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Sample records for learning behavioral model

  1. Car-following Behavior Model Learning Using Timed Automata

    NARCIS (Netherlands)

    Zhang, Yihuan; Lin, Q.; Wang, Jun; Verwer, S.E.; Dochain, D.; Henrion, D.; Peaucelle, D.

    Learning driving behavior is fundamental for autonomous vehicles to “understand” traffic situations. This paper proposes a novel method for learning a behavioral model of car-following using automata learning algorithms. The model is interpretable for car-following behavior analysis. Frequent common

  2. Learning Markov models for stationary system behaviors

    DEFF Research Database (Denmark)

    Chen, Yingke; Mao, Hua; Jaeger, Manfred

    2012-01-01

    to a single long observation sequence, and in these situations existing automatic learning methods cannot be applied. In this paper, we adapt algorithms for learning variable order Markov chains from a single observation sequence of a target system, so that stationary system properties can be verified using......Establishing an accurate model for formal verification of an existing hardware or software system is often a manual process that is both time consuming and resource demanding. In order to ease the model construction phase, methods have recently been proposed for automatically learning accurate...... the learned model. Experiments demonstrate that system properties (formulated as stationary probabilities of LTL formulas) can be reliably identified using the learned model....

  3. Learning Behavior Models for Interpreting and Predicting Traffic Situations

    OpenAIRE

    Gindele, Tobias

    2014-01-01

    In this thesis, we present Bayesian state estimation and machine learning methods for predicting traffic situations. The cognitive ability to assess situations and behaviors of traffic participants, and to anticipate possible developments is an essential requirement for several applications in the traffic domain, especially for self-driving cars. We present a method for learning behavior models from unlabeled traffic observations and develop improved learning methods for decision trees.

  4. A phenomenological memristor model for synaptic memory and learning behaviors

    Institute of Scientific and Technical Information of China (English)

    Nan Shao; Sheng-Bing Zhang; Shu-Yuan Shao

    2017-01-01

    Properties that are similar to the memory and learning functions in biological systems have been observed and reported in the experimental studies of memristors fabricated by different materials.These properties include the forgetting effect,the transition from short-term memory (STM) to long-term memory (LTM),learning-experience behavior,etc.The mathematical model of this kind of memristor would be very important for its theoretical analysis and application design.In our analysis of the existing memristor model with these properties,we find that some behaviors of the model are inconsistent with the reported experimental observations.A phenomenological memristor model is proposed for this kind of memristor.The model design is based on the forgetting effect and STM-to-LTM transition since these behaviors are two typical properties of these memristors.Further analyses of this model show that this model can also be used directly or modified to describe other experimentally observed behaviors.Simulations show that the proposed model can give a better description of the reported memory and learning behaviors of this kind of memristor than the existing model.

  5. Modeling individuals’ cognitive and affective responses in spatial learning behavior

    NARCIS (Netherlands)

    Han, Q.; Arentze, T.A.; Timmermans, H.J.P.; Janssens, D.; Wets, G.; Lo, H.P.; Leung, Stephen C.H.; Tan, Susanna M.L.

    2008-01-01

    Activity-based analysis has slowly shifted gear from analysis of daily activity patterns to analysis and modeling of dynamic activity-travel patterns. In this paper, we describe a dynamic model that is concerned with simulating cognitive and affective responses in spatial learning behavior for a

  6. Learning Methods for Dynamic Topic Modeling in Automated Behavior Analysis.

    Science.gov (United States)

    Isupova, Olga; Kuzin, Danil; Mihaylova, Lyudmila

    2017-09-27

    Semisupervised and unsupervised systems provide operators with invaluable support and can tremendously reduce the operators' load. In the light of the necessity to process large volumes of video data and provide autonomous decisions, this paper proposes new learning algorithms for activity analysis in video. The activities and behaviors are described by a dynamic topic model. Two novel learning algorithms based on the expectation maximization approach and variational Bayes inference are proposed. Theoretical derivations of the posterior estimates of model parameters are given. The designed learning algorithms are compared with the Gibbs sampling inference scheme introduced earlier in the literature. A detailed comparison of the learning algorithms is presented on real video data. We also propose an anomaly localization procedure, elegantly embedded in the topic modeling framework. It is shown that the developed learning algorithms can achieve 95% success rate. The proposed framework can be applied to a number of areas, including transportation systems, security, and surveillance.

  7. A Social Learning Model of Adolescent Contraceptive Behavior.

    Science.gov (United States)

    Balassone, Mary Lou

    1991-01-01

    Research findings and theories regarding adolescent contraceptive use are reviewed to propose an alternative framework relying on social learning theory. Environmental context, cognitive influences, and behavior execution constraints are suggested as the foundation for contraceptive behaviors. The behavioral skills teenagers need to use birth…

  8. Modeling the behavioral substrates of associate learning and memory - Adaptive neural models

    Science.gov (United States)

    Lee, Chuen-Chien

    1991-01-01

    Three adaptive single-neuron models based on neural analogies of behavior modification episodes are proposed, which attempt to bridge the gap between psychology and neurophysiology. The proposed models capture the predictive nature of Pavlovian conditioning, which is essential to the theory of adaptive/learning systems. The models learn to anticipate the occurrence of a conditioned response before the presence of a reinforcing stimulus when training is complete. Furthermore, each model can find the most nonredundant and earliest predictor of reinforcement. The behavior of the models accounts for several aspects of basic animal learning phenomena in Pavlovian conditioning beyond previous related models. Computer simulations show how well the models fit empirical data from various animal learning paradigms.

  9. Using LSTMs to learn physiological models of blood glucose behavior.

    Science.gov (United States)

    Mirshekarian, Sadegh; Bunescu, Razvan; Marling, Cindy; Schwartz, Frank

    2017-07-01

    For people with type 1 diabetes, good blood glucose control is essential to keeping serious disease complications at bay. This entails carefully monitoring blood glucose levels and taking corrective steps whenever they are too high or too low. If blood glucose levels could be accurately predicted, patients could take proactive steps to prevent blood glucose excursions from occurring. However, accurate predictions require complex physiological models of blood glucose behavior. Factors such as insulin boluses, carbohydrate intake, and exercise influence blood glucose in ways that are difficult to capture through manually engineered equations. In this paper, we describe a recursive neural network (RNN) approach that uses long short-term memory (LSTM) units to learn a physiological model of blood glucose. When trained on raw data from real patients, the LSTM networks (LSTMs) obtain results that are competitive with a previous state-of-the-art model based on manually engineered physiological equations. The RNN approach can incorporate arbitrary physiological parameters without the need for sophisticated manual engineering, thus holding the promise of further improvements in prediction accuracy.

  10. Sensorimotor learning biases choice behavior: a learning neural field model for decision making.

    Directory of Open Access Journals (Sweden)

    Christian Klaes

    Full Text Available According to a prominent view of sensorimotor processing in primates, selection and specification of possible actions are not sequential operations. Rather, a decision for an action emerges from competition between different movement plans, which are specified and selected in parallel. For action choices which are based on ambiguous sensory input, the frontoparietal sensorimotor areas are considered part of the common underlying neural substrate for selection and specification of action. These areas have been shown capable of encoding alternative spatial motor goals in parallel during movement planning, and show signatures of competitive value-based selection among these goals. Since the same network is also involved in learning sensorimotor associations, competitive action selection (decision making should not only be driven by the sensory evidence and expected reward in favor of either action, but also by the subject's learning history of different sensorimotor associations. Previous computational models of competitive neural decision making used predefined associations between sensory input and corresponding motor output. Such hard-wiring does not allow modeling of how decisions are influenced by sensorimotor learning or by changing reward contingencies. We present a dynamic neural field model which learns arbitrary sensorimotor associations with a reward-driven Hebbian learning algorithm. We show that the model accurately simulates the dynamics of action selection with different reward contingencies, as observed in monkey cortical recordings, and that it correctly predicted the pattern of choice errors in a control experiment. With our adaptive model we demonstrate how network plasticity, which is required for association learning and adaptation to new reward contingencies, can influence choice behavior. The field model provides an integrated and dynamic account for the operations of sensorimotor integration, working memory and action

  11. Learning and Behavior

    Science.gov (United States)

    ... List About PPMD Events News Login By Area Learning & Behavior Attention, Listening & Learning Autism Spectrum Disorder (ASD) ... Care Guidelines ❯ By Area ❯ Learning & Behavior Share Print Learning & Behavior Facts to Remember People with Duchenne may ...

  12. Behavioral Modeling for Mental Health using Machine Learning Algorithms.

    Science.gov (United States)

    Srividya, M; Mohanavalli, S; Bhalaji, N

    2018-04-03

    Mental health is an indicator of emotional, psychological and social well-being of an individual. It determines how an individual thinks, feels and handle situations. Positive mental health helps one to work productively and realize their full potential. Mental health is important at every stage of life, from childhood and adolescence through adulthood. Many factors contribute to mental health problems which lead to mental illness like stress, social anxiety, depression, obsessive compulsive disorder, drug addiction, and personality disorders. It is becoming increasingly important to determine the onset of the mental illness to maintain proper life balance. The nature of machine learning algorithms and Artificial Intelligence (AI) can be fully harnessed for predicting the onset of mental illness. Such applications when implemented in real time will benefit the society by serving as a monitoring tool for individuals with deviant behavior. This research work proposes to apply various machine learning algorithms such as support vector machines, decision trees, naïve bayes classifier, K-nearest neighbor classifier and logistic regression to identify state of mental health in a target group. The responses obtained from the target group for the designed questionnaire were first subject to unsupervised learning techniques. The labels obtained as a result of clustering were validated by computing the Mean Opinion Score. These cluster labels were then used to build classifiers to predict the mental health of an individual. Population from various groups like high school students, college students and working professionals were considered as target groups. The research presents an analysis of applying the aforementioned machine learning algorithms on the target groups and also suggests directions for future work.

  13. An Adolescent Nutrition Learning Model to Facilitate Behavior Change in Overweight Teens

    Science.gov (United States)

    Young, Kimberly J.; Ramsay, Samantha A.; Holyoke, Laura B.

    2016-01-01

    Understanding the process by which adolescents learn about nutrition is necessary for developing tailored education that leads to sustainable behavior change. Teens aged 15-17 participating in an obesity prevention program were interviewed. From the data, three themes emerged and informed development of an adolescent nutrition learning model. The…

  14. Modeling as a Technique for Promoting Classroom Learning and Prosocial Behavior. Theoretical Paper No. 39.

    Science.gov (United States)

    Frayer, Dorothy A.; Klausmeier, Herbert J.

    Research has shown that a behavior may be acquired through observing and imitating a model. A behavior which has already been acquired may be inhibited, disinhibited, or elicited by observing and imitating. A definition of imitation is given, and the effects of imitation on learning and performance are summarized. Research on factors which affect…

  15. A Model for the Transfer of Perceptual-Motor Skill Learning in Human Behaviors

    Science.gov (United States)

    Rosalie, Simon M.; Muller, Sean

    2012-01-01

    This paper presents a preliminary model that outlines the mechanisms underlying the transfer of perceptual-motor skill learning in sport and everyday tasks. Perceptual-motor behavior is motivated by performance demands and evolves over time to increase the probability of success through adaptation. Performance demands at the time of an event…

  16. A Quasi-Linear Behavioral Model and an Application to Self-Directed Learning

    Science.gov (United States)

    Ponton, Michael K.; Carr, Paul B.

    1999-01-01

    A model is presented that describes the relationship between one's knowledge of the world and the concomitant personal behaviors that serve as a mechanism to obtain desired outcomes. Integrated within this model are the differing roles that outcomes serve as motivators and as modifiers to one's worldview. The model is dichotomized between general and contextual applications. Because learner self-directedness (a personal characteristic) involves cognition and affection while self-directed learning (a pedagogic process) encompasses conation, behavior and introspection, the model can be dichotomized again in another direction. Presented also are the roles that cognitive motivation theories play in moving an individual through this behavioral model and the roles of wishes, self-efficacy, opportunity and self-influence.

  17. Modeling eating behaviors: The role of environment and positive food association learning via a Ratatouille effect.

    Science.gov (United States)

    Murillo, Anarina L; Safan, Muntaser; Castillo-Chavez, Carlos; Phillips, Elizabeth D Capaldi; Wadhera, Devina

    2016-08-01

    Eating behaviors among a large population of children are studied as a dynamic process driven by nonlinear interactions in the sociocultural school environment. The impact of food association learning on diet dynamics, inspired by a pilot study conducted among Arizona children in Pre-Kindergarten to 8th grades, is used to build simple population-level learning models. Qualitatively, mathematical studies are used to highlight the possible ramifications of instruction, learning in nutrition, and health at the community level. Model results suggest that nutrition education programs at the population-level have minimal impact on improving eating behaviors, findings that agree with prior field studies. Hence, the incorporation of food association learning may be a better strategy for creating resilient communities of healthy and non-healthy eaters. A Ratatouille effect can be observed when food association learners become food preference learners, a potential sustainable behavioral change, which in turn, may impact the overall distribution of healthy eaters. In short, this work evaluates the effectiveness of population-level intervention strategies and the importance of institutionalizing nutrition programs that factor in economical, social, cultural, and environmental elements that mesh well with the norms and values in the community.

  18. A Behavior-Based Circuit Model of How Outcome Expectations Organize Learned Behavior in Larval "Drosophila"

    Science.gov (United States)

    Schleyer, Michael; Saumweber, Timo; Nahrendorf, Wiebke; Fischer, Benjamin; von Alpen, Desiree; Pauls, Dennis; Thum, Andreas; Gerber, Bertram

    2011-01-01

    Drosophila larvae combine a numerically simple brain, a correspondingly moderate behavioral complexity, and the availability of a rich toolbox for transgenic manipulation. This makes them attractive as a study case when trying to achieve a circuit-level understanding of behavior organization. From a series of behavioral experiments, we suggest a…

  19. Iterative perceptual learning for social behavior synthesis

    NARCIS (Netherlands)

    de Kok, I.A.; Poppe, Ronald Walter; Heylen, Dirk K.J.

    We introduce Iterative Perceptual Learning (IPL), a novel approach to learn computational models for social behavior synthesis from corpora of human–human interactions. IPL combines perceptual evaluation with iterative model refinement. Human observers rate the appropriateness of synthesized

  20. Iterative Perceptual Learning for Social Behavior Synthesis

    NARCIS (Netherlands)

    de Kok, I.A.; Poppe, Ronald Walter; Heylen, Dirk K.J.

    We introduce Iterative Perceptual Learning (IPL), a novel approach for learning computational models for social behavior synthesis from corpora of human-human interactions. The IPL approach combines perceptual evaluation with iterative model refinement. Human observers rate the appropriateness of

  1. Soil-pipe interaction modeling for pipe behavior prediction with super learning based methods

    Science.gov (United States)

    Shi, Fang; Peng, Xiang; Liu, Huan; Hu, Yafei; Liu, Zheng; Li, Eric

    2018-03-01

    Underground pipelines are subject to severe distress from the surrounding expansive soil. To investigate the structural response of water mains to varying soil movements, field data, including pipe wall strains in situ soil water content, soil pressure and temperature, was collected. The research on monitoring data analysis has been reported, but the relationship between soil properties and pipe deformation has not been well-interpreted. To characterize the relationship between soil property and pipe deformation, this paper presents a super learning based approach combining feature selection algorithms to predict the water mains structural behavior in different soil environments. Furthermore, automatic variable selection method, e.i. recursive feature elimination algorithm, were used to identify the critical predictors contributing to the pipe deformations. To investigate the adaptability of super learning to different predictive models, this research employed super learning based methods to three different datasets. The predictive performance was evaluated by R-squared, root-mean-square error and mean absolute error. Based on the prediction performance evaluation, the superiority of super learning was validated and demonstrated by predicting three types of pipe deformations accurately. In addition, a comprehensive understand of the water mains working environments becomes possible.

  2. Disruption of model-based behavior and learning by cocaine self-administration in rats.

    Science.gov (United States)

    Wied, Heather M; Jones, Joshua L; Cooch, Nisha K; Berg, Benjamin A; Schoenbaum, Geoffrey

    2013-10-01

    Addiction is characterized by maladaptive decision-making, in which individuals seem unable to use adverse outcomes to modify their behavior. Adverse outcomes are often infrequent, delayed, and even rare events, especially when compared to the reliable rewarding drug-associated outcomes. As a result, recognizing and using information about their occurrence put a premium on the operation of so-called model-based systems of behavioral control, which allow one to mentally simulate outcomes of different courses of action based on knowledge of the underlying associative structure of the environment. This suggests that addiction may reflect, in part, drug-induced dysfunction in these systems. Here, we tested this hypothesis. This study aimed to test whether cocaine causes deficits in model-based behavior and learning independent of requirements for response inhibition or perception of costs or punishment. We trained rats to self-administer sucrose or cocaine for 2 weeks. Four weeks later, the rats began training on a sensory preconditioning and inferred value blocking task. Like devaluation, normal performance on this task requires representations of the underlying task structure; however, unlike devaluation, it does not require either response inhibition or adapting behavior to reflect aversive outcomes. Rats trained to self-administer cocaine failed to show conditioned responding or blocking to the preconditioned cue. These deficits were not observed in sucrose-trained rats nor did they reflect any changes in responding to cues paired directly with reward. These results imply that cocaine disrupts the operation of neural circuits that mediate model-based behavioral control.

  3. Ubiquitous learning model using interactive internet messenger group (IIMG) to improve engagement and behavior for smart campus

    Science.gov (United States)

    Umam, K.; Mardi, S. N. S.; Hariadi, M.

    2017-01-01

    The recent popularity of internet messenger based smartphone technologies has motivated some university lecturers to use them for educational activities. These technologies have enormous potential to enhance the teaching and ubiquitous learning experience for smart campus development. However, the design ubiquitous learning model using interactive internet messenger group (IIMG) and empirical evidence that would favor a broad application of mobile and ubiquitous learning in smart campus settings to improve engagement and behavior is still limited. In addition, the expectation that mobile learning could improve engagement and behavior on smart campus cannot be confirmed because the majority of the reviewed studies followed instructions paradigms. This article aims to present ubiquitous learning model design and showing learners’ experiences in improved engagement and behavior using IIMG for learner-learner and learner-lecturer interactions. The method applied in this paper includes design process and quantitative analysis techniques, with the purpose of identifying scenarios of ubiquitous learning and realize the impressions of learners and lecturers about engagement and behavior aspect, and its contribution to learning.

  4. Perspectives of Students’ Behavior Towards Mobile Learning (M-learning in Egypt: an Extension of the UTAUT Model

    Directory of Open Access Journals (Sweden)

    R. A. Ali

    2016-08-01

    Full Text Available The rapid development of third-generation (3G mobile technologies has led to the emergence of a new kind of learning called mobile learning (m-learning. M-learning means the use of mobile devices to access learning materials at anytime and anywhere with the aid of mobile terminals and networks. This paper explores the possibility of applying m-learning for schools in Egypt through a proposed model of acceptance factors that may affect the students’ intentions to adopt m-learning. We use the original model of Unified Theory of Acceptance and Use of Technology (UTAUT and extended it with three new factors mobility, interactivity, and enjoyment.

  5. Multi-agent models of spatial cognition, learning and complex choice behavior in urban environments

    NARCIS (Netherlands)

    Arentze, Theo; Timmermans, Harry; Portugali, J.

    2006-01-01

    This chapter provides an overview of ongoing research projects in the DDSS research program at TUE related to multi-agents. Projects include (a) the use of multi-agent models and concepts of artificial intelligence to develop models of activity-travel behavior; (b) the use of a multi-agent model to

  6. Lessons Learned Coaching Teachers in Behavior Management: The PBIS"plus" Coaching Model

    Science.gov (United States)

    Hershfeldt, Patricia A.; Pell, Karen; Sechrest, Richard; Pas, Elise T.; Bradshaw, Catherine P.

    2012-01-01

    There is growing interest in coaching as a means of promoting professional development and the use of evidence-based practices in schools. This article describes the PBIS"plus" coaching model used to provide technical assistance for classroom- and school-wide behavior management to elementary schools over the course of 3 years. This Tier…

  7. Learning Classification Models of Cognitive Conditions from Subtle Behaviors in the Digital Clock Drawing Test.

    Science.gov (United States)

    Souillard-Mandar, William; Davis, Randall; Rudin, Cynthia; Au, Rhoda; Libon, David J; Swenson, Rodney; Price, Catherine C; Lamar, Melissa; Penney, Dana L

    2016-03-01

    The Clock Drawing Test - a simple pencil and paper test - has been used for more than 50 years as a screening tool to differentiate normal individuals from those with cognitive impairment, and has proven useful in helping to diagnose cognitive dysfunction associated with neurological disorders such as Alzheimer's disease, Parkinson's disease, and other dementias and conditions. We have been administering the test using a digitizing ballpoint pen that reports its position with considerable spatial and temporal precision, making available far more detailed data about the subject's performance. Using pen stroke data from these drawings categorized by our software, we designed and computed a large collection of features, then explored the tradeoffs in performance and interpretability in classifiers built using a number of different subsets of these features and a variety of different machine learning techniques. We used traditional machine learning methods to build prediction models that achieve high accuracy. We operationalized widely used manual scoring systems so that we could use them as benchmarks for our models. We worked with clinicians to define guidelines for model interpretability, and constructed sparse linear models and rule lists designed to be as easy to use as scoring systems currently used by clinicians, but more accurate. While our models will require additional testing for validation, they offer the possibility of substantial improvement in detecting cognitive impairment earlier than currently possible, a development with considerable potential impact in practice.

  8. Frustration-Instigated Behavior and Learned Helplessness.

    Science.gov (United States)

    Winefield, Anthony H.

    1979-01-01

    Compares M. E. P. Seligman's recent work on learned helplessness with N. R. F. Maier's 30-year-old work on frustration behavior. Notes striking similarities between the two approaches. Concludes that the learned helplessness model might explain the "abnormal fixations" that Maier reported. (Author/RL)

  9. What can be learned from computer modeling? Comparing expository and modeling approaches to teaching dynamic systems behavior

    NARCIS (Netherlands)

    van Borkulo, S.P.; van Joolingen, W.R.; Savelsbergh, E.R.; de Jong, T.

    2012-01-01

    Computer modeling has been widely promoted as a means to attain higher order learning outcomes. Substantiating these benefits, however, has been problematic due to a lack of proper assessment tools. In this study, we compared computer modeling with expository instruction, using a tailored assessment

  10. University Students' Behavioral Intention to Use Mobile Learning: Evaluating the Technology Acceptance Model

    Science.gov (United States)

    Park, Sung Youl; Nam, Min-Woo; Cha, Seung-Bong

    2012-01-01

    As many Korean universities have recommended the implementation of mobile learning (m-learning) for various reasons, the number of such tertiary learning opportunities has steadily grown. However, little research has investigated the factors affecting university students' adoption and use of m-learning. A sample of 288 Konkuk university students…

  11. Learning Theory and Prosocial Behavior

    Science.gov (United States)

    Rosenhan, D. L.

    1972-01-01

    Although theories of learning which stress the role of reinforcement can help us understand altruistic behaviors, it seems clear that a more complete comprehension calls for an expansion of our notions of learning, such that they incorporate affect and cognition. (Author/JM)

  12. Better Behavior for Better Learning.

    Science.gov (United States)

    Novelli, Joan

    1993-01-01

    Presents strategies for banishing behavior problems in the classroom and creating a positive learning environment. The behaviors include name calling; hitting and pushing; tattling; poking and touching; overactivity; talking back; complaining about no playmates; being unprepared to work; and lying, cheating, and stealing. On-the-spot solutions are…

  13. Learning assessment for students with mental and behavioral disorders

    DEFF Research Database (Denmark)

    Dræby, Anders

    The session aims at presenting a learning-based model for how to conduct a comprehensive psychological evaluation of the learning resources and challenges amongst students with mental and behavioral disorders. In the learning assessment model the learning resources and challenges of the students...

  14. Malnutrition, Learning, and Behavior.

    Science.gov (United States)

    Read, Merrill S.; Felson, David

    The problems of those children who are chronically malnourished, the cultural environment of malnutrition, and the extent to which children are temporarily or permanently handicapped in learning because of malnutrition are discussed in this booklet. It also describes hunger and its effects on child development. The topics addressed are: definition…

  15. Visual Perceptual Learning and Models.

    Science.gov (United States)

    Dosher, Barbara; Lu, Zhong-Lin

    2017-09-15

    Visual perceptual learning through practice or training can significantly improve performance on visual tasks. Originally seen as a manifestation of plasticity in the primary visual cortex, perceptual learning is more readily understood as improvements in the function of brain networks that integrate processes, including sensory representations, decision, attention, and reward, and balance plasticity with system stability. This review considers the primary phenomena of perceptual learning, theories of perceptual learning, and perceptual learning's effect on signal and noise in visual processing and decision. Models, especially computational models, play a key role in behavioral and physiological investigations of the mechanisms of perceptual learning and for understanding, predicting, and optimizing human perceptual processes, learning, and performance. Performance improvements resulting from reweighting or readout of sensory inputs to decision provide a strong theoretical framework for interpreting perceptual learning and transfer that may prove useful in optimizing learning in real-world applications.

  16. Learning social attitudes: children's sensitivity to the nonverbal behaviors of adult models during interracial interactions.

    Science.gov (United States)

    Castelli, Luigi; De Dea, Cristina; Nesdale, Drew

    2008-11-01

    White children show marked ingroup race preferences and a relative devaluation of Black people. The origin of these early interracial attitudes is to a large extent still unclear. The studies here test the possibility that preschool-aged children are particularly sensitive to the nonverbal behaviors performed by White adults during interracial interactions. In Study 1, children were shown a video displaying an interaction between a White and a Black adult. Across conditions, the White adult's verbal behaviors were either friendly or neutral, whereas his nonverbal behaviors showed either easiness (e.g., closeness, high eye contact) or uneasiness (e.g., distance, avoidance of eye contact). Results revealed that participants shaped their attitudes toward the Black target accordingly, independently from the White adults' verbal behaviors. Study 2 replicated the basic findings and demonstrated that the observed effects generalized to other Black targets. Results are discussed in relation to current approaches to understanding the formation of racial attitudes among children.

  17. Wuling powder prevents the depression-like behavior in learned helplessness mice model through improving the TSPO mediated-mitophagy.

    Science.gov (United States)

    Li, Dongmei; Zheng, Ji; Wang, Mingyang; Feng, Lu; Liu, Yanyong; Yang, Nan; Zuo, Pingping

    2016-06-20

    Wuling powder (trade name: Wuling capsule), a traditional Chinese medicine (TCM), was extracted from mycelia of precious Xylaria Nigripes (Kl.) Sacc by modern fermentation technology, and has been claimed to be fully potent in improving the signs of insomnia and cognitive deficits. Moreover, Wuling capsule was effective in treating post-stroke and orther co-cormbid depression both in clinical and in basic research. In order to clarify the molecular mechanisms of the antidepressant effect of Wuling powder, we established learned helplessness (LH) depression animal model and focused on 18kDa translocator protein (TSPO) mediated-mitophagy pathway. Mice were exposed to the inescapable e-shock (IS) once a day for three consecutive days to establish the LH model. Then mice were orally administered Wuling powder for 2 weeks. For the behavioral assessment, Shuttle box test, novelty suppressed feeding test (NSF) and forced swimming test (FST) were performed. Following the behavioral assessment, we assessed the protein expression level that were related to TSPO-mediated mitophagy signaling pathway by Western blotting analysis. Finally, immunohistochemistry method was used to assess the neuroprotective effects of Wuling powder. Compared with mice that were subjected to inescapable e-shock, Wuling powder exhibited antidepressant effect in the multiple behavioral tests. In addition, Wuling powder altered the expression level of multiple proteins related to TSPO-mediated mitophagy signaling pathway. Our results suggested that Wuling powder exhibited an obvious antidepressant effect, which could be due to the improvement of TSPO-mediated mitophagy signaling pathway. Copyright © 2016. Published by Elsevier Ireland Ltd.

  18. Student Nutrition, Learning and Behavior.

    Science.gov (United States)

    Royster, Martha

    This discussion addresses several nutrition issues considered important to schools, students, and educators in the United States. Contents consist of a review of malnutrition and learning research and discussions of food additives and allergies, diet and hyperkinesia, the effects of caffeine and sugar on children's behavior, and the National…

  19. Learning Behavior and Achievement Analysis of a Digital Game-Based Learning Approach Integrating Mastery Learning Theory and Different Feedback Models

    Science.gov (United States)

    Yang, Kai-Hsiang

    2017-01-01

    It is widely accepted that the digital game-based learning approach has the advantage of stimulating students' learning motivation, but simply using digital games in the classroom does not guarantee satisfactory learning achievement, especially in the case of the absence of a teacher. Integrating appropriate learning strategies into a game can…

  20. Vicarious learning revisited: a contemporary behavior analytic interpretation.

    Science.gov (United States)

    Masia, C L; Chase, P N

    1997-03-01

    Beginning in the 1960s, social learning theorists argued that behavioral learning principles could not account for behavior acquired through observation. Such a viewpoint is still widely held today. This rejection of behavioral principles in explaining vicarious learning was based on three phenomena: (1) imitation that occurred without direct reinforcement of the observer's behavior; (2) imitation that occurred after a long delay following modeling; and (3) a greater probability of imitation of the model's reinforced behavior than of the model's nonreinforced or punished behavior. These observations convinced social learning theorists that cognitive variables were required to explain behavior. Such a viewpoint has progressed aggressively, as evidenced by the change in name from social learning theory to social cognitive theory, and has been accompanied by the inclusion of information-processing theory. Many criticisms of operant theory, however, have ignored the full range of behavioral concepts and principles that have been derived to account for complex behavior. This paper will discuss some problems with the social learning theory explanation of vicarious learning and provide an interpretation of vicarious learning from a contemporary behavior analytic viewpoint.

  1. [Change of hippocampal NMDA receptor and emotional behavior and spatial learning and memory in status epilepticus rat model].

    Science.gov (United States)

    Wang, Wei-Ping; Lou, Yan; Li, Zhen-Zhong; Li, Pan; Duan, Rui-Sheng

    2007-02-01

    SD rats were utilized for the purpose of the exploration of effects of status epilepticus (SE) on their emotional behavior, spatial learning and memory, and explorating its molecular mechanism. Forty maturity male SD rats, weighing (200 +/- 20) g were divided randomly and equally into SE group (SG) and normal control group (NG). The SG rats were induced by Pentylenetetrazole (PTZ) and the control animals received a saline (0.9%) solution. The change of emotional behavior in two groups were tested in elevated plus maze. Furthermore, Morris water maze was applied to evaluate the effects by SE on spatial learning and memory in rats. At the same time, N-methyl-D-aspartate (NMDA) receptor NR1 subunit mRNA in the hippocampus was determined by reverse transcription polymerase chain reaction (RT-PCR). In elevated plus test, SE rats increased the times of visits as well as the time spent on the open arms of the elevated plus maze (P emotional behavior and damage of spatial learning and memory in rats. NR1 might be involved in the patho- and physiological process in causing these behavioral changes.

  2. Neural Behavior Chain Learning of Mobile Robot Actions

    Directory of Open Access Journals (Sweden)

    Lejla Banjanovic-Mehmedovic

    2012-01-01

    Full Text Available This paper presents a visual/motor behavior learning approach, based on neural networks. We propose Behavior Chain Model (BCM in order to create a way of behavior learning. Our behavior-based system evolution task is a mobile robot detecting a target and driving/acting towards it. First, the mapping relations between the image feature domain of the object and the robot action domain are derived. Second, a multilayer neural network for offline learning of the mapping relations is used. This learning structure through neural network training process represents a connection between the visual perceptions and motor sequence of actions in order to grip a target. Last, using behavior learning through a noticed action chain, we can predict mobile robot behavior for a variety of similar tasks in similar environment. Prediction results suggest that the methodology is adequate and could be recognized as an idea for designing different mobile robot behaviour assistance.

  3. Active Learning for Player Modeling

    DEFF Research Database (Denmark)

    Shaker, Noor; Abou-Zleikha, Mohamed; Shaker, Mohammad

    2015-01-01

    Learning models of player behavior has been the focus of several studies. This work is motivated by better understanding of player behavior, a knowledge that can ultimately be employed to provide player-adapted or personalized content. In this paper, we propose the use of active learning for player...... experience modeling. We use a dataset from hundreds of players playing Infinite Mario Bros. as a case study and we employ the random forest method to learn mod- els of player experience through the active learning approach. The results obtained suggest that only part of the dataset (up to half the size...... that the method can be used online during the content generation process where the mod- els can improve and better content can be presented as the game is being played....

  4. Understanding the Consumers’ Behavior Intention in Using Green Ecolabel Product through Pro-Environmental Planned Behavior Model in Developing and Developed Regions: Lessons Learned from Taiwan and Indonesia

    Directory of Open Access Journals (Sweden)

    Ilma Mufidah

    2018-05-01

    Full Text Available An ecolabel product is an environmentally friendly substance that can be selected to maintain environmental sustainability. Both developed and developing regions are promoting the use of green products. The current study aimed to know the behavior intention on ecolabel product usage from citizens in developing and developed regions. The extended Theory of Planned Behavior, known as Pro-Environmental Planned Behavior Model (PEPB, was used as the assessment model. Two questionnaire surveys were conducted to extract the necessary information for analyzing user’s behavior intention in two different regions. Taiwan and Indonesia were selected as case studies of developed and developing regions, respectively. Structural Equation Modeling (SEM was used to analyze the proposed model and the result reveals that the model explains 49% of behavior intention to use ecolabel product in Taiwan’s case and 72% in Indonesia’s case. The findings revealed that attitude (AT is the key factor to determine the behavioral intention (BI in both Taiwan and Indonesia. Several practical recommendations based on the finding can be considered as input for the governments and related agencies to persuade manufacturing companies to produce more ecolabel products. Increased citizens’ intention to use ecolabel products help the company to reach broader target market and provide incentives to manufacturing companies to produce more environmentally friendly products.

  5. Three-dimensional visualization and a deep-learning model reveal complex fungal parasite networks in behaviorally manipulated ants.

    Science.gov (United States)

    Fredericksen, Maridel A; Zhang, Yizhe; Hazen, Missy L; Loreto, Raquel G; Mangold, Colleen A; Chen, Danny Z; Hughes, David P

    2017-11-21

    Some microbes possess the ability to adaptively manipulate host behavior. To better understand how such microbial parasites control animal behavior, we examine the cell-level interactions between the species-specific fungal parasite Ophiocordyceps unilateralis sensu lato and its carpenter ant host ( Camponotus castaneus ) at a crucial moment in the parasite's lifecycle: when the manipulated host fixes itself permanently to a substrate by its mandibles. The fungus is known to secrete tissue-specific metabolites and cause changes in host gene expression as well as atrophy in the mandible muscles of its ant host, but it is unknown how the fungus coordinates these effects to manipulate its host's behavior. In this study, we combine techniques in serial block-face scanning-electron microscopy and deep-learning-based image segmentation algorithms to visualize the distribution, abundance, and interactions of this fungus inside the body of its manipulated host. Fungal cells were found throughout the host body but not in the brain, implying that behavioral control of the animal body by this microbe occurs peripherally. Additionally, fungal cells invaded host muscle fibers and joined together to form networks that encircled the muscles. These networks may represent a collective foraging behavior of this parasite, which may in turn facilitate host manipulation. Copyright © 2017 the Author(s). Published by PNAS.

  6. Instructional control of reinforcement learning: a behavioral and neurocomputational investigation.

    Science.gov (United States)

    Doll, Bradley B; Jacobs, W Jake; Sanfey, Alan G; Frank, Michael J

    2009-11-24

    Humans learn how to behave directly through environmental experience and indirectly through rules and instructions. Behavior analytic research has shown that instructions can control behavior, even when such behavior leads to sub-optimal outcomes (Hayes, S. (Ed.). 1989. Rule-governed behavior: cognition, contingencies, and instructional control. Plenum Press.). Here we examine the control of behavior through instructions in a reinforcement learning task known to depend on striatal dopaminergic function. Participants selected between probabilistically reinforced stimuli, and were (incorrectly) told that a specific stimulus had the highest (or lowest) reinforcement probability. Despite experience to the contrary, instructions drove choice behavior. We present neural network simulations that capture the interactions between instruction-driven and reinforcement-driven behavior via two potential neural circuits: one in which the striatum is inaccurately trained by instruction representations coming from prefrontal cortex/hippocampus (PFC/HC), and another in which the striatum learns the environmentally based reinforcement contingencies, but is "overridden" at decision output. Both models capture the core behavioral phenomena but, because they differ fundamentally on what is learned, make distinct predictions for subsequent behavioral and neuroimaging experiments. Finally, we attempt to distinguish between the proposed computational mechanisms governing instructed behavior by fitting a series of abstract "Q-learning" and Bayesian models to subject data. The best-fitting model supports one of the neural models, suggesting the existence of a "confirmation bias" in which the PFC/HC system trains the reinforcement system by amplifying outcomes that are consistent with instructions while diminishing inconsistent outcomes.

  7. Learning a decision maker's utility function from (possibly) inconsistent behavior

    DEFF Research Database (Denmark)

    Nielsen, Thomas Dyhre; Jensen, Finn Verner

    2004-01-01

    developed for learning the probabilities from a database.However, methods for learning the utilities have only received limitedattention in the computer science community. A promising approach for learning a decision maker's utility function is to takeoutset in the decision maker's observed behavioral...... patterns, and then find autility function which (together with a domain model) can explainthis behavior. That is, it is assumed that decision maker's preferences arereflected in the behavior. Standard learning algorithmsalso assume that the decision maker is behavioralconsistent, i.e., given a model ofthe...... decision problem, there exists a utility function which canaccount for all the observed behavior. Unfortunately, this assumption israrely valid in real-world decision problems, and in these situationsexisting learning methods may only identify a trivial utilityfunction. In this paper we relax...

  8. Learning Markov Decision Processes for Model Checking

    DEFF Research Database (Denmark)

    Mao, Hua; Chen, Yingke; Jaeger, Manfred

    2012-01-01

    . The proposed learning algorithm is adapted from algorithms for learning deterministic probabilistic finite automata, and extended to include both probabilistic and nondeterministic transitions. The algorithm is empirically analyzed and evaluated by learning system models of slot machines. The evaluation......Constructing an accurate system model for formal model verification can be both resource demanding and time-consuming. To alleviate this shortcoming, algorithms have been proposed for automatically learning system models based on observed system behaviors. In this paper we extend the algorithm...... on learning probabilistic automata to reactive systems, where the observed system behavior is in the form of alternating sequences of inputs and outputs. We propose an algorithm for automatically learning a deterministic labeled Markov decision process model from the observed behavior of a reactive system...

  9. Examination of the Safety of Pediatric Vaccine Schedules in a Non-Human Primate Model: Assessments of Neurodevelopment, Learning, and Social Behavior

    Science.gov (United States)

    Curtis, Britni; Liberato, Noelle; Rulien, Megan; Morrisroe, Kelly; Kenney, Caroline; Yutuc, Vernon; Ferrier, Clayton; Marti, C. Nathan; Mandell, Dorothy; Burbacher, Thomas M.; Sackett, Gene P.

    2015-01-01

    Background In the 1990s, the mercury-based preservative thimerosal was used in most pediatric vaccines. Although there are currently only two thimerosal-containing vaccines (TCVs) recommended for pediatric use, parental perceptions that vaccines pose safety concerns are affecting vaccination rates, particularly in light of the much expanded and more complex schedule in place today. Objectives The objective of this study was to examine the safety of pediatric vaccine schedules in a non-human primate model. Methods We administered vaccines to six groups of infant male rhesus macaques (n = 12–16/group) using a standardized thimerosal dose where appropriate. Study groups included the recommended 1990s Pediatric vaccine schedule, an accelerated 1990s Primate schedule with or without the measles–mumps–rubella (MMR) vaccine, the MMR vaccine only, and the expanded 2008 schedule. We administered saline injections to age-matched control animals (n = 16). Infant development was assessed from birth to 12 months of age by examining the acquisition of neonatal reflexes, the development of object concept permanence (OCP), computerized tests of discrimination learning, and infant social behavior. Data were analyzed using analysis of variance, multilevel modeling, and survival analyses, where appropriate. Results We observed no group differences in the acquisition of OCP. During discrimination learning, animals receiving TCVs had improved performance on reversal testing, although some of these same animals showed poorer performance in subsequent learning-set testing. Analysis of social and nonsocial behaviors identified few instances of negative behaviors across the entire infancy period. Although some group differences in specific behaviors were reported at 2 months of age, by 12 months all infants, irrespective of vaccination status, had developed the typical repertoire of macaque behaviors. Conclusions This comprehensive 5-year case–control study, which closely examined

  10. Towards Behavioral Reflexion Models

    Science.gov (United States)

    Ackermann, Christopher; Lindvall, Mikael; Cleaveland, Rance

    2009-01-01

    Software architecture has become essential in the struggle to manage today s increasingly large and complex systems. Software architecture views are created to capture important system characteristics on an abstract and, thus, comprehensible level. As the system is implemented and later maintained, it often deviates from the original design specification. Such deviations can have implication for the quality of the system, such as reliability, security, and maintainability. Software architecture compliance checking approaches, such as the reflexion model technique, have been proposed to address this issue by comparing the implementation to a model of the systems architecture design. However, architecture compliance checking approaches focus solely on structural characteristics and ignore behavioral conformance. This is especially an issue in Systems-of- Systems. Systems-of-Systems (SoS) are decompositions of large systems, into smaller systems for the sake of flexibility. Deviations of the implementation to its behavioral design often reduce the reliability of the entire SoS. An approach is needed that supports the reasoning about behavioral conformance on architecture level. In order to address this issue, we have developed an approach for comparing the implementation of a SoS to an architecture model of its behavioral design. The approach follows the idea of reflexion models and adopts it to support the compliance checking of behaviors. In this paper, we focus on sequencing properties as they play an important role in many SoS. Sequencing deviations potentially have a severe impact on the SoS correctness and qualities. The desired behavioral specification is defined in UML sequence diagram notation and behaviors are extracted from the SoS implementation. The behaviors are then mapped to the model of the desired behavior and the two are compared. Finally, a reflexion model is constructed that shows the deviations between behavioral design and implementation. This

  11. Allergies and Learning/Behavioral Disorders.

    Science.gov (United States)

    McLoughlin, James A.; Nall, Michael

    1994-01-01

    This article describes various types of allergies, how they are diagnosed medically, and the different forms of medical treatment. It also considers how allergies may affect school learning and behavior, the connection between allergies and learning and behavioral disorders, the impact of allergy medications upon classroom performance, and various…

  12. Using Mobile Learning: Determinates Impacting Behavioral Intention

    Science.gov (United States)

    Lowenthal, Jeffrey N.

    2010-01-01

    This study examined the factors or determinates that impact the behavioral intention of students to use mobile learning (m-learning) technology. These determinates include performance expectancy, effort expectancy, and self-management of learning, all mediated by age, gender, or both. Regression coefficients showed strong and significant…

  13. Study on modeling of operator's learning mechanism

    International Nuclear Information System (INIS)

    Yoshimura, Seichi; Hasegawa, Naoko

    1998-01-01

    One effective method to analyze the causes of human errors is to model the behavior of human and to simulate it. The Central Research Institute of Electric Power Industry (CRIEPI) has developed an operator team behavior simulation system called SYBORG (Simulation System for the Behavior of an Operating Group) to analyze the human errors and to establish the countermeasures for them. As an operator behavior model which composes SYBORG has no learning mechanism and the knowledge of a plant is fixed, it cannot take suitable actions when unknown situations occur nor learn anything from the experience. However, considering actual operators, learning is an essential human factor to enhance their abilities to diagnose plant anomalies. In this paper, Q learning with 1/f fluctuation was proposed as a learning mechanism of an operator and simulation using the mechanism was conducted. The results showed the effectiveness of the learning mechanism. (author)

  14. Web-Based Instruction, Learning Effectiveness and Learning Behavior: The Impact of Relatedness

    Science.gov (United States)

    Shieh, Chich-Jen; Liao, Ying; Hu, Ridong

    2013-01-01

    This study aims to discuss the effects of Web-based Instruction and Learning Behavior on Learning Effectiveness. Web-based Instruction contains the dimensions of Active Learning, Simulation-based Learning, Interactive Learning, and Accumulative Learning; and, Learning Behavior covers Learning Approach, Learning Habit, and Learning Attitude. The…

  15. Chaotic behavior learning of Chua's circuit

    International Nuclear Information System (INIS)

    Sun Jian-Cheng

    2012-01-01

    Least-square support vector machines (LS-SVM) are applied for learning the chaotic behavior of Chua's circuit. The system is divided into three multiple-input single-output (MISO) structures and the LS-SVM are trained individually. Comparing with classical approaches, the proposed one reduces the structural complexity and the selection of parameters is avoided. Some parameters of the attractor are used to compare the chaotic behavior of the reconstructed and the original systems for model validation. Results show that the LS-SVM combined with the MISO can be trained to identify the underlying link among Chua's circuit state variables, and exhibit the chaotic attractors under the autonomous working mode

  16. Computational modeling of epiphany learning.

    Science.gov (United States)

    Chen, Wei James; Krajbich, Ian

    2017-05-02

    Models of reinforcement learning (RL) are prevalent in the decision-making literature, but not all behavior seems to conform to the gradual convergence that is a central feature of RL. In some cases learning seems to happen all at once. Limited prior research on these "epiphanies" has shown evidence of sudden changes in behavior, but it remains unclear how such epiphanies occur. We propose a sequential-sampling model of epiphany learning (EL) and test it using an eye-tracking experiment. In the experiment, subjects repeatedly play a strategic game that has an optimal strategy. Subjects can learn over time from feedback but are also allowed to commit to a strategy at any time, eliminating all other options and opportunities to learn. We find that the EL model is consistent with the choices, eye movements, and pupillary responses of subjects who commit to the optimal strategy (correct epiphany) but not always of those who commit to a suboptimal strategy or who do not commit at all. Our findings suggest that EL is driven by a latent evidence accumulation process that can be revealed with eye-tracking data.

  17. Shifting workplace behavior to inspire learning: a journey to building a learning culture.

    Science.gov (United States)

    Schoonbeek, Sue; Henderson, Amanda

    2011-01-01

    This article discusses the process of building a learning culture. It began with establishing acceptance and connection with the nurse unit manager and the ward team. In the early phases of developing rapport, bullying became apparent. Because bullying undermines sharing and trust, the hallmarks of learning environments, the early intervention work assisted staff to recognize and counteract bullying behaviors. When predominantly positive relationships were restored, interactions that facilitated open communication, including asking questions and providing feedback-behaviors commensurate with learning in the workplace-were developed during regular in-service sessions. Staff participated in role-play and role modeling desired behaviors. Once staff became knowledgeable about positive learning interactions, reward and recognition strategies began to reinforce attitudes and behaviors that align with learning. Through rewards, all nurses had the opportunity to be recognized for their contribution. Nurses who excelled were invited to become champions to continue engaging the key stakeholders to further build the learning environment. Copyright 2011, SLACK Incorporated.

  18. Team learning: building shared mental models

    NARCIS (Netherlands)

    Bossche, van den P.; Gijselaers, W.; Segers, M.; Woltjer, G.B.; Kirschner, P.

    2011-01-01

    To gain insight in the social processes that underlie knowledge sharing in teams, this article questions which team learning behaviors lead to the construction of a shared mental model. Additionally, it explores how the development of shared mental models mediates the relation between team learning

  19. Learning to pronounce first words in three languages: an investigation of caregiver and infant behavior using a computational model of an infant.

    Directory of Open Access Journals (Sweden)

    Ian S Howard

    Full Text Available Words are made up of speech sounds. Almost all accounts of child speech development assume that children learn the pronunciation of first language (L1 speech sounds by imitation, most claiming that the child performs some kind of auditory matching to the elements of ambient speech. However, there is evidence to support an alternative account and we investigate the non-imitative child behavior and well-attested caregiver behavior that this account posits using Elija, a computational model of an infant. Through unsupervised active learning, Elija began by discovering motor patterns, which produced sounds. In separate interaction experiments, native speakers of English, French and German then played the role of his caregiver. In their first interactions with Elija, they were allowed to respond to his sounds if they felt this was natural. We analyzed the interactions through phonemic transcriptions of the caregivers' utterances and found that they interpreted his output within the framework of their native languages. Their form of response was almost always a reformulation of Elija's utterance into well-formed sounds of L1. Elija retained those motor patterns to which a caregiver responded and formed associations between his motor pattern and the response it provoked. Thus in a second phase of interaction, he was able to parse input utterances in terms of the caregiver responses he had heard previously, and respond using his associated motor patterns. This capacity enabled the caregivers to teach Elija to pronounce some simple words in their native languages, by his serial imitation of the words' component speech sounds. Overall, our results demonstrate that the natural responses and behaviors of human subjects to infant-like vocalizations can take a computational model from a biologically plausible initial state through to word pronunciation. This provides support for an alternative to current auditory matching hypotheses for how children learn to

  20. Chaotic exploration and learning of locomotion behaviors.

    Science.gov (United States)

    Shim, Yoonsik; Husbands, Phil

    2012-08-01

    We present a general and fully dynamic neural system, which exploits intrinsic chaotic dynamics, for the real-time goal-directed exploration and learning of the possible locomotion patterns of an articulated robot of an arbitrary morphology in an unknown environment. The controller is modeled as a network of neural oscillators that are initially coupled only through physical embodiment, and goal-directed exploration of coordinated motor patterns is achieved by chaotic search using adaptive bifurcation. The phase space of the indirectly coupled neural-body-environment system contains multiple transient or permanent self-organized dynamics, each of which is a candidate for a locomotion behavior. The adaptive bifurcation enables the system orbit to wander through various phase-coordinated states, using its intrinsic chaotic dynamics as a driving force, and stabilizes on to one of the states matching the given goal criteria. In order to improve the sustainability of useful transient patterns, sensory homeostasis has been introduced, which results in an increased diversity of motor outputs, thus achieving multiscale exploration. A rhythmic pattern discovered by this process is memorized and sustained by changing the wiring between initially disconnected oscillators using an adaptive synchronization method. Our results show that the novel neurorobotic system is able to create and learn multiple locomotion behaviors for a wide range of body configurations and physical environments and can readapt in realtime after sustaining damage.

  1. Decentralized Reinforcement Learning of robot behaviors

    NARCIS (Netherlands)

    Leottau, David L.; Ruiz-del-Solar, Javier; Babuska, R.

