisition and dissemination of products and services in the global economy, in particular, e-learning.…

Improving Student Learning: A Strategic Planning Framework for an Integrated Student Information System in Charlotte-Mecklenburg Schools

ERIC Educational Resources Information Center

Ngoma, Sylvester

2010-01-01

There is growing recognition that an electronic Student Information System (SIS) affects student learning. Given the strategic importance of SIS in supporting school administration and enhancing student performance, school districts are increasingly interested in acquiring the most effective and efficient Student Information Systems for their…

Approximate, computationally efficient online learning in Bayesian spiking neurons.

PubMed

Kuhlmann, Levin; Hauser-Raspe, Michael; Manton, Jonathan H; Grayden, David B; Tapson, Jonathan; van Schaik, André

2014-03-01

Bayesian spiking neurons (BSNs) provide a probabilistic interpretation of how neurons perform inference and learning. Online learning in BSNs typically involves parameter estimation based on maximum-likelihood expectation-maximization (ML-EM) which is computationally slow and limits the potential of studying networks of BSNs. An online learning algorithm, fast learning (FL), is presented that is more computationally efficient than the benchmark ML-EM for a fixed number of time steps as the number of inputs to a BSN increases (e.g., 16.5 times faster run times for 20 inputs). Although ML-EM appears to converge 2.0 to 3.6 times faster than FL, the computational cost of ML-EM means that ML-EM takes longer to simulate to convergence than FL. FL also provides reasonable convergence performance that is robust to initialization of parameter estimates that are far from the true parameter values. However, parameter estimation depends on the range of true parameter values. Nevertheless, for a physiologically meaningful range of parameter values, FL gives very good average estimation accuracy, despite its approximate nature. The FL algorithm therefore provides an efficient tool, complementary to ML-EM, for exploring BSN networks in more detail in order to better understand their biological relevance. Moreover, the simplicity of the FL algorithm means it can be easily implemented in neuromorphic VLSI such that one can take advantage of the energy-efficient spike coding of BSNs.

Review on the administration and effectiveness of team-based learning in medical education.

PubMed

Hur, Yera; Cho, A Ra; Kim, Sun

2013-12-01

Team-based learning (TBL) is an active learning approach. In recent years, medical educators have been increasingly using TBL in their classes. We reviewed the concepts of TBL and discuss examples of international cases. Two types of TBL are administered: classic TBL and adapted TBL. Combining TBL and problem-based learning (PBL) might be a useful strategy for medical schools. TBL is an attainable and efficient educational approach in preparing large classes with regard to PBL. TBL improves student performance, team communication skills, leadership skills, problem solving skills, and cognitive conceptual structures and increases student engagement and satisfaction. This study suggests recommendations for administering TBL effectively in medical education.

Spoken word recognition by Latino children learning Spanish as their first language*

PubMed Central

HURTADO, NEREYDA; MARCHMAN, VIRGINIA A.; FERNALD, ANNE

2010-01-01

Research on the development of efficiency in spoken language understanding has focused largely on middle-class children learning English. Here we extend this research to Spanish-learning children (n=49; M=2;0; range=1;3–3;1) living in the USA in Latino families from primarily low socioeconomic backgrounds. Children looked at pictures of familiar objects while listening to speech naming one of the objects. Analyses of eye movements revealed developmental increases in the efficiency of speech processing. Older children and children with larger vocabularies were more efficient at processing spoken language as it unfolds in real time, as previously documented with English learners. Children whose mothers had less education tended to be slower and less accurate than children of comparable age and vocabulary size whose mothers had more schooling, consistent with previous findings of slower rates of language learning in children from disadvantaged backgrounds. These results add to the cross-linguistic literature on the development of spoken word recognition and to the study of the impact of socioeconomic status (SES) factors on early language development. PMID:17542157

Modeling the Player: Predictability of the Models of Bartle and Kolb Based on NEO-FFI (Big5) and the Implications for Game Based Learning

ERIC Educational Resources Information Center

Konert, Johannes; Gutjahr, Michael; Göbel, Stefan; Steinmetz, Ralf

2014-01-01

For adaptation and personalization of game play sophisticated player models and learner models are used in game-based learning environments. Thus, the game flow can be optimized to increase efficiency and effectiveness of gaming and learning in parallel. In the field of gaming still the Bartle model is commonly used due to its simplicity and good…

A Practical Decision Guide for Integrating Digital Applications and Handheld Devices into Advanced Individual Training

DTIC Science & Technology

2013-07-01

the devices increase efficiency and make instruction easier for them. (1) Demonstrate the ability of mobile learning to improve student learning ...predictors of learning , after controlling for the effects of cognitive ability and pre-training knowledge of the subject matter. Equally as...conventional teaching. PBL is an instructional model originally developed in medical schools , in which students are given a complex problem to solve that may

Lighting for Tomorrow: What have we learned and what about the day after tomorrow?

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

Gordon, Kelly L.; Foster, Rebecca; McGowan, Terry

2006-08-22

This paper describes Lighting for Tomorrow, a program sponsored by the US Department of Energy Emerging Technologies Program, the American Lighting Association, and the Consortium for Energy Efficiency. The program has conducted a design competition for residential decorative lighting fixtures using energy-efficient light sources. The paper discusses the reasons for development of the design competition, and the intended outcomes of the effort. The two competitive rounds completed to date are described in terms of their specific messaging and rules, direct results, and lessons learned. Experience to date is synthesized relative to the intended outcomes, including new product introductions, increased awarenessmore » of energy efficiency within the lighting industry, and increased participation by lighting showrooms in marketing and selling energy-efficient light fixtures. The paper also highlights the emergence of Lighting for Tomorrow as a forum for addressing market and technical barriers impeding use of energy-efficient lighting in the residential sector. Finally, it describes how Lighting for Tomorrow's current year (2006) program has been designed to respond to lessons from the previous competitions, feedback from the industry, and changes in lighting technology.« less

Off-line simulation inspires insight: A neurodynamics approach to efficient robot task learning.

PubMed

Sousa, Emanuel; Erlhagen, Wolfram; Ferreira, Flora; Bicho, Estela

2015-12-01

There is currently an increasing demand for robots able to acquire the sequential organization of tasks from social learning interactions with ordinary people. Interactive learning-by-demonstration and communication is a promising research topic in current robotics research. However, the efficient acquisition of generalized task representations that allow the robot to adapt to different users and contexts is a major challenge. In this paper, we present a dynamic neural field (DNF) model that is inspired by the hypothesis that the nervous system uses the off-line re-activation of initial memory traces to incrementally incorporate new information into structured knowledge. To achieve this, the model combines fast activation-based learning to robustly represent sequential information from single task demonstrations with slower, weight-based learning during internal simulations to establish longer-term associations between neural populations representing individual subtasks. The efficiency of the learning process is tested in an assembly paradigm in which the humanoid robot ARoS learns to construct a toy vehicle from its parts. User demonstrations with different serial orders together with the correction of initial prediction errors allow the robot to acquire generalized task knowledge about possible serial orders and the longer term dependencies between subgoals in very few social learning interactions. This success is shown in a joint action scenario in which ARoS uses the newly acquired assembly plan to construct the toy together with a human partner. Copyright © 2015 Elsevier Ltd. All rights reserved.

Balancing Online Teaching Activities: Strategies for Optimizing Efficiency and Effectiveness

ERIC Educational Resources Information Center

Raffo, Deana M.; Brinthaupt, Thomas M.; Gardner, Justin G.; Fisher, Lawanna S.

2015-01-01

Increased demands in professional expectations have required online faculty to learn how to balance multiple roles in an open-ended, changing, and relatively unstructured job. In this paper, we argue that being strategic about one's balance of the various facets of online teaching will improve one's teaching efficiency and effectiveness. We…

Successful Engagement: Guidance for Colleges and Providers on Effective Employer Engagement in Post-16 Learning.

ERIC Educational Resources Information Center

Hughes, Maria

Successful employer engagement requires that colleges in the United Kingdom secure employers' involvement in the design, development, management, and delivery of post-16 learning so that the skill needs of employers and the workforce will be met and the increased productivity, competitiveness, and efficiency of individual organizations and the…

Do School-Level Factors Influence the Educational Benefits of Digital Technology? A Critical Analysis of Teachers' Perceptions

ERIC Educational Resources Information Center

Perrotta, Carlo

2013-01-01

The supposed benefits of teachers' use of information and communications technology (digital technology) are well reported throughout the academic literature--most often involving issues of enhanced learning outcomes, increased pupil engagement and more efficient management and organisation of learning. This paper uses survey data from 683…

Identifying Factors that Contribute to the Satisfaction of Students in E-Learning

ERIC Educational Resources Information Center

Calli, Levent; Balcikanli, Cem; Calli, Fatih; Cebeci, Halil Ibrahim; Seymen, Omer Faruk

2013-01-01

There has been an increasing interest in the application of e-learning through the enhancement of internet and computer technologies. Satisfaction has appeared as a key factor in order to develop efficient course content in line with students' demands and expectations. Thus, a lot of research has been conducted on the concept of satisfaction in…

Collaborative learning using nursing student dyads in the clinical setting.

PubMed

Austria, Mary Jean; Baraki, Katie; Doig, Alexa K

2013-05-04

Formal pairing of student nurses to work collaboratively on one patient assignment is a strategy for improving the quality and efficiency of clinical instruction while better utilizing the limited resources at clinical agencies. The aim of this qualitative study was to explore the student nurse and patient experiences of collaborative learning when peer dyads are used in clinical nursing education. Interviews were conducted with 11 students and 9 patients. Students described the process of collaborative learning as information sharing, cross-checking when making clinical decisions, and group processing when assessing the outcomes of nursing interventions. Positive outcomes reported by students and patients included reduced student anxiety, increased confidence and task efficiency. Students' primary concern was reduced opportunity to perform hands-on skills which had to be negotiated within each dyad. Meeting the present and future challenges of educating nurses will require innovative models of clinical instruction such as collaborative learning using student peer dyads.

The neuroscience of learning.

PubMed

Collins, John W

2007-10-01

Significant advances have been made in understanding the neurophysiological basis of learning, including the discovery of mirror neurons and the role of cyclic adenosine monophosphate (cAMP) responsive element binding (CREB) protein in learning. Mirror neurons help us visually compare an observed activity with a remembered action in our memory, an ability that helps us imitate and learn through watching. Long-term potentiation, the Hebb rule, and CREB protein are associated with the formation of long-term memories. Conversely, protein phosphatase 1 and glucocorticoids are neurophysiological phenomena that limit what can be learned and cause forgetfulness. Gardner's theory of multiple intelligences contends that different areas of the brain are responsible for different competencies that we all possess to varying degrees. These multiple intelligences can be used as strategies for improved learning. Repeating material, using mnemonics, and avoiding overwhelming stress are other strategies for improving learning. Imaging studies have shown that practice with resultant learning results in significantly less use of brain areas, indicating that the brain becomes more efficient. Experts have advantages over novices, including increased cognitive processing efficiency. Nurses are in a unique position to use their understanding of neurophysiological principles to implement better educational strategies to provide quality education to patients and others.

Legionella pneumophila-induced visual learning impairment reversed by anti-interleukin-1 beta.

PubMed

Gibertini, M; Newton, C; Klein, T W; Friedman, H

1995-10-01

Infecting mice with the opportunistic intracellular pathogen Legionella pneumophila markedly inhibited place learning of infected C57BL/6 mice as determined by the Morris water maze test. Mice infected with L. pneumophila evinced much less ability to learn the position of a hidden platform than did normal noninfected mice, which quickly learned the location of the hidden platform and escaped from the cool water of the pool with increasing efficiency. However, infected mice treated with anti-interleukin-1 (anti-IL-1) neutralizing antibody learned the task with about the same efficiency as the controls. When the animals were tested 1 week after learning, control animals remembered the task well and were able to escape with near maximal efficacy. On the other hand, L. pneumophila-infected mice performed as poorly after the 1 week rest as during the training period, indicating that infection blocked learning and not merely performance. Mice infected with L. pneumophila and given the antibody treatment were found to be indistinguishable from controls in that they remembered the task and escaped with good efficiency. Thus, the results of this study suggest that the pro-inflammatory cytokine, IL-1 beta, is involved, at least partly, in the attenuation of spatial navigational learning in mice infected acutely with a sublethal concentration of L. pneumophila. These results, therefore, suggest that cognitive impairment of L. pneumophila-infected mice may be related to the cytokine IL-1 beta and, furthermore, that cytokines may be related to learning and memory changes experienced by individuals suffering acute bacterial infections.

Evolving models for medical physics education and training: a global perspective.

PubMed

Sprawls, P

2008-01-01

There is a significant need for high-quality medical physics education and training in all countries to support effective and safe use of modern medical technology for both diagnostic and treatment purposes. This is, and will continue to be, achieved using appropriate technology to increase both the effectiveness and efficiency of educational activities everywhere in the world. While the applications of technology to education and training are relatively new, the successful applications are based on theories and principles of the learning process developed by two pioneers in the field, Robert Gagne and Edgar Dale.The work of Gagne defines the different levels of learning that can occur and is used to show the types and levels of learning that are required for the application of physics and engineering principles to achieve appropriate diagnostic and therapeutic results from modern technology. The learning outcomes are determined by the effectiveness of the learning activity or experience. The extensive work of Dale as formulated in his Cone of Experience relates the effectiveness to the efficiency of educational activities. A major challenge in education is the development and conduction of learning activities (classroom discussions, laboratory and applied experiences, individual study, etc) that provide an optimum balance between effectiveness and efficiency. New and evolving models of the educational process use technology as the infrastructure to support education that is both more effective and efficient.The goal is to use technology to enhance human performance for both learners (students) and learning facilitators (teachers). A major contribution to global education is the trend in the development of shared educational resources. Two models of programs to support this effort with open and free shared resources are Physical Principles of Medical Imaging Online (http://www.sprawls.org/resources) and AAPM Continuing Education Courses (http://www.aapm.org/international).

Evolving models for medical physics education and training: a global perspective

PubMed Central

Sprawls, P

2008-01-01

There is a significant need for high-quality medical physics education and training in all countries to support effective and safe use of modern medical technology for both diagnostic and treatment purposes. This is, and will continue to be, achieved using appropriate technology to increase both the effectiveness and efficiency of educational activities everywhere in the world. While the applications of technology to education and training are relatively new, the successful applications are based on theories and principles of the learning process developed by two pioneers in the field, Robert Gagne and Edgar Dale. The work of Gagne defines the different levels of learning that can occur and is used to show the types and levels of learning that are required for the application of physics and engineering principles to achieve appropriate diagnostic and therapeutic results from modern technology. The learning outcomes are determined by the effectiveness of the learning activity or experience. The extensive work of Dale as formulated in his Cone of Experience relates the effectiveness to the efficiency of educational activities. A major challenge in education is the development and conduction of learning activities (classroom discussions, laboratory and applied experiences, individual study, etc) that provide an optimum balance between effectiveness and efficiency. New and evolving models of the educational process use technology as the infrastructure to support education that is both more effective and efficient. The goal is to use technology to enhance human performance for both learners (students) and learning facilitators (teachers). A major contribution to global education is the trend in the development of shared educational resources. Two models of programs to support this effort with open and free shared resources are Physical Principles of Medical Imaging Online (http://www.sprawls.org/resources) and AAPM Continuing Education Courses (http://www.aapm.org/international). PMID:21614309

Impact of Indoor Physical Environment on Learning Efficiency in Different Types of Tasks: A 3 × 4 × 3 Full Factorial Design Analysis.

PubMed

Xiong, Lilin; Huang, Xiao; Li, Jie; Mao, Peng; Wang, Xiang; Wang, Rubing; Tang, Meng

2018-06-13

Indoor physical environments appear to influence learning efficiency nowadays. For improvement in learning efficiency, environmental scenarios need to be designed when occupants engage in different learning tasks. However, how learning efficiency is affected by indoor physical environment based on task types are still not well understood. The present study aims to explore the impacts of three physical environmental factors (i.e., temperature, noise, and illuminance) on learning efficiency according to different types of tasks, including perception, memory, problem-solving, and attention-oriented tasks. A 3 × 4 × 3 full factorial design experiment was employed in a university classroom with 10 subjects recruited. Environmental scenarios were generated based on different levels of temperature (17 °C, 22 °C, and 27 °C), noise (40 dB(A), 50 dB(A), 60 dB(A), and 70 dB(A)) and illuminance (60 lx, 300 lx, and 2200 lx). Accuracy rate (AC), reaction time (RT), and the final performance indicator (PI) were used to quantify learning efficiency. The results showed ambient temperature, noise, and illuminance exerted significant main effect on learning efficiency based on four task types. Significant concurrent effects of the three factors on final learning efficiency was found in all tasks except problem-solving-oriented task. The optimal environmental scenarios for top learning efficiency were further identified under different environmental interactions. The highest learning efficiency came in thermoneutral, relatively quiet, and bright conditions in perception-oriented task. Subjects performed best under warm, relatively quiet, and moderately light exposure when recalling images in the memory-oriented task. Learning efficiency peaked to maxima in thermoneutral, fairly quiet, and moderately light environment in problem-solving process while in cool, fairly quiet and bright environment with regard to attention-oriented task. The study provides guidance for building users to conduct effective environmental intervention with simultaneous controls of ambient temperature, noise, and illuminance. It contributes to creating the most suitable indoor physical environment for improving occupants learning efficiency according to different task types. The findings could further supplement the present indoor environment-related standards or norms with providing empirical reference on environmental interactions.

Short term effects of methylphenidate on the cognitive, learning and academic performance of children with attention deficit disorder in the laboratory and the classroom.

PubMed

Douglas, V I; Barr, R G; O'Neill, M E; Britton, B G

1986-03-01

Sixteen children meeting diagnostic criteria for Attention Deficit Disorder with Hyperactivity (ADD-H) were tested on methylphenidate (0.3 mg/kg) and placebo on cognitive, learning, academic and behavioral measures in a double-blind study. Assessments were carried out in the laboratory and in the children's regular classrooms. Results indicate methylphenidate-induced improvements on a majority of the measures. Drug-induced changes reflected increased output, accuracy and efficiency and improved learning acquisition. There was also evidence of increased effort and self-correcting behaviours. It is argued that reviewers have underestimated the potential of stimulants to improve the performance of ADD-H children on academic, learning and cognitive tasks.

Video-task assessment of learning and memory in Macaques (Macaca mulatta) - Effects of stimulus movement on performance

NASA Technical Reports Server (NTRS)

Washburn, David A.; Hopkins, William D.; Rumbaugh, Duane M.

1989-01-01

Effects of stimulus movement on learning, transfer, matching, and short-term memory performance were assessed with 2 monkeys using a video-task paradigm in which the animals responded to computer-generated images by manipulating a joystick. Performance on tests of learning set, transfer index, matching to sample, and delayed matching to sample in the video-task paradigm was comparable to that obtained in previous investigations using the Wisconsin General Testing Apparatus. Additionally, learning, transfer, and matching were reliably and significantly better when the stimuli or discriminanda moved than when the stimuli were stationary. External manipulations such as stimulus movement may increase attention to the demands of a task, which in turn should increase the efficiency of learning. These findings have implications for the investigation of learning in other populations, as well as for the application of the video-task paradigm to comparative study.

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

PubMed

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

2016-11-23

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

The sales learning curve.

PubMed

Leslie, Mark; Holloway, Charles A

2006-01-01

When a company launches a new product into a new market, the temptation is to immediately ramp up sales force capacity to gain customers as quickly as possible. But hiring a full sales force too early just causes the firm to burn through cash and fail to meet revenue expectations. Before it can sell an innovative product efficiently, the entire organization needs to learn how customers will acquire and use it, a process the authors call the sales learning curve. The concept of a learning curve is well understood in manufacturing. Employees transfer knowledge and experience back and forth between the production line and purchasing, manufacturing, engineering, planning, and operations. The sales learning curve unfolds similarly through the give-and-take between the company--marketing, sales, product support, and product development--and its customers. As customers adopt the product, the firm modifies both the offering and the processes associated with making and selling it. Progress along the manufacturing curve is measured by tracking cost per unit: The more a firm learns about the manufacturing process, the more efficient it becomes, and the lower the unit cost goes. Progress along the sales learning curve is measured in an analogous way: The more a company learns about the sales process, the more efficient it becomes at selling, and the higher the sales yield. As the sales yield increases, the sales learning process unfolds in three distinct phases--initiation, transition, and execution. Each phase requires a different size--and kind--of sales force and represents a different stage in a company's production, marketing, and sales strategies. Adjusting those strategies as the firm progresses along the sales learning curve allows managers to plan resource allocation more accurately, set appropriate expectations, avoid disastrous cash shortfalls, and reduce both the time and money required to turn a profit.

Content-based VLE designs improve learning efficiency in constructivist statistics education.

PubMed

Wessa, Patrick; De Rycker, Antoon; Holliday, Ian Edward

2011-01-01

We introduced a series of computer-supported workshops in our undergraduate statistics courses, in the hope that it would help students to gain a deeper understanding of statistical concepts. This raised questions about the appropriate design of the Virtual Learning Environment (VLE) in which such an approach had to be implemented. Therefore, we investigated two competing software design models for VLEs. In the first system, all learning features were a function of the classical VLE. The second system was designed from the perspective that learning features should be a function of the course's core content (statistical analyses), which required us to develop a specific-purpose Statistical Learning Environment (SLE) based on Reproducible Computing and newly developed Peer Review (PR) technology. The main research question is whether the second VLE design improved learning efficiency as compared to the standard type of VLE design that is commonly used in education. As a secondary objective we provide empirical evidence about the usefulness of PR as a constructivist learning activity which supports non-rote learning. Finally, this paper illustrates that it is possible to introduce a constructivist learning approach in large student populations, based on adequately designed educational technology, without subsuming educational content to technological convenience. Both VLE systems were tested within a two-year quasi-experiment based on a Reliable Nonequivalent Group Design. This approach allowed us to draw valid conclusions about the treatment effect of the changed VLE design, even though the systems were implemented in successive years. The methodological aspects about the experiment's internal validity are explained extensively. The effect of the design change is shown to have substantially increased the efficiency of constructivist, computer-assisted learning activities for all cohorts of the student population under investigation. The findings demonstrate that a content-based design outperforms the traditional VLE-based design.

Today's Leaders for a Sustainable Tomorrow

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

Wood, Bryan

2013-02-27

Today's Leaders for a Sustainable Tomorrow is a collaboration of five residential environmental learning centers (Audubon Center of the North Woods, Deep Portage Learning Center, Laurentian Environmental Center, Long Lake Conservation Center and Wolf Ridge Environmental Learning Center) that together increased energy efficiency, energy conservation and renewable energy technologies through a number of different means appropriate for each unique center. For energy efficiency upgrades the centers installed envelope improvements to seal air barriers through better insulation in walls, ceilings, windows, doors as well as the installation of more energy efficient windows, doors, lighting and air ventilation systems. Through energy sub-metermore » monitoring the centers are able to accurately chart the usage of energy at each of their campuses and eliminate unnecessary energy usage. Facilities reduced their dependence on fossil fuel energy sources through the installation of renewable energy technologies including wood gasification, solar domestic hot water, solar photovoltaic, solar air heat, geothermal heating and wind power. Centers also installed energy education displays on the specific renewable energy technologies used at the center.« less

A diagram retrieval method with multi-label learning

NASA Astrophysics Data System (ADS)

Fu, Songping; Lu, Xiaoqing; Liu, Lu; Qu, Jingwei; Tang, Zhi

2015-01-01

In recent years, the retrieval of plane geometry figures (PGFs) has attracted increasing attention in the fields of mathematics education and computer science. However, the high cost of matching complex PGF features leads to the low efficiency of most retrieval systems. This paper proposes an indirect classification method based on multi-label learning, which improves retrieval efficiency by reducing the scope of compare operation from the whole database to small candidate groups. Label correlations among PGFs are taken into account for the multi-label classification task. The primitive feature selection for multi-label learning and the feature description of visual geometric elements are conducted individually to match similar PGFs. The experiment results show the competitive performance of the proposed method compared with existing PGF retrieval methods in terms of both time consumption and retrieval quality.

Manifold optimization-based analysis dictionary learning with an ℓ1∕2-norm regularizer.

PubMed

Li, Zhenni; Ding, Shuxue; Li, Yujie; Yang, Zuyuan; Xie, Shengli; Chen, Wuhui

2018-02-01

Recently there has been increasing attention towards analysis dictionary learning. In analysis dictionary learning, it is an open problem to obtain the strong sparsity-promoting solutions efficiently while simultaneously avoiding the trivial solutions of the dictionary. In this paper, to obtain the strong sparsity-promoting solutions, we employ the ℓ 1∕2 norm as a regularizer. The very recent study on ℓ 1∕2 norm regularization theory in compressive sensing shows that its solutions can give sparser results than using the ℓ 1 norm. We transform a complex nonconvex optimization into a number of one-dimensional minimization problems. Then the closed-form solutions can be obtained efficiently. To avoid trivial solutions, we apply manifold optimization to update the dictionary directly on the manifold satisfying the orthonormality constraint, so that the dictionary can avoid the trivial solutions well while simultaneously capturing the intrinsic properties of the dictionary. The experiments with synthetic and real-world data verify that the proposed algorithm for analysis dictionary learning can not only obtain strong sparsity-promoting solutions efficiently, but also learn more accurate dictionary in terms of dictionary recovery and image processing than the state-of-the-art algorithms. Copyright © 2017 Elsevier Ltd. All rights reserved.

Integrating the behavioral and neural dynamics of response selection in a dual-task paradigm: a dynamic neural field model of Dux et al. (2009).

PubMed

Buss, Aaron T; Wifall, Tim; Hazeltine, Eliot; Spencer, John P

2014-02-01

People are typically slower when executing two tasks than when only performing a single task. These dual-task costs are initially robust but are reduced with practice. Dux et al. (2009) explored the neural basis of dual-task costs and learning using fMRI. Inferior frontal junction (IFJ) showed a larger hemodynamic response on dual-task trials compared with single-task trial early in learning. As dual-task costs were eliminated, dual-task hemodynamics in IFJ reduced to single-task levels. Dux and colleagues concluded that the reduction of dual-task costs is accomplished through increased efficiency of information processing in IFJ. We present a dynamic field theory of response selection that addresses two questions regarding these results. First, what mechanism leads to the reduction of dual-task costs and associated changes in hemodynamics? We show that a simple Hebbian learning mechanism is able to capture the quantitative details of learning at both the behavioral and neural levels. Second, is efficiency isolated to cognitive control areas such as IFJ, or is it also evident in sensory motor areas? To investigate this, we restrict Hebbian learning to different parts of the neural model. None of the restricted learning models showed the same reductions in dual-task costs as the unrestricted learning model, suggesting that efficiency is distributed across cognitive control and sensory motor processing systems.

The Transition to Blended Learning in a School of Nursing at a Developing Country: An Evaluation

ERIC Educational Resources Information Center

Alarbeed, Adham; Al Hakim, Diala

2014-01-01

Within the past two decades, Blended Learning (BL) programs have become very prevalent. The number of offered courses is continually increasing. The factors which support this fact are mostly related to the technological advances that have made the obtainability both efficient and practical. A School of Nursing (SoN) started a Faculty Professional…

Design Process for Online Websites Created for Teaching Turkish as a Foreign Language in Web Based Environments

ERIC Educational Resources Information Center

Türker, Fatih Mehmet

2016-01-01

In today's world, where online learning environments have increased their efficiency in education and training, the design of the websites prepared for education and training purposes has become an important process. This study is about the teaching process of the online learning environments created to teach Turkish in web based environments, and…

A Neurocomputational Account of Taxonomic Responding and Fast Mapping in Early Word Learning

ERIC Educational Resources Information Center

Mayor, Julien; Plunkett, Kim

2010-01-01

We present a neurocomputational model with self-organizing maps that accounts for the emergence of taxonomic responding and fast mapping in early word learning, as well as a rapid increase in the rate of acquisition of words observed in late infancy. The quality and efficiency of generalization of word-object associations is directly related to…

Pre-Constructed Dynamic Geometry Materials in the Classroom--How Do They Facilitate the Learning of "Similar Triangles"?

ERIC Educational Resources Information Center

Poon, Kin Keung; Wong, Kwan Lam

2017-01-01

The use of dynamic geometry software (DGS) is becoming increasingly familiar among teachers, but letting students conduct inquiries using computers is still not a welcome idea. In addition to logistics and discipline concerns, many teachers believe that mathematics at the lower secondary level can be learned efficiently through practice alone.…

Developmental English Professors' Experiences with Learning Management Systems at an Urban Community College: Challenges, Benefits, and Other Perceptions

ERIC Educational Resources Information Center

Lewis, Amy L.

2016-01-01

Universities and colleges are embracing and utilizing technology to a rapidly increasing extent, responding to its cost-effectiveness and efficiency as well as the regularity with which 21st century students rely upon it in their everyday lives. Chief amongst the technology used in higher education are Learning Management Systems (LMS), such as…

Video Self-Modeling Technique That Can Be Used in Improving the Abilities of Fluent Reading and Fluent Speaking

ERIC Educational Resources Information Center

Sen, Ülker

2016-01-01

The use of technology in the field of education makes the educational process more efficient and motivating. Technological tools are used for developing the communication skills of students and teachers in the learning process increasing the participation, supporting the peer, the realization of collaborative learning. The use of technology is…

Age as a Factor in Second Language Acquisition: A Review of Some Recent Research.

ERIC Educational Resources Information Center

Singleton, D. M.

The assumed connection between ease of language learning and age has been investigated in recent years by researchers from a wide range of disciplines. With the exception of the findings of research that authentic accents are more easily acquired by children, studies seem to indicate that efficiency in language learning increases with maturation.…

Good, Fast, Cheap: How Centers of Teaching and Learning Can Capitalize in Today's Resource-Constrained Context

ERIC Educational Resources Information Center

Truong, Michael H.; Juillerat, Stephanie; Gin, Deborah H. C.

2016-01-01

This article provides leaders and educational developers of Centers for Teaching and Learning (CTL) with innovative and practical strategies on how to increase their centers' capacity and impact by focusing on quality, efficiency, and cost. This "good, fast, cheap" model represents a promising way that CTL can continue to grow, scale,…

Vocational Education and Training in Denmark. Short Description

ERIC Educational Resources Information Center

Cedefop - European Centre for the Development of Vocational Training, 2012

2012-01-01

Vocational education and training in Denmark has embarked on a process of modernisation aiming at, primarily, increasing flexibility, and individualisation, quality and efficiency. Assessment and recognition of informal and non-formal learning, competence-based curricula, innovative approaches to teaching, and increased possibilities for partial…

Orchid: a novel management, annotation and machine learning framework for analyzing cancer mutations.

PubMed

Cario, Clinton L; Witte, John S

2018-03-15

As whole-genome tumor sequence and biological annotation datasets grow in size, number and content, there is an increasing basic science and clinical need for efficient and accurate data management and analysis software. With the emergence of increasingly sophisticated data stores, execution environments and machine learning algorithms, there is also a need for the integration of functionality across frameworks. We present orchid, a python based software package for the management, annotation and machine learning of cancer mutations. Building on technologies of parallel workflow execution, in-memory database storage and machine learning analytics, orchid efficiently handles millions of mutations and hundreds of features in an easy-to-use manner. We describe the implementation of orchid and demonstrate its ability to distinguish tissue of origin in 12 tumor types based on 339 features using a random forest classifier. Orchid and our annotated tumor mutation database are freely available at https://github.com/wittelab/orchid. Software is implemented in python 2.7, and makes use of MySQL or MemSQL databases. Groovy 2.4.5 is optionally required for parallel workflow execution. JWitte@ucsf.edu. Supplementary data are available at Bioinformatics online.

A Learning Theory Conceptual Foundation for Using Capture Technology in Teaching

ERIC Educational Resources Information Center

Berardi, Victor; Blundell, Greg

2014-01-01

Lecture capture technologies are increasingly being used by instructors, programs, and institutions to deliver online lectures and courses. This lecture capture movement is important as it increases access to education opportunities that were not possible before, it can improve efficiency, and it can increase student engagement. However, this is…

Neuropsychological sequelae of exposure to welding fumes in a group of occupationally exposed men.

PubMed

Bowler, Rosemarie M; Gysens, Sabine; Diamond, Emily; Booty, Andrew; Hartney, Christopher; Roels, Harry A

2003-10-01

This study compares the neuropsychological function, emotional status, visual function, and illness prevalence of 76 former and current chemical industry welders primarily involved in steel welding, and exposed to welding fumes for an average of 24.9 years with that of 42 unexposed, non-welder controls. Health and occupational history questionnaires were administered, as were the neuropsychological tests included in the World Health Organization Neurobehavioral Core Test Battery, Luria Motor Test, and selected tests from the WAIS-III, and WMS-III. Emotional status tests included the BSI, POMS, BAI, and BDI, and vision tests included the Snellen near visual acuity, Lanthony d-15 color vision, Vistech Contrast Sensitivity, and Schirmer strips. While welders and controls performed similarly on tests of verbal skills, verbal retention, and auditory span, welders performed worse than controls on tests of verbal learning, working memory, cognitive flexibility, visuomotor processing speed, and motor efficiency. Welders had poorer color vision and emotional status, and increased prevalence of illnesses and psychiatric symptoms. The increased symptoms in welders were related to decreased scores on tasks measuring verbal learning, visuomotor abilities, visuospatial abilities, and information processing, and motor efficiency. Within the group of welders, the number of hours welding was negatively related to scores on verbal learning, auditory span, working memory, cognitive flexibility, and motor efficiency.

Learning to associate auditory and visual stimuli: behavioral and neural mechanisms.

PubMed

Altieri, Nicholas; Stevenson, Ryan A; Wallace, Mark T; Wenger, Michael J

2015-05-01

The ability to effectively combine sensory inputs across modalities is vital for acquiring a unified percept of events. For example, watching a hammer hit a nail while simultaneously identifying the sound as originating from the event requires the ability to identify spatio-temporal congruencies and statistical regularities. In this study, we applied a reaction time and hazard function measure known as capacity (e.g., Townsend and AshbyCognitive Theory 200-239, 1978) to quantify the extent to which observers learn paired associations between simple auditory and visual patterns in a model theoretic manner. As expected, results showed that learning was associated with an increase in accuracy, but more significantly, an increase in capacity. The aim of this study was to associate capacity measures of multisensory learning, with neural based measures, namely mean global field power (GFP). We observed a co-variation between an increase in capacity, and a decrease in GFP amplitude as learning occurred. This suggests that capacity constitutes a reliable behavioral index of efficient energy expenditure in the neural domain.

Efficiency of printed materials in worksite health promotion.

PubMed

Kishchuk, N; Anbar, F; O'Loughlin, J; Masson, P; Sacks-Silver, G

1991-01-01

Printed health promotion materials are widely believed to be an efficient means of achieving basic health promotion objectives, such as increasing knowledge of risk factors. This study examined the efficiency of cardiovascular health promotion leaflets in reaching employees in a heterogeneous sample of worksites. Two types of distribution were used: copies of the leaflets were either made available centrally or distributed to each individual employee. Interviews were conducted with 272 employees in six worksites. Respondents were asked whether they recognized, had read, and had learned something from the leaflets. Only one-quarter of respondents recognized the leaflets and only 14% stated that they had learned something. The efficiency of the leaflets was therefore much lower than expected. Z-tests for proportions showed that recognition, reading, and learning were significantly greater among those employees who had been given individual copies of the material. Among those who had been given individual copies, 45% reported recognizing the leaflet, 36% reading it, and 23% learning something from it. Among those who had only central access, the respective scores were 11%, 7% and 6%. These results suggest that the potential cost-effectiveness of printed materials such as leaflets and brochures should be weighed against alternative forms of intervention, given specific program objectives and characteristics of the target population. They also suggest that the cost and effort required in organizing the distribution of individual copies may be recouped in greater penetration.

The Application of Multiobjective Evolutionary Algorithms to an Educational Computational Model of Science Information Processing: A Computational Experiment in Science Education

ERIC Educational Resources Information Center

Lamb, Richard L.; Firestone, Jonah B.

2017-01-01

Conflicting explanations and unrelated information in science classrooms increase cognitive load and decrease efficiency in learning. This reduced efficiency ultimately limits one's ability to solve reasoning problems in the science. In reasoning, it is the ability of students to sift through and identify critical pieces of information that is of…

Data Mining Student Answers with Moodle to Investigate Learning Pathways in an Introductory Geohazards Course

NASA Astrophysics Data System (ADS)

Sit, S. M.; Brudzinski, M. R.; Colella, H. V.

2012-12-01

The recent growth of online learning in higher education is primarily motivated by a desire to (a) increase the availability of learning experiences for learners who cannot, or choose not, to attend traditional face-to-face offerings, (b) assemble and disseminate instructional content more cost-efficiently, or (c) enable instructors to handle more students while maintaining a learning outcome quality that is equivalent to that of comparable face-to-face instruction. However, a less recognized incentive is that online learning also provides an opportunity for data mining, or efficient discovery of non-obvious valuable patterns from a large collection of data, that can be used to investigate learning pathways as opposed to focusing solely on assessing student outcomes. Course management systems that enable online courses provide a means to collect a vast amount of information to analyze students' behavior and the learning process in general. One of the most commonly used is Moodle (modular object-oriented developmental learning environment), a free learning management system that enables creation of powerful, flexible, and engaging online courses and experiences. In order to examine student learning pathways, the online learning modules we are constructing take advantage of Moodle capabilities to provide immediate formative feedback, verifying answers as correct or incorrect and elaborating on knowledge components to guide students towards the correct answer. By permitting multiple attempts in which credit is diminished for each incorrect answer, we provide opportunities to use data mining strategies to assess thousands of students' actions for evidence of problem solving strategies and mastery of concepts. We will show preliminary results from application of this approach to a ~90 student introductory geohazard course that is migrating toward online instruction. We hope more continuous assessment of students' performances will help generate cognitive models that can inform instructional redesign, improve overall efficiency of student learning, and, potentially, be used to create an intelligent tutoring system.

Learning Efficiency: Identifying Individual Differences in Learning Rate and Retention in Healthy Adults.

PubMed

Zerr, Christopher L; Berg, Jeffrey J; Nelson, Steven M; Fishell, Andrew K; Savalia, Neil K; McDermott, Kathleen B

2018-06-01

People differ in how quickly they learn information and how long they remember it, yet individual differences in learning abilities within healthy adults have been relatively neglected. In two studies, we examined the relation between learning rate and subsequent retention using a new foreign-language paired-associates task (the learning-efficiency task), which was designed to eliminate ceiling effects that often accompany standardized tests of learning and memory in healthy adults. A key finding was that quicker learners were also more durable learners (i.e., exhibited better retention across a delay), despite studying the material for less time. Additionally, measures of learning and memory from this task were reliable in Study 1 ( N = 281) across 30 hr and Study 2 ( N = 92; follow-up n = 46) across 3 years. We conclude that people vary in how efficiently they learn, and we describe a reliable and valid method for assessing learning efficiency within healthy adults.

How Powerful Is Feedforward in University Education? A Case Study in Romanian Geography Education on Increasing Learning Efficiency

ERIC Educational Resources Information Center

Dulama, Maria Eliza; Ilovan, Oana-Ramona

2016-01-01

There are different opinions about the meaning of feedforward: some consider it a response to feedback, while others think it consists of suggestions given to a person in order to help them before learning or starting a task. This study analyzed the professor's and university students' actions during a seminar activity with a group of 60 students…

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…

Posttraining Increases in REM Sleep Intensity Implicate REM Sleep in Memory Processing and Provide a Biological Marker of Learning Potential

ERIC Educational Resources Information Center

Nader, Rebecca S.; Smith, Carlyle T.; Nixon, Margaret R.

2004-01-01

Posttraining rapid eye movement (REM) sleep has been reported to be important for efficient memory consolidation. The present results demonstrate increases in the intensity of REM sleep during the night of sleep following cognitive procedural/implicit task acquisition. These REM increases manifest as increases in total number of rapid eye…

Selection for delayed maturity : Does it take 20 years to learn to hunt and gather?

PubMed

Jones, Nicholas Blurton; Marlowe, Frank W

2002-06-01

Humans have a much longer juvenile period (weaning to first reproduction, 14 or more years) than their closest relatives (chimpanzees, 8 years). Three explanations are prominent in the literature. (a) Humans need the extra time to learn their complex subsistence techniques. (b) Among mammals, since length of the juvenile period bears a constant relationship to adult lifespan, the human juvenile period is just as expected. We therefore only need to explain the elongated adult lifespan, which can be explained by the opportunity for older individuals to increase their fitness by providing for grandchildren. (c) The recent model by Kaplan and colleagues suggests that longevity and investment in "embodied capital" will coevolve, and that the need to learn subsistence technology contributed to selection for our extended lifespan.We report experiments designed to test the first explanation: human subsistence technology takes many years to learn, and spending more time learning it gives reproductive benefits that outweight lost time. Taking away some of this time should lead to deficits in efficiency. We paid Hadza foragers to participate in tests of important subsistence skills. We compared efficiency of males and females at digging tubers. They differ greatly in time spent practicing digging but show no difference in efficiency. Children who lost "bush experience" by spending years in boarding school performed no worse at digging tubers or target archery than those who had spent their entire lives in the bush. Climbing baobab trees, an important and dangerous skill, showed no change with age among those who attempted it. We could show no effects of practice time.These findings do not support what we label "the practice theory," but we discuss ways in which the theory could be defended; for example, some as-yet-untested skill may be greatly impaired by loss of a few years of the juvenile period. Our data also show that it is not safe to assume that increases in skill with age are entirely due to learning or practice; they may instead be due to increases in size and strength.

Undergraduate Economics Journals: Learning by Doing

ERIC Educational Resources Information Center

Leekley, Robert M.; Davis-Kahl, Stephanie; Seeborg, Michael C.

2013-01-01

Although there are currently only a few undergraduate journals in economics, we expect their numbers to increase substantially in the future because of several developments: (1) research and writing activity is increasing in economics programs, (2) online publication is now more feasible and cost efficient than ever, and (3) students are…

Virtual Learning Simulations in High School: Effects on Cognitive and Non-cognitive Outcomes and Implications on the Development of STEM Academic and Career Choice.

PubMed

Thisgaard, Malene; Makransky, Guido

2017-01-01

The present study compared the value of using a virtual learning simulation compared to traditional lessons on the topic of evolution, and investigated if the virtual learning simulation could serve as a catalyst for STEM academic and career development, based on social cognitive career theory. The investigation was conducted using a crossover repeated measures design based on a sample of 128 high school biology/biotech students. The results showed that the virtual learning simulation increased knowledge of evolution significantly, compared to the traditional lesson. No significant differences between the simulation and lesson were found in their ability to increase the non-cognitive measures. Both interventions increased self-efficacy significantly, and none of them had a significant effect on motivation. In addition, the results showed that the simulation increased interest in biology related tasks, but not outcome expectations. The findings suggest that virtual learning simulations are at least as efficient in enhancing learning and self-efficacy as traditional lessons, and high schools can thus use them as supplementary educational methods. In addition, the findings indicate that virtual learning simulations may be a useful tool in enhancing student's interest in and goals toward STEM related careers.

Virtual Learning Simulations in High School: Effects on Cognitive and Non-cognitive Outcomes and Implications on the Development of STEM Academic and Career Choice

PubMed Central

Thisgaard, Malene; Makransky, Guido

2017-01-01

The present study compared the value of using a virtual learning simulation compared to traditional lessons on the topic of evolution, and investigated if the virtual learning simulation could serve as a catalyst for STEM academic and career development, based on social cognitive career theory. The investigation was conducted using a crossover repeated measures design based on a sample of 128 high school biology/biotech students. The results showed that the virtual learning simulation increased knowledge of evolution significantly, compared to the traditional lesson. No significant differences between the simulation and lesson were found in their ability to increase the non-cognitive measures. Both interventions increased self-efficacy significantly, and none of them had a significant effect on motivation. In addition, the results showed that the simulation increased interest in biology related tasks, but not outcome expectations. The findings suggest that virtual learning simulations are at least as efficient in enhancing learning and self-efficacy as traditional lessons, and high schools can thus use them as supplementary educational methods. In addition, the findings indicate that virtual learning simulations may be a useful tool in enhancing student’s interest in and goals toward STEM related careers. PMID:28611701

Support vector machine incremental learning triggered by wrongly predicted samples

NASA Astrophysics Data System (ADS)

Tang, Ting-long; Guan, Qiu; Wu, Yi-rong

2018-05-01

According to the classic Karush-Kuhn-Tucker (KKT) theorem, at every step of incremental support vector machine (SVM) learning, the newly adding sample which violates the KKT conditions will be a new support vector (SV) and migrate the old samples between SV set and non-support vector (NSV) set, and at the same time the learning model should be updated based on the SVs. However, it is not exactly clear at this moment that which of the old samples would change between SVs and NSVs. Additionally, the learning model will be unnecessarily updated, which will not greatly increase its accuracy but decrease the training speed. Therefore, how to choose the new SVs from old sets during the incremental stages and when to process incremental steps will greatly influence the accuracy and efficiency of incremental SVM learning. In this work, a new algorithm is proposed to select candidate SVs and use the wrongly predicted sample to trigger the incremental processing simultaneously. Experimental results show that the proposed algorithm can achieve good performance with high efficiency, high speed and good accuracy.

Learning Efficiency versus Low IQ and/or Teachers' Ratings as Predictors of Reading Ability of "Mentally Defective" Children: A Longitudinal Study.

ERIC Educational Resources Information Center

Gupta, R. M.

1985-01-01

Low IQ should not be deemed as an index of poor learning ability. Information about middle school children's learning efficiency as measured by the Learning Efficiency Test Battery was found to be more useful for predicting reading ability than conventional types of assessment. (Author/RM)

Validating YouTube Factors Affecting Learning Performance

NASA Astrophysics Data System (ADS)

Pratama, Yoga; Hartanto, Rudy; Suning Kusumawardani, Sri

2018-03-01

YouTube is often used as a companion medium or a learning supplement. One of the educational places that often uses is Jogja Audio School (JAS) which focuses on music production education. Music production is a difficult material to learn, especially at the audio mastering. With tutorial contents from YouTube, students find it easier to learn and understand audio mastering and improved their learning performance. This study aims to validate the role of YouTube as a medium of learning in improving student’s learning performance by looking at the factors that affect student learning performance. The sample involves 100 respondents from JAS at audio mastering level. The results showed that student learning performance increases seen from factors that have a significant influence of motivation, instructional content, and YouTube usefulness. Overall findings suggest that YouTube has a important role to student learning performance in music production education and as an innovative and efficient learning medium.

Learning curves for transapical transcatheter aortic valve replacement in the PARTNER-I trial: Technical performance, success, and safety.