    2018-01-01

    A multi-agent methodology is proposed for Decentralized Reinforcement Learning (DRL) of individual behaviors in problems where multi-dimensional action spaces are involved. When using this methodology, sub-tasks are learned in parallel by individual agents working toward a common goal. In

  2. Evidence of Antidepressive Effects of a Wakan-yaku, Hochuekkito, in Depression Model Mice with Learned-Helplessness Behavior

    Science.gov (United States)

    Tohda, Michihisa; Mingmalairak, Salin

    2013-01-01

    Wakan-yaku is a type of Japanese and Sino traditional, systematized medical care that has been practiced for hundreds of years. This medicinal system includes many antidepressive prescriptions. One of the candidates is Hochuekkito, although experimental evidence has not yet been established clearly. To obtain evidence, a depression model of learned-helplessness (LH) mice was used. Based on the score of escape failure, an index of the depression degree, mice with a depressive condition were selected to assess Hochuekkito's effects. This selection was significant and effective in the following two points: evaluation of the drug effect under disease conditions and minimization of the number of animals. Treatment with Hochuekkito (1 and 5 g/kg p.o.; estimated galenical amount) for 14 days significantly decreased the depression index, the number of escape failures, and desipramine (10 mg/kg p.o.) suggesting that Hochuekkito has an antidepressive effect. PMID:24454491

  3. Evidence of Antidepressive Effects of a Wakan-yaku, Hochuekkito, in Depression Model Mice with Learned-Helplessness Behavior

    Directory of Open Access Journals (Sweden)

    Michihisa Tohda

    2013-01-01

    Full Text Available Wakan-yaku is a type of Japanese and Sino traditional, systematized medical care that has been practiced for hundreds of years. This medicinal system includes many antidepressive prescriptions. One of the candidates is Hochuekkito, although experimental evidence has not yet been established clearly. To obtain evidence, a depression model of learned-helplessness (LH mice was used. Based on the score of escape failure, an index of the depression degree, mice with a depressive condition were selected to assess Hochuekkito’s effects. This selection was significant and effective in the following two points: evaluation of the drug effect under disease conditions and minimization of the number of animals. Treatment with Hochuekkito (1 and 5 g/kg p.o.; estimated galenical amount for 14 days significantly decreased the depression index, the number of escape failures, and desipramine (10 mg/kg p.o. suggesting that Hochuekkito has an antidepressive effect.

  4. Behavioral Style, Culture, and Teaching and Learning.

    Science.gov (United States)

    Hilliard, Asa G., III

    1992-01-01

    Argues that unique behavioral styles can be identified among African-American populations and that behavioral style may help explain differences in test performance for white and African-American students. Implications for all students of providing stylistic diversity in the schools and student ability to use multiple learning styles are…

  5. Hoarding behaviors in children with learning disabilities.

    Science.gov (United States)

    Testa, Renée; Pantelis, Christos; Fontenelle, Leonardo F

    2011-05-01

    Our objective was to describe the prevalence, comorbidity, and neuropsychological profiles of children with hoarding and learning disabilities. From 61 children with learning disabilities, 16.4% exhibited hoarding as a major clinical issue. Although children with learning disabilities and hoarding displayed greater rates of obsessive-compulsive disorder (30%) as compared to those with learning disabilities without hoarding (5.9%), the majority of patients belonging to the former group did not display obsessive-compulsive disorder diagnosis. When learning disability patients with hoarding were compared to age-, sex-, and IQ-matched learning disability subjects without hoarding, hoarders exhibited a slower learning curve on word list-learning task. In conclusion, salient hoarding behaviors were found to be relatively common in a sample of children with learning disabilities and not necessarily associated with obsessive-compulsive disorder, supporting its nosological independence. It is unclear whether underlying cognitive features may play a major role in the development of hoarding behaviors in children with learning disabilities.

  6. The Relationship among Self-Regulated Learning, Procrastination, and Learning Behaviors in Blended Learning Environment

    Science.gov (United States)

    Yamada, Masanori; Goda, Yoshiko; Matsuda, Takeshi; Kato, Hiroshi; Miyagawa, Hiroyuki

    2015-01-01

    This research aims to investigate the relationship among the awareness of self-regulated learning (SRL), procrastination, and learning behaviors in blended learning environment. One hundred seventy nine freshmen participated in this research, conducted in the blended learning style class using learning management system. Data collection was…

  7. Modelling intelligent behavior

    Science.gov (United States)

    Green, H. S.; Triffet, T.

    1993-01-01

    An introductory discussion of the related concepts of intelligence and consciousness suggests criteria to be met in the modeling of intelligence and the development of intelligent materials. Methods for the modeling of actual structure and activity of the animal cortex have been found, based on present knowledge of the ionic and cellular constitution of the nervous system. These have led to the development of a realistic neural network model, which has been used to study the formation of memory and the process of learning. An account is given of experiments with simple materials which exhibit almost all properties of biological synapses and suggest the possibility of a new type of computer architecture to implement an advanced type of artificial intelligence.

  8. A finite element-based machine learning approach for modeling the mechanical behavior of the breast tissues under compression in real-time.

    Science.gov (United States)

    Martínez-Martínez, F; Rupérez-Moreno, M J; Martínez-Sober, M; Solves-Llorens, J A; Lorente, D; Serrano-López, A J; Martínez-Sanchis, S; Monserrat, C; Martín-Guerrero, J D

    2017-11-01

    This work presents a data-driven method to simulate, in real-time, the biomechanical behavior of the breast tissues in some image-guided interventions such as biopsies or radiotherapy dose delivery as well as to speed up multimodal registration algorithms. Ten real breasts were used for this work. Their deformation due to the displacement of two compression plates was simulated off-line using the finite element (FE) method. Three machine learning models were trained with the data from those simulations. Then, they were used to predict in real-time the deformation of the breast tissues during the compression. The models were a decision tree and two tree-based ensemble methods (extremely randomized trees and random forest). Two different experimental setups were designed to validate and study the performance of these models under different conditions. The mean 3D Euclidean distance between nodes predicted by the models and those extracted from the FE simulations was calculated to assess the performance of the models in the validation set. The experiments proved that extremely randomized trees performed better than the other two models. The mean error committed by the three models in the prediction of the nodal displacements was under 2 mm, a threshold usually set for clinical applications. The time needed for breast compression prediction is sufficiently short to allow its use in real-time (<0.2 s). Copyright © 2017 Elsevier Ltd. All rights reserved.

  9. Amygdala subsystems and control of feeding behavior by learned cues.

    Science.gov (United States)

    Petrovich, Gorica D; Gallagher, Michela

    2003-04-01

    A combination of behavioral studies and a neural systems analysis approach has proven fruitful in defining the role of the amygdala complex and associated circuits in fear conditioning. The evidence presented in this chapter suggests that this approach is also informative in the study of other adaptive functions that involve the amygdala. In this chapter we present a novel model to study learning in an appetitive context. Furthermore, we demonstrate that long-recognized connections between the amygdala and the hypothalamus play a crucial role in allowing learning to modulate feeding behavior. In the first part we describe a behavioral model for motivational learning. In this model a cue that acquires motivational properties through pairings with food delivery when an animal is hungry can override satiety and promote eating in sated rats. Next, we present evidence that a specific amygdala subsystem (basolateral area) is responsible for allowing such learned cues to control eating (override satiety and promote eating in sated rats). We also show that basolateral amygdala mediates these actions via connectivity with the lateral hypothalamus. Lastly, we present evidence that the amygdalohypothalamic system is specific for the control of eating by learned motivational cues, as it does not mediate another function that depends on intact basolateral amygdala, namely, the ability of a conditioned cue to support new learning based on its acquired value. Knowledge about neural systems through which food-associated cues specifically control feeding behavior provides a defined model for the study of learning. In addition, this model may be informative for understanding mechanisms of maladaptive aspects of learned control of eating that contribute to eating disorders and more moderate forms of overeating.

  10. Learning Model of Unggah-Ungguh Basa Oriented to Noble Behavior in SMP (Junior High School) Jawa Timur (East Java) Indonesia

    Science.gov (United States)

    Sudikan, Setya Yuwana

    2017-01-01

    Learning problem of "unggah-ungguh basa", is very complicated. It is needed reorientation and re-setting the approaches, strategies, methods, techniques, and learning contents that can give rise to a new model of learning of "unggah-ungguh basa" oriented to the character formation of children, especially in Jawa Timur. In…

  11. Reactive behavior, learning, and anticipation

    Science.gov (United States)

    Whitehead, Steven D.; Ballard, Dana H.

    1989-01-01

    Reactive systems always act, thinking only long enough to 'look up' the action to execute. Traditional planning systems think a lot, and act only after generating fairly precise plans. Each represents an endpoint on a spectrum. It is argued that primitive forms of reasoning, like anticipation, play an important role in reducing the cost of learning and that the decision to act or think should be based on the uncertainty associated with the utility of executing an action in a particular situation. An architecture for an adaptable reactive system is presented and it is shown how it can be augmented with a simple anticipation mechanism that can substantially reduce the cost and time of learning.

  12. Behavioral tagging of extinction learning.

    Science.gov (United States)

    de Carvalho Myskiw, Jociane; Benetti, Fernando; Izquierdo, Iván

    2013-01-15

    Extinction of contextual fear in rats is enhanced by exposure to a novel environment at 1-2 h before or 1 h after extinction training. This effect is antagonized by administration of protein synthesis inhibitors anisomycin and rapamycin into the hippocampus, but not into the amygdala, immediately after either novelty or extinction training, as well as by the gene expression blocker 5,6-dichloro-1-beta-D-ribofuranosylbenzimidazole administered after novelty training, but not after extinction training. Thus, this effect can be attributed to a mechanism similar to synaptic tagging, through which long-term potentiation can be enhanced by other long-term potentiations or by exposure to a novel environment in a protein synthesis-dependent fashion. Extinction learning produces a tag at the appropriate synapses, whereas novelty learning causes the synthesis of plasticity-related proteins that are captured by the tag, strengthening the synapses that generated this tag.

  13. Enriching behavioral ecology with reinforcement learning methods.

    Science.gov (United States)

    Frankenhuis, Willem E; Panchanathan, Karthik; Barto, Andrew G

    2018-02-13

    This article focuses on the division of labor between evolution and development in solving sequential, state-dependent decision problems. Currently, behavioral ecologists tend to use dynamic programming methods to study such problems. These methods are successful at predicting animal behavior in a variety of contexts. However, they depend on a distinct set of assumptions. Here, we argue that behavioral ecology will benefit from drawing more than it currently does on a complementary collection of tools, called reinforcement learning methods. These methods allow for the study of behavior in highly complex environments, which conventional dynamic programming methods do not feasibly address. In addition, reinforcement learning methods are well-suited to studying how biological mechanisms solve developmental and learning problems. For instance, we can use them to study simple rules that perform well in complex environments. Or to investigate under what conditions natural selection favors fixed, non-plastic traits (which do not vary across individuals), cue-driven-switch plasticity (innate instructions for adaptive behavioral development based on experience), or developmental selection (the incremental acquisition of adaptive behavior based on experience). If natural selection favors developmental selection, which includes learning from environmental feedback, we can also make predictions about the design of reward systems. Our paper is written in an accessible manner and for a broad audience, though we believe some novel insights can be drawn from our discussion. We hope our paper will help advance the emerging bridge connecting the fields of behavioral ecology and reinforcement learning. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

  14. Multidimensionality of Teachers' Graded Responses for Preschoolers' Stylistic Learning Behavior: The Learning-to-Learn Scales

    Science.gov (United States)

    McDermott, Paul A.; Fantuzzo, John W.; Warley, Heather P.; Waterman, Clare; Angelo, Lauren E.; Gadsden, Vivian L.; Sekino, Yumiko

    2011-01-01

    Assessment of preschool learning behavior has become very popular as a mechanism to inform cognitive development and promote successful interventions. The most widely used measures offer sound predictions but distinguish only a few types of stylistic learning and lack sensitive growth detection. The Learning-to-Learn Scales was designed to…

  15. Reinforcement Learning and Savings Behavior.

    Science.gov (United States)

    Choi, James J; Laibson, David; Madrian, Brigitte C; Metrick, Andrew

    2009-12-01

    We show that individual investors over-extrapolate from their personal experience when making savings decisions. Investors who experience particularly rewarding outcomes from saving in their 401(k)-a high average and/or low variance return-increase their 401(k) savings rate more than investors who have less rewarding experiences with saving. This finding is not driven by aggregate time-series shocks, income effects, rational learning about investing skill, investor fixed effects, or time-varying investor-level heterogeneity that is correlated with portfolio allocations to stock, bond, and cash asset classes. We discuss implications for the equity premium puzzle and interventions aimed at improving household financial outcomes.

  16. Leader-Member Exchange, Learning Orientation and Innovative Work Behavior

    Science.gov (United States)

    Atitumpong, Aungkhana; Badir, Yuosre F.

    2018-01-01

    Purpose: This study aims to examine the effects of leader-member exchange (LMX) and employee learning orientation on employee innovative work behavior (IWB) through creative self-efficacy. Design/methodology/approach: Data have been collected from 337 employees and 137 direct managers from manufacturing sector. A hierarchical linear model has been…

  17. Predicting Teachers’ use of Digital Learning Materials: Combining Self-Determination Theory and the Integrative Model of Behavior Prediction

    NARCIS (Netherlands)

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

    2018-01-01

    In this article, we report on a study that investigated the motivational (e.g., intrinsic motivation) and dispositional variables (e.g., attitudes) that determine teachers’ intention to use or not to use Digital Learning Materials (DLMs). To understand the direct and indirect relationships between

  18. Organic Determinants of Learning and Behavioral Disorders.

    Science.gov (United States)

    Philpott, William H.; And Others

    Theories regarding organic determinants of learning and behavior disorders are reviewed historically. Cases illustrating how a bio-ecologic examination can isolate the substances to which a person reacts and some of the reasons for those reactions are presented; and the role of various disorders in relation to the central nervous system is…

  19. Reinforcement Learning and Savings Behavior*

    Science.gov (United States)

    Choi, James J.; Laibson, David; Madrian, Brigitte C.; Metrick, Andrew

    2009-01-01

    We show that individual investors over-extrapolate from their personal experience when making savings decisions. Investors who experience particularly rewarding outcomes from saving in their 401(k)—a high average and/or low variance return—increase their 401(k) savings rate more than investors who have less rewarding experiences with saving. This finding is not driven by aggregate time-series shocks, income effects, rational learning about investing skill, investor fixed effects, or time-varying investor-level heterogeneity that is correlated with portfolio allocations to stock, bond, and cash asset classes. We discuss implications for the equity premium puzzle and interventions aimed at improving household financial outcomes. PMID:20352013

  20. Learning behavior and learning opportunities as career stimuli

    NARCIS (Netherlands)

    van der Sluis, E.C.

    2002-01-01

    This paper presents some preliminary findings of a study in the field of work-related learning and management development from a managerial perspective. The interaction between individual and organisational characteristics builds the frame of reference to establish a management learning model, which

  1. Modeling taxi driver anticipatory behavior

    NARCIS (Netherlands)

    Zheng, Zhong; Rasouli, S.; Timmermans, H.J.P.

    2018-01-01

    As part of a wider behavioral agent-based model that simulates taxi drivers’ dynamic passenger-finding behavior under uncertainty, we present a model of strategic behavior of taxi drivers in anticipation of substantial time varying demand at locations such as airports and major train stations. The

  2. Self Modeling: Expanding the Theories of Learning

    Science.gov (United States)

    Dowrick, Peter W.

    2012-01-01

    Self modeling (SM) offers a unique expansion of learning theory. For several decades, a steady trickle of empirical studies has reported consistent evidence for the efficacy of SM as a procedure for positive behavior change across physical, social, educational, and diagnostic variations. SM became accepted as an extreme case of model similarity;…

  3. Learning About Climate and Atmospheric Models Through Machine Learning

    Science.gov (United States)

    Lucas, D. D.

    2017-12-01

    From the analysis of ensemble variability to improving simulation performance, machine learning algorithms can play a powerful role in understanding the behavior of atmospheric and climate models. To learn about model behavior, we create training and testing data sets through ensemble techniques that sample different model configurations and values of input parameters, and then use supervised machine learning to map the relationships between the inputs and outputs. Following this procedure, we have used support vector machines, random forests, gradient boosting and other methods to investigate a variety of atmospheric and climate model phenomena. We have used machine learning to predict simulation crashes, estimate the probability density function of climate sensitivity, optimize simulations of the Madden Julian oscillation, assess the impacts of weather and emissions uncertainty on atmospheric dispersion, and quantify the effects of model resolution changes on precipitation. This presentation highlights recent examples of our applications of machine learning to improve the understanding of climate and atmospheric models. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

  4. Place learning overrides innate behaviors in Drosophila.

    Science.gov (United States)

    Baggett, Vincent; Mishra, Aditi; Kehrer, Abigail L; Robinson, Abbey O; Shaw, Paul; Zars, Troy

    2018-03-01

    Animals in a natural environment confront many sensory cues. Some of these cues bias behavioral decisions independent of experience, and action selection can reveal a stimulus-response (S-R) connection. However, in a changing environment it would be a benefit for an animal to update behavioral action selection based on experience, and learning might modify even strong S-R relationships. How animals use learning to modify S-R relationships is a largely open question. Three sensory stimuli, air, light, and gravity sources were presented to individual Drosophila melanogaster in both naïve and place conditioning situations. Flies were tested for a potential modification of the S-R relationships of anemotaxis, phototaxis, and negative gravitaxis by a contingency that associated place with high temperature. With two stimuli, significant S-R relationships were abandoned when the cue was in conflict with the place learning contingency. The role of the dunce ( dnc ) cAMP-phosphodiesterase and the rutabaga ( rut ) adenylyl cyclase were examined in all conditions. Both dnc 1 and rut 2080 mutant flies failed to display significant S-R relationships with two attractive cues, and have characteristically lower conditioning scores under most conditions. Thus, learning can have profound effects on separate native S-R relationships in multiple contexts, and mutation of the dnc and rut genes reveal complex effects on behavior. © 2018 Baggett et al.; Published by Cold Spring Harbor Laboratory Press.

  5. Self-regulatory Behaviors and Approaches to Learning of Arts Students: A Comparison Between Professional Training and English Learning.

    Science.gov (United States)

    Tseng, Min-Chen; Chen, Chia-Cheng

    2017-06-01

    This study investigated the self-regulatory behaviors of arts students, namely memory strategy, goal-setting, self-evaluation, seeking assistance, environmental structuring, learning responsibility, and planning and organizing. We also explored approaches to learning, including deep approach (DA) and surface approach (SA), in a comparison between students' professional training and English learning. The participants consisted of 344 arts majors. The Academic Self-Regulation Questionnaire and the Revised Learning Process Questionnaire were adopted to examine students' self-regulatory behaviors and their approaches to learning. The results show that a positive and significant correlation was found in students' self-regulatory behaviors between professional training and English learning. The results indicated that increases in using self-regulatory behaviors in professional training were associated with increases in applying self-regulatory behaviors in learning English. Seeking assistance, self-evaluation, and planning and organizing were significant predictors for learning English. In addition, arts students used the deep approach more often than the surface approach in both their professional training and English learning. A positive correlation was found in DA, whereas a negative correlation was shown in SA between students' self-regulatory behaviors and their approaches to learning. Students with high self-regulation adopted a deep approach, and they applied the surface approach less in professional training and English learning. In addition, a SEM model confirmed that DA had a positive influence; however, SA had a negative influence on self-regulatory behaviors.

  6. Student Modeling and Machine Learning

    OpenAIRE

    Sison , Raymund; Shimura , Masamichi

    1998-01-01

    After identifying essential student modeling issues and machine learning approaches, this paper examines how machine learning techniques have been used to automate the construction of student models as well as the background knowledge necessary for student modeling. In the process, the paper sheds light on the difficulty, suitability and potential of using machine learning for student modeling processes, and, to a lesser extent, the potential of using student modeling techniques in machine le...

  7. A Driver Behavior Learning Framework for Enhancing Traffic Simulation

    Directory of Open Access Journals (Sweden)

    Ramona Maria Paven

    2014-06-01

    Full Text Available Traffic simulation provides an essential support for developing intelligent transportation systems. It allows affordable validation of such systems using a large variety of scenarios that involves massive data input. However, realistic traffic models are hard to be implemented especially for microscopic traffic simulation. One of the hardest problems in this context is to model the behavior of drivers, due the complexity of human nature. The work presented in this paper proposes a framework for learning driver behavior based on a Hidden Markov Model technique. Moreover, we propose also a practical method to inject this behavior in a traffic model used by the SUMO traffic simulator. To demonstrate the effectiveness of this method we present a case study involving real traffic collected from Timisoara city area.

  8. [Mathematical models of decision making and learning].

    Science.gov (United States)

    Ito, Makoto; Doya, Kenji

    2008-07-01

    Computational models of reinforcement learning have recently been applied to analysis of brain imaging and neural recording data to identity neural correlates of specific processes of decision making, such as valuation of action candidates and parameters of value learning. However, for such model-based analysis paradigms, selecting an appropriate model is crucial. In this study we analyze the process of choice learning in rats using stochastic rewards. We show that "Q-learning," which is a standard reinforcement learning algorithm, does not adequately reflect the features of choice behaviors. Thus, we propose a generalized reinforcement learning (GRL) algorithm that incorporates the negative reward effect of reward loss and forgetting of values of actions not chosen. Using the Bayesian estimation method for time-varying parameters, we demonstrated that the GRL algorithm can predict an animal's choice behaviors as efficiently as the best Markov model. The results suggest the usefulness of the GRL for the model-based analysis of neural processes involved in decision making.

  9. Behavior learning in differential games and reorientation maneuvers

    Science.gov (United States)

    Satak, Neha

    The purpose of this dissertation is to apply behavior learning concepts to incomplete- information continuous time games. Realistic game scenarios are often incomplete-information games in which the players withhold information. A player may not know its opponent's objectives and strategies prior to the start of the game. This lack of information can limit the player's ability to play optimally. If the player can observe the opponent's actions, it can better optimize its achievements by taking corrective actions. In this research, a framework to learn an opponent's behavior and take corrective actions is developed. The framework will allow a player to observe the opponent's actions and formulate behavior models. The developed behavior model can then be utilized to find the best actions for the player that optimizes the player's objective function. In addition, the framework proposes that the player plays a safe strategy at the beginning of the game. A safe strategy is defined in this research as a strategy that guarantees a minimum pay-off to the player independent of the other player's actions. During the initial part of the game, the player will play the safe strategy until it learns the opponent's behavior. Two methods to develop behavior models that differ in the formulation of the behavior model are proposed. The first method is the Cost-Strategy Recognition (CSR) method in which the player formulates an objective function and a strategy for the opponent. The opponent is presumed to be rational and therefore will play to optimize its objective function. The strategy of the opponent is dependent on the information available to the opponent about other players in the game. A strategy formulation presumes a certain level of information available to the opponent. The previous observations of the opponent's actions are used to estimate the parameters of the formulated behavior model. The estimated behavior model predicts the opponent's future actions. The second

  10. Mosaic model for sensorimotor learning and control.

    Science.gov (United States)

    Haruno, M; Wolpert, D M; Kawato, M

    2001-10-01

    Humans demonstrate a remarkable ability to generate accurate and appropriate motor behavior under many different and often uncertain environmental conditions. We previously proposed a new modular architecture, the modular selection and identification for control (MOSAIC) model, for motor learning and control based on multiple pairs of forward (predictor) and inverse (controller) models. The architecture simultaneously learns the multiple inverse models necessary for control as well as how to select the set of inverse models appropriate for a given environment. It combines both feedforward and feedback sensorimotor information so that the controllers can be selected both prior to movement and subsequently during movement. This article extends and evaluates the MOSAIC architecture in the following respects. The learning in the architecture was implemented by both the original gradient-descent method and the expectation-maximization (EM) algorithm. Unlike gradient descent, the newly derived EM algorithm is robust to the initial starting conditions and learning parameters. Second, simulations of an object manipulation task prove that the architecture can learn to manipulate multiple objects and switch between them appropriately. Moreover, after learning, the model shows generalization to novel objects whose dynamics lie within the polyhedra of already learned dynamics. Finally, when each of the dynamics is associated with a particular object shape, the model is able to select the appropriate controller before movement execution. When presented with a novel shape-dynamic pairing, inappropriate activation of modules is observed followed by on-line correction.

  11. Learning situation models in a smart home.

    Science.gov (United States)

    Brdiczka, Oliver; Crowley, James L; Reignier, Patrick

    2009-02-01

    This paper addresses the problem of learning situation models for providing context-aware services. Context for modeling human behavior in a smart environment is represented by a situation model describing environment, users, and their activities. A framework for acquiring and evolving different layers of a situation model in a smart environment is proposed. Different learning methods are presented as part of this framework: role detection per entity, unsupervised extraction of situations from multimodal data, supervised learning of situation representations, and evolution of a predefined situation model with feedback. The situation model serves as frame and support for the different methods, permitting to stay in an intuitive declarative framework. The proposed methods have been integrated into a whole system for smart home environment. The implementation is detailed, and two evaluations are conducted in the smart home environment. The obtained results validate the proposed approach.

  12. Online Behavior Analysis-Based Student Profile for Intelligent E-Learning

    Directory of Open Access Journals (Sweden)

    Kun Liang

    2017-01-01

    Full Text Available With the development of mobile platform, such as smart cellphone and pad, the E-Learning model has been rapidly developed. However, due to the low completion rate for E-Learning platform, it is very necessary to analyze the behavior characteristics of online learners to intelligently adjust online education strategy and enhance the quality of learning. In this paper, we analyzed the relation indicators of E-Learning to build the student profile and gave countermeasures. Adopting the similarity computation and Jaccard coefficient algorithm, we designed a system model to clean and dig into the educational data and also the students’ learning attitude and the duration of learning behavior to establish student profile. According to the E-Learning resources and learner behaviors, we also present the intelligent guide model to guide both E-Learning platform and learners to improve learning things. The study on student profile can help the E-Learning platform to meet and guide the students’ learning behavior deeply and also to provide personalized learning situation and promote the optimization of the E-Learning.

  13. Active Learning with Statistical Models.

    Science.gov (United States)

    1995-01-01

    Active Learning with Statistical Models ASC-9217041, NSF CDA-9309300 6. AUTHOR(S) David A. Cohn, Zoubin Ghahramani, and Michael I. Jordan 7. PERFORMING...TERMS 15. NUMBER OF PAGES Al, MIT, Artificial Intelligence, active learning , queries, locally weighted 6 regression, LOESS, mixtures of gaussians...COMPUTATIONAL LEARNING DEPARTMENT OF BRAIN AND COGNITIVE SCIENCES A.I. Memo No. 1522 January 9. 1995 C.B.C.L. Paper No. 110 Active Learning with

  14. THE CONCEPT OF LANGUAGE LEARNING IN BEHAVIORISM PERSPECTIVE

    Directory of Open Access Journals (Sweden)

    Khoiru Rakhman Abidin

    2016-07-01

    Full Text Available The aims of the study are (1 the concepts of language learning in behaviorism perspective, (2 the relation between language and learning in behaviorism perspective, (3 the influence of behaviorism in language learning. This is a descriptive qualitative study. The results showed that (1 behaviorism theories of languages also give good contribution in language learning process that describes a child can learn language from their environments, (2 behaviorism perspective defines as change of behavior through experience, it means human learn something from their environments, (3 human uses language for communication in the world and he also spreads his culture with his language so  human gets  knowledge of language through learning.

  15. Mirror neuron system and observational learning: behavioral and neurophysiological evidence.

    Science.gov (United States)

    Lago-Rodriguez, Angel; Lopez-Alonso, Virginia; Fernández-del-Olmo, Miguel

    2013-07-01

    Three experiments were performed to study observational learning using behavioral, perceptual, and neurophysiological data. Experiment 1 investigated whether observing an execution model, during physical practice of a transitive task that only presented one execution strategy, led to performance improvements compared with physical practice alone. Experiment 2 investigated whether performing an observational learning protocol improves subjects' action perception. In experiment 3 we evaluated whether the type of practice performed determined the activation of the Mirror Neuron System during action observation. Results showed that, compared with physical practice, observing an execution model during a task that only showed one execution strategy does not provide behavioral benefits. However, an observational learning protocol allows subjects to predict more precisely the outcome of the learned task. Finally, intersperse observation of an execution model with physical practice results in changes of primary motor cortex activity during the observation of the motor pattern previously practiced, whereas modulations in the connectivity between primary and non primary motor areas (PMv-M1; PPC-M1) were not affected by the practice protocol performed by the observer. Copyright © 2013 Elsevier B.V. All rights reserved.

  16. Modeling and Simulation of Elementary Robot Behaviors using Associative Memories

    Directory of Open Access Journals (Sweden)

    Claude F. Touzet

    2006-06-01

    Full Text Available Today, there are several drawbacks that impede the necessary and much needed use of robot learning techniques in real applications. First, the time needed to achieve the synthesis of any behavior is prohibitive. Second, the robot behavior during the learning phase is – by definition – bad, it may even be dangerous. Third, except within the lazy learning approach, a new behavior implies a new learning phase. We propose in this paper to use associative memories (self-organizing maps to encode the non explicit model of the robot-world interaction sampled by the lazy memory, and then generate a robot behavior by means of situations to be achieved, i.e., points on the self-organizing maps. Any behavior can instantaneously be synthesized by the definition of a goal situation. Its performance will be minimal (not necessarily bad and will improve by the mere repetition of the behavior.

  17. The effect of learning styles and study behavior on success of preclinical students in pharmacology.

    Science.gov (United States)

    Asci, Halil; Kulac, Esin; Sezik, Mekin; Cankara, F Nihan; Cicek, Ekrem

    2016-01-01

    To evaluate the effect of learning styles and study behaviors on preclinical medical students' pharmacology exam scores in a non-Western setting. Grasha-Reichmann Student Learning Study Scale and a modified Study Behavior Inventory were used to assess learning styles and study behaviors of preclinical medical students (n = 87). Logistic regression models were used to evaluate the independent effect of gender, age, learning style, and study behavior on pharmacology success. Collaborative (40%) and competitive (27%) dominant learning styles were frequent in the cohort. The most common study behavior subcategories were study reading (40%) and general study habits (38%). Adequate listening and note-taking skills were associated with pharmacology success, whereas students with adequate writing skills had lower exam scores. These effects were independent of gender. Preclinical medical students' study behaviors are independent predictive factors for short-term pharmacology success.

  18. The Game Enhanced Learning Model

    DEFF Research Database (Denmark)

    Reng, Lars; Schoenau-Fog, Henrik

    2016-01-01

    will describe the levels of the model, which is based on our experience in teaching professional game development at university level. Furthermore, we have been using the model to inspire numerous educators to improve their students’ motivation and skills. The model presents various game-based learning...... activities, and depicts their required planning and expected outcome through eight levels. At its lower levels, the model contains the possibilities of using stand-alone analogue and digital games as teachers, utilizing games as a facilitator of learning activities, exploiting gamification and motivating......In this paper, we will introduce the Game Enhanced learning Model (GEM), which describes a range of gameoriented learning activities. The model is intended to give an overview of the possibilities of game-based learning in general and all the way up to purposive game productions. In the paper, we...

  19. Gas Turbine Engine Behavioral Modeling

    OpenAIRE

    Meyer, Richard T; DeCarlo, Raymond A.; Pekarek, Steve; Doktorcik, Chris

    2014-01-01

    This paper develops and validates a power flow behavioral model of a gas tur- bine engine with a gas generator and free power turbine. “Simple” mathematical expressions to describe the engine’s power flow are derived from an understand- ing of basic thermodynamic and mechanical interactions taking place within the engine. The engine behavioral model presented is suitable for developing a supervisory level controller of an electrical power system that contains the en- gine connected to a gener...

  20. Behavior genetics: Bees as model

    International Nuclear Information System (INIS)

    Nates Parra, Guiomar

    2011-01-01

    The honeybee Apis mellifera (Apidae) is a model widely used in behavior because of its elaborate social life requiring coordinate actions among the members of the society. Within a colony, division of labor, the performance of tasks by different individuals, follows genetically determined physiological changes that go along with aging. Modern advances in tools of molecular biology and genomics, as well as the sequentiation of A. mellifera genome, have enabled a better understanding of honeybee behavior, in particular social behavior. Numerous studies show that aspects of worker behavior are genetically determined, including defensive, hygienic, reproductive and foraging behavior. For example, genetic diversity is associated with specialization to collect water, nectar and pollen. Also, control of worker reproduction is associated with genetic differences. In this paper, I review the methods and the main results from the study of the genetic and genomic basis of some behaviors in bees.

  1. Model-based machine learning.

    Science.gov (United States)

    Bishop, Christopher M

    2013-02-13

    Several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. To tackle a new application, a researcher typically tries to map their problem onto one of these existing methods, often influenced by their familiarity with specific algorithms and by the availability of corresponding software implementations. In this study, we describe an alternative methodology for applying machine learning, in which a bespoke solution is formulated for each new application. The solution is expressed through a compact modelling language, and the corresponding custom machine learning code is then generated automatically. This model-based approach offers several major advantages, including the opportunity to create highly tailored models for specific scenarios, as well as rapid prototyping and comparison of a range of alternative models. Furthermore, newcomers to the field of machine learning do not have to learn about the huge range of traditional methods, but instead can focus their attention on understanding a single modelling environment. In this study, we show how probabilistic graphical models, coupled with efficient inference algorithms, provide a very flexible foundation for model-based machine learning, and we outline a large-scale commercial application of this framework involving tens of millions of users. We also describe the concept of probabilistic programming as a powerful software environment for model-based machine learning, and we discuss a specific probabilistic programming language called Infer.NET, which has been widely used in practical applications.

  2. How Does Self-Regulated Learning Relate to Active Procrastination and Other Learning Behaviors?

    Science.gov (United States)

    Yamada, Masanori; Goda, Yoshiko; Matsuda, Takeshi; Saito, Yutaka; Kato, Hiroshi; Miyagawa, Hiroyuki

    2016-01-01

    This research investigates the relationship between self-regulated learning awareness, procrastination, and learning behaviors in a blended learning environment. Participants included 179 first-grade university students attending a blended learning-style class that used a learning management system. Data were collected using questionnaires on…

  3. Grounding the meanings in sensorimotor behavior using reinforcement learning

    Directory of Open Access Journals (Sweden)

    Igor eFarkaš

    2012-02-01

    Full Text Available The recent outburst of interest in cognitive developmental robotics is fueled by the ambition to propose ecologically plausible mechanisms of how, among other things, a learning agent/robot could ground linguistic meanings in its sensorimotor behaviour. Along this stream, we propose a model that allows the simulated iCub robot to learn the meanings of actions (point, touch and push oriented towards objects in robot's peripersonal space. In our experiments, the iCub learns to execute motor actions and comment on them. Architecturally, the model is composed of three neural-network-based modules that are trained in different ways. The first module, a two-layer perceptron, is trained by back-propagation to attend to the target position in the visual scene, given the low-level visual information and the feature-based target information. The second module, having the form of an actor-critic architecture, is the most distinguishing part of our model, and is trained by a continuous version of reinforcement learning to execute actions as sequences, based on a linguistic command. The third module, an echo-state network, is trained to provide the linguistic description of the executed actions. The trained model generalises well in case of novel action-target combinations with randomised initial arm positions. It can also promptly adapt its behavior if the action/target suddenly changes during motor execution.

  4. Learning to Eat: Behavioral and Psychological Aspects.

    Science.gov (United States)

    Birch, Leann L

    2016-01-01

    Because infants are totally dependent upon parents (or other caregivers) for care and sustenance, parents' feeding practices are a key feature of the family environments in which infants and young children learn about food and eating. Feeding practices include not only what the child is fed, but also the how, when, why and how much of feeding. Extensive evidence indicates that parenting behavior influences a variety of child outcomes, including cognitive and socioemotional development, as well as the development of self-regulatory skills. The focus of this chapter is on what is known about how parenting, particularly feeding practices, influences the early development of several aspects of children's eating behavior, including the acquisition of food preferences, self-regulatory skills, children's reactivity to food cues, satiety responsiveness and 'picky eating'. It is argued that traditional feeding practices, which evolved to protect children from environmental threats and ensure adequate intake in the context of food scarcity, can be maladaptive in current environments. An evidence base is needed to inform public policy to reduce early obesity risk in current environments, where too much palatable food is a major threat to child health. Results of recent research provides evidence that promoting responsive feeding practices can alter the development of eating behavior, sleep patterns and early self-regulatory skills, as well as reduce early obesity risk. © 2016 Nestec Ltd., Vevey/S. Karger AG, Basel.

  5. Online Behavior Analysis-Based Student Profile for Intelligent E-Learning

    OpenAIRE

    Liang, Kun; Zhang, Yiying; He, Yeshen; Zhou, Yilin; Tan, Wei; Li, Xiaoxia

    2017-01-01

    With the development of mobile platform, such as smart cellphone and pad, the E-Learning model has been rapidly developed. However, due to the low completion rate for E-Learning platform, it is very necessary to analyze the behavior characteristics of online learners to intelligently adjust online education strategy and enhance the quality of learning. In this paper, we analyzed the relation indicators of E-Learning to build the student profile and gave countermeasures. Adopting the similarit...

  6. Capability Building and Learning: An Emergent Behavior Approach

    Directory of Open Access Journals (Sweden)

    Andreu Rafael

    2014-12-01

    Full Text Available Economics-based models of firms typically overlook management acts and capability development. We propose a model that analyzes the aggregate behavior of a population of firms resulting from both specific management decisions and learning processes, that induce changes in companies’ capabilities. Decisions are made under imperfect information and bounded rationality, and managers may sacrifice short-term performance in exchange for qualitative outcomes that affect their firm’s future potential. The proposed model provides a structured setting in which these issues -often discussed only informally- can be systematically analyzed through simulation, producing a variety of hard-to-anticipate emergent behaviors. Economic performance is quite sensitive to managers’ estimates of their firms’ capabilities, and companies willing to sacrifice short-run results for future potential appear to be more stable than the rest. Also, bounded rationality can produce chaotic dynamics reminiscent of real life situations.

  7. A motivational model for environmentally responsible behavior.

    Science.gov (United States)

    Tabernero, Carmen; Hernández, Bernardo

    2012-07-01

    This paper presents a study examining whether self-efficacy and intrinsic motivation are related to environmentally responsible behavior (ERB). The study analysed past environmental behavior, self-regulatory mechanisms (self-efficacy, satisfaction, goals), and intrinsic and extrinsic motivation in relation to ERBs in a sample of 156 university students. Results show that all the motivational variables studied are linked to ERB. The effects of self-efficacy on ERB are mediated by the intrinsic motivation responses of the participants. A theoretical model was created by means of path analysis, revealing the power of motivational variables to predict ERB. Structural equation modeling was used to test and fit the research model. The role of motivational variables is discussed with a view to creating adequate learning contexts and experiences to generate interest and new sensations in which self-efficacy and affective reactions play an important role.

  8. Learning to Model in Engineering

    Science.gov (United States)

    Gainsburg, Julie

    2013-01-01

    Policymakers and education scholars recommend incorporating mathematical modeling into mathematics education. Limited implementation of modeling instruction in schools, however, has constrained research on how students learn to model, leaving unresolved debates about whether modeling should be reified and explicitly taught as a competence, whether…

  9. Determining the Effects of LMS Learning Behaviors on Academic Achievement in a Learning Analytic Perspective

    OpenAIRE

    Mehmet FIRAT

    2016-01-01

    Two of the most important outcomes of learning analytics are predicting students’ learning and providing effective feedback. Learning Management Systems (LMS), which are widely used to support online and face-to-face learning, provide extensive research opportunities with detailed records of background data regarding users’ behaviors. The purpose of this study was to investigate the effects of undergraduate students’ LMS learning behaviors on their academic achievements. In line with this pur...

  10. Instructional control of reinforcement learning: A behavioral and neurocomputational investigation

    NARCIS (Netherlands)

    Doll, B.B.; Jacobs, W.J.; Sanfey, A.G.; Frank, M.J.

    2009-01-01

    Humans learn how to behave directly through environmental experience and indirectly through rules and instructions. Behavior analytic research has shown that instructions can control behavior, even when such behavior leads to sub-optimal outcomes (Hayes, S (Ed) 1989. Rule-governed behavior:

  11. Learning of Behavior Trees for Autonomous Agents

    OpenAIRE

    Colledanchise, Michele; Parasuraman, Ramviyas; Ögren, Petter

    2015-01-01

    Definition of an accurate system model for Automated Planner (AP) is often impractical, especially for real-world problems. Conversely, off-the-shelf planners fail to scale up and are domain dependent. These drawbacks are inherited from conventional transition systems such as Finite State Machines (FSMs) that describes the action-plan execution generated by the AP. On the other hand, Behavior Trees (BTs) represent a valid alternative to FSMs presenting many advantages in terms of modularity, ...

  12. The Impact of Cognitive Dissonance on Learning Work Behavior

    Science.gov (United States)

    Dechawatanapaisal, Decha; Siengthai, Sununta

    2006-01-01

    Purpose: This research proposes a framework, which identifies the underlying factors that shape learning behavior in the workplace. It takes organizational members' perspectives into consideration to gain better understanding on managing people and their behavior in the organizational learning process. Design/methodology/approach: Primary data…

  13. The Learning Behaviors Scale: National Standardization in Trinidad and Tobago

    Science.gov (United States)

    Chao, Jessica L.; McDermott, Paul A.; Watkins, Marley W.; Drogalis, Anna Rhoad; Worrell, Frank C.; Hall, Tracey E.

    2018-01-01

    This study reports on the national standardization and validation of the Learning Behaviors Scale (LBS) for use in Trinidad and Tobago. The LBS is a teacher rating scale centering on observable behaviors relevant to identifying childhood approaches to classroom learning. Teachers observed a stratified sample of 900 students across the islands'…

  14. Observing Animal Behavior at the Zoo: A Learning Laboratory

    Science.gov (United States)

    Hull, Debra B.

    2003-01-01

    Undergraduate students in a learning laboratory course initially chose a species to study; researched that species' physical and behavioral characteristics; then learned skills necessary to select, operationalize, observe, and record animal behavior accurately. After their classroom preparation, students went to a local zoo to observe the behavior…

  15. Alterations in choice behavior by manipulations of world model.

    Science.gov (United States)

    Green, C S; Benson, C; Kersten, D; Schrater, P

    2010-09-14

    How to compute initially unknown reward values makes up one of the key problems in reinforcement learning theory, with two basic approaches being used. Model-free algorithms rely on the accumulation of substantial amounts of experience to compute the value of actions, whereas in model-based learning, the agent seeks to learn the generative process for outcomes from which the value of actions can be predicted. Here we show that (i) "probability matching"-a consistent example of suboptimal choice behavior seen in humans-occurs in an optimal Bayesian model-based learner using a max decision rule that is initialized with ecologically plausible, but incorrect beliefs about the generative process for outcomes and (ii) human behavior can be strongly and predictably altered by the presence of cues suggestive of various generative processes, despite statistically identical outcome generation. These results suggest human decision making is rational and model based and not consistent with model-free learning.

  16. Behavior model for performance assessment

    International Nuclear Information System (INIS)

    Brown-VanHoozer, S. A.

    1999-01-01

    Every individual channels information differently based on their preference of the sensory modality or representational system (visual auditory or kinesthetic) we tend to favor most (our primary representational system (PRS)). Therefore, some of us access and store our information primarily visually first, some auditorily, and others kinesthetically (through feel and touch); which in turn establishes our information processing patterns and strategies and external to internal (and subsequently vice versa) experiential language representation. Because of the different ways we channel our information, each of us will respond differently to a task--the way we gather and process the external information (input), our response time (process), and the outcome (behavior). Traditional human models of decision making and response time focus on perception, cognitive and motor systems stimulated and influenced by the three sensory modalities, visual, auditory and kinesthetic. For us, these are the building blocks to knowing how someone is thinking. Being aware of what is taking place and how to ask questions is essential in assessing performance toward reducing human errors. Existing models give predications based on time values or response times for a particular event, and may be summed and averaged for a generalization of behavior(s). However, by our not establishing a basic understanding of the foundation of how the behavior was predicated through a decision making strategy process, predicative models are overall inefficient in their analysis of the means by which behavior was generated. What is seen is the end result

  17. Behavior model for performance assessment.

    Energy Technology Data Exchange (ETDEWEB)

    Borwn-VanHoozer, S. A.

    1999-07-23

    Every individual channels information differently based on their preference of the sensory modality or representational system (visual auditory or kinesthetic) we tend to favor most (our primary representational system (PRS)). Therefore, some of us access and store our information primarily visually first, some auditorily, and others kinesthetically (through feel and touch); which in turn establishes our information processing patterns and strategies and external to internal (and subsequently vice versa) experiential language representation. Because of the different ways we channel our information, each of us will respond differently to a task--the way we gather and process the external information (input), our response time (process), and the outcome (behavior). Traditional human models of decision making and response time focus on perception, cognitive and motor systems stimulated and influenced by the three sensory modalities, visual, auditory and kinesthetic. For us, these are the building blocks to knowing how someone is thinking. Being aware of what is taking place and how to ask questions is essential in assessing performance toward reducing human errors. Existing models give predications based on time values or response times for a particular event, and may be summed and averaged for a generalization of behavior(s). However, by our not establishing a basic understanding of the foundation of how the behavior was predicated through a decision making strategy process, predicative models are overall inefficient in their analysis of the means by which behavior was generated. What is seen is the end result.

  18. Explaining clinical behaviors using multiple theoretical models

    Directory of Open Access Journals (Sweden)

    Eccles Martin P

    2012-10-01

    Full Text Available Abstract Background In the field of implementation research, there is an increased interest in use of theory when designing implementation research studies involving behavior change. In 2003, we initiated a series of five studies to establish a scientific rationale for interventions to translate research findings into clinical practice by exploring the performance of a number of different, commonly used, overlapping behavioral theories and models. We reflect on the strengths and weaknesses of the methods, the performance of the theories, and consider where these methods sit alongside the range of methods for studying healthcare professional behavior change. Methods These were five studies of the theory-based cognitions and clinical behaviors (taking dental radiographs, performing dental restorations, placing fissure sealants, managing upper respiratory tract infections without prescribing antibiotics, managing low back pain without ordering lumbar spine x-rays of random samples of primary care dentists and physicians. Measures were derived for the explanatory theoretical constructs in the Theory of Planned Behavior (TPB, Social Cognitive Theory (SCT, and Illness Representations specified by the Common Sense Self Regulation Model (CSSRM. We constructed self-report measures of two constructs from Learning Theory (LT, a measure of Implementation Intentions (II, and the Precaution Adoption Process. We collected data on theory-based cognitions (explanatory measures and two interim outcome measures (stated behavioral intention and simulated behavior by postal questionnaire survey during the 12-month period to which objective measures of behavior (collected from routine administrative sources were related. Planned analyses explored the predictive value of theories in explaining variance in intention, behavioral simulation and behavior. Results Response rates across the five surveys ranged from 21% to 48%; we achieved the target sample size for three of

  19. Explaining clinical behaviors using multiple theoretical models.

    Science.gov (United States)

    Eccles, Martin P; Grimshaw, Jeremy M; MacLennan, Graeme; Bonetti, Debbie; Glidewell, Liz; Pitts, Nigel B; Steen, Nick; Thomas, Ruth; Walker, Anne; Johnston, Marie

    2012-10-17

    In the field of implementation research, there is an increased interest in use of theory when designing implementation research studies involving behavior change. In 2003, we initiated a series of five studies to establish a scientific rationale for interventions to translate research findings into clinical practice by exploring the performance of a number of different, commonly used, overlapping behavioral theories and models. We reflect on the strengths and weaknesses of the methods, the performance of the theories, and consider where these methods sit alongside the range of methods for studying healthcare professional behavior change. These were five studies of the theory-based cognitions and clinical behaviors (taking dental radiographs, performing dental restorations, placing fissure sealants, managing upper respiratory tract infections without prescribing antibiotics, managing low back pain without ordering lumbar spine x-rays) of random samples of primary care dentists and physicians. Measures were derived for the explanatory theoretical constructs in the Theory of Planned Behavior (TPB), Social Cognitive Theory (SCT), and Illness Representations specified by the Common Sense Self Regulation Model (CSSRM). We constructed self-report measures of two constructs from Learning Theory (LT), a measure of Implementation Intentions (II), and the Precaution Adoption Process. We collected data on theory-based cognitions (explanatory measures) and two interim outcome measures (stated behavioral intention and simulated behavior) by postal questionnaire survey during the 12-month period to which objective measures of behavior (collected from routine administrative sources) were related. Planned analyses explored the predictive value of theories in explaining variance in intention, behavioral simulation and behavior. Response rates across the five surveys ranged from 21% to 48%; we achieved the target sample size for three of the five surveys. For the predictor variables

  20. A Model for Behavioral Management and Relationship Training for Parents in Groups,

    Science.gov (United States)

    Behavior, Human relations, *Training, *Families(Human), Symposia, Models, Children, Psychotherapy, Problem solving, Management, Control, Learning, Skills, Decision making , Group dynamics, Military psychology, Military medicine

  1. Behavior Modeling -- Foundations and Applications

    DEFF Research Database (Denmark)

    This book constitutes revised selected papers from the six International Workshops on Behavior Modelling - Foundations and Applications, BM-FA, which took place annually between 2009 and 2014. The 9 papers presented in this volume were carefully reviewed and selected from a total of 58 papers...

  2. Two Programs Educating the Public in Animal Learning and Behavior

    OpenAIRE

    Estep, Daniel Q.

    2002-01-01

    Two educational programs have been developed that teach basic principles of animal learning and behavior and how they can be used in day to day interactions with companion animals. The first program educates violators of animal control laws about animal learning and cat and dog behavior to help them resolve their problems with their animals and avoid future animal control violations. The second educates home service providers concerning basic principles of animal communication, dog behavior, ...

  3. Agent-based modeling of sustainable behaviors

    CERN Document Server

    Sánchez-Maroño, Noelia; Fontenla-Romero, Oscar; Polhill, J; Craig, Tony; Bajo, Javier; Corchado, Juan

    2017-01-01

    Using the O.D.D. (Overview, Design concepts, Detail) protocol, this title explores the role of agent-based modeling in predicting the feasibility of various approaches to sustainability. The chapters incorporated in this volume consist of real case studies to illustrate the utility of agent-based modeling and complexity theory in discovering a path to more efficient and sustainable lifestyles. The topics covered within include: households' attitudes toward recycling, designing decision trees for representing sustainable behaviors, negotiation-based parking allocation, auction-based traffic signal control, and others. This selection of papers will be of interest to social scientists who wish to learn more about agent-based modeling as well as experts in the field of agent-based modeling.

  4. Cognitive Modeling of Social Behaviors

    Science.gov (United States)

    Clancey, William J.; Sierhuis, Maarten; Damer. Bruce; Brodsky, Boris

    2004-01-01

    The driving theme of cognitive modeling for many decades has been that knowledge affects how and which goals are accomplished by an intelligent being (Newell 1991). But when one examines groups of people living and working together, one is forced to recognize that whose knowledge is called into play, at a particular time and location, directly affects what the group accomplishes. Indeed, constraints on participation, including roles, procedures, and norms, affect whether an individual is able to act at all (Lave & Wenger 1991; Jordan 1992; Scribner & Sachs 1991). To understand both individual cognition and collective activity, perhaps the greatest opportunity today is to integrate the cognitive modeling approach (which stresses how beliefs are formed and drive behavior) with social studies (which stress how relationships and informal practices drive behavior). The crucial insight is that norms are conceptualized in the individual &nd as ways of carrying out activities (Clancey 1997a, 2002b). This requires for the psychologist a shift from only modeling goals and tasks - why people do what they do - to modeling behavioral patterns-what people do-as they are engaged in purposeful activities. Instead of a model that exclusively deduces actions from goals, behaviors are also, if not primarily, driven by broader patterns of chronological and located activities (akin to scripts). This analysis is particular inspired by activity theory (Leont ev 1979). While acknowledging that knowledge (relating goals and operations) is fundamental for intelligent behavior, activity theory claims that a broader driver is the person s motives and conceptualization of activities. Such understanding of human interaction is normative (i.e., viewed with respect to social standards), affecting how knowledge is called into play and applied in practice. Put another way, how problems are discovered and framed, what methods are chosen, and indeed who even cares or has the authority to act, are all

  5. A Network Neuroscience of Human Learning: Potential to Inform Quantitative Theories of Brain and Behavior.

    Science.gov (United States)

    Bassett, Danielle S; Mattar, Marcelo G

    2017-04-01

    Humans adapt their behavior to their external environment in a process often facilitated by learning. Efforts to describe learning empirically can be complemented by quantitative theories that map changes in neurophysiology to changes in behavior. In this review we highlight recent advances in network science that offer a sets of tools and a general perspective that may be particularly useful in understanding types of learning that are supported by distributed neural circuits. We describe recent applications of these tools to neuroimaging data that provide unique insights into adaptive neural processes, the attainment of knowledge, and the acquisition of new skills, forming a network neuroscience of human learning. While promising, the tools have yet to be linked to the well-formulated models of behavior that are commonly utilized in cognitive psychology. We argue that continued progress will require the explicit marriage of network approaches to neuroimaging data and quantitative models of behavior. Copyright © 2017 Elsevier Ltd. All rights reserved.

  6. Efficient model learning methods for actor-critic control.

    Science.gov (United States)

    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.

  7. A Visual Detection Learning Model

    Science.gov (United States)

    Beard, Bettina L.; Ahumada, Albert J., Jr.; Trejo, Leonard (Technical Monitor)

    1998-01-01

    Our learning model has memory templates representing the target-plus-noise and noise-alone stimulus sets. The best correlating template determines the response. The correlations and the feedback participate in the additive template updating rule. The model can predict the relative thresholds for detection in random, fixed and twin noise.

  8. Temporal-pattern learning in neural models

    CERN Document Server

    Genís, Carme Torras

    1985-01-01

    While the ability of animals to learn rhythms is an unquestionable fact, the underlying neurophysiological mechanisms are still no more than conjectures. This monograph explores the requirements of such mechanisms, reviews those previously proposed and postulates a new one based on a direct electric coding of stimulation frequencies. Experi­ mental support for the option taken is provided both at the single neuron and neural network levels. More specifically, the material presented divides naturally into four parts: a description of the experimental and theoretical framework where this work becomes meaningful (Chapter 2), a detailed specifica­ tion of the pacemaker neuron model proposed together with its valida­ tion through simulation (Chapter 3), an analytic study of the behavior of this model when submitted to rhythmic stimulation (Chapter 4) and a description of the neural network model proposed for learning, together with an analysis of the simulation results obtained when varying seve­ ral factors r...

  9. Reappraising social insect behavior through aversive responsiveness and learning.

    Science.gov (United States)

    Roussel, Edith; Carcaud, Julie; Sandoz, Jean-Christophe; Giurfa, Martin

    2009-01-01

    The success of social insects can be in part attributed to their division of labor, which has been explained by a response threshold model. This model posits that individuals differ in their response thresholds to task-associated stimuli, so that individuals with lower thresholds specialize in this task. This model is at odds with findings on honeybee behavior as nectar and pollen foragers exhibit different responsiveness to sucrose, with nectar foragers having higher response thresholds to sucrose concentration. Moreover, it has been suggested that sucrose responsiveness correlates with responsiveness to most if not all other stimuli. If this is the case, explaining task specialization and the origins of division of labor on the basis of differences in response thresholds is difficult. To compare responsiveness to stimuli presenting clear-cut differences in hedonic value and behavioral contexts, we measured appetitive and aversive responsiveness in the same bees in the laboratory. We quantified proboscis extension responses to increasing sucrose concentrations and sting extension responses to electric shocks of increasing voltage. We analyzed the relationship between aversive responsiveness and aversive olfactory conditioning of the sting extension reflex, and determined how this relationship relates to division of labor. Sucrose and shock responsiveness measured in the same bees did not correlate, thus suggesting that they correspond to independent behavioral syndromes, a foraging and a defensive one. Bees which were more responsive to shock learned and memorized better aversive associations. Finally, guards were less responsive than nectar foragers to electric shocks, exhibiting higher tolerance to low voltage shocks. Consequently, foragers, which are more sensitive, were the ones learning and memorizing better in aversive conditioning. Our results constitute the first integrative study on how aversive responsiveness affects learning, memory and social

  10. Reappraising social insect behavior through aversive responsiveness and learning.

    Directory of Open Access Journals (Sweden)

    Edith Roussel

    Full Text Available The success of social insects can be in part attributed to their division of labor, which has been explained by a response threshold model. This model posits that individuals differ in their response thresholds to task-associated stimuli, so that individuals with lower thresholds specialize in this task. This model is at odds with findings on honeybee behavior as nectar and pollen foragers exhibit different responsiveness to sucrose, with nectar foragers having higher response thresholds to sucrose concentration. Moreover, it has been suggested that sucrose responsiveness correlates with responsiveness to most if not all other stimuli. If this is the case, explaining task specialization and the origins of division of labor on the basis of differences in response thresholds is difficult.To compare responsiveness to stimuli presenting clear-cut differences in hedonic value and behavioral contexts, we measured appetitive and aversive responsiveness in the same bees in the laboratory. We quantified proboscis extension responses to increasing sucrose concentrations and sting extension responses to electric shocks of increasing voltage. We analyzed the relationship between aversive responsiveness and aversive olfactory conditioning of the sting extension reflex, and determined how this relationship relates to division of labor.Sucrose and shock responsiveness measured in the same bees did not correlate, thus suggesting that they correspond to independent behavioral syndromes, a foraging and a defensive one. Bees which were more responsive to shock learned and memorized better aversive associations. Finally, guards were less responsive than nectar foragers to electric shocks, exhibiting higher tolerance to low voltage shocks. Consequently, foragers, which are more sensitive, were the ones learning and memorizing better in aversive conditioning.Our results constitute the first integrative study on how aversive responsiveness affects learning, memory and

  11. Intensitas Perilaku Pengguna E-learning System dengan Model Utaut

    OpenAIRE

    Sari, Fatma; Purnamasari, Susan Dian

    2013-01-01

    This study aims to determine behavioral intention in the use of e-learning system using models UTAUT. The phenomenon underlying the research is: It is not yet optimal use of e-learning by students information systems in the learning process, not yet optimal socialization of the existence of e-learning, so that is not maximized and yet utilization measurability of the impact of using e-learning for lecturers.This study is limited in its scope: analysis of the influence of performance expectanc...

  12. A Model for Teaching Rational Behavior Skills to Emotionally Disturbed Youth in a Public School Setting.

    Science.gov (United States)

    Patton, Patricia L.

    1985-01-01

    Describes a model used to teach rational behavior skills to 34 emotionally disturbed adolescents. Discusses teaching, training, and counseling strategies. The group demonstrated significant positive changes in learning and personality variables, but not behavior. (JAC)

  13. Assessing Student Behaviors and Motivation for Actively Learning Biology

    Science.gov (United States)

    Moore, Michael Edward

    Vision and Change states that one of the major changes in the way we design biology courses should be a switch in approach from teacher-centered learning to student-centered learning and identifies active learning as a recommended methods. Studies show performance benefits for students taking courses that use active learning. What is unknown is why active learning is such an effective instructional tool and the limits of this instructional method’s ability to influence performance. This dissertation builds a case in three steps for why active learning is an effective instructional tool. In step one, I assessed the influence of different types of active learning (clickers, group activities, and whole class discussions) on student engagement behavior in one semester of two different introductory biology courses and found that active learning positively influenced student engagement behavior significantly more than lecture. For step two, I examined over four semesters whether student engagement behavior was a predictor of performance and found participation (engagement behavior) in the online (video watching) and in-class course activities (clicker participation) that I measure were significant predictors of performance. In the third, I assessed whether certain active learning satisfied the psychological needs that lead to students’ intrinsic motivation to participate in those activities when compared over two semesters and across two different institutions of higher learning. Findings from this last step show us that student’s perceptions of autonomy, competency, and relatedness in doing various types of active learning are significantly higher than lecture and consistent across two institutions of higher learning. Lastly, I tie everything together, discuss implications of the research, and address future directions for research on biology student motivation and behavior.

  14. Learning Analytics for Networked Learning Models

    Science.gov (United States)

    Joksimovic, Srecko; Hatala, Marek; Gaševic, Dragan

    2014-01-01

    Teaching and learning in networked settings has attracted significant attention recently. The central topic of networked learning research is human-human and human-information interactions occurring within a networked learning environment. The nature of these interactions is highly complex and usually requires a multi-dimensional approach to…

  15. Behavioral Modeling Based on Probabilistic Finite Automata: An Empirical Study.

    Science.gov (United States)

    Tîrnăucă, Cristina; Montaña, José L; Ontañón, Santiago; González, Avelino J; Pardo, Luis M

    2016-06-24

    Imagine an agent that performs tasks according to different strategies. The goal of Behavioral Recognition (BR) is to identify which of the available strategies is the one being used by the agent, by simply observing the agent's actions and the environmental conditions during a certain period of time. The goal of Behavioral Cloning (BC) is more ambitious. In this last case, the learner must be able to build a model of the behavior of the agent. In both settings, the only assumption is that the learner has access to a training set that contains instances of observed behavioral traces for each available strategy. This paper studies a machine learning approach based on Probabilistic Finite Automata (PFAs), capable of achieving both the recognition and cloning tasks. We evaluate the performance of PFAs in the context of a simulated learning environment (in this case, a virtual Roomba vacuum cleaner robot), and compare it with a collection of other machine learning approaches.

  16. Effects of apomorphine on the expression of learned helplessness behavior.

    Science.gov (United States)

    Wang, Wen-Fu; Lei, Yen-Ping; Tseng, Ting; Hsu, Wen-Yu; Wang, Ching-Fu; Hsu, Cheng-Chin; Ho, Ying-Jui

    2007-04-30

    Dopaminergic system and its D1 as well as D2 receptors are involved in the modulation of emotional behavior. This experiment investigated the role of dopaminergic activity in the inescapable stress-induced learned helplessness, a widely used depression animal model, by using the pharmacological manipulation through the apomorphine (APO), an agonist for D1 and D2 receptors, and sulpiride (SUL), a selective D2 antagonist. Male Sprague Dawley rats were used and tested in a shuttle box. In the day-1 session, the rats received a 10-trial (1 min/trial) inescapable stressor: a 3 sec conditioned stimulus (CS; 75 db sound and 250 lux red light) followed by a 10 sec unconditioned stimulus (UCS; electrical foot shock, 0.5 mA). In the day-2 session, a 15-trial active avoidance test, 3 sec CS followed by UCS, was performed 30 min after the administration of APO (0, 0.05, 0.5, 1, and 5 mg/kg, i.p.). The number of failures was counted and the UCS was stopped when the rats did not escape after 15 sec UCS. The results showed that APO at the dosage of 0.5 mg/kg had a tendency to enhance the avoidance behavior. In contrast, the treatment of higher dose of APO, 1 and 5 mg/kg, reduced the number of escape but increased the number of failure. Pretreatment of SUL (5 mg/kg, i.p.), 10 min before 1 mg/kg of APO, significantly enhanced the failure behavior. The present data suggest that the activity of D2 receptor may be associated with the adaptive or protective role in the prevention of escape deficits after exposure to inescapable stress. However, the excessive stimulation of D1 receptor may participate in the failure of coping behavior leading to learned helplessness and therefore in the pathophysiological mechanisms underling the development of depression.

  17. A New Mobile Learning Adaptation Model

    OpenAIRE

    Mohamd Hassan Hassan; Jehad Al-Sadi

    2009-01-01

    This paper introduces a new model for m- Learning context adaptation due to the need of utilizing mobile technology in education. Mobile learning; m-Learning for short; in considered to be one of the hottest topics in the educational community, many researches had been done to conceptualize this new form of learning. We are presenting a promising design for a model to adapt the learning content in mobile learning applications in order to match the learner context, preferences and the educatio...

  18. The Impact on Career Development of Learning Opportunities and Learning Behavior at Work.

    Science.gov (United States)

    Van der Sluis, Lidewey E. C.; Poell, Rob E.

    2003-01-01

    Survey responses were received in 1998 (n=63) and 1999 (n=98) from master's of business administration graduates. Hierarchical regression and difference of means tests found that career development depended on learning opportunities at work and on individual learning behavior. Behavior was more predictive of objective career development measures,…

  19. Observational learning and workplace safety: the effects of viewing the collective behavior of multiple social models on the use of personal protective equipment.

    Science.gov (United States)

    Olson, Ryan; Grosshuesch, Ariel; Schmidt, Sara; Gray, Mary; Wipfli, Bradley

    2009-10-01

    The current project evaluated the effects of the collective behavior of multiple social models on the use of personal protective equipment (PPE). Prior to completing a simulated baggage-screening task, participants (N=64) watched a scripted training video that included three confederate trainees. Participants were randomly assigned to one of four manipulations, where different proportions of confederates were shown putting on over-ear sound mufflers before starting the task (0, 1, 2, or 3). White noise played at 70 decibels in the test room, and PPE use was observed unobtrusively through a lab window at five time intervals. The mean intervals of PPE use generally increased as the number of positive social models increased (0=0.63, 1=0.50, 2=1.25, 3=3.06), and differences between groups were significant [chi(2) (3, N=64)=14.92, preinforcement for compliance.

  20. Novas relações entre as interpretações funcionais do desamparo aprendido e do modelo comportamental de depressão New relations among functional interpretations of the learned helplessness and the behavioral model of depression

    Directory of Open Access Journals (Sweden)

    Paulo Roberto Abreu

    2011-01-01

    Full Text Available O desamparo aprendido tem sido referido como sendo um modelo animal de depressão. Sua hipótese tradicional afirma que sujeitos submetidos a estímulos aversivos incontroláveis desenvolverão dificuldades de aprendizagem, diminuindo a freqüência de atividade. Essa análise historicamente apresentou certa dissonância com o modelo clínico que afirmava que alguns comportamentos aumentavam de freqüência durante o episódio depressivo. Contudo, algumas pesquisas mostraram que os sujeitos pré-expostos a incontrolabilidade aprendem a resposta de fuga, a depender das propriedades da contingência de teste como a contigüidade da conseqüência e o controle discriminativo. Esses dados impulsionaram a formulação de uma nova hipótese funcional para o procedimento experimental. No artigo, sugere-se um diálogo possível entre a nova hipótese e o modelo comportamental da depressão.The learned helplessness has been referred as being an animal model of depression. Its traditional hypothesis affirms that subjects submitted to uncontrollable aversive stimuli will develop learning difficulties, reducing their activity frequency. Such analysis has historically presented certain dissonance with the clinical model that affirmed some behaviors increased in frequency during the depressive episode. However, some researches show that subjects pre-exposed to uncontrollability learn the escape response, depending on the properties of the test contingency such as the contiguity of the consequence and the specific discriminative control. Those data impelled the formulation of a new functional hypothesis for the experimental procedure. In this article, it is suggested a possible dialogue between the new hypothesis and the behavioral model of depression.

  1. Exercising during learning improves vocabulary acquisition: behavioral and ERP evidence.

    Science.gov (United States)

    Schmidt-Kassow, Maren; Kulka, Anna; Gunter, Thomas C; Rothermich, Kathrin; Kotz, Sonja A

    2010-09-20

    Numerous studies have provided evidence that physical activity promotes cortical plasticity in the adult brain and in turn facilitates learning. However, until now, the effect of simultaneous physical activity (e.g. bicycling) on learning performance has not been investigated systematically. The current study aims at clarifying whether simultaneous motor activity influences verbal learning compared to learning in a physically passive situation. Therefore the learning behavior of 12 healthy subjects (4 male, 19-33 years) was monitored over a period of 3 weeks. During that time, behavioral and electrophysiological responses to memorized materials were measured. We found a larger N400 effect and better performance in vocabulary tests when subjects were physically active during the encoding phase. Thus, our data indicate that simultaneous physical activity during vocabulary learning facilitates memorization of new items. Copyright 2010 Elsevier Ireland Ltd. All rights reserved.

  2. Individual Learning Accounts and Other Models of Financing Lifelong Learning

    Science.gov (United States)

    Schuetze, Hans G.

    2007-01-01

    To answer the question "Financing what?" this article distinguishes several models of lifelong learning as well as a variety of lifelong learning activities. Several financing methods are briefly reviewed, however the principal focus is on Individual Learning Accounts (ILAs) which were seen by some analysts as a promising model for…

  3. Urban Studies: A Learning Model.

    Science.gov (United States)

    Cooper, Terry L.; Sundeen, Richard

    1979-01-01

    The urban studies learning model described in this article was found to increase students' self-esteem, imbue a more flexible and open perspective, contribute to the capacity for self-direction, produce increases on the feeling reactivity, spontaneity, and acceptance of aggression scales, and expand interpersonal competence. (Author/WI)

  4. Mathematical models of human behavior

    DEFF Research Database (Denmark)

    Møllgaard, Anders Edsberg

    at the Technical University of Denmark. The data set includes face-to-face interaction (Bluetooth), communication (calls and texts), mobility (GPS), social network (Facebook), and general background information including a psychological profile (questionnaire). This thesis presents my work on the Social Fabric...... data set, along with work on other behavioral data. The overall goal is to contribute to a quantitative understanding of human behavior using big data and mathematical models. Central to the thesis is the determination of the predictability of different human activities. Upper limits are derived....... Evidence is provided, which implies that the asymmetry is caused by a self-enhancement in the initiation dynamics. These results have implications for the formation of social networks and the dynamics of the links. It is shown that the Big Five Inventory (BFI) representing a psychological profile only...

  5. The impact on career development of learning opportunities and learning behavior at work

    NARCIS (Netherlands)

    van der Sluis, E.C.; Poell, R.F.

    2003-01-01

    This study focuses on the individual career development process of M.B.A.s on the job, in an era emphasizing personal responsibility for learning and development. The impact of learning opportunities and individual learning behavior was analyzed through repeated measures. Hierarchical regressions

  6. Chiropractic management using a brain-based model of care for a 15-year-old adolescent boy with migraine headaches and behavioral and learning difficulties: a case report

    Science.gov (United States)

    Kuhn, Kurt W.; Cambron, Jerrilyn

    2013-01-01

    Objective The purpose of this report is to describe chiropractic management, using a brain-based model of care, of a teen who had migraine headaches and several social and learning difficulties. Clinical features A 15-year-old adolescent boy with a chronic history of migraines and more than 10 years of learning and behavioral difficulties, including attention-deficit/hyperactivity disorder, obsessive compulsive disorder, and Tourette syndrome, presented for chiropractic care. Intervention and outcome The patient received spinal manipulation and was given home physical coordination activities that were contralateral to the side of the involved basal ganglia and ipsilateral to the involved cerebellum, along with interactive metronome training. Quantitative changes were noted in neurological soft signs, tests of variables of attention Conners’ Parent Rating Scale, the California Achievement Test, grade point, and reduction of medications. The patient reported qualitative improvements in tics, attention, reading, vision, health, relationships with his peers and his family, and self-esteem. Conclusion The patient with migraine headaches and learning difficulties responded well to the course of chiropractic care. This study suggests that there may be value in a brain-based model of care in the chiropractic management of conditions that are beyond musculoskeletal in nature. PMID:24396330

  7. E-Learning Turkish Language and Grammar: Analyzing Learners' Behavior

    Science.gov (United States)

    Georgalas, Panagiotis

    2012-01-01

    This study analyses the behavior and the preferences of the Greek learners of Turkish language, who use a particular e-learning website in parallel with their studies, namely: http://turkish.pgeorgalas.gr. The website offers free online material in Greek and English language for learning the Turkish language and grammar. The traffic of several…

  8. Learner Behaviors and Perceptions of Autonomous Language Learning

    Science.gov (United States)

    Bekleyen, Nilüfer; Selimoglu, Figen

    2016-01-01

    The purpose of the present study was to investigate the learners' behaviors and perceptions about autonomous language learning at the university level in Turkey. It attempts to reveal what type of perceptions learners held regarding teachers' and their own responsibilities in the language learning process. Their autonomous language learning…

  9. Agreement among Classroom Observers of Children's Stylistic Learning Behaviors.

    Science.gov (United States)

    Buchanan, Helen Hamlet; McDermott, Paul A.; Schaefer, Barbara A.

    1998-01-01

    Investigates the interobserver agreement of the Learning Behavior Scale (LBS) by educators (n=16) observing students in special-education classes (n=72). No significant observer effect was found. Moreover, the LBS produced comparable levels of differential learning styles for assessments of individual children. (Author/MKA)

  10. Ontogeny of Classical and Operant Learning Behaviors in Zebrafish

    Science.gov (United States)

    Valente, Andre; Huang, Kuo-Hua; Portugues, Ruben; Engert, Florian

    2012-01-01

    The performance of developing zebrafish in both classical and operant conditioning assays was tested with a particular focus on the emergence of these learning behaviors during development. Strategically positioned visual cues paired with electroshocks were used in two fully automated assays to investigate both learning paradigms. These allow the…

  11. In Support of Coaching Models of Management and Leadership: A Comparative Study of Empirically Derived Managerial Coaching/Facilitating Learning Behaviors

    Science.gov (United States)

    Hamlin, Bob; Ellinger, Andrea D.; Beattie, Rona S.

    2004-01-01

    The concept of managers assuming developmental roles such as coaches and learning facilitators has gained considerable attention in recent years as organizations seek to leverage learning by creating infrastructures that foster employee learning and development. Despite the increased focus on coaching, the literature base remains atheoretical.…

  12. Teacher Behavioral Practices: Relations to Student Risk Behaviors, Learning Barriers, and School Climate

    Science.gov (United States)

    Martinez, Andrew; Mcmahon, Susan D.; Coker, Crystal; Keys, Christopher B.

    2016-01-01

    Student behavioral problems pose a myriad of challenges for schools. In this study, we examine the relations among teacher and school-level constructs (i.e., teacher collaboration, supervision/discipline, instructional management), and student-related outcomes (i.e., high-risk behaviors, barriers to learning, student social-behavioral climate).…

  13. Teaching Economics: A Cooperative Learning Model.

    Science.gov (United States)

    Caropreso, Edward J.; Haggerty, Mark

    2000-01-01

    Describes an alternative approach to introductory economics based on a cooperative learning model, "Learning Together." Discussion of issues in economics education and cooperative learning in higher education leads to explanation of how to adapt the Learning Together Model to lesson planning in economics. A flow chart illustrates the process for a…

  14. Learning from erroneous models using SCYDynamics

    NARCIS (Netherlands)

    Mulder, Y.G.; Bollen, Lars; de Jong, Anthonius J.M.

    2014-01-01

    Dynamic phenomena are common in science education. Students can learn about such system dynamic processes through model based learning activities. This paper describes a study on the effects of a learning from erroneous models approach using the learning environment SCYDynamics. The study compared

  15. From Learning Object to Learning Cell: A Resource Organization Model for Ubiquitous Learning

    Science.gov (United States)

    Yu, Shengquan; Yang, Xianmin; Cheng, Gang; Wang, Minjuan

    2015-01-01

    This paper presents a new model for organizing learning resources: Learning Cell. This model is open, evolving, cohesive, social, and context-aware. By introducing a time dimension into the organization of learning resources, Learning Cell supports the dynamic evolution of learning resources while they are being used. In addition, by introducing a…

  16. Crayfish Behavior: Observing Arthropods to Learn about Science & Scientific Inquiry

    Science.gov (United States)

    Rop, Charles J.

    2010-01-01

    This is a set of animal behavior investigations in which students will practice scientific inquiry as they observe crayfish, ask questions, and discuss territoriality, social interactions, and other behaviors. In doing this, they hone their skills of observation, learn to record and analyze data, control for variables, write hypotheses, make…

  17. A Coterminous Collaborative Learning Model: Interconnectivity of Leadership and Learning

    Directory of Open Access Journals (Sweden)

    Ilana Margolin

    2012-05-01

    Full Text Available This qualitative ethnographic study examines a collaborative leadership model focused on learning and socially just practices within a change context of a wide educational partnership. The study analyzes a range of perspectives of novice teachers, mentor teachers, teacher educators and district superintendents on leadership and learning. The findings reveal the emergence of a coalition of leaders crossing borders at all levels of the educational system: local school level, district level and teacher education level who were involved in coterminous collaborative learning. Four categories of learning were identified as critical to leading a change in the educational system: learning in professional communities, learning from practice, learning through theory and research and learning from and with leaders. The implications of the study for policy makers as well as for practitioners are to adopt a holistic approach to the educational environment and plan a collaborative learning continuum from initial pre-service programs through professional development learning at all levels.

  18. PWR fuel behavior: lessons learned from LOFT

    International Nuclear Information System (INIS)

    Russell, M.L.

    1981-01-01

    A summary of the experience with the Loss-of-Fluid Test (LOFT) fuel during loss-of-coolant experiments (LOCEs), operational and overpower transient tests and steady-state operation is presented. LOFT provides unique capabilities for obtaining pressurized water reactor (PWR) fuel behavior information because it features the representative thermal-hydraulic conditions which control fuel behavior during transient conditions and an elaborate measurement system to record the history of the fuel behavior

  19. Some chaotic behaviors in a MCA learning algorithm with a constant learning rate

    International Nuclear Information System (INIS)

    Lv Jiancheng; Yi Zhang

    2007-01-01

    Douglas's minor component analysis algorithm with a constant learning rate has both stability and chaotic dynamical behavior under some conditions. The paper explores such dynamical behavior of this algorithm. Certain stability and chaos of this algorithm are derived. Waveform plots, Lyapunov exponents and bifurcation diagrams are presented to illustrate the existence of chaotic behavior

  20. Analysis of learners’ behaviors and learning outcomes in a massive open online course

    Directory of Open Access Journals (Sweden)

    Dong Liang

    2014-09-01

    Full Text Available This paper introduces a massive open online course (MOOC on educational technology, and studies the factors that may influence learners’ participation and performance in the MOOC. Students’ learning records captured in the course management system and students’ feedback collected from a questionnaire survey are explored. Regression analysis is adopted to examine the correlation among perceived learning experience, learning activities and learning outcomes; data mining is applied to optimize the correlation models. The findings suggest that learners’ perceived usefulness rather than perceived ease of use of the MOOC, positively influences learners’ use of the system, and consequentially, the learning outcome. In addition, learners’ previous MOOC experience is not found to have a significant impact on their learning behavior and learning outcome in general. However, the performance of less active learners is found to be influenced by their prior MOOC experience.

  1. Learning Organization and Innovative Behavior: The Mediating Effect of Work Engagement

    Science.gov (United States)

    Park, Yu Kyoung; Song, Ji Hoon; Yoon, Seung Won; Kim, Jungwoo

    2014-01-01

    Purpose: The purpose of this study is to investigate the mediating effect of work engagement on the relationship between learning organization and innovative behavior. Design/methodology/approach: This study used surveys as a data collection tool and implemented structural equation modeling for empirically testing the proposed research model.…

  2. Place Learning Overrides Innate Behaviors in "Drosophila"

    Science.gov (United States)

    Baggett, Vincent; Mishra, Aditi; Kehrer, Abigail L.; Robinson, Abbey O.; Shaw, Paul; Zars, Troy

    2018-01-01

    Animals in a natural environment confront many sensory cues. Some of these cues bias behavioral decisions independent of experience, and action selection can reveal a stimulus-response (S-R) connection. However, in a changing environment it would be a benefit for an animal to update behavioral action selection based on experience, and learning…

  3. Learning Behavior Characterizations for Novelty Search

    DEFF Research Database (Denmark)

    Meyerson, Elliot; Lehman, Joel Anthony; Miikulainen, Risto

    2016-01-01

    Novelty search and related diversity-driven algorithms provide a promising approach to overcoming deception in complex domains. The behavior characterization (BC) is a critical choice in the application of such algorithms. The BC maps each evaluated individual to a behavior, i.e., some vector...

  4. Vicarious Learning from Human Models in Monkeys

    OpenAIRE

    Falcone, Rossella; Brunamonti, Emiliano; Genovesio, Aldo

    2012-01-01

    We examined whether monkeys can learn by observing a human model, through vicarious learning. Two monkeys observed a human model demonstrating an object-reward association and consuming food found underneath an object. The monkeys observed human models as they solved more than 30 learning problems. For each problem, the human models made a choice between two objects, one of which concealed a piece of apple. In the test phase afterwards, the monkeys made a choice of their own. Learning was app...

  5. Increasing Free Throw Accuracy through Behavior Modeling and Goal Setting.

    Science.gov (United States)

    Erffmeyer, Elizabeth S.

    A two-year behavior-modeling training program focusing on attention processes, retention processes, motor reproduction, and motivation processes was implemented to increase the accuracy of free throw shooting for a varsity intercollegiate women's basketball team. The training included specific learning keys, progressive relaxation, mental…

  6. Effect of behavior training on learning and memory of young rats with fetal growth restriction

    Institute of Scientific and Technical Information of China (English)

    Li Xuelan; Gou Wenli; Huang Pu; Li Chunfang; Sun Yunping

    2008-01-01

    Objective: To investigate the effect of behavior training on the learning and memory of young rats with fetal growth restriction (FGR). Methods: The model of FGR was established by passive smoking method to pregnant rats.The new-born rats were divided into FGR group and normal group, and then randomly subdivided into trained and untrained group respectively. Morris water maze behavior training was performed on postnatal months 2 and 4, then learning and memory abilities of young rats were measured by dark-avoidance testing and step-down testing. Results: In the dark-avoidance and step-down testing, the young rats' performance of FGR group was worse than that of control group, and the trained group was better than the untrained group significantly. Conclusion: FGR young rats have descended learning and memory abilities. Behavior training could improve the young rats' learning and memory abilities, especially for the FGR young rats.

  7. Towards a semantic learning model fostering learning object reusability

    OpenAIRE

    Fernandes , Emmanuel; Madhour , Hend; Wentland Forte , Maia; Miniaoui , Sami

    2005-01-01

    We try in this paper to propose a domain model for both author's and learner's needs concerning learning objects reuse. First of all, we present four key criteria for an efficient authoring tool: adaptive level of granularity, flexibility, integration and interoperability. Secondly, we introduce and describe our six-level Semantic Learning Model (SLM) designed to facilitate multi-level reuse of learning materials and search by defining a multi-layer model for metadata. Finally, after mapping ...

  8. Building entity models through observation and learning

    Science.gov (United States)

    Garcia, Richard; Kania, Robert; Fields, MaryAnne; Barnes, Laura

    2011-05-01

    To support the missions and tasks of mixed robotic/human teams, future robotic systems will need to adapt to the dynamic behavior of both teammates and opponents. One of the basic elements of this adaptation is the ability to exploit both long and short-term temporal data. This adaptation allows robotic systems to predict/anticipate, as well as influence, future behavior for both opponents and teammates and will afford the system the ability to adjust its own behavior in order to optimize its ability to achieve the mission goals. This work is a preliminary step in the effort to develop online entity behavior models through a combination of learning techniques and observations. As knowledge is extracted from the system through sensor and temporal feedback, agents within the multi-agent system attempt to develop and exploit a basic movement model of an opponent. For the purpose of this work, extraction and exploitation is performed through the use of a discretized two-dimensional game. The game consists of a predetermined number of sentries attempting to keep an unknown intruder agent from penetrating their territory. The sentries utilize temporal data coupled with past opponent observations to hypothesize the probable locations of the opponent and thus optimize their guarding locations.

  9. Model United Nations and Deep Learning: Theoretical and Professional Learning

    Science.gov (United States)

    Engel, Susan; Pallas, Josh; Lambert, Sarah

    2017-01-01

    This article demonstrates that the purposeful subject design, incorporating a Model United Nations (MUN), facilitated deep learning and professional skills attainment in the field of International Relations. Deep learning was promoted in subject design by linking learning objectives to Anderson and Krathwohl's (2001) four levels of knowledge or…

  10. The antecedents of e-learning outcome: an examination of system quality, technology readiness, and learning behavior.

    Science.gov (United States)

    Ho, Li-An

    2009-01-01

    The rapid advancement of Internet and computer technology has not only influenced the way we live, but also the way we learn. Due to the implementation of e-learning in urban junior high schools in Taiwan, it has become essential to find out how external and internal factors affect junior high school students' online learning behavior, which consequently affects their learning outcome. The present study aims to propose a conceptual structural equation model to investigate the relationships among e-Learning system quality (eLSQ), technology readiness (TR), learning behavior (LB), and learning outcome (LO), and to demonstrate the direct and indirect effect of eLSQ and TR on LO from the perspectives of LB. Data collected from 10 urban junior high schools in Taiwan (N = 376) were analyzed using structural equation modeling. Results reveal that both eLSQ and TR have a direct and significant impact on LB. However, eLSQ and TR influence LO indirectly through LB. In addition, LB has a direct and positive significant influence on LO. Managerial implications are proposed and research limitations are discussed.

  11. Toward A Dual-Learning Systems Model of Speech Category Learning

    Directory of Open Access Journals (Sweden)

    Bharath eChandrasekaran

    2014-07-01

    Full Text Available More than two decades of work in vision posits the existence of dual-learning systems of category learning. The reflective system uses working memory to develop and test rules for classifying in an explicit fashion, while the reflexive system operates by implicitly associating perception with actions that lead to reinforcement. Dual-learning systems models hypothesize that in learning natural categories, learners initially use the reflective system and, with practice, transfer control to the reflexive system. The role of reflective and reflexive systems in auditory category learning and more specifically in speech category learning has not been systematically examined. In this article we describe a neurobiologically-constrained dual-learning systems theoretical framework that is currently being developed in speech category learning and review recent applications of this framework. Using behavioral and computational modeling approaches, we provide evidence that speech category learning is predominantly mediated by the reflexive learning system. In one application, we explore the effects of normal aging on non-speech and speech category learning. We find an age related deficit in reflective-optimal but not reflexive-optimal auditory category learning. Prominently, we find a large age-related deficit in speech learning. The computational modeling suggests that older adults are less likely to transition from simple, reflective, uni-dimensional rules to more complex, reflexive, multi-dimensional rules. In a second application we summarize a recent study examining auditory category learning in individuals with elevated depressive symptoms. We find a deficit in reflective-optimal and an enhancement in reflexive-optimal auditory category learning. Interestingly, individuals with elevated depressive symptoms also show an advantage in learning speech categories. We end with a brief summary and description of a number of future directions.

  12. Learning Graphical Models With Hubs.

    Science.gov (United States)

    Tan, Kean Ming; London, Palma; Mohan, Karthik; Lee, Su-In; Fazel, Maryam; Witten, Daniela

    2014-10-01

    We consider the problem of learning a high-dimensional graphical model in which there are a few hub nodes that are densely-connected to many other nodes. Many authors have studied the use of an ℓ 1 penalty in order to learn a sparse graph in the high-dimensional setting. However, the ℓ 1 penalty implicitly assumes that each edge is equally likely and independent of all other edges. We propose a general framework to accommodate more realistic networks with hub nodes, using a convex formulation that involves a row-column overlap norm penalty. We apply this general framework to three widely-used probabilistic graphical models: the Gaussian graphical model, the covariance graph model, and the binary Ising model. An alternating direction method of multipliers algorithm is used to solve the corresponding convex optimization problems. On synthetic data, we demonstrate that our proposed framework outperforms competitors that do not explicitly model hub nodes. We illustrate our proposal on a webpage data set and a gene expression data set.

  13. Modeling lahar behavior and hazards

    Science.gov (United States)

    Manville, Vernon; Major, Jon J.; Fagents, Sarah A.

    2013-01-01

    Lahars are highly mobile mixtures of water and sediment of volcanic origin that are capable of traveling tens to > 100 km at speeds exceeding tens of km hr-1. Such flows are among the most serious ground-based hazards at many volcanoes because of their sudden onset, rapid advance rates, long runout distances, high energy, ability to transport large volumes of material, and tendency to flow along existing river channels where populations and infrastructure are commonly concentrated. They can grow in volume and peak discharge through erosion and incorporation of external sediment and/or water, inundate broad areas, and leave deposits many meters thick. Furthermore, lahars can recur for many years to decades after an initial volcanic eruption, as fresh pyroclastic material is eroded and redeposited during rainfall events, resulting in a spatially and temporally evolving hazard. Improving understanding of the behavior of these complex, gravitationally driven, multi-phase flows is key to mitigating the threat to communities at lahar-prone volcanoes. However, their complexity and evolving nature pose significant challenges to developing the models of flow behavior required for delineating their hazards and hazard zones.

  14. The Effect Of Islamic Education Learning Pai And Learning Results To Students Religious Behavior Of Stisip Widyapuri Mandiri Sukabumi Student

    Directory of Open Access Journals (Sweden)

    U. Abdullah Mumin

    2017-06-01

    Full Text Available The purpose of this study is to measure the level of the effect of Islamic Education learning and learning result on religious behaviour in STISIP Widyapuri Mandiri Sukabumi. The method used in this research is quantitative analysis based on inferential statistical model. The data collection is done by using observation techniques interviews and questionnaires. The researcher analize the data by using logic analysis for qualitative and statistical analysis for quantitative data by using descriptive statistics regression and correlation. Based on the hypothesis test simultaneously PAI learning and learning result have a positive and significant effect on students religious behaviour. Partially only PAI learning alone has a positive and significant impact on religious behavior.

  15. Behavioral Disorders, Learning Disabilities and Megavitamin Therapy.

    Science.gov (United States)

    LaPerchia, Phyllis

    1987-01-01

    Presents findings from several sources that give results of research in megavitamin nutritional therapy. Examines vitamin therapy in learning disabilities in general, schizophrenia, autism, mental retardation and Down's syndrome, and hyperkinesis. Concludes that holistic approach to treatment is needed and that vitamin therapy, if proven…

  16. Learning slow features for behavior analysis

    NARCIS (Netherlands)

    Zafeiriou, Lazaros; Nicolaou, Mihalis A.; Zafeiriou, Stefanos; Nikitids, Symeon; Pantic, Maja

    2013-01-01

    A recently introduced latent feature learning technique for time varying dynamic phenomena analysis is the socalled Slow Feature Analysis (SFA). SFA is a deterministic component analysis technique for multi-dimensional sequences that by minimizing the variance of the first order time derivative

  17. Vicarious learning from human models in monkeys.

    Science.gov (United States)

    Falcone, Rossella; Brunamonti, Emiliano; Genovesio, Aldo

    2012-01-01

    We examined whether monkeys can learn by observing a human model, through vicarious learning. Two monkeys observed a human model demonstrating an object-reward association and consuming food found underneath an object. The monkeys observed human models as they solved more than 30 learning problems. For each problem, the human models made a choice between two objects, one of which concealed a piece of apple. In the test phase afterwards, the monkeys made a choice of their own. Learning was apparent from the first trial of the test phase, confirming the ability of monkeys to learn by vicarious observation of human models.

  18. Vicarious learning from human models in monkeys.

    Directory of Open Access Journals (Sweden)

    Rossella Falcone

    Full Text Available We examined whether monkeys can learn by observing a human model, through vicarious learning. Two monkeys observed a human model demonstrating an object-reward association and consuming food found underneath an object. The monkeys observed human models as they solved more than 30 learning problems. For each problem, the human models made a choice between two objects, one of which concealed a piece of apple. In the test phase afterwards, the monkeys made a choice of their own. Learning was apparent from the first trial of the test phase, confirming the ability of monkeys to learn by vicarious observation of human models.

  19. Fast social-like learning of complex behaviors based on motor motifs

    Science.gov (United States)

    Calvo Tapia, Carlos; Tyukin, Ivan Y.; Makarov, Valeri A.

    2018-05-01

    Social learning is widely observed in many species. Less experienced agents copy successful behaviors exhibited by more experienced individuals. Nevertheless, the dynamical mechanisms behind this process remain largely unknown. Here we assume that a complex behavior can be decomposed into a sequence of n motor motifs. Then a neural network capable of activating motor motifs in a given sequence can drive an agent. To account for (n -1 )! possible sequences of motifs in a neural network, we employ the winnerless competition approach. We then consider a teacher-learner situation: one agent exhibits a complex movement, while another one aims at mimicking the teacher's behavior. Despite the huge variety of possible motif sequences we show that the learner, equipped with the provided learning model, can rewire "on the fly" its synaptic couplings in no more than (n -1 ) learning cycles and converge exponentially to the durations of the teacher's motifs. We validate the learning model on mobile robots. Experimental results show that the learner is indeed capable of copying the teacher's behavior composed of six motor motifs in a few learning cycles. The reported mechanism of learning is general and can be used for replicating different functions, including, for example, sound patterns or speech.

  20. Learning to Act: Qualitative Learning of Deterministic Action Models

    DEFF Research Database (Denmark)

    Bolander, Thomas; Gierasimczuk, Nina

    2017-01-01

    In this article we study learnability of fully observable, universally applicable action models of dynamic epistemic logic. We introduce a framework for actions seen as sets of transitions between propositional states and we relate them to their dynamic epistemic logic representations as action...... in the limit (inconclusive convergence to the right action model). We show that deterministic actions are finitely identifiable, while arbitrary (non-deterministic) actions require more learning power—they are identifiable in the limit. We then move on to a particular learning method, i.e. learning via update......, which proceeds via restriction of a space of events within a learning-specific action model. We show how this method can be adapted to learn conditional and unconditional deterministic action models. We propose update learning mechanisms for the afore mentioned classes of actions and analyse...

  1. Observations of Student Behavior in Collaborative Learning Groups

    Science.gov (United States)

    Adams, Jeffrey P.; Brissenden, Gina; Lindell, Rebecca S.; Slater, Timothy F.; Wallace, Joy

    In an effort to determine how our students were responding to the use of collaborative learning groups in our large enrollment introductory astronomy (ASTRO 101) courses, we systematically observed the behavior of 270 undergraduate students working in 48 self-formed groups. Their observed behaviors were classified as: (i) actively engaged; (ii) watching actively; (iii) watching passively; and (iv) disengaged. We found that male behavior is consistent regardless of the sex-composition of the groups. However, females were categorized as watching passively and or disengaged significantly more frequently when working in groups that contained uneven numbers of males and females. This case study observation suggests that faculty who use collaborative learning groups might find that the level of student participation in collaborative group learning activities can depend on the sex-composition of the group.

  2. Learning with hierarchical-deep models.

    Science.gov (United States)

    Salakhutdinov, Ruslan; Tenenbaum, Joshua B; Torralba, Antonio

    2013-08-01

    We introduce HD (or “Hierarchical-Deep”) models, a new compositional learning architecture that integrates deep learning models with structured hierarchical Bayesian (HB) models. Specifically, we show how we can learn a hierarchical Dirichlet process (HDP) prior over the activities of the top-level features in a deep Boltzmann machine (DBM). This compound HDP-DBM model learns to learn novel concepts from very few training example by learning low-level generic features, high-level features that capture correlations among low-level features, and a category hierarchy for sharing priors over the high-level features that are typical of different kinds of concepts. We present efficient learning and inference algorithms for the HDP-DBM model and show that it is able to learn new concepts from very few examples on CIFAR-100 object recognition, handwritten character recognition, and human motion capture datasets.

  3. Understanding E-Learning Adoption among Brazilian Universities: An Application of the Decomposed Theory of Planned Behavior

    Science.gov (United States)

    Dos Santos, Luiz Miguel Renda; Okazaki, Shintaro

    2013-01-01

    This study sheds light on the organizational dimensions underlying e-learning adoption among Brazilian universities. We propose an organizational e-learning adoption model based on the decomposed theory of planned behavior (TPB). A series of hypotheses are posited with regard to the relationships among the proposed constructs. The model is…

  4. Students’ mathematical learning in modelling activities

    DEFF Research Database (Denmark)

    Kjeldsen, Tinne Hoff; Blomhøj, Morten

    2013-01-01

    Ten years of experience with analyses of students’ learning in a modelling course for first year university students, led us to see modelling as a didactical activity with the dual goal of developing students’ modelling competency and enhancing their conceptual learning of mathematical concepts i...... create and help overcome hidden cognitive conflicts in students’ understanding; that reflections within modelling can play an important role for the students’ learning of mathematics. These findings are illustrated with a modelling project concerning the world population....

  5. Beliefs and Behaviors in Learning Critical Thinking Skills

    OpenAIRE

    Octavian REPOLSCHI

    2015-01-01

    The paper will present the relation between students’ beliefs and their behaviours observed in the process of learning critical thinking skills. In the first place some consideration concerning the fundamental epistemological concepts used in the research and about the particular critical thinking skills are to be sketched. Then the testing- learning procedure will be shortly summarized. Thirdly the evaluation of beliefs, their relations with knowledge and the associated behaviors are present...

  6. Learning Analytics focused on student behavior. Case study: dropout in distance learning institutions

    Directory of Open Access Journals (Sweden)

    Jose Aguilar

    2017-04-01

    Full Text Available Normally, Learning Analytics (LA can be focused on the analysis of the learning process or the student behavior. In this paper is analyzed the use of LA in the context of distance learning universities, particularly focuses on the students’ behavior. We propose to use a new concept, called "Autonomic Cycle of Learning Analysis Tasks", which defines a set of tasks of LA, whose common objective is to achieve an improvement in the process under study. In this paper, we develop the "Autonomic Cycle of LA Tasks" to analyze the dropout in distance learning institutions. We use a business intelligence methodology in order to develop the "Autonomic Cycle of LA Tasks" for the analysis of the dropout in distance learning. The Autonomic Cycle identifies factors that influence the decision of a student to abandon their studies, predicts the potentially susceptible students to abandon their university studies, and define a motivational pattern for these students.

  7. Peer-to-Peer Learning and the Army Learning Model

    Science.gov (United States)

    2012-06-08

    education will be delivered to the current and future force. This thesis examined the salient areas proposed by the ALM and its impact on P2P learning ...The Army Learning Model is the new educational model that develops adaptive leaders in an era of persistent conflict. Life-long, individual

  8. Inquiry based learning as didactic model in distant learning

    NARCIS (Netherlands)

    Rothkrantz, L.J.M.

    2015-01-01

    Recent years many universities are involved in development of Massive Open Online Courses (MOOCs). Unfortunately an appropriate didactic model for cooperated network learning is lacking. In this paper we introduce inquiry based learning as didactic model. Students are assumed to ask themselves

  9. Transfer Learning for Rodent Behavior Recognition

    NARCIS (Netherlands)

    Lorbach, M.T.; Poppe, R.W.; van Dam, Elsbeth; Veltkamp, R.C.; Noldus, Lucas

    2016-01-01

    Many behavior recognition systems are trained and tested on single datasets limiting their application to comparable datasets. While retraining the system with a novel dataset is possible, it involves laborious annotation effort. We propose to minimize the annotation effort by reusing the knowledge

  10. Leaders Who Learn: The Intersection of Behavioral Science, Adult Learning and Leadership

    Science.gov (United States)

    Sabga, Natalya I.

    2017-01-01

    This study examines if a relationship exists among three rich research streams, specifically the behavioral science of motivation, adult learning and leadership. What motivates adult professionals to continue learning and how is that connected to their style and efficacy as leaders? An extension of literature to connect Andragogy,…

  11. An interactive approach for new careers: The role of learning opportunities and learning behavior

    OpenAIRE

    van der Sluis, E.C.; Peiperl, M.A.

    2000-01-01

    This study examined the learning process at work from an individual perspective. Different kinds of learning opportunities and learning behavior were examined as (a) predictors of career development and (b) moderators of the development process on the job. Survey data from early-career MBAs were analyzed by performing hierarchical regressions and difference-of-means tests. Results indicated that the total amount of developmental job opportunities has a positive influence on individual percept...

  12. Early adversity and learning: implications for typical and atypical behavioral development.

    Science.gov (United States)

    Hanson, Jamie L; van den Bos, Wouter; Roeber, Barbara J; Rudolph, Karen D; Davidson, Richard J; Pollak, Seth D

    2017-07-01

    Children who experience early adversity often develop emotion regulatory problems, but little is known about the mechanisms that mediate this relation. We tested whether general associative learning processes contribute to associations between adversity, in the form of child maltreatment, and negative behavioral outcomes. Eighty-one participants between 12 and 17 years of age were recruited for this study and completed a probabilistic learning Task. Forty-one of these participants had been exposed to physical abuse, a form of early adversity. Forty additional participants without any known history of maltreatment served as a comparison group. All participants (and their parents) also completed portions of the Youth Life Stress Interview to understand adolescent's behavior. We calculated measures of associative learning, and also constructed mathematical models of learning. We found that adolescents exposed to high levels of adversity early in their lives had lower levels of associative learning than comparison adolescents. In addition, we found that impaired associative learning partially explained the higher levels of behavioral problems among youth who suffered early adversity. Using mathematical models, we also found that two components of learning were specifically affected in children exposed to adversity: choice variability and biases in their beliefs about the likelihood of rewards in the environment. Participants who had been exposed to early adversity were less able than their peers to correctly learn which stimuli were likely to result in reward, even after repeated feedback. These individuals also used information about known rewards in their environments less often. In addition, individuals exposed to adversity made decisions early in the learning process as if rewards were less consistent and occurred more at random. These data suggest one mechanism through which early life experience shapes behavioral development. © 2017 Association for Child and

  13. An evolutionary behavioral model for decision making

    OpenAIRE

    Romero Lopez, Dr Oscar Javier

    2011-01-01

    For autonomous agents the problem of deciding what to do next becomes increasingly complex when acting in unpredictable and dynamic environments pursuing multiple and possibly conflicting goals. One of the most relevant behavior-based model that tries to deal with this problem is the one proposed by Maes, the Bbehavior Network model. This model proposes a set of behaviors as purposive perception-action units which are linked in a nonhierarchical network, and whose behavior selection process i...