PubMed

Suri, Rakesh M; Minha, Sa'ar; Alli, Oluseun; Waksman, Ron; Rihal, Charanjit S; Satler, Lowell P; Greason, Kevin L; Torguson, Rebecca; Pichard, Augusto D; Mack, Michael; Svensson, Lars G; Rajeswaran, Jeevanantham; Lowry, Ashley M; Ehrlinger, John; Mick, Stephanie L; Tuzcu, E Murat; Thourani, Vinod H; Makkar, Raj; Holmes, David; Leon, Martin B; Blackstone, Eugene H

2016-09-01

Introduction of hybrid techniques, such as transapical transcatheter aortic valve replacement (TA-TAVR), requires skills that a heart team must master to achieve technical efficiency: the technical performance learning curve. To date, the learning curve for TA-TAVR remains unknown. We therefore evaluated the rate at which technical performance improved, assessed change in occurrence of adverse events in relation to technical performance, and determined whether adverse events after TA-TAVR were linked to acquiring technical performance efficiency (the learning curve). From April 2007 to February 2012, 1100 patients, average age 85.0 ± 6.4 years, underwent TA-TAVR in the PARTNER-I trial. Learning curves were defined by institution-specific patient sequence number using nonlinear mixed modeling. Mean procedure time decreased from 131 to 116 minutes within 30 cases (P = .06) and device success increased to 90% by case 45 (P = .0007). Within 30 days, 354 patients experienced a major adverse event (stroke in 29, death in 96), with possibly decreased complications over time (P ∼ .08). Although longer procedure time was associated with more adverse events (P < .0001), these events were associated with change in patient risk profile, not the technical performance learning curve (P = .8). The learning curve for TA-TAVR was 30 to 45 procedures performed, and technical efficiency was achieved without compromising patient safety. Although fewer patients are now undergoing TAVR via nontransfemoral access, understanding TA-TAVR learning curves and their relationship with outcomes is important as the field moves toward next-generation devices, such as those to replace the mitral valve, delivered via the left ventricular apex. Copyright © 2016 The American Association for Thoracic Surgery. Published by Elsevier Inc. All rights reserved.

Social learning of an associative foraging task in zebrafish

NASA Astrophysics Data System (ADS)

Zala, Sarah M.; Määttänen, Ilmari

2013-05-01

The zebrafish ( Danio rerio) is increasingly becoming an important model species for studies on the genetic and neural mechanisms controlling behaviour and cognition. Here, we utilized a conditioned place preference (CPP) paradigm to study social learning in zebrafish. We tested whether social interactions with conditioned demonstrators enhance the ability of focal naïve individuals to learn an associative foraging task. We found that the presence of conditioned demonstrators improved focal fish foraging behaviour through the process of social transmission, whereas the presence of inexperienced demonstrators interfered with the learning of the control focal fish. Our results indicate that zebrafish use social learning for finding food and that this CPP paradigm is an efficient assay to study social learning and memory in zebrafish.

Challenge-based instruction in biomedical engineering: a scalable method to increase the efficiency and effectiveness of teaching and learning in biomedical engineering.

PubMed

Harris, Thomas R; Brophy, Sean P

2005-09-01

Vanderbilt University, Northwestern University, the University of Texas and the Harvard/MIT Health Sciences Technology Program have collaborated since 1999 to develop means to improve bioengineering education. This effort, funded by the National Science Foundation as the VaNTH Engineering Research Center in Bioengineering Educational Technologies, has sought a synthesis of learning science, learning technology, assessment and the domains of bioengineering in order to improve learning by bioengineering students. Research has shown that bioengineering educational materials may be designed to emphasize challenges that engage the student and, when coupled with a learning cycle and appropriate technologies, can lead to improvements in instruction.

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).

Increasing the Effectiveness and Efficiency of Existing Public Investments in Early Childhood Education: Recommendations to Boost Program Outcomes and Efficiency

ERIC Educational Resources Information Center

Cooper, Donna; Costa, Kristina

2012-01-01

There is a mounting body of research demonstrating the impact of early learning on lifelong success. The quality of early child care is the most consistent predictor of young children's behavior, according to the National Institute of Child Health and Human Development Early Childcare Research Network. Children who receive high-quality child care…

Advanced Technologies in Safe and Efficient Operating Rooms

DTIC Science & Technology

2009-10-01

focused on the video, not (initially) any other sensors and ii) tried to capture using machine learning techniques the ability of an expert surgeon to...plant (with humans playing the role of team leader) o a learning environment (where humans play the role of students ). As can be seen, this work...increased cognitive demands associated with the one-handed technique occur because the surgeon is providing instructions to the assistant performing

Thermodynamic efficiency of learning a rule in neural networks

NASA Astrophysics Data System (ADS)

Goldt, Sebastian; Seifert, Udo

2017-11-01

Biological systems have to build models from their sensory input data that allow them to efficiently process previously unseen inputs. Here, we study a neural network learning a binary classification rule for these inputs from examples provided by a teacher. We analyse the ability of the network to apply the rule to new inputs, that is to generalise from past experience. Using stochastic thermodynamics, we show that the thermodynamic costs of the learning process provide an upper bound on the amount of information that the network is able to learn from its teacher for both batch and online learning. This allows us to introduce a thermodynamic efficiency of learning. We analytically compute the dynamics and the efficiency of a noisy neural network performing online learning in the thermodynamic limit. In particular, we analyse three popular learning algorithms, namely Hebbian, Perceptron and AdaTron learning. Our work extends the methods of stochastic thermodynamics to a new type of learning problem and might form a suitable basis for investigating the thermodynamics of decision-making.

Investigating the Efficiency of Scenario Based Learning and Reflective Learning Approaches in Teacher Education

ERIC Educational Resources Information Center

Hursen, Cigdem; Fasli, Funda Gezer

2017-01-01

The main purpose of this research is to investigate the efficiency of scenario based learning and reflective learning approaches in teacher education. The impact of applications of scenario based learning and reflective learning on prospective teachers' academic achievement and views regarding application and professional self-competence…

Increased efficiency of the GABAA and GABAB receptor–mediated neurotransmission in the Ts65Dn mouse model of Down syndrome

PubMed Central

Kleschevnikov, Alexander M.; Belichenko, Pavel V.; Gall, Jessica; George, Lizzy; Nosheny, Rachel; Maloney, Michael T.; Salehi, Ahmad; Mobley, William C.

2011-01-01

Cognitive impairment in Down syndrome (DS) involves the hippocampus. In the Ts65Dn mouse model of DS, deficits in hippocampus-dependent learning and synaptic plasticity were linked to enhanced inhibition. However, the mechanistic basis of changes in inhibitory efficiency remains largely unexplored, and efficiency of the GABAergic synaptic neurotransmission has not yet been investigated in direct electrophysiological experiments. To investigate this important feature of neurobiology of DS, we examined synaptic and molecular properties of the GABAergic system in the dentate gyrus (DG) of adult Ts65Dn mice. Both GABAA and GABAB receptor-mediated components of evoked inhibitory postsynaptic currents (IPSCs) were significantly increased in Ts65Dn vs. control (2N) DG granule cells. These changes were unaccompanied by alterations in hippocampal levels of GABAA (α1, α2, α3, α5 and γ2) or GABAB (Gbr1a and Gbr1b) receptor subunits. Immunoreactivity for GAD65, a marker for GABAergic terminals, was also unchanged. In contrast, there was a marked change in functional parameters of GABAergic synapses. Paired stimulations showed reduced paired-pulse ratios of both GABAA and GABAB receptor-mediated IPSC components (IPSC2/IPSC1), suggesting an increase in presynaptic release of GABA. Consistent with increased gene dose, the level of the Kir3.2 subunit of potassium channels, effectors for postsynaptic GABAB receptors, was increased. This change was associated with enhanced postsynaptic GABAB/Kir3.2 signaling following application of the GABAB receptor agonist baclofen. Thus, both GABAA and GABAB receptor-mediated synaptic efficiency is increased in the Ts65Dn DG, thus likely contributing to deficient synaptic plasticity and poor learning in DS. PMID:22062771

Content-Based VLE Designs Improve Learning Efficiency in Constructivist Statistics Education

PubMed Central

Wessa, Patrick; De Rycker, Antoon; Holliday, Ian Edward

2011-01-01

Background We introduced a series of computer-supported workshops in our undergraduate statistics courses, in the hope that it would help students to gain a deeper understanding of statistical concepts. This raised questions about the appropriate design of the Virtual Learning Environment (VLE) in which such an approach had to be implemented. Therefore, we investigated two competing software design models for VLEs. In the first system, all learning features were a function of the classical VLE. The second system was designed from the perspective that learning features should be a function of the course's core content (statistical analyses), which required us to develop a specific–purpose Statistical Learning Environment (SLE) based on Reproducible Computing and newly developed Peer Review (PR) technology. Objectives The main research question is whether the second VLE design improved learning efficiency as compared to the standard type of VLE design that is commonly used in education. As a secondary objective we provide empirical evidence about the usefulness of PR as a constructivist learning activity which supports non-rote learning. Finally, this paper illustrates that it is possible to introduce a constructivist learning approach in large student populations, based on adequately designed educational technology, without subsuming educational content to technological convenience. Methods Both VLE systems were tested within a two-year quasi-experiment based on a Reliable Nonequivalent Group Design. This approach allowed us to draw valid conclusions about the treatment effect of the changed VLE design, even though the systems were implemented in successive years. The methodological aspects about the experiment's internal validity are explained extensively. Results The effect of the design change is shown to have substantially increased the efficiency of constructivist, computer-assisted learning activities for all cohorts of the student population under investigation. The findings demonstrate that a content–based design outperforms the traditional VLE–based design. PMID:21998652

Differential theory of learning for efficient neural network pattern recognition

NASA Astrophysics Data System (ADS)

Hampshire, John B., II; Vijaya Kumar, Bhagavatula

1993-09-01

We describe a new theory of differential learning by which a broad family of pattern classifiers (including many well-known neural network paradigms) can learn stochastic concepts efficiently. We describe the relationship between a classifier's ability to generate well to unseen test examples and the efficiency of the strategy by which it learns. We list a series of proofs that differential learning is efficient in its information and computational resource requirements, whereas traditional probabilistic learning strategies are not. The proofs are illustrated by a simple example that lends itself to closed-form analysis. We conclude with an optical character recognition task for which three different types of differentially generated classifiers generalize significantly better than their probabilistically generated counterparts.

Differential theory of learning for efficient neural network pattern recognition

NASA Astrophysics Data System (ADS)

Hampshire, John B., II; Vijaya Kumar, Bhagavatula

1993-08-01

We describe a new theory of differential learning by which a broad family of pattern classifiers (including many well-known neural network paradigms) can learn stochastic concepts efficiently. We describe the relationship between a classifier's ability to generalize well to unseen test examples and the efficiency of the strategy by which it learns. We list a series of proofs that differential learning is efficient in its information and computational resource requirements, whereas traditional probabilistic learning strategies are not. The proofs are illustrated by a simple example that lends itself to closed-form analysis. We conclude with an optical character recognition task for which three different types of differentially generated classifiers generalize significantly better than their probabilistically generated counterparts.

Computational Properties of the Hippocampus Increase the Efficiency of Goal-Directed Foraging through Hierarchical Reinforcement Learning

PubMed Central

Chalmers, Eric; Luczak, Artur; Gruber, Aaron J.

2016-01-01

The mammalian brain is thought to use a version of Model-based Reinforcement Learning (MBRL) to guide “goal-directed” behavior, wherein animals consider goals and make plans to acquire desired outcomes. However, conventional MBRL algorithms do not fully explain animals' ability to rapidly adapt to environmental changes, or learn multiple complex tasks. They also require extensive computation, suggesting that goal-directed behavior is cognitively expensive. We propose here that key features of processing in the hippocampus support a flexible MBRL mechanism for spatial navigation that is computationally efficient and can adapt quickly to change. We investigate this idea by implementing a computational MBRL framework that incorporates features inspired by computational properties of the hippocampus: a hierarchical representation of space, “forward sweeps” through future spatial trajectories, and context-driven remapping of place cells. We find that a hierarchical abstraction of space greatly reduces the computational load (mental effort) required for adaptation to changing environmental conditions, and allows efficient scaling to large problems. It also allows abstract knowledge gained at high levels to guide adaptation to new obstacles. Moreover, a context-driven remapping mechanism allows learning and memory of multiple tasks. Simulating dorsal or ventral hippocampal lesions in our computational framework qualitatively reproduces behavioral deficits observed in rodents with analogous lesions. The framework may thus embody key features of how the brain organizes model-based RL to efficiently solve navigation and other difficult tasks. PMID:28018203

Interactive knowledge networks for interdisciplinary course navigation within Moodle.

PubMed

Scherl, Andre; Dethleffsen, Kathrin; Meyer, Michael

2012-12-01

Web-based hypermedia learning environments are widely used in modern education and seem particularly well suited for interdisciplinary learning. Previous work has identified guidance through these complex environments as a crucial problem of their acceptance and efficiency. We reasoned that map-based navigation might provide straightforward and effortless orientation. To achieve this, we developed a clickable and user-oriented concept map-based navigation plugin. This tool is implemented as an extension of Moodle, a widely used learning management system. It visualizes inner and interdisciplinary relations between learning objects and is generated dynamically depending on user set parameters and interactions. This plugin leaves the choice of navigation type to the user and supports direct guidance. Previously developed and evaluated face-to-face interdisciplinary learning materials bridging physiology and physics courses of a medical curriculum were integrated as learning objects, the relations of which were defined by metadata. Learning objects included text pages, self-assessments, videos, animations, and simulations. In a field study, we analyzed the effects of this learning environment on physiology and physics knowledge as well as the transfer ability of third-term medical students. Data were generated from pre- and posttest questionnaires and from tracking student navigation. Use of the hypermedia environment resulted in a significant increase of knowledge and transfer capability. Furthermore, the efficiency of learning was enhanced. We conclude that hypermedia environments based on Moodle and enriched by concept map-based navigation tools can significantly support interdisciplinary learning. Implementation of adaptivity may further strengthen this approach.

Safety, efficiency and learning curves in robotic surgery: a human factors analysis.

PubMed

Catchpole, Ken; Perkins, Colby; Bresee, Catherine; Solnik, M Jonathon; Sherman, Benjamin; Fritch, John; Gross, Bruno; Jagannathan, Samantha; Hakami-Majd, Niv; Avenido, Raymund; Anger, Jennifer T

2016-09-01

Expense, efficiency of use, learning curves, workflow integration and an increased prevalence of serious incidents can all be barriers to adoption. We explored an observational approach and initial diagnostics to enhance total system performance in robotic surgery. Eighty-nine robotic surgical cases were observed in multiple operating rooms using two different surgical robots (the S and Si), across several specialties (Urology, Gynecology, and Cardiac Surgery). The main measures were operative duration and rate of flow disruptions-described as 'deviations from the natural progression of an operation thereby potentially compromising safety or efficiency.' Contextual parameters collected were surgeon experience level and training, type of surgery, the model of robot and patient factors. Observations were conducted across four operative phases (operating room pre-incision; robot docking; main surgical intervention; post-console). A mean of 9.62 flow disruptions per hour (95 % CI 8.78-10.46) were predominantly caused by coordination, communication, equipment and training problems. Operative duration and flow disruption rate varied with surgeon experience (p = 0.039; p < 0.001, respectively), training cases (p = 0.012; p = 0.007) and surgical type (both p < 0.001). Flow disruption rates in some phases were also sensitive to the robot model and patient characteristics. Flow disruption rate is sensitive to system context and generates improvement diagnostics. Complex surgical robotic equipment increases opportunities for technological failures, increases communication requirements for the whole team, and can reduce the ability to maintain vision in the operative field. These data suggest specific opportunities to reduce the training costs and the learning curve.

Policies to Enhance Prescribing Efficiency in Europe: Findings and Future Implications

PubMed Central

Godman, Brian; Shrank, William; Andersen, Morten; Berg, Christian; Bishop, Iain; Burkhardt, Thomas; Garuoliene, Kristina; Herholz, Harald; Joppi, Roberta; Kalaba, Marija; Laius, Ott; Lonsdale, Julie; Malmström, Rickard E.; Martikainen, Jaana E.; Samaluk, Vita; Sermet, Catherine; Schwabe, Ulrich; Teixeira, Inês; Tilson, Lesley; Tulunay, F. Cankat; Vlahović-Palčevski, Vera; Wendykowska, Kamila; Wettermark, Bjorn; Zara, Corinne; Gustafsson, Lars L.

2010-01-01

Introduction: European countries need to learn from each other to address unsustainable increases in pharmaceutical expenditures. Objective: To assess the influence of the many supply and demand-side initiatives introduced across Europe to enhance prescribing efficiency in ambulatory care. As a result provide future guidance to countries. Methods: Cross national retrospective observational study of utilization (DDDs – defined daily doses) and expenditure (Euros and local currency) of proton pump inhibitors (PPIs) and statins among 19 European countries and regions principally from 2001 to 2007. Demand-side measures categorized under the “4Es” – education engineering, economics, and enforcement. Results: Instigating supply side initiatives to lower the price of generics combined with demand-side measures to enhance their prescribing is important to maximize prescribing efficiency. Just addressing one component will limit potential efficiency gains. The influence of demand-side reforms appears additive, with multiple initiatives typically having a greater influence on increasing prescribing efficiency than single measures apart from potentially “enforcement.” There are also appreciable differences in expenditure (€/1000 inhabitants/year) between countries. Countries that have not introduced multiple demand side measures to counteract commercial pressures to enhance the prescribing of generics have seen considerably higher expenditures than those that have instigated a range of measures. Conclusions: There are considerable opportunities for European countries to enhance their prescribing efficiency, with countries already learning from each other. The 4E methodology allows European countries to concisely capture the range of current demand-side measures and plan for the future knowing that initiatives can be additive to further enhance their prescribing efficiency. PMID:21833180

Outsourcing Support Services.

ERIC Educational Resources Information Center

McClure, James A.

2000-01-01

Successful outsourcing is a learning process demanding careful planning, commitment, and heavy communication. The process also requires a strong leadership and a cohesive school board ready to weather a cultural change. Service employee options, contractors' managerial expertise, increased efficiency, and partnership opportunities are possible…

Indoor airPLUS Videos, Podcasts, Webinars and Interviews

EPA Pesticide Factsheets

The Webinar presentations will help you discover how Indoor airPLUS homes are designed to improve indoor air quality and increase energy efficiency and learn about the key design and construction features included in Indoor airPLUS homes.

Stakeholders' views of shared learning models in general practice: a national survey.

PubMed

van de Mortel, Thea; Silberberg, Peter; Ahern, Christine; Pit, Sabrina

2014-09-01

The number of learners requiring general practice placements creates supervisory capacity constraints. This research examined how a shared learning model may affect training capacity. The number of learners requiring general practice placements creates supervisory capacity constraints. This research examined how a shared learning model may affect training capacity. A total of 1122 surveys were completed: 75% of learners had participated in shared learning; 25% of multi-level learner practices were not using shared learning. Learners were positive about shared learning (4.3-4.4/5), considering it an effective way to learn that created training capacity (4.1-4.2/5). 79-88% of learners preferred a mixture of one-to-one teaching and shared learning. Supervisors thought shared learning was more cost- and time-efficient, and created training capacity (4.3-4.4/5). Shared learning models have the potential to increase GP training capacity. Many practices are not utilising shared learning, representing capacity loss. Regional training providers should emphasise positive aspects of shared learning to facilitate uptake.

Computer-Based Learning: Interleaving Whole and Sectional Representation of Neuroanatomy

ERIC Educational Resources Information Center

Pani, John R.; Chariker, Julia H.; Naaz, Farah

2013-01-01

The large volume of material to be learned in biomedical disciplines requires optimizing the efficiency of instruction. In prior work with computer-based instruction of neuroanatomy, it was relatively efficient for learners to master whole anatomy and then transfer to learning sectional anatomy. It may, however, be more efficient to continuously…

Learning optimal eye movements to unusual faces

PubMed Central

Peterson, Matthew F.; Eckstein, Miguel P.

2014-01-01

Eye movements, which guide the fovea’s high resolution and computational power to relevant areas of the visual scene, are integral to efficient, successful completion of many visual tasks. How humans modify their eye movements through experience with their perceptual environments, and its functional role in learning new tasks, has not been fully investigated. Here, we used a face identification task where only the mouth discriminated exemplars to assess if, how, and when eye movement modulation may mediate learning. By interleaving trials of unconstrained eye movements with trials of forced fixation, we attempted to separate the contributions of eye movements and covert mechanisms to performance improvements. Without instruction, a majority of observers substantially increased accuracy and learned to direct their initial eye movements towards the optimal fixation point. The proximity of an observer’s default face identification eye movement behavior to the new optimal fixation point and the observer’s peripheral processing ability were predictive of performance gains and eye movement learning. After practice in a subsequent condition in which observers were directed to fixate different locations along the face, including the relevant mouth region, all observers learned to make eye movements to the optimal fixation point. In this fully learned state, augmented fixation strategy accounted for 43% of total efficiency improvements while covert mechanisms accounted for the remaining 57%. The findings suggest a critical role for eye movement planning to perceptual learning, and elucidate factors that can predict when and how well an observer can learn a new task with unusual exemplars. PMID:24291712

Acquisition of Motor and Cognitive Skills through Repetition in Typically Developing Children

PubMed Central

Magallón, Sara; Narbona, Juan; Crespo-Eguílaz, Nerea

2016-01-01

Background Procedural memory allows acquisition, consolidation and use of motor skills and cognitive routines. Automation of procedures is achieved through repeated practice. In children, improvement in procedural skills is a consequence of natural neurobiological development and experience. Methods The aim of the present research was to make a preliminary evaluation and description of repetition-based improvement of procedures in typically developing children (TDC). Ninety TDC children aged 6–12 years were asked to perform two procedural learning tasks. In an assembly learning task, which requires predominantly motor skills, we measured the number of assembled pieces in 60 seconds. In a mirror drawing learning task, which requires more cognitive functions, we measured time spent and efficiency. Participants were tested four times for each task: three trials were consecutive and the fourth trial was performed after a 10-minute nonverbal interference task. The influence of repeated practice on performance was evaluated by means of the analysis of variance with repeated measures and the paired-sample test. Correlation coefficients and simple linear regression test were used to examine the relationship between age and performance. Results TDC achieved higher scores in both tasks through repetition. Older children fitted more pieces than younger ones in assembling learning and they were faster and more efficient at the mirror drawing learning task. Conclusions These findings indicate that three consecutive trials at a procedural task increased speed and efficiency, and that age affected basal performance in motor-cognitive procedures. PMID:27384671

Acquisition of Motor and Cognitive Skills through Repetition in Typically Developing Children.

PubMed

Magallón, Sara; Narbona, Juan; Crespo-Eguílaz, Nerea

2016-01-01

Procedural memory allows acquisition, consolidation and use of motor skills and cognitive routines. Automation of procedures is achieved through repeated practice. In children, improvement in procedural skills is a consequence of natural neurobiological development and experience. The aim of the present research was to make a preliminary evaluation and description of repetition-based improvement of procedures in typically developing children (TDC). Ninety TDC children aged 6-12 years were asked to perform two procedural learning tasks. In an assembly learning task, which requires predominantly motor skills, we measured the number of assembled pieces in 60 seconds. In a mirror drawing learning task, which requires more cognitive functions, we measured time spent and efficiency. Participants were tested four times for each task: three trials were consecutive and the fourth trial was performed after a 10-minute nonverbal interference task. The influence of repeated practice on performance was evaluated by means of the analysis of variance with repeated measures and the paired-sample test. Correlation coefficients and simple linear regression test were used to examine the relationship between age and performance. TDC achieved higher scores in both tasks through repetition. Older children fitted more pieces than younger ones in assembling learning and they were faster and more efficient at the mirror drawing learning task. These findings indicate that three consecutive trials at a procedural task increased speed and efficiency, and that age affected basal performance in motor-cognitive procedures.

Use of videos to support teaching and learning of clinical skills in nursing education: A review.

PubMed

Forbes, Helen; Oprescu, Florin I; Downer, Terri; Phillips, Nicole M; McTier, Lauren; Lord, Bill; Barr, Nigel; Alla, Kristel; Bright, Peter; Dayton, Jeanne; Simbag, Vilma; Visser, Irene

2016-07-01

Information and communications technology is influencing the delivery of education in tertiary institutions. In particular, the increased use of videos for teaching and learning clinical skills in nursing may be a promising direction to pursue, yet we need to better document the current research in this area of inquiry. The aim of this paper was to explore and document the current areas of research into the use of videos to support teaching and learning of clinical skills in nursing education. The four main areas of current and future research are effectiveness, efficiency, usage, and quality of videos as teaching and learning materials. While there is a clear need for additional research in the area, the use of videos seems to be a promising, relevant, and increasingly used instructional strategy that could enhance the quality of clinical skills education. Copyright © 2016 Elsevier Ltd. All rights reserved.

Impacts of Scheduling Recess before Lunch in Elementary Schools: A Case Study Approach of Plate Waste and Perceived Behaviors

ERIC Educational Resources Information Center

Strohbehn, Catherine H.; Strohbehn, Garth W.; Lanningham-Foster, Lorraine; Litchfield, Ruth A.; Scheidel, Carrie; Delger, Patti

2016-01-01

Purpose/Objectives: Recess Before Lunch (RBL) for elementary students is considered a best practice related to increased nutrient intakes at lunch, decreased afternoon behavioral issues, and increased afternoon learning efficiency; however, school characteristics, such as amount of time for lunch, offer vs. serve, and scheduling factors can…

Flexible Learning via Web-Based Virtual Teaching and Virtual Laboratory Systems

ERIC Educational Resources Information Center

Chu, K. C.; Leung, Dennis

2003-01-01

In the current economic situation, most academic institutions would like to plan new courses to increase enrollment. Often, these changes do not follow with a proportional increase in cost or staff numbers to the institution. For cost-efficiency reasons, a reduction in student contact hours is most desirable, providing that this can maintain the…

Continuing Professional Education in the Military

ERIC Educational Resources Information Center

Gleiman, Ashley; Zacharakis, Jeff

2016-01-01

The military relies on continuing professional education as a key component to the success of its organization. With decreasing budgets and increasing importance for a force that operates efficiently and thinks critically, the cognitive tension among training, education, and learning comes center stage.

Safe, High-Performance, Sustainable Precast School Design

ERIC Educational Resources Information Center

Finsen, Peter I.

2011-01-01

School design utilizing integrated architectural and structural precast and prestressed concrete components has gained greater acceptance recently for numerous reasons, including increasingly sophisticated owners and improved learning environments based on material benefits such as: sustainability, energy efficiency, indoor air quality, storm…

Dissociable changes in functional network topology underlie early category learning and development of automaticity

PubMed Central

Soto, Fabian A.; Bassett, Danielle S.; Ashby, F. Gregory

2016-01-01

Recent work has shown that multimodal association areas–including frontal, temporal and parietal cortex–are focal points of functional network reconfiguration during human learning and performance of cognitive tasks. On the other hand, neurocomputational theories of category learning suggest that the basal ganglia and related subcortical structures are focal points of functional network reconfiguration during early learning of some categorization tasks, but become less so with the development of automatic categorization performance. Using a combination of network science and multilevel regression, we explore how changes in the connectivity of small brain regions can predict behavioral changes during training in a visual categorization task. We find that initial category learning, as indexed by changes in accuracy, is predicted by increasingly efficient integrative processing in subcortical areas, with higher functional specialization, more efficient integration across modules, but a lower cost in terms of redundancy of information processing. The development of automaticity, as indexed by changes in the speed of correct responses, was predicted by lower clustering (particularly in subcortical areas), higher strength (highest in cortical areas) and higher betweenness centrality. By combining neurocomputational theories and network scientific methods, these results synthesize the dissociative roles of multimodal association areas and subcortical structures in the development of automaticity during category learning. PMID:27453156

Computer-based learning: interleaving whole and sectional representation of neuroanatomy.

PubMed

Pani, John R; Chariker, Julia H; Naaz, Farah

2013-01-01

The large volume of material to be learned in biomedical disciplines requires optimizing the efficiency of instruction. In prior work with computer-based instruction of neuroanatomy, it was relatively efficient for learners to master whole anatomy and then transfer to learning sectional anatomy. It may, however, be more efficient to continuously integrate learning of whole and sectional anatomy. A study of computer-based learning of neuroanatomy was conducted to compare a basic transfer paradigm for learning whole and sectional neuroanatomy with a method in which the two forms of representation were interleaved (alternated). For all experimental groups, interactive computer programs supported an approach to instruction called adaptive exploration. Each learning trial consisted of time-limited exploration of neuroanatomy, self-timed testing, and graphical feedback. The primary result of this study was that interleaved learning of whole and sectional neuroanatomy was more efficient than the basic transfer method, without cost to long-term retention or generalization of knowledge to recognizing new images (Visible Human and MRI). Copyright © 2012 American Association of Anatomists.

Computer-Based Learning: Interleaving Whole and Sectional Representation of Neuroanatomy

PubMed Central

Pani, John R.; Chariker, Julia H.; Naaz, Farah

2015-01-01

The large volume of material to be learned in biomedical disciplines requires optimizing the efficiency of instruction. In prior work with computer-based instruction of neuroanatomy, it was relatively efficient for learners to master whole anatomy and then transfer to learning sectional anatomy. It may, however, be more efficient to continuously integrate learning of whole and sectional anatomy. A study of computer-based learning of neuroanatomy was conducted to compare a basic transfer paradigm for learning whole and sectional neuroanatomy with a method in which the two forms of representation were interleaved (alternated). For all experimental groups, interactive computer programs supported an approach to instruction called adaptive exploration. Each learning trial consisted of time-limited exploration of neuroanatomy, self-timed testing, and graphical feedback. The primary result of this study was that interleaved learning of whole and sectional neuroanatomy was more efficient than the basic transfer method, without cost to long-term retention or generalization of knowledge to recognizing new images (Visible Human and MRI). PMID:22761001

Progress in Energy Storage Technologies: Models and Methods for Policy Analysis

NASA Astrophysics Data System (ADS)

Matteson, Schuyler W.

Climate change and other sustainability challenges have led to the development of new technologies that increase energy efficiency and reduce the utilization of finite resources. To promote the adoption of technologies with social benefits, governments often enact policies that provide financial incentives at the point of purchase. In their current form, these subsidies have the potential to increase the diffusion of emerging technologies; however, accounting for technological progress can improve program success while decreasing net public investment. This research develops novel methods using experience curves for the development of more efficient subsidy policies. By providing case studies in the field of automotive energy storage technologies, this dissertation also applies the methods to show the impacts of incorporating technological progress into energy policies. Specific findings include learning-dependent tapering subsidies for electric vehicles based on the lithium-ion battery experience curve, the effects of residual learning rates in lead-acid batteries on emerging technology cost competitiveness, and a cascading diffusion assessment of plug-in hybrid electric vehicle subsidy programs. Notably, the results show that considering learning rates in policy development can save billions of dollars in public funds, while also lending insight into the decision of whether or not to subsidize a given technology.

Using S-P Chart and Bloom Taxonomy to Develop Intelligent Formative Assessment Tool

ERIC Educational Resources Information Center

Chang, Wen-Chih; Yang, Hsuan-Che; Shih, Timothy K.; Chao, Louis R.

2009-01-01

E-learning provides a convenient and efficient way for learning. Formative assessment not only guides student in instruction and learning, diagnose skill or knowledge gaps, but also measures progress and evaluation. An efficient and convenient e-learning formative assessment system is the key character for e-learning. However, most e-learning…

Media in teaching college level nutrition. Is it effective and efficient?

PubMed

Short, S H

1975-06-01

Several techniques have been used, studied, and tested to teach nutrition at Syracuse University. One self-paced course in nutrition and food science tutors students completely through audio tapes integrated with films, slides, video tapes, discussion groups, laboratory manual, and computer-assisted instruction. Evaluation is by computerized tests given after each module at the student's discretion. Compressed-speech tapes are used to increase learning efficiency. Dietetic, nutrition, nursing, and pre-medical students are taught nutrition via these methods for selected modules, but they mainly learn by lectures supplemented by pertinent films, slides, transparencies, television commercials, telectures, videotapes, and simulations. Multi-media "happenings" are presented which gain students' attention and change attitudes while imparting nutritional information which is well retained.

Misleading contextual cues: how do they affect visual search?

PubMed

Manginelli, Angela A; Pollmann, Stefan

2009-03-01

Contextual cueing occurs when repetitions of the distractor configuration are implicitly learned. This implicit learning leads to faster search times in repeated displays. Here, we investigated how search adapts to a change of the target location in old displays from a consistent location in the learning phase to a consistent new location in the transfer phase. In agreement with the literature, contextual cueing was accompanied by fewer fixations, a more efficient scan path and, specifically, an earlier onset of a monotonic gaze approach phase towards the target location in repeated displays. When the repeated context was no longer predictive of the old target location, search times and number of fixations for old displays increased to the level of novel displays. Along with this, scan paths for old and new displays became equally efficient. After the target location change, there was a bias of exploration towards the old target location, which soon disappeared. Thus, change of implicitly learned spatial relations between target and distractor configuration eliminated the advantageous effects of contextual cueing, but did not lead to a lasting impairment of search in repeated displays relative to novel displays.

More Efficient e-Learning through Design: Color of Text and Background

ERIC Educational Resources Information Center

Zufic, Janko; Kalpic, Damir

2009-01-01

Background: The area of research aimed for a more efficient e-learning is slowly widening from purely technical to the areas of psychology, didactics and methodology. The question is whether the text or background color influence the efficiency of memory, i.e. learning. If the answer to that question is positive, then another question arises which…

Semi-supervised learning for photometric supernova classification

NASA Astrophysics Data System (ADS)

Richards, Joseph W.; Homrighausen, Darren; Freeman, Peter E.; Schafer, Chad M.; Poznanski, Dovi

2012-01-01

We present a semi-supervised method for photometric supernova typing. Our approach is to first use the non-linear dimension reduction technique diffusion map to detect structure in a data base of supernova light curves and subsequently employ random forest classification on a spectroscopically confirmed training set to learn a model that can predict the type of each newly observed supernova. We demonstrate that this is an effective method for supernova typing. As supernova numbers increase, our semi-supervised method efficiently utilizes this information to improve classification, a property not enjoyed by template-based methods. Applied to supernova data simulated by Kessler et al. to mimic those of the Dark Energy Survey, our methods achieve (cross-validated) 95 per cent Type Ia purity and 87 per cent Type Ia efficiency on the spectroscopic sample, but only 50 per cent Type Ia purity and 50 per cent efficiency on the photometric sample due to their spectroscopic follow-up strategy. To improve the performance on the photometric sample, we search for better spectroscopic follow-up procedures by studying the sensitivity of our machine-learned supernova classification on the specific strategy used to obtain training sets. With a fixed amount of spectroscopic follow-up time, we find that, despite collecting data on a smaller number of supernovae, deeper magnitude-limited spectroscopic surveys are better for producing training sets. For supernova Ia (II-P) typing, we obtain a 44 per cent (1 per cent) increase in purity to 72 per cent (87 per cent) and 30 per cent (162 per cent) increase in efficiency to 65 per cent (84 per cent) of the sample using a 25th (24.5th) magnitude-limited survey instead of the shallower spectroscopic sample used in the original simulations. When redshift information is available, we incorporate it into our analysis using a novel method of altering the diffusion map representation of the supernovae. Incorporating host redshifts leads to a 5 per cent improvement in Type Ia purity and 13 per cent improvement in Type Ia efficiency. A web service for the supernova classification method used in this paper can be found at .

Evaluation of the clinical implementation of a large-scale online e-learning program on venous blood specimen collection guideline practices.

PubMed

Willman, Britta; Grankvist, Kjell; Bölenius, Karin

2018-05-11

When performed erroneously, the venous blood specimen collection (VBSC) practice steps patient identification, test request management and test tube labeling are at high risk to jeopardize patient safety. VBSC educational programs with the intention to minimize risk of harm to patients are therefore needed. In this study, we evaluate the efficiency of a large-scale online e-learning program on personnel's adherence to VBSC practices and their experience of the e-learning program. An interprofessional team transformed an implemented traditional VBSC education program to an online e-learning program developed to stimulate reflection with focus on the high-risk practice steps. We used questionnaires to evaluate the effect of the e-learning program on personnel's self-reported adherence to VBSC practices compared to questionnaire surveys before and after introduction of the traditional education program. We used content analysis to evaluate the participants free text experience of the VBSC e-learning program. Adherence to the VBSC guideline high-risk practice steps generally increased following the implementation of a traditional educational program followed by an e-learning program. We however found a negative trend over years regarding participation rates and the practice to always send/sign the request form following the introduction of an electronic request system. The participants were in general content with the VBSC e-learning program. Properly designed e-learning programs on VBSC practices supersedes traditional educational programs in usefulness and functionality. Inclusion of questionnaires in the e-learning program is necessary for follow-up of VBSC participant's practices and educational program efficiency.

Cognitive Load Theory vs. Constructivist Approaches: Which Best Leads to Efficient, Deep Learning?

ERIC Educational Resources Information Center

Vogel-Walcutt, J. J.; Gebrim, J. B.; Bowers, C.; Carper, T. M.; Nicholson, D.

2011-01-01

Computer-assisted learning, in the form of simulation-based training, is heavily focused upon by the military. Because computer-based learning offers highly portable, reusable, and cost-efficient training options, the military has dedicated significant resources to the investigation of instructional strategies that improve learning efficiency…

Developing Animated Cartoons for Economic Teaching

ERIC Educational Resources Information Center

Zhang, Yu Aimee

2012-01-01

Purpose: A picture is worth a thousand words. Multimedia teaching materials have been widely adopted by teachers in Physics, Biotechnology, Psychology, Religion, Analytical Science, and Economics nowadays. To assist with engaging students in their economic study, increase learning efficiency and understanding, solve misconception problems,…

Quality evaluation on an e-learning system in continuing professional education of nurses.

PubMed

Lin, I-Chun; Chien, Yu-Mei; Chang, I-Chiu

2006-01-01

Maintaining high quality in Web-based learning is a powerful means of increasing the overall efficiency and effectiveness of distance learning. Many studies have evaluated Web-based learning but seldom evaluate from the information systems (IS) perspective. This study applied the famous IS Success model in measuring the quality of a Web-based learning system using a Web-based questionnaire for data collection. One hundred and fifty four nurses participated in the survey. Based on confirmatory factor analysis, the variables of the research model fit for measuring the quality of a Web-based learning system. As Web-based education continues to grow worldwide, the results of this study may assist the system adopter (hospital executives), the learner (nurses), and the system designers in making reasonable and informed judgments with regard to the quality of Web-based learning system in continuing professional education.

The impact of social context on learning and cognitive demands for interactive virtual human simulations

PubMed Central

Lyons, Rebecca; Johnson, Teresa R.; Khalil, Mohammed K.

2014-01-01

Interactive virtual human (IVH) simulations offer a novel method for training skills involving person-to-person interactions. This article examines the effectiveness of an IVH simulation for teaching medical students to assess rare cranial nerve abnormalities in both individual and small-group learning contexts. Individual (n = 26) and small-group (n = 30) interaction with the IVH system was manipulated to examine the influence on learning, learner engagement, perceived cognitive demands of the learning task, and instructional efficiency. Results suggested the IVH activity was an equally effective and engaging instructional tool in both learning structures, despite learners in the group learning contexts having to share hands-on access to the simulation interface. Participants in both conditions demonstrated a significant increase in declarative knowledge post-training. Operation of the IVH simulation technology imposed moderate cognitive demand but did not exceed the demands of the task content or appear to impede learning. PMID:24883241

Efficient learning mechanisms hold in the social domain and are implemented in the medial prefrontal cortex

PubMed Central

Tobler, Philippe N.

2015-01-01

When we are learning to associate novel cues with outcomes, learning is more efficient if we take advantage of previously learned associations and thereby avoid redundant learning. The blocking effect represents this sort of efficiency mechanism and refers to the phenomenon in which a novel stimulus is blocked from learning when it is associated with a fully predicted outcome. Although there is sufficient evidence that this effect manifests itself when individuals learn about their own rewards, it remains unclear whether it also does when they learn about others’ rewards. We employed behavioral and neuroimaging methods to address this question. We demonstrate that blocking does indeed occur in the social domain and it does so to a similar degree as observed in the individual domain. On the neural level, activations in the medial prefrontal cortex (mPFC) show a specific contribution to blocking and learning-related prediction errors in the social domain. These findings suggest that the efficiency principle that applies to reward learning in the individual domain also applies to that in the social domain, with the mPFC playing a central role in implementing it. PMID:25326037

Optimal Learning for Efficient Experimentation in Nanotechnology and Biochemistry

DTIC Science & Technology

2015-12-22

AFRL-AFOSR-VA-TR-2016-0018 Optimal Learning for Efficient Experimentation in Nanotechnology , Biochemistry Warren Powell TRUSTEES OF PRINCETON...3. DATES COVERED (From - To) 01-07-2012 to 30-09-2015 4. TITLE AND SUBTITLE Optimal Learning for Efficient Experimentation in Nanotechnology and...in Nanotechnology and Biochemistry Principal Investigators: Warren B. Powell Princeton University Department of Operations Research and

Fidelity-Based Ant Colony Algorithm with Q-learning of Quantum System

NASA Astrophysics Data System (ADS)

Liao, Qin; Guo, Ying; Tu, Yifeng; Zhang, Hang

2018-03-01

Quantum ant colony algorithm (ACA) has potential applications in quantum information processing, such as solutions of traveling salesman problem, zero-one knapsack problem, robot route planning problem, and so on. To shorten the search time of the ACA, we suggest the fidelity-based ant colony algorithm (FACA) for the control of quantum system. Motivated by structure of the Q-learning algorithm, we demonstrate the combination of a FACA with the Q-learning algorithm and suggest the design of a fidelity-based ant colony algorithm with the Q-learning to improve the performance of the FACA in a spin-1/2 quantum system. The numeric simulation results show that the FACA with the Q-learning can efficiently avoid trapping into local optimal policies and increase the speed of convergence process of quantum system.

Fidelity-Based Ant Colony Algorithm with Q-learning of Quantum System

NASA Astrophysics Data System (ADS)

Liao, Qin; Guo, Ying; Tu, Yifeng; Zhang, Hang

2017-12-01

Quantum ant colony algorithm (ACA) has potential applications in quantum information processing, such as solutions of traveling salesman problem, zero-one knapsack problem, robot route planning problem, and so on. To shorten the search time of the ACA, we suggest the fidelity-based ant colony algorithm (FACA) for the control of quantum system. Motivated by structure of the Q-learning algorithm, we demonstrate the combination of a FACA with the Q-learning algorithm and suggest the design of a fidelity-based ant colony algorithm with the Q-learning to improve the performance of the FACA in a spin-1/2 quantum system. The numeric simulation results show that the FACA with the Q-learning can efficiently avoid trapping into local optimal policies and increase the speed of convergence process of quantum system.

Non-Constant Learning Rates in Retrospective Experience Curve Analyses and their Correlation to Deployment Programs

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

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

2015-07-16

A key challenge for policy-makers and technology market forecasters is to estimate future technology costs and in particular the rate of cost reduction versus production volume. A related, critical question is what role should state and federal governments have in advancing energy efficient and renewable energy technologies? This work provides retrospective experience curves and learning rates for several energy-related technologies, each of which have a known history of federal and state deployment programs. We derive learning rates for eight technologies including energy efficient lighting technologies, stationary fuel cell systems, and residential solar photovoltaics, and provide an overview and timeline ofmore » historical deployment programs such as state and federal standards and state and national incentive programs for each technology. Piecewise linear regimes are observed in a range of technology experience curves, and public investments or deployment programs are found to be strongly correlated to an increase in learning rate across multiple technologies. A downward bend in the experience curve is found in 5 out of the 8 energy-related technologies presented here (electronic ballasts, magnetic ballasts, compact fluorescent lighting, general service fluorescent lighting, and the installed cost of solar PV). In each of the five downward-bending experience curves, we believe that an increase in the learning rate can be linked to deployment programs to some degree. This work sheds light on the endogenous versus exogenous contributions to technological innovation and highlights the impact of exogenous government sponsored deployment programs. This work can inform future policy investment direction and can shed light on market transformation and technology learning behavior.« less

Can Social Networks and E-Portfolio be Used together for Enhancing Learning Effects and Attitudes?

ERIC Educational Resources Information Center

Baris, M. Fatih; Tosun, Nilgun

2013-01-01

As the choices that information technologies offer has increased, efficiency of these at education, the area and time it covers increases, as well. E-portfolio and social networks are the latest choices that informational technologies offer. In this study, both technologies have been used at education and results have been analyzed. For that…

Spitzer observatory operations: increasing efficiency in mission operations

NASA Astrophysics Data System (ADS)

Scott, Charles P.; Kahr, Bolinda E.; Sarrel, Marc A.

2006-06-01

This paper explores the how's and why's of the Spitzer Mission Operations System's (MOS) success, efficiency, and affordability in comparison to other observatory-class missions. MOS exploits today's flight, ground, and operations capabilities, embraces automation, and balances both risk and cost. With operational efficiency as the primary goal, MOS maintains a strong control process by translating lessons learned into efficiency improvements, thereby enabling the MOS processes, teams, and procedures to rapidly evolve from concept (through thorough validation) into in-flight implementation. Operational teaming, planning, and execution are designed to enable re-use. Mission changes, unforeseen events, and continuous improvement have often times forced us to learn to fly anew. Collaborative spacecraft operations and remote science and instrument teams have become well integrated, and worked together to improve and optimize each human, machine, and software-system element. Adaptation to tighter spacecraft margins has facilitated continuous operational improvements via automated and autonomous software coupled with improved human analysis. Based upon what we now know and what we need to improve, adapt, or fix, the projected mission lifetime continues to grow - as does the opportunity for numerous scientific discoveries.

Learning to ignore: A modeling study of a decremental cholinergic pathway and its influence on attention and learning

PubMed Central

Oros, Nicolas; Chiba, Andrea A.; Nitz, Douglas A.; Krichmar, Jeffrey L.

2014-01-01

Learning to ignore irrelevant stimuli is essential to achieving efficient and fluid attention, and serves as the complement to increasing attention to relevant stimuli. The different cholinergic (ACh) subsystems within the basal forebrain regulate attention in distinct but complementary ways. ACh projections from the substantia innominata/nucleus basalis region (SI/nBM) to the neocortex are necessary to increase attention to relevant stimuli and have been well studied. Lesser known are ACh projections from the medial septum/vertical limb of the diagonal band (MS/VDB) to the hippocampus and the cingulate that are necessary to reduce attention to irrelevant stimuli. We developed a neural simulation to provide insight into how ACh can decrement attention using this distinct pathway from the MS/VDB. We tested the model in behavioral paradigms that require decremental attention. The model exhibits behavioral effects such as associative learning, latent inhibition, and persisting behavior. Lesioning the MS/VDB disrupts latent inhibition, and drastically increases perseverative behavior. Taken together, the model demonstrates that the ACh decremental pathway is necessary for appropriate learning and attention under dynamic circumstances and suggests a canonical neural architecture for decrementing attention. PMID:24443744

Optimisation of GaN LEDs and the reduction of efficiency droop using active machine learning

DOE PAGES

Rouet-Leduc, Bertrand; Barros, Kipton Marcos; Lookman, Turab; ...