  14. Identifying Students' Characteristic Learning Behaviors in an Intelligent Tutoring System Fostering Self-Regulated Learning

    Science.gov (United States)

    Bouchet, Francois; Azevedo, Roger; Kinnebrew, John S.; Biswas, Gautam

    2012-01-01

    Identification of student learning behaviors, especially those that characterize or distinguish students, can yield important insights for the design of adaptation and feedback mechanisms in Intelligent Tutoring Systems (ITS). In this paper, we analyze trace data to identify distinguishing patterns of behavior in a study of 51 college students…

  15. Knowledge Management through the Equilibrium Pattern Model for Learning

    Science.gov (United States)

    Sarirete, Akila; Noble, Elizabeth; Chikh, Azeddine

    Contemporary students are characterized by having very applied learning styles and methods of acquiring knowledge. This behavior is consistent with the constructivist models where students are co-partners in the learning process. In the present work the authors developed a new model of learning based on the constructivist theory coupled with the cognitive development theory of Piaget. The model considers the level of learning based on several stages and the move from one stage to another requires learners' challenge. At each time a new concept is introduced creates a disequilibrium that needs to be worked out to return back to its equilibrium stage. This process of "disequilibrium/equilibrium" has been analyzed and validated using a course in computer networking as part of Cisco Networking Academy Program at Effat College, a women college in Saudi Arabia. The model provides a theoretical foundation for teaching especially in a complex knowledge domain such as engineering and can be used in a knowledge economy.

  16. Research Models in Developmental Behavioral Toxicology.

    Science.gov (United States)

    Dietrich, Kim N.; Pearson, Douglas T.

    Developmental models currently used by child behavioral toxicologists and teratologists are inadequate to address current issues in these fields. Both child behavioral teratology and toxicology scientifically study the impact of exposure to toxic agents on behavior development: teratology focuses on prenatal exposure and postnatal behavior…

  17. Preschool Interactive Peer Play Mediates Problem Behavior and Learning for Low-Income Children

    Science.gov (United States)

    Bulotsky-Shearer, Rebecca J.; Bell, Elizabeth R.; Romero, Sandy L.; Carter, Tracy M.

    2012-01-01

    The study employed a developmental, ecological, and resiliency framework to examine whether interactive peer play competencies mediated associations between teacher reported problem behavior and learning outcomes for a representative sample of urban low-income children (N = 507 across 46 Head Start classrooms). Structural equation models provided…

  18. Explaining Helping Behavior in a Cooperative Learning Classroom Setting Using Attribution Theory

    Science.gov (United States)

    Ahles, Paula M.; Contento, Jann M.

    2006-01-01

    This recently completed study examined whether attribution theory can explain helping behavior in an interdependent classroom environment that utilized a cooperative-learning model. The study focused on student participants enrolled in 6 community college communication classes taught by the same instructor. Three levels of cooperative-learning…

  19. Behavioral effects of nerve agents: laboratory animal models

    International Nuclear Information System (INIS)

    Myers, T. M.

    2009-01-01

    Diverse and often subtle behavioral consequences have been reported for humans exposed to nerve agents. Laboratory studies of nerve agent exposure offer rigorous control over important variables, but species other than man must be used. Nonhuman primate models offer the best means of identifying the toxic nervous system effects of nerve agent insult and the countermeasures best capable of preventing or attenuating these effects. Comprehensive behavioral models must evaluate preservation and recovery of function as well as new learning ability. The throughput and sensitivity of the tests chosen are important considerations. A few nonhuman primate studies will be discussed to elaborate recent successes, current limitations, and future directions.(author)

  20. Determining the Effects of LMS Learning Behaviors on Academic Achievement in a Learning Analytic Perspective

    Directory of Open Access Journals (Sweden)

    Mehmet FIRAT

    2016-02-01

    Full Text Available Two of the most important outcomes of learning analytics are predicting students’ learning and providing effective feedback. Learning Management Systems (LMS, which are widely used to support online and face-to-face learning, provide extensive research opportunities with detailed records of background data regarding users’ behaviors. The purpose of this study was to investigate the effects of undergraduate students’ LMS learning behaviors on their academic achievements. In line with this purpose, the participating students’ online learning behaviors in LMS were examined by using learning analytics for 14 weeks, and the relationship between students’ behaviors and their academic achievements was analyzed, followed by an analysis of their views about the influence of LMS on their academic achievement. The present study, in which quantitative and qualitative data were collected, was carried out with the explanatory mixed method. A total of 71 undergraduate students participated in the study. The results revealed that the students used LMSs as a support to face-to-face education more intensively on course days (at the beginning of the related lessons and at nights on course days and that they activated the content elements the most. Lastly, almost all the students agreed that LMSs helped increase their academic achievement only when LMSs included such features as effectiveness, interaction, reinforcement, attractive design, social media support, and accessibility.

  1. A Model for Learning Development

    Science.gov (United States)

    Kilfoil, W. R.

    2008-01-01

    This article looks at the way in which people perceive learning and the impact of these perceptions on teaching methods within the context of learning development in distance education. The context could, in fact, be any type of teaching and learning environment. The point is to balance approaches to teaching and learning depending on student…

  2. Model brain based learning (BBL and whole brain teaching (WBT in learning

    Directory of Open Access Journals (Sweden)

    Baiq Sri Handayani

    2017-08-01

    Full Text Available The learning process is a process of change in behavior as a form of the result of learning. The learning model is a crucial component of the success of the learning process. The learning model is growing fastly, and each model has different characteristics. Teachers are required to be able to understand each model to teach the students optimally by matching the materials and the learning model. The best of the learning model is the model that based on the brain system in learning that are the model of Brain Based Learning (BBL and the model of Whole Brain Teaching (WBT. The purposes of this article are to obtain information related to (1 the brain’s natural learning system, (2 analyze the characteristics of the model BBL and WBT based on theory, brain sections that play a role associated with syntax, similarities, and differences, (3 explain the distinctive characteristics of both models in comparison to other models. The results of this study are: (1 the brain’s natural learning system are: (a the nerves in each hemisphere do not work independently, (b doing more activities can connect more brain nerves, (c the right hemisphere controls the left side motoric sensor of the body, and vice versa; (2 the characteristics of BBL and WBT are: (a BBL is based on the brain’s structure and function, while the model WBT is based on the instructional approach, neurolinguistic, and body language, (b the parts of the brain that work in BBL are: cerebellum, cerebral cortex, frontal lobe, limbic system, and prefrontal cortex; whereas the parts that work WBT are: prefrontal cortex, visual cortex, motor cortex, limbic system, and amygdala, (c the similarities between them are that they both rely on the brain’s system and they both promote gesture in learning, whereas the differences are on the view of the purposes of gestures and the learning theory that they rely on. BBL relies on cognitive theory while WBT relies on social theory; (3 the typical

  3. Developmental song learning as a model to understand neural mechanisms that limit and promote the ability to learn.

    Science.gov (United States)

    London, Sarah E

    2017-11-20

    Songbirds famously learn their vocalizations. Some species can learn continuously, others seasonally, and still others just once. The zebra finch (Taeniopygia guttata) learns to sing during a single developmental "Critical Period," a restricted phase during which a specific experience has profound and permanent effects on brain function and behavioral patterns. The zebra finch can therefore provide fundamental insight into features that promote and limit the ability to acquire complex learned behaviors. For example, what properties permit the brain to come "on-line" for learning? How does experience become encoded to prevent future learning? What features define the brain in receptive compared to closed learning states? This piece will focus on epigenomic, genomic, and molecular levels of analysis that operate on the timescales of development and complex behavioral learning. Existing data will be discussed as they relate to Critical Period learning, and strategies for future studies to more directly address these questions will be considered. Birdsong learning is a powerful model for advancing knowledge of the biological intersections of maturation and experience. Lessons from its study not only have implications for understanding developmental song learning, but also broader questions of learning potential and the enduring effects of early life experience on neural systems and behavior. Copyright © 2017. Published by Elsevier B.V.

  4. Probability Learning: Changes in Behavior across Time and Development

    Science.gov (United States)

    Plate, Rista C.; Fulvio, Jacqueline M.; Shutts, Kristin; Green, C. Shawn; Pollak, Seth D.

    2018-01-01

    Individuals track probabilities, such as associations between events in their environments, but less is known about the degree to which experience--within a learning session and over development--influences people's use of incoming probabilistic information to guide behavior in real time. In two experiments, children (4-11 years) and adults…

  5. Demographic and Behavioral Characteristics of Osher Lifelong Learning Institute Members

    Science.gov (United States)

    Hansen, Robert Jack; Brady, E. Michael; Thaxton, Steven P.

    2016-01-01

    The number of lifelong learning institutes (LLIs) is growing across the United States and it is important for educational planners and administrators to know about current demographic and behavioral characteristics of program participants. A 14-question survey was administered via SurveyMonkey to members who use computers in eight Osher Lifelong…

  6. From rapid place learning to behavioral performance: a key role for the intermediate hippocampus.

    Directory of Open Access Journals (Sweden)

    Tobias Bast

    2009-04-01

    Full Text Available Rapid place encoding by hippocampal neurons, as reflected by place-related firing, has been intensely studied, whereas the substrates that translate hippocampal place codes into behavior have received little attention. A key point relevant to this translation is that hippocampal organization is characterized by functional-anatomical gradients along the septotemporal axis: Whereas the ability of hippocampal neurons to encode accurate place information declines from the septal to temporal end, hippocampal connectivity to prefrontal and subcortical sites that might relate such place information to behavioral-control processes shows an opposite gradient. We examined in rats the impact of selective lesions to relevant parts of the hippocampus on behavioral tests requiring place learning (watermaze procedures and on in vivo electrophysiological models of hippocampal encoding (long-term potentiation [LTP], place cells. We found that the intermediate hippocampus is necessary and largely sufficient for behavioral performance based on rapid place learning. In contrast, a residual septal pole of the hippocampus, although displaying intact electrophysiological indices of rapid information encoding (LTP, precise place-related firing, and rapid remapping, failed to sustain watermaze performance based on rapid place learning. These data highlight the important distinction between hippocampal encoding and the behavioral performance based on such encoding, and suggest that the intermediate hippocampus, where substrates of rapid accurate place encoding converge with links to behavioral control, is critical to translate rapid (one-trial place learning into navigational performance.

  7. Aversive Learning and Trait Aggression Influence Retaliatory Behavior.

    Science.gov (United States)

    Molapour, Tanaz; Lindström, Björn; Olsson, Andreas

    2016-01-01

    In two experiments (n = 35, n = 34), we used a modified fear-conditioning paradigm to investigate the role of aversive learning in retaliatory behavior in social context. Participants first completed an initial aversive learning phase in which the pairing of a neutral conditioned stimulus (CS; i.e., neutral face) with a naturally aversive unconditioned stimulus (US; electric shock) was learned. Then they were given an opportunity to interact (i.e., administer 0-2 shocks) with the same faces again, during a Test phase. In Experiment 2, we used the same paradigm with the addition of online trial-by-trial ratings (e.g., US expectancy and anger) to examine the role of aversive learning, anger, and the learned expectancy of receiving punishment more closely. Our results indicate that learned aversions influenced future retaliation in a social context. In both experiments, participants showed largest skin conductance responses (SCRs) to the faces paired with one or two shocks, demonstrating successful aversive learning. Importantly, participants administered more shocks to the faces paired with the most number of shocks when the opportunity was given during test. Also, our results revealed that aggressive traits (Buss and Perry Aggression scale) were associated with retaliation only toward CSs associated with aversive experiences. These two experiments show that aggressive traits, when paired with aversive learning experiences enhance the likelihood to act anti-socially toward others.

  8. Beliefs and Behaviors in Learning Critical Thinking Skills

    Directory of Open Access Journals (Sweden)

    Octavian REPOLSCHI

    2015-12-01

    Full Text Available The paper will present the relation between students’ beliefs and their behaviours observed in the process of learning critical thinking skills. In the first place some consideration concerning the fundamental epistemological concepts used in the research and about the particular critical thinking skills are to be sketched. Then the testing- learning procedure will be shortly summarized. Thirdly the evaluation of beliefs, their relations with knowledge and the associated behaviors are presented. The results of the periodic testing procedures that were taking place according to the established methodology are to be discussed. Finally, some general considerations concerning the relations between beliefs, behaviors and knowledge that have emerged in the process of learning are going to be presented.

  9. Model-observer similarity, error modeling and social learning in rhesus macaques.

    Directory of Open Access Journals (Sweden)

    Elisabetta Monfardini

    Full Text Available Monkeys readily learn to discriminate between rewarded and unrewarded items or actions by observing their conspecifics. However, they do not systematically learn from humans. Understanding what makes human-to-monkey transmission of knowledge work or fail could help identify mediators and moderators of social learning that operate regardless of language or culture, and transcend inter-species differences. Do monkeys fail to learn when human models show a behavior too dissimilar from the animals' own, or when they show a faultless performance devoid of error? To address this question, six rhesus macaques trained to find which object within a pair concealed a food reward were successively tested with three models: a familiar conspecific, a 'stimulus-enhancing' human actively drawing the animal's attention to one object of the pair without actually performing the task, and a 'monkey-like' human performing the task in the same way as the monkey model did. Reward was manipulated to ensure that all models showed equal proportions of errors and successes. The 'monkey-like' human model improved the animals' subsequent object discrimination learning as much as a conspecific did, whereas the 'stimulus-enhancing' human model tended on the contrary to retard learning. Modeling errors rather than successes optimized learning from the monkey and 'monkey-like' models, while exacerbating the adverse effect of the 'stimulus-enhancing' model. These findings identify error modeling as a moderator of social learning in monkeys that amplifies the models' influence, whether beneficial or detrimental. By contrast, model-observer similarity in behavior emerged as a mediator of social learning, that is, a prerequisite for a model to work in the first place. The latter finding suggests that, as preverbal infants, macaques need to perceive the model as 'like-me' and that, once this condition is fulfilled, any agent can become an effective model.

  10. Catalog Learning: Carabid Beetles Learn to Manipulate with Innate Coherent Behavioral Patterns

    Directory of Open Access Journals (Sweden)

    Zhanna Reznikova

    2013-07-01

    Full Text Available One of the most fascinating problems in comparative psychology is how learning contributes to solving specific functional problems in animal life, and which forms of learning our species shares with non-human animals. Simulating a natural situation of territorial conflicts between predatory carabids and red wood ants in field and laboratory experiments, we have revealed a relatively simple and quite natural form of learning that has been overlooked. We call it catalog learning, the name we give to the ability of animals to establish associations between stimuli and coherent behavioral patterns (patterns consist of elementary motor acts that have a fixed order. Instead of budgeting their motor acts gradually, from chaotic to rational sequences in order to learn something new, which is characteristic for a conditioning response, animals seem to be “cataloguing” their repertoire of innate coherent behavioral patterns in order to optimize their response to a certain repetitive event. This form of learning can be described as “stimulus-pattern” learning. In our experiments four “wild” carabid species, whose cognitive abilities have never been studied before, modified their behavior in a rather natural manner in order to avoid damage from aggressive ants. Beetles learned to select the relevant coherent behavioral patterns from the set of seven patterns, which are common to all four species and apparently innate. We suggest that this form of learning differs from the known forms of associative learning, and speculate that it is quite universal and can be present in a wide variety of species, both invertebrate and vertebrate. This study suggests a new link between the concepts of cognition and innateness.

  11. Hierarchical Bayesian Models of Subtask Learning

    Science.gov (United States)

    Anglim, Jeromy; Wynton, Sarah K. A.

    2015-01-01

    The current study used Bayesian hierarchical methods to challenge and extend previous work on subtask learning consistency. A general model of individual-level subtask learning was proposed focusing on power and exponential functions with constraints to test for inconsistency. To study subtask learning, we developed a novel computer-based booking…

  12. Modeling Architectural Patterns’ Behavior Using Architectural Primitives

    NARCIS (Netherlands)

    Waqas Kamal, Ahmad; Avgeriou, Paris

    2008-01-01

    Architectural patterns have an impact on both the structure and the behavior of a system at the architecture design level. However, it is challenging to model patterns’ behavior in a systematic way because modeling languages do not provide the appropriate abstractions and because each pattern

  13. A Learning-Style Theory for Understanding Autistic Behaviors

    Science.gov (United States)

    Qian, Ning; Lipkin, Richard M.

    2011-01-01

    Understanding autism's ever-expanding array of behaviors, from sensation to cognition, is a major challenge. We posit that autistic and typically developing brains implement different algorithms that are better suited to learn, represent, and process different tasks; consequently, they develop different interests and behaviors. Computationally, a continuum of algorithms exists, from lookup table (LUT) learning, which aims to store experiences precisely, to interpolation (INT) learning, which focuses on extracting underlying statistical structure (regularities) from experiences. We hypothesize that autistic and typical brains, respectively, are biased toward LUT and INT learning, in low- and high-dimensional feature spaces, possibly because of their narrow and broad tuning functions. The LUT style is good at learning relationships that are local, precise, rigid, and contain little regularity for generalization (e.g., the name–number association in a phonebook). However, it is poor at learning relationships that are context dependent, noisy, flexible, and do contain regularities for generalization (e.g., associations between gaze direction and intention, language and meaning, sensory input and interpretation, motor-control signal and movement, and social situation and proper response). The LUT style poorly compresses information, resulting in inefficiency, sensory overload (overwhelm), restricted interests, and resistance to change. It also leads to poor prediction and anticipation, frequent surprises and over-reaction (hyper-sensitivity), impaired attentional selection and switching, concreteness, strong local focus, weak adaptation, and superior and inferior performances on simple and complex tasks. The spectrum nature of autism can be explained by different degrees of LUT learning among different individuals, and in different systems of the same individual. Our theory suggests that therapy should focus on training autistic LUT algorithm to learn regularities

  14. A learning-style theory for understanding autistic behaviors

    Directory of Open Access Journals (Sweden)

    Ning eQian

    2011-08-01

    Full Text Available Understanding autism’s ever-expanding array of behaviors, from sensation to cognition, is a major challenge. We posit that autistic and typically-developing brains implement different algorithms that are better suited to learn, represent, and process different tasks; consequently, they develop different interests and behaviors. Computationally, a continuum of algorithms exists, from lookup-table (LUT learning, which aims to store experiences precisely, to interpolation (INT learning, which focuses on extracting underlying statistical structure (regularities from experiences. We hypothesize that autistic and typical brains, respectively, are biased toward LUT and INT learning, in low and high dimensional feature spaces, possibly because of their narrow and broad tuning functions. The LUT style is good at learning relationships that are local, precise, rigid, and contain little regularity for generalization (e.g., the name-number association in a phonebook. However, it is poor at learning relationships that are context dependent, noisy, flexible, and do contain regularities for generalization (e.g., associations between gaze direction and intention, language and meaning, sensory input and interpretation, motor-control signal and movement, and social situation and proper response. The LUT style poorly compresses information, resulting in inefficiency, sensory overload (overwhelm, restricted interests, and resistance to change. It also leads to poor prediction and anticipation, frequent surprises and over-reaction (hyper-sensitivity, impaired attentional selection and switching, concreteness, strong local focus, weak adaptation, and superior and inferior performances on simple and complex tasks. The spectrum nature of autism can be explained by different degrees of LUT learning among different individuals, and in different systems of the same individual. Our theory suggests that therapy should focus on training autistic LUT algorithm to learn

  15. Model for behavior observation training programs

    International Nuclear Information System (INIS)

    Berghausen, P.E. Jr.

    1987-01-01

    Continued behavior observation is mandated by ANSI/ANS 3.3. This paper presents a model for behavior observation training that is in accordance with this standard and the recommendations contained in US NRC publications. The model includes seventeen major topics or activities. Ten of these are discussed: Pretesting of supervisor's knowledge of behavior observation requirements, explanation of the goals of behavior observation programs, why behavior observation training programs are needed (legal and psychological issues), early indicators of emotional instability, use of videotaped interviews to demonstrate significant psychopathology, practice recording behaviors, what to do when unusual behaviors are observed, supervisor rationalizations for noncompliance, when to be especially vigilant, and prevention of emotional instability

  16. Explain the Behavior Intention to Use e-Learning Technologies: A Unified Theory of Acceptance and Use of Technology Perspective

    Science.gov (United States)

    Shaqrah, Amin A.

    2015-01-01

    The purpose of this study is to explain the behavior intention to use e-learning technologies. In order to achieve a better view and validate the study, researcher attempts to give details of how technology acceptance models help Jordanian trainees firms in accepting e-learning technology, and how if applied will result more attention to usage…

  17. Implication of Dopaminergic Modulation in Operant Reward Learning and the Induction of Compulsive-Like Feeding Behavior in "Aplysia"

    Science.gov (United States)

    Bedecarrats, Alexis; Cornet, Charles; Simmers, John; Nargeot, Romuald

    2013-01-01

    Feeding in "Aplysia" provides an amenable model system for analyzing the neuronal substrates of motivated behavior and its adaptability by associative reward learning and neuromodulation. Among such learning processes, appetitive operant conditioning that leads to a compulsive-like expression of feeding actions is known to be associated…

  18. Multiaxial behavior of foams - Experiments and modeling

    Science.gov (United States)

    Maheo, Laurent; Guérard, Sandra; Rio, Gérard; Donnard, Adrien; Viot, Philippe

    2015-09-01

    Cellular materials are strongly related to pressure level inside the material. It is therefore important to use experiments which can highlight (i) the pressure-volume behavior, (ii) the shear-shape behavior for different pressure level. Authors propose to use hydrostatic compressive, shear and combined pressure-shear tests to determine cellular materials behavior. Finite Element Modeling must take into account these behavior specificities. Authors chose to use a behavior law with a Hyperelastic, a Viscous and a Hysteretic contributions. Specific developments has been performed on the Hyperelastic one by separating the spherical and the deviatoric part to take into account volume change and shape change characteristics of cellular materials.

  19. Caka E-Learning Model

    Science.gov (United States)

    Gorsev, Gonca; Turkmen, Ugur; Askin, Cihat

    2017-01-01

    In today's world, in order to obtain the information in education, various approaches, methods and devices have been developed. Like many developing countries, e-learning and distance learning (internet based learning) are used today in many areas of education in Turkey. This research aims to contribute to education systems and develop a…

  20. An Ontology-Based Framework for Modeling User Behavior

    DEFF Research Database (Denmark)

    Razmerita, Liana

    2011-01-01

    and classifies its users according to their behavior. The user ontology is the backbone of OntobUMf and has been designed according to the Information Management System Learning Information Package (IMS LIP). The user ontology includes a Behavior concept that extends IMS LIP specification and defines...... characteristics of the users interacting with the system. Concrete examples of how OntobUMf is used in the context of a Knowledge Management (KM) System are provided. This paper discusses some of the implications of ontology-based user modeling for semantically enhanced KM and, in particular, for personal KM....... The results of this research may contribute to the development of other frameworks for modeling user behavior, other semantically enhanced user modeling frameworks, or other semantically enhanced information systems....

  1. Extinction of avoidance behavior by safety learning depends on endocannabinoid signaling in the hippocampus.

    Science.gov (United States)

    Micale, Vincenzo; Stepan, Jens; Jurik, Angela; Pamplona, Fabricio A; Marsch, Rudolph; Drago, Filippo; Eder, Matthias; Wotjak, Carsten T

    2017-07-01

    The development of exaggerated avoidance behavior is largely responsible for the decreased quality of life in patients suffering from anxiety disorders. Studies using animal models have contributed to the understanding of the neural mechanisms underlying the acquisition of avoidance responses. However, much less is known about its extinction. Here we provide evidence in mice that learning about the safety of an environment (i.e., safety learning) rather than repeated execution of the avoided response in absence of negative consequences (i.e., response extinction) allowed the animals to overcome their avoidance behavior in a step-down avoidance task. This process was context-dependent and could be blocked by pharmacological (3 mg/kg, s.c.; SR141716) or genetic (lack of cannabinoid CB1 receptors in neurons expressing dopamine D1 receptors) inactivation of CB1 receptors. In turn, the endocannabinoid reuptake inhibitor AM404 (3 mg/kg, i.p.) facilitated safety learning in a CB1-dependent manner and attenuated the relapse of avoidance behavior 28 days after conditioning. Safety learning crucially depended on endocannabinoid signaling at level of the hippocampus, since intrahippocampal SR141716 treatment impaired, whereas AM404 facilitated safety learning. Other than AM404, treatment with diazepam (1 mg/kg, i.p.) impaired safety learning. Drug effects on behavior were directly mirrored by drug effects on evoked activity propagation through the hippocampal trisynaptic circuit in brain slices: As revealed by voltage-sensitive dye imaging, diazepam impaired whereas AM404 facilitated activity propagation to CA1 in a CB1-dependent manner. In line with this, systemic AM404 enhanced safety learning-induced expression of Egr1 at level of CA1. Together, our data render it likely that AM404 promotes safety learning by enhancing information flow through the trisynaptic circuit to CA1. Copyright © 2017 Elsevier Ltd. All rights reserved.

  2. Learning Bayesian Dependence Model for Student Modelling

    Directory of Open Access Journals (Sweden)

    Adina COCU

    2008-12-01

    Full Text Available Learning a Bayesian network from a numeric set of data is a challenging task because of dual nature of learning process: initial need to learn network structure, and then to find out the distribution probability tables. In this paper, we propose a machine-learning algorithm based on hill climbing search combined with Tabu list. The aim of learning process is to discover the best network that represents dependences between nodes. Another issue in machine learning procedure is handling numeric attributes. In order to do that, we must perform an attribute discretization pre-processes. This discretization operation can influence the results of learning network structure. Therefore, we make a comparative study to find out the most suitable combination between discretization method and learning algorithm, for a specific data set.

  3. Genetic dissection of behavioral flexibility: reversal learning in mice.

    Science.gov (United States)

    Laughlin, Rick E; Grant, Tara L; Williams, Robert W; Jentsch, J David

    2011-06-01

    Behavioral inflexibility is a feature of schizophrenia, attention-deficit/hyperactivity disorder, and behavior addictions that likely results from heritable deficits in the inhibitory control over behavior. Here, we investigate the genetic basis of individual differences in flexibility, measured using an operant reversal learning task. We quantified discrimination acquisition and subsequent reversal learning in a cohort of 51 BXD strains of mice (2-5 mice/strain, n = 176) for which we have matched data on sequence, gene expression in key central nervous system regions, and neuroreceptor levels. Strain variation in trials to criterion on acquisition and reversal was high, with moderate heritability (∼.3). Acquisition and reversal learning phenotypes did not covary at the strain level, suggesting that these traits are effectively under independent genetic control. Reversal performance did covary with dopamine D2 receptor levels in the ventral midbrain, consistent with a similar observed relationship between impulsivity and D2 receptors in humans. Reversal, but not acquisition, is linked to a locus on mouse chromosome 10 with a peak likelihood ratio statistic at 86.2 megabase (p work demonstrates the clear trait independence between, and genetic control of, discrimination acquisition and reversal and illustrates how globally coherent data sets for a single panel of highly related strains can be interrogated and integrated to uncover genetic sources and molecular and neuropharmacological candidates of complex behavioral traits relevant to human psychopathology. Copyright © 2011 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

  4. Behavioral model of visual perception and recognition

    Science.gov (United States)

    Rybak, Ilya A.; Golovan, Alexander V.; Gusakova, Valentina I.

    1993-09-01

    In the processes of visual perception and recognition human eyes actively select essential information by way of successive fixations at the most informative points of the image. A behavioral program defining a scanpath of the image is formed at the stage of learning (object memorizing) and consists of sequential motor actions, which are shifts of attention from one to another point of fixation, and sensory signals expected to arrive in response to each shift of attention. In the modern view of the problem, invariant object recognition is provided by the following: (1) separated processing of `what' (object features) and `where' (spatial features) information at high levels of the visual system; (2) mechanisms of visual attention using `where' information; (3) representation of `what' information in an object-based frame of reference (OFR). However, most recent models of vision based on OFR have demonstrated the ability of invariant recognition of only simple objects like letters or binary objects without background, i.e. objects to which a frame of reference is easily attached. In contrast, we use not OFR, but a feature-based frame of reference (FFR), connected with the basic feature (edge) at the fixation point. This has provided for our model, the ability for invariant representation of complex objects in gray-level images, but demands realization of behavioral aspects of vision described above. The developed model contains a neural network subsystem of low-level vision which extracts a set of primary features (edges) in each fixation, and high- level subsystem consisting of `what' (Sensory Memory) and `where' (Motor Memory) modules. The resolution of primary features extraction decreases with distances from the point of fixation. FFR provides both the invariant representation of object features in Sensor Memory and shifts of attention in Motor Memory. Object recognition consists in successive recall (from Motor Memory) and execution of shifts of attention and

  5. Integrated Model for E-Learning Acceptance

    Science.gov (United States)

    Ramadiani; Rodziah, A.; Hasan, S. M.; Rusli, A.; Noraini, C.

    2016-01-01

    E-learning is not going to work if the system is not used in accordance with user needs. User Interface is very important to encourage using the application. Many theories had discuss about user interface usability evaluation and technology acceptance separately, actually why we do not make it correlation between interface usability evaluation and user acceptance to enhance e-learning process. Therefore, the evaluation model for e-learning interface acceptance is considered important to investigate. The aim of this study is to propose the integrated e-learning user interface acceptance evaluation model. This model was combined some theories of e-learning interface measurement such as, user learning style, usability evaluation, and the user benefit. We formulated in constructive questionnaires which were shared at 125 English Language School (ELS) students. This research statistics used Structural Equation Model using LISREL v8.80 and MANOVA analysis.

  6. Contextual Influences on Financial Behavior: A Proposed Model for Adult Financial Literacy Education

    Science.gov (United States)

    Way, Wendy L.

    2014-01-01

    This chapter presents an ecological model that highlights the importance of considering multiple contextual influences on behavior as well as other factors that may impact learning when designing research and practice aimed at enhancing financial capability.

  7. Biosocial Models of Deviant Behavior.

    Science.gov (United States)

    Rowe, David C.

    1995-01-01

    Describes biological influences on criminality. Illustrative data suggest a biological sex difference in criminality and heritable differences in this trait among individuals. Methods of isolating environmental influences are described. Author notes that using environment-friendly behavior genetic research designs is not only proper but would…

  8. The Effect of Education Level on Psychological Empowerment and Burnout-The Mediating Role of Workplace Learning Behaviors

    OpenAIRE

    Sarit Rashkovits; Yael Livne

    2013-01-01

    The study investigates the relationship between education level, workplace learning behaviors, psychological empowerment and burnout in a sample of 191 teachers. We hypothesized that education level will positively affect psychological state of increased empowerment and decreased burnout, and we purposed that these effects will be mediated by workplace learning behaviors. We used multiple regression analyses to test the model that included also the 6 following control var...

  9. SUSTAIN: a network model of category learning.

    Science.gov (United States)

    Love, Bradley C; Medin, Douglas L; Gureckis, Todd M

    2004-04-01

    SUSTAIN (Supervised and Unsupervised STratified Adaptive Incremental Network) is a model of how humans learn categories from examples. SUSTAIN initially assumes a simple category structure. If simple solutions prove inadequate and SUSTAIN is confronted with a surprising event (e.g., it is told that a bat is a mammal instead of a bird), SUSTAIN recruits an additional cluster to represent the surprising event. Newly recruited clusters are available to explain future events and can themselves evolve into prototypes-attractors-rules. SUSTAIN's discovery of category substructure is affected not only by the structure of the world but by the nature of the learning task and the learner's goals. SUSTAIN successfully extends category learning models to studies of inference learning, unsupervised learning, category construction, and contexts in which identification learning is faster than classification learning.

  10. Digital Competence Model of Distance Learning Students

    Science.gov (United States)

    da Silva, Ketia Kellen A.; Behar, Patricia A.

    2017-01-01

    This article presents the development of a digital competency model of Distance Learning (DL) students in Brazil called CompDigAl_EAD. The following topics were addressed in this study: Educational Competences, Digital Competences, and Distance Learning students. The model was developed between 2015 and 2016 and is being validated in 2017. It was…

  11. Learning models of activities involving interacting objects

    DEFF Research Database (Denmark)

    Manfredotti, Cristina; Pedersen, Kim Steenstrup; Hamilton, Howard J.

    2013-01-01

    We propose the LEMAIO multi-layer framework, which makes use of hierarchical abstraction to learn models for activities involving multiple interacting objects from time sequences of data concerning the individual objects. Experiments in the sea navigation domain yielded learned models that were t...

  12. Modelling and Optimizing Mathematics Learning in Children

    Science.gov (United States)

    Käser, Tanja; Busetto, Alberto Giovanni; Solenthaler, Barbara; Baschera, Gian-Marco; Kohn, Juliane; Kucian, Karin; von Aster, Michael; Gross, Markus

    2013-01-01

    This study introduces a student model and control algorithm, optimizing mathematics learning in children. The adaptive system is integrated into a computer-based training system for enhancing numerical cognition aimed at children with developmental dyscalculia or difficulties in learning mathematics. The student model consists of a dynamic…

  13. Rethinking Hearing Aid Fitting by Learning From Behavioral Patterns

    DEFF Research Database (Denmark)

    Johansen, Benjamin; Petersen, Michael Kai; Pontoppidan, Niels Henrik

    2017-01-01

    users to remotely enhance auditory focus and attenuate background noise to improve speech intelligibility. N=5, participants changed program settings and adjusted volume on their hearing instruments using their smartphones. We found that individual behavioral patterns affected the usage of the devices....... A significant difference between program usage, and weekdays versus weekends, were found. Users not only changed programs to modify aspects of directionality and noise reduction, but also continuously adjusted the volume. Rethinking hearing instruments as devices that adaptively learn behavioral patterns based...

  14. EFFECTS OF INQUIRY TRAINING LEARNING MODEL BASED MULTIMEDIA AND MOTIVATION OF PHYSICS STUDENT LEARNING OUTCOMES

    OpenAIRE

    Hayati .; Retno Dwi Suyanti

    2013-01-01

    The objective in this research: (1) Determine a better learning model to improve learning outcomes physics students among learning model Inquiry Training based multimedia and Inquiry Training learning model. (2) Determine the level of motivation to learn in affects physics student learning outcomes. (3) Knowing the interactions between the model of learning and motivation in influencing student learning outcomes. This research is a quasi experimental. The population in this research was all s...

  15. Neuromodulatory adaptive combination of correlation-based learning in cerebellum and reward-based learning in basal ganglia for goal-directed behavior control.

    Science.gov (United States)

    Dasgupta, Sakyasingha; Wörgötter, Florentin; Manoonpong, Poramate

    2014-01-01

    Goal-directed decision making in biological systems is broadly based on associations between conditional and unconditional stimuli. This can be further classified as classical conditioning (correlation-based learning) and operant conditioning (reward-based learning). A number of computational and experimental studies have well established the role of the basal ganglia in reward-based learning, where as the cerebellum plays an important role in developing specific conditioned responses. Although viewed as distinct learning systems, recent animal experiments point toward their complementary role in behavioral learning, and also show the existence of substantial two-way communication between these two brain structures. Based on this notion of co-operative learning, in this paper we hypothesize that the basal ganglia and cerebellar learning systems work in parallel and interact with each other. We envision that such an interaction is influenced by reward modulated heterosynaptic plasticity (RMHP) rule at the thalamus, guiding the overall goal directed behavior. Using a recurrent neural network actor-critic model of the basal ganglia and a feed-forward correlation-based learning model of the cerebellum, we demonstrate that the RMHP rule can effectively balance the outcomes of the two learning systems. This is tested using simulated environments of increasing complexity with a four-wheeled robot in a foraging task in both static and dynamic configurations. Although modeled with a simplified level of biological abstraction, we clearly demonstrate that such a RMHP induced combinatorial learning mechanism, leads to stabler and faster learning of goal-directed behaviors, in comparison to the individual systems. Thus, in this paper we provide a computational model for adaptive combination of the basal ganglia and cerebellum learning systems by way of neuromodulated plasticity for goal-directed decision making in biological and bio-mimetic organisms.

  16. A Conceptual Model of Investor Behavior

    OpenAIRE

    Lovric, M.; Kaymak, U.; Spronk, J.

    2008-01-01

    textabstractBased on a survey of behavioral finance literature, this paper presents a descriptive model of individual investor behavior in which investment decisions are seen as an iterative process of interactions between the investor and the investment environment. This investment process is influenced by a number of interdependent variables and driven by dual mental systems, the interplay of which contributes to boundedly rational behavior where investors use various heuristics and may exh...

  17. The Fragile X Syndrome: Behavioral Phenotype and Learning Disabilities

    Directory of Open Access Journals (Sweden)

    Claudia GRAU RUBIO

    2016-04-01

    Full Text Available In this article, we describe the behavioral phenotype of individuals with Fragile X Syndrome and its impact in the educational scope. This syndrome is characterized by difficulties in sensory integration, cognitive deficits (verbal reasoning, abstract/ visual and cuantitative skills, short term memory, sequential processing, attention and executive processes, language disorders (phonetic-phonologicals, semanticals, morphosyntacticals and pragmaticals and communication disorders, social anxiety, general hyperarousal, autism, non autistic social difficulties, attention deficit and hyperactivity, and learning disabilities. The behavioral phenotype is highly variable and depends on sex, age, and mutation status (full mutation or premutation. The behavioural phenotype has important repercussions in education, as it enables us to understand the learning disabilities and to develop specific intervention strategies.

  18. Inescapable Stress Changes Walking Behavior in Flies - Learned Helplessness Revisited.

    Science.gov (United States)

    Batsching, Sophie; Wolf, Reinhard; Heisenberg, Martin

    2016-01-01

    Like other animals flies develop a state of learned helplessness in response to unescapable aversive events. To show this, two flies, one 'master', one 'yoked', are each confined to a dark, small chamber and exposed to the same sequence of mild electric shocks. Both receive these shocks when the master fly stops walking for more than a second. Behavior in the two animals is differently affected by the shocks. Yoked flies are transiently impaired in place learning and take longer than master flies to exit from the chamber towards light. After the treatment they walk more slowly and take fewer and shorter walking bouts. The low activity is attributed to the fly's experience that its escape response, an innate behavior to terminate the electric shocks, does not help anymore. Earlier studies using heat pulses instead of electric shocks had shown similar effects. This parallel supports the interpretation that it is the uncontrollability that induces the state.

  19. Inescapable Stress Changes Walking Behavior in Flies - Learned Helplessness Revisited

    Science.gov (United States)

    Batsching, Sophie; Wolf, Reinhard; Heisenberg, Martin

    2016-01-01

    Like other animals flies develop a state of learned helplessness in response to unescapable aversive events. To show this, two flies, one 'master', one 'yoked', are each confined to a dark, small chamber and exposed to the same sequence of mild electric shocks. Both receive these shocks when the master fly stops walking for more than a second. Behavior in the two animals is differently affected by the shocks. Yoked flies are transiently impaired in place learning and take longer than master flies to exit from the chamber towards light. After the treatment they walk more slowly and take fewer and shorter walking bouts. The low activity is attributed to the fly's experience that its escape response, an innate behavior to terminate the electric shocks, does not help anymore. Earlier studies using heat pulses instead of electric shocks had shown similar effects. This parallel supports the interpretation that it is the uncontrollability that induces the state. PMID:27875580

  20. Inescapable Stress Changes Walking Behavior in Flies - Learned Helplessness Revisited.

    Directory of Open Access Journals (Sweden)

    Sophie Batsching

    Full Text Available Like other animals flies develop a state of learned helplessness in response to unescapable aversive events. To show this, two flies, one 'master', one 'yoked', are each confined to a dark, small chamber and exposed to the same sequence of mild electric shocks. Both receive these shocks when the master fly stops walking for more than a second. Behavior in the two animals is differently affected by the shocks. Yoked flies are transiently impaired in place learning and take longer than master flies to exit from the chamber towards light. After the treatment they walk more slowly and take fewer and shorter walking bouts. The low activity is attributed to the fly's experience that its escape response, an innate behavior to terminate the electric shocks, does not help anymore. Earlier studies using heat pulses instead of electric shocks had shown similar effects. This parallel supports the interpretation that it is the uncontrollability that induces the state.

  1. Model-Agnostic Interpretability of Machine Learning

    OpenAIRE

    Ribeiro, Marco Tulio; Singh, Sameer; Guestrin, Carlos

    2016-01-01

    Understanding why machine learning models behave the way they do empowers both system designers and end-users in many ways: in model selection, feature engineering, in order to trust and act upon the predictions, and in more intuitive user interfaces. Thus, interpretability has become a vital concern in machine learning, and work in the area of interpretable models has found renewed interest. In some applications, such models are as accurate as non-interpretable ones, and thus are preferred f...

  2. Mathematic anxiety, help seeking behavior and cooperative learning

    OpenAIRE

    Masoud Gholamali Lavasani; Farah Khandan

    2011-01-01

    Present project assess the effectiveness of cooperative learning over the mathematic anxiety and review the behavior of help seeking in first grade high school girl students. The experimental research procedure was in the form of pre-post tests after a period of 8 sessions of teaching. To measure the variables, the questionnaire of mathematic anxiety (Shokrani, 2002) and the questionnaire of help seeking technique (Ghadampour, 1998) were practiced (accepting or avoiding help seeking).To perfo...

  3. A Conceptual Model of Investor Behavior

    NARCIS (Netherlands)

    M. Lovric (Milan); U. Kaymak (Uzay); J. Spronk (Jaap)

    2008-01-01

    textabstractBased on a survey of behavioral finance literature, this paper presents a descriptive model of individual investor behavior in which investment decisions are seen as an iterative process of interactions between the investor and the investment environment. This investment process is

  4. Identification of animal behavioral strategies by inverse reinforcement learning.

    Directory of Open Access Journals (Sweden)

    Shoichiro Yamaguchi

    2018-05-01

    Full Text Available Animals are able to reach a desired state in an environment by controlling various behavioral patterns. Identification of the behavioral strategy used for this control is important for understanding animals' decision-making and is fundamental to dissect information processing done by the nervous system. However, methods for quantifying such behavioral strategies have not been fully established. In this study, we developed an inverse reinforcement-learning (IRL framework to identify an animal's behavioral strategy from behavioral time-series data. We applied this framework to C. elegans thermotactic behavior; after cultivation at a constant temperature with or without food, fed worms prefer, while starved worms avoid the cultivation temperature on a thermal gradient. Our IRL approach revealed that the fed worms used both the absolute temperature and its temporal derivative and that their behavior involved two strategies: directed migration (DM and isothermal migration (IM. With DM, worms efficiently reached specific temperatures, which explains their thermotactic behavior when fed. With IM, worms moved along a constant temperature, which reflects isothermal tracking, well-observed in previous studies. In contrast to fed animals, starved worms escaped the cultivation temperature using only the absolute, but not the temporal derivative of temperature. We also investigated the neural basis underlying these strategies, by applying our method to thermosensory neuron-deficient worms. Thus, our IRL-based approach is useful in identifying animal strategies from behavioral time-series data and could be applied to a wide range of behavioral studies, including decision-making, in other organisms.

  5. Hierarchical Neural Network (HNN) for Closed Loop Decision Making: Designing the Architecture of a Hierarchical Neural Network to Model Attention, Learning and Goal Oriented Behavior

    Science.gov (United States)

    1990-12-01

    other useful tasks. Simulation results of a 2 degrees of freedom (DOF) manipulator are given. Rigid Robot Dinamics The Lagrange-Euler formulation of...cells. In distributed models, the strength of patterns of activity over many units determines the degree of participation of these entities in functional

  6. CACNA1C gene regulates behavioral strategies in operant rule learning.

    Science.gov (United States)

    Koppe, Georgia; Mallien, Anne Stephanie; Berger, Stefan; Bartsch, Dusan; Gass, Peter; Vollmayr, Barbara; Durstewitz, Daniel

    2017-06-01

    Behavioral experiments are usually designed to tap into a specific cognitive function, but animals may solve a given task through a variety of different and individual behavioral strategies, some of them not foreseen by the experimenter. Animal learning may therefore be seen more as the process of selecting among, and adapting, potential behavioral policies, rather than mere strengthening of associative links. Calcium influx through high-voltage-gated Ca2+ channels is central to synaptic plasticity, and altered expression of Cav1.2 channels and the CACNA1C gene have been associated with severe learning deficits and psychiatric disorders. Given this, we were interested in how specifically a selective functional ablation of the Cacna1c gene would modulate the learning process. Using a detailed, individual-level analysis of learning on an operant cue discrimination task in terms of behavioral strategies, combined with Bayesian selection among computational models estimated from the empirical data, we show that a Cacna1c knockout does not impair learning in general but has a much more specific effect: the majority of Cacna1c knockout mice still managed to increase reward feedback across trials but did so by adapting an outcome-based strategy, while the majority of matched controls adopted the experimentally intended cue-association rule. Our results thus point to a quite specific role of a single gene in learning and highlight that much more mechanistic insight could be gained by examining response patterns in terms of a larger repertoire of potential behavioral strategies. The results may also have clinical implications for treating psychiatric disorders.

  7. A preclinical murine model for the early detection of radiation-induced brain injury using magnetic resonance imaging and behavioral tests for learning and memory: with applications for the evaluation of possible stem cell imaging agents and therapies.

    Science.gov (United States)

    Ngen, Ethel J; Wang, Lee; Gandhi, Nishant; Kato, Yoshinori; Armour, Michael; Zhu, Wenlian; Wong, John; Gabrielson, Kathleen L; Artemov, Dmitri

    2016-06-01

    Stem cell therapies are being developed for radiotherapy-induced brain injuries (RIBI). Magnetic resonance imaging (MRI) offers advantages for imaging transplanted stem cells. However, most MRI cell-tracking techniques employ superparamagnetic iron oxide particles (SPIOs), which are difficult to distinguish from hemorrhage. In current preclinical RIBI models, hemorrhage occurs concurrently with other injury markers. This makes the evaluation of the recruitment of transplanted SPIO-labeled stem cells to injury sites difficult. Here, we developed a RIBI model, with early injury markers reflective of hippocampal dysfunction, which can be detected noninvasively with MRI and behavioral tests. Lesions were generated by sub-hemispheric irradiation of mouse hippocampi with single X-ray beams of 80 Gy. Lesion formation was monitored with anatomical and contrast-enhanced MRI and changes in memory and learning were assessed with fear-conditioning tests. Early injury markers were detected 2 weeks after irradiation. These included an increase in the permeability of the blood-brain barrier, demonstrated by a 92 ± 20 % contrast enhancement of the irradiated versus the non-irradiated brain hemispheres, within 15 min of the administration of an MRI contrast agent. A change in short-term memory was also detected, as demonstrated by a 40.88 ± 5.03 % decrease in the freezing time measured during the short-term memory context test at this time point, compared to that before irradiation. SPIO-labeled stem cells transplanted contralateral to the lesion migrated toward the lesion at this time point. No hemorrhage was detected up to 10 weeks after irradiation. This model can be used to evaluate SPIO-based stem cell-tracking agents, short-term.

  8. Relational models for knowledge sharing behavior

    NARCIS (Netherlands)

    Boer, N.I.; Berends, J.J.; Baalen, P.

    2011-01-01

    In this paper we explore the relational dimension of knowledge sharing behavior by proposing a comprehensive theoretical framework for studying knowledge sharing in organizations. This theoretical framework originates from (Fiske, 1991) and (Fiske, 1992) Relational Models Theory (RMT). The RMT

  9. Enhanced democratic learning within the Aalborg Model

    DEFF Research Database (Denmark)

    Qvist, Palle

    2010-01-01

    The Aalborg PBL Model [Kjersdam & Enemark, 1997; Kolmos et al., 2004] is an example of a democratic learning system [Qvist, 2008]. Writing one project each semester in teams is an important element in the model. Medicine with Industrial Specialisation - a study at the Faculties of Engineering......, Science and Medicine at Aalborg University - has combined the Aalborg Model with solving cases as used by other models. A questionnaire survey related to democratic learning indicates that the democratic learning has been enhanced. This paper presents the results....

  10. Punishment models of addictive behavior

    NARCIS (Netherlands)

    Vanderschuren, L.J.M.J.|info:eu-repo/dai/nl/126514917; Minnaard, A.M.|info:eu-repo/dai/nl/413292533; Smeets, J.A.S.|info:eu-repo/dai/nl/413578577; Lesscher, H.M.B.|info:eu-repo/dai/nl/258637196

    2017-01-01

    Substance addiction is a chronic relapsing brain disorder, characterized by loss of control over substance use. In recent years, there has been a lively interest in animal models of loss of control over substance use, using punishment paradigms. We provide an overview of punishment models of

  11. Online Learning of Industrial Manipulators' Dynamics Models

    DEFF Research Database (Denmark)

    Polydoros, Athanasios

    2017-01-01

    , it was compared with multiple other state-of-the-art machine learning algorithms. Moreover, the thesis presents the application of the proposed learning method on robot control for achieving trajectory execution while learning the inverse dynamics models  on-the-fly . Also it is presented the application...... of the dynamics models. Those mainly derive from physics-based methods and thus they are based on physical properties which are hard to be calculated.  In this thesis, is presented, a novel online machine learning approach  which is able to model both inverse and forward dynamics models of industrial manipulators....... The proposed method belongs to the class of deep learning and exploits the concepts of self-organization, recurrent neural networks and iterative multivariate Bayesian regression. It has been evaluated on multiple datasets captured from industrial robots while they were performing various tasks. Also...

  12. Resident Space Object Characterization and Behavior Understanding via Machine Learning and Ontology-based Bayesian Networks

    Science.gov (United States)

    Furfaro, R.; Linares, R.; Gaylor, D.; Jah, M.; Walls, R.

    2016-09-01

    In this paper, we present an end-to-end approach that employs machine learning techniques and Ontology-based Bayesian Networks (BN) to characterize the behavior of resident space objects. State-of-the-Art machine learning architectures (e.g. Extreme Learning Machines, Convolutional Deep Networks) are trained on physical models to learn the Resident Space Object (RSO) features in the vectorized energy and momentum states and parameters. The mapping from measurements to vectorized energy and momentum states and parameters enables behavior characterization via clustering in the features space and subsequent RSO classification. Additionally, Space Object Behavioral Ontologies (SOBO) are employed to define and capture the domain knowledge-base (KB) and BNs are constructed from the SOBO in a semi-automatic fashion to execute probabilistic reasoning over conclusions drawn from trained classifiers and/or directly from processed data. Such an approach enables integrating machine learning classifiers and probabilistic reasoning to support higher-level decision making for space domain awareness applications. The innovation here is to use these methods (which have enjoyed great success in other domains) in synergy so that it enables a "from data to discovery" paradigm by facilitating the linkage and fusion of large and disparate sources of information via a Big Data Science and Analytics framework.

  13. A Hybrid Teaching and Learning Model

    Science.gov (United States)

    Juhary, Jowati Binti

    This paper aims at analysing the needs for a specific teaching and learning model for the National Defence University of Malaysia (NDUM). The main argument is that whether there are differences between teaching and learning for academic component versus military component at the university. It is further argued that in order to achieve excellence, there should be one teaching and learning culture. Data were collected through interviews with military cadets. It is found that there are variations of teaching and learning strategies for academic courses, in comparison to a dominant teaching and learning style for military courses. Thus, in the interest of delivering quality education and training for students at the university, the paper argues that possibly a hybrid model for teaching and learning is fundamental in order to generate a one culture of academic and military excellence for the NDUM.

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

    Science.gov (United States)

    Sugiharto

    2015-01-01

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

  15. Automated measurement of mouse social behaviors using depth sensing, video tracking, and machine learning.

    Science.gov (United States)

    Hong, Weizhe; Kennedy, Ann; Burgos-Artizzu, Xavier P; Zelikowsky, Moriel; Navonne, Santiago G; Perona, Pietro; Anderson, David J

    2015-09-22

    A lack of automated, quantitative, and accurate assessment of social behaviors in mammalian animal models has limited progress toward understanding mechanisms underlying social interactions and their disorders such as autism. Here we present a new integrated hardware and software system that combines video tracking, depth sensing, and machine learning for automatic detection and quantification of social behaviors involving close and dynamic interactions between two mice of different coat colors in their home cage. We designed a hardware setup that integrates traditional video cameras with a depth camera, developed computer vision tools to extract the body "pose" of individual animals in a social context, and used a supervised learning algorithm to classify several well-described social behaviors. We validated the robustness of the automated classifiers in various experimental settings and used them to examine how genetic background, such as that of Black and Tan Brachyury (BTBR) mice (a previously reported autism model), influences social behavior. Our integrated approach allows for rapid, automated measurement of social behaviors across diverse experimental designs and also affords the ability to develop new, objective behavioral metrics.

  16. E-LEARNING TURKISH LANGUAGE AND GRAMMAR: Analyzing Learners' Behavior

    Directory of Open Access Journals (Sweden)

    Panagiotis GEORGALAS

    2012-01-01

    Full Text Available This study analyses the behavior and the preferences of the Greek learners of Turkish language, who use a particular e-learning website in parallel with their studies, namely: http://turkish.pgeorgalas.gr. The website offers free online material in Greek and English language for learning the Turkish language and grammar. The traffic of several modules of the website has been measured, examined and analyzed. The research was carried out between the years 2010- 2011 and included the analysis of several million clicks. The results show particular attitudes, habits and preferences throughout the e-learning process. There is a preference of users to exercises against theory. Fast cross-link exercises are preferred to slower “fill in” ones. During the weekends, visitors tend to use less e-learning facilities and select more light activities than the rest days of the week. Society trends and fashions like TV serials have a serious impact to the number of people who decide to learn a new foreign language, in particular Turkish. There is a strong preference of the audience to use online TV against online radio facilities for language practice. The subjects that Greek learners of Turkish language spend more time are verbs conjugation and vocabulary learning. They focus on elementary grammar subjects like the Alphabet, the numbers and the formation of plural. Finally, they try to learn the syntax of Turkish language through sentence structure puzzles and give priority to special grammar issues like noun compounds that are not present in Greek language.

  17. Learning the Task Management Space of an Aircraft Approach Model

    Science.gov (United States)

    Krall, Joseph; Menzies, Tim; Davies, Misty

    2014-01-01

    Validating models of airspace operations is a particular challenge. These models are often aimed at finding and exploring safety violations, and aim to be accurate representations of real-world behavior. However, the rules governing the behavior are quite complex: nonlinear physics, operational modes, human behavior, and stochastic environmental concerns all determine the responses of the system. In this paper, we present a study on aircraft runway approaches as modeled in Georgia Tech's Work Models that Compute (WMC) simulation. We use a new learner, Genetic-Active Learning for Search-Based Software Engineering (GALE) to discover the Pareto frontiers defined by cognitive structures. These cognitive structures organize the prioritization and assignment of tasks of each pilot during approaches. We discuss the benefits of our approach, and also discuss future work necessary to enable uncertainty quantification.

  18. Learning sparse generative models of audiovisual signals

    OpenAIRE

    Monaci, Gianluca; Sommer, Friedrich T.; Vandergheynst, Pierre

    2008-01-01

    This paper presents a novel framework to learn sparse represen- tations for audiovisual signals. An audiovisual signal is modeled as a sparse sum of audiovisual kernels. The kernels are bimodal functions made of synchronous audio and video components that can be positioned independently and arbitrarily in space and time. We design an algorithm capable of learning sets of such audiovi- sual, synchronous, shift-invariant functions by alternatingly solving a coding and a learning pr...

  19. Condition monitoring with wind turbine SCADA data using Neuro-Fuzzy normal behavior models

    DEFF Research Database (Denmark)

    Schlechtingen, Meik; Santos, Ilmar

    2012-01-01

    System (ANFIS) models are employed to learn the normal behavior in a training phase, where the component condition can be considered healthy. In the application phase the trained models are applied to predict the target signals, e.g. temperatures, pressures, currents, power output, etc. The behavior......This paper presents the latest research results of a project that focuses on normal behavior models for condition monitoring of wind turbines and their components, via ordinary Supervisory Control And Data Acquisition (SCADA) data. In this machine learning approach Adaptive Neuro-Fuzzy Interference...... of the prediction error is used as an indicator for normal and abnormal behavior, with respect to the learned behavior. The advantage of this approach is that the prediction error is widely decoupled from the typical fluctuations of the SCADA data caused by the different turbine operational modes. To classify...

  20. A Bayesian Approach for Structural Learning with Hidden Markov Models

    Directory of Open Access Journals (Sweden)

    Cen Li

    2002-01-01

    Full Text Available Hidden Markov Models(HMM have proved to be a successful modeling paradigm for dynamic and spatial processes in many domains, such as speech recognition, genomics, and general sequence alignment. Typically, in these applications, the model structures are predefined by domain experts. Therefore, the HMM learning problem focuses on the learning of the parameter values of the model to fit the given data sequences. However, when one considers other domains, such as, economics and physiology, model structure capturing the system dynamic behavior is not available. In order to successfully apply the HMM methodology in these domains, it is important that a mechanism is available for automatically deriving the model structure from the data. This paper presents a HMM learning procedure that simultaneously learns the model structure and the maximum likelihood parameter values of a HMM from data. The HMM model structures are derived based on the Bayesian model selection methodology. In addition, we introduce a new initialization procedure for HMM parameter value estimation based on the K-means clustering method. Experimental results with artificially generated data show the effectiveness of the approach.

  1. Student Modelling in Adaptive E-Learning Systems

    Directory of Open Access Journals (Sweden)

    Clemens Bechter

    2011-09-01

    Full Text Available Most e-Learning systems provide web-based learning so that students can access the same online courses via the Internet without adaptation, based on each student's profile and behavior. In an e-Learning system, one size does not fit all. Therefore, it is a challenge to make e-Learning systems that are suitably “adaptive”. The aim of adaptive e-Learning is to provide the students the appropriate content at the right time, means that the system is able to determine the knowledge level, keep track of usage, and arrange content automatically for each student for the best learning result. This study presents a proposed system which includes major adaptive features based on a student model. The proposed system is able to initialize the student model for determining the knowledge level of a student when the student registers for the course. After a student starts learning the lessons and doing many activities, the system can track information of the student until he/she takes a test. The student’s knowledge level, based on the test scores, is updated into the system for use in the adaptation process, which combines the student model with the domain model in order to deliver suitable course contents to the students. In this study, the proposed adaptive e-Learning system is implemented on an “Introduction to Java Programming Language” course, using LearnSquare software. After the system was tested, the results showed positive feedback towards the proposed system, especially in its adaptive capability.

  2. An integrative model of organizational safety behavior.

    Science.gov (United States)

    Cui, Lin; Fan, Di; Fu, Gui; Zhu, Cherrie Jiuhua

    2013-06-01

    This study develops an integrative model of safety management based on social cognitive theory and the total safety culture triadic framework. The purpose of the model is to reveal the causal linkages between a hazardous environment, safety climate, and individual safety behaviors. Based on primary survey data from 209 front-line workers in one of the largest state-owned coal mining corporations in China, the model is tested using structural equation modeling techniques. An employee's perception of a hazardous environment is found to have a statistically significant impact on employee safety behaviors through a psychological process mediated by the perception of management commitment to safety and individual beliefs about safety. The integrative model developed here leads to a comprehensive solution that takes into consideration the environmental, organizational and employees' psychological and behavioral aspects of safety management. Copyright © 2013 National Safety Council and Elsevier Ltd. All rights reserved.

  3. Inference in models with adaptive learning

    NARCIS (Netherlands)

    Chevillon, G.; Massmann, M.; Mavroeidis, S.

    2010-01-01

    Identification of structural parameters in models with adaptive learning can be weak, causing standard inference procedures to become unreliable. Learning also induces persistent dynamics, and this makes the distribution of estimators and test statistics non-standard. Valid inference can be

  4. Learning curves in energy planning models

    Energy Technology Data Exchange (ETDEWEB)

    Barreto, L; Kypreos, S [Paul Scherrer Inst. (PSI), Villigen (Switzerland)

    1999-08-01

    This study describes the endogenous representation of investment cost learning curves into the MARKAL energy planning model. A piece-wise representation of the learning curves is implemented using Mixed Integer Programming. The approach is briefly described and some results are presented. (author) 3 figs., 5 refs.

  5. How adolescents learn about risk perception and behavior in regards to alcohol use in light of social learning theory: a qualitative study in Bogotá, Colombia.

    Science.gov (United States)

    Trujillo, Elena María; Suárez, Daniel Enrique; Lema, Mariana; Londoño, Alicia

    2015-02-01

    In Colombia, the use of alcohol is one of the main risky behaviors carried out by adolescents, given that alcohol is the principal drug of abuse in this age group. Understanding how adolescents learn about risk and behavior is important in developing effective prevention programs. The Theory of Social learning underlines the importance of social interaction in the learning process. It suggests that learning can occur in three ways: a live model in which a person is enacting the desired behavior, verbal instruction when the desired behavior is described, and symbolic learning in which modeling occurs by influence of the media. This study explores these three forms of learning in the perception of risk and behavior related to the use of alcohol in a group of students between 12 and 14 years of age in Bogotá, Colombia. This is a qualitative research study, which is part of a larger study exploring the social representations of risk and alcohol use in adolescents and their communities. The sample group included 160 students from two middle schools (7th and 8th graders) in Bogotá, Colombia. Six sessions of participant observation, 12 semi-structured interviews, and 12 focus group discussions were conducted for data collection. Data were analyzed using the Atlas ti software (V7.0) (ATLAS.ti Scientific Software Development GmbH, London, UK), and categories of analysis were developed using a framework analysis approach. Adolescents can identify several risks related to the use of alcohol, which for the most part, appear to have been learned through verbal instruction. However, this risk recognition does not appear to correlate with their behavior. Parental modeling and messages conveyed by the media represent two other significant sources of learning that are constantly contradicting the messages relayed through verbal instruction and correlate to a greater extent with adolescent behavior. The three different forms of learning described by Social Learning Theory play a

  6. Using John M. Keller's MVP Model in Teaching Professional Values and Behaviors

    Science.gov (United States)

    Theall, Michael; Graham, DeBorah D.

    2017-01-01

    This chapter discusses teaching and learning in the affective domain and the development of beliefs, values, and behaviors common in professional school education. We use Keller's MVP model as the basis for designing a teacher education course where professional "dispositions" are critical learning outcomes.

  7. Technology Learning Ratios in Global Energy Models

    International Nuclear Information System (INIS)

    Varela, M.

    2001-01-01

    The process of introduction of a new technology supposes that while its production and utilisation increases, also its operation improves and its investment costs and production decreases. The accumulation of experience and learning of a new technology increase in parallel with the increase of its market share. This process is represented by the technological learning curves and the energy sector is not detached from this process of substitution of old technologies by new ones. The present paper carries out a brief revision of the main energy models that include the technology dynamics (learning). The energy scenarios, developed by global energy models, assume that the characteristics of the technologies are variables with time. But this trend is incorporated in a exogenous way in these energy models, that is to say, it is only a time function. This practice is applied to the cost indicators of the technology such as the specific investment costs or to the efficiency of the energy technologies. In the last years, the new concept of endogenous technological learning has been integrated within these global energy models. This paper examines the concept of technological learning in global energy models. It also analyses the technological dynamics of the energy system including the endogenous modelling of the process of technological progress. Finally, it makes a comparison of several of the most used global energy models (MARKAL, MESSAGE and ERIS) and, more concretely, about the use these models make of the concept of technological learning. (Author) 17 refs

  8. Predicting Pilot Behavior in Medium Scale Scenarios Using Game Theory and Reinforcement Learning

    Science.gov (United States)

    Yildiz, Yildiray; Agogino, Adrian; Brat, Guillaume

    2013-01-01

    Effective automation is critical in achieving the capacity and safety goals of the Next Generation Air Traffic System. Unfortunately creating integration and validation tools for such automation is difficult as the interactions between automation and their human counterparts is complex and unpredictable. This validation becomes even more difficult as we integrate wide-reaching technologies that affect the behavior of different decision makers in the system such as pilots, controllers and airlines. While overt short-term behavior changes can be explicitly modeled with traditional agent modeling systems, subtle behavior changes caused by the integration of new technologies may snowball into larger problems and be very hard to detect. To overcome these obstacles, we show how integration of new technologies can be validated by learning behavior models based on goals. In this framework, human participants are not modeled explicitly. Instead, their goals are modeled and through reinforcement learning their actions are predicted. The main advantage to this approach is that modeling is done within the context of the entire system allowing for accurate modeling of all participants as they interact as a whole. In addition such an approach allows for efficient trade studies and feasibility testing on a wide range of automation scenarios. The goal of this paper is to test that such an approach is feasible. To do this we implement this approach using a simple discrete-state learning system on a scenario where 50 aircraft need to self-navigate using Automatic Dependent Surveillance-Broadcast (ADS-B) information. In this scenario, we show how the approach can be used to predict the ability of pilots to adequately balance aircraft separation and fly efficient paths. We present results with several levels of complexity and airspace congestion.

  9. Childhood Fish Consumption and Learning and Behavioral Disorders

    Directory of Open Access Journals (Sweden)

    Jenny L. Carwile

    2016-11-01

    Full Text Available Fish is a major source of nutrients critical for brain development during early life. The importance of childhood fish consumption is supported by several studies reporting associations of n-3 polyunsaturated fatty acid (n-3 PUFA supplementation with better behavior and school performance. However, fish may have a different effect than n-3 PUFA alone due to the neurotoxic effects of methylmercury, a frequent contaminant. We investigated associations of childhood fish consumption with learning and behavioral disorders in birth cohort study of the neurotoxic effects of early life exposure to solvent-contaminated drinking water. Childhood (age 7–12 years fish consumption and learning and behavioral problems were reported in self-administered questionnaires (age 23–41 at questionnaire completion. Fish consumption was not meaningfully associated with repeating a grade, tutoring, attending summer school, special class placement, or low educational attainment. However, participants who ate fish several times a week had an elevated odds of Attention Deficit Disorder/Attention Deficit Hyperactivity Disorder (odds ratio: 5.2; 95% confidence interval: 1.5–18 compared to participants who did not eat fish. While these findings generally support the safety of the observed level of fish consumption, the absence of a beneficial effect may be attributed to insufficient fish intake or the choice of relatively low n-3 PUFA fish.

  10. Models of iodine behavior in reactor containments

    Energy Technology Data Exchange (ETDEWEB)

    Weber, C.F.; Beahm, E.C.; Kress, T.S.

    1992-10-01

    Models are developed for many phenomena of interest concerning iodine behavior in reactor containments during severe accidents. Processes include speciation in both gas and liquid phases, reactions with surfaces, airborne aerosols, and other materials, and gas-liquid interface behavior. Although some models are largely empirical formulations, every effort has been made to construct mechanistic and rigorous descriptions of relevant chemical processes. All are based on actual experimental data generated at the Oak Ridge National Laboratory (ORNL) or elsewhere, and, hence, considerable data evaluation and parameter estimation are contained in this study. No application or encoding is attempted, but each model is stated in terms of rate processes, with the intention of allowing mechanistic simulation. Taken together, this collection of models represents a best estimate iodine behavior and transport in reactor accidents.

  11. Models of iodine behavior in reactor containments

    International Nuclear Information System (INIS)

    Weber, C.F.; Beahm, E.C.; Kress, T.S.

    1992-10-01

    Models are developed for many phenomena of interest concerning iodine behavior in reactor containments during severe accidents. Processes include speciation in both gas and liquid phases, reactions with surfaces, airborne aerosols, and other materials, and gas-liquid interface behavior. Although some models are largely empirical formulations, every effort has been made to construct mechanistic and rigorous descriptions of relevant chemical processes. All are based on actual experimental data generated at the Oak Ridge National Laboratory (ORNL) or elsewhere, and, hence, considerable data evaluation and parameter estimation are contained in this study. No application or encoding is attempted, but each model is stated in terms of rate processes, with the intention of allowing mechanistic simulation. Taken together, this collection of models represents a best estimate iodine behavior and transport in reactor accidents

  12. Model analysis of adaptive car driving behavior

    NARCIS (Netherlands)

    Wewerinke, P.H.

    1996-01-01

    This paper deals with two modeling approaches to car driving. The first one is a system theoretic approach to describe adaptive human driving behavior. The second approach utilizes neural networks. As an illustrative example the overtaking task is considered and modeled in system theoretic terms.

  13. Democratic learning in the Aalborg Model

    DEFF Research Database (Denmark)

    Qvist, Palle

    A democratic learning system can be defined as a system where decisions, processes and behaviour related to learning are established through argumentation (discussion) or negotiation (dialog), voting or consensus (alone or in combination) between those affected by the decision simultaneously...... reaching the learning outcomes, the technical and professional knowledge and insight. In principle the participants must be equal with equal rights and feel committed to the values of rationality and impartiality. The Aalborg Model is an example of a democratic learning system although not 100% democratic......, processes and behaviour related to learning can be established through argumentation (discussion) or negotiation (dialog), voting or consensus (alone or in combination) within the group simultaneously reaching the learning outcomes, the technical and professional knowledge and insight. This article...

  14. Deep learning for constructing microblog behavior representation to identify social media user’s personality

    Directory of Open Access Journals (Sweden)

    Xiaoqian Liu

    2016-09-01

    Full Text Available Due to the rapid development of information technology, the Internet has gradually become a part of everyday life. People would like to communicate with friends to share their opinions on social networks. The diverse behavior on socials networks is an ideal reflection of users’ personality traits. Existing behavior analysis methods for personality prediction mostly extract behavior attributes with heuristic analysis. Although they work fairly well, they are hard to extend and maintain. In this paper, we utilize a deep learning algorithm to build a feature learning model for personality prediction, which could perform an unsupervised extraction of the Linguistic Representation Feature Vector (LRFV activity without supervision from text actively published on the Sina microblog. Compared with other feature extractsion methods, LRFV, as an abstract representation of microblog content, could describe a user’s semantic information more objectively and comprehensively. In the experiments, the personality prediction model is built using a linear regression algorithm, and different attributes obtained through different feature extraction methods are taken as input of the prediction model, respectively. The results show that LRFV performs better in microblog behavior descriptions, and improves the performance of the personality prediction model.

  15. The Self-Regulated Learning Model and Music Education

    Directory of Open Access Journals (Sweden)

    Maja Marijan

    2017-02-01

    Full Text Available Self-regulation and self-regulated learning (SRL are important features in music education. In this research self-regulated learning model is presented as a complex, multidimensional structure. SRL starts with the self-regulation. Self-regulation is formed through interaction with the environment, thus self-learning, self-analysis, self-judgment, self-instruction, and self-monitoring are the main functions in self-regulatory structure. Co-regulation is needed, and helps self-regulation to be activated and monitored. In music education, co-regulation refers to the instructions that teacher introduces in the lessons. These instructions have to enhance learning and develop regulation over emotions, cognitive, auditor, and motor skills in students. Learning techniques and learning strategies are core components in music education. Adapting those, students become aware of their learning processes, actions, thoughts, feelings and behaviors that are involved in learning. It is suggested that every teaching methodology has to develop learning techniques, as well as metamemory and metacognition in students, in order to gain expertise. The author has emphasized her attention to every aspect that is believed to belong to SRL. There are not many articles on the SRL in music education, written by musicians, in compare with those written by psychologists and neurologists,. Therefore, the author has suggested that this paper would encourage music teachers and performers to take an advantage in the research of SRL. These researches would help music educational systems and teachers to develop and promote learning techniques and strategies. The results would show improvement in student’s learning and self-regulation.

  16. Kolb's Experiential Learning Model: Critique from a Modeling Perspective

    Science.gov (United States)

    Bergsteiner, Harald; Avery, Gayle C.; Neumann, Ruth

    2010-01-01

    Kolb's experiential learning theory has been widely influential in adult learning. The theory and associated instruments continue to be criticized, but rarely is the graphical model itself examined. This is significant because models can aid scientific understanding and progress, as well as theory development and research. Applying accepted…

  17. Variability in Dopamine Genes Dissociates Model-Based and Model-Free Reinforcement Learning.

    Science.gov (United States)

    Doll, Bradley B; Bath, Kevin G; Daw, Nathaniel D; Frank, Michael J

    2016-01-27

    Considerable evidence suggests that multiple learning systems can drive behavior. Choice can proceed reflexively from previous actions and their associated outcomes, as captured by "model-free" learning algorithms, or flexibly from prospective consideration of outcomes that might occur, as captured by "model-based" learning algorithms. However, differential contributions of dopamine to these systems are poorly understood. Dopamine is widely thought to support model-free learning by modulating plasticity in striatum. Model-based learning may also be affected by these striatal effects, or by other dopaminergic effects elsewhere, notably on prefrontal working memory function. Indeed, prominent demonstrations linking striatal dopamine to putatively model-free learning did not rule out model-based effects, whereas other studies have reported dopaminergic modulation of verifiably model-based learning, but without distinguishing a prefrontal versus striatal locus. To clarify the relationships between dopamine, neural systems, and learning strategies, we combine a genetic association approach in humans with two well-studied reinforcement learning tasks: one isolating model-based from model-free behavior and the other sensitive to key aspects of striatal plasticity. Prefrontal function was indexed by a polymorphism in the COMT gene, differences of which reflect dopamine levels in the prefrontal cortex. This polymorphism has been associated with differences in prefrontal activity and working memory. Striatal function was indexed by a gene coding for DARPP-32, which is densely expressed in the striatum where it is necessary for synaptic plasticity. We found evidence for our hypothesis that variations in prefrontal dopamine relate to model-based learning, whereas variations in striatal dopamine function relate to model-free learning. Decisions can stem reflexively from their previously associated outcomes or flexibly from deliberative consideration of potential choice outcomes

  18. Explaining clinical behaviors using multiple theoretical models

    OpenAIRE

    Eccles, Martin P; Grimshaw, Jeremy M; MacLennan, Graeme; Bonetti, Debbie; Glidewell, Liz; Pitts, Nigel B; Steen, Nick; Thomas, Ruth; Walker, Anne; Johnston, Marie

    2012-01-01

    Abstract Background In the field of implementation research, there is an increased interest in use of theory when designing implementation research studies involving behavior change. In 2003, we initiated a series of five studies to establish a scientific rationale for interventions to translate research findings into clinical practice by exploring the performance of a number of different, commonly used, overlapping behavioral theories and models. We reflect on the strengths and weaknesses of...

  19. Applying incentive sensitization models to behavioral addiction

    DEFF Research Database (Denmark)

    Rømer Thomsen, Kristine; Fjorback, Lone; Møller, Arne

    2014-01-01

    The incentive sensitization theory is a promising model for understanding the mechanisms underlying drug addiction, and has received support in animal and human studies. So far the theory has not been applied to the case of behavioral addictions like Gambling Disorder, despite sharing clinical...... symptoms and underlying neurobiology. We examine the relevance of this theory for Gambling Disorder and point to predictions for future studies. The theory promises a significant contribution to the understanding of behavioral addiction and opens new avenues for treatment....

  20. Organizational buying behavior: An integrated model

    Directory of Open Access Journals (Sweden)

    Rakić Beba

    2002-01-01

    Full Text Available Organizational buying behavior is decision making process by which formal organizations establish the need for purchased products and services, and identify, evaluate, and choose among alternative brands and suppliers. Understanding the buying decision processes is essential to developing the marketing programs of companies that sell to organizations, or to 'industrial customers'. In business (industrial marketing, exchange relationships between the organizational selling center and the organizational buying center are crucial. Integrative model of organizational buying behavior offers a systematic framework in analyzing the complementary factors and what effect they have on the behavior of those involved in making buying decisions.

  1. Learning strategies: a synthesis and conceptual model

    Science.gov (United States)

    Hattie, John A. C.; Donoghue, Gregory M.

    2016-08-01

    The purpose of this article is to explore a model of learning that proposes that various learning strategies are powerful at certain stages in the learning cycle. The model describes three inputs and outcomes (skill, will and thrill), success criteria, three phases of learning (surface, deep and transfer) and an acquiring and consolidation phase within each of the surface and deep phases. A synthesis of 228 meta-analyses led to the identification of the most effective strategies. The results indicate that there is a subset of strategies that are effective, but this effectiveness depends on the phase of the model in which they are implemented. Further, it is best not to run separate sessions on learning strategies but to embed the various strategies within the content of the subject, to be clearer about developing both surface and deep learning, and promoting their associated optimal strategies and to teach the skills of transfer of learning. The article concludes with a discussion of questions raised by the model that need further research.

  2. Problem Solving Model for Science Learning

    Science.gov (United States)

    Alberida, H.; Lufri; Festiyed; Barlian, E.

    2018-04-01

    This research aims to develop problem solving model for science learning in junior high school. The learning model was developed using the ADDIE model. An analysis phase includes curriculum analysis, analysis of students of SMP Kota Padang, analysis of SMP science teachers, learning analysis, as well as the literature review. The design phase includes product planning a science-learning problem-solving model, which consists of syntax, reaction principle, social system, support system, instructional impact and support. Implementation of problem-solving model in science learning to improve students' science process skills. The development stage consists of three steps: a) designing a prototype, b) performing a formative evaluation and c) a prototype revision. Implementation stage is done through a limited trial. A limited trial was conducted on 24 and 26 August 2015 in Class VII 2 SMPN 12 Padang. The evaluation phase was conducted in the form of experiments at SMPN 1 Padang, SMPN 12 Padang and SMP National Padang. Based on the development research done, the syntax model problem solving for science learning at junior high school consists of the introduction, observation, initial problems, data collection, data organization, data analysis/generalization, and communicating.

  3. Learning Probabilistic Logic Models from Probabilistic Examples.

    Science.gov (United States)

    Chen, Jianzhong; Muggleton, Stephen; Santos, José

    2008-10-01

    We revisit an application developed originally using abductive Inductive Logic Programming (ILP) for modeling inhibition in metabolic networks. The example data was derived from studies of the effects of toxins on rats using Nuclear Magnetic Resonance (NMR) time-trace analysis of their biofluids together with background knowledge representing a subset of the Kyoto Encyclopedia of Genes and Genomes (KEGG). We now apply two Probabilistic ILP (PILP) approaches - abductive Stochastic Logic Programs (SLPs) and PRogramming In Statistical modeling (PRISM) to the application. Both approaches support abductive learning and probability predictions. Abductive SLPs are a PILP framework that provides possible worlds semantics to SLPs through abduction. Instead of learning logic models from non-probabilistic examples as done in ILP, the PILP approach applied in this paper is based on a general technique for introducing probability labels within a standard scientific experimental setting involving control and treated data. Our results demonstrate that the PILP approach provides a way of learning probabilistic logic models from probabilistic examples, and the PILP models learned from probabilistic examples lead to a significant decrease in error accompanied by improved insight from the learned results compared with the PILP models learned from non-probabilistic examples.

  4. A Concept Transformation Learning Model for Architectural Design Learning Process

    Science.gov (United States)

    Wu, Yun-Wu; Weng, Kuo-Hua; Young, Li-Ming

    2016-01-01

    Generally, in the foundation course of architectural design, much emphasis is placed on teaching of the basic design skills without focusing on teaching students to apply the basic design concepts in their architectural designs or promoting students' own creativity. Therefore, this study aims to propose a concept transformation learning model to…

  5. Learning, Learning Analytics, Activity Visualisation and Open learner Model

    DEFF Research Database (Denmark)

    Bull, Susan; Kickmeier-Rust, Michael; Vatrapu, Ravi

    2013-01-01

    This paper draws on visualisation approaches in learning analytics, considering how classroom visualisations can come together in practice. We suggest an open learner model in situations where many tools and activity visualisations produce more visual information than can be readily interpreted....

  6. Feeding Behavior of Aplysia: A Model System for Comparing Cellular Mechanisms of Classical and Operant Conditioning

    Science.gov (United States)

    Baxter, Douglas A.; Byrne, John H.

    2006-01-01

    Feeding behavior of Aplysia provides an excellent model system for analyzing and comparing mechanisms underlying appetitive classical conditioning and reward operant conditioning. Behavioral protocols have been developed for both forms of associative learning, both of which increase the occurrence of biting following training. Because the neural…

  7. Modelling aerosol behavior in reactor cooling systems

    International Nuclear Information System (INIS)

    McDonald, B.H.

    1990-01-01

    This paper presents an overview of some of the areas of concern in using computer codes to model fission-product aerosol behavior in the reactor cooling system (RCS) of a water-cooled nuclear reactor during a loss-of-coolant accident. The basic physical processes that require modelling include: fission product release and aerosol formation in the reactor core, aerosol transport and deposition in the reactor core and throughout the rest of the RCS, and the interaction between aerosol transport processes and the thermalhydraulics. In addition to these basic physical processes, chemical reactions can have a large influence on the nature of the aerosol and its behavior in the RCS. The focus is on the physics and the implications of numerical methods used in the computer codes to model aerosol behavior in the RCS

  8. Student perceptions of their biology teacher's interpersonal teaching behaviors and student achievement and affective learning outcomes

    Science.gov (United States)

    Smith, Wade Clay, Jr.

    The primary goals of this dissertation were to determine the relationships between interpersonal teaching behaviors and student achievement and affective learning outcomes. The instrument used to collect student perceptions of teacher interpersonal teaching behaviors was the Questionnaire on Teacher Interactions (QTI). The instrument used to assess student affective learning outcomes was the Biology Student Affective Instrument (BSAI). The interpersonal teaching behavior data were collected using students as the observers. 111 students in an urban influenced, rural high school answered the QTI and BSAI in September 1997 and again in April 1998. At the same time students were pre and post tested using the Biology End of Course Examination (BECE). The QTI has been used primarily in European and Oceanic areas. The instrument was also primarily used in educational stratified environment. This was the first time the BSAI was used to assess student affective learning outcomes. The BECE is a Texas normed cognitive assessment test and it is used by Texas schools districts as the end of course examination in biology. The interpersonal teaching behaviors model was tested to ascertain if predictive power in the USA and in a non-stratified educational environment. Findings indicate that the QTI is an adequate predictor of student achievement in biology. The results were not congruent with the non-USA data and results, this indicates that the QTI is a society/culturally sensitive instrument and the instrument needs to be normed to a particular society/culture before it is used to affect teachers' and students' educational environments.

  9. Driver's Behavior Modeling Using Fuzzy Logic

    Directory of Open Access Journals (Sweden)

    Sehraneh Ghaemi

    2010-01-01

    Full Text Available In this study, we propose a hierarchical fuzzy system for human in a driver-vehicle-environment system to model takeover by different drivers. The driver's behavior is affected by the environment. The climate, road and car conditions are included in fuzzy modeling. For obtaining fuzzy rules, experts' opinions are benefited by means of questionnaires on effects of parameters such as climate, road and car conditions on driving capabilities. Also the precision, age and driving individuality are used to model the driver's behavior. Three different positions are considered for driving and decision making. A fuzzy model called Model I is presented for modeling the change of steering angle and speed control by considering time distances with existing cars in these three positions, the information about the speed and direction of car, and the steering angle of car. Also we obtained two other models based on fuzzy rules called Model II and Model III by using Sugeno fuzzy inference. Model II and Model III have less linguistic terms than Model I for the steering angle and direction of car. The results of three models are compared for a driver who drives based on driving laws.

  10. Collaborative Inquiry Learning: Models, tools, and challenges

    Science.gov (United States)

    Bell, Thorsten; Urhahne, Detlef; Schanze, Sascha; Ploetzner, Rolf

    2010-02-01

    Collaborative inquiry learning is one of the most challenging and exciting ventures for today's schools. It aims at bringing a new and promising culture of teaching and learning into the classroom where students in groups engage in self-regulated learning activities supported by the teacher. It is expected that this way of learning fosters students' motivation and interest in science, that they learn to perform steps of inquiry similar to scientists and that they gain knowledge on scientific processes. Starting from general pedagogical reflections and science standards, the article reviews some prominent models of inquiry learning. This comparison results in a set of inquiry processes being the basis for cooperation in the scientific network NetCoIL. Inquiry learning is conceived in several ways with emphasis on different processes. For an illustration of the spectrum, some main conceptions of inquiry and their focuses are described. In the next step, the article describes exemplary computer tools and environments from within and outside the NetCoIL network that were designed to support processes of collaborative inquiry learning. These tools are analysed by describing their functionalities as well as effects on student learning known from the literature. The article closes with challenges for further developments elaborated by the NetCoIL network.

  11. Examining Culture's Impact on the Learning Behaviors of International Students from Confucius Culture Studying in Western Online Learning Context

    Science.gov (United States)

    Kang, Haijun; Chang, Bo

    2016-01-01

    There is a lack of shared understanding of how culture impacts learning in online environment. Utilizing document analysis, the authors in this research study culture's impact on the learning behaviors of student sojourners from Confucius culture studying in Western online learning context. The shared understandings of Confucius culture and…

  12. Motivation Matters? The Relationship among Different Types of Learning Motivation, Engagement Behaviors and Learning Outcomes of Undergraduate Students in Taiwan

    Science.gov (United States)

    Hsieh, Tzu-Ling

    2014-01-01

    The purpose of this study is to understand predictors of different learning outcomes among various student background characteristics, types of learning motivation and engagement behaviors. 178 junior students were surveyed at a 4-year research university in Taiwan. The scales of motivation, engagement and perceived learning outcomes were adapted…

  13. Mechanisms of social avoidance learning can explain the emergence of adaptive and arbitrary behavioral traditions in humans.

    Science.gov (United States)

    Lindström, Björn; Olsson, Andreas

    2015-06-01

    Many nonhuman animals preferentially copy the actions of others when the environment contains predation risk or other types of danger. In humans, the role of social learning in avoidance of danger is still unknown, despite the fundamental importance of social learning for complex social behaviors. Critically, many social behaviors, such as cooperation and adherence to religious taboos, are maintained by threat of punishment. However, the psychological mechanisms allowing threat of punishment to generate such behaviors, even when actual punishment is rare or absent, are largely unknown. To address this, we used both computer simulations and behavioral experiments. First, we constructed a model where simulated agents interacted under threat of punishment and showed that mechanisms' (a) tendency to copy the actions of others through social learning, together with (b) the rewarding properties of avoiding a threatening punishment, could explain the emergence, maintenance, and transmission of large-scale behavioral traditions, both when punishment is common and when it is rare or nonexistent. To provide empirical support for our model, including the 2 mechanisms, we conducted 4 experiments, showing that humans, if threatened with punishment, are exceptionally prone to copy and transmit the behavior observed in others. Our results show that humans, similar to many nonhuman animals, use social learning if the environment is perceived as dangerous. We provide a novel psychological and computational basis for a range of human behaviors characterized by the threat of punishment, such as the adherence to cultural norms and religious taboos. (c) 2015 APA, all rights reserved).

  14. Classroom behavior and family climate in students with learning disabilities and hyperactive behavior.

    Science.gov (United States)

    Margalit, M; Almougy, K

    1991-01-01

    The present study aimed to identify subtypes of the learning disabilities (LD) syndrome by examining classroom behavior and family climate among four groups of Israeli students ranging in age from 7 to 10 years: 22 students with LD and hyperactive behavior (HB), 22 nonhyperactive students with LD, 20 nondisabled students with HB, and 20 nondisabled nonhyperactive students. Schaefer's Classroom Behavior Inventory and Moos's Family Environmental Scale were administered to teachers and mothers, respectively. The results revealed that higher distractibility and hostility among both groups with HB differentiated between the two groups with LD. Families of children with HB were reported as less supportive and as emphasizing control less. The academic competence and temperament of the nondisabled students with HB were rated as similar to those of the two groups of students with LD. Both groups with LD were characterized by dependent interpersonal relations and by more conflictual families who fostered more achievement but less personal growth.

  15. Does temporal discounting explain unhealthy behavior? A systematic review and reinforcement learning perspective

    Science.gov (United States)

    Story, Giles W.; Vlaev, Ivo; Seymour, Ben; Darzi, Ara; Dolan, Raymond J.

    2014-01-01

    The tendency to make unhealthy choices is hypothesized to be related to an individual's temporal discount rate, the theoretical rate at which they devalue delayed rewards. Furthermore, a particular form of temporal discounting, hyperbolic discounting, has been proposed to explain why unhealthy behavior can occur despite healthy intentions. We examine these two hypotheses in turn. We first systematically review studies which investigate whether discount rates can predict unhealthy behavior. These studies reveal that high discount rates for money (and in some instances food or drug rewards) are associated with several unhealthy behaviors and markers of health status, establishing discounting as a promising predictive measure. We secondly examine whether intention-incongruent unhealthy actions are consistent with hyperbolic discounting. We conclude that intention-incongruent actions are often triggered by environmental cues or changes in motivational state, whose effects are not parameterized by hyperbolic discounting. We propose a framework for understanding these state-based effects in terms of the interplay of two distinct reinforcement learning mechanisms: a “model-based” (or goal-directed) system and a “model-free” (or habitual) system. Under this framework, while discounting of delayed health may contribute to the initiation of unhealthy behavior, with repetition, many unhealthy behaviors become habitual; if health goals then change, habitual behavior can still arise in response to environmental cues. We propose that the burgeoning development of computational models of these processes will permit further identification of health decision-making phenotypes. PMID:24659960

  16. Roles of Parents and Annotation Sharing in Children's Learning Behavior and Achievement Using E-Readers

    Science.gov (United States)

    Hwang, Wu-Yuin; Liu, Yi-Fan; Chen, Hon-Ren; Huang, Jian-Wun; Li, Jin-Yi

    2015-01-01

    Although previous studies have highlighted the advantages of using e-books for learning, most have compared learning achieved with traditional textbooks with that achieved with e-books in a classroom situation. These studies focused on individual learning instead of on interactions among learners, learning behavior using ebooks after school, and…

  17. The Interaction of Motivation, Self-Regulatory Strategies, and Autonomous Learning Behavior in Different Learner Groups

    Science.gov (United States)

    Kormos, Judit; Csizér, Kata

    2014-01-01

    Autonomous learning and effective self-regulatory strategies are increasingly important in foreign language learning; without these, students might not be able to exploit learning opportunities outside language classrooms. This study investigated the influence of motivational factors and self-regulatory strategies on autonomous learning behavior.…

  18. The Emergence of the Open Networked ``i-Learning'' Model

    Science.gov (United States)

    Elia, Gianluca

    The most significant forces that are changing the business world and the society behaviors in this beginning of the twenty-first century can be identified into the globalization of the economy, technological evolution and convergence, change of the workers' expectations, workplace diversity and mobility, and mostly, knowledge and learning as major organizational assets. But which type of ­learning dynamics must be nurtured and pursued within the organizations, today, in order to generate valuable knowledge and its effective applications? After a brief discussion on the main changes observable in management, ICT and society/workplace in the last years, this chapter aims to answer to this question, through the proposition of the “Π-shaped” profile (a new professional archetype for leading change), and through the discussion of the open networked “i-Learning” model (a new framework to “incubate” innovation in learning processes). Actually, the “i” stands for “innovation” (to highlight the nature of the impact on traditional ­learning model), but also it stands for “incubation” (to underline the urgency to have new environments in which incubating new professional profiles). Specifically, the main key characteristics at the basis of the innovation of the learning processes will be ­presented and described, by highlighting the managerial, technological and societal aspects of their nature. A set of operational guidelines will be also ­provided to ­activate and sustain the innovation process, so implementing changes in the strategic dimensions of the model. Finally, the “i-Learning Radar” is presented as an operational tool to design, communicate and control an “i-Learning experience”. This tool is represented by a radar diagram with six strategic dimensions of a ­learning initiative.

  19. Data-Driven Design: Learning from Student Experiences and Behaviors

    Science.gov (United States)

    Horodyskyj, L.; Mead, C.; Buxner, S.; Semken, S. C.; Anbar, A. D.

    2015-12-01

    Good instructors know that lessons and courses change over time. Limitations in time and data often prevent instructors from making changes that will most benefit their students. For example, in traditional in-person classrooms an instructor may only have access to the final product of a student's thought processes (such as a term paper, homework assignment, or exam). The thought processes that lead to a given answer are opaque to the instructor, making future modifications to course content an exercise in trial-and-error and instinct. Modern online intelligent tutoring systems can provide insight into a student's behavior, providing transparency to a previously opaque process and providing the instructor with better information for course modification. Habitable Worlds is an introductory level online-only astrobiology lab course that has been offered at Arizona State University since Fall 2011. The course is built and offered through an intelligent tutoring system, Smart Sparrow's Adaptive eLearning Platform, which provides in-depth analytics that allow the instructor to investigate detailed student behavior, from time spent on question to number of attempts to patterns of answers. We will detail the process we employ of informed modification of course content, including time and trial comparisons between semesters, analysis of submitted answers, analysis of alternative learning pathways taken, and A/B testing.

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

    Science.gov (United States)

    Liao, Peiwen; Yu, Chien; Yi, Chincheh

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

  1. Multidimensional Learner Model In Intelligent Learning System

    Science.gov (United States)

    Deliyska, B.; Rozeva, A.

    2009-11-01

    The learner model in an intelligent learning system (ILS) has to ensure the personalization (individualization) and the adaptability of e-learning in an online learner-centered environment. ILS is a distributed e-learning system whose modules can be independent and located in different nodes (servers) on the Web. This kind of e-learning is achieved through the resources of the Semantic Web and is designed and developed around a course, group of courses or specialty. An essential part of ILS is learner model database which contains structured data about learner profile and temporal status in the learning process of one or more courses. In the paper a learner model position in ILS is considered and a relational database is designed from learner's domain ontology. Multidimensional modeling agent for the source database is designed and resultant learner data cube is presented. Agent's modules are proposed with corresponding algorithms and procedures. Multidimensional (OLAP) analysis guidelines on the resultant learner module for designing dynamic learning strategy have been highlighted.

  2. Utilizing Gaze Behavior for Inferring Task Transitions Using Abstract Hidden Markov Models

    Directory of Open Access Journals (Sweden)

    Daniel Fernando Tello Gamarra

    2016-12-01

    Full Text Available We demonstrate an improved method for utilizing observed gaze behavior and show that it is useful in inferring hand movement intent during goal directed tasks. The task dynamics and the relationship between hand and gaze behavior are learned using an Abstract Hidden Markov Model (AHMM. We show that the predicted hand movement transitions occur consistently earlier in AHMM models with gaze than those models that do not include gaze observations.

  3. Attributional processes in the learned helplessness paradigm: behavioral effects of global attributions.

    Science.gov (United States)

    Mikulincer, M

    1986-12-01

    Following the learned helplessness paradigm, I assessed in this study the effects of global and specific attributions for failure on the generalization of performance deficits in a dissimilar situation. Helplessness training consisted of experience with noncontingent failures on four cognitive discrimination problems attributed to either global or specific causes. Experiment 1 found that performance in a dissimilar situation was impaired following exposure to globally attributed failure. Experiment 2 examined the behavioral effects of the interaction between stable and global attributions of failure. Exposure to unsolvable problems resulted in reduced performance in a dissimilar situation only when failure was attributed to global and stable causes. Finally, Experiment 3 found that learned helplessness deficits were a product of the interaction of global and internal attribution. Performance deficits following unsolvable problems were recorded when failure was attributed to global and internal causes. Results were discussed in terms of the reformulated learned helplessness model.

  4. Fear learning and memory across adolescent development Hormones and Behavior Special Issue: Puberty and Adolescence

    Science.gov (United States)

    Pattwell, Siobhan S.; Lee, Francis S.; Casey, B.J.

    2013-01-01

    Throughout the past several decades, studies have uncovered a wealth of information about the neural circuitry underlying fear learning and extinction that has helped to inform treatments for fear-related disorders such as post-traumatic stress and anxiety. Yet, up to 40 percent of people do not respond to such treatments. Adolescence, in particular, is a developmental stage during which anxiety disorders peak, yet little is known about the development of fear-related neural circuitry during this period. Moreover, pharmacological and behavioral therapies that have been developed are based on mature circuitry and function. Here, we review neural circuitry implicated in fear learning and data from adolescent mouse and human fear learning studies. In addition, we propose a developmental model of fear neural circuitry that may optimize current treatments and inform when, during development, specific treatments for anxiety may be most effective. PMID:23998679

  5. Error Resilient Video Compression Using Behavior Models

    Directory of Open Access Journals (Sweden)

    Jacco R. Taal

    2004-03-01

    Full Text Available Wireless and Internet video applications are inherently subjected to bit errors and packet errors, respectively. This is especially so if constraints on the end-to-end compression and transmission latencies are imposed. Therefore, it is necessary to develop methods to optimize the video compression parameters and the rate allocation of these applications that take into account residual channel bit errors. In this paper, we study the behavior of a predictive (interframe video encoder and model the encoders behavior using only the statistics of the original input data and of the underlying channel prone to bit errors. The resulting data-driven behavior models are then used to carry out group-of-pictures partitioning and to control the rate of the video encoder in such a way that the overall quality of the decoded video with compression and channel errors is optimized.

  6. Toward Self-Referential Autonomous Learning of Object and Situation Models.

    Science.gov (United States)

    Damerow, Florian; Knoblauch, Andreas; Körner, Ursula; Eggert, Julian; Körner, Edgar

    2016-01-01

    Most current approaches to scene understanding lack the capability to adapt object and situation models to behavioral needs not anticipated by the human system designer. Here, we give a detailed description of a system architecture for self-referential autonomous learning which enables the refinement of object and situation models during operation in order to optimize behavior. This includes structural learning of hierarchical models for situations and behaviors that is triggered by a mismatch between expected and actual action outcome. Besides proposing architectural concepts, we also describe a first implementation of our system within a simulated traffic scenario to demonstrate the feasibility of our approach.

  7. Building a Model of Successful Collaborative Learning for Company Innovativeness

    Directory of Open Access Journals (Sweden)

    Agata Sudolska

    2014-01-01

    Full Text Available The aim of the paper is to develop a model of successful collaborative learning for company innovativeness. First of all, the paper explores the issue of inter-firm learning, focusing its attention on collaborative learning. Secondly, inter-firm learning relationships are considered. Thirdly, the ex ante conditions of collaborative learning and the intra-organizational enhancers of inter-firm learning processes are studied. Finally, a model of the critical success factors for collaborative learning is developed.

  8. Behavioral and statistical models of educational inequality

    DEFF Research Database (Denmark)

    Holm, Anders; Breen, Richard

    2016-01-01

    This paper addresses the question of how students and their families make educational decisions. We describe three types of behavioral model that might underlie decision-making and we show that they have consequences for what decisions are made. Our study thus has policy implications if we wish...

  9. Modeling landowner behavior regarding forest certification

    Science.gov (United States)

    David C. Mercker; Donald G. Hodges

    2008-01-01

    Nonindustrial private forest owners in western Tennessee were surveyed to assess their awareness, acceptance, and perceived benefits of forest certification. More than 80 percent of the landowners indicated a willingness to consider certification for their lands. A model was created to explain landowner behavior regarding their willingness to consider certification....

  10. A conceptual model of investor behavior

    NARCIS (Netherlands)

    Lovric, M.; Kaymak, U.; Spronk, J.; Nefti, S.; Gray, J.O.

    2010-01-01

    Behavioral finance is a subdiscipline of finance that uses insights from cogni tive and social psychology to enrich our knowledge of how investors make their financial decisions. Agent-based artificial financial markets are bottomup models of financial markets that start from the micro level of

  11. Models of behavioral change and adaptation

    NARCIS (Netherlands)

    Rasouli, S.; Timmermans, H.J.P.; Zhang, J.

    2017-01-01

    This chapter explains and summarizes models of behavioral change and adaptation, which have received less application in the life choice analysis associated with urban policy. Related to various life choices, life trajectory events are major decisions with a relatively long-lasting impact, such as

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

    Science.gov (United States)

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

    2014-06-01

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

  13. Situated learning theory: adding rate and complexity effects via Kauffman's NK model.

    Science.gov (United States)

    Yuan, Yu; McKelvey, Bill

    2004-01-01

    For many firms, producing information, knowledge, and enhancing learning capability have become the primary basis of competitive advantage. A review of organizational learning theory identifies two approaches: (1) those that treat symbolic information processing as fundamental to learning, and (2) those that view the situated nature of cognition as fundamental. After noting that the former is inadequate because it focuses primarily on behavioral and cognitive aspects of individual learning, this paper argues the importance of studying learning as interactions among people in the context of their environment. It contributes to organizational learning in three ways. First, it argues that situated learning theory is to be preferred over traditional behavioral and cognitive learning theories, because it treats organizations as complex adaptive systems rather than mere information processors. Second, it adds rate and nonlinear learning effects. Third, following model-centered epistemology, it uses an agent-based computational model, in particular a "humanized" version of Kauffman's NK model, to study the situated nature of learning. Using simulation results, we test eight hypotheses extending situated learning theory in new directions. The paper ends with a discussion of possible extensions of the current study to better address key issues in situated learning.