2016-04-26

A fundamental challenge in the design of LEDs is to maximise electro-luminescence efficiency at high current densities. We simulate GaN-based LED structures that delay the onset of efficiency droop by spreading carrier concentrations evenly across the active region. Statistical analysis and machine learning effectively guide the selection of the next LED structure to be examined based upon its expected efficiency as well as model uncertainty. This active learning strategy rapidly constructs a model that predicts Poisson-Schrödinger simulations of devices, and that simultaneously produces structures with higher simulated efficiencies.

Learning with Computer-Based Multimedia: Gender Effects on Efficiency

ERIC Educational Resources Information Center

Pohnl, Sabine; Bogner, Franz X.

2012-01-01

Up to now, only a few studies in multimedia learning have focused on gender effects. While research has mostly focused on learning success, the effect of gender on instructional efficiency (IE) has not yet been considered. Consequently, we used a quasi-experimental design to examine possible gender differences in the learning success, mental…

Time and Learning Efficiency in Internet-Based Learning: A Systematic Review and Meta-Analysis

ERIC Educational Resources Information Center

Cook, David A.; Levinson, Anthony J.; Garside, Sarah

2010-01-01

Authors have claimed that Internet-based instruction promotes greater learning efficiency than non-computer methods. Objectives Determine, through a systematic synthesis of evidence in health professions education, how Internet-based instruction compares with non-computer instruction in time spent learning, and what features of Internet-based…

Efficient Actor-Critic Algorithm with Hierarchical Model Learning and Planning

PubMed Central

Fu, QiMing

2016-01-01

To improve the convergence rate and the sample efficiency, two efficient learning methods AC-HMLP and RAC-HMLP (AC-HMLP with ℓ 2-regularization) are proposed by combining actor-critic algorithm with hierarchical model learning and planning. The hierarchical models consisting of the local and the global models, which are learned at the same time during learning of the value function and the policy, are approximated by local linear regression (LLR) and linear function approximation (LFA), respectively. Both the local model and the global model are applied to generate samples for planning; the former is used only if the state-prediction error does not surpass the threshold at each time step, while the latter is utilized at the end of each episode. The purpose of taking both models is to improve the sample efficiency and accelerate the convergence rate of the whole algorithm through fully utilizing the local and global information. Experimentally, AC-HMLP and RAC-HMLP are compared with three representative algorithms on two Reinforcement Learning (RL) benchmark problems. The results demonstrate that they perform best in terms of convergence rate and sample efficiency. PMID:27795704

Efficient Actor-Critic Algorithm with Hierarchical Model Learning and Planning.

PubMed

Zhong, Shan; Liu, Quan; Fu, QiMing

2016-01-01

To improve the convergence rate and the sample efficiency, two efficient learning methods AC-HMLP and RAC-HMLP (AC-HMLP with ℓ 2 -regularization) are proposed by combining actor-critic algorithm with hierarchical model learning and planning. The hierarchical models consisting of the local and the global models, which are learned at the same time during learning of the value function and the policy, are approximated by local linear regression (LLR) and linear function approximation (LFA), respectively. Both the local model and the global model are applied to generate samples for planning; the former is used only if the state-prediction error does not surpass the threshold at each time step, while the latter is utilized at the end of each episode. The purpose of taking both models is to improve the sample efficiency and accelerate the convergence rate of the whole algorithm through fully utilizing the local and global information. Experimentally, AC-HMLP and RAC-HMLP are compared with three representative algorithms on two Reinforcement Learning (RL) benchmark problems. The results demonstrate that they perform best in terms of convergence rate and sample efficiency.

Artificial Intelligence: Threat or Boon to Radiologists?

PubMed

Recht, Michael; Bryan, R Nick

2017-11-01

The development and integration of machine learning/artificial intelligence into routine clinical practice will significantly alter the current practice of radiology. Changes in reimbursement and practice patterns will also continue to affect radiology. But rather than being a significant threat to radiologists, we believe these changes, particularly machine learning/artificial intelligence, will be a boon to radiologists by increasing their value, efficiency, accuracy, and personal satisfaction. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Spectrum Access In Cognitive Radio Using a Two-Stage Reinforcement Learning Approach

NASA Astrophysics Data System (ADS)

Raj, Vishnu; Dias, Irene; Tholeti, Thulasi; Kalyani, Sheetal

2018-02-01

With the advent of the 5th generation of wireless standards and an increasing demand for higher throughput, methods to improve the spectral efficiency of wireless systems have become very important. In the context of cognitive radio, a substantial increase in throughput is possible if the secondary user can make smart decisions regarding which channel to sense and when or how often to sense. Here, we propose an algorithm to not only select a channel for data transmission but also to predict how long the channel will remain unoccupied so that the time spent on channel sensing can be minimized. Our algorithm learns in two stages - a reinforcement learning approach for channel selection and a Bayesian approach to determine the optimal duration for which sensing can be skipped. Comparisons with other learning methods are provided through extensive simulations. We show that the number of sensing is minimized with negligible increase in primary interference; this implies that lesser energy is spent by the secondary user in sensing and also higher throughput is achieved by saving on sensing.

The effect of repeated bouts of backward walking on physiologic efficiency.

PubMed

Childs, John D; Gantt, Christy; Higgins, Dan; Papazis, Janet A; Franklin, Ronald; Metzler, Terri; Underwood, Frank B

2002-08-01

Previous studies have demonstrated an increased energy expenditure with novel tasks. With practice, the energy cost decreases as the body more efficiently recruits motor units. This study examined whether one becomes more efficient after repeated bouts of backward walking. The subjects were 7 healthy subjects between the ages of 23 and 49 years. A backward walking speed was calculated to elicit a VO(2) equal to 60% of the VO(2)max. There were 18 training sessions at the prescribed walking speed 3 d x wk(-1) for 20 min x d(-1). The backward walking speed required to elicit a fixed VO(2) increased between weeks 4 and 6 of the training period. This finding suggests that backward walking is indeed a novel task and that motor learning occurs as a result of practice, leading to a more efficient recruitment of motor units.

An Experimental Investigation into the Efficiency of Cooperative Learning with Consideration of Multiple Grouping Criteria

ERIC Educational Resources Information Center

Hsiung, C. -M.

2010-01-01

The present study conducts an experimental investigation to compare the efficiency of the cooperative learning method with that of the traditional learning method. A total of 42 engineering students are randomly assigned to the two learning conditions and are formed into mixed-ability groups comprising three team members. In addition to the…

Efficient Learning Algorithms with Limited Information

ERIC Educational Resources Information Center

De, Anindya

2013-01-01

The thesis explores efficient learning algorithms in settings which are more restrictive than the PAC model of learning (Valiant) in one of the following two senses: (i) The learning algorithm has a very weak access to the unknown function, as in, it does not get labeled samples for the unknown function (ii) The error guarantee required from the…

Using Email to Enable E[superscript 3] (Effective, Efficient, and Engaging) Learning

ERIC Educational Resources Information Center

Kim, ChanMin

2008-01-01

This article argues that technology that supports both noncognitive and cognitive aspects can make learning more effective, efficient, and engaging (e[superscript 3]-learning). The technology of interest in this article is email. The investigation focuses on characteristics of email that are likely to enable e[superscript 3]-learning. In addition,…

Conceptions of Efficiency: Applications in Learning and Problem Solving

ERIC Educational Resources Information Center

Hoffman, Bobby; Schraw, Gregory

2010-01-01

The purpose of this article is to clarify conceptions, definitions, and applications of learning and problem-solving efficiency. Conceptions of efficiency vary within the field of educational psychology, and there is little consensus as to how to define, measure, and interpret the efficiency construct. We compare three diverse models that differ…

Efficient learning mechanisms hold in the social domain and are implemented in the medial prefrontal cortex.

PubMed

Seid-Fatemi, Azade; Tobler, Philippe N

2015-05-01

When we are learning to associate novel cues with outcomes, learning is more efficient if we take advantage of previously learned associations and thereby avoid redundant learning. The blocking effect represents this sort of efficiency mechanism and refers to the phenomenon in which a novel stimulus is blocked from learning when it is associated with a fully predicted outcome. Although there is sufficient evidence that this effect manifests itself when individuals learn about their own rewards, it remains unclear whether it also does when they learn about others' rewards. We employed behavioral and neuroimaging methods to address this question. We demonstrate that blocking does indeed occur in the social domain and it does so to a similar degree as observed in the individual domain. On the neural level, activations in the medial prefrontal cortex (mPFC) show a specific contribution to blocking and learning-related prediction errors in the social domain. These findings suggest that the efficiency principle that applies to reward learning in the individual domain also applies to that in the social domain, with the mPFC playing a central role in implementing it. © The Author (2014). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Q-Learning-Based Adjustable Fixed-Phase Quantum Grover Search Algorithm

NASA Astrophysics Data System (ADS)

Guo, Ying; Shi, Wensha; Wang, Yijun; Hu, Jiankun

2017-02-01

We demonstrate that the rotation phase can be suitably chosen to increase the efficiency of the phase-based quantum search algorithm, leading to a dynamic balance between iterations and success probabilities of the fixed-phase quantum Grover search algorithm with Q-learning for a given number of solutions. In this search algorithm, the proposed Q-learning algorithm, which is a model-free reinforcement learning strategy in essence, is used for performing a matching algorithm based on the fraction of marked items λ and the rotation phase α. After establishing the policy function α = π(λ), we complete the fixed-phase Grover algorithm, where the phase parameter is selected via the learned policy. Simulation results show that the Q-learning-based Grover search algorithm (QLGA) enables fewer iterations and gives birth to higher success probabilities. Compared with the conventional Grover algorithms, it avoids the optimal local situations, thereby enabling success probabilities to approach one.

Sparsity-promoting orthogonal dictionary updating for image reconstruction from highly undersampled magnetic resonance data.

PubMed

Huang, Jinhong; Guo, Li; Feng, Qianjin; Chen, Wufan; Feng, Yanqiu

2015-07-21

Image reconstruction from undersampled k-space data accelerates magnetic resonance imaging (MRI) by exploiting image sparseness in certain transform domains. Employing image patch representation over a learned dictionary has the advantage of being adaptive to local image structures and thus can better sparsify images than using fixed transforms (e.g. wavelets and total variations). Dictionary learning methods have recently been introduced to MRI reconstruction, and these methods demonstrate significantly reduced reconstruction errors compared to sparse MRI reconstruction using fixed transforms. However, the synthesis sparse coding problem in dictionary learning is NP-hard and computationally expensive. In this paper, we present a novel sparsity-promoting orthogonal dictionary updating method for efficient image reconstruction from highly undersampled MRI data. The orthogonality imposed on the learned dictionary enables the minimization problem in the reconstruction to be solved by an efficient optimization algorithm which alternately updates representation coefficients, orthogonal dictionary, and missing k-space data. Moreover, both sparsity level and sparse representation contribution using updated dictionaries gradually increase during iterations to recover more details, assuming the progressively improved quality of the dictionary. Simulation and real data experimental results both demonstrate that the proposed method is approximately 10 to 100 times faster than the K-SVD-based dictionary learning MRI method and simultaneously improves reconstruction accuracy.

``Astrophysique sur Mesure'', E-learning in Astronomy and Astrophysics

NASA Astrophysics Data System (ADS)

Mosser, Benoît; Delsanti, Audrey; Guillaume, Damien; Balança, Christian; Balkowski, Chantal

2011-06-01

``Astrophysique sur Mesure'' (astrophysics made-to-measure) is a set of e-learning programmes started 4 years ago at the Paris Observatory. In order to deliver attractive and efficient programmes, we have added many multimedia tools to usual lectures: animations, Java applets. The programmes are presented on two different platforms. The first one offers the content of all the lectures in free access. A second platform with restricted access is provided to registered students taking part in the e-learning program and benefiting from the help of tutors. The development of these programs helps to increase the sphere of influence of astronomy taught at the Paris Observatory, hence to increase the presence of astronomy in various degree courses. Instead of teaching classical astronomy lectures to a happy few, we can bring astronomy and astrophysics to a wider audience.

Toward a Neurobiology of Child Psychotherapy

ERIC Educational Resources Information Center

Kay, Jerald

2009-01-01

Brain imaging studies have demonstrated that psychotherapy alters brain structure and function. Learning and memory, both implicit and explicit, play central roles in this process through the creation of new genetic material that leads to increased synaptic efficiency through the creation of new neuronal connections. Although there is substantial…

Influence of the Internet on Studying English

ERIC Educational Resources Information Center

Molchanova, Irma Igorevna

2015-01-01

The article considers theoretical aspects of influence of the Internet on studying English, including on the opportunities of listening and increase of motivation for studying English. The characteristic of blended learning technology in studying the foreign languages is given. The practical justification of the efficiency of studying English…

Neural Signatures of Phonetic Learning in Adulthood: A Magnetoencephalography Study

PubMed Central

Zhang, Yang; Kuhl, Patricia K.; Imada, Toshiaki; Iverson, Paul; Pruitt, John; Stevens, Erica B.; Kawakatsu, Masaki; Tohkura, Yoh'ichi; Nemoto, Iku

2010-01-01

The present study used magnetoencephalography (MEG) to examine perceptual learning of American English /r/ and /l/ categories by Japanese adults who had limited English exposure. A training software program was developed based on the principles of infant phonetic learning, featuring systematic acoustic exaggeration, multi-talker variability, visible articulation, and adaptive listening. The program was designed to help Japanese listeners utilize an acoustic dimension relevant for phonemic categorization of /r-l/ in English. Although training did not produce native-like phonetic boundary along the /r-l/ synthetic continuum in the second language learners, success was seen in highly significant identification improvement over twelve training sessions and transfer of learning to novel stimuli. Consistent with behavioral results, pre-post MEG measures showed not only enhanced neural sensitivity to the /r-l/ distinction in the left-hemisphere mismatch field (MMF) response but also bilateral decreases in equivalent current dipole (ECD) cluster and duration measures for stimulus coding in the inferior parietal region. The learning-induced increases in neural sensitivity and efficiency were also found in distributed source analysis using Minimum Current Estimates (MCE). Furthermore, the pre-post changes exhibited significant brain-behavior correlations between speech discrimination scores and MMF amplitudes as well as between the behavioral scores and ECD measures of neural efficiency. Together, the data provide corroborating evidence that substantial neural plasticity for second-language learning in adulthood can be induced with adaptive and enriched linguistic exposure. Like the MMF, the ECD cluster and duration measures are sensitive neural markers of phonetic learning. PMID:19457395

Effects of Mobile Augmented Reality Learning Compared to Textbook Learning on Medical Students: Randomized Controlled Pilot Study

PubMed Central

2013-01-01

Background By adding new levels of experience, mobile Augmented Reality (mAR) can significantly increase the attractiveness of mobile learning applications in medical education. Objective To compare the impact of the heightened realism of a self-developed mAR blended learning environment (mARble) on learners to textbook material, especially for ethically sensitive subjects such as forensic medicine, while taking into account basic psychological aspects (usability and higher level of emotional involvement) as well as learning outcomes (increased learning efficiency). Methods A prestudy was conducted based on a convenience sample of 10 third-year medical students. The initial emotional status was captured using the “Profile of Mood States” questionnaire (POMS, German variation); previous knowledge about forensic medicine was determined using a 10-item single-choice (SC) test. During the 30-minute learning period, the students were randomized into two groups: the first group consisted of pairs of students, each equipped with one iPhone with a preinstalled copy of mARble, while the second group was provided with textbook material. Subsequently, both groups were asked to once again complete the POMS questionnaire and SC test to measure changes in emotional state and knowledge gain. Usability as well as pragmatic and hedonic qualities of the learning material was captured using AttrakDiff2 questionnaires. Data evaluation was conducted anonymously. Descriptive statistics for the score in total and the subgroups were calculated before and after the intervention. The scores of both groups were tested against each other using paired and unpaired signed-rank tests. An item analysis was performed for the SC test to objectify difficulty and selectivity. Results Statistically significant, the mARble group (6/10) showed greater knowledge gain than the control group (4/10) (Wilcoxon z=2.232, P=.03). The item analysis of the SC test showed a difficulty of P=0.768 (s=0.09) and a selectivity of RPB=0.2. For mARble, fatigue (z=2.214, P=.03) and numbness (z=2.07, P=.04) decreased with statistical significance when comparing pre- and post-tests. Vigor rose slightly, while irritability did not increase significantly. Changes in the control group were insignificant. Regarding hedonic quality (identification, stimulation, attractiveness), there were significant differences between mARble (mean 1.179, CI −0.440 to 0.440) and the book chapter (mean −0.982, CI −0.959 to 0.959); the pragmatic quality mean only differed slightly. Conclusions The mARble group performed considerably better regarding learning efficiency; there are hints for activating components of the mAR concept that may serve to fascinate the participants and possibly boost interest in the topic for the remainder of the class. While the small sample size reduces our study’s conclusiveness, its design seems appropriate for determining the effects of interactive eLearning material with respect to emotions, learning efficiency, and hedonic and pragmatic qualities using a larger group. Trial Registration German Clinical Trial Register (DRKS), DRKS-ID: DRKS00004685; https://drks-neu.uniklinik-freiburg.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00004685. PMID:23963306

Effects of mobile augmented reality learning compared to textbook learning on medical students: randomized controlled pilot study.

PubMed

Albrecht, Urs-Vito; Folta-Schoofs, Kristian; Behrends, Marianne; von Jan, Ute

2013-08-20

By adding new levels of experience, mobile Augmented Reality (mAR) can significantly increase the attractiveness of mobile learning applications in medical education. To compare the impact of the heightened realism of a self-developed mAR blended learning environment (mARble) on learners to textbook material, especially for ethically sensitive subjects such as forensic medicine, while taking into account basic psychological aspects (usability and higher level of emotional involvement) as well as learning outcomes (increased learning efficiency). A prestudy was conducted based on a convenience sample of 10 third-year medical students. The initial emotional status was captured using the "Profile of Mood States" questionnaire (POMS, German variation); previous knowledge about forensic medicine was determined using a 10-item single-choice (SC) test. During the 30-minute learning period, the students were randomized into two groups: the first group consisted of pairs of students, each equipped with one iPhone with a preinstalled copy of mARble, while the second group was provided with textbook material. Subsequently, both groups were asked to once again complete the POMS questionnaire and SC test to measure changes in emotional state and knowledge gain. Usability as well as pragmatic and hedonic qualities of the learning material was captured using AttrakDiff2 questionnaires. Data evaluation was conducted anonymously. Descriptive statistics for the score in total and the subgroups were calculated before and after the intervention. The scores of both groups were tested against each other using paired and unpaired signed-rank tests. An item analysis was performed for the SC test to objectify difficulty and selectivity. Statistically significant, the mARble group (6/10) showed greater knowledge gain than the control group (4/10) (Wilcoxon z=2.232, P=.03). The item analysis of the SC test showed a difficulty of P=0.768 (s=0.09) and a selectivity of RPB=0.2. For mARble, fatigue (z=2.214, P=.03) and numbness (z=2.07, P=.04) decreased with statistical significance when comparing pre- and post-tests. Vigor rose slightly, while irritability did not increase significantly. Changes in the control group were insignificant. Regarding hedonic quality (identification, stimulation, attractiveness), there were significant differences between mARble (mean 1.179, CI -0.440 to 0.440) and the book chapter (mean -0.982, CI -0.959 to 0.959); the pragmatic quality mean only differed slightly. The mARble group performed considerably better regarding learning efficiency; there are hints for activating components of the mAR concept that may serve to fascinate the participants and possibly boost interest in the topic for the remainder of the class. While the small sample size reduces our study's conclusiveness, its design seems appropriate for determining the effects of interactive eLearning material with respect to emotions, learning efficiency, and hedonic and pragmatic qualities using a larger group. German Clinical Trial Register (DRKS), DRKS-ID: DRKS00004685; https://drks-neu.uniklinik-freiburg.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00004685.

Learning to soar in turbulent environments

NASA Astrophysics Data System (ADS)

Reddy, Gautam; Celani, Antonio; Sejnowski, Terrence; Vergassola, Massimo

Birds and gliders exploit warm, rising atmospheric currents (thermals) to reach heights comparable to low-lying clouds with a reduced expenditure of energy. Soaring provides a remarkable instance of complex decision-making in biology and requires a long-term strategy to effectively use the ascending thermals. Furthermore, the problem is technologically relevant to extend the flying range of autonomous gliders. The formation of thermals unavoidably generates strong turbulent fluctuations, which make deriving an efficient policy harder and thus constitute an essential element of soaring. Here, we approach soaring flight as a problem of learning to navigate highly fluctuating turbulent environments. We simulate the atmospheric boundary layer by numerical models of turbulent convective flow and combine them with model-free, experience-based, reinforcement learning algorithms to train the virtual gliders. For the learned policies in the regimes of moderate and strong turbulence levels, the virtual glider adopts an increasingly conservative policy as turbulence levels increase, quantifying the degree of risk affordable in turbulent environments. Reinforcement learning uncovers those sensorimotor cues that permit effective control over soaring in turbulent environments.

The WEB 2.0 induced paradigm shift in the e-learning and the role of crowdsourcing in dental education.

PubMed

Thurzo, A; Stanko, P; Urbanova, W; Lysy, J; Suchancova, B; Makovnik, M; Javorka, V

2010-01-01

Authors evaluated the effect of the WEB 2.0 environment on dental education and estimated the difference in retention of knowledge by cephalometric analysis in orthodontics between conventional education and off-line e-learning. Five years of experience with complex web-based e-learning system allowed the evaluation by retrospective analysis and on-line questionnaire. The results revealed the current trends in on-line behavior of students based on the WEB 2.0 innovative technologies like Ajax. Results confirmed an increasing number of resources with a rising frequency of e-learning materials. The study confirmed that e-learning of the same subject is more efficient in immediate examination after the lecture with even better results after 12 and 24 months against the control group (Tab. 3, Fig. 1, Ref. 26).

Single-machine group scheduling problems with deteriorating and learning effect

NASA Astrophysics Data System (ADS)

Xingong, Zhang; Yong, Wang; Shikun, Bai

2016-07-01

The concepts of deteriorating jobs and learning effects have been individually studied in many scheduling problems. However, most studies considering the deteriorating and learning effects ignore the fact that production efficiency can be increased by grouping various parts and products with similar designs and/or production processes. This phenomenon is known as 'group technology' in the literature. In this paper, a new group scheduling model with deteriorating and learning effects is proposed, where learning effect depends not only on job position, but also on the position of the corresponding job group; deteriorating effect depends on its starting time of the job. This paper shows that the makespan and the total completion time problems remain polynomial optimal solvable under the proposed model. In addition, a polynomial optimal solution is also presented to minimise the maximum lateness problem under certain agreeable restriction.

Positive Attitude Toward Math Supports Early Academic Success: Behavioral Evidence and Neurocognitive Mechanisms.

PubMed

Chen, Lang; Bae, Se Ri; Battista, Christian; Qin, Shaozheng; Chen, Tianwen; Evans, Tanya M; Menon, Vinod

2018-03-01

Positive attitude is thought to impact academic achievement and learning in children, but little is known about its underlying neurocognitive mechanisms. Using a large behavioral sample of 240 children, we found that positive attitude toward math uniquely predicted math achievement, even after we accounted for multiple other cognitive-affective factors. We then investigated the neural mechanisms underlying the link between positive attitude and academic achievement in two independent cohorts of children (discovery cohort: n = 47; replication cohort: n = 28) and tested competing hypotheses regarding the differential roles of affective-motivational and learning-memory systems. In both cohorts, we found that positive attitude was associated with increased engagement of the hippocampal learning-memory system. Structural equation modeling further revealed that, in both cohorts, increased hippocampal activity and more frequent use of efficient memory-based strategies mediated the relation between positive attitude and higher math achievement. Our study is the first to elucidate the neurocognitive mechanisms by which positive attitude influences learning and academic achievement.

Investigating the Structural Relationships among Organisational Support, Learning Flow, Learners' Satisfaction and Learning Transfer in Corporate E-Learning

ERIC Educational Resources Information Center

Joo, Young Ju; Lim, Kyu Yon; Park, Su Yeong

2011-01-01

E-learning in corporate training has been growing rapidly because of the pursuit of time and budget efficiency in course development and delivery. However, according to previous studies, efficiency does not always guarantee training effectiveness, which is the major concern of human resource development. It is therefore necessary to identify the…

LEARNING EFFICIENCY AS A FUNCTION OF DEPICTION, VERBALIZATION, GRADE AND SOCIAL CLASS.

ERIC Educational Resources Information Center

ROHWER, WILLIAM D., JR.; AND OTHERS

LEARNING EFFICIENCY AS A FUNCTION OF DEPICTION, VERBALIZATION, GRADE LEVEL, AND SOCIAL CLASS WAS EXPLORED BY ASKING 384 KINDERGARTEN, FIRST-, THIRD-, AND SIXTH-GRADE CHILDREN FROM BOTH MIDDLE-CLASS AND LOWER-CLASS AREAS TO LEARN A LIST OF 24 PAIRED ASSOCIATES. ALL PAIRS WERE PRESENTED PICTORIALLY BY A STUDY-TEST METHOD FOR TWO LEARNING TRIALS. THE…

Teaching customer-centric operations management - evidence from an experiential learning-oriented mass customisation class

NASA Astrophysics Data System (ADS)

Medini, Khaled

2018-01-01

The increase of individualised customer demands and tough competition in the manufacturing sector gave rise to more customer-centric operations management such as products and services (mass) customisation. Mass customisation (MC), which inherits the 'economy of scale' from mass production (MP), aims to meet specific customer demands with near MP efficiency. Such an overarching concept has multiple impacts on operations management. This requires highly qualified and multi-skilled engineers who are well prepared for managing MC. Therefore, this concept should be properly addressed by engineering education curricula which needs to keep up with the emerging business trends. This paper introduces a novel course about MC and variety in operations management which recalls several Experiential Learning (EL) practices consistently with the principle of an active learning. The paper aims to analyse to which extent EL can improve the efficiency of the teaching methods and the retention rate in the context of operations management. The proposed course is given to engineering students whose' perceptions are collected using semi-structured questionnaires and analysed quantitatively and qualitatively. The paper highlights the relevance (i) of teaching MC, and (ii) of active learning in engineering education, through the specific application in the domain of MC.

Efficient coding of spectrotemporal binaural sounds leads to emergence of the auditory space representation

PubMed Central

Młynarski, Wiktor

2014-01-01

To date a number of studies have shown that receptive field shapes of early sensory neurons can be reproduced by optimizing coding efficiency of natural stimulus ensembles. A still unresolved question is whether the efficient coding hypothesis explains formation of neurons which explicitly represent environmental features of different functional importance. This paper proposes that the spatial selectivity of higher auditory neurons emerges as a direct consequence of learning efficient codes for natural binaural sounds. Firstly, it is demonstrated that a linear efficient coding transform—Independent Component Analysis (ICA) trained on spectrograms of naturalistic simulated binaural sounds extracts spatial information present in the signal. A simple hierarchical ICA extension allowing for decoding of sound position is proposed. Furthermore, it is shown that units revealing spatial selectivity can be learned from a binaural recording of a natural auditory scene. In both cases a relatively small subpopulation of learned spectrogram features suffices to perform accurate sound localization. Representation of the auditory space is therefore learned in a purely unsupervised way by maximizing the coding efficiency and without any task-specific constraints. This results imply that efficient coding is a useful strategy for learning structures which allow for making behaviorally vital inferences about the environment. PMID:24639644

Compressed Air System Redesign Results in Increased Production at a Fuel System Plant (Caterpillar Fuel Systems Pontiac Plant)

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

None

2001-06-01

This case study is one in a series on industrial firms who are implementing energy efficient technologies and system improvements into their manufacturing processes. This case study documents the activities, savings, and lessons learned on the Caterpillar's Pontiac Plant project.

Improved System Yields $100,000 Annual Savings (Systems Analysis at Alcoa Yields Significant Savings)

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

None

1999-01-01

In another Office of Industrial Technologies Motor Challenge Success Story, Alcoa (formerly Alumax) aluminum reduced annual energy consumption by 12% and reduced both maintenance and noise levels. Order this fact sheet now to learn how your company can both increase energy efficiency and decrease pollution.

Using Machine Learning to Increase Research Efficiency: A New Approach in Environmental Sciences

USDA-ARS?s Scientific Manuscript database

Data collection has evolved from tedious in-person fieldwork to automatic data gathering from multiple sensor remotely. Scientist in environmental sciences have not fully exploited this data deluge, including legacy and new data, because the traditional scientific method is focused on small, high qu...

U.S. Public Administration Programs: Increasing Academic Achievement by Identifying and Utilizing Student Learning Styles

ERIC Educational Resources Information Center

Naylor, Lorenda A; Wooldridge, Blue; Lyles, Alan

2014-01-01

Global economic shifts are forcing universities to become more competitive and operationally efficient. As a result, universities emphasize access, affordability, and achievement. More specifically, U.S. universities have responded by emphasizing course assessment, retention rates, and graduation rates. Both university administrators and faculty…

Remote RF Laboratory Requirements: Engineers' and Technicians' Perspective

ERIC Educational Resources Information Center

Cagiltay, Nergiz Ercil; Aydin, Elif Uray; Kara, Ali

2007-01-01

This study aims to find out requirements and needs to be fulfilled in developing remote Radio Frequency (RF) laboratory. Remote laboratories are newly emerging solutions for better supporting of e-learning platforms and for increasing their efficiency and effectiveness in technical education. By this way, modern universities aim to provide…

Organizational Problems of Nutrition in the Context of Modernization of Education

ERIC Educational Resources Information Center

Platonovaa, Raisa I.; Lebedeva, Uljana M.; Cherkashina, Anna G.; Ammosova, Liliya I.; Dokhunaeva, Alyona V.

2016-01-01

The realization of the project of regional educational systems' modernization was started in 2011. The main goal of the project is to achieve systemic positive changes in the school education, improving of learning conditions, increasing of openness, availability, efficiency of General education, introduction of modern educational technologies. In…

Six Myths about Spatial Thinking

ERIC Educational Resources Information Center

Newcombe, Nora S.; Stieff, Mike

2012-01-01

Visualizations are an increasingly important part of scientific education and discovery. However, users often do not gain knowledge from them in a complete or efficient way. This article aims to direct research on visualizations in science education in productive directions by reviewing the evidence for widespread assumptions that learning styles,…

Individual differences in working memory capacity and search efficiency.

PubMed

Miller, Ashley L; Unsworth, Nash

2018-05-29

In two experiments, we examined how various learning conditions impact the relation between working memory capacity (WMC) and memory search abilities. Experiment 1 employed a delayed free recall task with semantically related words to induce the buildup of proactive interference (PI) and revealed that the buildup of PI differentially impacted recall accuracy and recall latency for low-WMC and high-WMC individuals. Namely, the buildup of PI impaired recall accuracy and slowed recall latency for low-WMC individuals to a greater extent than what was observed for high-WMC individuals. To provide a circumstance in which previously learned information remains relevant over the course of learning, Experiment 2 required participants to complete a multitrial delayed free recall task with unrelated words. Results revealed that with increased practice with the same word list, WMC-related differences were eventually eliminated in interresponse times (IRTs) and recall accuracy, but not recall latency. Thus, despite still accumulating larger search sets, low-WMC individuals searched LTM as efficiently as high-WMC individuals. Collectively, these results are consistent with the notion that under normal free recall conditions, low-WMC individuals search LTM less efficiently than do high-WMC individuals because of their reliance on noisy temporal-contextual cues at retrieval. However, it appears that under conditions in which previously learned items remain relevant at recall, this tendency to rely on vague self-generated retrieval cues can actually facilitate the ability to accurately and quickly recall information.

Multimedia learning for increasing knowledge on energy efficiency and promotion of proenvironmental behavior: A study of undergraduate students in Costa Rica

NASA Astrophysics Data System (ADS)

Walsh-Zuniga, Yoselyn

Promotion of energy efficiency practices among household has been employed in many interventions with a varying degree of success, mainly on developed countries. The purpose of the study is to promote and measure knowledge of proenvironmental behavior in undergraduate students in the Costa Rica Institute of Technology. The intervention used for this purpose provided personal and altruistic information about the impact of energy consumption activities in household. People's perceptions and attitudes about behaviors that contribute and mitigate climate change were also investigated. Participants were students from undergraduate programs who are also inhabitants of the residence hall provided by the institution. The participation consisted in two surveys and a learning module. Students responded a survey before and after exposure to a learning module. Surveys focused on identifying knowledge, attitudes and intentions. The learning module provided information about three hypothetical scenarios and corresponding energy consumption estimates for each one. Participants did not significantly improve their knowledge on energy efficiency topics and did not change perceptions about the topic of climate change. Yet for both, knowledge and perceptions, participants demonstrated an average knowledge on topics associated to climate change. In addition, participants did not use technical information to explain concepts and perceptions. Another important finding was that participants wrote their responses more third-person than in first person singular or plural, meaning that, excluding themselves from the solution and the problem. Results suggest that there is an average knowledge among participants about 2.5 out of 5 points that represent a start point to design more successful interventions that promote energy efficiency behaviors. A major recommendation to improve energy efficiency behaviors is to place a greater emphasis and awareness in personal consequences of the misuse of energy in household as part of future interventions. More studies based on real consumption data along with more engaging visualizations are highly encouraged.

Neural correlates of skill acquisition: decreased cortical activity during a serial interception sequence learning task.

PubMed

Gobel, Eric W; Parrish, Todd B; Reber, Paul J

2011-10-15

Learning of complex motor skills requires learning of component movements as well as the sequential structure of their order and timing. Using a Serial Interception Sequence Learning (SISL) task, participants learned a sequence of precisely timed interception responses through training with a repeating sequence. Following initial implicit learning of the repeating sequence, functional MRI data were collected during performance of that known sequence and compared with activity evoked during novel sequences of actions, novel timing patterns, or both. Reduced activity was observed during the practiced sequence in a distributed bilateral network including extrastriate occipital, parietal, and premotor cortical regions. These reductions in evoked activity likely reflect improved efficiency in visuospatial processing, spatio-motor integration, motor planning, and motor execution for the trained sequence, which is likely supported by nondeclarative skill learning. In addition, the practiced sequence evoked increased activity in the left ventral striatum and medial prefrontal cortex, while the posterior cingulate was more active during periods of better performance. Many prior studies of perceptual-motor skill learning have found increased activity in motor areas of the frontal cortex (e.g., motor and premotor cortex, SMA) and striatal areas (e.g., the putamen). The change in activity observed here (i.e., decreased activity across a cortical network) may reflect skill learning that is predominantly expressed through more accurate performance rather than decreased reaction time. Copyright © 2011 Elsevier Inc. All rights reserved.

Neural Correlates of Skill Acquisition: Decreased Cortical Activity During a Serial Interception Sequence Learning Task

PubMed Central

Gobel, Eric W.; Parrish, Todd B.; Reber, Paul J.

2011-01-01

Learning of complex motor skills requires learning of component movements as well as the sequential structure of their order and timing. Using a Serial Interception Sequence Learning (SISL) task, participants learned a sequence of precisely timed interception responses through training with a repeating sequence. Following initial implicit learning of the repeating sequence, functional MRI data were collected during performance of that known sequence and compared with activity evoked during novel sequences of actions, novel timing patterns, or both. Reduced activity was observed during the practiced sequence in a distributed bilateral network including extrastriate occipital, parietal, and premotor cortical regions. These reductions in evoked activity likely reflect improved efficiency in visuospatial processing, spatio-motor integration, motor planning, and motor execution for the trained sequence, which is likely supported by nondeclarative skill learning. In addition, the practiced sequence evoked increased activity in the left ventral striatum and medial prefrontal cortex, while the posterior cingulate was more active during periods of better performance. Many prior studies of perceptual-motor skill learning have found increased activity in motor areas of frontal cortex (e.g., motor and premotor cortex, SMA) and striatal areas (e.g., the putamen). The change in activity observed here (i.e., decreased activity across a cortical network) may reflect skill learning that is predominantly expressed through more accurate performance rather than decreased reaction time. PMID:21771663

IRB Process Improvements: A Machine Learning Analysis.

PubMed

Shoenbill, Kimberly; Song, Yiqiang; Cobb, Nichelle L; Drezner, Marc K; Mendonca, Eneida A

2017-06-01

Clinical research involving humans is critically important, but it is a lengthy and expensive process. Most studies require institutional review board (IRB) approval. Our objective is to identify predictors of delays or accelerations in the IRB review process and apply this knowledge to inform process change in an effort to improve IRB efficiency, transparency, consistency and communication. We analyzed timelines of protocol submissions to determine protocol or IRB characteristics associated with different processing times. Our evaluation included single variable analysis to identify significant predictors of IRB processing time and machine learning methods to predict processing times through the IRB review system. Based on initial identified predictors, changes to IRB workflow and staffing procedures were instituted and we repeated our analysis. Our analysis identified several predictors of delays in the IRB review process including type of IRB review to be conducted, whether a protocol falls under Veteran's Administration purview and specific staff in charge of a protocol's review. We have identified several predictors of delays in IRB protocol review processing times using statistical and machine learning methods. Application of this knowledge to process improvement efforts in two IRBs has led to increased efficiency in protocol review. The workflow and system enhancements that are being made support our four-part goal of improving IRB efficiency, consistency, transparency, and communication.

Improving Learning Performance Through Rational Resource Allocation

NASA Technical Reports Server (NTRS)

Gratch, J.; Chien, S.; DeJong, G.

1994-01-01

This article shows how rational analysis can be used to minimize learning cost for a general class of statistical learning problems. We discuss the factors that influence learning cost and show that the problem of efficient learning can be cast as a resource optimization problem. Solutions found in this way can be significantly more efficient than the best solutions that do not account for these factors. We introduce a heuristic learning algorithm that approximately solves this optimization problem and document its performance improvements on synthetic and real-world problems.

The Effect of Cognitive Activity on Sleep Maintenance in a Subsequent Daytime Nap.

PubMed

Arzilli, Cinzia; Cerasuolo, Mariangela; Conte, Francesca; Bittoni, Valentina; Gatteschi, Claudia; Albinni, Benedetta; Giganti, Fiorenza; Ficca, Gianluca

2018-01-25

The aim of this study is to assess the effects of a learning task on the characteristics of a subsequent daytime nap. Thirty-eight subjects were administered a control nap (C) and one preceded by a cognitive training session (TR). Relative to C, TR naps showed significantly increased sleep duration with decreased sleep latency, as well as significantly increased sleep efficiency due to reduced awakening frequency. Meaningful trends were also found toward an increase of Stage 2 sleep proportion and a reduction of Stage 1 sleep, percentage of wake after sleep onset (WASO), and frequency of state transitions. Our results indicate that presleep learning favors sleep propensity and maintenance, offering the possibility to explore planned cognitive training as a low-cost treatment for sleep impairments.

Practice makes proficient: pigeons (Columba livia) learn efficient routes on full-circuit navigational traveling salesperson problems.

PubMed

Baron, Danielle M; Ramirez, Alejandro J; Bulitko, Vadim; Madan, Christopher R; Greiner, Ariel; Hurd, Peter L; Spetch, Marcia L

2015-01-01

Visiting multiple locations and returning to the start via the shortest route, referred to as the traveling salesman (or salesperson) problem (TSP), is a valuable skill for both humans and non-humans. In the current study, pigeons were trained with increasing set sizes of up to six goals, with each set size presented in three distinct configurations, until consistency in route selection emerged. After training at each set size, the pigeons were tested with two novel configurations. All pigeons acquired routes that were significantly more efficient (i.e., shorter in length) than expected by chance selection of the goals. On average, the pigeons also selected routes that were more efficient than expected based on a local nearest-neighbor strategy and were as efficient as the average route generated by a crossing-avoidance strategy. Analysis of the routes taken indicated that they conformed to both a nearest-neighbor and a crossing-avoidance strategy significantly more often than expected by chance. Both the time taken to visit all goals and the actual distance traveled decreased from the first to the last trials of training in each set size. On the first trial with novel configurations, average efficiency was higher than chance, but was not higher than expected from a nearest-neighbor or crossing-avoidance strategy. These results indicate that pigeons can learn to select efficient routes on a TSP problem.

Technical efficiency in the use of health care resources: a comparison of OECD countries.

PubMed

Retzlaff-Roberts, Donna; Chang, Cyril F; Rubin, Rose M

2004-07-01

Our paper analyzes technical efficiency in the production of aggregate health outcomes of reduced infant mortality and increased life expectancy, using Organization for Economic Cooperation and Development (OECD) health data. Application of data envelopment analysis (DEA) reveals that some countries achieve relative efficiency advantages, including those with good health outcomes (Japan, Sweden, Norway, and Canada) and those with modest health outcomes (Mexico and Turkey). We conclude the USA may learn from countries more economical in their allocation of healthcare resources that more is not necessarily better. Specifically, we find that the USA can substantially reduce inputs while maintaining the current level of life expectancy.

[Gender-dependent effects of histone deacetylase inhibitor sodium valproate on early olfactory learning in 129Sv mice].

PubMed

Burenkova, O V; Aleksandrova, E A; Zaraĭskaia, I Iu

2013-02-01

In the brain, histone acetylation underlies both learning and the maintenance of long-term sustained effects of early experience which is further epigenetically inherited. However, the role of acetylation in learning previously has only been studied in adult animals: high level of learning could be dependent on high levels of histone H3 acetylation in the brain. The role of acetylation in the mechanisms of early learning has not been studied. In the present work, we were interested whether histone deacetylase inhibitor sodium valproate which increases the level of histone H3 acetylation will affect early olfactory discrimination learning in 8-day-old pups of 129Sv mice that are characterized by low efficiency of learning with imitation of maternal grooming. Multiple valproate injections from 3rd to 6th postnatal day had a gender-dependent effect: learning was selectively improved in male but not in female pups. In the female pups, learning improvement was observed after multiple injections of saline. Possible epigenetic mechanisms underlying these sex differences are discussed.

Transfer Learning to Accelerate Interface Structure Searches

NASA Astrophysics Data System (ADS)

Oda, Hiromi; Kiyohara, Shin; Tsuda, Koji; Mizoguchi, Teruyasu

2017-12-01

Interfaces have atomic structures that are significantly different from those in the bulk, and play crucial roles in material properties. The central structures at the interfaces that provide properties have been extensively investigated. However, determination of even one interface structure requires searching for the stable configuration among many thousands of candidates. Here, a powerful combination of machine learning techniques based on kriging and transfer learning (TL) is proposed as a method for unveiling the interface structures. Using the kriging+TL method, thirty-three grain boundaries were systematically determined from 1,650,660 candidates in only 462 calculations, representing an increase in efficiency over conventional all-candidate calculation methods, by a factor of approximately 3,600.

Development of Efficient Authoring Software for e-Learning Contents

NASA Astrophysics Data System (ADS)

Kozono, Kazutake; Teramoto, Akemi; Akiyama, Hidenori

The contents creation in e-Learning system becomes an important problem. The contents of e-Learning should include figure and voice media for a high-level educational effect. However, the use of figure and voice complicates the operation of authoring software considerably. A new authoring software, which can build e-Learning contents efficiently, has been developed to solve this problem. This paper reports development results of the authoring software.

The Efficiency of the "Learning Management System (LMS)" in AOU, Kuwait, as a Communication Tool in an E-Learning System

ERIC Educational Resources Information Center

Alfadly, Ahmad Assaf

2013-01-01

Purpose: The integration of a Learning Management System (LMS) at the Arab Open University (AOU), Kuwait, opens new possibilities for online interaction between teachers and students. The purpose of this paper is to evaluate the efficiency of the LMS at AOU, Kuwait as a communication tool in the E-learning system and to find the best automated…

Applications of Deep Learning in Biomedicine.

PubMed

Mamoshina, Polina; Vieira, Armando; Putin, Evgeny; Zhavoronkov, Alex

2016-05-02

Increases in throughput and installed base of biomedical research equipment led to a massive accumulation of -omics data known to be highly variable, high-dimensional, and sourced from multiple often incompatible data platforms. While this data may be useful for biomarker identification and drug discovery, the bulk of it remains underutilized. Deep neural networks (DNNs) are efficient algorithms based on the use of compositional layers of neurons, with advantages well matched to the challenges -omics data presents. While achieving state-of-the-art results and even surpassing human accuracy in many challenging tasks, the adoption of deep learning in biomedicine has been comparatively slow. Here, we discuss key features of deep learning that may give this approach an edge over other machine learning methods. We then consider limitations and review a number of applications of deep learning in biomedical studies demonstrating proof of concept and practical utility.

Discussion of the enabling environments for decentralised water systems.

PubMed

Moglia, M; Alexander, K S; Sharma, A

2011-01-01

Decentralised water supply systems are becoming increasingly affordable and commonplace in Australia and have the potential to alleviate urban water shortages and reduce pollution into natural receiving marine and freshwater streams. Learning processes are necessary to support the efficient implementation of decentralised systems. These processes reveal the complex socio-technical and institutional factors to be considered when developing an enabling environment supporting decentralised water and wastewater servicing solutions. Critical to the technological transition towards established decentralised systems is the ability to create strategic and adaptive capacity to promote learning and dialogue. Learning processes require institutional mechanisms to ensure the lessons are incorporated into the formulation of policy and regulation, through constructive involvement of key government institutions. Engagement of stakeholders is essential to the enabling environment. Collaborative learning environments using systems analysis with communities (social learning) and adaptive management techniques are useful in refining and applying scientists' and managers' knowledge (knowledge management).

Immunization against social fear learning.

PubMed

Golkar, Armita; Olsson, Andreas

2016-06-01

Social fear learning offers an efficient way to transmit information about potential threats; little is known, however, about the learning processes that counteract the social transmission of fear. In three separate experiments, we found that safety information transmitted from another individual (i.e., demonstrator) during preexposure prevented subsequent observational fear learning (Experiments 1-3), and this effect was maintained in a new context involving direct threat confrontation (Experiment 3). This protection from observational fear learning was specific to conditions in which information about both safety and danger was transmitted from the same demonstrator (Experiments 2-3) and was unaffected by increasing the number of the safety demonstrators (Experiment 3). Collectively, these findings demonstrate that observational preexposure can limit social transmission of fear. Future research is needed to better understand the conditions under which such effects generalize across individual demonstrators. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Set size manipulations reveal the boundary conditions of perceptual ensemble learning.

PubMed

Chetverikov, Andrey; Campana, Gianluca; Kristjánsson, Árni

2017-11-01

Recent evidence suggests that observers can grasp patterns of feature variations in the environment with surprising efficiency. During visual search tasks where all distractors are randomly drawn from a certain distribution rather than all being homogeneous, observers are capable of learning highly complex statistical properties of distractor sets. After only a few trials (learning phase), the statistical properties of distributions - mean, variance and crucially, shape - can be learned, and these representations affect search during a subsequent test phase (Chetverikov, Campana, & Kristjánsson, 2016). To assess the limits of such distribution learning, we varied the information available to observers about the underlying distractor distributions by manipulating set size during the learning phase in two experiments. We found that robust distribution learning only occurred for large set sizes. We also used set size to assess whether the learning of distribution properties makes search more efficient. The results reveal how a certain minimum of information is required for learning to occur, thereby delineating the boundary conditions of learning of statistical variation in the environment. However, the benefits of distribution learning for search efficiency remain unclear. Copyright © 2017 Elsevier Ltd. All rights reserved.

Efficient E-Learning by Dint of Cognitive Abilities

ERIC Educational Resources Information Center

Asaph, Amudha; Raja, B. William Dharma

2016-01-01

The purpose of this article is to portray the effective ways of utilizing cognitive abilities for efficient e-learning. In the present scenario, globalization and advancements in technology have driven changes in the sphere of social, technological, economic environment and political landscapes at a rapid rate. E-learning is, one among the new…

Rapid Prototyping of Mobile Learning Games

ERIC Educational Resources Information Center

Federley, Maija; Sorsa, Timo; Paavilainen, Janne; Boissonnier, Kimo; Seisto, Anu

2014-01-01

This position paper presents the first results of an on-going project, in which we explore rapid prototyping method to efficiently produce digital learning solutions that are commercially viable. In this first phase, rapid game prototyping and an iterative approach was tested as a quick and efficient way to create learning games and to evaluate…

Energy consumption analysis for various memristive networks under different learning strategies

NASA Astrophysics Data System (ADS)

Deng, Lei; Wang, Dong; Zhang, Ziyang; Tang, Pei; Li, Guoqi; Pei, Jing

2016-02-01

Recently, various memristive systems emerge to emulate the efficient computing paradigm of the brain cortex; whereas, how to make them energy efficient still remains unclear, especially from an overall perspective. Here, a systematical and bottom-up energy consumption analysis is demonstrated, including the memristor device level and the network learning level. We propose an energy estimating methodology when modulating the memristive synapses, which is simulated in three typical neural networks with different synaptic structures and learning strategies for both offline and online learning. These results provide an in-depth insight to create energy efficient brain-inspired neuromorphic devices in the future.

Multimodal Discriminative Binary Embedding for Large-Scale Cross-Modal Retrieval.

PubMed

Wang, Di; Gao, Xinbo; Wang, Xiumei; He, Lihuo; Yuan, Bo

2016-10-01

Multimodal hashing, which conducts effective and efficient nearest neighbor search across heterogeneous data on large-scale multimedia databases, has been attracting increasing interest, given the explosive growth of multimedia content on the Internet. Recent multimodal hashing research mainly aims at learning the compact binary codes to preserve semantic information given by labels. The overwhelming majority of these methods are similarity preserving approaches which approximate pairwise similarity matrix with Hamming distances between the to-be-learnt binary hash codes. However, these methods ignore the discriminative property in hash learning process, which results in hash codes from different classes undistinguished, and therefore reduces the accuracy and robustness for the nearest neighbor search. To this end, we present a novel multimodal hashing method, named multimodal discriminative binary embedding (MDBE), which focuses on learning discriminative hash codes. First, the proposed method formulates the hash function learning in terms of classification, where the binary codes generated by the learned hash functions are expected to be discriminative. And then, it exploits the label information to discover the shared structures inside heterogeneous data. Finally, the learned structures are preserved for hash codes to produce similar binary codes in the same class. Hence, the proposed MDBE can preserve both discriminability and similarity for hash codes, and will enhance retrieval accuracy. Thorough experiments on benchmark data sets demonstrate that the proposed method achieves excellent accuracy and competitive computational efficiency compared with the state-of-the-art methods for large-scale cross-modal retrieval task.

Neural correlates of context-dependent feature conjunction learning in visual search tasks.

PubMed

Reavis, Eric A; Frank, Sebastian M; Greenlee, Mark W; Tse, Peter U

2016-06-01

Many perceptual learning experiments show that repeated exposure to a basic visual feature such as a specific orientation or spatial frequency can modify perception of that feature, and that those perceptual changes are associated with changes in neural tuning early in visual processing. Such perceptual learning effects thus exert a bottom-up influence on subsequent stimulus processing, independent of task-demands or endogenous influences (e.g., volitional attention). However, it is unclear whether such bottom-up changes in perception can occur as more complex stimuli such as conjunctions of visual features are learned. It is not known whether changes in the efficiency with which people learn to process feature conjunctions in a task (e.g., visual search) reflect true bottom-up perceptual learning versus top-down, task-related learning (e.g., learning better control of endogenous attention). Here we show that feature conjunction learning in visual search leads to bottom-up changes in stimulus processing. First, using fMRI, we demonstrate that conjunction learning in visual search has a distinct neural signature: an increase in target-evoked activity relative to distractor-evoked activity (i.e., a relative increase in target salience). Second, we demonstrate that after learning, this neural signature is still evident even when participants passively view learned stimuli while performing an unrelated, attention-demanding task. This suggests that conjunction learning results in altered bottom-up perceptual processing of the learned conjunction stimuli (i.e., a perceptual change independent of the task). We further show that the acquired change in target-evoked activity is contextually dependent on the presence of distractors, suggesting that search array Gestalts are learned. Hum Brain Mapp 37:2319-2330, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Orthopaedic resident preparedness for closed reduction and pinning of pediatric supracondylar fractures is improved by e-learning: a multisite randomized controlled study.

PubMed

Hearty, Thomas; Maizels, Max; Pring, Maya; Mazur, John; Liu, Raymond; Sarwark, John; Janicki, Joseph

2013-09-04

There is a need to provide more efficient surgical training methods for orthopaedic residents. E-learning could possibly increase resident surgical preparedness, confidence, and comfort for surgery. Using closed reduction and pinning of pediatric supracondylar humeral fractures as the index case, we hypothesized that e-learning could increase resident knowledge acquisition for case preparation in the operating room. An e-learning surgical training module was created on the Computer Enhanced Visual Learning platform. The module provides a detailed and focused road map of the procedure utilizing a multimedia format. A multisite prospective randomized controlled study design compared residents who used a textbook for case preparation (control group) with residents who used the same textbook plus completed the e-learning module (test group). All subjects completed a sixty-question test on the theory and methods of the case. After completion of the test, the control group then completed the module as well. All subjects were surveyed on their opinion regarding the effectiveness of the module after performing an actual surgical case. Twenty-eight subjects with no previous experience in this surgery were enrolled at four academic centers. Subjects were randomized into two equal groups. The test group scored significantly better (p < 0.001) and demonstrated competence on the test compared with the control group; the mean correct test score (and standard deviation) was 90.9% ± 6.8% for the test group and 73.5% ± 6.4% for the control group. All residents surveyed (n = 27) agreed that the module is a useful supplement to traditional methods for case preparation and twenty-two of twenty-seven residents agreed that it reduced their anxiety during the case and improved their attention to surgical detail. E-learning using the Computer Enhanced Visual Learning platform significantly improved preparedness, confidence, and comfort with percutaneous closed reduction and pinning of a pediatric supracondylar humeral fracture. We believe that adapting such methods into residency training programs will improve efficiency in surgical training.

Improving The Quality of Education through School-Based Management: Learning from International Experiences

ERIC Educational Resources Information Center

De Grauwe, Anton

2005-01-01

School-based management is being increasingly advocated as a shortcut to more efficient management and quality improvement in education. Research, however, has been unable to prove conclusively such a linkage. Especially in developing countries, concerns remain about the possible detrimental impact of school-based management on school quality;…

Brain Gym[R]: Building Stronger Brains or Wishful Thinking?

ERIC Educational Resources Information Center

Hyatt, Keith J.

2007-01-01

As part of the accountability movement, schools are increasingly called upon to provide interventions that are based on sound scientific research and that provide measurable outcomes for children. Brain Gym[R] is a popular commercial program claiming that adherence to its regimen will result in more efficient learning in an almost miraculous…

Commentary: How Readily Can Findings from Basic Cognitive Psychology Research Be Applied in the Classroom?

ERIC Educational Resources Information Center

Efklides, Anastasia

2012-01-01

The commentary discusses phenomena highlighted in the studies of the special issue such as the hypercorrection effect, overconfidence, and the efficiency of interventions designed to increase monitoring accuracy. The discussion is based on a broader theoretical framework of self-regulation of learning that stresses the inferential character of…

Multiplicity in public health supply systems: a learning agenda.

PubMed

Bornbusch, Alan; Bates, James

2013-08-01

Supply chain integration-merging products for health programs into a single supply chain-tends to be the dominant model in health sector reform. However, multiplicity in a supply system may be justified as a risk management strategy that can better ensure product availability, advance specific health program objectives, and increase efficiency.

Online Program Capacity: Limited, Static, Elastic, or Infinite?

ERIC Educational Resources Information Center

Meyer, Katrina A.

2008-01-01

What is the capacity of online programs? Can these types of programs enroll more students than their face-to-face counterparts or not? This article looks at research on achieving cost-efficiencies through online learning, identifies the parts of an online program that can be changed to increase enrollments, and discusses whether a program's…

Putting Educational Research to Use through Knowledge Transformation. The Agency Comments.

ERIC Educational Resources Information Center

Desforges, Charles

Educational research is distinctive inasmuch as it shares the moral objective of education, which is to help people make the best of themselves through processes of learning. Educational research is a service industry for education. As such, its contribution to the efficiency and effectiveness of educational processes can be increased by focusing…

Course Management Systems in Higher Education: Understanding Student Experiences

ERIC Educational Resources Information Center

Yuen, Allan; Fox, Robert; Sun, Angie; Deng, Liping

2009-01-01

Purpose: The course management system (CMS), as an evolving tool and innovation, is increasingly used to promote the quality, efficiency and flexibility of teaching and learning in higher education. This paper aims to examine students' experiences of CMSs across faculties at a comprehensive university in Hong Kong. Design/methodology/approach:…

Towards Deeper Comprehension in Higher Engineering Education: "Method of Cornerstones"

ERIC Educational Resources Information Center

Korpela, Aki; Tarhasaari, Timo; Kettunen, Lauri; Mikkonen, Risto; Kinnari-Korpela, Hanna

2016-01-01

During the current millennium, universities have faced a new kind of problem: there is not enough higher learning in higher education. Driving forces have mainly been economical, since financial pressure and effort for increasing efficiency have given rise to growing amount of accessed and graduated students. In addition, a pressure for…

Multimedia: Bringing the Sciences to Life--Experiences with Multimedia in the Life Sciences.

ERIC Educational Resources Information Center

Cavender, Jane F.; Rutter, Steve M.

"Straight" lecturing as the only method for information delivery was at one time an efficient means of college teaching. Increased enrollment in the biological sciences, the diversity of preparedness of the students, and the variety of learning preferences of the students require new ways of disseminating information and assessing classroom…

Individual Differences and Acquiring Computer Literacy: Are Women More Efficient Than Men?

ERIC Educational Resources Information Center

Gattiker, Urs E.

The training of computer users is becoming increasingly important to all industrialized nations. This study examined how individual differences (e.g. ability and gender) may affect learning outcomes when acquiring computer skills. Subjects (N=347) were college students who took a computer literacy course from a college of business administration…

Web-Based Learning Support System

NASA Astrophysics Data System (ADS)

Fan, Lisa

Web-based learning support system offers many benefits over traditional learning environments and has become very popular. The Web is a powerful environment for distributing information and delivering knowledge to an increasingly wide and diverse audience. Typical Web-based learning environments, such as Web-CT, Blackboard, include course content delivery tools, quiz modules, grade reporting systems, assignment submission components, etc. They are powerful integrated learning management systems (LMS) that support a number of activities performed by teachers and students during the learning process [1]. However, students who study a course on the Internet tend to be more heterogeneously distributed than those found in a traditional classroom situation. In order to achieve optimal efficiency in a learning process, an individual learner needs his or her own personalized assistance. For a web-based open and dynamic learning environment, personalized support for learners becomes more important. This chapter demonstrates how to realize personalized learning support in dynamic and heterogeneous learning environments by utilizing Adaptive Web technologies. It focuses on course personalization in terms of contents and teaching materials that is according to each student's needs and capabilities. An example of using Rough Set to analyze student personal information to assist students with effective learning and predict student performance is presented.

IDEAL: Images Across Domains, Experiments, Algorithms and Learning

NASA Astrophysics Data System (ADS)

Ushizima, Daniela M.; Bale, Hrishikesh A.; Bethel, E. Wes; Ercius, Peter; Helms, Brett A.; Krishnan, Harinarayan; Grinberg, Lea T.; Haranczyk, Maciej; Macdowell, Alastair A.; Odziomek, Katarzyna; Parkinson, Dilworth Y.; Perciano, Talita; Ritchie, Robert O.; Yang, Chao

2016-11-01

Research across science domains is increasingly reliant on image-centric data. Software tools are in high demand to uncover relevant, but hidden, information in digital images, such as those coming from faster next generation high-throughput imaging platforms. The challenge is to analyze the data torrent generated by the advanced instruments efficiently, and provide insights such as measurements for decision-making. In this paper, we overview work performed by an interdisciplinary team of computational and materials scientists, aimed at designing software applications and coordinating research efforts connecting (1) emerging algorithms for dealing with large and complex datasets; (2) data analysis methods with emphasis in pattern recognition and machine learning; and (3) advances in evolving computer architectures. Engineering tools around these efforts accelerate the analyses of image-based recordings, improve reusability and reproducibility, scale scientific procedures by reducing time between experiments, increase efficiency, and open opportunities for more users of the imaging facilities. This paper describes our algorithms and software tools, showing results across image scales, demonstrating how our framework plays a role in improving image understanding for quality control of existent materials and discovery of new compounds.

Towards a mLearning training solution to the adoption of a CPOE system.

PubMed

Pakonstantinou, Despoina; Poulymenopoulou, Mikaela; Malamateniou, Flora; Vassilacopoulos, George

2012-01-01

Computerized Physician Order Entry (CPOE) has been introduced as a solution that can fundamentally change the way healthcare is provided, affecting all types of healthcare stakeholders and improving healthcare decisions, patient outcomes, patient safety and efficiency. However, a relatively small proportion of healthcare organizations have implemented CPOE systems, due to its technological complexity and to its low acceptance rate by healthcare professionals who largely disregard the value of CPOE in efficient healthcare delivery. An online training facility embedded within a CPOE service may increase the likelihood of its adoption by healthcare professionals as it offers them guidelines on how to perform each task of the CPOE service. In contrast to CPOE, on the other hand, handheld devices and other mobile technologies have showed an increased adoption rate. This paper considers a CPOE service that can be accessed by authorized healthcare professionals through their mobile devices anytime anywhere, and allows embedded training content, which has been developed through a learning management system (LMS) to be presented to the user automatically upon request.

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

PubMed

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

2016-01-01

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

Learning may need only a few bits of synaptic precision

NASA Astrophysics Data System (ADS)

Baldassi, Carlo; Gerace, Federica; Lucibello, Carlo; Saglietti, Luca; Zecchina, Riccardo

2016-05-01

Learning in neural networks poses peculiar challenges when using discretized rather then continuous synaptic states. The choice of discrete synapses is motivated by biological reasoning and experiments, and possibly by hardware implementation considerations as well. In this paper we extend a previous large deviations analysis which unveiled the existence of peculiar dense regions in the space of synaptic states which accounts for the possibility of learning efficiently in networks with binary synapses. We extend the analysis to synapses with multiple states and generally more plausible biological features. The results clearly indicate that the overall qualitative picture is unchanged with respect to the binary case, and very robust to variation of the details of the model. We also provide quantitative results which suggest that the advantages of increasing the synaptic precision (i.e., the number of internal synaptic states) rapidly vanish after the first few bits, and therefore that, for practical applications, only few bits may be needed for near-optimal performance, consistent with recent biological findings. Finally, we demonstrate how the theoretical analysis can be exploited to design efficient algorithmic search strategies.

Predicting Efficiency of Travel in Young, Visually Impaired Children from Their Other Spatial Skills.

ERIC Educational Resources Information Center

Hill, Anita; And Others

1985-01-01

To test ways of predicting how efficiently visually impaired children learn travel skills, a criteria checklist of spatial skills was developed for close-body space, local space, and geographical/travel space. Comparison was made between predictors of efficient learning including subjective ratings of teachers, personal qualities and factors of…

The community comes to campus: the Patient and Community Fair.

PubMed

Towle, Angela; Godolphin, William; Kline, Cathy

2015-08-01

Community-based learning connects students with local communities so that they learn about the broad context in which health and social care is provided; however, students usually interact with only one or a few organisations that serve a particular population. One example of a community-based learning activity is the health fair in which students provide health promotion and screening for local communities. We adapted the health fair concept to develop a multi-professional educational event at which, instead of providing service, students learn from and about the expertise and resources of not-for-profit organisations. The fair is an annual 1-day event that students can attend between, or in place of, classes. Each community organisation has a booth to display information. One-hour 'patient panels' are held on a variety of topics throughout the day. Evaluation methods include questionnaires, exit interviews and visitor tracking sheets. Over 5 years (2009-2013), the fair increased in size with respect to estimated attendance, number of participating organisations, number of patient panels and number of students for whom the fair is a required curriculum component. Students learn about a range of patient experiences and community resources, and information about specific diseases or conditions. The fair is an efficient way for students to learn about a range of community organisations. It fosters university-community engagement through continuing connections between students, faculty members and community organisations. Lessons learned include the need for community organisations to have techniques to engage students, and ways to overcome challenges of evaluating an informal 'drop-in' event. The fair is an efficient way for students to learn about a range of community organisations. © 2015 John Wiley & Sons Ltd.

Transitioning a bachelor of science in nursing program to blended learning: Successes, challenges & outcomes.

PubMed

Posey, Laurie; Pintz, Christine

2017-09-01

To help address the challenges of providing undergraduate nursing education in an accelerated time frame, the Teaching and Transforming through Technology (T3) project was funded to transition a second-degree ABSN program to a blended learning format. The project has explored the use of blended learning to: enable flexible solutions to support teaching goals and address course challenges; provide students with new types of independent learning activities outside of the traditional classroom; increase opportunities for active learning in the classroom; and improve students' digital literacy and lifelong learning skills. Program evaluation included quality reviews of the redesigned courses, surveys of student perceptions, pre- and post-program assessment of students' digital literacy and interviews with faculty about their experiences with the new teaching methods. Adopting an established quality framework to guide course design and evaluation for quality contributed to the efficient and effective development of a high-quality undergraduate blended nursing program. Program outcomes and lessons learned are presented to inform future teaching innovation and research related to blended learning in undergraduate nursing education. Copyright © 2016 Elsevier Ltd. All rights reserved.

Mapping the dynamics of cortical neuroplasticity of skilled motor learning using micro X-ray fluorescence and histofluorescence imaging of zinc in the rat

PubMed Central

Alaverdashvili, Mariam; Paterson, Phyllis G.

2017-01-01

Synchrotron-based X-ray fluorescence imaging (XFI) of zinc (Zn) has been recently implemented to understand the efficiency of various therapeutic interventions targeting post-stroke neuroprotection and neuroplasticity. However, it is uncertain if micro XFI can resolve neuroplasticity-induced changes. Thus, we explored if learning-associated behavioral changes would be accompanied by changes in cortical Zn concentration measured by XFI in healthy adult rats. Proficiency in a skilled reach-to-eat task during early and late stages of motor learning served as a functional measure of neuroplasticity. c-Fos protein and vesicular Zn expression were employed as indirect neuronal measures of brain plasticity. A total Zn map (20 × 20 × 30 μm3 resolution) generated by micro XFI failed to reflect increases in either c-Fos or vesicular Zn in the motor cortex contralateral to the trained forelimb or improved proficiency in the skilled reaching task. Remarkably, vesicular Zn increased in the late stage of motor learning along with a concurrent decrease in the number of c-fos-ip neurons relative to the early stage of motor learning. This inverse dynamics of c-fos and vesicular Zn level as the motor skill advances suggest that a qualitatively different neural population, comprised of fewer active but more efficiently connected neurons, supports a skilled action in the late versus early stage of motor learning. The lack of sensitivity of the XFI-generated Zn map to visualize the plasticity-associated changes in vesicular Zn suggests that the Zn level measured by micro XFI should not be used as a surrogate marker of neuroplasticity in response to the acquisition of skilled motor actions. Nanoscopic XFI could be explored in future as a means of imaging these subtle physiological changes. PMID:27840249

Optimizing Schedules of Retrieval Practice for Durable and Efficient Learning: How Much Is Enough?

ERIC Educational Resources Information Center

Rawson, Katherine A.; Dunlosky, John

2011-01-01

The literature on testing effects is vast but supports surprisingly few prescriptive conclusions for how to schedule practice to achieve both durable and efficient learning. Key limitations are that few studies have examined the effects of initial learning criterion or the effects of relearning, and no prior research has examined the combined…

The Goal Specificity Effect on Strategy Use and Instructional Efficiency during Computer-Based Scientific Discovery Learning

ERIC Educational Resources Information Center

Kunsting, Josef; Wirth, Joachim; Paas, Fred

2011-01-01

Using a computer-based scientific discovery learning environment on buoyancy in fluids we investigated the "effects of goal specificity" (nonspecific goals vs. specific goals) for two goal types (problem solving goals vs. learning goals) on "strategy use" and "instructional efficiency". Our empirical findings close an important research gap,…

Ideal regularization for learning kernels from labels.

PubMed

Pan, Binbin; Lai, Jianhuang; Shen, Lixin

2014-08-01

In this paper, we propose a new form of regularization that is able to utilize the label information of a data set for learning kernels. The proposed regularization, referred to as ideal regularization, is a linear function of the kernel matrix to be learned. The ideal regularization allows us to develop efficient algorithms to exploit labels. Three applications of the ideal regularization are considered. Firstly, we use the ideal regularization to incorporate the labels into a standard kernel, making the resulting kernel more appropriate for learning tasks. Next, we employ the ideal regularization to learn a data-dependent kernel matrix from an initial kernel matrix (which contains prior similarity information, geometric structures, and labels of the data). Finally, we incorporate the ideal regularization to some state-of-the-art kernel learning problems. With this regularization, these learning problems can be formulated as simpler ones which permit more efficient solvers. Empirical results show that the ideal regularization exploits the labels effectively and efficiently. Copyright © 2014 Elsevier Ltd. All rights reserved.

The curse of planning: dissecting multiple reinforcement-learning systems by taxing the central executive.

PubMed

Otto, A Ross; Gershman, Samuel J; Markman, Arthur B; Daw, Nathaniel D

2013-05-01

A number of accounts of human and animal behavior posit the operation of parallel and competing valuation systems in the control of choice behavior. In these accounts, a flexible but computationally expensive model-based reinforcement-learning system has been contrasted with a less flexible but more efficient model-free reinforcement-learning system. The factors governing which system controls behavior-and under what circumstances-are still unclear. Following the hypothesis that model-based reinforcement learning requires cognitive resources, we demonstrated that having human decision makers perform a demanding secondary task engenders increased reliance on a model-free reinforcement-learning strategy. Further, we showed that, across trials, people negotiate the trade-off between the two systems dynamically as a function of concurrent executive-function demands, and people's choice latencies reflect the computational expenses of the strategy they employ. These results demonstrate that competition between multiple learning systems can be controlled on a trial-by-trial basis by modulating the availability of cognitive resources.

The Curse of Planning: Dissecting multiple reinforcement learning systems by taxing the central executive

PubMed Central

Otto, A. Ross; Gershman, Samuel J.; Markman, Arthur B.; Daw, Nathaniel D.

2013-01-01

A number of accounts of human and animal behavior posit the operation of parallel and competing valuation systems in the control of choice behavior. Along these lines, a flexible but computationally expensive model-based reinforcement learning system has been contrasted with a less flexible but more efficient model-free reinforcement learning system. The factors governing which system controls behavior—and under what circumstances—are still unclear. Based on the hypothesis that model-based reinforcement learning requires cognitive resources, we demonstrate that having human decision-makers perform a demanding secondary task engenders increased reliance on a model-free reinforcement learning strategy. Further, we show that across trials, people negotiate this tradeoff dynamically as a function of concurrent executive function demands and their choice latencies reflect the computational expenses of the strategy employed. These results demonstrate that competition between multiple learning systems can be controlled on a trial-by-trial basis by modulating the availability of cognitive resources. PMID:23558545

The Impact of Team-Based Learning on Nervous System Examination Knowledge of Nursing Students.

PubMed

Hemmati Maslakpak, Masomeh; Parizad, Naser; Zareie, Farzad

2015-12-01

Team-based learning is one of the active learning approaches in which independent learning is combined with small group discussion in the class. This study aimed to determine the impact of team-based learning in nervous system examination knowledge of nursing students. This quasi-experimental study was conducted on 3(rd) grade nursing students, including 5th semester (intervention group) and 6(th) semester (control group). The traditional lecture method and the team-based learning method were used for educating the examination of the nervous system for intervention and control groups, respectively. The data were collected by a test covering 40-questions (multiple choice, matching, gap-filling and descriptive questions) before and after intervention in both groups. Individual Readiness Assurance Test (RAT) and Group Readiness Assurance Test (GRAT) used to collect data in the intervention group. In the end, the collected data were analyzed by SPSS ver. 13 using descriptive and inferential statistical tests. In team-based learning group, mean and standard deviation was 13.39 (4.52) before the intervention, which had been increased to 31.07 (3.20) after the intervention and this increase was statistically significant. Also, there was a statistically significant difference between the scores of RAT and GRAT in team-based learning group. Using team-based learning approach resulted in much better improvement and stability in the nervous system examination knowledge of nursing students compared to traditional lecture method; therefore, this method could be efficiently used as an effective educational approach in nursing education.

Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis

NASA Astrophysics Data System (ADS)

Litjens, Geert; Sánchez, Clara I.; Timofeeva, Nadya; Hermsen, Meyke; Nagtegaal, Iris; Kovacs, Iringo; Hulsbergen-van de Kaa, Christina; Bult, Peter; van Ginneken, Bram; van der Laak, Jeroen

2016-05-01

Pathologists face a substantial increase in workload and complexity of histopathologic cancer diagnosis due to the advent of personalized medicine. Therefore, diagnostic protocols have to focus equally on efficiency and accuracy. In this paper we introduce ‘deep learning’ as a technique to improve the objectivity and efficiency of histopathologic slide analysis. Through two examples, prostate cancer identification in biopsy specimens and breast cancer metastasis detection in sentinel lymph nodes, we show the potential of this new methodology to reduce the workload for pathologists, while at the same time increasing objectivity of diagnoses. We found that all slides containing prostate cancer and micro- and macro-metastases of breast cancer could be identified automatically while 30-40% of the slides containing benign and normal tissue could be excluded without the use of any additional immunohistochemical markers or human intervention. We conclude that ‘deep learning’ holds great promise to improve the efficacy of prostate cancer diagnosis and breast cancer staging.

Energy Efficient Sparse Connectivity from Imbalanced Synaptic Plasticity Rules

PubMed Central

Sacramento, João; Wichert, Andreas; van Rossum, Mark C. W.

2015-01-01

It is believed that energy efficiency is an important constraint in brain evolution. As synaptic transmission dominates energy consumption, energy can be saved by ensuring that only a few synapses are active. It is therefore likely that the formation of sparse codes and sparse connectivity are fundamental objectives of synaptic plasticity. In this work we study how sparse connectivity can result from a synaptic learning rule of excitatory synapses. Information is maximised when potentiation and depression are balanced according to the mean presynaptic activity level and the resulting fraction of zero-weight synapses is around 50%. However, an imbalance towards depression increases the fraction of zero-weight synapses without significantly affecting performance. We show that imbalanced plasticity corresponds to imposing a regularising constraint on the L 1-norm of the synaptic weight vector, a procedure that is well-known to induce sparseness. Imbalanced plasticity is biophysically plausible and leads to more efficient synaptic configurations than a previously suggested approach that prunes synapses after learning. Our framework gives a novel interpretation to the high fraction of silent synapses found in brain regions like the cerebellum. PMID:26046817

A virtual environment for medical radiation collaborative learning.

PubMed

Bridge, Pete; Trapp, Jamie V; Kastanis, Lazaros; Pack, Darren; Parker, Jacqui C

2015-06-01

A software-based environment was developed to provide practical training in medical radiation principles and safety. The Virtual Radiation Laboratory application allowed students to conduct virtual experiments using simulated diagnostic and radiotherapy X-ray generators. The experiments were designed to teach students about the inverse square law, half value layer and radiation protection measures and utilised genuine clinical and experimental data. Evaluation of the application was conducted in order to ascertain the impact of the software on students' understanding, satisfaction and collaborative learning skills and also to determine potential further improvements to the software and guidelines for its continued use. Feedback was gathered via an anonymous online survey consisting of a mixture of Likert-style questions and short answer open questions. Student feedback was highly positive with 80 % of students reporting increased understanding of radiation protection principles. Furthermore 72 % enjoyed using the software and 87 % of students felt that the project facilitated collaboration within small groups. The main themes arising in the qualitative feedback comments related to efficiency and effectiveness of teaching, safety of environment, collaboration and realism. Staff and students both report gains in efficiency and effectiveness associated with the virtual experiments. In addition students particularly value the visualisation of "invisible" physical principles and increased opportunity for experimentation and collaborative problem-based learning. Similar ventures will benefit from adopting an approach that allows for individual experimentation while visualizing challenging concepts.

Increased reward in ankle robotics training enhances motor control and cortical efficiency in stroke.

PubMed

Goodman, Ronald N; Rietschel, Jeremy C; Roy, Anindo; Jung, Brian C; Diaz, Jason; Macko, Richard F; Forrester, Larry W

2014-01-01

Robotics is rapidly emerging as a viable approach to enhance motor recovery after disabling stroke. Current principles of cognitive motor learning recognize a positive relationship between reward and motor learning. Yet no prior studies have established explicitly whether reward improves the rate or efficacy of robotics-assisted rehabilitation or produces neurophysiologic adaptations associated with motor learning. We conducted a 3 wk, 9-session clinical pilot with 10 people with chronic hemiparetic stroke, randomly assigned to train with an impedance-controlled ankle robot (anklebot) under either high reward (HR) or low reward conditions. The 1 h training sessions entailed playing a seated video game by moving the paretic ankle to hit moving onscreen targets with the anklebot only providing assistance as needed. Assessments included paretic ankle motor control, learning curves, electroencephalograpy (EEG) coherence and spectral power during unassisted trials, and gait function. While both groups exhibited changes in EEG, the HR group had faster learning curves (p = 0.05), smoother movements (p

Body painting to promote self-active learning of hand anatomy for preclinical medical students.

PubMed

Jariyapong, Pitchanee; Punsawad, Chuchard; Bunratsami, Suchirat; Kongthong, Paranyu

2016-01-01

The purpose of this study was to use the body painting method to teach hand anatomy to a group of preclinical medical students. Students reviewed hand anatomy using the traditional method and body painting exercise. Feedback and retention of the anatomy-related information were examined by a questionnaire and multiple-choice questions, respectively, immediately and 1 month after the painting exercise. Students agreed that the exercise was advantageous and helped facilitate self-active learning after in-class anatomy lessons. While there was no significant difference in knowledge retention between the control and experimental groups, the students appreciated the exercise in which they applied body paint to the human body to learn anatomy. The body painting was an efficient tool for aiding the interactive learning of medical students and increasing the understanding of gross anatomy.

A random forest learning assisted "divide and conquer" approach for peptide conformation search.

PubMed

Chen, Xin; Yang, Bing; Lin, Zijing

2018-06-11

Computational determination of peptide conformations is challenging as it is a problem of finding minima in a high-dimensional space. The "divide and conquer" approach is promising for reliably reducing the search space size. A random forest learning model is proposed here to expand the scope of applicability of the "divide and conquer" approach. A random forest classification algorithm is used to characterize the distributions of the backbone φ-ψ units ("words"). A random forest supervised learning model is developed to analyze the combinations of the φ-ψ units ("grammar"). It is found that amino acid residues may be grouped as equivalent "words", while the φ-ψ combinations in low-energy peptide conformations follow a distinct "grammar". The finding of equivalent words empowers the "divide and conquer" method with the flexibility of fragment substitution. The learnt grammar is used to improve the efficiency of the "divide and conquer" method by removing unfavorable φ-ψ combinations without the need of dedicated human effort. The machine learning assisted search method is illustrated by efficiently searching the conformations of GGG/AAA/GGGG/AAAA/GGGGG through assembling the structures of GFG/GFGG. Moreover, the computational cost of the new method is shown to increase rather slowly with the peptide length.

Fast Sparse Coding for Range Data Denoising with Sparse Ridges Constraint.

PubMed

Gao, Zhi; Lao, Mingjie; Sang, Yongsheng; Wen, Fei; Ramesh, Bharath; Zhai, Ruifang

2018-05-06

Light detection and ranging (LiDAR) sensors have been widely deployed on intelligent systems such as unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) to perform localization, obstacle detection, and navigation tasks. Thus, research into range data processing with competitive performance in terms of both accuracy and efficiency has attracted increasing attention. Sparse coding has revolutionized signal processing and led to state-of-the-art performance in a variety of applications. However, dictionary learning, which plays the central role in sparse coding techniques, is computationally demanding, resulting in its limited applicability in real-time systems. In this study, we propose sparse coding algorithms with a fixed pre-learned ridge dictionary to realize range data denoising via leveraging the regularity of laser range measurements in man-made environments. Experiments on both synthesized data and real data demonstrate that our method obtains accuracy comparable to that of sophisticated sparse coding methods, but with much higher computational efficiency.

State-of-the-art in design rules for drug delivery platforms: lessons learned from FDA-approved nanomedicines.

PubMed

Dawidczyk, Charlene M; Kim, Chloe; Park, Jea Ho; Russell, Luisa M; Lee, Kwan Hyi; Pomper, Martin G; Searson, Peter C

2014-08-10

The ability to efficiently deliver a drug to a tumor site is dependent on a wide range of physiologically imposed design constraints. Nanotechnology provides the possibility of creating delivery vehicles where these design constraints can be decoupled, allowing new approaches for reducing the unwanted side effects of systemic delivery, increasing targeting efficiency and efficacy. Here we review the design strategies of the two FDA-approved antibody-drug conjugates (Brentuximab vedotin and Trastuzumab emtansine) and the four FDA-approved nanoparticle-based drug delivery platforms (Doxil, DaunoXome, Marqibo, and Abraxane) in the context of the challenges associated with systemic targeted delivery of a drug to a solid tumor. The lessons learned from these nanomedicines provide an important insight into the key challenges associated with the development of new platforms for systemic delivery of anti-cancer drugs. Copyright © 2014 Elsevier B.V. All rights reserved.

Transfer of piano practice in fast performance of skilled finger movements.

PubMed

Furuya, Shinichi; Nakamura, Ayumi; Nagata, Noriko

2013-11-01

Transfer of learning facilitates the efficient mastery of various skills without practicing all possible sensory-motor repertoires. The present study assessed whether motor practice at a submaximal speed, which is typical in sports and music performance, results in an increase in a maximum speed of finger movements of trained and untrained skills. Piano practice of sequential finger movements at a submaximal speed over days progressively increased the maximum speed of trained movements. This increased maximum speed of finger movements was maintained two months after the practice. The learning transferred within the hand to some extent, but not across the hands. The present study confirmed facilitation of fast finger movements following a piano practice at a submaximal speed. In addition, the findings indicated the intra-manual transfer effects of piano practice on the maximum speed of skilled finger movements.

Cost-Efficiencies in Online Learning. ASHE Higher Education Report, Volume 32, Number 1

ERIC Educational Resources Information Center

Meyer, Katrina A.

2006-01-01

This monograph is divided into five chapters. The first chapter provides a road map for understanding the review of studies on cost-efficiencies of online learning, including understanding why cost-efficiencies are so important to many higher education institutions and the framework used to categorize and discuss these studies. The second chapter…

The Efficiency Challenge: Creating a Transformative Learning Experience in a Principles of Management Course

ERIC Educational Resources Information Center

Durant, Rita A.; Carlon, Donna M.; Downs, Alexis

2017-01-01

This article describes the results of the "Efficiency Challenge," a 10-week, Principles of Management course activity that uses reflection and goal setting to help students understand the concept of operational efficiency. With transformative learning theory as a lens, we base our report on 4 years' worth of student reflections regarding…

Control of nitromethane photoionization efficiency with shaped femtosecond pulses.

PubMed

Roslund, Jonathan; Shir, Ofer M; Dogariu, Arthur; Miles, Richard; Rabitz, Herschel

2011-04-21

The applicability of adaptive femtosecond pulse shaping is studied for achieving selectivity in the photoionization of low-density polyatomic targets. In particular, optimal dynamic discrimination (ODD) techniques exploit intermediate molecular electronic resonances that allow a significant increase in the photoionization efficiency of nitromethane with shaped near-infrared femtosecond pulses. The intensity bias typical of high-photon number, nonresonant ionization is accounted for by reference to a strictly intensity-dependent process. Closed-loop adaptive learning is then able to discover a pulse form that increases the ionization efficiency of nitromethane by ∼150%. The optimally induced molecular dynamics result from entry into a region of parameter space inaccessible with intensity-only control. Finally, the discovered pulse shape is demonstrated to interact with the molecular system in a coherent fashion as assessed from the asymmetry between the response to the optimal field and its time-reversed counterpart.

The impact of privacy protection filters on gender recognition

NASA Astrophysics Data System (ADS)

Ruchaud, Natacha; Antipov, Grigory; Korshunov, Pavel; Dugelay, Jean-Luc; Ebrahimi, Touradj; Berrani, Sid-Ahmed

2015-09-01

Deep learning-based algorithms have become increasingly efficient in recognition and detection tasks, especially when they are trained on large-scale datasets. Such recent success has led to a speculation that deep learning methods are comparable to or even outperform human visual system in its ability to detect and recognize objects and their features. In this paper, we focus on the specific task of gender recognition in images when they have been processed by privacy protection filters (e.g., blurring, masking, and pixelization) applied at different strengths. Assuming a privacy protection scenario, we compare the performance of state of the art deep learning algorithms with a subjective evaluation obtained via crowdsourcing to understand how privacy protection filters affect both machine and human vision.

Health professional learner attitudes and use of digital learning resources.

PubMed

Maloney, Stephen; Chamberlain, Michael; Morrison, Shane; Kotsanas, George; Keating, Jennifer L; Ilic, Dragan

2013-01-16

Web-based digital repositories allow educational resources to be accessed efficiently and conveniently from diverse geographic locations, hold a variety of resource formats, enable interactive learning, and facilitate targeted access for the user. Unlike some other learning management systems (LMS), resources can be retrieved through search engines and meta-tagged labels, and content can be streamed, which is particularly useful for multimedia resources. The aim of this study was to examine usage and user experiences of an online learning repository (Physeek) in a population of physiotherapy students. The secondary aim of this project was to examine how students prefer to access resources and which resources they find most helpful. The following data were examined using an audit of the repository server: (1) number of online resources accessed per day in 2010, (2) number of each type of resource accessed, (3) number of resources accessed during business hours (9 am to 5 pm) and outside business hours (years 1-4), (4) session length of each log-on (years 1-4), and (5) video quality (bit rate) of each video accessed. An online questionnaire and 3 focus groups assessed student feedback and self-reported experiences of Physeek. Students preferred the support provided by Physeek to other sources of educational material primarily because of its efficiency. Peak usage commonly occurred at times of increased academic need (ie, examination times). Students perceived online repositories as a potential tool to support lifelong learning and health care delivery. The results of this study indicate that today's health professional students welcome the benefits of online learning resources because of their convenience and usability. This represents a transition away from traditional learning styles and toward technological learning support and may indicate a growing link between social immersions in Internet-based connections and learning styles. The true potential for Web-based resources to support student learning is as yet unknown.

Health Professional Learner Attitudes and Use of Digital Learning Resources

PubMed Central

Chamberlain, Michael; Morrison, Shane; Kotsanas, George; Keating, Jennifer L; Ilic, Dragan

2013-01-01

Background Web-based digital repositories allow educational resources to be accessed efficiently and conveniently from diverse geographic locations, hold a variety of resource formats, enable interactive learning, and facilitate targeted access for the user. Unlike some other learning management systems (LMS), resources can be retrieved through search engines and meta-tagged labels, and content can be streamed, which is particularly useful for multimedia resources. Objective The aim of this study was to examine usage and user experiences of an online learning repository (Physeek) in a population of physiotherapy students. The secondary aim of this project was to examine how students prefer to access resources and which resources they find most helpful. Methods The following data were examined using an audit of the repository server: (1) number of online resources accessed per day in 2010, (2) number of each type of resource accessed, (3) number of resources accessed during business hours (9 am to 5 pm) and outside business hours (years 1-4), (4) session length of each log-on (years 1-4), and (5) video quality (bit rate) of each video accessed. An online questionnaire and 3 focus groups assessed student feedback and self-reported experiences of Physeek. Results Students preferred the support provided by Physeek to other sources of educational material primarily because of its efficiency. Peak usage commonly occurred at times of increased academic need (ie, examination times). Students perceived online repositories as a potential tool to support lifelong learning and health care delivery. Conclusions The results of this study indicate that today’s health professional students welcome the benefits of online learning resources because of their convenience and usability. This represents a transition away from traditional learning styles and toward technological learning support and may indicate a growing link between social immersions in Internet-based connections and learning styles. The true potential for Web-based resources to support student learning is as yet unknown. PMID:23324800

Developing Web-Based Training for Public Health Practitioners: What Can We Learn from a Review of Five Disciplines?

ERIC Educational Resources Information Center

Ballew, Paula; Castro, Sarah; Claus, Julie; Kittur, Nupur; Brennan, Laura; Brownson, Ross C.

2013-01-01

During a time when governmental funding, resources and staff are decreasing and travel restrictions are increasing, attention to efficient methods of public health workforce training is essential. A literature review was conducted to inform the development and delivery of web-based trainings for public health practitioners. Literature was gathered…

Assessing and Measuring Statistics Cognition in Higher Education Online Environments: Emerging Research and Opportunities

ERIC Educational Resources Information Center

Chase, Justin P.; Yan, Zheng

2017-01-01

The ability to effective learn, process, and retain new information is critical to the success of any student. Since mathematics are becoming increasingly more important in our educational systems, it is imperative that we devise an efficient system to measure these types of information recall. "Assessing and Measuring Statistics Cognition in…

The Relationship between Time in Computer-Assisted Instruction and the Increase in Reading Skills

ERIC Educational Resources Information Center

Shannon, Rene M.

2013-01-01

Educational leadership appropriates significant amounts of money for technology in school budgets. Teachers must decide how to use technology to maximize student learning and make the most efficient use of instructional minutes. The purpose of this quantitative correlational study was to determine if a relationship existed between the amount of…

A Comparison of Two Sight Word Interventions: Traditional Drill and Wordsheets

ERIC Educational Resources Information Center

Mule, Christina Marie

2013-01-01

Traditional drill and practice (TDP) is a sight word intervention that is well supported in the literature as being both effective and efficient. However, with growing demands in school systems, there is increased pressure to employ interventions that enhance learning outcomes with less instructional time. WordSheets (WS) was created as a method…

The Influence of Rater Effects in Training Sets on the Psychometric Quality of Automated Scoring for Writing Assessments

ERIC Educational Resources Information Center

Wind, Stefanie A.; Wolfe, Edward W.; Engelhard, George, Jr.; Foltz, Peter; Rosenstein, Mark

2018-01-01

Automated essay scoring engines (AESEs) are becoming increasingly popular as an efficient method for performance assessments in writing, including many language assessments that are used worldwide. Before they can be used operationally, AESEs must be "trained" using machine-learning techniques that incorporate human ratings. However, the…

Sustaining Higher Education Using Wal-Mart's Best Supply Chain Management Practices

ERIC Educational Resources Information Center

Comm, Clare L.; Mathaisel, Dennis F. X.

2008-01-01

Purpose: The costs in higher education are increasing and need to be controlled. This paper aims to demonstrate what lessons higher education could learn from Wal-Mart's reasons for its financial success with its focus on efficient and effective supply chain management (SCM) best practices. Design/methodology/approach: Wal-Mart's best practices in…

Critical Transport: A Serious Game to Teach the Recommendations for the Transport of Critically Ill Patients

ERIC Educational Resources Information Center

Ribeiro, Claudia; Antunes, Tiago; Pereira, João; Monteiro, Micaela

2014-01-01

At present, medical knowledge is experiencing an exponential growth. This results in serious difficulties to healthcare professionals in keeping up to date. At the same time, medical education is mostly taught using traditional learning methodologies, not always the most efficient. Recently however, there has been a significant increase in the use…

The Foreign-Language Teacher and Cognitive Psychology or Where Do We Go from Here?

ERIC Educational Resources Information Center

Rivers, Wilga M.

Research into the psychology of perception can uncover important discoveries for more efficient learning. There must be increased understanding of the processing of input and the pre-processing of output for improved language instruction. Educators must at the present time be extremely wary of basing what they do in the foreign-language classroom…

Social Capital and Entrepreneurial Behaviour Advancing Innovativeness in Interaction between Small Rural Entrepreneurs and Researchers: A Phenomenographic Study

ERIC Educational Resources Information Center

Iivonen, Sari; Kyro, Paula; Mynttinen, Sinikka; Sarkka-Tirkkonen, Marjo; Kahiluoto, Helena

2011-01-01

Innovation processes between entrepreneurs and researchers are activated by interaction. Social capital increases the efficiency of action, for example, information dissemination by minimising redundancy. To learn more about how to build and develop social capital assumes that we understand how entrepreneurs behave and what their expectations of…

Interim Report on Ed Tech PILOTS. A Report to the 78th Texas Legislature from the Texas Education Agency.

ERIC Educational Resources Information Center

Texas Education Agency, Austin.

The primary goals of the Texas Education Agency's Educational Technologies Providing Increased Learning Opportunities for Texas Students (Ed Tech PILOTS) are to employ technology to more efficiently and effectively delivery information to students and teachers to enhance the efficacy of classroom instruction. This interim report discusses the…

Overhauling Technical Handouts for Active Student Participation: A Model for Improving Lecture Efficiency and Increasing Attendance

ERIC Educational Resources Information Center

Jakee, Keith

2011-01-01

This instructional paper is intended to provide an alternative approach to developing lecture materials, including handouts and PowerPoint slides, successfully developed over several years. The principal objective is to aid in the bridging of traditional "chalk and talk" lecture approaches with more active learning techniques, especially in more…

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

PubMed

ALQahtani, Dalal A; Al-Gahtani, Sara M

2014-06-01

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

Active learning methods for interactive image retrieval.