  14. Traditional Classroom vs E-learning in Higher Education: Difference between Students' Behavioral Engagement

    Directory of Open Access Journals (Sweden)

    Fei Li

    2014-03-01

    Full Text Available We discuss traditional classroom, e-learning, behavioral engagement and difference between behavioral engagements in two kind of instruction environment. Results from variance analyses suggest that there is no significant difference between engagements of active learning in different classroom conditions, and there exist significant differences on higher-level learning of innovative and critical thinking. Our findings highlight students' behavioral engagements in two environments have no significant advantage over each other, but e-learning facilitates higher-level learning better.

  15. Modeling human learning involved in car driving

    NARCIS (Netherlands)

    Wewerinke, P.H.

    1994-01-01

    In this paper, car driving is considered at the level of human tracking and maneuvering in the context of other traffic. A model analysis revealed the most salient features determining driving performance and safety. Learning car driving is modelled based on a system theoretical approach and based

  16. Learning in AN Oscillatory Cortical Model

    Science.gov (United States)

    Scarpetta, Silvia; Li, Zhaoping; Hertz, John

    We study a model of generalized-Hebbian learning in asymmetric oscillatory neural networks modeling cortical areas such as hippocampus and olfactory cortex. The learning rule is based on the synaptic plasticity observed experimentally, in particular long-term potentiation and long-term depression of the synaptic efficacies depending on the relative timing of the pre- and postsynaptic activities during learning. The learned memory or representational states can be encoded by both the amplitude and the phase patterns of the oscillating neural populations, enabling more efficient and robust information coding than in conventional models of associative memory or input representation. Depending on the class of nonlinearity of the activation function, the model can function as an associative memory for oscillatory patterns (nonlinearity of class II) or can generalize from or interpolate between the learned states, appropriate for the function of input representation (nonlinearity of class I). In the former case, simulations of the model exhibits a first order transition between the "disordered state" and the "ordered" memory state.

  17. Animal behaviour learning environment: software to facilitate learning in canine and feline behavior therapy.

    Science.gov (United States)

    McGreevy, P D; Della Torre, P K; Evans, D L

    2003-01-01

    Interactive software has been developed on CD-ROM to facilitate learning of problem formulation, diagnostic methodology, and therapeutic options in dog and cat behavior problems. Students working in small groups are presented with a signalment, a case history, and brief description of the problem behavior as perceived by the client. Students then navigate through the case history by asking the client questions from an icon-driven question pad. Animated video responses to the questions are provided. Students are then required to rate the significance of the questions and answers with respect to the development of the unwelcome behavior. Links to online self-assessments and to resource materials about causation and treatment options are provided to assist students in their decision-making process. The activity concludes with a software-generated e-mail submission that includes the recorded history, diagnosis, and recommended treatment for assessment purposes.

  18. Learning Actions Models: Qualitative Approach

    DEFF Research Database (Denmark)

    Bolander, Thomas; Gierasimczuk, Nina

    2015-01-01

    In dynamic epistemic logic, actions are described using action models. In this paper we introduce a framework for studying learnability of action models from observations. We present first results concerning propositional action models. First we check two basic learnability criteria: finite ident...

  19. Reversal learning and resurgence of operant behavior in zebrafish (Danio rerio).

    Science.gov (United States)

    Kuroda, Toshikazu; Mizutani, Yuto; Cançado, Carlos R X; Podlesnik, Christopher A

    2017-09-01

    Zebrafish are used extensively as vertebrate animal models in biomedical research for having such features as a fully sequenced genome and transparent embryo. Yet, operant-conditioning studies with this species are scarce. The present study investigated reversal learning and resurgence of operant behavior in zebrafish. A target response (approaching a sensor) was reinforced in Phase 1. In Phase 2, the target response was extinguished while reinforcing an alternative response (approaching a different sensor). In Phase 3, extinction was in effect for the target and alternative responses. Reversal learning was demonstrated when responding tracked contingency changes between Phases 1 and 2. Moreover, resurgence occurred in 10 of 13 fish in Phase 3: Target response rates increased transiently and exceeded rates of an unreinforced control response. The present study provides the first evidence with zebrafish supporting reversal learning between discrete operant responses and a laboratory model of relapse. These findings open the possibility to assessing genetic influences of operant behavior generally and in models of relapse (e.g., resurgence, renewal, reinstatement). Copyright © 2017 Elsevier B.V. All rights reserved.

  20. A multiplicative reinforcement learning model capturing learning dynamics and interindividual variability in mice

    OpenAIRE

    Bathellier, Brice; Tee, Sui Poh; Hrovat, Christina; Rumpel, Simon

    2013-01-01

    Learning speed can strongly differ across individuals. This is seen in humans and animals. Here, we measured learning speed in mice performing a discrimination task and developed a theoretical model based on the reinforcement learning framework to account for differences between individual mice. We found that, when using a multiplicative learning rule, the starting connectivity values of the model strongly determine the shape of learning curves. This is in contrast to current learning models ...

  1. Discrete time modelization of human pilot behavior

    Science.gov (United States)

    Cavalli, D.; Soulatges, D.

    1975-01-01

    This modelization starts from the following hypotheses: pilot's behavior is a time discrete process, he can perform only one task at a time and his operating mode depends on the considered flight subphase. Pilot's behavior was observed using an electro oculometer and a simulator cockpit. A FORTRAN program has been elaborated using two strategies. The first one is a Markovian process in which the successive instrument readings are governed by a matrix of conditional probabilities. In the second one, strategy is an heuristic process and the concepts of mental load and performance are described. The results of the two aspects have been compared with simulation data.

  2. E-Model for Online Learning Communities.

    Science.gov (United States)

    Rogo, Ellen J; Portillo, Karen M

    2015-10-01

    The purpose of this study was to explore the students' perspectives on the phenomenon of online learning communities while enrolled in a graduate dental hygiene program. A qualitative case study method was designed to investigate the learners' experiences with communities in an online environment. A cross-sectional purposive sampling method was used. Interviews were the data collection method. As the original data were being analyzed, the researchers noted a pattern evolved indicating the phenomenon developed in stages. The data were re-analyzed and validated by 2 member checks. The participants' experiences revealed an e-model consisting of 3 stages of formal learning community development as core courses in the curriculum were completed and 1 stage related to transmuting the community to an informal entity as students experienced the independent coursework in the program. The development of the formal learning communities followed 3 stages: Building a Foundation for the Learning Community, Building a Supportive Network within the Learning Community and Investing in the Community to Enhance Learning. The last stage, Transforming the Learning Community, signaled a transition to an informal network of learners. The e-model was represented by 3 key elements: metamorphosis of relationships, metamorphosis through the affective domain and metamorphosis through the cognitive domain, with the most influential element being the affective development. The e-model describes a 4 stage process through which learners experience a metamorphosis in their affective, relationship and cognitive development. Synergistic learning was possible based on the interaction between synergistic relationships and affective actions. Copyright © 2015 The American Dental Hygienists’ Association.

  3. Modeling irrigation behavior in groundwater systems

    Science.gov (United States)

    Foster, Timothy; Brozović, Nicholas; Butler, Adrian P.

    2014-08-01

    Integrated hydro-economic models have been widely applied to water management problems in regions of intensive groundwater-fed irrigation. However, policy interpretations may be limited as most existing models do not explicitly consider two important aspects of observed irrigation decision making, namely the limits on instantaneous irrigation rates imposed by well yield and the intraseasonal structure of irrigation planning. We develop a new modeling approach for determining irrigation demand that is based on observed farmer behavior and captures the impacts on production and water use of both well yield and climate. Through a case study of irrigated corn production in the Texas High Plains region of the United States we predict optimal irrigation strategies under variable levels of groundwater supply, and assess the limits of existing models for predicting land and groundwater use decisions by farmers. Our results show that irrigation behavior exhibits complex nonlinear responses to changes in groundwater availability. Declining well yields induce large reductions in the optimal size of irrigated area and irrigation use as constraints on instantaneous application rates limit the ability to maintain sufficient soil moisture to avoid negative impacts on crop yield. We demonstrate that this important behavioral response to limited groundwater availability is not captured by existing modeling approaches, which therefore may be unreliable predictors of irrigation demand, agricultural profitability, and resilience to climate change and aquifer depletion.

  4. A Professionalism Curricular Model to Promote Transformative Learning Among Residents.

    Science.gov (United States)

    Foshee, Cecile M; Mehdi, Ali; Bierer, S Beth; Traboulsi, Elias I; Isaacson, J Harry; Spencer, Abby; Calabrese, Cassandra; Burkey, Brian B

    2017-06-01

    Using the frameworks of transformational learning and situated learning theory, we developed a technology-enhanced professionalism curricular model to build a learning community aimed at promoting residents' self-reflection and self-awareness. The RAPR model had 4 components: (1) R ecognize : elicit awareness; (2) A ppreciate : question assumptions and take multiple perspectives; (3) P ractice : try new/changed perspectives; and (4) R eflect : articulate implications of transformed views on future actions. The authors explored the acceptability and practicality of the RAPR model in teaching professionalism in a residency setting, including how residents and faculty perceive the model, how well residents carry out the curricular activities, and whether these activities support transformational learning. A convenience sample of 52 postgraduate years 1 through 3 internal medicine residents participated in the 10-hour curriculum over 4 weeks. A constructivist approach guided the thematic analysis of residents' written reflections, which were a required curricular task. A total of 94% (49 of 52) of residents participated in 2 implementation periods (January and March 2015). Findings suggested that RAPR has the potential to foster professionalism transformation in 3 domains: (1) attitudinal, with participants reporting they viewed professionalism in a more positive light and felt more empathetic toward patients; (2) behavioral, with residents indicating their ability to listen to patients increased; and (3) cognitive, with residents indicating the discussions improved their ability to reflect, and this helped them create meaning from experiences. Our findings suggest that RAPR offers an acceptable and practical strategy to teach professionalism to residents.

  5. Treatment of avoidance behavior as an adjunct to exposure therapy: Insights from modern learning theory.

    Science.gov (United States)

    Treanor, Michael; Barry, Tom J

    2017-09-01

    Pathological avoidance of benign stimuli is a hallmark of anxiety and related disorders, and exposure-based treatments have often encouraged the removal of avoidance, or safety behaviors, due to their negative effects on extinction learning. Unfortunately, empirical evidence suggests that avoidance behaviors can persist following treatment, and the mere availability of avoidance behavior can be sufficient to renew fear following successful extinction learning. The present paper critically examines the function of avoidance behavior through the lens of modern learning theory, and speculates on novel behavioral and pharmacological strategies for targeting avoidance as an adjunct to current evidence-based treatments. Copyright © 2017. Published by Elsevier Ltd.

  6. A low concentration of ethanol impairs learning but not motor and sensory behavior in Drosophila larvae.

    Directory of Open Access Journals (Sweden)

    Brooks G Robinson

    Full Text Available Drosophila melanogaster has proven to be a useful model system for the genetic analysis of ethanol-associated behaviors. However, past studies have focused on the response of the adult fly to large, and often sedating, doses of ethanol. The pharmacological effects of low and moderate quantities of ethanol have remained understudied. In this study, we tested the acute effects of low doses of ethanol (∼7 mM internal concentration on Drosophila larvae. While ethanol did not affect locomotion or the response to an odorant, we observed that ethanol impaired associative olfactory learning when the heat shock unconditioned stimulus (US intensity was low but not when the heat shock US intensity was high. We determined that the reduction in learning at low US intensity was not a result of ethanol anesthesia since ethanol-treated larvae responded to the heat shock in the same manner as untreated animals. Instead, low doses of ethanol likely impair the neuronal plasticity that underlies olfactory associative learning. This impairment in learning was reversible indicating that exposure to low doses of ethanol does not leave any long lasting behavioral or physiological effects.

  7. Modeling Adaptive Behavior for Systems Design

    DEFF Research Database (Denmark)

    Rasmussen, Jens

    1994-01-01

    Field studies in modern work systems and analysis of recent major accidents have pointed to a need for better models of the adaptive behavior of individuals and organizations operating in a dynamic and highly competitive environment. The paper presents a discussion of some key characteristics.......) The basic difference between the models of system functions used in engineering and design and those evolving from basic research within the various academic disciplines and finally 3.) The models and methods required for closed-loop, feedback system design....

  8. Learning about physical parameters: the importance of model discrepancy

    International Nuclear Information System (INIS)

    Brynjarsdóttir, Jenný; O'Hagan, Anthony

    2014-01-01

    Science-based simulation models are widely used to predict the behavior of complex physical systems. It is also common to use observations of the physical system to solve the inverse problem, that is, to learn about the values of parameters within the model, a process which is often called calibration. The main goal of calibration is usually to improve the predictive performance of the simulator but the values of the parameters in the model may also be of intrinsic scientific interest in their own right. In order to make appropriate use of observations of the physical system it is important to recognize model discrepancy, the difference between reality and the simulator output. We illustrate through a simple example that an analysis that does not account for model discrepancy may lead to biased and over-confident parameter estimates and predictions. The challenge with incorporating model discrepancy in statistical inverse problems is being confounded with calibration parameters, which will only be resolved with meaningful priors. For our simple example, we model the model-discrepancy via a Gaussian process and demonstrate that through accounting for model discrepancy our prediction within the range of data is correct. However, only with realistic priors on the model discrepancy do we uncover the true parameter values. Through theoretical arguments we show that these findings are typical of the general problem of learning about physical parameters and the underlying physical system using science-based mechanistic models. (paper)

  9. Two Undergraduate Process Modeling Courses Taught Using Inductive Learning Methods

    Science.gov (United States)

    Soroush, Masoud; Weinberger, Charles B.

    2010-01-01

    This manuscript presents a successful application of inductive learning in process modeling. It describes two process modeling courses that use inductive learning methods such as inquiry learning and problem-based learning, among others. The courses include a novel collection of multi-disciplinary complementary process modeling examples. They were…

  10. Development of a Model for Whole Brain Learning of Physiology

    Science.gov (United States)

    Eagleton, Saramarie; Muller, Anton

    2011-01-01

    In this report, a model was developed for whole brain learning based on Curry's onion model. Curry described the effect of personality traits as the inner layer of learning, information-processing styles as the middle layer of learning, and environmental and instructional preferences as the outer layer of learning. The model that was developed…

  11. Small grant management in health and behavioral sciences: Lessons learned.

    Science.gov (United States)

    Sakraida, Teresa J; D'Amico, Jessica; Thibault, Erica

    2010-08-01

    This article describes considerations in health and behavioral sciences small grant management and describes lessons learned during post-award implementation. Using the components by W. Sahlman [Sahlman, W. (1997). How to write a great business plan. Harvard Business Review, 75(4), 98-108] as a business framework, a plan was developed that included (a) building relationships with people in the research program and with external parties providing key resources, (b) establishing a perspective of opportunity for research advancement, (c) identifying the larger context of scientific culture and regulatory environment, and (d) anticipating problems with a flexible response and rewarding teamwork. Small grant management included developing a day-to-day system, building a grant/study program development plan, and initiating a marketing plan. Copyright 2010 Elsevier Inc. All rights reserved.

  12. Human Guidance Behavior Decomposition and Modeling

    Science.gov (United States)

    Feit, Andrew James

    Trained humans are capable of high performance, adaptable, and robust first-person dynamic motion guidance behavior. This behavior is exhibited in a wide variety of activities such as driving, piloting aircraft, skiing, biking, and many others. Human performance in such activities far exceeds the current capability of autonomous systems in terms of adaptability to new tasks, real-time motion planning, robustness, and trading safety for performance. The present work investigates the structure of human dynamic motion guidance that enables these performance qualities. This work uses a first-person experimental framework that presents a driving task to the subject, measuring control inputs, vehicle motion, and operator visual gaze movement. The resulting data is decomposed into subspace segment clusters that form primitive elements of action-perception interactive behavior. Subspace clusters are defined by both agent-environment system dynamic constraints and operator control strategies. A key contribution of this work is to define transitions between subspace cluster segments, or subgoals, as points where the set of active constraints, either system or operator defined, changes. This definition provides necessary conditions to determine transition points for a given task-environment scenario that allow a solution trajectory to be planned from known behavior elements. In addition, human gaze behavior during this task contains predictive behavior elements, indicating that the identified control modes are internally modeled. Based on these ideas, a generative, autonomous guidance framework is introduced that efficiently generates optimal dynamic motion behavior in new tasks. The new subgoal planning algorithm is shown to generate solutions to certain tasks more quickly than existing approaches currently used in robotics.

  13. Prototype-based models in machine learning.

    Science.gov (United States)

    Biehl, Michael; Hammer, Barbara; Villmann, Thomas

    2016-01-01

    An overview is given of prototype-based models in machine learning. In this framework, observations, i.e., data, are stored in terms of typical representatives. Together with a suitable measure of similarity, the systems can be employed in the context of unsupervised and supervised analysis of potentially high-dimensional, complex datasets. We discuss basic schemes of competitive vector quantization as well as the so-called neural gas approach and Kohonen's topology-preserving self-organizing map. Supervised learning in prototype systems is exemplified in terms of learning vector quantization. Most frequently, the familiar Euclidean distance serves as a dissimilarity measure. We present extensions of the framework to nonstandard measures and give an introduction to the use of adaptive distances in relevance learning. © 2016 Wiley Periodicals, Inc.

  14. Culture in Transition: A learning model

    DEFF Research Database (Denmark)

    Baca, Susan

    2010-01-01

    of organizational transition, and 3) demonstrating the efficacy of the model by using it to explain empirical research findings. It is argued that learning new cultural currency involves the use of active intelligence to locate and answer relevant questions, and further that this process requires the interplay......This paper addresses the problem of resistance to attempted changes in organizational culture, particularly those involving diversity, by 1) identifying precisely what is meant by organizational as opposed to societal culture, 2) developing a theoretical model of learning useful in contexts...... is useful for both management and labor in regulating transition processes, thus making a contribution to industrial relations....

  15. Encouraging IS developers to learn business skills: an examination of the MARS model

    OpenAIRE

    Tsay, Han-Huei (Crystal)

    2016-01-01

    Though prior research has recognized business skills as one of the keys to successful information system development, few studies have investigated the determinants of an IS developer’s behavioral intention to learn such skills. Based on the Motivation–Ability–Role Perception–Situational factors (i.e., the MARS model), this study argues that the intention of IS developers to acquire business skills is influenced by learning motivation (M), learning self-efficacy (A), change agent role percept...

  16. Studies of radiation effects on the indicator of behavior and learning of the nematode caenorhabditis elegans

    International Nuclear Information System (INIS)

    Sakashita, Tetsuya; Suzuki, Michiyo

    2011-01-01

    Radiation effects on the essential behavior and its higher level of learning are described mainly on authors' studies of the title nematode (N), a unique model of which, in a whole, genome is mapped, genealogy of cells is defined, and anatomical distribution and linkage of nervous cells are known. N exhibits various behaviors such as meandering on the culture agar/swimming in water responding to given stimuli like touch, temperature, and chemical. Authors have found that the locomotive activity of N is reduced by gamma-irradiation. Their subsequent irradiation study of 60 Co gamma ray at 300-900 Gy (32 Gy/sec) to wild type and cat-2 mutant lacking tyrosine hydroxylase in the presence/absence of N's food has revealed that dopaminergic nerves do not participate in the mechanism of the reduction unexpectedly. Rather, participation of an active oxygen species is suggested by their following study with H 2 O 2 , but exact nervous system responsible to the reduction is still to be elucidated in future. For radiation effect on N's associative learning, authors have used the reversal of salt preference, where Ns cultured on NaCl-containing agar covered by E. coli (food), being chemotactic to the salt, are conditioned by food removal: salt preference is reversed after learning. The gamma ray irradiation at the conditioning stage but neither before nor after learning, is found to lead to reduction of the chemotaxis (promotion of learning), and this radiation response is found to occur in N lacking gpc-1 gene coding G-protein gamma-subunit which is localized in a part of sensory nerves. Authors think this radiation effect is a modulation of nervous circuit for chemotaxis of N, but of which relation with the complicated nervous functions in higher animals is further to be elucidated in aspects of learning and memory. (T.T.)

  17. An examination of the impact of non-formal and informal learning on adult environmental knowledge, attitudes, and behaviors

    Science.gov (United States)

    Digby, Cynthia Louise Barrett

    The purpose of this research is to consider the environmental knowledge, attitudes, and behaviors, of adults in Minnesota, and possible factors that influence environmental literacy. Specifically, this study is designed to: (1) measure the environmental literacy of Minnesota adults, (2) explore possible relationships between Minnesota adults, environmental literacy variables and their demographic, non-formal and informal learning, and (3) determine the relative contribution of demographic and learning variables for predicting environmental knowledge, attitudes and behaviors. This research was accomplished by conducting a secondary data analysis of The Third Minnesota Report Card on Environmental Literacy: A Survey of Adult Environmental Knowledge, Attitudes and Behavior (Murphy & Olson, 2008). Phone interviews were completed between August and November 2007 with one thousand adults throughout Minnesota. Findings indicated that for age, education, and income, there was a weak positive relationship with environmental knowledge, attitude and behavior scores. There was a significant effect for gender and environmental knowledge scores, with males receiving higher environmental knowledge scores than females. There was a significant effect for gender and environmental attitudes, and behavior scores as well, with females receiving slightly higher environmental attitude and behavior scores than males. After controlling for the effects of demographic variables on environmental knowledge, attitudes and behaviors, non-formal learning participation appears to be a moderate contributor to both environmental knowledge and environmental behaviors. After controlling for the effects of demographic variables on environmental knowledge, attitudes and behaviors, informal learning participation appears to be a slight contributor to environmental attitudes, and a moderate contributor to environmental knowledge and behaviors. Overall, the results of this study suggest that participation

  18. Understanding Social Learning Behaviors via a Virtual Field Trip

    Directory of Open Access Journals (Sweden)

    Xin Bai

    2014-06-01

    Full Text Available This is a multidisciplinary study investigating how a virtual rather than face-to-face field trip can be conducted in a real-world setting and how students respond to such a social learning opportunity. Our participants followed a story of a stroke patient at her virtual home and in a virtual hospital via a teaching vignette. They were then given a new case and got on a virtual trip via a multiuser virtual environment. They played the roles of patients, relatives, doctors, or nurses, experiencing the emotional, physical, or social impacts those stakeholders may go through. Our study finds the overall participation of the Virtual Group is 50% more than the Text Group. Although the Virtual Group generates much more nodes in total, they focused much less on knowledge sharing and comparing than the Text Group (46 vs. 67, but more on other higher-level aspects of social interactions, such as knowledge discovery (57 vs. 42, co-construction (66 vs. 39, testing and modification (58 vs. 24 and application of newly constructed meaning (60 vs. 16. Analysis of students’ virtual field activities and in-depth discussions of important issues implied are included to help understand social learning behaviors during a virtual field trip. Sustainability of such systems is discussed.

  19. Aids to determining fuel models for estimating fire behavior

    Science.gov (United States)

    Hal E. Anderson

    1982-01-01

    Presents photographs of wildland vegetation appropriate for the 13 fuel models used in mathematical models of fire behavior. Fuel model descriptions include fire behavior associated with each fuel and its physical characteristics. A similarity chart cross-references the 13 fire behavior fuel models to the 20 fuel models used in the National Fire Danger Rating System....

  20. Machine Learning and Deep Learning Models to Predict Runoff Water Quantity and Quality

    Science.gov (United States)

    Bradford, S. A.; Liang, J.; Li, W.; Murata, T.; Simunek, J.

    2017-12-01

    Contaminants can be rapidly transported at the soil surface by runoff to surface water bodies. Physically-based models, which are based on the mathematical description of main hydrological processes, are key tools for predicting surface water impairment. Along with physically-based models, data-driven models are becoming increasingly popular for describing the behavior of hydrological and water resources systems since these models can be used to complement or even replace physically based-models. In this presentation we propose a new data-driven model as an alternative to a physically-based overland flow and transport model. First, we have developed a physically-based numerical model to simulate overland flow and contaminant transport (the HYDRUS-1D overland flow module). A large number of numerical simulations were carried out to develop a database containing information about the impact of various input parameters (weather patterns, surface topography, vegetation, soil conditions, contaminants, and best management practices) on runoff water quantity and quality outputs. This database was used to train data-driven models. Three different methods (Neural Networks, Support Vector Machines, and Recurrence Neural Networks) were explored to prepare input- output functional relations. Results demonstrate the ability and limitations of machine learning and deep learning models to predict runoff water quantity and quality.

  1. EFFECTS OF INQUIRY TRAINING LEARNING MODEL BASED MULTIMEDIA AND MOTIVATION OF PHYSICS STUDENT LEARNING OUTCOMES

    Directory of Open Access Journals (Sweden)

    Hayati .

    2013-06-01

    Full Text Available The objective in this research: (1 Determine a better learning model to improve learning outcomes physics students among learning model Inquiry Training based multimedia and Inquiry Training learning model. (2 Determine the level of motivation to learn in affects physics student learning outcomes. (3 Knowing the interactions between the model of learning and motivation in influencing student learning outcomes. This research is a quasi experimental. The population in this research was all students in class XI SMA Negeri 1 T.P Sunggal Semester I 2012/2013. The sample of this research was consisted of two classes with a sample of 70 peoples who are determined by purposive sampling, the IPA XI-2 as a class experiment using a model-based multimedia learning Training Inquiry as many as 35 peoples and XI IPA-3 as a control class using learning model Inquiry Training 35 peoples. Hypotheses were analyzed using the GLM at significant level of 0.05 using SPSS 17.0 for Windows. Based on data analysis and hypothesis testing conducted found that: (1 Training Inquiry-based multimedia learning model in improving student learning outcomes rather than learning model physics Inquiry Training. (2 The results of studying physics students who have high motivation to learn better than students who have a low learning motivation. (3 From this research there was an interaction between learning model inquiry-based multimedia training and motivation to study on learning outcomes of students.

  2. The 8 Learning Events Model: a Pedagogic Conceptual Tool Supporting Diversification of Learning Methods

    NARCIS (Netherlands)

    Verpoorten, Dominique; Poumay, M; Leclercq, D

    2006-01-01

    Please, cite this publication as: Verpoorten, D., Poumay, M., & Leclercq, D. (2006). The 8 Learning Events Model: a Pedagogic Conceptual Tool Supporting Diversification of Learning Methods. Proceedings of International Workshop in Learning Networks for Lifelong Competence Development, TENCompetence

  3. Learning Adversary Modeling from Games

    National Research Council Canada - National Science Library

    Avellino, Paul

    2007-01-01

    .... In the computer age, highly accurate models and simulations of the enemy can be created. However, including the effects of motivations, capabilities, and weaknesses of adversaries in current wars is still extremely difficult...

  4. Behavior genetic modeling of human fertility

    DEFF Research Database (Denmark)

    Rodgers, J L; Kohler, H P; Kyvik, K O

    2001-01-01

    Behavior genetic designs and analysis can be used to address issues of central importance to demography. We use this methodology to document genetic influence on human fertility. Our data come from Danish twin pairs born from 1953 to 1959, measured on age at first attempt to get pregnant (First......Try) and number of children (NumCh). Behavior genetic models were fitted using structural equation modeling and DF analysis. A consistent medium-level additive genetic influence was found for NumCh, equal across genders; a stronger genetic influence was identified for FirstTry, greater for females than for males....... A bivariate analysis indicated significant shared genetic variance between NumCh and FirstTry....

  5. Personalized learning: From neurogenetics of behaviors to designing optimal language training.

    Science.gov (United States)

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

    2017-04-01

    Variability in drug responsivity has prompted the development of Personalized Medicine, which has shown great promise in utilizing genotypic information to develop safer and more effective drug regimens for patients. Similarly, individual variability in learning outcomes has puzzled researchers who seek to create optimal learning environments for students. "Personalized Learning" seeks to identify genetic, neural and behavioral predictors of individual differences in learning and aims to use predictors to help create optimal teaching paradigms. Evidence for Personalized Learning can be observed by connecting research in pharmacogenomics, cognitive genetics and behavioral experiments across domains of learning, which provides a framework for conducting empirical studies from the laboratory to the classroom and holds promise for addressing learning effectiveness in the individual learners. Evidence can also be seen in the subdomain of speech learning, thus providing initial support for the applicability of Personalized Learning to language. Copyright © 2016 Elsevier Ltd. All rights reserved.

  6. Behavioral Reference Model for Pervasive Healthcare Systems.

    Science.gov (United States)

    Tahmasbi, Arezoo; Adabi, Sahar; Rezaee, Ali

    2016-12-01

    The emergence of mobile healthcare systems is an important outcome of application of pervasive computing concepts for medical care purposes. These systems provide the facilities and infrastructure required for automatic and ubiquitous sharing of medical information. Healthcare systems have a dynamic structure and configuration, therefore having an architecture is essential for future development of these systems. The need for increased response rate, problem limited storage, accelerated processing and etc. the tendency toward creating a new generation of healthcare system architecture highlight the need for further focus on cloud-based solutions for transfer data and data processing challenges. Integrity and reliability of healthcare systems are of critical importance, as even the slightest error may put the patients' lives in danger; therefore acquiring a behavioral model for these systems and developing the tools required to model their behaviors are of significant importance. The high-level designs may contain some flaws, therefor the system must be fully examined for different scenarios and conditions. This paper presents a software architecture for development of healthcare systems based on pervasive computing concepts, and then models the behavior of described system. A set of solutions are then proposed to improve the design's qualitative characteristics including, availability, interoperability and performance.

  7. A Bilingual Child Learns Social Communication Skills through Video Modeling--A Single Case Study in a Norwegian School Setting

    Science.gov (United States)

    Özerk, Meral; Özerk, Kamil

    2015-01-01

    "Video modeling" is one of the recognized methods used in the training and teaching of children with Autism Spectrum Disorders (ASD). The model's theoretical base stems from Albert Bandura's (1977; 1986) social learning theory in which he asserts that children can learn many skills and behaviors observationally through modeling. One can…

  8. Adult neurogenesis is reduced in the dorsal hippocampus of rats displaying learned helplessness behavior.

    Science.gov (United States)

    Ho, Y C; Wang, S

    2010-11-24

    Clinical and preclinical studies suggest that the hippocampus has a role in the pathophysiology of major depression. In the learned helplessness (LH) animal model of depression after inescapable shocks (ISs) animals that display LH behavior have reduced cell proliferation in the hippocampus; this effect can be reversed by antidepressant treatment. Using this model, we compared rats that displayed LH behavior and rats that did not show LH behavior (NoLH) after ISs to determine whether reduced hippocampal cell proliferation is associated with the manifestation of LH behavior or is a general response to stress. Specifically, we examined cell proliferation, neurogenesis, and synaptic function in dorsal and ventral hippocampus of LH and NoLH animals and control rats that were not shocked. The LH rats had showed reduced cell proliferation, neurogenesis, and synaptic transmission in the dorsal hippocampus, whereas no changes were seen in the ventral hippocampus. These changes were not observed in the NoLH animals. In a group of NoLH rats that received the same amount of electrical shock as the LH rats to control for the unequal shocks received in these two groups, we observed changes in Ki-67(+) cells associated with acute stress. We conclude that reduced hippocampal cell proliferation and neurogenesis are associated with the manifestation of LH behavior and that the dorsal hippocampus is the most affected area. Copyright © 2010 IBRO. Published by Elsevier Ltd. All rights reserved.

  9. A model of positive and negative learning : Learning demands and resources, learning engagement, critical thinking, and fake news detection

    NARCIS (Netherlands)

    Dormann, Christian; Demerouti, Eva; Bakker, Arnold; Zlatkin-Troitschanskaia, O.; Wittum, G.; Dengel, A.

    2018-01-01

    This chapter proposes a model of positive and negative learning (PNL model). We use the term negative learning when stress among students occurs, and when knowledge and abilities are not properly developed. We use the term positive learning if motivation is high and active learning occurs. The PNL

  10. Assessment of Readiness to Participate in Distance Learning of the Certified Florida Behavioral Workforce

    Science.gov (United States)

    Baston, George R.

    2011-01-01

    This research study explored perceptions of readiness to participate in distance learning among the certified behavioral workforce in Florida. The study sought to determine if there were significant differences in perception of readiness to participate in distance learning between certified behavioral health professionals at the administrator…

  11. Learning Contracts in Undergraduate Courses: Impacts on Student Behaviors and Academic Performance

    Science.gov (United States)

    Frank, Timothy; Scharf, Lauren F. V

    2013-01-01

    This project studied the effect of individualized, voluntary learning contracts for 18 students who performed poorly in the first part of the semester. Contracts were hypothesized to increase commitment and motivation, and lead to changes in behaviors and course performance. Self-reported prioritization and learning-related behaviors (completion…

  12. Organizational Learning Supported by Reference Architecture Models

    DEFF Research Database (Denmark)

    Nardello, Marco; Møller, Charles; Gøtze, John

    2017-01-01

    of an emerging technical standard specific for the manufacturing industry. Global manufacturing experts consider the Reference Architecture Model Industry 4.0 (RAMI4.0) as one of the corner stones for the implementation of Industry 4.0. The instantiation contributed to organizational learning in the laboratory...

  13. Prototype-based models in machine learning

    NARCIS (Netherlands)

    Biehl, Michael; Hammer, Barbara; Villmann, Thomas

    2016-01-01

    An overview is given of prototype-based models in machine learning. In this framework, observations, i.e., data, are stored in terms of typical representatives. Together with a suitable measure of similarity, the systems can be employed in the context of unsupervised and supervised analysis of

  14. School Improvement Model to Foster Student Learning

    Science.gov (United States)

    Rulloda, Rudolfo Barcena

    2011-01-01

    Many classroom teachers are still using the traditional teaching methods. The traditional teaching methods are one-way learning process, where teachers would introduce subject contents such as language arts, English, mathematics, science, and reading separately. However, the school improvement model takes into account that all students have…

  15. How the challenge of explaining learning influenced the origins and development of John B. Watson's behaviorism.

    Science.gov (United States)

    Rilling, M

    2000-01-01

    Before he invented behaviorism, John B. Watson considered learning one of the most important topics in psychology. Watson conducted excellent empirical research on animal learning. He developed behaviorism in part to promote research and elevate the status of learning in psychology. Watson was much less successful in the adequacy and originality of the mechanisms he proposed to explain learning. By assimilating the method of classical conditioning and adopting Pavlov's theory of stimulus substitution, Watson linked behaviorism with a new method that could compete with both Titchener's method of introspection and Freud's methods of psychoanalysis. Watson's interest in explaining psychopathology led to the discovery of conditioned emotional responses and a behavioristic explanation for the learning of phobic behavior. Watson established learning as a central topic for basic research and application in American psychology.

  16. Social Learning Theory and Behavioral Therapy: Considering Human Behaviors within the Social and Cultural Context of Individuals and Families.

    Science.gov (United States)

    McCullough Chavis, Annie

    2011-01-01

    This article examines theoretical thoughts of social learning theory and behavioral therapy and their influences on human behavior within a social and cultural context. The article utilizes two case illustrations with applications for consumers. It points out the abundance of research studies concerning the effectiveness of social learning theory, and the paucity of research studies regarding effectiveness and evidence-based practices with diverse groups. Providing a social and cultural context in working with diverse groups with reference to social learning theory adds to the literature for more cultural considerations in adapting the theory to women, African Americans, and diverse groups.

  17. Mob control models of threshold collective behavior

    CERN Document Server

    Breer, Vladimir V; Rogatkin, Andrey D

    2017-01-01

    This book presents mathematical models of mob control with threshold (conformity) collective decision-making of the agents. Based on the results of analysis of the interconnection between the micro- and macromodels of active network structures, it considers the static (deterministic, stochastic and game-theoretic) and dynamic (discrete- and continuous-time) models of mob control, and highlights models of informational confrontation. Many of the results are applicable not only to mob control problems, but also to control problems arising in social groups, online social networks, etc. Aimed at researchers and practitioners, it is also a valuable resource for undergraduate and postgraduate students as well as doctoral candidates specializing in the field of collective behavior modeling.

  18. Identifying Learning Behaviors by Contextualizing Differential Sequence Mining with Action Features and Performance Evolution

    Science.gov (United States)

    Kinnebrew, John S.; Biswas, Gautam

    2012-01-01

    Our learning-by-teaching environment, Betty's Brain, captures a wealth of data on students' learning interactions as they teach a virtual agent. This paper extends an exploratory data mining methodology for assessing and comparing students' learning behaviors from these interaction traces. The core algorithm employs sequence mining techniques to…

  19. A Contextualized, Differential Sequence Mining Method to Derive Students' Learning Behavior Patterns

    Science.gov (United States)

    Kinnebrew, John S.; Loretz, Kirk M.; Biswas, Gautam

    2013-01-01

    Computer-based learning environments can produce a wealth of data on student learning interactions. This paper presents an exploratory data mining methodology for assessing and comparing students' learning behaviors from these interaction traces. The core algorithm employs a novel combination of sequence mining techniques to identify deferentially…

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

    Science.gov (United States)

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

    2016-01-01

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

  1. Analysis of Learning Behavior in a Flipped Programing Classroom Adopting Problem-Solving Strategies

    Science.gov (United States)

    Chiang, Tosti Hsu-Cheng

    2017-01-01

    Programing is difficult for beginners because they need to learn the new language of computers. Developing software, especially complex software, is bound to result in problems, frustration, and the need to think in new ways. Identifying the learning behavior behind programing by way of empirical studies can help beginners learn more easily. In…

  2. Common marmoset (Callithrix jacchus) as a primate model for behavioral neuroscience studies.

    Science.gov (United States)

    Prins, Noeline W; Pohlmeyer, Eric A; Debnath, Shubham; Mylavarapu, Ramanamurthy; Geng, Shijia; Sanchez, Justin C; Rothen, Daniel; Prasad, Abhishek

    2017-06-01

    The common marmoset (Callithrix jacchus) has been proposed as a suitable bridge between rodents and larger primates. They have been used in several types of research including auditory, vocal, visual, pharmacological and genetics studies. However, marmosets have not been used as much for behavioral studies. Here we present data from training 12 adult marmosets for behavioral neuroscience studies. We discuss the husbandry, food preferences, handling, acclimation to laboratory environments and neurosurgical techniques. In this paper, we also present a custom built "scoop" and a monkey chair suitable for training of these animals. The animals were trained for three tasks: 4 target center-out reaching task, reaching tasks that involved controlling robot actions, and touch screen task. All animals learned the center-out reaching task within 1-2 weeks whereas learning reaching tasks controlling robot actions task took several months of behavioral training where the monkeys learned to associate robot actions with food rewards. We propose the marmoset as a novel model for behavioral neuroscience research as an alternate for larger primate models. This is due to the ease of handling, quick reproduction, available neuroanatomy, sensorimotor system similar to larger primates and humans, and a lissencephalic brain that can enable implantation of microelectrode arrays relatively easier at various cortical locations compared to larger primates. All animals were able to learn behavioral tasks well and we present the marmosets as an alternate model for simple behavioral neuroscience tasks. Copyright © 2017 Elsevier B.V. All rights reserved.

  3. Applications of operant learning theory to the management of challenging behavior after traumatic brain injury.

    Science.gov (United States)

    Wood, Rodger Ll; Alderman, Nick

    2011-01-01

    For more than 3 decades, interventions derived from learning theory have been delivered within a neurobehavioral framework to manage challenging behavior after traumatic brain injury with the aim of promoting engagement in the rehabilitation process and ameliorating social handicap. Learning theory provides a conceptual structure that facilitates our ability to understand the relationship between challenging behavior and environmental contingencies, while accommodating the constraints upon learning imposed by impaired cognition. Interventions derived from operant learning theory have most frequently been described in the literature because this method of associational learning provides good evidence for the effectiveness of differential reinforcement methods. This article therefore examines the efficacy of applying operant learning theory to manage challenging behavior after TBI as well as some of the limitations of this approach. Future developments in the application of learning theory are also considered.

  4. Modeling the thermotaxis behavior of C.elegans based on the artificial neural network.

    Science.gov (United States)

    Li, Mingxu; Deng, Xin; Wang, Jin; Chen, Qiaosong; Tang, Yun

    2016-07-03

    ASBTRACT This research aims at modeling the thermotaxis behavior of C.elegans which is a kind of nematode with full clarified neuronal connections. Firstly, this work establishes the motion model which can perform the undulatory locomotion with turning behavior. Secondly, the thermotaxis behavior is modeled by nonlinear functions and the nonlinear functions are learned by artificial neural network. Once the artificial neural networks have been well trained, they can perform the desired thermotaxis behavior. Last, several testing simulations are carried out to verify the effectiveness of the model for thermotaxis behavior. This work also analyzes the different performances of the model under different environments. The testing results reveal the essence of the thermotaxis of C.elegans to some extent, and theoretically support the research on the navigation of the crawling robots.

  5. Constructing Pedagogical Models For E-learning

    OpenAIRE

    Patricia Alejandra Behar

    2011-01-01

    This article brings forth an overview of the paradigmatic crisis and the introduction of new pedagogical practices. It also discusses the relationship between paradigm and pedagogical model, presenting a theoretical discussion on the concepts of pedagogical model for E-learning and its pedagogical architecture. To do so, the elements that are part of it such as organizational aspects, content, methodological and technological aspects are discussed. This theoretical discussion underlies the co...

  6. A conceptual model of nurses' goal orientation, service behavior, and service performance.

    Science.gov (United States)

    Chien, Chun-Cheng; Chou, Hsin-Kai; Hung, Shuo-Tsung

    2008-01-01

    Based on the conceptual framework known as the "service triangle," the authors constructed a model of nurses' goal orientation, service behavior, and service performance to investigate the antecedents and consequences of the medical service behavior provided by nurses. This cross-sectional study collected data from 127 nurses in six hospitals using a mail-in questionnaire. Analysis of the model revealed that the customer-oriented behavior of nurses had a positive influence on organizational citizenship behavior; and both of these behaviors had a significant positive influence on service performance. The results also indicate that a higher learning goal orientation among nurses was associated with the performance of both observable customer-oriented behavior and organizational-citizenship behavior.

  7. Developing Autonomous Vehicles That Learn to Navigate by Mimicking Human Behavior

    Science.gov (United States)

    2006-09-28

    navigate in an unstructured environment to a specific target or location. 15. SUBJECT TERMS autonomous vehicles , fuzzy logic, learning behavior...ANSI-Std Z39-18 Developing Autonomous Vehicles That Learn to Navigate by Mimicking Human Behavior FINAL REPORT 9/28/2006 Dean B. Edwards Department...the future, as greater numbers of autonomous vehicles are employed, it is hoped that lower LONG-TERM GOALS Use LAGR (Learning Applied to Ground Robots

  8. Use of Peer Tutoring, Cooperative Learning, and Collaborative Learning: Implications for Reducing Anti-Social Behavior of Schooling Adolescents

    Science.gov (United States)

    Eskay, M.; Onu, V. C.; Obiyo, N.; Obidoa, M.

    2012-01-01

    The study investigated the use of peer tutoring, cooperative learning, and collaborative learning as strategies to reduce anti-social behavior among schooling adolescents. The study is a descriptive survey study. The area of study was Nsukka education zone in Enugu State of Nigeria. The sample of the study was 200 teachers randomly sampled from…

  9. Facilitating Effective Digital Game-Based Learning Behaviors and Learning Performances of Students Based on a Collaborative Knowledge Construction Strategy

    Science.gov (United States)

    Sung, Han-Yu; Hwang, Gwo-Jen

    2018-01-01

    Researchers have recognized the potential of educational computer games in improving students' learning engagement and outcomes; however, facilitating effective learning behaviors during the gaming process remains an important and challenging issue. In this paper, a collaborative knowledge construction strategy was incorporated into an educational…

  10. Effects of Locus of Control on Behavioral Intention and Learning Performance of Energy Knowledge in Game-Based Learning

    Science.gov (United States)

    Yang, Jie Chi; Lin, Yi Lung; Liu, Yi-Chun

    2017-01-01

    Game-based learning has been gradually adopted in energy education as an effective learning tool because digital games have the potential to increase energy literacy and encourage behavior change. However, not every learner can benefit from this support. There is a need to examine how human factors affect learners' reactions to digital games for…

  11. Self-Regulatory Behaviors and Approaches to Learning of Arts Students: A Comparison between Professional Training and English Learning

    Science.gov (United States)

    Tseng, Min-chen; Chen, Chia-cheng

    2017-01-01

    This study investigated the self-regulatory behaviors of arts students, namely memory strategy, goal-setting, self-evaluation, seeking assistance, environmental structuring, learning responsibility, and planning and organizing. We also explored approaches to learning, including deep approach (DA) and surface approach (SA), in a comparison between…

  12. Correlation Between Blended Learning Model With The Perspective Of Learning Effectiveness For Nursing Student

    Directory of Open Access Journals (Sweden)

    Susila Sumartiningsih

    2015-06-01

    Full Text Available ABSTRACT The learning model is one of the enabling factors that influence the achievement of students. That students have a good learning outcomes the lecturer must choose appropriate learning models. But in fact not all lecturers choose the most appropriate learning model with the demands of learning outcomes and student characteristics.The study design was descriptive quantitative correlation. Total population of 785 the number of samples are 202 were taken by purposive sampling. Techniques of data collection is done by cross-sectional and then processed through the Spearman test. The results showed no significant relationship between classroom lecture method in the context of blended learning models to study the effectiveness perspective the p value of 0.001. There is a significant relationship between e-learning methods in the context of blended learning models with perspective of activities study of nursing students the p value of 0.028. There is a significant relationship between learning model of blended learning with the perspective of nursing students learning effectiveness p value 0.167. Researchers recommend to future researchers conduct more research on the comparison between the effectiveness of the learning model based on student learning centers with the e-learning models and its impact on student achievement of learning competencies as well as to the implications for other dimensions of learning outcomes and others.

  13. Effect Of Inquiry Learning Model And Motivation On Physics Outcomes Learning Students

    OpenAIRE

    Pardede, Dahlia Megawati; Manurung, Sondang Rina

    2016-01-01

    The purposes of the research are: (a) to determine differences in learning outcomes of students with Inquiry Training models and conventional models, (b) to determine differences in physics learning outcomes of students who have high motivation and low motivation, (c) to determine the interaction between learning models with the level of motivation in improving student Physics learning outcomes. The results were found: (a) there are differences in physical students learning outcomes are taugh...

  14. Learning topic models by belief propagation.

    Science.gov (United States)

    Zeng, Jia; Cheung, William K; Liu, Jiming

    2013-05-01

    Latent Dirichlet allocation (LDA) is an important hierarchical Bayesian model for probabilistic topic modeling, which attracts worldwide interest and touches on many important applications in text mining, computer vision and computational biology. This paper represents the collapsed LDA as a factor graph, which enables the classic loopy belief propagation (BP) algorithm for approximate inference and parameter estimation. Although two commonly used approximate inference methods, such as variational Bayes (VB) and collapsed Gibbs sampling (GS), have gained great success in learning LDA, the proposed BP is competitive in both speed and accuracy, as validated by encouraging experimental results on four large-scale document datasets. Furthermore, the BP algorithm has the potential to become a generic scheme for learning variants of LDA-based topic models in the collapsed space. To this end, we show how to learn two typical variants of LDA-based topic models, such as author-topic models (ATM) and relational topic models (RTM), using BP based on the factor graph representations.

  15. The Aalborg Model and participant directed learning

    DEFF Research Database (Denmark)

    Qvist, Palle

    2009-01-01

    Preparing students for a life as active citizens in a democratic society is one of the aims within the Bologna process. The Council of Europe has also stressed the importance of focus on democracy in Higher Education. Higher Education is seen as important to develop a democratic culture among...... students. Teaching democracy should be promoted in lessons and curricula. Creating democratic learning systems in institutions of higher education could be the answer to reaching the aim related to democracy. The Aalborg Model practised at Aalborg University is a learning system which has collaborative...

  16. A model of olfactory associative learning

    Science.gov (United States)

    Tavoni, Gaia; Balasubramanian, Vijay

    We propose a mechanism, rooted in the known anatomy and physiology of the vertebrate olfactory system, by which presentations of rewarded and unrewarded odors lead to formation of odor-valence associations between piriform cortex (PC) and anterior olfactory nucleus (AON) which, in concert with neuromodulators release in the bulb, entrains a direct feedback from the AON representation of valence to a group of mitral cells (MCs). The model makes several predictions concerning MC activity during and after associative learning: (a) AON feedback produces synchronous divergent responses in a localized subset of MCs; (b) such divergence propagates to other MCs by lateral inhibition; (c) after learning, MC responses reconverge; (d) recall of the newly formed associations in the PC increases feedback inhibition in the MCs. These predictions have been confirmed in disparate experiments which we now explain in a unified framework. For cortex, our model further predicts that the response divergence developed during learning reshapes odor representations in the PC, with the effects of (a) decorrelating PC representations of odors with different valences, (b) increasing the size and reliability of those representations, and enabling recall correction and redundancy reduction after learning. Simons Foundation for Mathematical Modeling of Living Systems.

  17. Assessing Student Behaviors and Motivation for Actively Learning Biology

    Science.gov (United States)

    Moore, Michael Edward

    2017-01-01

    Vision and Change states that one of the major changes in the way we design biology courses should be a switch in approach from teacher-centered learning to student-centered learning and identifies active learning as a recommended methods. Studies show performance benefits for students taking courses that use active learning. What is unknown is…

  18. VOTERS DECIDE. CLASSICAL MODELS OF ELECTORAL BEHAVIOR.

    Directory of Open Access Journals (Sweden)

    Constantin SASU

    2015-04-01

    Full Text Available The decision to vote and choosing among the candidates is a extremely important one with repercussions on everyday life by determining, in global mode, its quality for the whole society. Therefore the whole process by which the voter decide becomes a central concern. In this paper we intend to locate the determinants of the vote decision in the electoral behavior classical theoretical models developed over time. After doing synthesis of classical schools of thought on electoral behavior we conclude that it has been made a journey through the mind, soul and cheek, as follows: the mind as reason in theory developed by Downs, soul as preferably for an actor in Campbell's theory, etc. and cheek as an expression of the impossibility of detachment from social groups to which we belong in Lazarsfeld's theory.