PubMed

Gosselin, Philippe Henri; Cord, Matthieu

2008-07-01

Active learning methods have been considered with increased interest in the statistical learning community. Initially developed within a classification framework, a lot of extensions are now being proposed to handle multimedia applications. This paper provides algorithms within a statistical framework to extend active learning for online content-based image retrieval (CBIR). The classification framework is presented with experiments to compare several powerful classification techniques in this information retrieval context. Focusing on interactive methods, active learning strategy is then described. The limitations of this approach for CBIR are emphasized before presenting our new active selection process RETIN. First, as any active method is sensitive to the boundary estimation between classes, the RETIN strategy carries out a boundary correction to make the retrieval process more robust. Second, the criterion of generalization error to optimize the active learning selection is modified to better represent the CBIR objective of database ranking. Third, a batch processing of images is proposed. Our strategy leads to a fast and efficient active learning scheme to retrieve sets of online images (query concept). Experiments on large databases show that the RETIN method performs well in comparison to several other active strategies.

Learning by Association in Plants.

PubMed

Gagliano, Monica; Vyazovskiy, Vladyslav V; Borbély, Alexander A; Grimonprez, Mavra; Depczynski, Martial

2016-12-02

In complex and ever-changing environments, resources such as food are often scarce and unevenly distributed in space and time. Therefore, utilizing external cues to locate and remember high-quality sources allows more efficient foraging, thus increasing chances for survival. Associations between environmental cues and food are readily formed because of the tangible benefits they confer. While examples of the key role they play in shaping foraging behaviours are widespread in the animal world, the possibility that plants are also able to acquire learned associations to guide their foraging behaviour has never been demonstrated. Here we show that this type of learning occurs in the garden pea, Pisum sativum. By using a Y-maze task, we show that the position of a neutral cue, predicting the location of a light source, affected the direction of plant growth. This learned behaviour prevailed over innate phototropism. Notably, learning was successful only when it occurred during the subjective day, suggesting that behavioural performance is regulated by metabolic demands. Our results show that associative learning is an essential component of plant behaviour. We conclude that associative learning represents a universal adaptive mechanism shared by both animals and plants.

Evolving Educational Techniques in Surgical Training.

PubMed

Evans, Charity H; Schenarts, Kimberly D

2016-02-01

Training competent and professional surgeons efficiently and effectively requires innovation and modernization of educational methods. Today's medical learner is quite adept at using multiple platforms to gain information, providing surgical educators with numerous innovative avenues to promote learning. With the growth of technology, and the restriction of work hours in surgical education, there has been an increase in use of simulation, including virtual reality, robotics, telemedicine, and gaming. The use of simulation has shifted the learning of basic surgical skills to the laboratory, reserving limited time in the operating room for the acquisition of complex surgical skills". Copyright © 2016 Elsevier Inc. All rights reserved.

Testing of dual-junction SCARLET modules and cells plus lessons learned

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

Eskenazi, M.I.; Murphy, D.M.; Ralph, E.L.

1997-12-31

Key simulator test methods and results for Solar Concentrator Array with Refractive Linear Element Technology (SCARLET) cells, modules, and module strings are presented from the NASA/JPL New Millennium DS1 program. Important observations and lessons learned are discussed. These findings include: (1) a significant efficiency increase for shunted low performing 1 sun cells at SCARLET`s {approximately}7 sun concentration, (2) a decrease in temperature coefficient under SCARLET concentration, and (3) the importance of active germanium (third junction) screening during GaInP{sub 2}/GaAs/Ge cell production especially when red reflecting covers are used.

EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks.

PubMed

Berka, Chris; Levendowski, Daniel J; Lumicao, Michelle N; Yau, Alan; Davis, Gene; Zivkovic, Vladimir T; Olmstead, Richard E; Tremoulet, Patrice D; Craven, Patrick L

2007-05-01

The ability to continuously and unobtrusively monitor levels of task engagement and mental workload in an operational environment could be useful in identifying more accurate and efficient methods for humans to interact with technology. This information could also be used to optimize the design of safer, more efficient work environments that increase motivation and productivity. The present study explored the feasibility of monitoring electroencephalo-graphic (EEG) indices of engagement and workload acquired unobtrusively and quantified during performance of cognitive tests. EEG was acquired from 80 healthy participants with a wireless sensor headset (F3-F4,C3-C4,Cz-POz,F3-Cz,Fz-C3,Fz-POz) during tasks including: multi-level forward/backward-digit-span, grid-recall, trails, mental-addition, 20-min 3-Choice Vigilance, and image-learning and memory tests. EEG metrics for engagement and workload were calculated for each 1 -s of EEG. Across participants, engagement but not workload decreased over the 20-min vigilance test. Engagement and workload were significantly increased during the encoding period of verbal and image-learning and memory tests when compared with the recognition/ recall period. Workload but not engagement increased linearly as level of difficulty increased in forward and backward-digit-span, grid-recall, and mental-addition tests. EEG measures correlated with both subjective and objective performance metrics. These data in combination with previous studies suggest that EEG engagement reflects information-gathering, visual processing, and allocation of attention. EEG workload increases with increasing working memory load and during problem solving, integration of information, analytical reasoning, and may be more reflective of executive functions. Inspection of EEG on a second-by-second timescale revealed associations between workload and engagement levels when aligned with specific task events providing preliminary evidence that second-by-second classifications reflect parameters of task performance.

Efficient model learning methods for actor-critic control.

PubMed

Grondman, Ivo; Vaandrager, Maarten; Buşoniu, Lucian; Babuska, Robert; Schuitema, Erik

2012-06-01

We propose two new actor-critic algorithms for reinforcement learning. Both algorithms use local linear regression (LLR) to learn approximations of the functions involved. A crucial feature of the algorithms is that they also learn a process model, and this, in combination with LLR, provides an efficient policy update for faster learning. The first algorithm uses a novel model-based update rule for the actor parameters. The second algorithm does not use an explicit actor but learns a reference model which represents a desired behavior, from which desired control actions can be calculated using the inverse of the learned process model. The two novel methods and a standard actor-critic algorithm are applied to the pendulum swing-up problem, in which the novel methods achieve faster learning than the standard algorithm.

Industrial Energy Efficiency Practices in Indonesia: Lesson Learned from Astra Green Energy (AGen) Award

NASA Astrophysics Data System (ADS)

Telaga, A. S.; Hartanto, I. D.

2017-03-01

Many countries have used award system to promote energy efficiency practices in industry. The award system has been found to have significant impact to increase energy conservation and sustainability adoption in companies. Astra International (AI) as a holding company of more than 200 companies also organised Astra green energy (AGen) award to all affiliated companies (AFFCO) in Astra group. The event has been used to share energy efficiency best practices among AFFCO in Astra group. AFFCOs of Astra International are among the biggest and the leader in their industrial sectors Therefore, analyses from AFFO’s energy efficiency case studies represents current practices in Indonesia industrial sectors. Analyses are divided into industry, building, and renewable energy. The results from analyses found that AFFCOs already aware of energy conservation and have implemented projects to promote energy efficiency. However, the AFFCOs do not optimally use monitoring data for energy reduction.

Cooperative learning with role play in Chinese pharmacology education.

PubMed

Wang, Jun; Hu, Xiamin; Xi, Jinglei

2012-03-01

Cooperative learning (CL) and role play are both efficient educational tools for enhancing Chinese student active learning and communication skills. This study was designed to obtain student feedback on the format of CL together with role play in the study of pharmacology in Chinese pharmaceutical undergraduates. CL was used in the self-study of new drugs used clinically but neglected in textbook and class teaching, so that groups of students were assigned to become "specialists" in one area of new drugs. Then, these "specialists" taught their new-found knowledge to other groups in role play approach involving an interaction between the pharmacist and a patient. Student perceptions of CL together with role play were examined using an eight-item survey instrument. Students were satisfied with CL together with role play. Majority of the students believed this teaching method enhanced their learning experience, made them gain more pharmacological expertise, increased the awareness of their career in future and self-educational abilities, and fostered their cooperation spirit and confidence. The materials on CL and role play were also believed pertinent. Only 63.4-76.5% and 63.1-37.3% of the students thought "CL and role-play were very funny" and "I felt very relaxed during CL together with role-play", respectively. CL together with role play is an efficient educational tool for enhancing student active-learning and communication skills. But Chinese students will take some time to adapt to this new teaching method.

Optimal Learning Paths in Information Networks

PubMed Central

Rodi, G. C.; Loreto, V.; Servedio, V. D. P.; Tria, F.

2015-01-01

Each sphere of knowledge and information could be depicted as a complex mesh of correlated items. By properly exploiting these connections, innovative and more efficient navigation strategies could be defined, possibly leading to a faster learning process and an enduring retention of information. In this work we investigate how the topological structure embedding the items to be learned can affect the efficiency of the learning dynamics. To this end we introduce a general class of algorithms that simulate the exploration of knowledge/information networks standing on well-established findings on educational scheduling, namely the spacing and lag effects. While constructing their learning schedules, individuals move along connections, periodically revisiting some concepts, and sometimes jumping on very distant ones. In order to investigate the effect of networked information structures on the proposed learning dynamics we focused both on synthetic and real-world graphs such as subsections of Wikipedia and word-association graphs. We highlight the existence of optimal topological structures for the simulated learning dynamics whose efficiency is affected by the balance between hubs and the least connected items. Interestingly, the real-world graphs we considered lead naturally to almost optimal learning performances. PMID:26030508

Body painting to promote self-active learning of hand anatomy for preclinical medical students.

PubMed

Jariyapong, Pitchanee; Punsawad, Chuchard; Bunratsami, Suchirat; Kongthong, Paranyu

2016-01-01

Background The purpose of this study was to use the body painting method to teach hand anatomy to a group of preclinical medical students. Methods Students reviewed hand anatomy using the traditional method and body painting exercise. Feedback and retention of the anatomy-related information were examined by a questionnaire and multiple-choice questions, respectively, immediately and 1 month after the painting exercise. Results Students agreed that the exercise was advantageous and helped facilitate self-active learning after in-class anatomy lessons. While there was no significant difference in knowledge retention between the control and experimental groups, the students appreciated the exercise in which they applied body paint to the human body to learn anatomy. Conclusion The body painting was an efficient tool for aiding the interactive learning of medical students and increasing the understanding of gross anatomy.

In search for instructional techniques to maximize the use of germane cognitive resources: A case of teaching complex tasks in physics

NASA Astrophysics Data System (ADS)

Sliva, Yekaterina

The purpose of this study was to introduce an instructional technique for teaching complex tasks in physics, test its effectiveness and efficiency, and understand cognitive processes taking place in learners' minds while they are exposed to this technique. The study was based primarily on cognitive load theory (CLT). CLT determines the amount of total cognitive load imposed on a learner by a learning task as combined intrinsic (invested in comprehending task complexity) and extraneous (wasteful) cognitive load. Working memory resources associated with intrinsic cognitive load are defined as germane resources caused by element interactivity that lead to learning, in contrast to extraneous working memory resources that are devoted to dealing with extraneous cognitive load. However, the amount of learner's working memory resources actually devoted to a task depends on how well the learner is engaged in the learning environment. Since total cognitive load has to stay within limits of working memory capacity, both extraneous and intrinsic cognitive load need to be reduced. In order for effective learning to occur, the use of germane cognitive resources should be maximized. In this study, the use of germane resources was maximized for two experimental groups by providing a learning environment that combined problem-solving procedure with prompts to self-explain with and without completion problems. The study tested three hypotheses and answered two research questions. The first hypothesis predicting that experimental treatments would reduce total cognitive load was not supported. The second hypothesis predicting that experimental treatments would increase performance was supported for the self-explanation group only. The third hypothesis that tested efficiency measure as adopted from Paas and van Merrienboer (1993) was not supported. As for the research question of whether the quality of self-explanations would change with time for the two experimental conditions, it was determined that time had a positive effect on such quality. The research question that investigated learners' attitudes towards the instructions revealed that experimental groups understood the main idea behind the suggested technique and positively reacted to it. The results of the study support the conclusions that (a) prompting learners to self-explain while independently solving problems can increase performance, especially on far transfer questions; (b) better performance is achieved in combination with increased mental effort; (c) self-explanations do not increase time on task; and (d) quality of self-explanations can be improved with time. Results based on the analyses of learners' attitudes further support that learners in the experimental groups understood the main idea behind the suggested techniques and positively reacted to them. The study also raised concern about application of efficiency formula for instructional conditions that increase both performance and mental effort in CLT. As a result, an alternative model was suggested to explain the relationship between performance and mental effort based on Yerkes-Dodson law (1908). Keywords: instructional design, cognitive load, complex tasks, problem-solving, self-explanation.

Fast-Spiking Interneurons Supply Feedforward Control of Bursting, Calcium, and Plasticity for Efficient Learning.

PubMed

Owen, Scott F; Berke, Joshua D; Kreitzer, Anatol C

2018-02-08

Fast-spiking interneurons (FSIs) are a prominent class of forebrain GABAergic cells implicated in two seemingly independent network functions: gain control and network plasticity. Little is known, however, about how these roles interact. Here, we use a combination of cell-type-specific ablation, optogenetics, electrophysiology, imaging, and behavior to describe a unified mechanism by which striatal FSIs control burst firing, calcium influx, and synaptic plasticity in neighboring medium spiny projection neurons (MSNs). In vivo silencing of FSIs increased bursting, calcium transients, and AMPA/NMDA ratios in MSNs. In a motor sequence task, FSI silencing increased the frequency of calcium transients but reduced the specificity with which transients aligned to individual task events. Consistent with this, ablation of FSIs disrupted the acquisition of striatum-dependent egocentric learning strategies. Together, our data support a model in which feedforward inhibition from FSIs temporally restricts MSN bursting and calcium-dependent synaptic plasticity to facilitate striatum-dependent sequence learning. Copyright © 2018 Elsevier Inc. All rights reserved.

A Reinforcement-Based Learning Paradigm Increases Anatomical Learning and Retention—A Neuroeducation Study

PubMed Central

Anderson, Sarah J.; Hecker, Kent G.; Krigolson, Olave E.; Jamniczky, Heather A.

2018-01-01

In anatomy education, a key hurdle to engaging in higher-level discussion in the classroom is recognizing and understanding the extensive terminology used to identify and describe anatomical structures. Given the time-limited classroom environment, seeking methods to impart this foundational knowledge to students in an efficient manner is essential. Just-in-Time Teaching (JiTT) methods incorporate pre-class exercises (typically online) meant to establish foundational knowledge in novice learners so subsequent instructor-led sessions can focus on deeper, more complex concepts. Determining how best do we design and assess pre-class exercises requires a detailed examination of learning and retention in an applied educational context. Here we used electroencephalography (EEG) as a quantitative dependent variable to track learning and examine the efficacy of JiTT activities to teach anatomy. Specifically, we examined changes in the amplitude of the N250 and reward positivity event-related brain potential (ERP) components alongside behavioral performance as novice students participated in a series of computerized reinforcement-based learning modules to teach neuroanatomical structures. We found that as students learned to identify anatomical structures, the amplitude of the N250 increased and reward positivity amplitude decreased in response to positive feedback. Both on a retention and transfer exercise when learners successfully remembered and translated their knowledge to novel images, the amplitude of the reward positivity remained decreased compared to early learning. Our findings suggest ERPs can be used as a tool to track learning, retention, and transfer of knowledge and that employing the reinforcement learning paradigm is an effective educational approach for developing anatomical expertise. PMID:29467638

A Reinforcement-Based Learning Paradigm Increases Anatomical Learning and Retention-A Neuroeducation Study.

PubMed

Anderson, Sarah J; Hecker, Kent G; Krigolson, Olave E; Jamniczky, Heather A

2018-01-01

In anatomy education, a key hurdle to engaging in higher-level discussion in the classroom is recognizing and understanding the extensive terminology used to identify and describe anatomical structures. Given the time-limited classroom environment, seeking methods to impart this foundational knowledge to students in an efficient manner is essential. Just-in-Time Teaching (JiTT) methods incorporate pre-class exercises (typically online) meant to establish foundational knowledge in novice learners so subsequent instructor-led sessions can focus on deeper, more complex concepts. Determining how best do we design and assess pre-class exercises requires a detailed examination of learning and retention in an applied educational context. Here we used electroencephalography (EEG) as a quantitative dependent variable to track learning and examine the efficacy of JiTT activities to teach anatomy. Specifically, we examined changes in the amplitude of the N250 and reward positivity event-related brain potential (ERP) components alongside behavioral performance as novice students participated in a series of computerized reinforcement-based learning modules to teach neuroanatomical structures. We found that as students learned to identify anatomical structures, the amplitude of the N250 increased and reward positivity amplitude decreased in response to positive feedback. Both on a retention and transfer exercise when learners successfully remembered and translated their knowledge to novel images, the amplitude of the reward positivity remained decreased compared to early learning. Our findings suggest ERPs can be used as a tool to track learning, retention, and transfer of knowledge and that employing the reinforcement learning paradigm is an effective educational approach for developing anatomical expertise.

The role of affordances in children's learning performance and efficiency when using virtual manipulative mathematics touch-screen apps

NASA Astrophysics Data System (ADS)

Moyer-Packenham, Patricia S.; Bullock, Emma K.; Shumway, Jessica F.; Tucker, Stephen I.; Watts, Christina M.; Westenskow, Arla; Anderson-Pence, Katie L.; Maahs-Fladung, Cathy; Boyer-Thurgood, Jennifer; Gulkilik, Hilal; Jordan, Kerry

2016-03-01

This paper focuses on understanding the role that affordances played in children's learning performance and efficiency during clinical interviews of their interactions with mathematics apps on touch-screen devices. One hundred children, ages 3 to 8, each used six different virtual manipulative mathematics apps during 30-40-min interviews. The study used a convergent mixed methods design, in which quantitative and qualitative data were collected concurrently to answer the research questions (Creswell and Plano Clark 2011). Videos were used to capture each child's interactions with the virtual manipulative mathematics apps, document learning performance and efficiency, and record children's interactions with the affordances within the apps. Quantitized video data answered the research question on differences in children's learning performance and efficiency between pre- and post-assessments. A Wilcoxon matched pairs signed-rank test was used to explore these data. Qualitative video data was used to identify affordance access by children when using each app, identifying 95 potential helping and hindering affordances among the 18 apps. The results showed that there were changes in children's learning performance and efficiency when children accessed a helping or a hindering affordance. Helping affordances were more likely to be accessed by children who progressed between the pre- and post-assessments, and the same affordances had helping and hindering effects for different children. These results have important implications for the design of virtual manipulative mathematics learning apps.

Learner Performance in Multimedia Learning Arrangements: An Analysis across Instructional Approaches

ERIC Educational Resources Information Center

Eysink, Tessa H. S.; de Jong, Ton; Berthold, Kirsten; Kolloffel, Bas; Opfermann, Maria; Wouters, Pieter

2009-01-01

In this study, the authors compared four multimedia learning arrangements differing in instructional approach on effectiveness and efficiency for learning: (a) hypermedia learning, (b) observational learning, (c) self-explanation-based learning, and (d) inquiry learning. The approaches all advocate learners' active attitude toward the learning…

[Changes of the neuronal membrane excitability as cellular mechanisms of learning and memory].

PubMed

Gaĭnutdinov, Kh L; Andrianov, V V; Gaĭnutdinova, T Kh

2011-01-01

In the presented review given literature and results of own studies of dynamics of electrical characteristics of neurons, which change are included in processes both an elaboration of learning, and retention of the long-term memory. Literary datas and our results allow to conclusion, that long-term retention of behavioural reactions during learning is accompanied not only by changing efficiency of synaptic transmission, as well as increasing of excitability of command neurons of the defensive reflex. This means, that in the process of learning are involved long-term changes of the characteristics a membrane of certain elements of neuronal network, dependent from the metabolism of the cells. see text). Thou phenomena possible mark as cellular (electrophysiological) correlates of long-term plastic modifications of the behaviour. The analyses of having results demonstrates an important role of membrane characteristics of neurons (their excitability) and parameters an synaptic transmission not only in initial stage of learning, as well as in long-term modifications of the behaviour (long-term memory).

Fast and Epsilon-Optimal Discretized Pursuit Learning Automata.

PubMed

Zhang, JunQi; Wang, Cheng; Zhou, MengChu

2015-10-01

Learning automata (LA) are powerful tools for reinforcement learning. A discretized pursuit LA is the most popular one among them. During an iteration its operation consists of three basic phases: 1) selecting the next action; 2) finding the optimal estimated action; and 3) updating the state probability. However, when the number of actions is large, the learning becomes extremely slow because there are too many updates to be made at each iteration. The increased updates are mostly from phases 1 and 3. A new fast discretized pursuit LA with assured ε -optimality is proposed to perform both phases 1 and 3 with the computational complexity independent of the number of actions. Apart from its low computational complexity, it achieves faster convergence speed than the classical one when operating in stationary environments. This paper can promote the applications of LA toward the large-scale-action oriented area that requires efficient reinforcement learning tools with assured ε -optimality, fast convergence speed, and low computational complexity for each iteration.

Encouraging information sharing to boost the name-your-own-price auction

NASA Astrophysics Data System (ADS)

Chen, Yahong; Li, Jinlin; Huang, He; Ran, Lun; Hu, Yusheng

2017-08-01

During a name-your-own-price (NYOP) auction, buyers can learn a lot of knowledge from their socially connected peers. Such social learning process makes them become more active to attend the auction and also helps them make decisions on what price to submit. Combining an information diffusion model and a belief decision model, we explore three effects of bidders' information sharing on the buyers' behaviors and the seller profit. The results indicate that information sharing significantly increases the NYOP popularity and the seller profit. When enlarging the quality or quantity of information sharing, or increasing the spreading efficiency of the network topology, the number of attenders and the seller profit are increased significantly. However, the spread of information may make bidders be more likely to bid higher and consequently lose surplus. In addition, the different but interdependent influence of the successful information and failure information are discussed in this work.

Transfer of piano practice in fast performance of skilled finger movements

PubMed Central

2013-01-01

Background Transfer of learning facilitates the efficient mastery of various skills without practicing all possible sensory-motor repertoires. The present study assessed whether motor practice at a submaximal speed, which is typical in sports and music performance, results in an increase in a maximum speed of finger movements of trained and untrained skills. Results Piano practice of sequential finger movements at a submaximal speed over days progressively increased the maximum speed of trained movements. This increased maximum speed of finger movements was maintained two months after the practice. The learning transferred within the hand to some extent, but not across the hands. Conclusions The present study confirmed facilitation of fast finger movements following a piano practice at a submaximal speed. In addition, the findings indicated the intra-manual transfer effects of piano practice on the maximum speed of skilled finger movements. PMID:24175946

Instructional Strategy: Administration of Injury Scripts

ERIC Educational Resources Information Center

Schilling, Jim

2016-01-01

Context: Learning how to form accurate and efficient clinical examinations is a critical factor in becoming a competent athletic training practitioner, and instructional strategies differ for this complex task. Objective: To introduce an instructional strategy consistent with complex learning to encourage improved efficiency by minimizing…

What is the optimal task difficulty for reinforcement learning of brain self-regulation?

PubMed

Bauer, Robert; Vukelić, Mathias; Gharabaghi, Alireza

2016-09-01

The balance between action and reward during neurofeedback may influence reinforcement learning of brain self-regulation. Eleven healthy volunteers participated in three runs of motor imagery-based brain-machine interface feedback where a robot passively opened the hand contingent to β-band modulation. For each run, the β-desynchronization threshold to initiate the hand robot movement increased in difficulty (low, moderate, and demanding). In this context, the incentive to learn was estimated by the change of reward per action, operationalized as the change in reward duration per movement onset. Variance analysis revealed a significant interaction between threshold difficulty and the relationship between reward duration and number of movement onsets (p<0.001), indicating a negative learning incentive for low difficulty, but a positive learning incentive for moderate and demanding runs. Exploration of different thresholds in the same data set indicated that the learning incentive peaked at higher thresholds than the threshold which resulted in maximum classification accuracy. Specificity is more important than sensitivity of neurofeedback for reinforcement learning of brain self-regulation. Learning efficiency requires adequate challenge by neurofeedback interventions. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Self-directed exploration provides a Ncs1-dependent learning bonus

PubMed Central

Mun, Ho-Suk; Saab, Bechara J.; Ng, Enoch; McGirr, Alexander; Lipina, Tatiana V.; Gondo, Yoichi; Georgiou, John; Roder, John C.

2015-01-01

Understanding the mechanisms of memory formation is fundamental to establishing optimal educational practices and restoring cognitive function in brain disease. Here, we show for the first time in a non-primate species, that spatial learning receives a special bonus from self-directed exploration. In contrast, when exploration is escape-oriented, or when the full repertoire of exploratory behaviors is reduced, no learning bonus occurs. These findings permitted the first molecular and cellular examinations into the coupling of exploration to learning. We found elevated expression of neuronal calcium sensor 1 (Ncs1) and dopamine type-2 receptors upon self-directed exploration, in concert with increased neuronal activity in the hippocampal dentate gyrus and area CA3, as well as the nucleus accumbens. We probed further into the learning bonus by developing a point mutant mouse (Ncs1P144S/P144S) harboring a destabilized NCS-1 protein, and found this line lacked the equivalent self-directed exploration learning bonus. Acute knock-down of Ncs1 in the hippocampus also decoupled exploration from efficient learning. These results are potentially relevant for augmenting learning and memory in health and disease, and provide the basis for further molecular and circuit analyses in this direction. PMID:26639399

Memristive Mixed-Signal Neuromorphic Systems: Energy-Efficient Learning at the Circuit-Level

DOE PAGES

Chakma, Gangotree; Adnan, Md Musabbir; Wyer, Austin R.; ...

2017-11-23

Neuromorphic computing is non-von Neumann computer architecture for the post Moore’s law era of computing. Since a main focus of the post Moore’s law era is energy-efficient computing with fewer resources and less area, neuromorphic computing contributes effectively in this research. Here in this paper, we present a memristive neuromorphic system for improved power and area efficiency. Our particular mixed-signal approach implements neural networks with spiking events in a synchronous way. Moreover, the use of nano-scale memristive devices saves both area and power in the system. We also provide device-level considerations that make the system more energy-efficient. The proposed systemmore » additionally includes synchronous digital long term plasticity, an online learning methodology that helps the system train the neural networks during the operation phase and improves the efficiency in learning considering the power consumption and area overhead.« less

Memristive Mixed-Signal Neuromorphic Systems: Energy-Efficient Learning at the Circuit-Level

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

Chakma, Gangotree; Adnan, Md Musabbir; Wyer, Austin R.

Neuromorphic computing is non-von Neumann computer architecture for the post Moore’s law era of computing. Since a main focus of the post Moore’s law era is energy-efficient computing with fewer resources and less area, neuromorphic computing contributes effectively in this research. Here in this paper, we present a memristive neuromorphic system for improved power and area efficiency. Our particular mixed-signal approach implements neural networks with spiking events in a synchronous way. Moreover, the use of nano-scale memristive devices saves both area and power in the system. We also provide device-level considerations that make the system more energy-efficient. The proposed systemmore » additionally includes synchronous digital long term plasticity, an online learning methodology that helps the system train the neural networks during the operation phase and improves the efficiency in learning considering the power consumption and area overhead.« less

An efficient ensemble learning method for gene microarray classification.

PubMed

Osareh, Alireza; Shadgar, Bita

2013-01-01

The gene microarray analysis and classification have demonstrated an effective way for the effective diagnosis of diseases and cancers. However, it has been also revealed that the basic classification techniques have intrinsic drawbacks in achieving accurate gene classification and cancer diagnosis. On the other hand, classifier ensembles have received increasing attention in various applications. Here, we address the gene classification issue using RotBoost ensemble methodology. This method is a combination of Rotation Forest and AdaBoost techniques which in turn preserve both desirable features of an ensemble architecture, that is, accuracy and diversity. To select a concise subset of informative genes, 5 different feature selection algorithms are considered. To assess the efficiency of the RotBoost, other nonensemble/ensemble techniques including Decision Trees, Support Vector Machines, Rotation Forest, AdaBoost, and Bagging are also deployed. Experimental results have revealed that the combination of the fast correlation-based feature selection method with ICA-based RotBoost ensemble is highly effective for gene classification. In fact, the proposed method can create ensemble classifiers which outperform not only the classifiers produced by the conventional machine learning but also the classifiers generated by two widely used conventional ensemble learning methods, that is, Bagging and AdaBoost.

Joint detection and localization of multiple anatomical landmarks through learning

NASA Astrophysics Data System (ADS)

Dikmen, Mert; Zhan, Yiqiang; Zhou, Xiang Sean

2008-03-01

Reliable landmark detection in medical images provides the essential groundwork for successful automation of various open problems such as localization, segmentation, and registration of anatomical structures. In this paper, we present a learning-based system to jointly detect (is it there?) and localize (where?) multiple anatomical landmarks in medical images. The contributions of this work exist in two aspects. First, this method takes the advantage from the learning scenario that is able to automatically extract the most distinctive features for multi-landmark detection. Therefore, it is easily adaptable to detect arbitrary landmarks in various kinds of imaging modalities, e.g., CT, MRI and PET. Second, the use of multi-class/cascaded classifier architecture in different phases of the detection stage combined with robust features that are highly efficient in terms of computation time enables a seemingly real time performance, with very high localization accuracy. This method is validated on CT scans of different body sections, e.g., whole body scans, chest scans and abdominal scans. Aside from improved robustness (due to the exploitation of spatial correlations), it gains a run time efficiency in landmark detection. It also shows good scalability performance under increasing number of landmarks.

Uncertainty Quantification of Medium-Term Heat Storage From Short-Term Geophysical Experiments Using Bayesian Evidential Learning

NASA Astrophysics Data System (ADS)

Hermans, Thomas; Nguyen, Frédéric; Klepikova, Maria; Dassargues, Alain; Caers, Jef

2018-04-01

In theory, aquifer thermal energy storage (ATES) systems can recover in winter the heat stored in the aquifer during summer to increase the energy efficiency of the system. In practice, the energy efficiency is often lower than expected from simulations due to spatial heterogeneity of hydraulic properties or non-favorable hydrogeological conditions. A proper design of ATES systems should therefore consider the uncertainty of the prediction related to those parameters. We use a novel framework called Bayesian Evidential Learning (BEL) to estimate the heat storage capacity of an alluvial aquifer using a heat tracing experiment. BEL is based on two main stages: pre- and postfield data acquisition. Before data acquisition, Monte Carlo simulations and global sensitivity analysis are used to assess the information content of the data to reduce the uncertainty of the prediction. After data acquisition, prior falsification and machine learning based on the same Monte Carlo are used to directly assess uncertainty on key prediction variables from observations. The result is a full quantification of the posterior distribution of the prediction conditioned to observed data, without any explicit full model inversion. We demonstrate the methodology in field conditions and validate the framework using independent measurements.

Improved Learning Efficiency and Increased Student Collaboration through Use of Virtual Microscopy in the Teaching of Human Pathology

ERIC Educational Resources Information Center

Braun, Mark W.; Kearns, Katherine D.

2008-01-01

The implementation of virtual microscopy in the teaching of pathology at the Bloomington, Indiana extension of the Indiana University School of Medicine permitted the assessment of student attitudes, use and academic performance with respect to this new technology. A gradual and integrated approach allowed the parallel assessment with respect to…

Examining the Use of Spacing Effect to Increase the Efficiency of Incremental Rehearsal

ERIC Educational Resources Information Center

Swehla, Sarah E.; Burns, Matthew K.; Zaslofsky, Anne F.; Hall, Matthew S.; Varma, Sashank; Volpe, Robert J.

2016-01-01

Incremental rehearsal (IR) is a highly effective intervention that uses high repetition and a high ratio of known to unknown items with linearly spaced known items between the new items. It has been hypothesized that narrowly spaced practice would result in quick learning, whereas items that are widely spaced would result in longer-term retention.…

Teaching Social Skills to Students with Autism Spectrum Disorders: Efficacy of a Social Learning Approach

ERIC Educational Resources Information Center

Bellinger, Jillian M.

2012-01-01

As the number of children diagnosed with an autism spectrum disorder (ASD) is rising, there is increasing demand for evidence-based interventions that are efficient and easy to implement in school settings. The purpose of the current study was to evaluate the effectiveness of an intervention protocol featuring guided practice, coaching, modeling,…

Increasing Instructional Efficiency When Using Simultaneous Prompting Procedure in Teaching Academic Skills to Students with Autism Spectrum Disorders

ERIC Educational Resources Information Center

Tekin-Iftar, Elif; Olcay-Gul, Seray

2016-01-01

A multiple probe design across behaviors replicated across participants was used to examine the effects of a simultaneous prompting procedure delivered along with instructive feedback and observational learning stimuli when teaching academic skills to a small group of students with ASD. Different target skills were taught to each student in the…

Integrating Farm Production and Natural Resource Management in Tasmania, Australia

ERIC Educational Resources Information Center

Cotching, W. E.; Sherriff, L.; Kilpatrick, S.

2009-01-01

This paper reports on the social learning from a project aimed to increase the knowledge and capacity of a group of farmers in Tasmania, Australia, to reduce the impacts of intensive agriculture on soil health and waterways, and to optimise the efficient use of on-farm inputs. The plan-do-check-review cycle adopted in this project required the…

The Impact of Learning Context on Intent to Use Marketing and Sales Technology: A Comparison of Scenario-Based and Task-Based Approaches

ERIC Educational Resources Information Center

Mallin, Michael L.; Jones, Deirdre E.; Cordell, Jennifer L.

2010-01-01

With firms focused on increasing efficiency and effectiveness in today's marketing and sales environment, it is crucial that salesforce training methods facilitate greater adoption of salesforce automation technology. Given the growth in sales education at colleges and universities, firms are looking to recruit their frontline marketing and sales…

"Blending" Docent Learning: Using Google Forms Quizzes to Increase Efficiency in Interpreter Education at Fort Henry

ERIC Educational Resources Information Center

Murphy, Michael P. A.

2018-01-01

The introduction of online elements to museums and cultural sites has opened up new ways for visitors to engage with the past, with nature, with culture, and all other treasures of the museum. However, docent training has lagged behind visitor-facing educational initiatives. By blending online elements into docent education programs, staff…

Newly qualified doctors' perceptions of informal learning from nurses: implications for interprofessional education and practice.

PubMed

Burford, Bryan; Morrow, Gill; Morrison, Jill; Baldauf, Beate; Spencer, John; Johnson, Neil; Rothwell, Charlotte; Peile, Ed; Davies, Carol; Allen, Maggie; Illing, Jan

2013-09-01

Newly qualified doctors spend much of their time with nurses, but little research has considered informal learning during that formative contact. This article reports findings from a multiple case study that explored what newly qualified doctors felt they learned from nurses in the workplace. Analysis of interviews conducted with UK doctors in their first year of practice identified four overarching themes: attitudes towards working with nurses, learning about roles, professional hierarchies and learning skills. Informal learning was found to contribute to the newly qualified doctors' knowledge of their own and others' roles. A dynamic hierarchy was identified: one in which a "pragmatic hierarchy" recognising nurses' expertise was superseded by a "normative structural hierarchy" that reinforced the notion of medical dominance. Alongside the implicit learning of roles, nurses contributed to the explicit learning of skills and captured doctors' errors, with implications for patient safety. The findings are discussed in relation to professional socialisation. Issues of power between the professions are also considered. It is concluded that increasing both medical and nursing professions' awareness of informal workplace learning may improve the efficiency of education in restricted working hours. A culture in which informal learning is embedded may also have benefits for patient safety.

Efficient convolutional sparse coding

DOEpatents

Wohlberg, Brendt

2017-06-20

Computationally efficient algorithms may be applied for fast dictionary learning solving the convolutional sparse coding problem in the Fourier domain. More specifically, efficient convolutional sparse coding may be derived within an alternating direction method of multipliers (ADMM) framework that utilizes fast Fourier transforms (FFT) to solve the main linear system in the frequency domain. Such algorithms may enable a significant reduction in computational cost over conventional approaches by implementing a linear solver for the most critical and computationally expensive component of the conventional iterative algorithm. The theoretical computational cost of the algorithm may be reduced from O(M.sup.3N) to O(MN log N), where N is the dimensionality of the data and M is the number of elements in the dictionary. This significant improvement in efficiency may greatly increase the range of problems that can practically be addressed via convolutional sparse representations.

Multilayer Extreme Learning Machine With Subnetwork Nodes for Representation Learning.

PubMed

Yang, Yimin; Wu, Q M Jonathan

2016-11-01

The extreme learning machine (ELM), which was originally proposed for "generalized" single-hidden layer feedforward neural networks, provides efficient unified learning solutions for the applications of clustering, regression, and classification. It presents competitive accuracy with superb efficiency in many applications. However, ELM with subnetwork nodes architecture has not attracted much research attentions. Recently, many methods have been proposed for supervised/unsupervised dimension reduction or representation learning, but these methods normally only work for one type of problem. This paper studies the general architecture of multilayer ELM (ML-ELM) with subnetwork nodes, showing that: 1) the proposed method provides a representation learning platform with unsupervised/supervised and compressed/sparse representation learning and 2) experimental results on ten image datasets and 16 classification datasets show that, compared to other conventional feature learning methods, the proposed ML-ELM with subnetwork nodes performs competitively or much better than other feature learning methods.

Machine learning methods can replace 3D profile method in classification of amyloidogenic hexapeptides.

PubMed

Stanislawski, Jerzy; Kotulska, Malgorzata; Unold, Olgierd

2013-01-17

Amyloids are proteins capable of forming fibrils. Many of them underlie serious diseases, like Alzheimer disease. The number of amyloid-associated diseases is constantly increasing. Recent studies indicate that amyloidogenic properties can be associated with short segments of aminoacids, which transform the structure when exposed. A few hundreds of such peptides have been experimentally found. Experimental testing of all possible aminoacid combinations is currently not feasible. Instead, they can be predicted by computational methods. 3D profile is a physicochemical-based method that has generated the most numerous dataset - ZipperDB. However, it is computationally very demanding. Here, we show that dataset generation can be accelerated. Two methods to increase the classification efficiency of amyloidogenic candidates are presented and tested: simplified 3D profile generation and machine learning methods. We generated a new dataset of hexapeptides, using more economical 3D profile algorithm, which showed very good classification overlap with ZipperDB (93.5%). The new part of our dataset contains 1779 segments, with 204 classified as amyloidogenic. The dataset of 6-residue sequences with their binary classification, based on the energy of the segment, was applied for training machine learning methods. A separate set of sequences from ZipperDB was used as a test set. The most effective methods were Alternating Decision Tree and Multilayer Perceptron. Both methods obtained area under ROC curve of 0.96, accuracy 91%, true positive rate ca. 78%, and true negative rate 95%. A few other machine learning methods also achieved a good performance. The computational time was reduced from 18-20 CPU-hours (full 3D profile) to 0.5 CPU-hours (simplified 3D profile) to seconds (machine learning). We showed that the simplified profile generation method does not introduce an error with regard to the original method, while increasing the computational efficiency. Our new dataset proved representative enough to use simple statistical methods for testing the amylogenicity based only on six letter sequences. Statistical machine learning methods such as Alternating Decision Tree and Multilayer Perceptron can replace the energy based classifier, with advantage of very significantly reduced computational time and simplicity to perform the analysis. Additionally, a decision tree provides a set of very easily interpretable rules.

Efficiency Goals

ERIC Educational Resources Information Center

Graham, Donald

2009-01-01

The lighting of learning environments is an important focus in designing new schools and renovating older schools. Studies long have shown that appropriate lighting levels and daylighting improve learning; now, climbing energy budgets have spurred school administrators to seek more efficient use of lighting. Electricity rates are expected to rise…

What Lies beyond Effectiveness and Efficiency? Adventure Learning Design

ERIC Educational Resources Information Center

Doering, Aaron; Veletsianos, George

2008-01-01

Educational technology and instructional design research has focused on evaluating interventions and innovations in terms of their effectiveness, efficiency, and appeal. While such indicators of learning outcomes are important, designers should also strive for engaging, socially just, and transformational instruction. To illuminate the…

A study of metaheuristic algorithms for high dimensional feature selection on microarray data

NASA Astrophysics Data System (ADS)

Dankolo, Muhammad Nasiru; Radzi, Nor Haizan Mohamed; Sallehuddin, Roselina; Mustaffa, Noorfa Haszlinna

2017-11-01

Microarray systems enable experts to examine gene profile at molecular level using machine learning algorithms. It increases the potentials of classification and diagnosis of many diseases at gene expression level. Though, numerous difficulties may affect the efficiency of machine learning algorithms which includes vast number of genes features comprised in the original data. Many of these features may be unrelated to the intended analysis. Therefore, feature selection is necessary to be performed in the data pre-processing. Many feature selection algorithms are developed and applied on microarray which including the metaheuristic optimization algorithms. This paper discusses the application of the metaheuristics algorithms for feature selection in microarray dataset. This study reveals that, the algorithms have yield an interesting result with limited resources thereby saving computational expenses of machine learning algorithms.

Lessons Learned Designing and Using an Online Discussion Forum for Care Coordinators in Primary Care.

PubMed

Ferrante, Jeanne M; Friedman, Asia; Shaw, Eric K; Howard, Jenna; Cohen, Deborah J; Shahidi, Laleh

2015-10-18

While an increasing number of researchers are using online discussion forums for qualitative research, few authors have documented their experiences and lessons learned to demonstrate this method's viability and validity in health services research. We comprehensively describe our experiences, from start to finish, of designing and using an asynchronous online discussion forum for collecting and analyzing information elicited from care coordinators in Patient-Centered Medical Homes across the United States. Our lessons learned from each phase, including planning, designing, implementing, using, and ending this private online discussion forum, provide some recommendations for other health services researchers considering this method. An asynchronous online discussion forum is a feasible, efficient, and effective method to conduct a qualitative study, particularly when subjects are health professionals. © The Author(s) 2015.

Lessons Learned Designing and Using an Online Discussion Forum for Care Coordinators in Primary Care

PubMed Central

Ferrante, Jeanne M.; Friedman, Asia; Shaw, Eric K.; Howard, Jenna; Cohen, Deborah J.; Shahidi, Laleh

2016-01-01

While an increasing number of researchers are using online discussion forums for qualitative research, few authors have documented their experiences and lessons learned to demonstrate this method’s viability and validity in health services research. We comprehensively describe our experiences, from start to finish, of designing and using an asynchronous online discussion forum for collecting and analyzing information elicited from care coordinators in Patient-Centered Medical Homes across the United States. Our lessons learned from each phase, including planning, designing, implementing, using, and ending this private online discussion forum, provide some recommendations for other health services researchers considering this method. An asynchronous online discussion forum is a feasible, efficient, and effective method to conduct a qualitative study, particularly when subjects are health professionals. PMID:26481942

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

Aznar, Alexandra; Day, Megan; Doris, Elizabeth

The report analyzes and presents information learned from a sample of 20 cities across the United States, from New York City to Park City, Utah, including a diverse sample of population size, utility type, region, annual greenhouse gas reduction targets, vehicle use, and median household income. The report compares climate, sustainability, and energy plans to better understand where cities are taking energy-related actions and how they are measuring impacts. Some common energy-related goals focus on reducing city-wide carbon emissions, improving energy efficiency across sectors, increasing renewable energy, and increasing biking and walking.

Magnetic stimulation of visual cortex impairs perceptual learning.

PubMed

Baldassarre, Antonello; Capotosto, Paolo; Committeri, Giorgia; Corbetta, Maurizio

2016-12-01

The ability to learn and process visual stimuli more efficiently is important for survival. Previous neuroimaging studies have shown that perceptual learning on a shape identification task differently modulates activity in both frontal-parietal cortical regions and visual cortex (Sigman et al., 2005;Lewis et al., 2009). Specifically, fronto-parietal regions (i.e. intra parietal sulcus, pIPS) became less activated for trained as compared to untrained stimuli, while visual regions (i.e. V2d/V3 and LO) exhibited higher activation for familiar shape. Here, after the intensive training, we employed transcranial magnetic stimulation over both visual occipital and parietal regions, previously shown to be modulated, to investigate their causal role in learning the shape identification task. We report that interference with V2d/V3 and LO increased reaction times to learned stimuli as compared to pIPS and Sham control condition. Moreover, the impairment observed after stimulation over the two visual regions was positive correlated. These results strongly support the causal role of the visual network in the control of the perceptual learning. Copyright © 2016 Elsevier Inc. All rights reserved.

E-learning on antibiotic prescribing-the role of autonomous motivation in participation: a prospective cohort study.

PubMed

Caris, Martine G; Sikkens, Jonne J; Kusurkar, Rashmi A; van Agtmael, Michiel A

2018-05-10

E-learning is increasingly used in education on antimicrobial stewardship, but participation rates are often low. Insight into factors that affect participation is therefore needed. Autonomous motivation is associated with higher achievements in medical education and could also play a role in e-learning participation. We therefore aimed to investigate the role of residents' autonomous motivation in their participation in e-learning on antibiotic prescribing. We performed a multicentre cohort study in two academic and two teaching hospitals. Residents who filled out questionnaires on antibiotic knowledge, the perceived importance of antibiotics and motivation [Self-Regulation Questionnaire - Academic (SRQ-a)] received e-learning access. We used the SRQ-a to calculate relative autonomous motivation (RAM), an index that estimates the amount of autonomous motivation compared with the amount of controlled motivation. We then analysed associations between RAM and participation in e-learning with logistic regression. Eighty-six residents participated (74% female, mean age 30 years). Overall e-learning participation was 58% (n = 50). Participation was 41% in residents with negative RAM (i.e. more controlled motivation) and 62% in residents with positive RAM (i.e. more autonomous motivation). RAM was positively associated with participation, adjusted for residency in an academic hospital (adjusted OR 2.6, 95% CI 1.5-4.6). Participation in non-obligatory e-learning on antibiotic prescribing is higher in residents with more autonomous motivation. Interventions to increase autonomous motivation could improve participation. Preceding e-learning on antibiotic prescribing with face-to-face education, to explain the importance of the subject, could enhance autonomous motivation and thus optimize e-learning efficiency.

Building on prior knowledge without building it in.

PubMed

Hansen, Steven S; Lampinen, Andrew K; Suri, Gaurav; McClelland, James L

2017-01-01

Lake et al. propose that people rely on "start-up software," "causal models," and "intuitive theories" built using compositional representations to learn new tasks more efficiently than some deep neural network models. We highlight the many drawbacks of a commitment to compositional representations and describe our continuing effort to explore how the ability to build on prior knowledge and to learn new tasks efficiently could arise through learning in deep neural networks.

The Framework of Intervention Engine Based on Learning Analytics

ERIC Educational Resources Information Center

Sahin, Muhittin; Yurdugül, Halil

2017-01-01

Learning analytics primarily deals with the optimization of learning environments and the ultimate goal of learning analytics is to improve learning and teaching efficiency. Studies on learning analytics seem to have been made in the form of adaptation engine and intervention engine. Adaptation engine studies are quite widespread, but intervention…

The Brain as an Efficient and Robust Adaptive Learner.

PubMed

Denève, Sophie; Alemi, Alireza; Bourdoukan, Ralph

2017-06-07

Understanding how the brain learns to compute functions reliably, efficiently, and robustly with noisy spiking activity is a fundamental challenge in neuroscience. Most sensory and motor tasks can be described as dynamical systems and could presumably be learned by adjusting connection weights in a recurrent biological neural network. However, this is greatly complicated by the credit assignment problem for learning in recurrent networks, e.g., the contribution of each connection to the global output error cannot be determined based only on locally accessible quantities to the synapse. Combining tools from adaptive control theory and efficient coding theories, we propose that neural circuits can indeed learn complex dynamic tasks with local synaptic plasticity rules as long as they associate two experimentally established neural mechanisms. First, they should receive top-down feedbacks driving both their activity and their synaptic plasticity. Second, inhibitory interneurons should maintain a tight balance between excitation and inhibition in the circuit. The resulting networks could learn arbitrary dynamical systems and produce irregular spike trains as variable as those observed experimentally. Yet, this variability in single neurons may hide an extremely efficient and robust computation at the population level. Copyright © 2017 Elsevier Inc. All rights reserved.

Just Say No to Carbon Emissions (LBNL Science at the Theater)

ScienceCinema

Ramesh, Ramamoorthy; Zhou, Nan; Oldenburg, Curt

2018-06-15

Learn about three efforts our grandchildren may thank us for: cheap solar energy, bringing energy efficiency to China, and learning how to store carbon deep underground. Can solar energy be dirt cheap? We're all potentially billionaires when it comes to solar energy. The trick is learning how to convert sunlight to electricity using cheap and plentiful materials. Ramamoorthy Ramesh, an innovative materials scientist at Berkeley Lab, will discuss how he and other researchers are working to make photovoltaic cells using the most abundant elements in the Earth's crust -- materials that are literally as common as dirt. Energy efficiency in China: Nan Zhou is a researcher with Berkeley Labs China Energy Group. She will speak about Chinas energy use and the policies that have been implemented to increase energy efficiency and reduce CO2 emission growth. Her work focuses on building China's capacity to evaluate, adopt and implement low-carbon development strategies. Zhou has an architecture degree from China, and a Master and Ph.D. in Engineering from Japan. Understanding geologic carbon sequestration: Even with continued growth of renewable energy sources such as wind and solar, fossil fuels will likely remain cheap and plentiful for decades to come. Geologist Curt Oldenburg, who heads Berkeley Lab's Geologic Carbon Sequestration Program, will discuss a strategy to reduce carbon emissions from coal and natural gas. It involves pumping compressed CO2 captured from large stationary sources into underground rock formations that can store it for geological time scales.

Just Say No to Carbon Emissions (LBNL Science at the Theater)

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

Ramesh, Ramamoorthy; Zhou, Nan; Oldenburg, Curt

2010-04-26

Learn about three efforts our grandchildren may thank us for: cheap solar energy, bringing energy efficiency to China, and learning how to store carbon deep underground. Can solar energy be dirt cheap? We're all potentially billionaires when it comes to solar energy. The trick is learning how to convert sunlight to electricity using cheap and plentiful materials. Ramamoorthy Ramesh, an innovative materials scientist at Berkeley Lab, will discuss how he and other researchers are working to make photovoltaic cells using the most abundant elements in the Earth's crust -- materials that are literally as common as dirt. Energy efficiency inmore » China: Nan Zhou is a researcher with Berkeley Labs China Energy Group. She will speak about Chinas energy use and the policies that have been implemented to increase energy efficiency and reduce CO2 emission growth. Her work focuses on building China's capacity to evaluate, adopt and implement low-carbon development strategies. Zhou has an architecture degree from China, and a Master and Ph.D. in Engineering from Japan. Understanding geologic carbon sequestration: Even with continued growth of renewable energy sources such as wind and solar, fossil fuels will likely remain cheap and plentiful for decades to come. Geologist Curt Oldenburg, who heads Berkeley Lab's Geologic Carbon Sequestration Program, will discuss a strategy to reduce carbon emissions from coal and natural gas. It involves pumping compressed CO2 captured from large stationary sources into underground rock formations that can store it for geological time scales.« less

Efficient Ways to Learn Weather Radar Polarimetry

ERIC Educational Resources Information Center

Cao, Qing; Yeary, M. B.; Zhang, Guifu

2012-01-01

The U.S. weather radar network is currently being upgraded with dual-polarization capability. Weather radar polarimetry is an interdisciplinary area of engineering and meteorology. This paper presents efficient ways to learn weather radar polarimetry through several basic and practical topics. These topics include: 1) hydrometeor scattering model…

A Health Belief Model-Social Learning Theory approach to adolescents' fertility control: findings from a controlled field trial.

PubMed

Eisen, M; Zellman, G L; McAlister, A L

1992-01-01

We evaluated an 8- to 12-hour Health Belief Model-Social Learning Theory (HBM-SLT)-based sex education program against several community- and school-based interventions in a controlled field experiment. Data on sexual and contraceptive behavior were collected from 1,444 adolescents unselected for gender, race/ethnicity, or virginity status in a pretest-posttest design. Over 60% completed the one-year follow-up. Multivariate analyses were conducted separately for each preintervention virginity status by gender grouping. The results revealed differential program impacts. First, for preintervention virgins, there were no gender or intervention differences in abstinence maintenance over the follow-up year. Second, female preintervention Comparison program virgins used effective contraceptive methods more consistently than those who attended the HBM-SLT program (p less than 0.01); among males, the intervention programs were equally effective. Third, both interventions significantly increased contraceptive efficiency for teenagers who were sexually active before attending the programs. For males, the HBM-SLT program led to significantly greater follow-up contraceptive efficiency than the Comparison program with preintervention contraceptive efficiency controlled (p less than 0.05); for females, the programs produced equivalent improvement. Implications for program planning and evaluation are discussed.

Teaching the Fundamentals of Energy Efficiency

NASA Astrophysics Data System (ADS)

Meier, Alan

2010-02-01

A course on energy efficiency is a surprisingly valuable complement to a student's education in physics and many other disciplines. The Univ. of California, Davis, offers a 1-quarter course on ``understanding the other side of the meter.'' Lectures begin by giving students a demand-side perspective on how, where, and why energy is used. Students measure energy use of appliances in their homes and then report results. This gives students a practical sense of the difference between energy and power and learn how appliances transform energy into useful services. Lectures introduce the types of direct conservation measures--reducing demand, reducing fixed consumptions, and increasing efficiency. Practical examples draw upon simple concepts in heat transfer, thermodynamics, and mechanics. Graphical techniques, strengthened through problem sets, explain the interdependence of conservation measures. Lectures then examine indirect energy savings from measures and consider questions like ``where can one achieve the greatest fuel savings in a car by removing one gram of mass?'' Finally, students learn about conservation measures that circumvent physical limits by adopting new processes. By the end of the course, students have a gained a new perspective on energy consumption and the opportunities to reduce it. )

Invasion fitness for gene-culture co-evolution in family-structured populations and an application to cumulative culture under vertical transmission.

PubMed

Mullon, Charles; Lehmann, Laurent

2017-08-01

Human evolution depends on the co-evolution between genetically determined behaviors and socially transmitted information. Although vertical transmission of cultural information from parent to offspring is common in hominins, its effects on cumulative cultural evolution are not fully understood. Here, we investigate gene-culture co-evolution in a family-structured population by studying the invasion fitness of a mutant allele that influences a deterministic level of cultural information (e.g., amount of knowledge or skill) to which diploid carriers of the mutant are exposed in subsequent generations. We show that the selection gradient on such a mutant, and the concomitant level of cultural information it generates, can be evaluated analytically under the assumption that the cultural dynamic has a single attractor point, thereby making gene-culture co-evolution in family-structured populations with multigenerational effects mathematically tractable. We apply our result to study how genetically determined phenotypes of individual and social learning co-evolve with the level of adaptive information they generate under vertical transmission. We find that vertical transmission increases adaptive information due to kin selection effects, but when information is transmitted as efficiently between family members as between unrelated individuals, this increase is moderate in diploids. By contrast, we show that the way resource allocation into learning trades off with allocation into reproduction (the "learning-reproduction trade-off") significantly influences levels of adaptive information. We also show that vertical transmission prevents evolutionary branching and may therefore play a qualitative role in gene-culture co-evolutionary dynamics. More generally, our analysis of selection suggests that vertical transmission can significantly increase levels of adaptive information under the biologically plausible condition that information transmission between relatives is more efficient than between unrelated individuals. Copyright © 2017 Elsevier Inc. All rights reserved.

Efficient identification of nationally mandated reportable cancer cases using natural language processing and machine learning.

PubMed

Osborne, John D; Wyatt, Matthew; Westfall, Andrew O; Willig, James; Bethard, Steven; Gordon, Geoff

2016-11-01

To help cancer registrars efficiently and accurately identify reportable cancer cases. The Cancer Registry Control Panel (CRCP) was developed to detect mentions of reportable cancer cases using a pipeline built on the Unstructured Information Management Architecture - Asynchronous Scaleout (UIMA-AS) architecture containing the National Library of Medicine's UIMA MetaMap annotator as well as a variety of rule-based UIMA annotators that primarily act to filter out concepts referring to nonreportable cancers. CRCP inspects pathology reports nightly to identify pathology records containing relevant cancer concepts and combines this with diagnosis codes from the Clinical Electronic Data Warehouse to identify candidate cancer patients using supervised machine learning. Cancer mentions are highlighted in all candidate clinical notes and then sorted in CRCP's web interface for faster validation by cancer registrars. CRCP achieved an accuracy of 0.872 and detected reportable cancer cases with a precision of 0.843 and a recall of 0.848. CRCP increases throughput by 22.6% over a baseline (manual review) pathology report inspection system while achieving a higher precision and recall. Depending on registrar time constraints, CRCP can increase recall to 0.939 at the expense of precision by incorporating a data source information feature. CRCP demonstrates accurate results when applying natural language processing features to the problem of detecting patients with cases of reportable cancer from clinical notes. We show that implementing only a portion of cancer reporting rules in the form of regular expressions is sufficient to increase the precision, recall, and speed of the detection of reportable cancer cases when combined with off-the-shelf information extraction software and machine learning. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Optimizing biomedical science learning in a veterinary curriculum: a review.

PubMed

Warren, Amy L; Donnon, Tyrone

2013-01-01

As veterinary medical curricula evolve, the time dedicated to biomedical science teaching, as well as the role of biomedical science knowledge in veterinary education, has been scrutinized. Aside from being mandated by accrediting bodies, biomedical science knowledge plays an important role in developing clinical, diagnostic, and therapeutic reasoning skills in the application of clinical skills, in supporting evidence-based veterinary practice and life-long learning, and in advancing biomedical knowledge and comparative medicine. With an increasing volume and fast pace of change in biomedical knowledge, as well as increased demands on curricular time, there has been pressure to make biomedical science education efficient and relevant for veterinary medicine. This has lead to a shift in biomedical education from fact-based, teacher-centered and discipline-based teaching to applicable, student-centered, integrated teaching. This movement is supported by adult learning theories and is thought to enhance students' transference of biomedical science into their clinical practice. The importance of biomedical science in veterinary education and the theories of biomedical science learning will be discussed in this article. In addition, we will explore current advances in biomedical teaching methodologies that are aimed to maximize knowledge retention and application for clinical veterinary training and practice.

Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network.

PubMed

Li, Yuexiang; Shen, Linlin

2018-02-11

Skin lesions are a severe disease globally. Early detection of melanoma in dermoscopy images significantly increases the survival rate. However, the accurate recognition of melanoma is extremely challenging due to the following reasons: low contrast between lesions and skin, visual similarity between melanoma and non-melanoma lesions, etc. Hence, reliable automatic detection of skin tumors is very useful to increase the accuracy and efficiency of pathologists. In this paper, we proposed two deep learning methods to address three main tasks emerging in the area of skin lesion image processing, i.e., lesion segmentation (task 1), lesion dermoscopic feature extraction (task 2) and lesion classification (task 3). A deep learning framework consisting of two fully convolutional residual networks (FCRN) is proposed to simultaneously produce the segmentation result and the coarse classification result. A lesion index calculation unit (LICU) is developed to refine the coarse classification results by calculating the distance heat-map. A straight-forward CNN is proposed for the dermoscopic feature extraction task. The proposed deep learning frameworks were evaluated on the ISIC 2017 dataset. Experimental results show the promising accuracies of our frameworks, i.e., 0.753 for task 1, 0.848 for task 2 and 0.912 for task 3 were achieved.

Efficacy of plaque removal and learning effect of a powered and a manual toothbrush.

PubMed

Lazarescu, D; Boccaneala, S; Illiescu, A; De Boever, J A

2003-08-01

Subjects with high plaque and gingivitis scores can profit most from the introduction of new manual or powered tooth brushes. To improve their hygiene, not only the technical characteristics of new brushes but also the learning effect in efficient handling are of importance. : The present study compared the efficacy in plaque removal of an electric and a manual toothbrush in a general population and analysed the learning effect in efficient handling. Eighty healthy subjects, unfamiliar with electric brushes, were divided into two groups: group 1 used the Philips/Jordan HP 735 powered brush and group 2 used a manual brush, Oral-B40+. Plaque index (PI) and gingival bleeding index (GBI) were assessed at baseline and at weeks 3, 6, 12 and 18. After each evaluation, patients abstained from oral hygiene for 24 h. The next day a 3-min supervised brushing was performed. Before and after this brushing, PI was assessed for the estimation of the individual learning effect. The study was single blinded. Over the 18-week period, PI reduced gradually and statistically significantly (p<0.001) in group 1 from 2.9 (+/-0.38) to 1.5 (+/-0.24) and in group 2 from 2.9 (+/-0.34) to 2.2 (+/-0.23). From week 3 onwards, the difference between groups was statistically significant (p<0.001). The bleeding index decreased in group 1 from 28% (+/-17%) to 7% (+/-5%) (p<0.001) and in group 2 from 30% (+/-12%) to 12% (+/-6%) (p<0.001). The difference between groups was statistically significant (p<0.001) from week 6 onwards. The learning effect, expressed as the percentage of plaque reduction after 3 min of supervised brushing, was 33% for group 1 and 26% for group 2 at week 0. This percentage increased at week 18 to 64% in group 1 and 44% in group 2 (difference between groups statistically significant: p<0.001). The powered brush was significantly more efficient in removing plaque and improving gingival health than the manual brush in the group of subjects unfamiliar with electric brushes. There was also a significant learning effect that was more pronounced with the electric toothbrush.

Impaired discrimination learning in interneuronal NMDAR-GluN2B mutant mice.

PubMed

Brigman, Jonathan L; Daut, Rachel A; Saksida, Lisa; Bussey, Timothy J; Nakazawa, Kazu; Holmes, Andrew

2015-06-17

Previous studies have established a role for N-methyl-D-aspartate receptor (NMDAR) containing the GluN2B subunit in efficient learning behavior on a variety of tasks. Recent findings have suggested that NMDAR on GABAergic interneurons may underlie the modulation of striatal function necessary to balance efficient action with cortical excitatory input. Here we investigated how loss of GluN2B-containing NMDAR on GABAergic interneurons altered corticostriatal-mediated associative learning. Mutant mice (floxed-GluN2B×Ppp1r2-Cre) were generated to produce loss of GluN2B on forebrain interneurons and phenotyped on a touchscreen-based pairwise visual learning paradigm. We found that the mutants showed normal performance during Pavlovian and instrumental pretraining, but were significantly impaired on a discrimination learning task. Detailed analysis of the microstructure of discrimination performance revealed reduced win→stay behavior in the mutants. These results further support the role of NMDAR, and GluN2B in particular, on modulation of striatal function necessary for efficient choice behavior and suggest that NMDAR on interneurons may play a critical role in associative learning.

Incidental learning speeds visual search by lowering response thresholds, not by improving efficiency: evidence from eye movements.

PubMed

Hout, Michael C; Goldinger, Stephen D

2012-02-01

When observers search for a target object, they incidentally learn the identities and locations of "background" objects in the same display. This learning can facilitate search performance, eliciting faster reaction times for repeated displays. Despite these findings, visual search has been successfully modeled using architectures that maintain no history of attentional deployments; they are amnesic (e.g., Guided Search Theory). In the current study, we asked two questions: 1) under what conditions does such incidental learning occur? And 2) what does viewing behavior reveal about the efficiency of attentional deployments over time? In two experiments, we tracked eye movements during repeated visual search, and we tested incidental memory for repeated nontarget objects. Across conditions, the consistency of search sets and spatial layouts were manipulated to assess their respective contributions to learning. Using viewing behavior, we contrasted three potential accounts for faster searching with experience. The results indicate that learning does not result in faster object identification or greater search efficiency. Instead, familiar search arrays appear to allow faster resolution of search decisions, whether targets are present or absent.

Not just passengers: pigeons, Columba livia, can learn homing routes while flying with a more experienced conspecific.

PubMed

Pettit, Benjamin; Flack, Andrea; Freeman, Robin; Guilford, Tim; Biro, Dora

2013-01-07

For animals that travel in groups, the directional choices of conspecifics are potentially a rich source of information for spatial learning. In this study, we investigate how the opportunity to follow a locally experienced demonstrator affects route learning by pigeons over repeated homing flights. This test of social influences on navigation takes advantage of the individually distinctive routes that pigeons establish when trained alone. We found that pigeons learn routes just as effectively while flying with a partner as control pigeons do while flying alone. However, rather than learning the exact route of the demonstrator, the paired routes shifted over repeated flights, which suggests that the birds with less local experience also took an active role in the navigational task. The efficiency of the original routes was a key factor in how far they shifted, with less efficient routes undergoing the greatest changes. In this context, inefficient routes are unlikely to be maintained through repeated rounds of social transmission, and instead more efficient routes are achieved because of the interaction between social learning and information pooling.

Procedural learning: A developmental study of motor sequence learning and probabilistic classification learning in school-aged children.

PubMed

Mayor-Dubois, Claire; Zesiger, Pascal; Van der Linden, Martial; Roulet-Perez, Eliane

2016-01-01

In this study, we investigated motor and cognitive procedural learning in typically developing children aged 8-12 years with a serial reaction time (SRT) task and a probabilistic classification learning (PCL) task. The aims were to replicate and extend the results of previous SRT studies, to investigate PCL in school-aged children, to explore the contribution of declarative knowledge to SRT and PCL performance, to explore the strategies used by children in the PCL task via a mathematical model, and to see whether performances obtained in motor and cognitive tasks correlated. The results showed similar learning effects in the three age groups in the SRT and in the first half of the PCL tasks. Participants did not develop explicit knowledge in the SRT task whereas declarative knowledge of the cue-outcome associations correlated with the performances in the second half of the PCL task, suggesting a participation of explicit knowledge after some time of exposure in PCL. An increasing proportion of the optimal strategy use with increasing age was observed in the PCL task. Finally, no correlation appeared between cognitive and motor performance. In conclusion, we extended the hypothesis of age invariance from motor to cognitive procedural learning, which had not been done previously. The ability to adopt more efficient learning strategies with age may rely on the maturation of the fronto-striatal loops. The lack of correlation between performance in the SRT task and the first part of the PCL task suggests dissociable developmental trajectories within the procedural memory system.

Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines

PubMed Central

Neftci, Emre O.; Augustine, Charles; Paul, Somnath; Detorakis, Georgios

2017-01-01

An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Gradient Back Propagation (BP) rule, often relies on the immediate availability of network-wide information stored with high-precision memory during learning, and precise operations that are difficult to realize in neuromorphic hardware. Remarkably, recent work showed that exact backpropagated gradients are not essential for learning deep representations. Building on these results, we demonstrate an event-driven random BP (eRBP) rule that uses an error-modulated synaptic plasticity for learning deep representations. Using a two-compartment Leaky Integrate & Fire (I&F) neuron, the rule requires only one addition and two comparisons for each synaptic weight, making it very suitable for implementation in digital or mixed-signal neuromorphic hardware. Our results show that using eRBP, deep representations are rapidly learned, achieving classification accuracies on permutation invariant datasets comparable to those obtained in artificial neural network simulations on GPUs, while being robust to neural and synaptic state quantizations during learning. PMID:28680387

Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines.

PubMed

Neftci, Emre O; Augustine, Charles; Paul, Somnath; Detorakis, Georgios

2017-01-01

An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Gradient Back Propagation (BP) rule, often relies on the immediate availability of network-wide information stored with high-precision memory during learning, and precise operations that are difficult to realize in neuromorphic hardware. Remarkably, recent work showed that exact backpropagated gradients are not essential for learning deep representations. Building on these results, we demonstrate an event-driven random BP (eRBP) rule that uses an error-modulated synaptic plasticity for learning deep representations. Using a two-compartment Leaky Integrate & Fire (I&F) neuron, the rule requires only one addition and two comparisons for each synaptic weight, making it very suitable for implementation in digital or mixed-signal neuromorphic hardware. Our results show that using eRBP, deep representations are rapidly learned, achieving classification accuracies on permutation invariant datasets comparable to those obtained in artificial neural network simulations on GPUs, while being robust to neural and synaptic state quantizations during learning.

Repetition Suppression in the Left Inferior Frontal Gyrus Predicts Tone Learning Performance.

PubMed

Asaridou, Salomi S; Takashima, Atsuko; Dediu, Dan; Hagoort, Peter; McQueen, James M

2016-06-01

Do individuals differ in how efficiently they process non-native sounds? To what extent do these differences relate to individual variability in sound-learning aptitude? We addressed these questions by assessing the sound-learning abilities of Dutch native speakers as they were trained on non-native tone contrasts. We used fMRI repetition suppression to the non-native tones to measure participants' neuronal processing efficiency before and after training. Although all participants improved in tone identification with training, there was large individual variability in learning performance. A repetition suppression effect to tone was found in the bilateral inferior frontal gyri (IFGs) before training. No whole-brain effect was found after training; a region-of-interest analysis, however, showed that, after training, repetition suppression to tone in the left IFG correlated positively with learning. That is, individuals who were better in learning the non-native tones showed larger repetition suppression in this area. Crucially, this was true even before training. These findings add to existing evidence that the left IFG plays an important role in sound learning and indicate that individual differences in learning aptitude stem from differences in the neuronal efficiency with which non-native sounds are processed. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Improving the Efficiency of Virtual Reality Training by Integrating Partly Observational Learning

ERIC Educational Resources Information Center

Yuviler-Gavish, Nirit; Rodríguez, Jorge; Gutiérrez, Teresa; Sánchez, Emilio; Casado, Sara

2014-01-01

The current study hypothesized that integrating partly observational learning into virtual reality training systems (VRTS) can enhance training efficiency for procedural tasks. A common approach in designing VRTS is the enactive approach, which stresses the importance of physical actions within the environment to enhance perception and improve…

Designing Strategies for an Efficient Language MOOC

ERIC Educational Resources Information Center

Perifanou, Maria

2016-01-01

The advent of Massive Open Online Courses (MOOCs) has dramatically changed the way people learn a language. But how can we design an efficient language learning environment for a massive number of learners? Are there any good practices that showcase successful Massive Open Online Language Course (MOOLC) design strategies? According to recent…

Energy Transformation: Teaching Youth about Energy Efficiency while Meeting Science Essential Standards

ERIC Educational Resources Information Center

Kirby, Sarah D.; Chilcote, Amy G.

2014-01-01

This article describes the Energy Transformation 4-H school enrichment curriculum. The curriculum addresses energy efficiency and conservation while meeting sixth-grade science essential standards requirements. Through experiential learning, including building and testing a model home, youth learn the relationship between various technologies and…

Development and implementation of a radiation therapy incident learning system compatible with local workflow and a national taxonomy.

PubMed

Montgomery, Logan; Fava, Palma; Freeman, Carolyn R; Hijal, Tarek; Maietta, Ciro; Parker, William; Kildea, John

2018-01-01

Collaborative incident learning initiatives in radiation therapy promise to improve and standardize the quality of care provided by participating institutions. However, the software interfaces provided with such initiatives must accommodate all participants and thus are not optimized for the workflows of individual radiation therapy centers. This article describes the development and implementation of a radiation therapy incident learning system that is optimized for a clinical workflow and uses the taxonomy of the Canadian National System for Incident Reporting - Radiation Treatment (NSIR-RT). The described incident learning system is a novel version of an open-source software called the Safety and Incident Learning System (SaILS). A needs assessment was conducted prior to development to ensure SaILS (a) was intuitive and efficient (b) met changing staff needs and (c) accommodated revisions to NSIR-RT. The core functionality of SaILS includes incident reporting, investigations, tracking, and data visualization. Postlaunch modifications of SaILS were informed by discussion and a survey of radiation therapy staff. There were 240 incidents detected and reported using SaILS in 2016 and the number of incidents per month tended to increase throughout the year. An increase in incident reporting occurred after switching to fully online incident reporting from an initial hybrid paper-electronic system. Incident templating functionality and a connection with our center's oncology information system were incorporated into the investigation interface to minimize repetitive data entry. A taskable actions feature was also incorporated to document outcomes of incident reports and has since been utilized for 36% of reported incidents. Use of SaILS and the NSIR-RT taxonomy has improved the structure of, and staff engagement with, incident learning in our center. Software and workflow modifications informed by staff feedback improved the utility of SaILS and yielded an efficient and transparent solution to categorize incidents with the NSIR-RT taxonomy. © 2017 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

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.

Deep Learning for Flow Sculpting: Insights into Efficient Learning using Scientific Simulation Data

NASA Astrophysics Data System (ADS)

Stoecklein, Daniel; Lore, Kin Gwn; Davies, Michael; Sarkar, Soumik; Ganapathysubramanian, Baskar

2017-04-01

A new technique for shaping microfluid flow, known as flow sculpting, offers an unprecedented level of passive fluid flow control, with potential breakthrough applications in advancing manufacturing, biology, and chemistry research at the microscale. However, efficiently solving the inverse problem of designing a flow sculpting device for a desired fluid flow shape remains a challenge. Current approaches struggle with the many-to-one design space, requiring substantial user interaction and the necessity of building intuition, all of which are time and resource intensive. Deep learning has emerged as an efficient function approximation technique for high-dimensional spaces, and presents a fast solution to the inverse problem, yet the science of its implementation in similarly defined problems remains largely unexplored. We propose that deep learning methods can completely outpace current approaches for scientific inverse problems while delivering comparable designs. To this end, we show how intelligent sampling of the design space inputs can make deep learning methods more competitive in accuracy, while illustrating their generalization capability to out-of-sample predictions.

Machine-Learning Approach for Design of Nanomagnetic-Based Antennas

NASA Astrophysics Data System (ADS)

Gianfagna, Carmine; Yu, Huan; Swaminathan, Madhavan; Pulugurtha, Raj; Tummala, Rao; Antonini, Giulio

2017-08-01

We propose a machine-learning approach for design of planar inverted-F antennas with a magneto-dielectric nanocomposite substrate. It is shown that machine-learning techniques can be efficiently used to characterize nanomagnetic-based antennas by accurately mapping the particle radius and volume fraction of the nanomagnetic material to antenna parameters such as gain, bandwidth, radiation efficiency, and resonant frequency. A modified mixing rule model is also presented. In addition, the inverse problem is addressed through machine learning as well, where given the antenna parameters, the corresponding design space of possible material parameters is identified.

Fast Sparse Coding for Range Data Denoising with Sparse Ridges Constraint

PubMed Central

Lao, Mingjie; Sang, Yongsheng; Wen, Fei; Zhai, Ruifang

2018-01-01

Light detection and ranging (LiDAR) sensors have been widely deployed on intelligent systems such as unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) to perform localization, obstacle detection, and navigation tasks. Thus, research into range data processing with competitive performance in terms of both accuracy and efficiency has attracted increasing attention. Sparse coding has revolutionized signal processing and led to state-of-the-art performance in a variety of applications. However, dictionary learning, which plays the central role in sparse coding techniques, is computationally demanding, resulting in its limited applicability in real-time systems. In this study, we propose sparse coding algorithms with a fixed pre-learned ridge dictionary to realize range data denoising via leveraging the regularity of laser range measurements in man-made environments. Experiments on both synthesized data and real data demonstrate that our method obtains accuracy comparable to that of sophisticated sparse coding methods, but with much higher computational efficiency. PMID:29734793

Integration and segregation of large-scale brain networks during short-term task automatization

PubMed Central

Mohr, Holger; Wolfensteller, Uta; Betzel, Richard F.; Mišić, Bratislav; Sporns, Olaf; Richiardi, Jonas; Ruge, Hannes

2016-01-01

The human brain is organized into large-scale functional networks that can flexibly reconfigure their connectivity patterns, supporting both rapid adaptive control and long-term learning processes. However, it has remained unclear how short-term network dynamics support the rapid transformation of instructions into fluent behaviour. Comparing fMRI data of a learning sample (N=70) with a control sample (N=67), we find that increasingly efficient task processing during short-term practice is associated with a reorganization of large-scale network interactions. Practice-related efficiency gains are facilitated by enhanced coupling between the cingulo-opercular network and the dorsal attention network. Simultaneously, short-term task automatization is accompanied by decreasing activation of the fronto-parietal network, indicating a release of high-level cognitive control, and a segregation of the default mode network from task-related networks. These findings suggest that short-term task automatization is enabled by the brain's ability to rapidly reconfigure its large-scale network organization involving complementary integration and segregation processes. PMID:27808095

Collaborative testing for key-term definitions under representative conditions: Efficiency costs and no learning benefits.

PubMed

Wissman, Kathryn T; Rawson, Katherine A

2018-01-01

Students are expected to learn key-term definitions across many different grade levels and academic disciplines. Thus, investigating ways to promote understanding of key-term definitions is of critical importance for applied purposes. A recent survey showed that learners report engaging in collaborative practice testing when learning key-term definitions, with outcomes also shedding light on the way in which learners report engaging in collaborative testing in real-world contexts (Wissman & Rawson, 2016, Memory, 24, 223-239). However, no research has directly explored the effectiveness of engaging in collaborative testing under representative conditions. Accordingly, the current research evaluates the costs (with respect to efficiency) and the benefits (with respect to learning) of collaborative testing for key-term definitions under representative conditions. In three experiments (ns = 94, 74, 95), learners individually studied key-term definitions and then completed retrieval practice, which occurred either individually or collaboratively (in dyads). Two days later, all learners completed a final individual test. Results from Experiments 1-2 showed a cost (with respect to efficiency) and no benefit (with respect to learning) of engaging in collaborative testing for key-term definitions. Experiment 3 evaluated a theoretical explanation for why collaborative benefits do not emerge under representative conditions. Collectively, outcomes indicate that collaborative testing versus individual testing is less effective and less efficient when learning key-term definitions under representative conditions.

Human Machine Learning Symbiosis

ERIC Educational Resources Information Center

Walsh, Kenneth R.; Hoque, Md Tamjidul; Williams, Kim H.

2017-01-01

Human Machine Learning Symbiosis is a cooperative system where both the human learner and the machine learner learn from each other to create an effective and efficient learning environment adapted to the needs of the human learner. Such a system can be used in online learning modules so that the modules adapt to each learner's learning state both…

Time-Decayed User Profile for Second Language Vocabulary Learning System

ERIC Educational Resources Information Center

Li, Li; Wei, Xiao

2014-01-01

Vocabulary learning is the foundation of second language learning. Many E-learning systems have been developed to help learners to learn vocabulary efficiently. Most of these systems employ Ebbinghaus Forgetting Curve to make the review schedule for learners. However, learners are different in learning ability and the review schedule based on…

Commodity durability, trader specialization, and market performance

PubMed Central

Dickhaut, John; Lin, Shengle; Porter, David; Smith, Vernon

2012-01-01

The original double auction studies of supply and demand markets established their strong efficiency and equilibrium convergence behavior using economically unsophisticated and untrained subjects. The results were unexpected because all individual costs and values were private and dependent entirely on the market trading process to aggregate the dispersed information into socially desirable outcomes. The exchange environment, however, corresponded to that of perishable, and not re-traded goods in which participants were specialized as buyers or sellers. We report experiments in repeated single-period markets where tradability, and buyer-seller role specialization, is varied by imposing or relaxing a restriction on re-trade within each period. In re-trade markets scope is given to speculative motives unavailable where goods perish on purchase. We observe greatly increased trade volume and decreased efficiency but subject experience increases efficiency. Observed speculation slows convergence by impeding the process whereby individuals learn from the market whether their private circumstances lead them to specialize as buyers or sellers. PMID:22307595

Increasing Touch-Keyboarding Skills in the Middle School Student: "KeyWords" vs. "Type To Learn," Hand Covers vs. No Hand Covers.

ERIC Educational Resources Information Center

Reagan, Steven Dallas

A computer teacher in a middle school in East Tennessee observed that his students were entering the middle school program with computer familiarity but without the touch keyboarding skills necessary to operate the computer efficiently. It was also observed that even with instruction and practice using drill and practice keyboarding software, the…

Teaching Business Statistics with Real Data to Undergraduates and the Use of Technology in the Class Room

ERIC Educational Resources Information Center

Singamsetti, Rao

2007-01-01

In this paper an attempt is made to highlight some issues of interpretation of statistical concepts and interpretation of results as taught in undergraduate Business statistics courses. The use of modern technology in the class room is shown to have increased the efficiency and the ease of learning and teaching in statistics. The importance of…

Investigating the Use of Text Messages in Mobile Learning

ERIC Educational Resources Information Center

Geng, Gretchen

2013-01-01

Nowadays, teaching and learning have been shifted from traditional classrooms to technology-supported learning environment. By offering a convenient, efficient and financially affordable information technology learning environment, mobile learning is a topic that is of considerable interest for education audiences owing to the pervasive nature of…

A neurocomputational account of taxonomic responding and fast mapping in early word learning.

PubMed

Mayor, Julien; Plunkett, Kim

2010-01-01

We present a neurocomputational model with self-organizing maps that accounts for the emergence of taxonomic responding and fast mapping in early word learning, as well as a rapid increase in the rate of acquisition of words observed in late infancy. The quality and efficiency of generalization of word-object associations is directly related to the quality of prelexical, categorical representations in the model. We show how synaptogenesis supports coherent generalization of word-object associations and show that later synaptic pruning minimizes metabolic costs without being detrimental to word learning. The role played by joint-attentional activities is identified in the model, both at the level of selecting efficient cross-modal synapses and at the behavioral level, by accelerating and refining overall vocabulary acquisition. The model can account for the qualitative shift in the way infants use words, from an associative to a referential-like use, for the pattern of overextension errors in production and comprehension observed during early childhood and typicality effects observed in lexical development. Interesting by-products of the model include a potential explanation of the shift from prototype to exemplar-based effects reported for adult category formation, an account of mispronunciation effects in early lexical development, and extendability to include accounts of individual differences in lexical development and specific disorders such as Williams syndrome. The model demonstrates how an established constraint on lexical learning, which has often been regarded as domain-specific, can emerge from domain-general learning principles that are simultaneously biologically, psychologically, and socially plausible.

Fast Gaussian kernel learning for classification tasks based on specially structured global optimization.

PubMed

Zhong, Shangping; Chen, Tianshun; He, Fengying; Niu, Yuzhen

2014-09-01

For a practical pattern classification task solved by kernel methods, the computing time is mainly spent on kernel learning (or training). However, the current kernel learning approaches are based on local optimization techniques, and hard to have good time performances, especially for large datasets. Thus the existing algorithms cannot be easily extended to large-scale tasks. In this paper, we present a fast Gaussian kernel learning method by solving a specially structured global optimization (SSGO) problem. We optimize the Gaussian kernel function by using the formulated kernel target alignment criterion, which is a difference of increasing (d.i.) functions. Through using a power-transformation based convexification method, the objective criterion can be represented as a difference of convex (d.c.) functions with a fixed power-transformation parameter. And the objective programming problem can then be converted to a SSGO problem: globally minimizing a concave function over a convex set. The SSGO problem is classical and has good solvability. Thus, to find the global optimal solution efficiently, we can adopt the improved Hoffman's outer approximation method, which need not repeat the searching procedure with different starting points to locate the best local minimum. Also, the proposed method can be proven to converge to the global solution for any classification task. We evaluate the proposed method on twenty benchmark datasets, and compare it with four other Gaussian kernel learning methods. Experimental results show that the proposed method stably achieves both good time-efficiency performance and good classification performance. Copyright © 2014 Elsevier Ltd. All rights reserved.

Alignment-free genome tree inference by learning group-specific distance metrics.

PubMed

Patil, Kaustubh R; McHardy, Alice C

2013-01-01

Understanding the evolutionary relationships between organisms is vital for their in-depth study. Gene-based methods are often used to infer such relationships, which are not without drawbacks. One can now attempt to use genome-scale information, because of the ever increasing number of genomes available. This opportunity also presents a challenge in terms of computational efficiency. Two fundamentally different methods are often employed for sequence comparisons, namely alignment-based and alignment-free methods. Alignment-free methods rely on the genome signature concept and provide a computationally efficient way that is also applicable to nonhomologous sequences. The genome signature contains evolutionary signal as it is more similar for closely related organisms than for distantly related ones. We used genome-scale sequence information to infer taxonomic distances between organisms without additional information such as gene annotations. We propose a method to improve genome tree inference by learning specific distance metrics over the genome signature for groups of organisms with similar phylogenetic, genomic, or ecological properties. Specifically, our method learns a Mahalanobis metric for a set of genomes and a reference taxonomy to guide the learning process. By applying this method to more than a thousand prokaryotic genomes, we showed that, indeed, better distance metrics could be learned for most of the 18 groups of organisms tested here. Once a group-specific metric is available, it can be used to estimate the taxonomic distances for other sequenced organisms from the group. This study also presents a large scale comparison between 10 methods--9 alignment-free and 1 alignment-based.

Online CKD education for medical students, residents, and fellows: training in a new era.

PubMed

Bhasin, Bhavna; Estrella, Michelle M; Choi, Michael J

2013-07-01

CKD and its complications are associated with substantial morbidity and mortality. Studies have highlighted significant deficiencies in resident knowledge and awareness of CKD and its complications. There is a need to improve CKD education through medical school and residency. There is also a need to provide alternatives to traditional teaching methods to meet the challenges of learning in the context of work-hour restrictions and increasing workload among residents and fellows. Internet-based learning resources offer various educational tools, including websites, kidney blogs, online modules, and smartphone applications, which could potentially and efficiently advance CKD knowledge among medical trainees. In this review, we describe several online resources for CKD education that could be useful for medical students, residents, and fellows. Increased awareness of these tools and their utilization may significantly influence and hopefully improve the recognition and management of patients with CKD. Future studies may help evaluate the effectiveness of these online learning methods and their effect on CKD patient outcomes. In addition, in light of increased concern about nephrology workforce issues, the potential for these online tools to augment interest in nephrology careers should be investigated. Copyright © 2013 National Kidney Foundation, Inc. Published by Elsevier Inc. All rights reserved.

Memory as discrimination: a challenge to the encoding-retrieval match principle.

PubMed

Poirier, Marie; Nairne, James S; Morin, Caroline; Zimmermann, Friederike G S; Koutmeridou, Kyriaki; Fowler, James

2012-01-01

Four experiments contrasted the predictions of a general encoding-retrieval match hypothesis with those of a view claiming that the distinctiveness of the cue-target relationship is the causal factor in retrieval. In Experiments 1, 2, and 4 participants learned the relationships between 4 targets and trios of cues; in Experiment 3 there were 3 targets, each associated with a pair of cues. A learning phase was followed by a cued-recognition task where the correct target had to be identified based on 1 or more of the cues. The main performance measurement was response time. Learning was designed to lead to high accuracy so effects could be attributed to retrieval efficiency rather than to variations in encoding. The nature of the cues and targets was varied across experiments. The critical factor was whether each cue was uniquely associated with the to-be-recalled target. All experiments orthogonally manipulated (a) how discriminative-or uniquely associated with a target-each cue was and (b) the degree of overlap between the cues present during learning and those present at retrieval. The novel finding reported here is that increasing the encoding-retrieval match can hinder performance if the increase simultaneously reduces how well cues predict a target-that is, a cue's diagnostic value. Encoding-retrieval match was not the factor that determined the effectiveness of retrieval. Our findings suggest that increasing the encoding-retrieval match can lead to no change, an increase, or a decrease in retrieval performance.

Social organization and the evolution of cumulative technology in apes and hominins.

PubMed

Pradhan, Gauri R; Tennie, Claudio; van Schaik, Carel P

2012-07-01

Culturally supported accumulation (or ratcheting) of technological complexity is widely seen as characterizing hominin technology relative to that of the extant great apes, and thus as representing a threshold in cultural evolution. To explain this divide, we modeled the process of cultural accumulation of technology, which we defined as adding new actions to existing ones to create new functional combinations, based on a model for great ape tool use. The model shows that intraspecific and interspecific variation in the presence of simple and cumulative technology among extant orangutans and chimpanzees is largely due to variation in sociability, and hence opportunities for social learning. The model also suggests that the adoption of extensive allomaternal care (cooperative breeding) in early Pleistocene Homo, which led to an increase in sociability and to teaching, and hence increased efficiency of social learning, was enough to facilitate technological ratcheting. Hence, socioecological changes, rather than advances in cognitive abilities, can account for the cumulative cultural changes seen until the origin of the Acheulean. The consequent increase in the reliance on technology could have served as the pacemaker for increased cognitive abilities. Our results also suggest that a more important watershed in cultural evolution was the rise of donated culture (technology or concepts), in which technology or concepts was transferred to naïve individuals, allowing them to skip many learning steps, and specialization arose, which allowed individuals to learn only a subset of the population's skills. Copyright © 2012 Elsevier Ltd. All rights reserved.

Intrinsic dimensionality predicts the saliency of natural dynamic scenes.

PubMed

Vig, Eleonora; Dorr, Michael; Martinetz, Thomas; Barth, Erhardt

2012-06-01

Since visual attention-based computer vision applications have gained popularity, ever more complex, biologically inspired models seem to be needed to predict salient locations (or interest points) in naturalistic scenes. In this paper, we explore how far one can go in predicting eye movements by using only basic signal processing, such as image representations derived from efficient coding principles, and machine learning. To this end, we gradually increase the complexity of a model from simple single-scale saliency maps computed on grayscale videos to spatiotemporal multiscale and multispectral representations. Using a large collection of eye movements on high-resolution videos, supervised learning techniques fine-tune the free parameters whose addition is inevitable with increasing complexity. The proposed model, although very simple, demonstrates significant improvement in predicting salient locations in naturalistic videos over four selected baseline models and two distinct data labeling scenarios.

Self-learning Monte Carlo method

DOE PAGES

Liu, Junwei; Qi, Yang; Meng, Zi Yang; ...

2017-01-04

Monte Carlo simulation is an unbiased numerical tool for studying classical and quantum many-body systems. One of its bottlenecks is the lack of a general and efficient update algorithm for large size systems close to the phase transition, for which local updates perform badly. In this Rapid Communication, we propose a general-purpose Monte Carlo method, dubbed self-learning Monte Carlo (SLMC), in which an efficient update algorithm is first learned from the training data generated in trial simulations and then used to speed up the actual simulation. Lastly, we demonstrate the efficiency of SLMC in a spin model at the phasemore » transition point, achieving a 10–20 times speedup.« less

Incremental learning of skill collections based on intrinsic motivation

PubMed Central

Metzen, Jan H.; Kirchner, Frank

2013-01-01

Life-long learning of reusable, versatile skills is a key prerequisite for embodied agents that act in a complex, dynamic environment and are faced with different tasks over their lifetime. We address the question of how an agent can learn useful skills efficiently during a developmental period, i.e., when no task is imposed on him and no external reward signal is provided. Learning of skills in a developmental period needs to be incremental and self-motivated. We propose a new incremental, task-independent skill discovery approach that is suited for continuous domains. Furthermore, the agent learns specific skills based on intrinsic motivation mechanisms that determine on which skills learning is focused at a given point in time. We evaluate the approach in a reinforcement learning setup in two continuous domains with complex dynamics. We show that an intrinsically motivated, skill learning agent outperforms an agent which learns task solutions from scratch. Furthermore, we compare different intrinsic motivation mechanisms and how efficiently they make use of the agent's developmental period. PMID:23898265

Peer Learning Network: Implementing and Sustaining Cooperative Learning by Teacher Collaboration

ERIC Educational Resources Information Center

Miquel, Ester; Duran, David

2017-01-01

This article describes an in-service teachers', staff-development model "Peer Learning Network" and presents results about its efficiency. "Peer Learning Network" promotes three levels of peer learning simultaneously (among pupils, teachers, and schools). It supports pairs of teachers from several schools, who are linked…

Learning Potential Tests for Ethnic Minorities.

ERIC Educational Resources Information Center

Hamers, J. H. M.; Pennings, A. H.

1995-01-01

This article presents the results of three studies in the learning-potential testing of children from minority groups: (1) Test of Children's Learning Ability; (2) Learning Efficiency Battery; and (3) Learning Potential Test for Ethnic Minorities. It concludes that, although the development of culture-free tests is impossible, it is possible to…

Learning Path Recommendation Based on Modified Variable Length Genetic Algorithm

ERIC Educational Resources Information Center

Dwivedi, Pragya; Kant, Vibhor; Bharadwaj, Kamal K.

2018-01-01

With the rapid advancement of information and communication technologies, e-learning has gained a considerable attention in recent years. Many researchers have attempted to develop various e-learning systems with personalized learning mechanisms for assisting learners so that they can learn more efficiently. In this context, curriculum sequencing…

Efficiency of goal-oriented communicating agents in different graph topologies: A study with Internet crawlers

NASA Astrophysics Data System (ADS)

Lőrincz, András; Lázár, Katalin A.; Palotai, Zsolt

2007-05-01

To what extent does the communication make a goal-oriented community efficient in different topologies? In order to gain insight into this problem, we study the influence of learning method as well as that of the topology of the environment on the communication efficiency of crawlers in quest of novel information in different topics on the Internet. Individual crawlers employ selective learning, function approximation-based reinforcement learning (RL), and their combination. Selective learning, in effect, modifies the starting URL lists of the crawlers, whilst RL alters the URL orderings. Real data have been collected from the web and scale-free worlds, scale-free small world (SFSW), and random world environments (RWEs) have been created by link reorganization. In our previous experiments [ Zs. Palotai, Cs. Farkas, A. Lőrincz, Is selection optimal in scale-free small worlds?, ComPlexUs 3 (2006) 158-168], the crawlers searched for novel, genuine documents and direct communication was not possible. Herein, our finding is reproduced: selective learning performs the best and RL the worst in SFSW, whereas the combined, i.e., selective learning coupled with RL is the best-by a slight margin-in scale-free worlds. This effect is demonstrated to be more pronounced when the crawlers search for different topic-specific documents: the relative performance of the combined learning algorithm improves in all worlds, i.e., in SFSW, in SFW, and in RWE. If the tasks are more complex and the work sharing is enforced by the environment then the combined learning algorithm becomes at least equal, even superior to both the selective and the RL algorithms in most cases, irrespective of the efficiency of communication. Furthermore, communication improves the performance by a large margin and adaptive communication is advantageous in the majority of the cases.