  19. EFFECT OF INQUIRY LEARNING MODEL AND MOTIVATION ON PHYSICS OUTCOMES LEARNING STUDENTS

    Directory of Open Access Journals (Sweden)

    Dahlia Megawati Pardede

    2016-06-01

    Full Text Available The purposes of the research are: (a to determine differences in learning outcomes of students with Inquiry Training models and conventional models, (b to determine differences in physics learning outcomes of students who have high motivation and low motivation, (c to determine the interaction between learning models with the level of motivation in improving student Physics learning outcomes. The results were found: (a there are differences in physical students learning outcomes are taught by Inquiry Training models and conventional models. (b learning outcomes of students who are taught by Inquiry Learning Model Training better than student learning outcomes are taught with conventional model. (c there is a difference in student's learning outcomes that have high motivation and low motivation. (d Student learning outcomes that have a high motivation better than student learning outcomes than have a low motivation. (e there is interaction between learning and motivation to student learning outcomes. Learning outcomes of students who are taught by the model is influenced also by the motivation, while learning outcomes of students who are taught with conventional models are not affected by motivation.

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

    Science.gov (United States)

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

    2016-01-01

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

  1. Modeling Individual Cyclic Variation in Human Behavior.

    Science.gov (United States)

    Pierson, Emma; Althoff, Tim; Leskovec, Jure

    2018-04-01

    Cycles are fundamental to human health and behavior. Examples include mood cycles, circadian rhythms, and the menstrual cycle. However, modeling cycles in time series data is challenging because in most cases the cycles are not labeled or directly observed and need to be inferred from multidimensional measurements taken over time. Here, we present Cyclic Hidden Markov Models (CyH-MMs) for detecting and modeling cycles in a collection of multidimensional heterogeneous time series data. In contrast to previous cycle modeling methods, CyHMMs deal with a number of challenges encountered in modeling real-world cycles: they can model multivariate data with both discrete and continuous dimensions; they explicitly model and are robust to missing data; and they can share information across individuals to accommodate variation both within and between individual time series. Experiments on synthetic and real-world health-tracking data demonstrate that CyHMMs infer cycle lengths more accurately than existing methods, with 58% lower error on simulated data and 63% lower error on real-world data compared to the best-performing baseline. CyHMMs can also perform functions which baselines cannot: they can model the progression of individual features/symptoms over the course of the cycle, identify the most variable features, and cluster individual time series into groups with distinct characteristics. Applying CyHMMs to two real-world health-tracking datasets-of human menstrual cycle symptoms and physical activity tracking data-yields important insights including which symptoms to expect at each point during the cycle. We also find that people fall into several groups with distinct cycle patterns, and that these groups differ along dimensions not provided to the model. For example, by modeling missing data in the menstrual cycles dataset, we are able to discover a medically relevant group of birth control users even though information on birth control is not given to the model.

  2. Coaching Model + Clinical Playbook = Transformative Learning.

    Science.gov (United States)

    Fletcher, Katherine A; Meyer, Mary

    2016-01-01

    Health care employers demand that workers be skilled in clinical reasoning, able to work within complex interprofessional teams to provide safe, quality patient-centered care in a complex evolving system. To this end, there have been calls for radical transformation of nursing education including the development of a baccalaureate generalist nurse. Based on recommendations from the American Association of Colleges of Nursing, faculty concluded that clinical education must change moving beyond direct patient care by applying the concepts associated with designer, manager, and coordinator of care and being a member of a profession. To accomplish this, the faculty utilized a system of focused learning assignments (FLAs) that present transformative learning opportunities that expose students to "disorienting dilemmas," alternative perspectives, and repeated opportunities to reflect and challenge their own beliefs. The FLAs collected in a "Playbook" were scaffolded to build the student's competencies over the course of the clinical experience. The FLAs were centered on the 6 Quality and Safety Education for Nurses competencies, with 2 additional concepts of professionalism and systems-based practice. The FLAs were competency-based exercises that students performed when not assigned to direct patient care or had free clinical time. Each FLA had a lesson plan that allowed the student and faculty member to see the competency addressed by the lesson, resources, time on task, student instructions, guide for reflection, grading rubric, and recommendations for clinical instructor. The major advantages of the model included (a) consistent implementation of structured learning experiences by a diverse teaching staff using a coaching model of instruction; (b) more systematic approach to present learning activities that build upon each other; (c) increased time for faculty to interact with students providing direct patient care; (d) guaranteed capture of selected transformative

  3. Learning versus correct models: influence of model type on the learning of a free-weight squat lift.

    Science.gov (United States)

    McCullagh, P; Meyer, K N

    1997-03-01

    It has been assumed that demonstrating the correct movement is the best way to impart task-relevant information. However, empirical verification with simple laboratory skills has shown that using a learning model (showing an individual in the process of acquiring the skill to be learned) may accelerate skill acquisition and increase retention more than using a correct model. The purpose of the present study was to compare the effectiveness of viewing correct versus learning models on the acquisition of a sport skill (free-weight squat lift). Forty female participants were assigned to four learning conditions: physical practice receiving feedback, learning model with model feedback, correct model with model feedback, and learning model without model feedback. Results indicated that viewing either a correct or learning model was equally effective in learning correct form in the squat lift.

  4. A CONCEPTUAL MODEL FOR EFFECTIVE DISTANCE LEARNING IN HIGHER A CONCEPTUAL MODEL FOR EFFECTIVE DISTANCE LEARNING IN HIGHER A CONCEPTUAL MODEL FOR EFFECTIVE DISTANCE LEARNING IN HIGHER EDUCATION

    Directory of Open Access Journals (Sweden)

    Mehran FARAJOLLAHI

    2010-07-01

    Full Text Available The present research aims at presenting a conceptual model for effective distance learning in higher education. Findings of this research shows that an understanding of the technological capabilities and learning theories especially constructive theory and independent learning theory and communicative and interaction theory in Distance learning is an efficient factor in the planning of effective Distance learning in higher education. Considering the theoretical foundations of the present research, in the effective distance learning model, the learner is situated at the center of learning environment. For this purpose, the learner needs to be ready for successful learning and the teacher has to be ready to design the teaching- learning activities when they initially enter the environment. In the present model, group and individual active teaching-learning approach, timely feedback, using IT and eight types of interactions have been designed with respect to theoretical foundations and current university missions. From among the issues emphasized in this model, one can refer to the Initial, Formative and Summative evaluations. In an effective distance learning environment, evaluation should be part of the learning process and the feedback resulting from it should be used to improve learning. For validating the specified features, the opinions of Distance learning experts in Payame Noor, Shiraz, Science and Technology and Amirkabir Universities have been used which verified a high percentage of the statistical sample of the above mentioned features.

  5. Modellus: Learning Physics with Mathematical Modelling

    Science.gov (United States)

    Teodoro, Vitor

    Computers are now a major tool in research and development in almost all scientific and technological fields. Despite recent developments, this is far from true for learning environments in schools and most undergraduate studies. This thesis proposes a framework for designing curricula where computers, and computer modelling in particular, are a major tool for learning. The framework, based on research on learning science and mathematics and on computer user interface, assumes that: 1) learning is an active process of creating meaning from representations; 2) learning takes place in a community of practice where students learn both from their own effort and from external guidance; 3) learning is a process of becoming familiar with concepts, with links between concepts, and with representations; 4) direct manipulation user interfaces allow students to explore concrete-abstract objects such as those of physics and can be used by students with minimal computer knowledge. Physics is the science of constructing models and explanations about the physical world. And mathematical models are an important type of models that are difficult for many students. These difficulties can be rooted in the fact that most students do not have an environment where they can explore functions, differential equations and iterations as primary objects that model physical phenomena--as objects-to-think-with, reifying the formal objects of physics. The framework proposes that students should be introduced to modelling in a very early stage of learning physics and mathematics, two scientific areas that must be taught in very closely related way, as they were developed since Galileo and Newton until the beginning of our century, before the rise of overspecialisation in science. At an early stage, functions are the main type of objects used to model real phenomena, such as motions. At a later stage, rates of change and equations with rates of change play an important role. This type of equations

  6. Using IMS Learning Design to model collaborative learning activities

    NARCIS (Netherlands)

    Tattersall, Colin

    2006-01-01

    IMS Learning Design provides a counter to the trend towards designing for lone-learners reading from screens. It guides staff and educational developers to start not with content, but with learning activities and the achievement of learning objectives. It recognises that learning can happen without

  7. Machine-learning-based calving prediction from activity, lying, and ruminating behaviors in dairy cattle.

    Science.gov (United States)

    Borchers, M R; Chang, Y M; Proudfoot, K L; Wadsworth, B A; Stone, A E; Bewley, J M

    2017-07-01

    The objective of this study was to use automated activity, lying, and rumination monitors to characterize prepartum behavior and predict calving in dairy cattle. Data were collected from 20 primiparous and 33 multiparous Holstein dairy cattle from September 2011 to May 2013 at the University of Kentucky Coldstream Dairy. The HR Tag (SCR Engineers Ltd., Netanya, Israel) automatically collected neck activity and rumination data in 2-h increments. The IceQube (IceRobotics Ltd., South Queensferry, United Kingdom) automatically collected number of steps, lying time, standing time, number of transitions from standing to lying (lying bouts), and total motion, summed in 15-min increments. IceQube data were summed in 2-h increments to match HR Tag data. All behavioral data were collected for 14 d before the predicted calving date. Retrospective data analysis was performed using mixed linear models to examine behavioral changes by day in the 14 d before calving. Bihourly behavioral differences from baseline values over the 14 d before calving were also evaluated using mixed linear models. Changes in daily rumination time, total motion, lying time, and lying bouts occurred in the 14 d before calving. In the bihourly analysis, extreme values for all behaviors occurred in the final 24 h, indicating that the monitored behaviors may be useful in calving prediction. To determine whether technologies were useful at predicting calving, random forest, linear discriminant analysis, and neural network machine-learning techniques were constructed and implemented using R version 3.1.0 (R Foundation for Statistical Computing, Vienna, Austria). These methods were used on variables from each technology and all combined variables from both technologies. A neural network analysis that combined variables from both technologies at the daily level yielded 100.0% sensitivity and 86.8% specificity. A neural network analysis that combined variables from both technologies in bihourly increments was

  8. Modeling Pseudo-elastic Behavior of Springback

    International Nuclear Information System (INIS)

    Xia, Z. Cedric

    2005-01-01

    One of the principal foundations of mathematical theory of conventional plasticity for rate-independent metals is that there exists a well-defined yield surface in stress space for any material point under deformation. A material point can undergo further plastic deformation if the applied stresses are beyond current yield surface which is generally referred as 'plastic loading'. On the other hand, if the applied stress state falls within or on the yield surface, the metal will deform elastically only and is said to be undergoing 'elastic unloading'. Although it has been always recognized throughout the history of development of plasticity theory that there is indeed inelastic deformation accompanying elastic unloading, which leads to metal's hysteresis behavior, its effects were thought to be negligible and were largely ignored in the mathematical treatment.Recently there have been renewed interests in the study of unloading behavior of sheet metals upon large plastic deformation and its implications on springback prediction. Springback is essentially an elastic recovery process of a formed sheet metal blank when it is released from the forming dies. Its magnitude depends on the stress states and compliances of the deformed sheet metal if no further plastic loading occurs during the relaxation process. Therefore the accurate determination of material compliances during springback and its effective incorporation into simulation software are important aspects for springback calculation. Some of the studies suggest that the unloading curve might deviate from linearity, and suggestions were made that a reduced elastic modulus be used for springback simulation.The aim of this study is NOT to take a position on the debate of whether elastic moduli are changed during sheet metal forming process. Instead we propose an approach of modeling observed psuedoelastic behavior within the context of mathematical theory of plasticity, where elastic moduli are treated to be

  9. Service Learning In Physics: The Consultant Model

    Science.gov (United States)

    Guerra, David

    2005-04-01

    Each year thousands of students across the country and across the academic disciplines participate in service learning. Unfortunately, with no clear model for integrating community service into the physics curriculum, there are very few physics students engaged in service learning. To overcome this shortfall, a consultant based service-learning program has been developed and successfully implemented at Saint Anselm College (SAC). As consultants, students in upper level physics courses apply their problem solving skills in the service of others. Most recently, SAC students provided technical and managerial support to a group from Girl's Inc., a national empowerment program for girls in high-risk, underserved areas, who were participating in the national FIRST Lego League Robotics competition. In their role as consultants the SAC students provided technical information through brainstorming sessions and helped the girls stay on task with project management techniques, like milestone charting. This consultant model of service-learning, provides technical support to groups that may not have a great deal of resources and gives physics students a way to improve their interpersonal skills, test their technical expertise, and better define the marketable skill set they are developing through the physics curriculum.

  10. Applicability of the theory of planned behavior in explaining the general practitioners eLearning use in continuing medical education.

    Science.gov (United States)

    Hadadgar, Arash; Changiz, Tahereh; Masiello, Italo; Dehghani, Zahra; Mirshahzadeh, Nahidossadat; Zary, Nabil

    2016-08-22

    General practitioners (GP) update their knowledge and skills by participating in continuing medical education (CME) programs either in a traditional or an e-Learning format. GPs' beliefs about electronic format of CME have been studied but without an explicit theoretical framework which makes the findings difficult to interpret. In other health disciplines, researchers used theory of planned behavior (TPB) to predict user's behavior. In this study, an instrument was developed to investigate GPs' intention to use e-Learning in CME based on TPB. The goodness of fit of TPB was measured using confirmatory factor analysis and the relationship between latent variables was assessed using structural equation modeling. A total of 148 GPs participated in the study. Most of the items in the questionnaire related well to the TPB theoretical constructs, and the model had good fitness. The perceived behavioral control and attitudinal constructs were included, and the subjective norms construct was excluded from the structural model. The developed questionnaire could explain 66 % of the GPs' intention variance. The TPB could be used as a model to construct instruments that investigate GPs' intention to participate in e-Learning programs in CME. The findings from the study will encourage CME managers and researchers to explore the developed instrument as a mean to explain and improve the GPs' intentions to use eLearning in CME.

  11. Theory and learning protocols for the material tempotron model

    International Nuclear Information System (INIS)

    Baldassi, Carlo; Braunstein, Alfredo; Zecchina, Riccardo

    2013-01-01

    Neural networks are able to extract information from the timing of spikes. Here we provide new results on the behavior of the simplest neuronal model which is able to decode information embedded in temporal spike patterns, the so-called tempotron. Using statistical physics techniques we compute the capacity for the case of sparse, time-discretized input, and ‘material’ discrete synapses, showing that the device saturates the information theoretic bounds with a statistics of output spikes that is consistent with the statistics of the inputs. We also derive two simple and highly efficient learning algorithms which are able to learn a number of associations which are close to the theoretical limit. The simplest versions of these algorithms correspond to distributed online protocols of interest for neuromorphic devices, and can be adapted to address the more biologically relevant continuous-time version of the classification problem, hopefully allowing the understanding of some aspects of synaptic plasticity. (paper)

  12. Academic Competence and Social Adjustment of Boys with Learning Disabilities and Boys with Behavior Disorders.

    Science.gov (United States)

    Margalit, Malka

    1989-01-01

    Comparison of 31 elementary grade boys with learning disabilities and 52 boys with behavior disorders who either did or did not also display hyperactive behavior found significant differences between groups on the Classroom Behavior Inventory in three areas: Hostility versus Consideration, Extroversion versus Introversion, and Independence versus…

  13. Classification and Validation of Behavioral Subtypes of Learning-Disabled Children.

    Science.gov (United States)

    Speece, Deborah L.; And Others

    1985-01-01

    Using the Classroom Behavior Inventory, teachers rated the behaviors of 63 school-identified, learning-disabled first and second graders. Hierarchical cluster analysis techniques identified seven distinct behavioral subtypes. Internal validation techniques indicated that the subtypes were replicable and had profile patterns different from a sample…

  14. Behavioral Ethics in Practice: Integrating Service Learning into a Graduate Business Ethics Course

    Science.gov (United States)

    O'Brien, Kevin; Wittmer, Dennis; Ebrahimi, Bahman Paul

    2017-01-01

    Adopting a broad definition that distinguishes behavioral ethics as science and behavioral ethics in practice, we describe how service learning can be a meaningful component of a four-credit, one-quarter graduate business ethics course by blending both normative/prescriptive and behavioral/descriptive ethics. We provide a conceptual and…

  15. A joint model of word segmentation and meaning acquisition through cross-situational learning.

    Science.gov (United States)

    Räsänen, Okko; Rasilo, Heikki

    2015-10-01

    Human infants learn meanings for spoken words in complex interactions with other people, but the exact learning mechanisms are unknown. Among researchers, a widely studied learning mechanism is called cross-situational learning (XSL). In XSL, word meanings are learned when learners accumulate statistical information between spoken words and co-occurring objects or events, allowing the learner to overcome referential uncertainty after having sufficient experience with individually ambiguous scenarios. Existing models in this area have mainly assumed that the learner is capable of segmenting words from speech before grounding them to their referential meaning, while segmentation itself has been treated relatively independently of the meaning acquisition. In this article, we argue that XSL is not just a mechanism for word-to-meaning mapping, but that it provides strong cues for proto-lexical word segmentation. If a learner directly solves the correspondence problem between continuous speech input and the contextual referents being talked about, segmentation of the input into word-like units emerges as a by-product of the learning. We present a theoretical model for joint acquisition of proto-lexical segments and their meanings without assuming a priori knowledge of the language. We also investigate the behavior of the model using a computational implementation, making use of transition probability-based statistical learning. Results from simulations show that the model is not only capable of replicating behavioral data on word learning in artificial languages, but also shows effective learning of word segments and their meanings from continuous speech. Moreover, when augmented with a simple familiarity preference during learning, the model shows a good fit to human behavioral data in XSL tasks. These results support the idea of simultaneous segmentation and meaning acquisition and show that comprehensive models of early word segmentation should take referential word

  16. Bayesian Belief Networks Approach for Modeling Irrigation Behavior

    Science.gov (United States)

    Andriyas, S.; McKee, M.

    2012-12-01

    Canal operators need information to manage water deliveries to irrigators. Short-term irrigation demand forecasts can potentially valuable information for a canal operator who must manage an on-demand system. Such forecasts could be generated by using information about the decision-making processes of irrigators. Bayesian models of irrigation behavior can provide insight into the likely criteria which farmers use to make irrigation decisions. This paper develops a Bayesian belief network (BBN) to learn irrigation decision-making behavior of farmers and utilizes the resulting model to make forecasts of future irrigation decisions based on factor interaction and posterior probabilities. Models for studying irrigation behavior have been rarely explored in the past. The model discussed here was built from a combination of data about biotic, climatic, and edaphic conditions under which observed irrigation decisions were made. The paper includes a case study using data collected from the Canal B region of the Sevier River, near Delta, Utah. Alfalfa, barley and corn are the main crops of the location. The model has been tested with a portion of the data to affirm the model predictive capabilities. Irrigation rules were deduced in the process of learning and verified in the testing phase. It was found that most of the farmers used consistent rules throughout all years and across different types of crops. Soil moisture stress, which indicates the level of water available to the plant in the soil profile, was found to be one of the most significant likely driving forces for irrigation. Irrigations appeared to be triggered by a farmer's perception of soil stress, or by a perception of combined factors such as information about a neighbor irrigating or an apparent preference to irrigate on a weekend. Soil stress resulted in irrigation probabilities of 94.4% for alfalfa. With additional factors like weekend and irrigating when a neighbor irrigates, alfalfa irrigation

  17. Representing adaptive and adaptable Units of Learning. How to model personalized eLearning in IMS Learning Design

    OpenAIRE

    Burgos, Daniel; Tattersall, Colin; Koper, Rob

    2006-01-01

    Burgos, D., Tattersall, C., & Koper, E. J. R. (2007). Representing adaptive and adaptable Units of Learning. How to model personalized eLearning in IMS Learning Design. In B. Fernández Manjon, J. M. Sanchez Perez, J. A. Gómez Pulido, M. A. Vega Rodriguez & J. Bravo (Eds.), Computers and Education: E-learning - from theory to practice. Germany: Kluwer.

  18. Modeling Avoidance in Mood and Anxiety Disorders Using Reinforcement Learning.

    Science.gov (United States)

    Mkrtchian, Anahit; Aylward, Jessica; Dayan, Peter; Roiser, Jonathan P; Robinson, Oliver J

    2017-10-01

    Serious and debilitating symptoms of anxiety are the most common mental health problem worldwide, accounting for around 5% of all adult years lived with disability in the developed world. Avoidance behavior-avoiding social situations for fear of embarrassment, for instance-is a core feature of such anxiety. However, as for many other psychiatric symptoms the biological mechanisms underlying avoidance remain unclear. Reinforcement learning models provide formal and testable characterizations of the mechanisms of decision making; here, we examine avoidance in these terms. A total of 101 healthy participants and individuals with mood and anxiety disorders completed an approach-avoidance go/no-go task under stress induced by threat of unpredictable shock. We show an increased reliance in the mood and anxiety group on a parameter of our reinforcement learning model that characterizes a prepotent (pavlovian) bias to withhold responding in the face of negative outcomes. This was particularly the case when the mood and anxiety group was under stress. This formal description of avoidance within the reinforcement learning framework provides a new means of linking clinical symptoms with biophysically plausible models of neural circuitry and, as such, takes us closer to a mechanistic understanding of mood and anxiety disorders. Copyright © 2017 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

  19. Facilitating behavioral learning and habit change in voice therapy—theoretic premises and practical strategies

    DEFF Research Database (Denmark)

    Iwarsson, Jenny

    2014-01-01

    A typical goal of voice therapy is a behavioral change in the patient’s everyday speech. The SLP’s plan for voice therapy should therefore optimally include strategies for automatization. The aim of the present study was to identify and describe factors that promote behavioral learning and habit...... are described and discussed from a learning theory perspective. Nine factors that seem to be relevant to facilitate behavioral learning and habit change in voice therapy are presented, together with related practical strategies and theoretical underpinnings. These are: 1) Cue-altering; 2) Attention exercises; 3...... change in voice behavior and have the potential to affect patient compliance and thus therapy outcome. Research literature from the areas of motor and behavioral learning, habit formation, and habit change was consulted. Also, specific elements from personal experience of clinical voice therapy...

  20. Behavioral optimization models for multicriteria portfolio selection

    Directory of Open Access Journals (Sweden)

    Mehlawat Mukesh Kumar

    2013-01-01

    Full Text Available In this paper, behavioral construct of suitability is used to develop a multicriteria decision making framework for portfolio selection. To achieve this purpose, we rely on multiple methodologies. Analytical hierarchy process technique is used to model the suitability considerations with a view to obtaining the suitability performance score in respect of each asset. A fuzzy multiple criteria decision making method is used to obtain the financial quality score of each asset based upon investor's rating on the financial criteria. Two optimization models are developed for optimal asset allocation considering simultaneously financial and suitability criteria. An empirical study is conducted on randomly selected assets from National Stock Exchange, Mumbai, India to demonstrate the effectiveness of the proposed methodology.

  1. Generalized Penner models and multicritical behavior

    International Nuclear Information System (INIS)

    Tan, C.

    1992-01-01

    In this paper, we are interested in the critical behavior of generalized Penner models at t∼-1+μ/N where the topological expansion for the free energy develops logarithmic singularities: Γ∼-(χ 0 μ 2 lnμ+χ 1 lnμ+...). We demonstrate that these criticalities can best be characterized by the fact that the large-N generating function becomes meromorphic with a single pole term of unit residue, F(z)→1/(z-a), where a is the location of the ''sink.'' For a one-band eigenvalue distribution, we identify multicritical potentials; we find that none of these can be associated with the c=1 string compactified at an integral multiple of the self-dual radius. We also give an exact solution to the Gaussian Penner model and explicitly demonstrate that, at criticality, this solution does not correspond to a c=1 string compactified at twice the self-dual radius

  2. The Theory of Reasoned Action as Parallel Constraint Satisfaction: Towards a Dynamic Computational Model of Health Behavior

    OpenAIRE

    Orr, Mark G.; Thrush, Roxanne; Plaut, David C.

    2013-01-01

    The reasoned action approach, although ubiquitous in health behavior theory (e.g., Theory of Reasoned Action/Planned Behavior), does not adequately address two key dynamical aspects of health behavior: learning and the effect of immediate social context (i.e., social influence). To remedy this, we put forth a computational implementation of the Theory of Reasoned Action (TRA) using artificial-neural networks. Our model re-conceptualized behavioral intention as arising from a dynamic constrain...

  3. THE USAGE OF SOCIAL MEDIA FOR LEARNING AND TEACHING PURPOSES: AN IMPLEMENTATION OF EXTENDED THEORY OF REASONED ACTION MODEL

    OpenAIRE

    AKMAN, İbrahim; TURHAN, Çiğdem

    2014-01-01

    The growing popularity of the social networking siteshas presented new options for the development of learning and teachingenvironments to provide informal learning. In this study, the usage of socialnetworking sites for the purpose of learning and teaching has been analyzedusing the extended Theory of Reasoned Action (TRA) model. A survey has beenconducted to analyze the behavior in regard to the acceptance of social mediafor learning and teaching and the results were systematically analyzed...

  4. Online constrained model-based reinforcement learning

    CSIR Research Space (South Africa)

    Van Niekerk, B

    2017-08-01

    Full Text Available Constrained Model-based Reinforcement Learning Benjamin van Niekerk School of Computer Science University of the Witwatersrand South Africa Andreas Damianou∗ Amazon.com Cambridge, UK Benjamin Rosman Council for Scientific and Industrial Research, and School... MULTIPLE SHOOTING Using direct multiple shooting (Bock and Plitt, 1984), problem (1) can be transformed into a structured non- linear program (NLP). First, the time horizon [t0, t0 + T ] is partitioned into N equal subintervals [tk, tk+1] for k = 0...

  5. Behavior Intention To Use Of Learning Management System Among Malaysian Pre-Service Teachers: A Confirmatory Factor Analysis

    Directory of Open Access Journals (Sweden)

    Sousan Baleghi-Zadeh

    2014-01-01

    Full Text Available Learning Management system is a type of Information system that many universities invest on to be integrated with their curriculum. Therefore, factors which make students accept or reject Learning Management System is crucial for educational managers of universities. The main purpose of the present study is to modify and validate a measurement model based on two models of Technology Acceptance Model and Fit Model. The proposed measurement model included five constructs of perceived ease of use, perceived usefulness, behavior intention to use, technical support and task-technology fit. The sample size of the study was 300 pre-service teachers studying at Universiti Putra Malaysia (UPM and Universiti of Malaya (UM. The results of the study revealed that after deleting eleven items, the proposed measurement model was validated and fit. Therefore, the modified measurement model was able to present the theoretical patterns of the actual data.

  6. Streamlined Modeling for Characterizing Spacecraft Anomalous Behavior

    Science.gov (United States)

    Klem, B.; Swann, D.

    2011-09-01

    Anomalous behavior of on-orbit spacecraft can often be detected using passive, remote sensors which measure electro-optical signatures that vary in time and spectral content. Analysts responsible for assessing spacecraft operational status and detecting detrimental anomalies using non-resolved imaging sensors are often presented with various sensing and identification issues. Modeling and measuring spacecraft self emission and reflected radiant intensity when the radiation patterns exhibit a time varying reflective glint superimposed on an underlying diffuse signal contribute to assessment of spacecraft behavior in two ways: (1) providing information on body component orientation and attitude; and, (2) detecting changes in surface material properties due to the space environment. Simple convex and cube-shaped spacecraft, designed to operate without protruding solar panel appendages, may require an enhanced level of preflight characterization to support interpretation of the various physical effects observed during on-orbit monitoring. This paper describes selected portions of the signature database generated using streamlined signature modeling and simulations of basic geometry shapes apparent to non-imaging sensors. With this database, summarization of key observable features for such shapes as spheres, cylinders, flat plates, cones, and cubes in specific spectral bands that include the visible, mid wave, and long wave infrared provide the analyst with input to the decision process algorithms contained in the overall sensing and identification architectures. The models typically utilize baseline materials such as Kapton, paints, aluminum surface end plates, and radiators, along with solar cell representations covering the cylindrical and side portions of the spacecraft. Multiple space and ground-based sensors are assumed to be located at key locations to describe the comprehensive multi-viewing aspect scenarios that can result in significant specular reflection

  7. SMARTbot: A Behavioral Analysis Framework Augmented with Machine Learning to Identify Mobile Botnet Applications.

    Directory of Open Access Journals (Sweden)

    Ahmad Karim

    Full Text Available Botnet phenomenon in smartphones is evolving with the proliferation in mobile phone technologies after leaving imperative impact on personal computers. It refers to the network of computers, laptops, mobile devices or tablets which is remotely controlled by the cybercriminals to initiate various distributed coordinated attacks including spam emails, ad-click fraud, Bitcoin mining, Distributed Denial of Service (DDoS, disseminating other malwares and much more. Likewise traditional PC based botnet, Mobile botnets have the same operational impact except the target audience is particular to smartphone users. Therefore, it is import to uncover this security issue prior to its widespread adaptation. We propose SMARTbot, a novel dynamic analysis framework augmented with machine learning techniques to automatically detect botnet binaries from malicious corpus. SMARTbot is a component based off-device behavioral analysis framework which can generate mobile botnet learning model by inducing Artificial Neural Networks' back-propagation method. Moreover, this framework can detect mobile botnet binaries with remarkable accuracy even in case of obfuscated program code. The results conclude that, a classifier model based on simple logistic regression outperform other machine learning classifier for botnet apps' detection, i.e 99.49% accuracy is achieved. Further, from manual inspection of botnet dataset we have extracted interesting trends in those applications. As an outcome of this research, a mobile botnet dataset is devised which will become the benchmark for future studies.

  8. SMARTbot: A Behavioral Analysis Framework Augmented with Machine Learning to Identify Mobile Botnet Applications.

    Science.gov (United States)

    Karim, Ahmad; Salleh, Rosli; Khan, Muhammad Khurram

    2016-01-01

    Botnet phenomenon in smartphones is evolving with the proliferation in mobile phone technologies after leaving imperative impact on personal computers. It refers to the network of computers, laptops, mobile devices or tablets which is remotely controlled by the cybercriminals to initiate various distributed coordinated attacks including spam emails, ad-click fraud, Bitcoin mining, Distributed Denial of Service (DDoS), disseminating other malwares and much more. Likewise traditional PC based botnet, Mobile botnets have the same operational impact except the target audience is particular to smartphone users. Therefore, it is import to uncover this security issue prior to its widespread adaptation. We propose SMARTbot, a novel dynamic analysis framework augmented with machine learning techniques to automatically detect botnet binaries from malicious corpus. SMARTbot is a component based off-device behavioral analysis framework which can generate mobile botnet learning model by inducing Artificial Neural Networks' back-propagation method. Moreover, this framework can detect mobile botnet binaries with remarkable accuracy even in case of obfuscated program code. The results conclude that, a classifier model based on simple logistic regression outperform other machine learning classifier for botnet apps' detection, i.e 99.49% accuracy is achieved. Further, from manual inspection of botnet dataset we have extracted interesting trends in those applications. As an outcome of this research, a mobile botnet dataset is devised which will become the benchmark for future studies.

  9. SMARTbot: A Behavioral Analysis Framework Augmented with Machine Learning to Identify Mobile Botnet Applications

    Science.gov (United States)

    Karim, Ahmad; Salleh, Rosli; Khan, Muhammad Khurram

    2016-01-01

    Botnet phenomenon in smartphones is evolving with the proliferation in mobile phone technologies after leaving imperative impact on personal computers. It refers to the network of computers, laptops, mobile devices or tablets which is remotely controlled by the cybercriminals to initiate various distributed coordinated attacks including spam emails, ad-click fraud, Bitcoin mining, Distributed Denial of Service (DDoS), disseminating other malwares and much more. Likewise traditional PC based botnet, Mobile botnets have the same operational impact except the target audience is particular to smartphone users. Therefore, it is import to uncover this security issue prior to its widespread adaptation. We propose SMARTbot, a novel dynamic analysis framework augmented with machine learning techniques to automatically detect botnet binaries from malicious corpus. SMARTbot is a component based off-device behavioral analysis framework which can generate mobile botnet learning model by inducing Artificial Neural Networks’ back-propagation method. Moreover, this framework can detect mobile botnet binaries with remarkable accuracy even in case of obfuscated program code. The results conclude that, a classifier model based on simple logistic regression outperform other machine learning classifier for botnet apps’ detection, i.e 99.49% accuracy is achieved. Further, from manual inspection of botnet dataset we have extracted interesting trends in those applications. As an outcome of this research, a mobile botnet dataset is devised which will become the benchmark for future studies. PMID:26978523

  10. Analysis of dynamic Cournot learning models for generation companies based on conjectural variations and forward expectation

    International Nuclear Information System (INIS)

    Gutierrez-Alcaraz, G.; Tovar-Hernandez, Jose H.; Moreno-Goytia, Edgar L.

    2009-01-01

    Electricity spot markets generally operate on an hourly basis; under this condition GENCOs can closely observe their competitors' market behavior. For this purposes, a detailed dynamic model is one of the tools used by GENCOs to understand the behavioral variations of competitors over time. The required abilities to rapidly adjust one's own decision-making create a need for new learning procedures and models. Conjectural variations (CV) have been proposed as a learning approach. In this paper a model based on forward expectations (FE) is proposed as a learning approach, and through illustrative examples it is shown that the market equilibria found by the CV model are also obtained by the FE model. (author)

  11. From Behaviorism to Connectivism of Modern E-Learning

    Directory of Open Access Journals (Sweden)

    Zbigniew Meger

    2012-06-01

    Full Text Available Wider use of e-learning methods and universality of learning platform encourage to running learning processes in different forms and to preparing new teaching materials. However, it appears that such materials are usually prepared in the simplest form of programmed learning course, where knowledge is transmitted in form of programmed instruction without or only with a weak feedback from the learner. Meanwhile, in the past four decades are developing cognitive methods, which are only slightly reflected in nowadays techniques and methods of distance learning. However, it appears in the last years, that the important role in education can play constructivism, whose ideas can be also implement in distance learning environment. All of these trends is trying to cover the theory of connectivism, and glimpse of this theory can indicate the opportunities and threats of modern e-learning.

  12. THE EFFECTS OF COOPERATIVE LEARNING MODEL GROUP INVESTIGATION AND MOTIVATION TOWARD PHYSICS LEARNING RESULTS MAN TANJUNGBALAI

    Directory of Open Access Journals (Sweden)

    Amalia Febri Aristi

    2014-12-01

    Full Text Available This study aimed to determine: (1 Is there a difference in student's learning outcomes with the application of learning models Investigation Group and Direct Instruction teaching model. (2 Is there a difference in students' motivation with the application of learning models Investigation Group and Direct Instruction teaching model, (3 Is there an interaction between learning models Investigation Group and Direct Instruction to improve students' motivation in learning outcomes Physics. This research is a quasi experimental. The study population was a student of class XII Tanjung Balai MAN. Random sample selection is done by randomizing the class. The instrument used consisted of: (1 achievement test (2 students' motivation questionnaire. The tests are used to obtain the data is shaped essay. The data in this study were analyzed using ANOVA analysis of two paths. The results showed that: (1 there were differences in learning outcomes between students who used the physics model of Group Investigation learning compared with students who used the Direct Instruction teaching model. (2 There was a difference in student's learning outcomes that had a low learning motivation and high motivation to learn both in the classroom and in the classroom Investigation Group Direct Instruction. (3 There was interaction between learning models Instruction Direct Group Investigation and motivation to learn in improving learning outcomes Physics.

  13. Bio-Inspired Neural Model for Learning Dynamic Models

    Science.gov (United States)

    Duong, Tuan; Duong, Vu; Suri, Ronald

    2009-01-01

    A neural-network mathematical model that, relative to prior such models, places greater emphasis on some of the temporal aspects of real neural physical processes, has been proposed as a basis for massively parallel, distributed algorithms that learn dynamic models of possibly complex external processes by means of learning rules that are local in space and time. The algorithms could be made to perform such functions as recognition and prediction of words in speech and of objects depicted in video images. The approach embodied in this model is said to be "hardware-friendly" in the following sense: The algorithms would be amenable to execution by special-purpose computers implemented as very-large-scale integrated (VLSI) circuits that would operate at relatively high speeds and low power demands.

  14. Patterns in clinical students' self-regulated learning behavior: a Q-methodology study.

    Science.gov (United States)

    Berkhout, Joris J; Teunissen, Pim W; Helmich, Esther; van Exel, Job; van der Vleuten, Cees P M; Jaarsma, Debbie A D C

    2017-03-01

    Students feel insufficiently supported in clinical environments to engage in active learning and achieve a high level of self-regulation. As a result clinical learning is highly demanding for students. Because of large differences between students, supervisors may not know how to support them in their learning process. We explored patterns in undergraduate students' self-regulated learning behavior in the clinical environment, to improve tailored supervision, using Q-methodology. Q-methodology uses features of both qualitative and quantitative methods for the systematic investigation of subjective issues by having participants sort statements along a continuum to represent their opinion. We enrolled 74 students between December 2014 and April 2015 and had them characterize their learning behavior by sorting 52 statements about self-regulated learning behavior and explaining their response. The statements used for the sorting were extracted from a previous study. The data was analyzed using by-person factor analysis to identify clusters of individuals with similar sorts of the statements. The resulting factors and qualitative data were used to interpret and describe the patterns that emerged. Five resulting patterns were identified in students' self-regulated learning behavior in the clinical environment, which we labelled: Engaged, Critically opportunistic, Uncertain, Restrained and Effortful. The five patterns varied mostly regarding goals, metacognition, communication, effort, and dependence on external regulation for learning. These discrete patterns in students' self-regulated learning behavior in the clinical environment are part of a complex interaction between student and learning context. The results suggest that developing self-regulated learning behavior might best be supported regarding individual students' needs.

  15. A Multiobjective Sparse Feature Learning Model for Deep Neural Networks.

    Science.gov (United States)

    Gong, Maoguo; Liu, Jia; Li, Hao; Cai, Qing; Su, Linzhi

    2015-12-01

    Hierarchical deep neural networks are currently popular learning models for imitating the hierarchical architecture of human brain. Single-layer feature extractors are the bricks to build deep networks. Sparse feature learning models are popular models that can learn useful representations. But most of those models need a user-defined constant to control the sparsity of representations. In this paper, we propose a multiobjective sparse feature learning model based on the autoencoder. The parameters of the model are learnt by optimizing two objectives, reconstruction error and the sparsity of hidden units simultaneously to find a reasonable compromise between them automatically. We design a multiobjective induced learning procedure for this model based on a multiobjective evolutionary algorithm. In the experiments, we demonstrate that the learning procedure is effective, and the proposed multiobjective model can learn useful sparse features.

  16. Proposing a Model of Co-Regulated Learning for Graduate Medical Education.

    Science.gov (United States)

    Rich, Jessica V

    2017-08-01

    Primarily grounded in Zimmerman's social cognitive model of self-regulation, graduate medical education is guided by principles that self-regulated learning takes place within social context and influence, and that the social context and physical environment reciprocally influence persons and their cognition, behavior, and development. However, contemporary perspectives on self-regulation are moving beyond Zimmerman's triadic reciprocal orientation to models that consider social transactions as the central core of regulated learning. Such co-regulated learning models emphasize shared control of learning and the role more advanced others play in scaffolding novices' metacognitive engagement.Models of co-regulated learning describe social transactions as periods of distributed regulation among individuals, which instrumentally promote or inhibit the capacity for individuals to independently self-regulate. Social transactions with other regulators, including attending physicians, more experienced residents, and allied health care professionals, are known to mediate residents' learning and to support or hamper the development of their self-regulated learning competence. Given that social transactions are at the heart of learning-oriented assessment and entrustment decisions, an appreciation for co-regulated learning is likely important for advancing medical education research and practice-especially given the momentum of new innovations such as entrustable professional activities.In this article, the author explains why graduate medical educators should consider adopting a model of co-regulated learning to complement and extend Zimmerman's models of self-regulated learning. In doing so, the author suggests a model of co-regulated learning and provides practical examples of how the model is relevant to graduate medical education research and practice.

  17. Modelization of cognition, activity and motivation as indicators for Interactive Learning Environment

    Directory of Open Access Journals (Sweden)

    Asmaa Darouich

    2017-06-01

    Full Text Available In Interactive Learning Environment (ILE, the cognitive activity and behavior of learners are the center of the researchers’ concerns. The improvement of learning through combining these axes as a structure of indicators for well-designed learning environment, encloses the measurement of the educational activity as a part of the learning process. In this paper, we propose a mathematical modeling approach based on learners actions to estimate the cognitive activity, learning behavior and motivation, in accordance with a proposed course content structure. This Cognitive indicator includes the study of knowledge, memory and reasoning. While, activity indicator aims to study effort, resistance and intensity. The results recovered on a sample of students with different levels of education, assume that the proposed approach presents a relation among all these indicators which is relatively reliable in the term of cognitive system.

  18. The Impact of Robot Tutor Nonverbal Social Behavior on Child Learning

    Directory of Open Access Journals (Sweden)

    James Kennedy

    2017-04-01

    Full Text Available Several studies have indicated that interacting with social robots in educational contexts may lead to a greater learning than interactions with computers or virtual agents. As such, an increasing amount of social human–robot interaction research is being conducted in the learning domain, particularly with children. However, it is unclear precisely what social behavior a robot should employ in such interactions. Inspiration can be taken from human–human studies; this often leads to an assumption that the more social behavior an agent utilizes, the better the learning outcome will be. We apply a nonverbal behavior metric to a series of studies in which children are taught how to identify prime numbers by a robot with various behavioral manipulations. We find a trend, which generally agrees with the pedagogy literature, but also that overt nonverbal behavior does not account for all learning differences. We discuss the impact of novelty, child expectations, and responses to social cues to further the understanding of the relationship between robot social behavior and learning. We suggest that the combination of nonverbal behavior and social cue congruency is necessary to facilitate learning.

  19. Dynamic Socialized Gaussian Process Models for Human Behavior Prediction in a Health Social Network

    Science.gov (United States)

    Shen, Yelong; Phan, NhatHai; Xiao, Xiao; Jin, Ruoming; Sun, Junfeng; Piniewski, Brigitte; Kil, David; Dou, Dejing

    2016-01-01

    Modeling and predicting human behaviors, such as the level and intensity of physical activity, is a key to preventing the cascade of obesity and helping spread healthy behaviors in a social network. In our conference paper, we have developed a social influence model, named Socialized Gaussian Process (SGP), for socialized human behavior modeling. Instead of explicitly modeling social influence as individuals' behaviors influenced by their friends' previous behaviors, SGP models the dynamic social correlation as the result of social influence. The SGP model naturally incorporates personal behavior factor and social correlation factor (i.e., the homophily principle: Friends tend to perform similar behaviors) into a unified model. And it models the social influence factor (i.e., an individual's behavior can be affected by his/her friends) implicitly in dynamic social correlation schemes. The detailed experimental evaluation has shown the SGP model achieves better prediction accuracy compared with most of baseline methods. However, a Socialized Random Forest model may perform better at the beginning compared with the SGP model. One of the main reasons is the dynamic social correlation function is purely based on the users' sequential behaviors without considering other physical activity-related features. To address this issue, we further propose a novel “multi-feature SGP model” (mfSGP) which improves the SGP model by using multiple physical activity-related features in the dynamic social correlation learning. Extensive experimental results illustrate that the mfSGP model clearly outperforms all other models in terms of prediction accuracy and running time. PMID:27746515

  20. Uncertainty in action-value estimation affects both action choice and learning rate of the choice behaviors of rats.

    Science.gov (United States)

    Funamizu, Akihiro; Ito, Makoto; Doya, Kenji; Kanzaki, Ryohei; Takahashi, Hirokazu

    2012-04-01

    The estimation of reward outcomes for action candidates is essential for decision making. In this study, we examined whether and how the uncertainty in reward outcome estimation affects the action choice and learning rate. We designed a choice task in which rats selected either the left-poking or right-poking hole and received a reward of a food pellet stochastically. The reward probabilities of the left and right holes were chosen from six settings (high, 100% vs. 66%; mid, 66% vs. 33%; low, 33% vs. 0% for the left vs. right holes, and the opposites) in every 20-549 trials. We used Bayesian Q-learning models to estimate the time course of the probability distribution of action values and tested if they better explain the behaviors of rats than standard Q-learning models that estimate only the mean of action values. Model comparison by cross-validation revealed that a Bayesian Q-learning model with an asymmetric update for reward and non-reward outcomes fit the choice time course of the rats best. In the action-choice equation of the Bayesian Q-learning model, the estimated coefficient for the variance of action value was positive, meaning that rats were uncertainty seeking. Further analysis of the Bayesian Q-learning model suggested that the uncertainty facilitated the effective learning rate. These results suggest that the rats consider uncertainty in action-value estimation and that they have an uncertainty-seeking action policy and uncertainty-dependent modulation of the effective learning rate. © 2012 The Authors. European Journal of Neuroscience © 2012 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.

  1. An entropy model for artificial grammar learning

    Directory of Open Access Journals (Sweden)

    Emmanuel Pothos

    2010-06-01

    Full Text Available A model is proposed to characterize the type of knowledge acquired in Artificial Grammar Learning (AGL. In particular, Shannon entropy is employed to compute the complexity of different test items in an AGL task, relative to the training items. According to this model, the more predictable a test item is from the training items, the more likely it is that this item should be selected as compatible with the training items. The predictions of the entropy model are explored in relation to the results from several previous AGL datasets and compared to other AGL measures. This particular approach in AGL resonates well with similar models in categorization and reasoning which also postulate that cognitive processing is geared towards the reduction of entropy.

  2. An analysis of intergroup rivalry using Ising model and reinforcement learning

    Science.gov (United States)

    Zhao, Feng-Fei; Qin, Zheng; Shao, Zhuo

    2014-01-01

    Modeling of intergroup rivalry can help us better understand economic competitions, political elections and other similar activities. The result of intergroup rivalry depends on the co-evolution of individual behavior within one group and the impact from the rival group. In this paper, we model the rivalry behavior using Ising model. Different from other simulation studies using Ising model, the evolution rules of each individual in our model are not static, but have the ability to learn from historical experience using reinforcement learning technique, which makes the simulation more close to real human behavior. We studied the phase transition in intergroup rivalry and focused on the impact of the degree of social freedom, the personality of group members and the social experience of individuals. The results of computer simulation show that a society with a low degree of social freedom and highly educated, experienced individuals is more likely to be one-sided in intergroup rivalry.

  3. Organizational Learning Supported by Reference Architecture Models

    DEFF Research Database (Denmark)

    Nardello, Marco; Møller, Charles; Gøtze, John

    2017-01-01

    The wave of the fourth industrial revolution (Industry 4.0) is bringing a new vision of the manufacturing industry. In manufacturing, one of the buzzwords of the moment is “Smart production”. Smart production involves manufacturing equipment with many sensors that can generate and transmit large...... amounts of data. These data and information from manufacturing operations are however not shared in the organization. Therefore the organization is not using them to learn and improve their operations. To address this problem, the authors implemented in an Industry 4.0 laboratory an instance...... of an emerging technical standard specific for the manufacturing industry. Global manufacturing experts consider the Reference Architecture Model Industry 4.0 (RAMI4.0) as one of the corner stones for the implementation of Industry 4.0. The instantiation contributed to organizational learning in the laboratory...

  4. Swarming behavior of simple model squirmers

    International Nuclear Information System (INIS)

    Thutupalli, Shashi; Seemann, Ralf; Herminghaus, Stephan

    2011-01-01

    We have studied experimentally the collective behavior of self-propelling liquid droplets, which closely mimic the locomotion of some protozoal organisms, the so-called squirmers. For the sake of simplicity, we concentrate on quasi-two-dimensional (2D) settings, although our swimmers provide a fully 3D propulsion scheme. At an areal density of 0.46, we find strong polar correlation of the locomotion velocities of neighboring droplets, which decays over less than one droplet diameter. When the areal density is increased to 0.78, distinct peaks show up in the angular correlation function, which point to the formation of ordered rafts. This shows that pronounced textures, beyond what has been seen in simulations so far, may show up in crowds of simple model squirmers, despite the simplicity of their (purely physical) mutual interaction.

  5. Behavior of cosmological models with varying G

    International Nuclear Information System (INIS)

    Barrow, J.D.; Parsons, P.

    1997-01-01

    We provide a detailed analysis of Friedmann-Robertson-Walker universes in a wide range of scalar-tensor theories of gravity. We apply solution-generating methods to three parametrized classes of scalar-tensor theory which lead naturally to general relativity in the weak-field limit. We restrict the parameters which specify these theories by the requirements imposed by the weak-field tests of gravitation theories in the solar system and by the requirement that viable cosmological solutions be obtained. We construct a range of exact solutions for open, closed, and flat isotropic universes containing matter with equation of state p≤(1)/(3)ρ and in vacuum. We study the range of early- and late-time behaviors displayed, examine when there is a open-quotes bounceclose quotes at early times, and expansion maxima in closed models. copyright 1997 The American Physical Society

  6. Swarming behavior of simple model squirmers

    Energy Technology Data Exchange (ETDEWEB)

    Thutupalli, Shashi; Seemann, Ralf; Herminghaus, Stephan, E-mail: shashi.thutupalli@ds.mpg.de, E-mail: stephan.herminghaus@ds.mpg.de [Max Planck Institute for Dynamics and Self-Organization, Bunsenstrasse 10, 37073 Goettingen (Germany)

    2011-07-15

    We have studied experimentally the collective behavior of self-propelling liquid droplets, which closely mimic the locomotion of some protozoal organisms, the so-called squirmers. For the sake of simplicity, we concentrate on quasi-two-dimensional (2D) settings, although our swimmers provide a fully 3D propulsion scheme. At an areal density of 0.46, we find strong polar correlation of the locomotion velocities of neighboring droplets, which decays over less than one droplet diameter. When the areal density is increased to 0.78, distinct peaks show up in the angular correlation function, which point to the formation of ordered rafts. This shows that pronounced textures, beyond what has been seen in simulations so far, may show up in crowds of simple model squirmers, despite the simplicity of their (purely physical) mutual interaction.

  7. Modeling Human Behavior to Anticipate Insider Attacks

    Directory of Open Access Journals (Sweden)

    Ryan E Hohimer

    2011-01-01

    Full Text Available The insider threat ranks among the most pressing cyber-security challenges that threaten government and industry information infrastructures. To date, no systematic methods have been developed that provide a complete and effective approach to prevent data leakage, espionage, and sabotage. Current practice is forensic in nature, relegating to the analyst the bulk of the responsibility to monitor, analyze, and correlate an overwhelming amount of data. We describe a predictive modeling framework that integrates a diverse set of data sources from the cyber domain, as well as inferred psychological/motivational factors that may underlie malicious insider exploits. This comprehensive threat assessment approach provides automated support for the detection of high-risk behavioral "triggers" to help focus the analyst's attention and inform the analysis. Designed to be domain-independent, the system may be applied to many different threat and warning analysis/sense-making problems.