Students' Metacognitive Awareness and Physics Learning Efficiency and Correlation between Them

ERIC Educational Resources Information Center

Bogdanovic, Ivana; Obadovic, Dušanka Ž.; Cvjeticanin, Stanko; Segedinac, Mirjana; Budic, Spomenka

2015-01-01

This paper presents a research directed to examine the relation between students' metacognitive awareness and physics learning efficiency. Questionnaire of metacognitive awareness and physics knowledge test were applied on the sample of 746 subjects of both sexes, first graders of Grammar Schools in Novi Sad, Republic of Serbia. Obtained results…

Learning Efficiency of Two ICT-Based Instructional Strategies in Greek Sheep Farmers

ERIC Educational Resources Information Center

Bellos, Georgios; Mikropoulos, Tassos A.; Deligeorgis, Stylianos; Kominakis, Antonis

2016-01-01

Purpose: The objective of the present study was to compare the learning efficiency of two information and communications technology (ICT)-based instructional strategies (multimedia presentation (MP) and concept mapping) in a sample (n = 187) of Greek sheep farmers operating mainly in Western Greece. Design/methodology/approach: In total, 15…

A Study on the Learning Efficiency of Multimedia-Presented, Computer-Based Science Information

ERIC Educational Resources Information Center

Guan, Ying-Hua

2009-01-01

This study investigated the effects of multimedia presentations on the efficiency of learning scientific information (i.e. information on basic anatomy of human brains and their functions, the definition of cognitive psychology, and the structure of human memory). Experiment 1 investigated whether the modality effect could be observed when the…

A Study of the Effectiveness of Teaching Listening.

ERIC Educational Resources Information Center

Broiles, Mack R.

Listening, the most efficient means of learning in the early grades, is replaced by reading as an efficient method for learning after the seventh grade. For an investigation of the effectiveness with which college students may be taught listening, lesson plans were developed from a programed instruction book --Principles of Selective Listening--…

A Truthful Incentive Mechanism for Online Recruitment in Mobile Crowd Sensing System.

PubMed

Chen, Xiao; Liu, Min; Zhou, Yaqin; Li, Zhongcheng; Chen, Shuang; He, Xiangnan

2017-01-01

We investigate emerging mobile crowd sensing (MCS) systems, in which new cloud-based platforms sequentially allocate homogenous sensing jobs to dynamically-arriving users with uncertain service qualities. Given that human beings are selfish in nature, it is crucial yet challenging to design an efficient and truthful incentive mechanism to encourage users to participate. To address the challenge, we propose a novel truthful online auction mechanism that can efficiently learn to make irreversible online decisions on winner selections for new MCS systems without requiring previous knowledge of users. Moreover, we theoretically prove that our incentive possesses truthfulness, individual rationality and computational efficiency. Extensive simulation results under both real and synthetic traces demonstrate that our incentive mechanism can reduce the payment of the platform, increase the utility of the platform and social welfare.

Learning SAS’s Perl Regular Expression Matching the Easy Way: By Doing

DTIC Science & Technology

2015-01-12

Doing 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Paul Genovesi 5d. PROJECT NUMBER 5e. TASK NUMBER 5f...regex_learning_tool allows both beginner and expert to efficiently practice PRX matching by selecting and processing only the match records that the user is interested...perl regular expression and/or source string. The regex_learning_tool allows both beginner and expert to efficiently practice PRX matching by

The study of selective property of college student’s learning space

NASA Astrophysics Data System (ADS)

Nagai, Mizuki; Matsumoto, Yuji; Naka, Ryusuke

2018-05-01

These days, college students study not only at places designed for learning such as libraries in colleges, but also cafes in downtown while the number of facilities for learning run by colleges is increasing. Then I have researched facilities in college and those in downtown to find selective properties of college students’ learning space. First, I found by questionnaire survey that students chose “3rd place” such as cafes and fast food shops, second to their houses and libraries in college. Next, I found “psychological factor” were also affected their choice. Furthermore, they studied different subjects at different places. In experiments, I researched how effectively they studied each subject at every place. The results show that I find that places you like and places where learning efficiency is good are different. They learned the least effective at “3d place” regardless of what they learned. The result of how long they kept high-level intellectual activity at each place shows that they could work on the study with more motivation at their favorite place and 3rd place. On the other hand, at the 2nd place, they could study rather effectively, but could not keep concentration and motivation for a long time. In this way, college students have 2 patterns of choosing learning space.

A Neural Network Model to Learn Multiple Tasks under Dynamic Environments

NASA Astrophysics Data System (ADS)

Tsumori, Kenji; Ozawa, Seiichi

When environments are dynamically changed for agents, the knowledge acquired in an environment might be useless in future. In such dynamic environments, agents should be able to not only acquire new knowledge but also modify old knowledge in learning. However, modifying all knowledge acquired before is not efficient because the knowledge once acquired may be useful again when similar environment reappears and some knowledge can be shared among different environments. To learn efficiently in such environments, we propose a neural network model that consists of the following modules: resource allocating network, long-term & short-term memory, and environment change detector. We evaluate the model under a class of dynamic environments where multiple function approximation tasks are sequentially given. The experimental results demonstrate that the proposed model possesses stable incremental learning, accurate environmental change detection, proper association and recall of old knowledge, and efficient knowledge transfer.

Learning to Predict Combinatorial Structures

NASA Astrophysics Data System (ADS)

Vembu, Shankar

2009-12-01

The major challenge in designing a discriminative learning algorithm for predicting structured data is to address the computational issues arising from the exponential size of the output space. Existing algorithms make different assumptions to ensure efficient, polynomial time estimation of model parameters. For several combinatorial structures, including cycles, partially ordered sets, permutations and other graph classes, these assumptions do not hold. In this thesis, we address the problem of designing learning algorithms for predicting combinatorial structures by introducing two new assumptions: (i) The first assumption is that a particular counting problem can be solved efficiently. The consequence is a generalisation of the classical ridge regression for structured prediction. (ii) The second assumption is that a particular sampling problem can be solved efficiently. The consequence is a new technique for designing and analysing probabilistic structured prediction models. These results can be applied to solve several complex learning problems including but not limited to multi-label classification, multi-category hierarchical classification, and label ranking.

Tensorial dynamic time warping with articulation index representation for efficient audio-template learning.

PubMed

Le, Long N; Jones, Douglas L

2018-03-01

Audio classification techniques often depend on the availability of a large labeled training dataset for successful performance. However, in many application domains of audio classification (e.g., wildlife monitoring), obtaining labeled data is still a costly and laborious process. Motivated by this observation, a technique is proposed to efficiently learn a clean template from a few labeled, but likely corrupted (by noise and interferences), data samples. This learning can be done efficiently via tensorial dynamic time warping on the articulation index-based time-frequency representations of audio data. The learned template can then be used in audio classification following the standard template-based approach. Experimental results show that the proposed approach outperforms both (1) the recurrent neural network approach and (2) the state-of-the-art in the template-based approach on a wildlife detection application with few training samples.

Crack Damage Detection Method via Multiple Visual Features and Efficient Multi-Task Learning Model.

PubMed

Wang, Baoxian; Zhao, Weigang; Gao, Po; Zhang, Yufeng; Wang, Zhe

2018-06-02

This paper proposes an effective and efficient model for concrete crack detection. The presented work consists of two modules: multi-view image feature extraction and multi-task crack region detection. Specifically, multiple visual features (such as texture, edge, etc.) of image regions are calculated, which can suppress various background noises (such as illumination, pockmark, stripe, blurring, etc.). With the computed multiple visual features, a novel crack region detector is advocated using a multi-task learning framework, which involves restraining the variability for different crack region features and emphasizing the separability between crack region features and complex background ones. Furthermore, the extreme learning machine is utilized to construct this multi-task learning model, thereby leading to high computing efficiency and good generalization. Experimental results of the practical concrete images demonstrate that the developed algorithm can achieve favorable crack detection performance compared with traditional crack detectors.

Imitation learning based on an intrinsic motivation mechanism for efficient coding

PubMed Central

Triesch, Jochen

2013-01-01

A hypothesis regarding the development of imitation learning is presented that is rooted in intrinsic motivations. It is derived from a recently proposed form of intrinsically motivated learning (IML) for efficient coding in active perception, wherein an agent learns to perform actions with its sense organs to facilitate efficient encoding of the sensory data. To this end, actions of the sense organs that improve the encoding of the sensory data trigger an internally generated reinforcement signal. Here it is argued that the same IML mechanism might also support the development of imitation when general actions beyond those of the sense organs are considered: The learner first observes a tutor performing a behavior and learns a model of the the behavior's sensory consequences. The learner then acts itself and receives an internally generated reinforcement signal reflecting how well the sensory consequences of its own behavior are encoded by the sensory model. Actions that are more similar to those of the tutor will lead to sensory signals that are easier to encode and produce a higher reinforcement signal. Through this, the learner's behavior is progressively tuned to make the sensory consequences of its actions match the learned sensory model. I discuss this mechanism in the context of human language acquisition and bird song learning where similar ideas have been proposed. The suggested mechanism also offers an account for the development of mirror neurons and makes a number of predictions. Overall, it establishes a connection between principles of efficient coding, intrinsic motivations and imitation. PMID:24204350

Efficient and Effective Use of Peer Teaching for Medical Student Simulation.

PubMed

House, Joseph B; Choe, Carol H; Wourman, Heather L; Berg, Kristin M; Fischer, Jonathan P; Santen, Sally A

2017-01-01

Simulation is increasingly used in medical education, promoting active learning and retention; however, increasing use also requires considerable instructor resources. Simulation may provide a safe environment for students to teach each other, which many will need to do when they enter residency. Along with reinforcing learning and increasing retention, peer teaching could decrease instructor demands. Our objective was to determine the effectiveness of peer-taught simulation compared to physician-led simulation. We hypothesized that peer-taught simulation would lead to equivalent knowledge acquisition when compared to physician-taught sessions and would be viewed positively by participants. This was a quasi-experimental study in an emergency medicine clerkship. The control group was faculty taught. In the peer-taught intervention group, students were assigned to teach one of the three simulation-based medical emergency cases. Each student was instructed to master their topic and teach it to their peers using the provided objectives and resource materials. The students were assigned to groups of three, with all three cases represented; students took turns leading their case. Three groups ran simultaneously. During the intervention sessions, one physician was present to monitor the accuracy of learning and to answer questions, while three physicians were required for the control groups. Outcomes compared pre-test and post-test knowledge and student reaction between control and intervention groups. Both methods led to equally improved knowledge; mean score for the post-test was 75% for both groups (p=0.6) and were viewed positively. Students in the intervention group agreed that peer-directed learning was an effective way to learn. However, students in the control group scored their simulation experience more favorably. In general, students' response to peer teaching was positive, students learned equally well, and found peer-taught sessions to be interactive and beneficial.

Distance learning and the internet in respiratory therapy education.

PubMed

Varekojis, Sarah M; Sergakis, Georgianna G; Dunlevy, Crystal L; Foote, Elbie; Clutter, Jill

2011-11-01

The profession of respiratory therapy (RT) continues to grow both in number, due to population growth and an ever-increasing aging population, and scope of practice, due to both new and expanded roles and responsibilities in divergent areas of clinical practice. Instructional technology, including distance learning, will probably play a key role in training, educating, and assessing RT students to meet the increasing demand for practitioners. To assess current uses of distance learning and opinions concerning the appropriate use of distance education in RT education programs nationwide. A 13-item on-line survey was designed to collect information about the frequency of use of various types of distance education typically utilized in RT education programs. The survey was sent to directors of 343 Committee on Accreditation for Respiratory Care accredited programs of RT education that offer entry-level or advanced courses of study. The response rate was 50% (169 respondents). Fifty-two percent of the respondents indicated that their courses included some form of on-line learning component. Most directors anticipated that the distance composition of their course offerings will remain unchanged or increase in the near future. Our results indicate that, while distance education plays an important supportive role in RT education, there is still a preference for face-to-face instruction and Internet-facilitated courses among program directors. Program directors continue to view the laboratory and clinical settings as hands-on environments that require instructor supervision in order for students to demonstrate proficiency and critical thinking skills. When used appropriately, distance learning may be an efficient and effective approach to address the many barriers to education faced by the health workforce in general, including budget constraints, overloaded schedules, the need for on-the-job learning opportunities, and lack of access.

Self-paced model learning for robust visual tracking

NASA Astrophysics Data System (ADS)

Huang, Wenhui; Gu, Jason; Ma, Xin; Li, Yibin

2017-01-01

In visual tracking, learning a robust and efficient appearance model is a challenging task. Model learning determines both the strategy and the frequency of model updating, which contains many details that could affect the tracking results. Self-paced learning (SPL) has recently been attracting considerable interest in the fields of machine learning and computer vision. SPL is inspired by the learning principle underlying the cognitive process of humans, whose learning process is generally from easier samples to more complex aspects of a task. We propose a tracking method that integrates the learning paradigm of SPL into visual tracking, so reliable samples can be automatically selected for model learning. In contrast to many existing model learning strategies in visual tracking, we discover the missing link between sample selection and model learning, which are combined into a single objective function in our approach. Sample weights and model parameters can be learned by minimizing this single objective function. Additionally, to solve the real-valued learning weight of samples, an error-tolerant self-paced function that considers the characteristics of visual tracking is proposed. We demonstrate the robustness and efficiency of our tracker on a recent tracking benchmark data set with 50 video sequences.

Hemispheric dominance underlying the neural substrate for learned vocalizations develops with experience.

PubMed

Chirathivat, Napim; Raja, Sahitya C; Gobes, Sharon M H

2015-06-22

Many aspects of song learning in songbirds resemble characteristics of speech acquisition in humans. Genetic, anatomical and behavioural parallels have most recently been extended with demonstrated similarities in hemispheric dominance between humans and songbirds: the avian higher order auditory cortex is left-lateralized for processing song memories in juvenile zebra finches that already have formed a memory of their fathers' song, just like Wernicke's area in the left hemisphere of the human brain is dominant for speech perception. However, it is unclear if hemispheric specialization is due to pre-existing functional asymmetry or the result of learning itself. Here we show that in juvenile male and female zebra finches that had never heard an adult song before, neuronal activation after initial exposure to a conspecific song is bilateral. Thus, like in humans, hemispheric dominance develops with vocal proficiency. A left-lateralized functional system that develops through auditory-vocal learning may be an evolutionary adaptation that could increase the efficiency of transferring information within one hemisphere, benefiting the production and perception of learned communication signals.

Hemispheric dominance underlying the neural substrate for learned vocalizations develops with experience

PubMed Central

Chirathivat, Napim; Raja, Sahitya C.; Gobes, Sharon M. H.

2015-01-01

Many aspects of song learning in songbirds resemble characteristics of speech acquisition in humans. Genetic, anatomical and behavioural parallels have most recently been extended with demonstrated similarities in hemispheric dominance between humans and songbirds: the avian higher order auditory cortex is left-lateralized for processing song memories in juvenile zebra finches that already have formed a memory of their fathers’ song, just like Wernicke’s area in the left hemisphere of the human brain is dominant for speech perception. However, it is unclear if hemispheric specialization is due to pre-existing functional asymmetry or the result of learning itself. Here we show that in juvenile male and female zebra finches that had never heard an adult song before, neuronal activation after initial exposure to a conspecific song is bilateral. Thus, like in humans, hemispheric dominance develops with vocal proficiency. A left-lateralized functional system that develops through auditory-vocal learning may be an evolutionary adaptation that could increase the efficiency of transferring information within one hemisphere, benefiting the production and perception of learned communication signals. PMID:26098840

Optimizing support vector machine learning for semi-arid vegetation mapping by using clustering analysis

NASA Astrophysics Data System (ADS)

Su, Lihong

In remote sensing communities, support vector machine (SVM) learning has recently received increasing attention. SVM learning usually requires large memory and enormous amounts of computation time on large training sets. According to SVM algorithms, the SVM classification decision function is fully determined by support vectors, which compose a subset of the training sets. In this regard, a solution to optimize SVM learning is to efficiently reduce training sets. In this paper, a data reduction method based on agglomerative hierarchical clustering is proposed to obtain smaller training sets for SVM learning. Using a multiple angle remote sensing dataset of a semi-arid region, the effectiveness of the proposed method is evaluated by classification experiments with a series of reduced training sets. The experiments show that there is no loss of SVM accuracy when the original training set is reduced to 34% using the proposed approach. Maximum likelihood classification (MLC) also is applied on the reduced training sets. The results show that MLC can also maintain the classification accuracy. This implies that the most informative data instances can be retained by this approach.

Students' perceptions of clinical teaching and learning strategies: a Pakistani perspective.

PubMed

Khan, Basnama Ayaz; Ali, Fauziya; Vazir, Nilofar; Barolia, Rubina; Rehan, Seema

2012-01-01

The complexity of the health care environment is increasing with the explosion of technology, coupled with the issues of patients' access, equity, time efficiency, and cost containment. Nursing education must focus on means that enable students to develop the processes of active learning, problem-solving, and critical thinking, in order to enable them to deal with the complexities. This study aims at identifying the nursing students' perceptions about the effectiveness of utilized teaching and learning strategies of clinical education, in improving students' knowledge, skills, and attitudes. A descriptive cross sectional study design was utilized using both qualitative and quantitative approaches. Data were collected from 74 students, using a questionnaire that was developed for the purpose of the study and analyzed using descriptive and non-parametric statistics. The findings revealed that demonstration was the most effective strategy for improving students' skills; reflection, for improving attitudes; and problem based learning and concept map for improving their knowledge. Students' responses to open-ended questions confirmed the effectiveness of these strategies in improving their learning outcomes. Recommendations have been provided based on the findings. Copyright © 2011 Elsevier Ltd. All rights reserved.

Incidental learning speeds visual search by lowering response thresholds, not by improving efficiency: Evidence from eye movements

PubMed Central

Hout, Michael C.; Goldinger, Stephen D.

2011-01-01

When observers search for a target object, they incidentally learn the identities and locations of “background” objects in the same display. This learning can facilitate search performance, eliciting faster reaction times for repeated displays (Hout & Goldinger, 2010). Despite these findings, visual search has been successfully modeled using architectures that maintain no history of attentional deployments; they are amnesic (e.g., Guided Search Theory; Wolfe, 2007). In the current study, we asked two questions: 1) under what conditions does such incidental learning occur? And 2) what does viewing behavior reveal about the efficiency of attentional deployments over time? In two experiments, we tracked eye movements during repeated visual search, and we tested incidental memory for repeated non-target objects. Across conditions, the consistency of search sets and spatial layouts were manipulated to assess their respective contributions to learning. Using viewing behavior, we contrasted three potential accounts for faster searching with experience. The results indicate that learning does not result in faster object identification or greater search efficiency. Instead, familiar search arrays appear to allow faster resolution of search decisions, whether targets are present or absent. PMID:21574743

Semi-supervised and unsupervised extreme learning machines.

PubMed

Huang, Gao; Song, Shiji; Gupta, Jatinder N D; Wu, Cheng

2014-12-01

Extreme learning machines (ELMs) have proven to be efficient and effective learning mechanisms for pattern classification and regression. However, ELMs are primarily applied to supervised learning problems. Only a few existing research papers have used ELMs to explore unlabeled data. In this paper, we extend ELMs for both semi-supervised and unsupervised tasks based on the manifold regularization, thus greatly expanding the applicability of ELMs. The key advantages of the proposed algorithms are as follows: 1) both the semi-supervised ELM (SS-ELM) and the unsupervised ELM (US-ELM) exhibit learning capability and computational efficiency of ELMs; 2) both algorithms naturally handle multiclass classification or multicluster clustering; and 3) both algorithms are inductive and can handle unseen data at test time directly. Moreover, it is shown in this paper that all the supervised, semi-supervised, and unsupervised ELMs can actually be put into a unified framework. This provides new perspectives for understanding the mechanism of random feature mapping, which is the key concept in ELM theory. Empirical study on a wide range of data sets demonstrates that the proposed algorithms are competitive with the state-of-the-art semi-supervised or unsupervised learning algorithms in terms of accuracy and efficiency.

Visual search and contextual cueing: differential effects in 10-year-old children and adults.

PubMed

Couperus, Jane W; Hunt, Ruskin H; Nelson, Charles A; Thomas, Kathleen M

2011-02-01

The development of contextual cueing specifically in relation to attention was examined in two experiments. Adult and 10-year-old participants completed a context cueing visual search task (Jiang & Chun, The Quarterly Journal of Experimental Psychology, 54A(4), 1105-1124, 2001) containing stimuli presented in an attended (e.g., red) and unattended (e.g., green) color. When the spatial configuration of stimuli in the attended and unattended color was invariant and consistently paired with the target location, adult reaction times improved, demonstrating learning. Learning also occurred if only the attended stimuli's configuration remained fixed. In contrast, while 10 year olds, like adults, showed incrementally slower reaction times as the number of attended stimuli increased, they did not show learning in the standard paradigm. However, they did show learning when the ratio of attended to unattended stimuli was high, irrespective of the total number of attended stimuli. Findings suggest children show efficient attentional guidance by color in visual search but differences in contextual cueing.

Considering Research Outcomes as Essential Tools for Medical Education Decision Making.

PubMed

Miller, Karen Hughes; Miller, Bonnie M; Karani, Reena

2015-11-01

As medical educators face the challenge of incorporating new content, learning methods, and assessment techniques into the curriculum, the need for rigorous medical education research to guide efficient and effective instructional planning increases. When done properly, well-designed education research can provide guidance for complex education decision making. In this Commentary, the authors consider the 2015 Research in Medical Education (RIME) research and review articles in terms of the critical areas in teaching and learning that they address. The broad categories include (1) assessment (the largest collection of RIME articles, including both feedback from learners and instructors and the reliability of learner assessment), (2) the institution's impact on the learning environment, (3) what can be learned from program evaluation, and (4) emerging issues in faculty development. While the articles in this issue are broad in scope and potential impact, the RIME committee noted few studies of sufficient rigor focusing on areas of diversity and diverse learners. Although challenging to investigate, the authors encourage continuing innovation in research focused on these important areas.

There is No Free Lunch: Tradeoffs in the Utility of Learned Knowledge

NASA Technical Reports Server (NTRS)

Kedar, Smadar T.; McKusick, Kathleen B.

1992-01-01

With the recent introduction of learning in integrated systems, there is a need to measure the utility of learned knowledge for these more complex systems. A difficulty arrises when there are multiple, possibly conflicting, utility metrics to be measured. In this paper, we present schemes which trade off conflicting utility metrics in order to achieve some global performance objectives. In particular, we present a case study of a multi-strategy machine learning system, mutual theory refinement, which refines world models for an integrated reactive system, the Entropy Reduction Engine. We provide experimental results on the utility of learned knowledge in two conflicting metrics - improved accuracy and degraded efficiency. We then demonstrate two ways to trade off these metrics. In each, some learned knowledge is either approximated or dynamically 'forgotten' so as to improve efficiency while degrading accuracy only slightly.

Supervised Learning in Spiking Neural Networks for Precise Temporal Encoding

PubMed Central

Gardner, Brian; Grüning, André

2016-01-01

Precise spike timing as a means to encode information in neural networks is biologically supported, and is advantageous over frequency-based codes by processing input features on a much shorter time-scale. For these reasons, much recent attention has been focused on the development of supervised learning rules for spiking neural networks that utilise a temporal coding scheme. However, despite significant progress in this area, there still lack rules that have a theoretical basis, and yet can be considered biologically relevant. Here we examine the general conditions under which synaptic plasticity most effectively takes place to support the supervised learning of a precise temporal code. As part of our analysis we examine two spike-based learning methods: one of which relies on an instantaneous error signal to modify synaptic weights in a network (INST rule), and the other one relying on a filtered error signal for smoother synaptic weight modifications (FILT rule). We test the accuracy of the solutions provided by each rule with respect to their temporal encoding precision, and then measure the maximum number of input patterns they can learn to memorise using the precise timings of individual spikes as an indication of their storage capacity. Our results demonstrate the high performance of the FILT rule in most cases, underpinned by the rule’s error-filtering mechanism, which is predicted to provide smooth convergence towards a desired solution during learning. We also find the FILT rule to be most efficient at performing input pattern memorisations, and most noticeably when patterns are identified using spikes with sub-millisecond temporal precision. In comparison with existing work, we determine the performance of the FILT rule to be consistent with that of the highly efficient E-learning Chronotron rule, but with the distinct advantage that our FILT rule is also implementable as an online method for increased biological realism. PMID:27532262

Supervised Learning in Spiking Neural Networks for Precise Temporal Encoding.

PubMed

Gardner, Brian; Grüning, André

2016-01-01

Precise spike timing as a means to encode information in neural networks is biologically supported, and is advantageous over frequency-based codes by processing input features on a much shorter time-scale. For these reasons, much recent attention has been focused on the development of supervised learning rules for spiking neural networks that utilise a temporal coding scheme. However, despite significant progress in this area, there still lack rules that have a theoretical basis, and yet can be considered biologically relevant. Here we examine the general conditions under which synaptic plasticity most effectively takes place to support the supervised learning of a precise temporal code. As part of our analysis we examine two spike-based learning methods: one of which relies on an instantaneous error signal to modify synaptic weights in a network (INST rule), and the other one relying on a filtered error signal for smoother synaptic weight modifications (FILT rule). We test the accuracy of the solutions provided by each rule with respect to their temporal encoding precision, and then measure the maximum number of input patterns they can learn to memorise using the precise timings of individual spikes as an indication of their storage capacity. Our results demonstrate the high performance of the FILT rule in most cases, underpinned by the rule's error-filtering mechanism, which is predicted to provide smooth convergence towards a desired solution during learning. We also find the FILT rule to be most efficient at performing input pattern memorisations, and most noticeably when patterns are identified using spikes with sub-millisecond temporal precision. In comparison with existing work, we determine the performance of the FILT rule to be consistent with that of the highly efficient E-learning Chronotron rule, but with the distinct advantage that our FILT rule is also implementable as an online method for increased biological realism.

Teaching Skill Acquisition and Development in Dental Education.

PubMed

Lyon, Lucinda J; Hoover, Terry E; Giusti, Lola; Booth, Mark T; Mahdavi, Elham

2016-08-01

Development of dental faculty members is paramount to providing outstanding education and role modeling for students. With the large number of second career educators in dental schools, an efficient method of acquiring teaching skills is important for new faculty members. Knowing the skill progression and learning experiences identified by dental educators of varying rank may lead to more efficient, effective faculty development. The aims of this study were to identify the perceptions of a group of faculty members about the knowledge, skills, attitudes, and learning experiences that contribute to developing teaching expertise and to compare and contrast the perceptions of new and more senior faculty members on these subjects. The Dreyfus skill acquisition continuum of novice to expert performance was used as a construct reference. The study used a mixed-methods approach in which qualitative and quantitative data were collected concurrently in an electronic survey of faculty members at one U.S. dental school. Of the 492 total faculty members, 80 survey responses were received, for a 16% response rate. Open coding and analysis of responses revealed some common themes. Building rich content knowledge and learning varied methodologies for teaching and assessment, supported by an awareness of peer role models, were perceived to be features of early growth. Content prioritization, clarity, and customization appropriate for the learner characterized mid growth. As theorized in the Dreyfus model, more experienced faculty members described a fluid, less structured teaching process, increased reflection, and appreciation of the strength of the educational community. The results of this study may help increase dental educators' understanding of teaching skill acquisition and inform faculty development and support.

VariantSpark: population scale clustering of genotype information.

PubMed

O'Brien, Aidan R; Saunders, Neil F W; Guo, Yi; Buske, Fabian A; Scott, Rodney J; Bauer, Denis C

2015-12-10

Genomic information is increasingly used in medical practice giving rise to the need for efficient analysis methodology able to cope with thousands of individuals and millions of variants. The widely used Hadoop MapReduce architecture and associated machine learning library, Mahout, provide the means for tackling computationally challenging tasks. However, many genomic analyses do not fit the Map-Reduce paradigm. We therefore utilise the recently developed SPARK engine, along with its associated machine learning library, MLlib, which offers more flexibility in the parallelisation of population-scale bioinformatics tasks. The resulting tool, VARIANTSPARK provides an interface from MLlib to the standard variant format (VCF), offers seamless genome-wide sampling of variants and provides a pipeline for visualising results. To demonstrate the capabilities of VARIANTSPARK, we clustered more than 3,000 individuals with 80 Million variants each to determine the population structure in the dataset. VARIANTSPARK is 80 % faster than the SPARK-based genome clustering approach, ADAM, the comparable implementation using Hadoop/Mahout, as well as ADMIXTURE, a commonly used tool for determining individual ancestries. It is over 90 % faster than traditional implementations using R and Python. The benefits of speed, resource consumption and scalability enables VARIANTSPARK to open up the usage of advanced, efficient machine learning algorithms to genomic data.

A Situated Cultural Festival Learning System Based on Motion Sensing

ERIC Educational Resources Information Center

Chang, Yi-Hsing; Lin, Yu-Kai; Fang, Rong-Jyue; Lu, You-Te

2017-01-01

A situated Chinese cultural festival learning system based on motion sensing is developed in this study. The primary design principle is to create a highly interactive learning environment, allowing learners to interact with Kinect through natural gestures in the designed learning situation to achieve efficient learning. The system has the…

E-Learning System Overview Based on Semantic Web

ERIC Educational Resources Information Center

Alsultanny, Yas A.

2006-01-01

The challenge of the semantic web is the provision of distributed information with well-defined meaning, understandable for different parties. e-Learning is efficient task relevant and just-in-time learning grown from the learning requirements of the new dynamically changing, distributed business world. In this paper we design an e-Learning system…

Investigating Team Learning in a Military Context

ERIC Educational Resources Information Center

Veestraeten, Marlies; Kyndt, Eva; Dochy, Filip

2014-01-01

As teams have become fundamental parts of today's organisations, the need for these teams to function and learn efficiently and effectively is widely emphasised. Also in military contexts team learning is vital. The current article examines team learning behaviour in military teams as it aims to cross-validate a team learning model that was…

An Automatic Detection System of Lung Nodule Based on Multi-Group Patch-Based Deep Learning Network.

PubMed

Jiang, Hongyang; Ma, He; Qian, Wei; Gao, Mengdi; Li, Yan

2017-07-14

High-efficiency lung nodule detection dramatically contributes to the risk assessment of lung cancer. It is a significant and challenging task to quickly locate the exact positions of lung nodules. Extensive work has been done by researchers around this domain for approximately two decades. However, previous computer aided detection (CADe) schemes are mostly intricate and time-consuming since they may require more image processing modules, such as the computed tomography (CT) image transformation, the lung nodule segmentation and the feature extraction, to construct a whole CADe system. It is difficult for those schemes to process and analyze enormous data when the medical images continue to increase. Besides, some state of the art deep learning schemes may be strict in the standard of database. This study proposes an effective lung nodule detection scheme based on multi-group patches cut out from the lung images, which are enhanced by the Frangi filter. Through combining two groups of images, a four-channel convolution neural networks (CNN) model is designed to learn the knowledge of radiologists for detecting nodules of four levels. This CADe scheme can acquire the sensitivity of 80.06% with 4.7 false positives per scan and the sensitivity of 94% with 15.1 false positives per scan. The results demonstrate that the multi-group patch-based learning system is efficient to improve the performance of lung nodule detection and greatly reduce the false positives under a huge amount of image data.

Long-range dismount activity classification: LODAC

NASA Astrophysics Data System (ADS)

Garagic, Denis; Peskoe, Jacob; Liu, Fang; Cuevas, Manuel; Freeman, Andrew M.; Rhodes, Bradley J.

2014-06-01

Continuous classification of dismount types (including gender, age, ethnicity) and their activities (such as walking, running) evolving over space and time is challenging. Limited sensor resolution (often exacerbated as a function of platform standoff distance) and clutter from shadows in dense target environments, unfavorable environmental conditions, and the normal properties of real data all contribute to the challenge. The unique and innovative aspect of our approach is a synthesis of multimodal signal processing with incremental non-parametric, hierarchical Bayesian machine learning methods to create a new kind of target classification architecture. This architecture is designed from the ground up to optimally exploit correlations among the multiple sensing modalities (multimodal data fusion) and rapidly and continuously learns (online self-tuning) patterns of distinct classes of dismounts given little a priori information. This increases classification performance in the presence of challenges posed by anti-access/area denial (A2/AD) sensing. To fuse multimodal features, Long-range Dismount Activity Classification (LODAC) develops a novel statistical information theoretic approach for multimodal data fusion that jointly models multimodal data (i.e., a probabilistic model for cross-modal signal generation) and discovers the critical cross-modal correlations by identifying components (features) with maximal mutual information (MI) which is efficiently estimated using non-parametric entropy models. LODAC develops a generic probabilistic pattern learning and classification framework based on a new class of hierarchical Bayesian learning algorithms for efficiently discovering recurring patterns (classes of dismounts) in multiple simultaneous time series (sensor modalities) at multiple levels of feature granularity.

Metacognition and Successful Learning Strategies in Higher Education

ERIC Educational Resources Information Center

Railean, Elena, Ed.; Alev Elçi, Ed.; Elçi, Atilla, Ed.

2017-01-01

Metacognition plays an important role in numerous aspects of higher educational learning strategies. When properly integrated in the educational system, schools are better equipped to build more efficient and successful learning strategies for students in higher education. "Metacognition and Successful Learning Strategies in Higher…

Empirical Linkages between Firm Competencies and Organisational Learning.

ERIC Educational Resources Information Center

Murray, Peter; Donegan, Kevin

2003-01-01

Management, operational, technology, and learning competencies in 26 large and 15 small Australian construction contractors were identified at five levels: simplistic, structure, efficiency, value, and dynamic. Organizational learning appeared useful when combined with competency development embedded in the routines of a learning culture.…

Distance Metric Learning via Iterated Support Vector Machines.

PubMed

Zuo, Wangmeng; Wang, Faqiang; Zhang, David; Lin, Liang; Huang, Yuchi; Meng, Deyu; Zhang, Lei

2017-07-11

Distance metric learning aims to learn from the given training data a valid distance metric, with which the similarity between data samples can be more effectively evaluated for classification. Metric learning is often formulated as a convex or nonconvex optimization problem, while most existing methods are based on customized optimizers and become inefficient for large scale problems. In this paper, we formulate metric learning as a kernel classification problem with the positive semi-definite constraint, and solve it by iterated training of support vector machines (SVMs). The new formulation is easy to implement and efficient in training with the off-the-shelf SVM solvers. Two novel metric learning models, namely Positive-semidefinite Constrained Metric Learning (PCML) and Nonnegative-coefficient Constrained Metric Learning (NCML), are developed. Both PCML and NCML can guarantee the global optimality of their solutions. Experiments are conducted on general classification, face verification and person re-identification to evaluate our methods. Compared with the state-of-the-art approaches, our methods can achieve comparable classification accuracy and are efficient in training.

A sparse structure learning algorithm for Gaussian Bayesian Network identification from high-dimensional data.

PubMed

Huang, Shuai; Li, Jing; Ye, Jieping; Fleisher, Adam; Chen, Kewei; Wu, Teresa; Reiman, Eric

2013-06-01

Structure learning of Bayesian Networks (BNs) is an important topic in machine learning. Driven by modern applications in genetics and brain sciences, accurate and efficient learning of large-scale BN structures from high-dimensional data becomes a challenging problem. To tackle this challenge, we propose a Sparse Bayesian Network (SBN) structure learning algorithm that employs a novel formulation involving one L1-norm penalty term to impose sparsity and another penalty term to ensure that the learned BN is a Directed Acyclic Graph--a required property of BNs. Through both theoretical analysis and extensive experiments on 11 moderate and large benchmark networks with various sample sizes, we show that SBN leads to improved learning accuracy, scalability, and efficiency as compared with 10 existing popular BN learning algorithms. We apply SBN to a real-world application of brain connectivity modeling for Alzheimer's disease (AD) and reveal findings that could lead to advancements in AD research.

A Sparse Structure Learning Algorithm for Gaussian Bayesian Network Identification from High-Dimensional Data

PubMed Central

Huang, Shuai; Li, Jing; Ye, Jieping; Fleisher, Adam; Chen, Kewei; Wu, Teresa; Reiman, Eric

2014-01-01

Structure learning of Bayesian Networks (BNs) is an important topic in machine learning. Driven by modern applications in genetics and brain sciences, accurate and efficient learning of large-scale BN structures from high-dimensional data becomes a challenging problem. To tackle this challenge, we propose a Sparse Bayesian Network (SBN) structure learning algorithm that employs a novel formulation involving one L1-norm penalty term to impose sparsity and another penalty term to ensure that the learned BN is a Directed Acyclic Graph (DAG)—a required property of BNs. Through both theoretical analysis and extensive experiments on 11 moderate and large benchmark networks with various sample sizes, we show that SBN leads to improved learning accuracy, scalability, and efficiency as compared with 10 existing popular BN learning algorithms. We apply SBN to a real-world application of brain connectivity modeling for Alzheimer’s disease (AD) and reveal findings that could lead to advancements in AD research. PMID:22665720

The use of mathematical models in teaching wastewater treatment engineering.

PubMed

Morgenroth, E; Arvin, E; Vanrolleghem, P

2002-01-01

Mathematical modeling of wastewater treatment processes has become increasingly popular in recent years. To prepare students for their future careers, environmental engineering education should provide students with sufficient background and experiences to understand and apply mathematical models efficiently and responsibly. Approaches for introducing mathematical modeling into courses on wastewater treatment engineering are discussed depending on the learning objectives, level of the course and the time available.

Anytime Prediction: Efficient Ensemble Methods for Any Computational Budget

DTIC Science & Technology

2014-01-21

difficult problem and is the focus of this work. 1.1 Motivation The number of machine learning applications which involve real time and latency sensitive pre...significantly increasing latency , and the computational costs associated with hosting a service are often critical to its viability. For such...balancing training costs, concerns such as scalability and tractability are often more important, as opposed to factors such as latency which are more

Young Children's Learning Performance and Efficiency When Using Virtual Manipulative Mathematics iPad Apps

ERIC Educational Resources Information Center

Moyer-Packenham, Patricia S.; Shumway, Jessica F.; Bullock, Emma; Tucker, Stephen I.; Anderson-Pence, Katie L.; Westenskow, Arla; Boyer-Thurgood, Jennifer; Maahs-Fladung, Cathy; Symanzik, Juergen; Mahamane, Salif; MacDonald, Beth; Jordan, Kerry

2015-01-01

Part of a larger initiation mixed methods study (Greene, Caracelli, & Graham, 1989), this paper discusses the changes in young children's learning performance and efficiency (one element of the quantitative portion of the larger study) during clinical interviews in which each child interacted with a variety of virtual manipulative…

Effectiveness, Efficiency, and Appeal: Pick Any Two? The Influence of Learning Domains and Learning Outcomes on Designer Judgments of Useful Instructional Methods

ERIC Educational Resources Information Center

Honebein, Peter C.; Honebein, Cass H.

2015-01-01

When choosing instructional methods, instructional designers trade-off or sacrifice an outcome, such as effectiveness, efficiency, or appeal. In instructional planning theory, this is referred to as values about priorities. When "values about priorities" are combined with "conditions about content," we expect that a different…

A Study of the Effects of Shift Operations on Student Achievement in Electronics Training.

ERIC Educational Resources Information Center

Johnson, Frank F., Jr.

This study was designed to determine if the hours during which students participated in electronics training had any influence on their learning efficiency and their ability to function effectively as students, and to identify those factors that contributed to diminished learning efficiency. The three shifts used for the experiment were the night…

Through Efficient Use of LORs: Prospective Teachers' Views on Operational Aspects of Learning Object Repositories

ERIC Educational Resources Information Center

Yalcinalp, Serpil; Emiroglu, Bulent

2012-01-01

Although many developments have been made in the design and development of learning object repositories (LORs), the efficient use of such systems is still questionable. Without realising the functional use of such systems or considering the involvement of their dynamic users, these systems would probably become obsolete. This study includes both…

Developing an Efficient Computational Method that Estimates the Ability of Students in a Web-Based Learning Environment

ERIC Educational Resources Information Center

Lee, Young-Jin

2012-01-01

This paper presents a computational method that can efficiently estimate the ability of students from the log files of a Web-based learning environment capturing their problem solving processes. The computational method developed in this study approximates the posterior distribution of the student's ability obtained from the conventional Bayes…

Evolution of co-management: role of knowledge generation, bridging organizations and social learning.

PubMed

Berkes, Fikret

2009-04-01

Over a period of some 20 years, different aspects of co-management (the sharing of power and responsibility between the government and local resource users) have come to the forefront. The paper focuses on a selection of these: knowledge generation, bridging organizations, social learning, and the emergence of adaptive co-management. Co-management can be considered a knowledge partnership. Different levels of organization, from local to international, have comparative advantages in the generation and mobilization of knowledge acquired at different scales. Bridging organizations provide a forum for the interaction of these different kinds of knowledge, and the coordination of other tasks that enable co-operation: accessing resources, bringing together different actors, building trust, resolving conflict, and networking. Social learning is one of these tasks, essential both for the co-operation of partners and an outcome of the co-operation of partners. It occurs most efficiently through joint problem solving and reflection within learning networks. Through successive rounds of learning and problem solving, learning networks can incorporate new knowledge to deal with problems at increasingly larger scales, with the result that maturing co-management arrangements become adaptive co-management in time.

How much is too much assessment? Insight into assessment-driven student learning gains in large-scale undergraduate microbiology courses.

PubMed

Wang, Jack T H; Schembri, Mark A; Hall, Roy A

2013-01-01

Designing and implementing assessment tasks in large-scale undergraduate science courses is a labor-intensive process subject to increasing scrutiny from students and quality assurance authorities alike. Recent pedagogical research has provided conceptual frameworks for teaching introductory undergraduate microbiology, but has yet to define best-practice assessment guidelines. This study assessed the applicability of Biggs' theory of constructive alignment in designing consistent learning objectives, activities, and assessment items that aligned with the American Society for Microbiology's concept-based microbiology curriculum in MICR2000, an introductory microbiology course offered at the University of Queensland, Australia. By improving the internal consistency in assessment criteria and increasing the number of assessment items explicitly aligned to the course learning objectives, the teaching team was able to efficiently provide adequate feedback on numerous assessment tasks throughout the semester, which contributed to improved student performance and learning gains. When comparing the constructively aligned 2011 offering of MICR2000 with its 2010 counterpart, students obtained higher marks in both coursework assignments and examinations as the semester progressed. Students also valued the additional feedback provided, as student rankings for course feedback provision increased in 2011 and assessment and feedback was identified as a key strength of MICR2000. By designing MICR2000 using constructive alignment and iterative assessment tasks that followed a common set of learning outcomes, the teaching team was able to effectively deliver detailed and timely feedback in a large introductory microbiology course. This study serves as a case study for how constructive alignment can be integrated into modern teaching practices for large-scale courses.

Learners' Ensemble Based Security Conceptual Model for M-Learning System in Malaysian Higher Learning Institution

ERIC Educational Resources Information Center

Mahalingam, Sheila; Abdollah, Faizal Mohd; Sahib, Shahrin

2014-01-01

M-Learning has a potential to improve efficiency in the education sector and has a tendency to grow advance and transform the learning environment in the future. Yet there are challenges in many areas faced when introducing and implementing m-learning. The learner centered attribute in mobile learning implies deployment in untrustworthy learning…

The value of online learning and MRI: finding a niche for expensive technologies.

PubMed

Cook, David A

2014-11-01

The benefits of online learning come at a price. How can we optimize the overall value? Critically appraise the value of online learning. Narrative review. Several prevalent myths overinflate the value of online learning. These include that online learning is cheap and easy (it is usually more expensive), that it is more efficient (efficiency depends on the instructional design, not the modality), that it will transform education (fundamental learning principles have not changed), and that the Net Generation expects it (there is no evidence of pent-up demand). However, online learning does add real value by enhancing flexibility, control and analytics. Costs may also go down if disruptive innovations (e.g. low-cost, low-tech, but instructionally sound "good enough" online learning) supplant technically superior but more expensive online learning products. Cost-lowering strategies include focusing on core principles of learning rather than technologies, using easy-to-learn authoring tools, repurposing content (organizing and sequencing existing resources rather than creating new content) and using course templates. Online learning represents just one tool in an educator's toolbox, as does the MRI for clinicians. We need to use the right tool(s) for the right learner at the right dose, time and route.

An Energy-Efficient and Scalable Deep Learning/Inference Processor With Tetra-Parallel MIMD Architecture for Big Data Applications.

PubMed

Park, Seong-Wook; Park, Junyoung; Bong, Kyeongryeol; Shin, Dongjoo; Lee, Jinmook; Choi, Sungpill; Yoo, Hoi-Jun

2015-12-01

Deep Learning algorithm is widely used for various pattern recognition applications such as text recognition, object recognition and action recognition because of its best-in-class recognition accuracy compared to hand-crafted algorithm and shallow learning based algorithms. Long learning time caused by its complex structure, however, limits its usage only in high-cost servers or many-core GPU platforms so far. On the other hand, the demand on customized pattern recognition within personal devices will grow gradually as more deep learning applications will be developed. This paper presents a SoC implementation to enable deep learning applications to run with low cost platforms such as mobile or portable devices. Different from conventional works which have adopted massively-parallel architecture, this work adopts task-flexible architecture and exploits multiple parallelism to cover complex functions of convolutional deep belief network which is one of popular deep learning/inference algorithms. In this paper, we implement the most energy-efficient deep learning and inference processor for wearable system. The implemented 2.5 mm × 4.0 mm deep learning/inference processor is fabricated using 65 nm 8-metal CMOS technology for a battery-powered platform with real-time deep inference and deep learning operation. It consumes 185 mW average power, and 213.1 mW peak power at 200 MHz operating frequency and 1.2 V supply voltage. It achieves 411.3 GOPS peak performance and 1.93 TOPS/W energy efficiency, which is 2.07× higher than the state-of-the-art.

Learning efficient visual search for stimuli containing diagnostic spatial configurations and color-shape conjunctions.

PubMed

Reavis, Eric A; Frank, Sebastian M; Tse, Peter U

2018-04-12

Visual search is often slow and difficult for complex stimuli such as feature conjunctions. Search efficiency, however, can improve with training. Search for stimuli that can be identified by the spatial configuration of two elements (e.g., the relative position of two colored shapes) improves dramatically within a few hundred trials of practice. Several recent imaging studies have identified neural correlates of this learning, but it remains unclear what stimulus properties participants learn to use to search efficiently. Influential models, such as reverse hierarchy theory, propose two major possibilities: learning to use information contained in low-level image statistics (e.g., single features at particular retinotopic locations) or in high-level characteristics (e.g., feature conjunctions) of the task-relevant stimuli. In a series of experiments, we tested these two hypotheses, which make different predictions about the effect of various stimulus manipulations after training. We find relatively small effects of manipulating low-level properties of the stimuli (e.g., changing their retinotopic location) and some conjunctive properties (e.g., color-position), whereas the effects of manipulating other conjunctive properties (e.g., color-shape) are larger. Overall, the findings suggest conjunction learning involving such stimuli might be an emergent phenomenon that reflects multiple different learning processes, each of which capitalizes on different types of information contained in the stimuli. We also show that both targets and distractors are learned, and that reversing learned target and distractor identities impairs performance. This suggests that participants do not merely learn to discriminate target and distractor stimuli, they also learn stimulus identity mappings that contribute to performance improvements.

A psychoengineering paradigm for the neurocognitive mechanisms of biofeedback and neurofeedback.

PubMed

Gaume, A; Vialatte, A; Mora-Sánchez, A; Ramdani, C; Vialatte, F B

2016-09-01

We believe that the missing keystone to design effective and efficient biofeedback and neurofeedback protocols is a comprehensive model of the mechanisms of feedback learning. In this manuscript we review the learning models in behavioral, developmental and cognitive psychology, and derive a synthetic model of the psychological perspective on biofeedback. We afterwards review the neural correlates of feedback learning mechanisms, and present a general neuroscience model of biofeedback. We subsequently show how biomedical engineering principles can be applied to design efficient feedback protocols. We finally present an integrative psychoengineering model of the feedback learning processes, and provide new guidelines for the efficient design of biofeedback and neurofeedback protocols. We identify five key properties, (1) perceptibility=can the subject perceive the biosignal?, (2) autonomy=can the subject regulate by himself?, (3) mastery=degree of control over the biosignal, (4) motivation=rewards system of the biofeedback, and (5) learnability=possibility of learning. We conclude with guidelines for the investigation and promotion of these properties in biofeedback protocols. Copyright © 2016 Elsevier Ltd. All rights reserved.

Deep Learning for Flow Sculpting: Insights into Efficient Learning using Scientific Simulation Data

PubMed Central

Stoecklein, Daniel; Lore, Kin Gwn; Davies, Michael; Sarkar, Soumik; Ganapathysubramanian, Baskar

2017-01-01

A new technique for shaping microfluid flow, known as flow sculpting, offers an unprecedented level of passive fluid flow control, with potential breakthrough applications in advancing manufacturing, biology, and chemistry research at the microscale. However, efficiently solving the inverse problem of designing a flow sculpting device for a desired fluid flow shape remains a challenge. Current approaches struggle with the many-to-one design space, requiring substantial user interaction and the necessity of building intuition, all of which are time and resource intensive. Deep learning has emerged as an efficient function approximation technique for high-dimensional spaces, and presents a fast solution to the inverse problem, yet the science of its implementation in similarly defined problems remains largely unexplored. We propose that deep learning methods can completely outpace current approaches for scientific inverse problems while delivering comparable designs. To this end, we show how intelligent sampling of the design space inputs can make deep learning methods more competitive in accuracy, while illustrating their generalization capability to out-of-sample predictions. PMID:28402332

Team-Based Learning Enhances Performance in Introductory Biology

ERIC Educational Resources Information Center

Carmichael, Jeffrey

2009-01-01

Given the problems associated with the traditional lecture method, the constraints associated with large classes, and the effectiveness of active learning, continued development and testing of efficient student-centered learning approaches are needed. This study explores the effectiveness of team-based learning (TBL) in a large-enrollment…

Designing Multimedia for Meaningful Online Teaching and Learning

ERIC Educational Resources Information Center

Terry, Krista P.; Doolittle, Peter E.; Scheer, Stephanie B.; McNeill, Andrea

2004-01-01

The development of distance and distributed learning environments on college campuses has created a need to reconsider traditional approaches to teaching and learning by integrating research and theories in human learning, pedagogy, and instructional technology. Creating effective and efficient multimedia for Web-based instruction requires a…

Distributed Learning Metadata Standards

ERIC Educational Resources Information Center

McClelland, Marilyn

2004-01-01

Significant economies can be achieved in distributed learning systems architected with a focus on interoperability and reuse. The key building blocks of an efficient distributed learning architecture are the use of standards and XML technologies. The goal of plug and play capability among various components of a distributed learning system…

Learning Strategy Instruction Innovation Configuration

ERIC Educational Resources Information Center

Schumaker, Jean B.

2011-01-01

One way of helping students with learning disabilities and other struggling students to be independent life-long learners is to teach them how to use learning strategies in efficient ways. Learning strategy instruction can provide students the opportunity to succeed in today's schools and meet rigorous standards, transforming ineffective learners…

Efficient Training of Supervised Spiking Neural Network via Accurate Synaptic-Efficiency Adjustment Method.

PubMed

Xie, Xiurui; Qu, Hong; Yi, Zhang; Kurths, Jurgen

2017-06-01

The spiking neural network (SNN) is the third generation of neural networks and performs remarkably well in cognitive tasks, such as pattern recognition. The temporal neural encode mechanism found in biological hippocampus enables SNN to possess more powerful computation capability than networks with other encoding schemes. However, this temporal encoding approach requires neurons to process information serially on time, which reduces learning efficiency significantly. To keep the powerful computation capability of the temporal encoding mechanism and to overcome its low efficiency in the training of SNNs, a new training algorithm, the accurate synaptic-efficiency adjustment method is proposed in this paper. Inspired by the selective attention mechanism of the primate visual system, our algorithm selects only the target spike time as attention areas, and ignores voltage states of the untarget ones, resulting in a significant reduction of training time. Besides, our algorithm employs a cost function based on the voltage difference between the potential of the output neuron and the firing threshold of the SNN, instead of the traditional precise firing time distance. A normalized spike-timing-dependent-plasticity learning window is applied to assigning this error to different synapses for instructing their training. Comprehensive simulations are conducted to investigate the learning properties of our algorithm, with input neurons emitting both single spike and multiple spikes. Simulation results indicate that our algorithm possesses higher learning performance than the existing other methods and achieves the state-of-the-art efficiency in the training of SNN.

The reverse classroom: lectures on your own and homework with faculty.

PubMed

Sherbino, Jonathan; Chan, Teresa; Schiff, Karen

2013-05-01

With the arrival of a technologically proficient generation of learners (often described with the moniker "digital natives") into Canadian medical schools and residency programs, there is an increasing trend toward harnessing technology to enhance education and increase teaching efficiency. We present an instructional method that allows medical educators to "reverse" the traditional classroom paradigm. Imagine that prior to an academic half-day session, learners watch an e-lecture on their own time; then during class, they do "homework" with tailored consultations from a content expert. The reverse classroom uses simple, readily accessible technology to allow faculty members to engage learners in high-order learning such as information analysis and synthesis. With this instructional method, the inefficient, repetitious delivery of recurring core lectures is no longer required. The reverse classroom is an effective instructional method. Using this technique, learners engage in high-order learning and interaction with teachers, and teachers are able to optimally share their expertise.

Massive open online courses: a resource for health education in developing countries.

PubMed

Liyanagunawardena, Tharindu R; Aboshady, Omar A

2017-01-01

Developing countries are suffering from increasing burdens presented by both non-communicable and emerging infectious diseases. Health education is an important step to fight against these mostly preventable diseases. E-learning has been shown to be one of the tools that address some of the training challenges experienced in developing countries by supporting efficient content delivery, decreasing costs and increasing access. Massive open online courses (MOOCs) are a recent innovative presentation of online learning that have attracted millions of learners from all over the world. In this commentary, we propose MOOCs as a potential tool to offer a tremendous opportunity to fulfil the unmet training needs of the health sector in developing countries in two complementary ways: as a resource for training healthcare professionals; and as a resource for the general public. Potential barriers to accessing MOOCs and possible solutions are also discussed.

Evolving neural networks through augmenting topologies.

PubMed

Stanley, Kenneth O; Miikkulainen, Risto

2002-01-01

An important question in neuroevolution is how to gain an advantage from evolving neural network topologies along with weights. We present a method, NeuroEvolution of Augmenting Topologies (NEAT), which outperforms the best fixed-topology method on a challenging benchmark reinforcement learning task. We claim that the increased efficiency is due to (1) employing a principled method of crossover of different topologies, (2) protecting structural innovation using speciation, and (3) incrementally growing from minimal structure. We test this claim through a series of ablation studies that demonstrate that each component is necessary to the system as a whole and to each other. What results is significantly faster learning. NEAT is also an important contribution to GAs because it shows how it is possible for evolution to both optimize and complexify solutions simultaneously, offering the possibility of evolving increasingly complex solutions over generations, and strengthening the analogy with biological evolution.

Enhancing Spatial Resolution of Remotely Sensed Imagery Using Deep Learning

NASA Astrophysics Data System (ADS)

Beck, J. M.; Bridges, S.; Collins, C.; Rushing, J.; Graves, S. J.

2017-12-01

Researchers at the Information Technology and Systems Center at the University of Alabama in Huntsville are using Deep Learning with Convolutional Neural Networks (CNNs) to develop a method for enhancing the spatial resolutions of moderate resolution (10-60m) multispectral satellite imagery. This enhancement will effectively match the resolutions of imagery from multiple sensors to provide increased global temporal-spatial coverage for a variety of Earth science products. Our research is centered on using Deep Learning for automatically generating transformations for increasing the spatial resolution of remotely sensed images with different spatial, spectral, and temporal resolutions. One of the most important steps in using images from multiple sensors is to transform the different image layers into the same spatial resolution, preferably the highest spatial resolution, without compromising the spectral information. Recent advances in Deep Learning have shown that CNNs can be used to effectively and efficiently upscale or enhance the spatial resolution of multispectral images with the use of an auxiliary data source such as a high spatial resolution panchromatic image. In contrast, we are using both the spatial and spectral details inherent in low spatial resolution multispectral images for image enhancement without the use of a panchromatic image. This presentation will discuss how this technology will benefit many Earth Science applications that use remotely sensed images with moderate spatial resolutions.

Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network

PubMed Central

2018-01-01

Skin lesions are a severe disease globally. Early detection of melanoma in dermoscopy images significantly increases the survival rate. However, the accurate recognition of melanoma is extremely challenging due to the following reasons: low contrast between lesions and skin, visual similarity between melanoma and non-melanoma lesions, etc. Hence, reliable automatic detection of skin tumors is very useful to increase the accuracy and efficiency of pathologists. In this paper, we proposed two deep learning methods to address three main tasks emerging in the area of skin lesion image processing, i.e., lesion segmentation (task 1), lesion dermoscopic feature extraction (task 2) and lesion classification (task 3). A deep learning framework consisting of two fully convolutional residual networks (FCRN) is proposed to simultaneously produce the segmentation result and the coarse classification result. A lesion index calculation unit (LICU) is developed to refine the coarse classification results by calculating the distance heat-map. A straight-forward CNN is proposed for the dermoscopic feature extraction task. The proposed deep learning frameworks were evaluated on the ISIC 2017 dataset. Experimental results show the promising accuracies of our frameworks, i.e., 0.753 for task 1, 0.848 for task 2 and 0.912 for task 3 were achieved. PMID:29439500

Cognitive consequences of cannabis use: comparison with abuse of stimulants and heroin with regard to attention, memory and executive functions.

PubMed

Lundqvist, Thomas

2005-06-01

This review aims to compare cognitive consequence between cannabis, and stimulants and heroin with regards to attention, memory and executive functions. The available studies using brain imaging techniques and neuropsychological tests show that acutely, all drugs create a disharmony in the neuropsychological network, causing a decrease of activity in areas responsible for short-term memory and attention, with the possible exception of heroin. Cannabis induces loss of internal control and cognitive impairment, especially of attention and memory, for the duration of intoxication. Heavy cannabis use is associated with reduced function of the attentional/executive system, as exhibited by decreased mental flexibility, increased perserveration, and reduced learning, to shift and/or sustain attention. Recent investigations on amphetamine/methamphetamine have documented deficits in learning, delayed recall, processing speed, and working memory. MDMA users exhibit difficulties in coding information into long-term memory, display impaired verbal learning, are more easily distracted, and are less efficient at focusing attention on complex tasks. The degree of executive impairment increases with the severity of use, and the impairments are relatively lasting over time. Chronic cocaine users display impaired attention, learning, memory, reaction time and cognitive flexibility. Heroin addiction may have a negative effect on impulse control, and selective processing.

Trends in free WWW-based E-learning Modules seen from the Learning Resource Server Medicine (LRSMed).

PubMed

Stausberg, Jürgen; Geueke, Martin; Bludßat, Kevin

2005-01-01

Despite the lost enthusiasm concerning E-learning a lot of material is available on the World Wide Web (WWW) free of charge. This material is collected and systematically described by services like the Learning Resource Server Medicine (LRSMed) at http://mmedia.medizin.uni-essen.de/portal/. With the LRSMed E-learning modules are made available for medical students by means of a metadata description that can be used for a catalogue search. The number of resources included has risen enormously from 100 in 1999 up to 805 today. Especially in 2004 there was an exponential increase in the LRSMed's content. Anatomy is still the field with the highest amount of available material, but general medicine has improved its position over the years and is now the second one. Technically and didactically simple material as scripts, textbooks, and link lists (called info services) is still dominating. Similar to 1999, there is not one module which could be truly referred to as tutorial dialogue. Simple material can not replace face-to-face-teaching. But it could be combined with conventional courses to establish some kind of blending learning. The scene of free E-learning modules on the WWW is ready to meet current challenges for efficient training of students and continuing education in medicine.

Efficient k-Winner-Take-All Competitive Learning Hardware Architecture for On-Chip Learning

PubMed Central

Ou, Chien-Min; Li, Hui-Ya; Hwang, Wen-Jyi

2012-01-01

A novel k-winners-take-all (k-WTA) competitive learning (CL) hardware architecture is presented for on-chip learning in this paper. The architecture is based on an efficient pipeline allowing k-WTA competition processes associated with different training vectors to be performed concurrently. The pipeline architecture employs a novel codeword swapping scheme so that neurons failing the competition for a training vector are immediately available for the competitions for the subsequent training vectors. The architecture is implemented by the field programmable gate array (FPGA). It is used as a hardware accelerator in a system on programmable chip (SOPC) for realtime on-chip learning. Experimental results show that the SOPC has significantly lower training time than that of other k-WTA CL counterparts operating with or without hardware support.

Co-Creation Learning Procedures: Comparing Interactive Language Lessons for Deaf and Hearing Students.

PubMed

Hosono, Naotsune; Inoue, Hiromitsu; Tomita, Yutaka

2017-01-01

This paper discusses co-creation learning procedures of second language lessons for deaf students, and sign language lessons by a deaf lecturer. The analyses focus on the learning procedure and resulting assessment, considering the disability. Through questionnaires ICT-based co-creative learning technologies are effective and efficient and promote spontaneous learning motivation goals.

The Effects of Locus of Control on University Students' Mobile Learning Adoption

ERIC Educational Resources Information Center

Hsia, Jung-Wen

2016-01-01

Since mobile devices have become cheaper, easily accessible, powerful, and popular and the cost of wireless access has declined gradually, mobile learning (m-learning) has begun to spread rapidly. To further improve the effectiveness and efficiency of m-learning for university students, it is critical to understand whether they use m-learning.…

Interactive and Non-Interactive Pictures in Multimedia Learning Environments: Effects on Learning Outcomes and Learning Efficiency

ERIC Educational Resources Information Center

Rasch, Thorsten; Schnotz, Wolfgang

2009-01-01

New technologies enable flexible combinations of text and interactive or non-interactive pictures. The aim of the present study was to investigate (a) whether adding pictures to texts is generally beneficial for learning or whether it can also have detrimental effects, (b) how interactivity of pictures affects learning, (c) whether the…

Constructing a User-Friendly and Smart Ubiquitous Personalized Learning Environment by Using a Context-Aware Mechanism

ERIC Educational Resources Information Center

Yao, Ching-Bang

2017-01-01

Although m-learning applications have been widely researched, few studies have investigated applying adaptive learning content to various learning environments and efficient input interfaces. This study combined a context-aware mechanism, which can be used to provide suitable learning information anytime and anyplace by using GPS technology, with…

Active and Passive Technology Integration: A Novel Approach for Managing Technology's Influence on Learning Experiences in Context-Aware Learning Spaces

ERIC Educational Resources Information Center

Laine, Teemu H.; Nygren, Eeva

2016-01-01

Technology integration is the process of overcoming different barriers that hinder efficient utilisation of learning technologies. The authors divide technology integration into two components based on technology's role in the integration process. In active integration, the technology integrates learning resources into a learning space, making it…

Piece-wise quadratic approximations of arbitrary error functions for fast and robust machine learning.

PubMed

Gorban, A N; Mirkes, E M; Zinovyev, A

2016-12-01

Most of machine learning approaches have stemmed from the application of minimizing the mean squared distance principle, based on the computationally efficient quadratic optimization methods. However, when faced with high-dimensional and noisy data, the quadratic error functionals demonstrated many weaknesses including high sensitivity to contaminating factors and dimensionality curse. Therefore, a lot of recent applications in machine learning exploited properties of non-quadratic error functionals based on L 1 norm or even sub-linear potentials corresponding to quasinorms L p (0

Nitric oxide facilitates active avoidance learning via enhancement of glutamate levels in the hippocampal dentate gyrus.

PubMed

Wang, Shi; Pan, De-Xi; Wang, Dan; Wan, Peng; Qiu, De-Lai; Jin, Qing-Hua

2014-09-01

The hippocampus is a key structure for learning and memory in mammals, and long-term potentiation (LTP) is an important cellular mechanism responsible for learning and memory. Despite a number of studies indicating that nitric oxide (NO) is involved in the formation and maintenance of LTP as a retrograde messenger, few studies have used neurotransmitter release as a visual indicator in awake animals to explore the role of NO in learning-dependent long-term enhancement of synaptic efficiency. Therefore, in the present study, the effects of l-NMMA (a NO synthase inhibitor) and SNP (a NO donor) on extracellular glutamate (Glu) concentrations and amplitudes of field excitatory postsynaptic potential (fEPSP) were measured in the hippocampal dentate gyrus (DG) region during the acquisition and extinction of active-avoidance behavior in freely-moving conscious rats. In the control group, the extracellular concentration of Glu in the DG was significantly increased during the acquisition of active-avoidance behavior and gradually returned to baseline levels following extinction training. In the experimental group, the change in Glu concentration was significantly reduced by local microinjection of l-NMMA, as was the acquisition of the active-avoidance behavior. In contrast, the change in Glu concentration was significantly enhanced by SNP, and the acquisition of the active-avoidance behavior was significantly accelerated. Furthermore, in all groups, the changes in extracellular Glu were accompanied by corresponding changes in fEPSP amplitude and active-avoidance behavior. Our results suggest that NO in the hippocampal DG facilitates active avoidance learning via enhancements of glutamate levels and synaptic efficiency in rats. Copyright © 2014 Elsevier B.V. All rights reserved.

Memory and Learning: A Case Study.

ERIC Educational Resources Information Center

Webster, Raymond E.

1986-01-01

The usefulness of the Learning Efficency Test (LET), an approach to assessing the learning efficiency and short-term memory recall capacity in children, is described via a case study demonstrating the test's use to develop instructional strategies. (CL)

Choices and Consequences.

ERIC Educational Resources Information Center

Thorp, Carmany

1995-01-01

Describes student use of Hyperstudio computer software to create history adventure games. History came alive while students learned efficient writing skills; learned to understand and manipulate cause, effect choice and consequence; and learned to incorporate succinct locational, climatic, and historical detail. (ET)

Modularization: An Attempt at Collegiate Level in India.

ERIC Educational Resources Information Center

Gabriel, J.; Pillai, J. K.

1981-01-01

The effectiveness of a modular approach to learning in a botany unit as compared to the traditional teaching approach in terms of learning efficiency, learning time, and mastery level is reported. Three references are cited. (Author/CHC)

Man-Robot Symbiosis: A Framework For Cooperative Intelligence And Control

NASA Astrophysics Data System (ADS)

Parker, Lynne E.; Pin, Francois G.

1988-10-01

The man-robot symbiosis concept has the fundamental objective of bridging the gap between fully human-controlled and fully autonomous systems to achieve true man-robot cooperative control and intelligence. Such a system would allow improved speed, accuracy, and efficiency of task execution, while retaining the man in the loop for innovative reasoning and decision-making. The symbiont would have capabilities for supervised and unsupervised learning, allowing an increase of expertise in a wide task domain. This paper describes a robotic system architecture facilitating the symbiotic integration of teleoperative and automated modes of task execution. The architecture reflects a unique blend of many disciplines of artificial intelligence into a working system, including job or mission planning, dynamic task allocation, man-robot communication, automated monitoring, and machine learning. These disciplines are embodied in five major components of the symbiotic framework: the Job Planner, the Dynamic Task Allocator, the Presenter/Interpreter, the Automated Monitor, and the Learning System.

Affordable Integrated Technology Projects Science Education towards New Horizons

NASA Astrophysics Data System (ADS)

Paoletti, Franco; Carlucci, Lisa Marie

2009-03-01

The new-era concept of education supports a type of instruction whereby technology directly acts as a conduit of change, fundamentally altering what is learned, how it is learned, and the role of the educator in the classroom. In our current world, the learning about technology itself has become a goal and a means to successful participation in today's society. Efficient integration of technology to enhance and support the educational process will: 1) provide educators with the resources and the freedom to actualize innovative educational programs; 2) allow educators to be successful in challenging each student to reach his/her highest potential to ultimately increase academic achievement. This study analyzes what technology integration into education means identifying the benefits and the challenges that educators need to meet in order to be successful in their efforts while providing examples of how to successfully implement effective programs under budgetary constraints.

Multivariate temporal dictionary learning for EEG.

PubMed

Barthélemy, Q; Gouy-Pailler, C; Isaac, Y; Souloumiac, A; Larue, A; Mars, J I

2013-04-30

This article addresses the issue of representing electroencephalographic (EEG) signals in an efficient way. While classical approaches use a fixed Gabor dictionary to analyze EEG signals, this article proposes a data-driven method to obtain an adapted dictionary. To reach an efficient dictionary learning, appropriate spatial and temporal modeling is required. Inter-channels links are taken into account in the spatial multivariate model, and shift-invariance is used for the temporal model. Multivariate learned kernels are informative (a few atoms code plentiful energy) and interpretable (the atoms can have a physiological meaning). Using real EEG data, the proposed method is shown to outperform the classical multichannel matching pursuit used with a Gabor dictionary, as measured by the representative power of the learned dictionary and its spatial flexibility. Moreover, dictionary learning can capture interpretable patterns: this ability is illustrated on real data, learning a P300 evoked potential. Copyright © 2013 Elsevier B.V. All rights reserved.

Developing Course Materials for Technology-Mediated Chinese Language Learning

ERIC Educational Resources Information Center

Kubler, Cornelius C.

2018-01-01

This article discusses principles involved in developing course materials for technology-mediated Chinese language learning, with examples from a new course designed to take into account the needs of distance and independent learners. Which learning environment is most efficient for a given learning activity needs to be carefully considered. It…

Architectures for Developing Multiuser, Immersive Learning Scenarios

ERIC Educational Resources Information Center

Nadolski, Rob J.; Hummel, Hans G. K.; Slootmaker, Aad; van der Vegt, Wim

2012-01-01

Multiuser immersive learning scenarios hold strong potential for lifelong learning as they can support the acquisition of higher order skills in an effective, efficient, and attractive way. Existing virtual worlds, game development platforms, and game engines only partly cater for the proliferation of such learning scenarios as they are often…

Green School--A Service Learning Instrument to Enhance School Society Relation

ERIC Educational Resources Information Center

Madhusoodanan, Harikrishnan; Vitus, Geetha Janet

2014-01-01

A Green school is energy efficient, higher performing school that can be environmentally beneficial. Importance of Green school lies in the environmental friendliness value it upholds. Service learning has emanated out of philosophies of progressiveness and pragmatism. Service learning enables students to grow and learn through active…

Designing Ensemble Based Security Framework for M-Learning System

ERIC Educational Resources Information Center

Mahalingam, Sheila; Abdollah, Mohd Faizal; bin Sahibuddin, Shahrin

2014-01-01

Mobile Learning has a potential to improve efficiency in the education sector and expand educational opportunities to underserved remote area in higher learning institutions. However there are multi challenges in different altitude faced when introducing and implementing m-learning. Despite the evolution of technology changes in education,…

The Theory of Investigative Study and the Development of People

ERIC Educational Resources Information Center

Chen, Yuejun; Xu, Zhenhui

2017-01-01

Based on explaining investigative learning, this paper analyzed the characteristics of investigative learning and efficient methods to develop investigative learning, further to state the effects of investigative learning to train the abilities of university students especially the creative ability and the promotion effect to the mutual…

Collaborative E-Learning Using Semantic Course Blog

ERIC Educational Resources Information Center

Lu, Lai-Chen; Yeh, Ching-Long

2008-01-01

Collaborative e-learning delivers many enhancements to e-learning technology; it enables students to collaborate with each other and improves their learning efficiency. Semantic blog combines semantic Web and blog technology that users can import, export, view, navigate, and query the blog. We developed a semantic course blog for collaborative…

Development and Design of Problem Based Learning Game-Based Courseware

ERIC Educational Resources Information Center

Chang, Chiung-Sui; Chen, Jui-Fa; Chen, Fei-Ling

2015-01-01

In an educational environment, instructors would always think of ways to provide students with motivational learning materials and efficient learning strategies. Hence, many researchers have proposed that students' problem-solving ability enhances their learning. Problem-solving ability plays an important role for users in dealing with problems…

Effects of an Instructional Gaming Characteristic on Learning Effectiveness, Efficiency, and Engagement: Using a Storyline for Teaching Basic Statistical Skills

ERIC Educational Resources Information Center

Novak, Elena; Johnson, Tristan E.; Tenenbaum, Gershon; Shute, Valerie J.

2016-01-01

The study explored instructional benefits of a storyline gaming characteristic (GC) on learning effectiveness, efficiency, and engagement with the use of an online instructional simulation for graduate students in an introductory statistics course. A storyline is a game-design element that connects scenes with the educational content. In order to…

Efficiency of English for Academic Purposes Activity in Students' Language Education: Developing the System of External and Internal Perspectives

ERIC Educational Resources Information Center

Zascerinska, Jelena

2010-01-01

The paradigm change from an input based teaching/learning process to an outcome based process (D. Bluma, 2008, p. 673) reveals efficiency of contribution applied to enhance students' learning outcomes to become particularly important for the development of education and culture change in the constantly changing environment. Aim of the research is…

Effects of an Instructional Gaming Characteristic on Learning Effectiveness, Efficiency, and Engagement: Using a Storyline to Teach Basic Statistical Analytical Skills

ERIC Educational Resources Information Center

Novak, Elena

2012-01-01

The study explored instructional benefits of a storyline gaming characteristic (GC) on learning effectiveness, efficiency, and engagement with the use of an online instructional simulation for graduate students in an introductory statistics course. In addition, the study focused on examining the effects of a storyline GC on specific learning…

Improving the Effectiveness and Efficiency of Teaching Large Classes: Development and Evaluation of a Novel e-Resource in Cancer Biology

ERIC Educational Resources Information Center

Hejmadi, Momna V.

2007-01-01

This paper describes the development and evaluation of a blended learning resource in the biosciences, created by combining online learning with formal face-face lectures and supported by formative assessments. In order to improve the effectiveness and efficiency of teaching large classes with mixed student cohorts, teaching was delivered through…

An Exploratory Study of the Effects of Online Course Efficiency Perceptions on Student Evaluation of Teaching (SET) Measures

ERIC Educational Resources Information Center

Estelami, Hooman

2016-01-01

One of the fundamental drivers of the growing use of distance learning methods in modern business education has been the efficiency gains associated with this method of educational delivery. Distance methods benefit both students and educational institutions as they facilitate the processing of large volumes of learning material to overcome…

The Role of Affordances in Children's Learning Performance and Efficiency When Using Virtual Manipulative Mathematics Touch-Screen Apps

ERIC Educational Resources Information Center

Moyer-Packenham, Patricia S.; Bullock, Emma K.; Shumway, Jessica F.; Tucker, Stephen I.; Watts, Christina M.; Westenskow, Arla; Anderson-Pence, Katie L.; Maahs-Fladung, Cathy; Boyer-Thurgood, Jennifer; Gulkilik, Hilal; Jordan, Kerry

2016-01-01

This paper focuses on understanding the role that affordances played in children's learning performance and efficiency during clinical interviews of their interactions with mathematics apps on touch-screen devices. One hundred children, ages 3 to 8, each used six different virtual manipulative mathematics apps during 30-40-min interviews. The…

An efficient dictionary learning algorithm and its application to 3-D medical image denoising.

PubMed

Li, Shutao; Fang, Leyuan; Yin, Haitao

2012-02-01

In this paper, we propose an efficient dictionary learning algorithm for sparse representation of given data and suggest a way to apply this algorithm to 3-D medical image denoising. Our learning approach is composed of two main parts: sparse coding and dictionary updating. On the sparse coding stage, an efficient algorithm named multiple clusters pursuit (MCP) is proposed. The MCP first applies a dictionary structuring strategy to cluster the atoms with high coherence together, and then employs a multiple-selection strategy to select several competitive atoms at each iteration. These two strategies can greatly reduce the computation complexity of the MCP and assist it to obtain better sparse solution. On the dictionary updating stage, the alternating optimization that efficiently approximates the singular value decomposition is introduced. Furthermore, in the 3-D medical image denoising application, a joint 3-D operation is proposed for taking the learning capabilities of the presented algorithm to simultaneously capture the correlations within each slice and correlations across the nearby slices, thereby obtaining better denoising results. The experiments on both synthetically generated data and real 3-D medical images demonstrate that the proposed approach has superior performance compared to some well-known methods. © 2011 IEEE

A Truthful Incentive Mechanism for Online Recruitment in Mobile Crowd Sensing System

PubMed Central

Chen, Xiao; Liu, Min; Zhou, Yaqin; Li, Zhongcheng; Chen, Shuang; He, Xiangnan

2017-01-01

We investigate emerging mobile crowd sensing (MCS) systems, in which new cloud-based platforms sequentially allocate homogenous sensing jobs to dynamically-arriving users with uncertain service qualities. Given that human beings are selfish in nature, it is crucial yet challenging to design an efficient and truthful incentive mechanism to encourage users to participate. To address the challenge, we propose a novel truthful online auction mechanism that can efficiently learn to make irreversible online decisions on winner selections for new MCS systems without requiring previous knowledge of users. Moreover, we theoretically prove that our incentive possesses truthfulness, individual rationality and computational efficiency. Extensive simulation results under both real and synthetic traces demonstrate that our incentive mechanism can reduce the payment of the platform, increase the utility of the platform and social welfare. PMID:28045441

Efficiency in energy production and consumption

NASA Astrophysics Data System (ADS)

Kellogg, Ryan Mayer

This dissertation deals with economic efficiency in the energy industry and consists of three parts. The first examines how joint experience between pairs of firms working together in oil and gas drilling improves productivity. Part two asks whether oil producers time their drilling optimally by taking real options effects into consideration. Finally, I investigate the efficiency with which energy is consumed, asking whether extending Daylight Saving Time (DST) reduces electricity use. The chapter "Learning by Drilling: Inter-Firm Learning and Relationship Persistence in the Texas Oilpatch" examines how oil production companies and the drilling rigs they hire improve drilling productivity by learning through joint experience. I find that the joint productivity of a lead firm and its drilling contractor is enhanced significantly as they accumulate experience working together. Moreover, this result is robust to other relationship specificities and standard firm-specific learning-by-doing effects. The second chapter, "Drill Now or Drill Later: The Effect of Expected Volatility on Investment," investigates the extent to which firms' drilling behavior accords with a key prescription of real options theory: irreversible investments such as drilling should be deferred when the expected volatility of the investments' payoffs increases. I combine detailed data on oil drilling with expectations of future oil price volatility that I derive from the NYMEX futures options market. Conditioning on expected price levels, I find that oil production companies significantly reduce the number of wells they drill when expected price volatility is high. I conclude with "Daylight Time and Energy: Evidence from an Australian Experiment," co-authored with Hendrik Wolff. This chapter assesses DST's impact on electricity demand using a quasi-experiment in which parts of Australia extended DST in 2000 to facilitate the Sydney Olympics. We show that the extension did not reduce overall electricity consumption, but did cause a substantial intra-day shift in demand consistent with activity patterns that are tied to the clock rather than sunrise and sunset.

Self-Learning Monte Carlo Method

NASA Astrophysics Data System (ADS)

Liu, Junwei; Qi, Yang; Meng, Zi Yang; Fu, Liang

Monte Carlo simulation is an unbiased numerical tool for studying classical and quantum many-body systems. One of its bottlenecks is the lack of general and efficient update algorithm for large size systems close to phase transition or with strong frustrations, for which local updates perform badly. In this work, we propose a new general-purpose Monte Carlo method, dubbed self-learning Monte Carlo (SLMC), in which an efficient update algorithm is first learned from the training data generated in trial simulations and then used to speed up the actual simulation. We demonstrate the efficiency of SLMC in a spin model at the phase transition point, achieving a 10-20 times speedup. This work is supported by the DOE Office of Basic Energy Sciences, Division of Materials Sciences and Engineering under Award DE-SC0010526.

A collaborative framework for Distributed Privacy-Preserving Support Vector Machine learning.

PubMed

Que, Jialan; Jiang, Xiaoqian; Ohno-Machado, Lucila

2012-01-01

A Support Vector Machine (SVM) is a popular tool for decision support. The traditional way to build an SVM model is to estimate parameters based on a centralized repository of data. However, in the field of biomedicine, patient data are sometimes stored in local repositories or institutions where they were collected, and may not be easily shared due to privacy concerns. This creates a substantial barrier for researchers to effectively learn from the distributed data using machine learning tools like SVMs. To overcome this difficulty and promote efficient information exchange without sharing sensitive raw data, we developed a Distributed Privacy Preserving Support Vector Machine (DPP-SVM). The DPP-SVM enables privacy-preserving collaborative learning, in which a trusted server integrates "privacy-insensitive" intermediary results. The globally learned model is guaranteed to be exactly the same as learned from combined data. We also provide a free web-service (http://privacy.ucsd.edu:8080/ppsvm/) for multiple participants to collaborate and complete the SVM-learning task in an efficient and privacy-preserving manner.

Rules and mechanisms for efficient two-stage learning in neural circuits.

PubMed

Teşileanu, Tiberiu; Ölveczky, Bence; Balasubramanian, Vijay

2017-04-04

Trial-and-error learning requires evaluating variable actions and reinforcing successful variants. In songbirds, vocal exploration is induced by LMAN, the output of a basal ganglia-related circuit that also contributes a corrective bias to the vocal output. This bias is gradually consolidated in RA, a motor cortex analogue downstream of LMAN. We develop a new model of such two-stage learning. Using stochastic gradient descent, we derive how the activity in 'tutor' circuits ( e.g., LMAN) should match plasticity mechanisms in 'student' circuits ( e.g., RA) to achieve efficient learning. We further describe a reinforcement learning framework through which the tutor can build its teaching signal. We show that mismatches between the tutor signal and the plasticity mechanism can impair learning. Applied to birdsong, our results predict the temporal structure of the corrective bias from LMAN given a plasticity rule in RA. Our framework can be applied predictively to other paired brain areas showing two-stage learning.

Single-hidden-layer feed-forward quantum neural network based on Grover learning.

PubMed

Liu, Cheng-Yi; Chen, Chein; Chang, Ching-Ter; Shih, Lun-Min

2013-09-01

In this paper, a novel single-hidden-layer feed-forward quantum neural network model is proposed based on some concepts and principles in the quantum theory. By combining the quantum mechanism with the feed-forward neural network, we defined quantum hidden neurons and connected quantum weights, and used them as the fundamental information processing unit in a single-hidden-layer feed-forward neural network. The quantum neurons make a wide range of nonlinear functions serve as the activation functions in the hidden layer of the network, and the Grover searching algorithm outstands the optimal parameter setting iteratively and thus makes very efficient neural network learning possible. The quantum neuron and weights, along with a Grover searching algorithm based learning, result in a novel and efficient neural network characteristic of reduced network, high efficient training and prospect application in future. Some simulations are taken to investigate the performance of the proposed quantum network and the result show that it can achieve accurate learning. Copyright © 2013 Elsevier Ltd. All rights reserved.

Efficient Learning of Continuous-Time Hidden Markov Models for Disease Progression

PubMed Central

Liu, Yu-Ying; Li, Shuang; Li, Fuxin; Song, Le; Rehg, James M.

2016-01-01

The Continuous-Time Hidden Markov Model (CT-HMM) is an attractive approach to modeling disease progression due to its ability to describe noisy observations arriving irregularly in time. However, the lack of an efficient parameter learning algorithm for CT-HMM restricts its use to very small models or requires unrealistic constraints on the state transitions. In this paper, we present the first complete characterization of efficient EM-based learning methods for CT-HMM models. We demonstrate that the learning problem consists of two challenges: the estimation of posterior state probabilities and the computation of end-state conditioned statistics. We solve the first challenge by reformulating the estimation problem in terms of an equivalent discrete time-inhomogeneous hidden Markov model. The second challenge is addressed by adapting three approaches from the continuous time Markov chain literature to the CT-HMM domain. We demonstrate the use of CT-HMMs with more than 100 states to visualize and predict disease progression using a glaucoma dataset and an Alzheimer’s disease dataset. PMID:27019571

Considering student choice when selecting instructional strategies: a comparison of three prompting systems.

PubMed

Taber-Doughty, Teresa

2005-01-01

Three secondary age students with moderate intellectual disabilities learned to use the system of least prompts, a self-operated picture prompting system, and a self-operated auditory prompting system to use a copy machine and a debit machine. Both the effectiveness and efficiency of these prompting systems were compared. Additionally, student preference of instructional method was examined. The results demonstrated that each prompting system was effective and efficient with varying students when skill acquisition and duration of task performance were measured. All students demonstrated increased independence in completing both tasks. This study found that the preferred prompting systems were more effective in terms of both skill acquisition and duration for completing tasks for students.

Learning to detect and combine the features of an object

PubMed Central

Suchow, Jordan W.; Pelli, Denis G.

2013-01-01

To recognize an object, it is widely supposed that we first detect and then combine its features. Familiar objects are recognized effortlessly, but unfamiliar objects—like new faces or foreign-language letters—are hard to distinguish and must be learned through practice. Here, we describe a method that separates detection and combination and reveals how each improves as the observer learns. We dissociate the steps by two independent manipulations: For each step, we do or do not provide a bionic crutch that performs it optimally. Thus, the two steps may be performed solely by the human, solely by the crutches, or cooperatively, when the human takes one step and a crutch takes the other. The crutches reveal a double dissociation between detecting and combining. Relative to the two-step ideal, the human observer’s overall efficiency for unconstrained identification equals the product of the efficiencies with which the human performs the steps separately. The two-step strategy is inefficient: Constraining the ideal to take two steps roughly halves its identification efficiency. In contrast, we find that humans constrained to take two steps perform just as well as when unconstrained, which suggests that they normally take two steps. Measuring threshold contrast (the faintness of a barely identifiable letter) as it improves with practice, we find that detection is inefficient and learned slowly. Combining is learned at a rate that is 4× higher and, after 1,000 trials, 7× more efficient. This difference explains much of the diversity of rates reported in perceptual learning studies, including effects of complexity and familiarity. PMID:23267067

Efficient Learning Using a Virtual Learning Environment in a University Class

ERIC Educational Resources Information Center

Stricker, Daniel; Weibel, David; Wissmath, Bartholomaus

2011-01-01

This study examines a blended learning setting in an undergraduate course in psychology. A virtual learning environment (VLE) complemented the face-to-face lecture. The usage was voluntary and the VLE was designed to support the learning process of the students. Data from users (N = 80) and non-users (N = 82) from two cohorts were collected.…

Harnessing the Power of Learning Management Systems: An E-Learning Approach for Professional Development.

PubMed

White, Meagan; Shellenbarger, Teresa

E-learning provides an alternative approach to traditional professional development activities. A learning management system may help nursing professional development practitioners deliver content more efficiently and effectively; however, careful consideration is needed during planning and implementation. This article provides essential information in the selection and use of a learning management system for professional development.

CARSVM: a class association rule-based classification framework and its application to gene expression data.

PubMed

Kianmehr, Keivan; Alhajj, Reda

2008-09-01

In this study, we aim at building a classification framework, namely the CARSVM model, which integrates association rule mining and support vector machine (SVM). The goal is to benefit from advantages of both, the discriminative knowledge represented by class association rules and the classification power of the SVM algorithm, to construct an efficient and accurate classifier model that improves the interpretability problem of SVM as a traditional machine learning technique and overcomes the efficiency issues of associative classification algorithms. In our proposed framework: instead of using the original training set, a set of rule-based feature vectors, which are generated based on the discriminative ability of class association rules over the training samples, are presented to the learning component of the SVM algorithm. We show that rule-based feature vectors present a high-qualified source of discrimination knowledge that can impact substantially the prediction power of SVM and associative classification techniques. They provide users with more conveniences in terms of understandability and interpretability as well. We have used four datasets from UCI ML repository to evaluate the performance of the developed system in comparison with five well-known existing classification methods. Because of the importance and popularity of gene expression analysis as real world application of the classification model, we present an extension of CARSVM combined with feature selection to be applied to gene expression data. Then, we describe how this combination will provide biologists with an efficient and understandable classifier model. The reported test results and their biological interpretation demonstrate the applicability, efficiency and effectiveness of the proposed model. From the results, it can be concluded that a considerable increase in classification accuracy can be obtained when the rule-based feature vectors are integrated in the learning process of the SVM algorithm. In the context of applicability, according to the results obtained from gene expression analysis, we can conclude that the CARSVM system can be utilized in a variety of real world applications with some adjustments.

Measuring learning gain: Comparing anatomy drawing screencasts and paper-based resources.

PubMed

Pickering, James D

2017-07-01

The use of technology-enhanced learning (TEL) resources is now a common tool across a variety of healthcare programs. Despite this popular approach to curriculum delivery there remains a paucity in empirical evidence that quantifies the change in learning gain. The aim of the study was to measure the changes in learning gain observed with anatomy drawing screencasts in comparison to a traditional paper-based resource. Learning gain is a widely used term to describe the tangible changes in learning outcomes that have been achieved after a specific intervention. In regard to this study, a cohort of Year 2 medical students voluntarily participated and were randomly assigned to either a screencast or textbook group to compare changes in learning gain across resource type. Using a pre-test/post-test protocol, and a range of statistical analyses, the learning gain was calculated at three test points: immediate post-test, 1-week post-test and 4-week post-test. Results at all test points revealed a significant increase in learning gain and large effect sizes for the screencast group compared to the textbook group. Possible reasons behind the difference in learning gain are explored by comparing the instructional design of both resources. Strengths and weaknesses of the study design are also considered. This work adds to the growing area of research that supports the effective design of TEL resources which are complimentary to the cognitive theory of multimedia learning to achieve both an effective and efficient learning resource for anatomical education. Anat Sci Educ 10: 307-316. © 2016 American Association of Anatomists. © 2016 American Association of Anatomists.

Projected regression method for solving Fredholm integral equations arising in the analytic continuation problem of quantum physics

NASA Astrophysics Data System (ADS)

Arsenault, Louis-François; Neuberg, Richard; Hannah, Lauren A.; Millis, Andrew J.

2017-11-01

We present a supervised machine learning approach to the inversion of Fredholm integrals of the first kind as they arise, for example, in the analytic continuation problem of quantum many-body physics. The approach provides a natural regularization for the ill-conditioned inverse of the Fredholm kernel, as well as an efficient and stable treatment of constraints. The key observation is that the stability of the forward problem permits the construction of a large database of outputs for physically meaningful inputs. Applying machine learning to this database generates a regression function of controlled complexity, which returns approximate solutions for previously unseen inputs; the approximate solutions are then projected onto the subspace of functions satisfying relevant constraints. Under standard error metrics the method performs as well or better than the Maximum Entropy method for low input noise and is substantially more robust to increased input noise. We suggest that the methodology will be similarly effective for other problems involving a formally ill-conditioned inversion of an integral operator, provided that the forward problem can be efficiently solved.

The development of cortical sensitivity to visual word forms.

PubMed

Ben-Shachar, Michal; Dougherty, Robert F; Deutsch, Gayle K; Wandell, Brian A

2011-09-01

The ability to extract visual word forms quickly and efficiently is essential for using reading as a tool for learning. We describe the first longitudinal fMRI study to chart individual changes in cortical sensitivity to written words as reading develops. We conducted four annual measurements of brain function and reading skills in a heterogeneous group of children, initially 7-12 years old. The results show age-related increase in children's cortical sensitivity to word visibility in posterior left occipito-temporal sulcus (LOTS), nearby the anatomical location of the visual word form area. Moreover, the rate of increase in LOTS word sensitivity specifically correlates with the rate of improvement in sight word efficiency, a measure of speeded overt word reading. Other cortical regions, including V1, posterior parietal cortex, and the right homologue of LOTS, did not demonstrate such developmental changes. These results provide developmental support for the hypothesis that LOTS is part of the cortical circuitry that extracts visual word forms quickly and efficiently and highlight the importance of developing cortical sensitivity to word visibility in reading acquisition.

The Development of Cortical Sensitivity to Visual Word Forms

PubMed Central

Ben-Shachar, Michal; Dougherty, Robert F.; Deutsch, Gayle K.; Wandell, Brian A.

2011-01-01

The ability to extract visual word forms quickly and efficiently is essential for using reading as a tool for learning. We describe the first longitudinal fMRI study to chart individual changes in cortical sensitivity to written words as reading develops. We conducted four annual measurements of brain function and reading skills in a heterogeneous group of children, initially 7–12 years old. The results show age-related increase in children's cortical sensitivity to word visibility in posterior left occipito-temporal sulcus (LOTS), nearby the anatomical location of the visual word form area. Moreover, the rate of increase in LOTS word sensitivity specifically correlates with the rate of improvement in sight word efficiency, a measure of speeded overt word reading. Other cortical regions, including V1, posterior parietal cortex, and the right homologue of LOTS, did not demonstrate such developmental changes. These results provide developmental support for the hypothesis that LOTS is part of the cortical circuitry that extracts visual word forms quickly and efficiently and highlight the importance of developing cortical sensitivity to word visibility in reading acquisition. PMID:21261451

Web Applications That Promote Learning Communities in Today's Online Classrooms

ERIC Educational Resources Information Center

Reigle, Rosemary R.

2015-01-01

The changing online learning environment requires that instructors depend less on the standard tools built into most educational learning platforms and turn their focus to use of Open Educational Resources (OERs) and free or low-cost commercial applications. These applications permit new and more efficient ways to build online learning communities…