  8. Modeling the exergy behavior of human body

    International Nuclear Information System (INIS)

    Keutenedjian Mady, Carlos Eduardo; Silva Ferreira, Maurício; Itizo Yanagihara, Jurandir; Hilário Nascimento Saldiva, Paulo; Oliveira Junior, Silvio de

    2012-01-01

    Exergy analysis is applied to assess the energy conversion processes that take place in the human body, aiming at developing indicators of health and performance based on the concepts of exergy destroyed rate and exergy efficiency. The thermal behavior of the human body is simulated by a model composed of 15 cylinders with elliptical cross section representing: head, neck, trunk, arms, forearms, hands, thighs, legs, and feet. For each, a combination of tissues is considered. The energy equation is solved for each cylinder, being possible to obtain transitory response from the body due to a variation in environmental conditions. With this model, it is possible to obtain heat and mass flow rates to the environment due to radiation, convection, evaporation and respiration. The exergy balances provide the exergy variation due to heat and mass exchange over the body, and the exergy variation over time for each compartments tissue and blood, the sum of which leads to the total variation of the body. Results indicate that exergy destroyed and exergy efficiency decrease over lifespan and the human body is more efficient and destroys less exergy in lower relative humidities and higher temperatures. -- Highlights: ► In this article it is indicated an overview of the human thermal model. ► It is performed the energy and exergy analysis of the human body. ► Exergy destruction and exergy efficiency decreases with lifespan. ► Exergy destruction and exergy efficiency are a function of environmental conditions.

  9. A Conceptual Model of Leisure-Time Choice Behavior.

    Science.gov (United States)

    Bergier, Michel J.

    1981-01-01

    Methods of studying the gap between predisposition and actual behavior of consumers of spectator sports is discussed. A model is drawn from the areas of behavioral sciences, consumer behavior, and leisure research. The model is constructed around the premise that choice is primarily a function of personal, product, and environmental factors. (JN)

  10. Behavioral characterization of mouse models of neuroferritinopathy.

    Directory of Open Access Journals (Sweden)

    Sara Capoccia

    Full Text Available Ferritin is the main intracellular protein of iron storage with a central role in the regulation of iron metabolism and detoxification. Nucleotide insertions in the last exon of the ferritin light chain cause a neurodegenerative disease known as Neuroferritinopathy, characterized by iron deposition in the brain, particularly in the cerebellum, basal ganglia and motor cortex. The disease progresses relentlessly, leading to dystonia, chorea, motor disability and neuropsychiatry features. The characterization of a good animal model is required to compare and contrast specific features with the human disease, in order to gain new insights on the consequences of chronic iron overload on brain function and behavior. To this aim we studied an animal model expressing the pathogenic human FTL mutant 498InsTC under the phosphoglycerate kinase (PGK promoter. Transgenic (Tg mice showed strong accumulation of the mutated protein in the brain, which increased with age, and this was accompanied by brain accumulation of ferritin/iron bodies, the main pathologic hallmark of human neuroferritinopathy. Tg-mice were tested throughout development and aging at 2-, 8- and 18-months for motor coordination and balance (Beam Walking and Footprint tests. The Tg-mice showed a significant decrease in motor coordination at 8 and 18 months of age, with a shorter latency to fall and abnormal gait. Furthermore, one group of aged naïve subjects was challenged with two herbicides (Paraquat and Maneb known to cause oxidative damage. The treatment led to a paradoxical increase in behavioral activation in the transgenic mice, suggestive of altered functioning of the dopaminergic system. Overall, data indicate that mice carrying the pathogenic FTL498InsTC mutation show motor deficits with a developmental profile suggestive of a progressive pathology, as in the human disease. These mice could be a powerful tool to study the neurodegenerative mechanisms leading to the disease and help

  11. Behavioral characterization of mouse models of neuroferritinopathy.

    Science.gov (United States)

    Capoccia, Sara; Maccarinelli, Federica; Buffoli, Barbara; Rodella, Luigi F; Cremona, Ottavio; Arosio, Paolo; Cirulli, Francesca

    2015-01-01

    Ferritin is the main intracellular protein of iron storage with a central role in the regulation of iron metabolism and detoxification. Nucleotide insertions in the last exon of the ferritin light chain cause a neurodegenerative disease known as Neuroferritinopathy, characterized by iron deposition in the brain, particularly in the cerebellum, basal ganglia and motor cortex. The disease progresses relentlessly, leading to dystonia, chorea, motor disability and neuropsychiatry features. The characterization of a good animal model is required to compare and contrast specific features with the human disease, in order to gain new insights on the consequences of chronic iron overload on brain function and behavior. To this aim we studied an animal model expressing the pathogenic human FTL mutant 498InsTC under the phosphoglycerate kinase (PGK) promoter. Transgenic (Tg) mice showed strong accumulation of the mutated protein in the brain, which increased with age, and this was accompanied by brain accumulation of ferritin/iron bodies, the main pathologic hallmark of human neuroferritinopathy. Tg-mice were tested throughout development and aging at 2-, 8- and 18-months for motor coordination and balance (Beam Walking and Footprint tests). The Tg-mice showed a significant decrease in motor coordination at 8 and 18 months of age, with a shorter latency to fall and abnormal gait. Furthermore, one group of aged naïve subjects was challenged with two herbicides (Paraquat and Maneb) known to cause oxidative damage. The treatment led to a paradoxical increase in behavioral activation in the transgenic mice, suggestive of altered functioning of the dopaminergic system. Overall, data indicate that mice carrying the pathogenic FTL498InsTC mutation show motor deficits with a developmental profile suggestive of a progressive pathology, as in the human disease. These mice could be a powerful tool to study the neurodegenerative mechanisms leading to the disease and help developing

  12. Cognitive components underpinning the development of model-based learning.

    Science.gov (United States)

    Potter, Tracey C S; Bryce, Nessa V; Hartley, Catherine A

    2017-06-01

    Reinforcement learning theory distinguishes "model-free" learning, which fosters reflexive repetition of previously rewarded actions, from "model-based" learning, which recruits a mental model of the environment to flexibly select goal-directed actions. Whereas model-free learning is evident across development, recruitment of model-based learning appears to increase with age. However, the cognitive processes underlying the development of model-based learning remain poorly characterized. Here, we examined whether age-related differences in cognitive processes underlying the construction and flexible recruitment of mental models predict developmental increases in model-based choice. In a cohort of participants aged 9-25, we examined whether the abilities to infer sequential regularities in the environment ("statistical learning"), maintain information in an active state ("working memory") and integrate distant concepts to solve problems ("fluid reasoning") predicted age-related improvements in model-based choice. We found that age-related improvements in statistical learning performance did not mediate the relationship between age and model-based choice. Ceiling performance on our working memory assay prevented examination of its contribution to model-based learning. However, age-related improvements in fluid reasoning statistically mediated the developmental increase in the recruitment of a model-based strategy. These findings suggest that gradual development of fluid reasoning may be a critical component process underlying the emergence of model-based learning. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

  13. Seemingly irrational driving behavior model: The effect of habit strength and anticipated affective reactions.

    Science.gov (United States)

    Chung, Yi-Shih

    2015-09-01

    An increasing amount of evidence suggests that aberrant driving behaviors are not entirely rational. On the basis of the dual-process theory, this study postulates that drivers may learn to perform irrational aberrant driving behaviors, and these behaviors could be derived either from a deliberate or an intuitive decision-making approach. Accordingly, a seemingly irrational driving behavior model is proposed; in this model, the theory of planned behavior (TPB) was adopted to represent the deliberate decision-making mechanism, and habit strength was incorporated to reflect the intuitive decision process. A multiple trivariate mediation structure was designed to reflect the process through which driving behaviors are learned. Anticipated affective reactions (AARs) were further included to examine the effect of affect on aberrant driving behaviors. Considering the example of speeding behaviors, this study developed scales and conducted a two-wave survey of students in two departments at a university in Northern Taiwan. The analysis results show that habit strength consists of multiple aspects, and frequency of past behavior cannot be a complete repository for accumulating habit strength. Habit strength appeared to be a crucial mediator between intention antecedents (e.g., attitude) and the intention itself. Including habit strength in the TPB model enhanced the explained variance of speeding intention by 26.7%. In addition, AARs were different from attitudes; particularly, young drivers tended to perform speeding behaviors to reduce negative feelings such as regret. The proposed model provides an effective alternative approach for investigating aberrant driving behaviors; corresponding countermeasures are discussed. Copyright © 2015 Elsevier Ltd. All rights reserved.

  14. The Relationships among Learning Behaviors, Major Satisfaction, and Study Skills of First-Year Medical Students.

    Science.gov (United States)

    Park, Minjung

    2011-06-01

    This study aims at increasing our understanding of first-year medical students' learning behaviors, major satisfaction, and study skills. We investigate different features of freshmen's behavior in relation to learning and explore the extent to which freshmen were satisfied with their major and perceived their study skills. A total of 106 freshmen participated in this study. At midyear, first-year medical students were asked to complete a questionnaire that included the learning behaviors, major satisfaction, and study skills. The data collected from the survey were analyzed using t-test, ANOVA, chi-square test, correlation analysis, and multiple regression analysis. The study reported that most of freshmen had a lot of difficulties in studying at medical school by lack of prior learning. Despite first-year students, they were studying hard their major. Freshmen spent studying an average of 1 hour or less than 2 hours every day. The study also indicated that of major satisfaction, the overall satisfaction of the department was the highest and the satisfaction in learning environment was the lowest. There were significant differences among the freshmen on the major satisfaction due to admission process, academic performance, and housing type. Of 11 study skills, while freshman highly perceived their teamwork, stress management, and reading skills, their weak study skills identified in this study were writing, note taking, time management, and test taking skills. There were significant differences among the freshmen on the study skills due to gender and academic performance. Finally, freshmen's learning behaviors and major satisfaction were significantly associated with some of study skills. This study may have implications for the academic adjustment and learning processes in the first year. We need to consider variables such as learning behaviors, major satisfaction, and study skills, when discussing about how to maximize the learning potential of medical students

  15. Medial thalamic 18-FDG uptake following inescapable shock correlates with subsequent learned helpless behavior

    International Nuclear Information System (INIS)

    Mirrione, M.M.; Schulz, D.; Dewey, S.L.; Henn, F.A.

    2009-01-01

    The learned helplessness paradigm has been repeatedly shown to correlate with neurobiological aspects of depression in humans. In this model, rodents are exposed inescapable foot-shock in order to reveal susceptibility to escape deficit, defined as 'learned helplessness' (LH). Few methods are available to probe the neurobiological aspects underlying the differences in susceptibility in the living animal, thus far being limited to studies examining regional neurochemical changes with microdialysis. With the widespread implementation of small animal neuroimaging methods, including positron emission tomography (PET), it is now possible to explore the living brain on a systems level to define regional changes that may correlate with vulnerability to stress. In this study, 12 wild type Sprague-Dawley rats were exposed to 40 minutes of inescapable foot-shock followed by metabolic imaging using 2-deoxy-2[ 18 F]fluoro-D-glucose (18-FDG) 1 hour later. The escape test was performed on these rats 48 hours later (to accommodate radiotracer decay), where they were given the opportunity to press a lever to shut off the shock. A region of interest (ROI) analysis was used to investigate potential correlations (Pearson Regression Coefficients) between regional 18-FDG uptake following inescapable shock and subsequent learned helpless behavior (time to finish the test; number of successful lever presses within 20 seconds of shock onset). ROI analysis revealed a significant positive correlation between time to finish and 18-FDG uptake, and a negative correlation between lever presses and uptake, in the medial thalamic area (p=0.033, p=0.036). This ROI included the paraventricular thalamus, mediodorsal thalamus, and the habenula. In an effort to account for possible spillover artifact, the posterior thalamic area (including ventral medial and lateral portions) was also evaluated but did not reveal significant correlations (p=0.870, p=0.897). No other significant correlations were found

  16. Modeling in the Classroom: An Evolving Learning Tool

    Science.gov (United States)

    Few, A. A.; Marlino, M. R.; Low, R.

    2006-12-01

    Among the early programs (early 1990s) focused on teaching Earth System Science were the Global Change Instruction Program (GCIP) funded by NSF through UCAR and the Earth System Science Education Program (ESSE) funded by NASA through USRA. These two programs introduced modeling as a learning tool from the beginning, and they provided workshops, demonstrations and lectures for their participating universities. These programs were aimed at university-level education. Recently, classroom modeling is experiencing a revival of interest. Drs John Snow and Arthur Few conducted two workshops on modeling at the ESSE21 meeting in Fairbanks, Alaska, in August 2005. The Digital Library for Earth System Education (DLESE) at http://www.dlese.org provides web access to STELLA models and tutorials, and UCAR's Education and Outreach (EO) program holds workshops that include training in modeling. An important innovation to the STELLA modeling software by isee systems, http://www.iseesystems.com, called "isee Player" is available as a free download. The Player allows users to view and run STELLA models, change model parameters, share models with colleagues and students, and make working models available on the web. This is important because the expert can create models, and the user can learn how the modeled system works. Another aspect of this innovation is that the educational benefits of modeling concepts can be extended throughout most of the curriculum. The procedure for building a working computer model of an Earth Science System follows this general format: (1) carefully define the question(s) for which you seek the answer(s); (2) identify the interacting system components and inputs contributing to the system's behavior; (3) collect the information and data that will be required to complete the conceptual model; (4) construct a system diagram (graphic) of the system that displays all of system's central questions, components, relationships and required inputs. At this stage

  17. A strategy learning model for autonomous agents based on classification

    Directory of Open Access Journals (Sweden)

    Śnieżyński Bartłomiej

    2015-09-01

    Full Text Available In this paper we propose a strategy learning model for autonomous agents based on classification. In the literature, the most commonly used learning method in agent-based systems is reinforcement learning. In our opinion, classification can be considered a good alternative. This type of supervised learning can be used to generate a classifier that allows the agent to choose an appropriate action for execution. Experimental results show that this model can be successfully applied for strategy generation even if rewards are delayed. We compare the efficiency of the proposed model and reinforcement learning using the farmer-pest domain and configurations of various complexity. In complex environments, supervised learning can improve the performance of agents much faster that reinforcement learning. If an appropriate knowledge representation is used, the learned knowledge may be analyzed by humans, which allows tracking the learning process

  18. Personal Coaching: Reflection on a Model for Effective Learning

    Science.gov (United States)

    Griffiths, Kerryn

    2015-01-01

    The article "Personal Coaching: A Model for Effective Learning" (Griffiths, 2006) appeared in the "Journal of Learning Design" Volume 1, Issue 2 in 2006. Almost ten years on, Kerryn Griffiths reflects upon her original article. Specifically, Griffiths looks back at the combined coaching-learning model she suggested in her…

  19. Validating a Technology Enhanced Student-Centered Learning Model

    Science.gov (United States)

    Kang, Myunghee; Hahn, Jungsun; Chung, Warren

    2015-01-01

    The Technology Enhanced Student Centered Learning (TESCL) Model in this study presents the core factors that ensure the quality of learning in a technology-supported environment. Although the model was conceptually constructed using a student-centered learning framework and drawing upon previous studies, it should be validated through real-world…

  20. A Judgement-Based Model of Workplace Learning

    Science.gov (United States)

    Athanasou, James A.

    2004-01-01

    The purpose of this paper is to outline a judgement-based model of adult learning. This approach is set out as a Perceptual-Judgemental-Reinforcement approach to social learning under conditions of complexity and where there is no single, clearly identified correct response. The model builds upon the Hager-Halliday thesis of workplace learning and…

  1. Sex differences in vicarious trial-and-error behavior during radial arm maze learning.

    Science.gov (United States)

    Bimonte, H A; Denenberg, V H

    2000-02-01

    We investigated sex differences in VTE behavior in rats during radial arm maze learning. Females made more VTEs than males, although there were no sex differences in learning. Further, VTEs and errors were positively correlated during the latter testing sessions in females, but not in males. This sex difference may be a reflection of differences between the sexes in conflict behavior or cognitive strategy while solving the maze.

  2. Associative learning and the control of human dietary behavior.

    Science.gov (United States)

    Brunstrom, Jeffrey M

    2007-07-01

    Most of our food likes and disliked are learned. Relevant forms of associative learning have been identified in animals. However, observations of the same associative processes are relatively scarce in humans. The first section of this paper outlines reasons why this might be the case. Emphasis is placed on recent research exploring individual differences and the importance or otherwise of hunger and contingency awareness. The second section briefly considers the effect of learning on meal size, and the author revisits the question of how learned associations might come to influence energy intake in humans.

  3. Competition-Based Learning: A Model for the Integration of Competitions with Project-Based Learning Using Open Source LMS

    Science.gov (United States)

    Issa, Ghassan; Hussain, Shakir M.; Al-Bahadili, Hussein

    2014-01-01

    In an effort to enhance the learning process in higher education, a new model for Competition-Based Learning (CBL) is presented. The new model utilizes two well-known learning models, namely, the Project-Based Learning (PBL) and competitions. The new model is also applied in a networked environment with emphasis on collective learning as well as…

  4. College English Students’ Autonomous Learning Motivation and Cultivation Model Research

    Institute of Scientific and Technical Information of China (English)

    王艳荣; 李娥

    2015-01-01

    Studying the autonomous learning motivation and excitation model can stimulate intrinsic motivation of foreign language learners,develop students self-management strategy evaluation are very necessary.The purpose of this paper is to give students the skills of listening and speaking for their autonomous learning.Then study the cultivation and motivation of college English students autonomous learning,hoping to make students to learn autonomous learning and stimulate their motivation fully.

  5. The layered learning practice model: Lessons learned from implementation.

    Science.gov (United States)

    Pinelli, Nicole R; Eckel, Stephen F; Vu, Maihan B; Weinberger, Morris; Roth, Mary T

    2016-12-15

    Pharmacists' views about the implementation, benefits, and attributes of a layered learning practice model (LLPM) were examined. Eligible and willing attending pharmacists at the same institution that had implemented an LLPM completed an individual, 90-minute, face-to-face interview using a structured interview guide developed by the interdisciplinary study team. Interviews were digitally recorded and transcribed verbatim without personal identifiers. Three researchers independently reviewed preliminary findings to reach consensus on emerging themes. In cases where thematic coding diverged, the researchers discussed their analyses until consensus was reached. Of 25 eligible attending pharmacists, 24 (96%) agreed to participate. The sample was drawn from both acute and ambulatory care practice settings and all clinical specialty areas. Attending pharmacists described several experiences implementing the LLPM and perceived benefits of the model. Attending pharmacists identified seven key attributes for hospital and health-system pharmacy departments that are needed to design and implement effective LLPMs: shared leadership, a systematic approach, good communication, flexibility for attending pharmacists, adequate resources, commitment, and evaluation. Participants also highlighted several potential challenges and obstacles for organizations to consider before implementing an LLPM. According to attending pharmacists involved in an LLPM, successful implementation of an LLPM required shared leadership, a systematic approach, communication, flexibility, resources, commitment, and a process for evaluation. Copyright © 2016 by the American Society of Health-System Pharmacists, Inc. All rights reserved.

  6. Extended child and caregiver benefits of behavior-based child contingency learning games.

    Science.gov (United States)

    Dunst, Carl J; Raab, Melinda; Trivette, Carol M; Wilson, Linda L; Hamby, Deborah W; Parkey, Cindy

    2010-08-01

    Findings from 2 studies of the relationship between response-contingent child behavior and child, caregiver-child, and caregiver behavior not directly associated with child contingency learning are described. The participants were 19 children with significant developmental delays and their mothers in 1 study and 22 children with significant developmental delays and their teachers in the second study. Caregivers engaged the children in learning games characterized by behavior-based contingencies for 15 weeks. Research staff observed the children and their caregivers in everyday routines and activities and rated child and caregiver behavior while the children and caregivers were not playing the games. Results from both studies showed that the degree of response-contingent responding during the games was related to child and caregiver behavior, not the focus of the contingency learning opportunities afforded the children. Implications for practice are described.

  7. An explorative analysis of the links between learning behavior and change orientation

    NARCIS (Netherlands)

    Sluis, van der E.C. (Lidewey); Caluwé, L.I.A.; Nistelrooij, van A.T.M.

    2005-01-01

    The article presents an explorative study on the links between learning behavior and change orientation of individuals. When reading literature on how to develop employees and organizations, it strikes one how less focus there is on learning and change needs of individuals. This paper deals with

  8. Influence of course characteristics, student characteristics, and behavior in learning management systems on student performance

    NARCIS (Netherlands)

    Conijn, Rianne; Kleingeld, Ad; Matzat, Uwe; Snijders, Chris; van Zaanen, Menno

    2016-01-01

    The use of learning management systems (LMS) in education make it possible to track students’ online behavior. This data can be used for educational data mining and learning analytics, for example, by predicting student performance. Although LMS data might contain useful predictors, course

  9. Experience the city : analysis of space-time behavior and spatial learning

    NARCIS (Netherlands)

    Moiseeva, A.

    2013-01-01

    Learning plays an important role by coding information into individual cognitive maps that can be used to make decisions concerning individual behavior in space. Through traveling people learn about the urban environment and update their knowledge. In this regard, the growing concern in the field of

  10. Designing a mobile learning game to investigate the impact of role-playing on helping behavior

    NARCIS (Netherlands)

    Schmitz, Birgit; Ternier, Stefaan; Klemke, Roland; Kalz, Marco; Specht, Marcus

    2013-01-01

    Schmitz, B., Ternier, S., Klemke, R., Kalz, M., & Specht, M. (2013). Designing a mobile learning game to investigate the impact of role-playing on helping behavior. In D. Hernández-Leo et al. (Eds.), Scaling up Learning for Sustained Impact. Proceedings of European Conference on Technology Enhanced

  11. The Learning Disabled Adolescent: Eriksonian Psychosocial Development, Self-Concept, and Delinquent Behavior.

    Science.gov (United States)

    Pickar, Daniel B.; Tori, Christopher D.

    1986-01-01

    Using a developmental perspective, this study contrasted learning and nonlearning disabled adolescents on three variables: Erikson's stages of psychosocial development; self-concept; and delinquent behavior. The results indicated that the learning disabled subjects, due to years of failing, were unable to develop a sense of industry and…

  12. On the relationships among work characteristics and learning-related behavior : Does age matter?

    NARCIS (Netherlands)

    De Lange, Annet H.; Taris, Toon W.; Jansen, Paul; Kompier, Michiel A. J.; Houtman, Irene L. D.; Bongers, Paulien M.

    2010-01-01

    This 3-wave longitudinal study examined (a) the causal direction of the relationships among psychosocial work characteristics (e.g., job demands, job control, and supervisor support) and indicators of learning-related behavior (e.g., motivation to learn and active problem solving), and (b) whether

  13. The development of learning material using learning cycle 5E model based stem to improve students’ learning outcomes in Thermochemistry

    Science.gov (United States)

    sugiarti, A. C.; suyatno, S.; Sanjaya, I. G. M.

    2018-04-01

    The objective of this study is describing the feasibility of Learning Cycle 5E STEM (Science, Technology, Engineering, and Mathematics) based learning material which is appropriate to improve students’ learning achievement in Thermochemistry. The study design used 4-D models and one group pretest-posttest design to obtain the information about the improvement of sudents’ learning outcomes. The subject was learning cycle 5E based STEM learning materials which the data were collected from 30 students of Science class at 11th Grade. The techniques used in this study were validation, observation, test, and questionnaire. Some result attain: (1) all the learning materials contents were valid, (2) the practicality and the effectiveness of all the learning materials contents were classified as good. The conclution of this study based on those three condition, the Learnig Cycle 5E based STEM learning materials is appropriate to improve students’ learning outcomes in studying Thermochemistry.

  14. Automatic, Global and Dynamic Student Modeling in a Ubiquitous Learning Environment

    Directory of Open Access Journals (Sweden)

    Sabine Graf

    2009-03-01

    Full Text Available Ubiquitous learning allows students to learn at any time and any place. Adaptivity plays an important role in ubiquitous learning, aiming at providing students with adaptive and personalized learning material, activities, and information at the right place and the right time. However, for providing rich adaptivity, the student model needs to be able to gather a variety of information about the students. In this paper, an automatic, global, and dynamic student modeling approach is introduced, which aims at identifying and frequently updating information about students’ progress, learning styles, interests and knowledge level, problem solving abilities, preferences for using the system, social connectivity, and current location. This information is gathered in an automatic way, using students’ behavior and actions in different learning situations provided by different components/services of the ubiquitous learning environment. By providing a comprehensive student model, students can be supported by rich adaptivity in every component/service of the learning environment. Furthermore, the information in the student model can help in giving teachers a better understanding about the students’ learning process.

  15. Educational Modelling Language and Learning Design: new challenges for instructional re-usability and personalized learning

    NARCIS (Netherlands)

    Hummel, Hans; Manderveld, Jocelyn; Tattersall, Colin; Koper, Rob

    2003-01-01

    Published: Hummel, H. G. K., Manderveld, J. M., Tattersall, C.,& Koper, E. J. R. (2004). Educational Modelling Language: new challenges for instructional re-usability and personalized learning. International Journal of Learning Technology, 1, 1, 110-111.

  16. Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling.

    Science.gov (United States)

    Cuperlovic-Culf, Miroslava

    2018-01-11

    Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies.

  17. Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling

    Science.gov (United States)

    Cuperlovic-Culf, Miroslava

    2018-01-01

    Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies. PMID:29324649

  18. Game Based Learning (GBL) adoption model for universities: cesim ...

    African Journals Online (AJOL)

    Game Based Learning (GBL) adoption model for universities: cesim simulation. ... The global market has escalated the need of Game Based Learning (GBL) to offer a wide range of courses since there is a ... AJOL African Journals Online.

  19. Cognitive Models for Learning to Control Dynamic Systems

    National Research Council Canada - National Science Library

    Eberhart, Russ; Hu, Xiaohui; Chen, Yaobin

    2008-01-01

    Report developed under STTR contract for topic "Cognitive models for learning to control dynamic systems" demonstrated a swarm intelligence learning algorithm and its application in unmanned aerial vehicle (UAV) mission planning...

  20. The Game Object Model and expansive learning: Creation ...

    African Journals Online (AJOL)

    The Game Object Model and expansive learning: Creation, instantiation, ... The aim of the paper is to develop insights into the design, integration, evaluation and use of video games in learning and teaching. ... AJOL African Journals Online.

  1. Learning and inference using complex generative models in a spatial localization task.

    Science.gov (United States)

    Bejjanki, Vikranth R; Knill, David C; Aslin, Richard N

    2016-01-01

    A large body of research has established that, under relatively simple task conditions, human observers integrate uncertain sensory information with learned prior knowledge in an approximately Bayes-optimal manner. However, in many natural tasks, observers must perform this sensory-plus-prior integration when the underlying generative model of the environment consists of multiple causes. Here we ask if the Bayes-optimal integration seen with simple tasks also applies to such natural tasks when the generative model is more complex, or whether observers rely instead on a less efficient set of heuristics that approximate ideal performance. Participants localized a "hidden" target whose position on a touch screen was sampled from a location-contingent bimodal generative model with different variances around each mode. Over repeated exposure to this task, participants learned the a priori locations of the target (i.e., the bimodal generative model), and integrated this learned knowledge with uncertain sensory information on a trial-by-trial basis in a manner consistent with the predictions of Bayes-optimal behavior. In particular, participants rapidly learned the locations of the two modes of the generative model, but the relative variances of the modes were learned much more slowly. Taken together, our results suggest that human performance in a more complex localization task, which requires the integration of sensory information with learned knowledge of a bimodal generative model, is consistent with the predictions of Bayes-optimal behavior, but involves a much longer time-course than in simpler tasks.

  2. Learning classification models with soft-label information.

    Science.gov (United States)

    Nguyen, Quang; Valizadegan, Hamed; Hauskrecht, Milos

    2014-01-01

    Learning of classification models in medicine often relies on data labeled by a human expert. Since labeling of clinical data may be time-consuming, finding ways of alleviating the labeling costs is critical for our ability to automatically learn such models. In this paper we propose a new machine learning approach that is able to learn improved binary classification models more efficiently by refining the binary class information in the training phase with soft labels that reflect how strongly the human expert feels about the original class labels. Two types of methods that can learn improved binary classification models from soft labels are proposed. The first relies on probabilistic/numeric labels, the other on ordinal categorical labels. We study and demonstrate the benefits of these methods for learning an alerting model for heparin induced thrombocytopenia. The experiments are conducted on the data of 377 patient instances labeled by three different human experts. The methods are compared using the area under the receiver operating characteristic curve (AUC) score. Our AUC results show that the new approach is capable of learning classification models more efficiently compared to traditional learning methods. The improvement in AUC is most remarkable when the number of examples we learn from is small. A new classification learning framework that lets us learn from auxiliary soft-label information provided by a human expert is a promising new direction for learning classification models from expert labels, reducing the time and cost needed to label data.

  3. Guiding Moral Behavior through a Reflective Learning Practice

    Science.gov (United States)

    Hedberg, Patricia R.

    2017-01-01

    Reflective learning practice embedded across the business curriculum is a powerful way to equip students with intentionally formed moral habits of the mind and heart. This article explores why and how to apply reflective learning to the teaching of business ethics. To act with integrity in complicated work organizations, students need skills and…

  4. What’s about Peer Tutoring Learning Model?

    Science.gov (United States)

    Muthma'innah, M.

    2017-09-01

    Mathematics learning outcomes in Indonesia in general is still far from satisfactory. One effort that could be expected to solve the problem is to apply the model of peer tutoring learning in mathematics. This study aims to determine whether the results of students’ mathematics learning can be enhanced through peer tutoring learning models. This type of research is the study of literature, so that the method used is to summarize and analyze the results of relevant research that has been done. Peer tutoring learning model is a model of learning in which students learn in small groups that are grouped with different ability levels, all group members to work together and help each other to understand the material. By paying attention to the syntax of the learning, then learning will be invaluable peer tutoring for students who served as teachers and students are taught. In mathematics, the implementation of this learning model can make students understand each other mathematical concepts and help students in solving mathematical problems that are poorly understood, due to the interaction between students in learning. Then it will be able to improve learning outcomes in mathematics. The impact, it can be applied in mathematics learning.

  5. Empirical Models of Social Learning in a Large, Evolving Network.

    Directory of Open Access Journals (Sweden)

    Ayşe Başar Bener

    Full Text Available This paper advances theories of social learning through an empirical examination of how social networks change over time. Social networks are important for learning because they constrain individuals' access to information about the behaviors and cognitions of other people. Using data on a large social network of mobile device users over a one-month time period, we test three hypotheses: 1 attraction homophily causes individuals to form ties on the basis of attribute similarity, 2 aversion homophily causes individuals to delete existing ties on the basis of attribute dissimilarity, and 3 social influence causes individuals to adopt the attributes of others they share direct ties with. Statistical models offer varied degrees of support for all three hypotheses and show that these mechanisms are more complex than assumed in prior work. Although homophily is normally thought of as a process of attraction, people also avoid relationships with others who are different. These mechanisms have distinct effects on network structure. While social influence does help explain behavior, people tend to follow global trends more than they follow their friends.

  6. The Answering Process for Multiple-Choice Questions in Collaborative Learning: A Mathematical Learning Model Analysis

    Science.gov (United States)

    Nakamura, Yasuyuki; Nishi, Shinnosuke; Muramatsu, Yuta; Yasutake, Koichi; Yamakawa, Osamu; Tagawa, Takahiro

    2014-01-01

    In this paper, we introduce a mathematical model for collaborative learning and the answering process for multiple-choice questions. The collaborative learning model is inspired by the Ising spin model and the model for answering multiple-choice questions is based on their difficulty level. An intensive simulation study predicts the possibility of…

  7. Learning from video modeling examples: Does gender matter?

    NARCIS (Netherlands)

    Hoogerheide, V.; Loyens, S.M.M.; van Gog, T.

    2016-01-01

    Online learning from video modeling examples, in which a human model demonstrates and explains how to perform a learning task, is an effective instructional method that is increasingly used nowadays. However, model characteristics such as gender tend to differ across videos, and the model-observer

  8. Learning from Video Modeling Examples: Does Gender Matter?

    Science.gov (United States)

    Hoogerheide, Vincent; Loyens, Sofie M. M.; van Gog, Tamara

    2016-01-01

    Online learning from video modeling examples, in which a human model demonstrates and explains how to perform a learning task, is an effective instructional method that is increasingly used nowadays. However, model characteristics such as gender tend to differ across videos, and the model-observer similarity hypothesis suggests that such…

  9. Collective (Team) Learning Process Models: A Conceptual Review

    Science.gov (United States)

    Knapp, Randall

    2010-01-01

    Teams have become a key resource for learning and accomplishing work in organizations. The development of collective learning in specific contexts is not well understood, yet has become critical to organizational success. The purpose of this conceptual review is to inform human resource development (HRD) practice about specific team behaviors and…

  10. LEARNING CREATIVE WRITING MODEL BASED ON NEUROLINGUISTIC PROGRAMMING

    OpenAIRE

    Rustan, Edhy

    2017-01-01

    The objectives of the study are to determine: (1) condition on learning creative writing at high school students in Makassar, (2) requirement of learning model in creative writing, (3) program planning and design model in ideal creative writing, (4) feasibility of model study based on creative writing in neurolinguistic programming, and (5) the effectiveness of the learning model based on creative writing in neurolinguisticprogramming.The method of this research uses research development of L...

  11. Electronic learning and constructivism: a model for nursing education.

    Science.gov (United States)

    Kala, Sasikarn; Isaramalai, Sang-Arun; Pohthong, Amnart

    2010-01-01

    Nurse educators are challenged to teach nursing students to become competent professionals, who have both in-depth knowledge and decision-making skills. The use of electronic learning methods has been found to facilitate the teaching-learning process in nursing education. Although learning theories are acknowledged as useful guides to design strategies and activities of learning, integration of these theories into technology-based courses appears limited. Constructivism is a theoretical paradigm that could prove to be effective in guiding the design of electronic learning experiences for the purpose of providing positive outcomes, such as the acquisition of knowledge and decision-making skills. Therefore, the purposes of this paper are to: describe electronic learning, present a brief overview of what is known about the outcomes of electronic learning, discuss constructivism theory, present a model for electronic learning using constructivism, and describe educators' roles emphasizing the utilization of the model in developing electronic learning experiences in nursing education.

  12. Designing for Learning and Play - The Smiley Model as Framework

    DEFF Research Database (Denmark)

    Weitze, Charlotte Lærke

    2016-01-01

    digital games. The Smiley Model inspired and provided a scaffold or a heuristic for the overall gamified learning design –- as well as for the students’ learning game design processes when creating small games turning the learning situation into an engaging experience. The audience for the experiments......This paper presents a framework for designing engaging learning experiences in games – the Smiley Model. In this Design-Based Research project, student-game-designers were learning inside a gamified learning design - while designing and implementing learning goals from curriculum into the small...... was adult upper secondary general students as well as 7th grade primary school students. The intention with this article is to inspire future learning designers that would like to experiment with integrating learning and play....

  13. Innovation and entrepreneurship as pathways for new teaching / learning models

    Directory of Open Access Journals (Sweden)

    Helen Kelle dos Santos Costa

    2017-10-01

    Full Text Available Introduction: Modern times demand from society a new attitude, new attitudes, a new way of thinking and seeing the world. It is thus necessary that Education, the foundation for building a society, once again reinvents, innovates and adapts the demands that the process of human development requires. Objective: To emphasize the importance of Innovation and Entrepreneurship as tools for the development of new models of teaching / learning so that there is an education that meets the new social demands. Methodology: The article was structured from a Bibliographic research on theories and models of teaching / learning through an analytical reading, able to identify the characteristics for the effective realization of entrepreneurship in education in an innovative way. Results: The models of education are in constant process of evolution, the adoption of good practices and new resources that can help in teachinglearning as motivating agent of entrepreneurship in education through innovation is a reality to be reviewed by society as a whole. Conclusions: This study is expected to be an important tool for behavioral and / or economic change, with the aim of making the results successful for all parties involved in the attempt to corroborate with the entrepreneurship ecosystem through continuous and increasing multiplication of knowledge.

  14. Middle School Teachers' Expectations of Organizational Behaviors of Students with Learning Disabilities

    Science.gov (United States)

    McMullen, Rebecca C.; Shippen, Margaret E.; Dangel, Harry L.

    2007-01-01

    The purpose of this pilot study was to investigate the specific classroom organizational behaviors that middle school inclusive teachers report as expectations for students with learning disabilities. Practicing middle school science and social studies teachers (n = 12) responded to a survey about organization behaviors of students with learning…

  15. Schools' Mental Health Services and Young Children's Emotions, Behavior, and Learning

    Science.gov (United States)

    Reback, Randall

    2010-01-01

    Recent empirical research has found that children's noncognitive skills play a critical role in their own success, young children's behavioral and psychological disorders can severely harm their future outcomes, and disruptive students harm the behavior and learning of their classmates. Yet relatively little is known about wide-scale interventions…

  16. Low-Back Pain Patients Learn to Adapt Motor Behavior with Adverse Secondary Consequences

    NARCIS (Netherlands)

    van Dieën, Jaap H.; Flor, Herta; Hodges, Paul W.

    2017-01-01

    ABSTRACT: We hypothesize that changes in motor behavior in individuals with low-back pain are adaptations aimed at minimizing the real or perceived risk of further pain. Through reinforcement learning, pain and subsequent adaptions result in less dynamic motor behavior, leading to increased loading

  17. Behavioral Objectives, the Cult of Efficiency, and Foreign Language Learning: Are They Compatible?

    Science.gov (United States)

    Tumposky, Nancy Rennau

    1984-01-01

    Surveys the literature regarding the use of behavioral objectives in education and in foreign language instruction and examines the roots of the behavioral objectives movement in behaviorist psychology and the scientific management movement of the 1920s. Discusses implications for foreign and second language learning and provides suggestions for…

  18. Using Ants, Animal Behavior & the Learning Cycle to Investigate Scientific Processes

    Science.gov (United States)

    Ligon, Russell A.; Dolezal, Adam G.; Hicks, Michael R.; Butler, Michael W.; Morehouse, Nathan I.; Ganesh, Tirupalavanam G.

    2014-01-01

    The behavior of animals is an intrinsically fascinating topic for students from a wide array of backgrounds. We describe a learning experience using animal behavior that we created for middle school students as part of a graduate-student outreach program, Graduate Partners in Science Education, at Arizona State University in collaboration with a…

  19. Phase behavior of model ABC triblock copolymers

    Science.gov (United States)

    Chatterjee, Joon

    The phase behavior of poly(isoprene-b-styrene- b-ethylene oxide) (ISO), a model ABC triblock copolymer has been studied. This class of materials exhibit self-assembly, forming a large array of ordered morphologies at length scales of 5-100 nm. The formation of stable three-dimensionally continuous network morphologies is of special interest in this study. Since these nanostructures considerably impact the material properties, fundamental knowledge for designing ABC systems have high technological importance for realizing applications in the areas of nanofabrication, nanoporous media, separation membranes, drug delivery and high surface area catalysts. A comprehensive framework was developed to describe the phase behavior of the ISO triblock copolymers at weak to intermediate segregation strengths spanning a wide range of composition. Phases were characterized through a combination of characterization techniques, including small angle x-ray scattering, dynamic mechanical spectroscopy, transmission electron microscopy, and birefringence measurements. Combined with previous investigations on ISO, six different stable ordered state symmetries have been identified: lamellae (LAM), Fddd orthorhombic network (O70), double gyroid (Q230), alternating gyroid (Q214), hexagonal (HEX), and body-centered cubic (BCC). The phase map was found to be somewhat asymmetric around the fI = fO isopleth. This work provides a guide for theoretical studies and gives insight into the intricate effects of various parameters on the self-assembly of ABC triblock copolymers. Experimental SAXS data evaluated with a simple scattering intensity model show that local mixing varies continuously across the phase map between states of two- and three-domain segregation. Strategies of blending homopolymers with ISO triblock copolymer were employed for studying the swelling properties of a lamellar state. Results demonstrate that lamellar domains swell or shrink depending upon the type of homopolymer that

  20. Cognitive Model of Trust Dynamics Predicts Human Behavior within and between Two Games of Strategic Interaction with Computerized Confederate Agents.

    Science.gov (United States)

    Collins, Michael G; Juvina, Ion; Gluck, Kevin A

    2016-01-01

    When playing games of strategic interaction, such as iterated Prisoner's Dilemma and iterated Chicken Game, people exhibit specific within-game learning (e.g., learning a game's optimal outcome) as well as transfer of learning between games (e.g., a game's optimal outcome occurring at a higher proportion when played after another game). The reciprocal trust players develop during the first game is thought to mediate transfer of learning effects. Recently, a computational cognitive model using a novel trust mechanism has been shown to account for human behavior in both games, including the transfer between games. We present the results of a study in which we evaluate the model's a priori predictions of human learning and transfer in 16 different conditions. The model's predictive validity is compared against five model variants that lacked a trust mechanism. The results suggest that a trust mechanism is necessary to explain human behavior across multiple conditions, even when a human plays against a non-human agent. The addition of a trust mechanism to the other learning mechanisms within the cognitive architecture, such as sequence learning, instance-based learning, and utility learning, leads to better prediction of the empirical data. It is argued that computational cognitive modeling is a useful tool for studying trust development, calibration, and repair.

  1. Learner Open Modeling in Adaptive Mobile Learning System for Supporting Student to Learn English

    Directory of Open Access Journals (Sweden)

    Van Cong Pham

    2011-10-01

    Full Text Available This paper represents a personalized context-aware mobile learning architecture for supporting student to learn English as foreign language in order to prepare for TOEFL test. We consider how to apply open learner modeling techniques to adapt contents for different learners based on context, which includes location, amount of time to learn, the manner as well as learner's knowledge in learning progress. Through negotiation with system, the editable learner model will be updated to support adaptive engine to select adaptive contents meeting learner's demands. Empirical testing results for students who used application prototype indicate that interaction user modeling is helpful in supporting learner to learn adaptive materials.

  2. Development of a model for whole brain learning of physiology.

    Science.gov (United States)

    Eagleton, Saramarie; Muller, Anton

    2011-12-01

    In this report, a model was developed for whole brain learning based on Curry's onion model. Curry described the effect of personality traits as the inner layer of learning, information-processing styles as the middle layer of learning, and environmental and instructional preferences as the outer layer of learning. The model that was developed elaborates on these layers by relating the personality traits central to learning to the different quadrants of brain preference, as described by Neethling's brain profile, as the inner layer of the onion. This layer is encircled by the learning styles that describe different information-processing preferences for each brain quadrant. For the middle layer, the different stages of Kolb's learning cycle are classified into the four brain quadrants associated with the different brain processing strategies within the information processing circle. Each of the stages of Kolb's learning cycle is also associated with a specific cognitive learning strategy. These two inner circles are enclosed by the circle representing the role of the environment and instruction on learning. It relates environmental factors that affect learning and distinguishes between face-to-face and technology-assisted learning. This model informs on the design of instructional interventions for physiology to encourage whole brain learning.

  3. Effect of quantum learning model in improving creativity and memory

    Science.gov (United States)

    Sujatmika, S.; Hasanah, D.; Hakim, L. L.

    2018-04-01

    Quantum learning is a combination of many interactions that exist during learning. This model can be applied by current interesting topic, contextual, repetitive, and give opportunities to students to demonstrate their abilities. The basis of the quantum learning model are left brain theory, right brain theory, triune, visual, auditorial, kinesthetic, game, symbol, holistic, and experiential learning theory. Creativity plays an important role to be success in the working world. Creativity shows alternatives way to problem-solving or creates something. Good memory plays a role in the success of learning. Through quantum learning, students will use all of their abilities, interested in learning and create their own ways of memorizing concepts of the material being studied. From this idea, researchers assume that quantum learning models can improve creativity and memory of the students.

  4. Scanpath Based N-Gram Models for Predicting Reading Behavior

    DEFF Research Database (Denmark)

    Mishra, Abhijit; Bhattacharyya, Pushpak; Carl, Michael

    2013-01-01

    Predicting reading behavior is a difficult task. Reading behavior depends on various linguistic factors (e.g. sentence length, structural complexity etc.) and other factors (e.g individual's reading style, age etc.). Ideally, a reading model should be similar to a language model where the model i...

  5. Representing adaptive and adaptable Units of Learning. How to model personalized eLearning in IMS Learning Design

    NARCIS (Netherlands)

    Burgos, Daniel; Tattersall, Colin; Koper, Rob

    2006-01-01

    Burgos, D., Tattersall, C., & Koper, E. J. R. (2007). Representing adaptive and adaptable Units of Learning. How to model personalized eLearning in IMS Learning Design. In B. Fernández Manjon, J. M. Sanchez Perez, J. A. Gómez Pulido, M. A. Vega Rodriguez & J. Bravo (Eds.), Computers and Education:

  6. A Bootstrapping Model of Frequency and Context Effects in Word Learning.

    Science.gov (United States)

    Kachergis, George; Yu, Chen; Shiffrin, Richard M

    2017-04-01

    Prior research has shown that people can learn many nouns (i.e., word-object mappings) from a short series of ambiguous situations containing multiple words and objects. For successful cross-situational learning, people must approximately track which words and referents co-occur most frequently. This study investigates the effects of allowing some word-referent pairs to appear more frequently than others, as is true in real-world learning environments. Surprisingly, high-frequency pairs are not always learned better, but can also boost learning of other pairs. Using a recent associative model (Kachergis, Yu, & Shiffrin, 2012), we explain how mixing pairs of different frequencies can bootstrap late learning of the low-frequency pairs based on early learning of higher frequency pairs. We also manipulate contextual diversity, the number of pairs a given pair appears with across training, since it is naturalistically confounded with frequency. The associative model has competing familiarity and uncertainty biases, and their interaction is able to capture the individual and combined effects of frequency and contextual diversity on human learning. Two other recent word-learning models do not account for the behavioral findings. Copyright © 2016 Cognitive Science Society, Inc.

  7. Group Modeling in Social Learning Environments

    Science.gov (United States)

    Stankov, Slavomir; Glavinic, Vlado; Krpan, Divna

    2012-01-01

    Students' collaboration while learning could provide better learning environments. Collaboration assumes social interactions which occur in student groups. Social theories emphasize positive influence of such interactions on learning. In order to create an appropriate learning environment that enables social interactions, it is important to…

  8. Obesity, Physical Activity, and Sedentary Behavior of Youth With Learning Disabilities and ADHD.

    Science.gov (United States)

    Cook, Bryan G; Li, Dongmei; Heinrich, Katie M

    2015-01-01

    Obesity, physical activity, and sedentary behavior in childhood are important indicators of present and future health and are associated with school-related outcomes such as academic achievement, behavior, peer relationships, and self-esteem. Using logistic regression models that controlled for gender, age, ethnicity/race, and socioeconomic status, we investigated the likelihood that youth with learning disabilities (LD) and attention-deficit/hyperactivity disorder (ADHD) are obese, physically active, and sedentary using a nationally representative sample of 45,897 youth in the United States from 10 to 17 years of age. Results indicated that youth with comorbid LD/ADHD were significantly more likely than peers without LD or ADHD to be obese; that youth with LD only, ADHD only, and comorbid LD/ADHD were significantly less likely to meet recommended levels of physical activity; and that youth with LD only were significantly more likely to exceed recommended levels of sedentary behavior. Medication status mediated outcomes for youth with ADHD. We offer school-based recommendations for improving health-related outcomes for students with LD and ADHD. © Hammill Institute on Disabilities 2014.

  9. Behavior Self-Organization in Multi-Agent Learning

    National Research Council Canada - National Science Library

    Bay, John

    1999-01-01

    There are four primary results of the first year of the project: It was discovered that clustering algorithms for pre-sorting high-dimensional datasets was not effective in improving subsequent processing by reinforcement learning methods...

  10. Business Models for E-Learning

    OpenAIRE

    Hoppe, Gabriela; Breitner, Michael H.

    2003-01-01

    E(Electronic)-learning becomes more and more important. Reasons are the paramount importance of knowledge, life-time learning, globalization and mobility. Not all providers of e-learning products succeed in closing the gap between production costs and revenues. Especially in the academic sector e-learning projects suffer more and more from decreasing funding. For many currently active research groups it is essential to market their research results, e. g. e-learning applications, in order to ...

  11. Procrastinating Behavior in Computer-Based Learning Environments to Predict Performance: A Case Study in Moodle

    Science.gov (United States)

    Cerezo, Rebeca; Esteban, María; Sánchez-Santillán, Miguel; Núñez, José C.

    2017-01-01

    Introduction: Research about student performance has traditionally considered academic procrastination as a behavior that has negative effects on academic achievement. Although there is much evidence for this in class-based environments, there is a lack of research on Computer-Based Learning Environments (CBLEs). Therefore, the purpose of this study is to evaluate student behavior in a blended learning program and specifically procrastination behavior in relation to performance through Data Mining techniques. Materials and Methods: A sample of 140 undergraduate students participated in a blended learning experience implemented in a Moodle (Modular Object Oriented Developmental Learning Environment) Management System. Relevant interaction variables were selected for the study, taking into account student achievement and analyzing data by means of association rules, a mining technique. The association rules were arrived at and filtered through two selection criteria: 1, rules must have an accuracy over 0.8 and 2, they must be present in both sub-samples. Results: The findings of our study highlight the influence of time management in online learning environments, particularly on academic achievement, as there is an association between procrastination variables and student performance. Conclusion: Negative impact of procrastination in learning outcomes has been observed again but in virtual learning environments where practical implications, prevention of, and intervention in, are different from class-based learning. These aspects are discussed to help resolve student difficulties at various ages. PMID:28883801

  12. Procrastinating Behavior in Computer-Based Learning Environments to Predict Performance: A Case Study in Moodle.

    Science.gov (United States)

    Cerezo, Rebeca; Esteban, María; Sánchez-Santillán, Miguel; Núñez, José C

    2017-01-01

    Introduction: Research about student performance has traditionally considered academic procrastination as a behavior that has negative effects on academic achievement. Although there is much evidence for this in class-based environments, there is a lack of research on Computer-Based Learning Environments (CBLEs) . Therefore, the purpose of this study is to evaluate student behavior in a blended learning program and specifically procrastination behavior in relation to performance through Data Mining techniques. Materials and Methods: A sample of 140 undergraduate students participated in a blended learning experience implemented in a Moodle (Modular Object Oriented Developmental Learning Environment) Management System. Relevant interaction variables were selected for the study, taking into account student achievement and analyzing data by means of association rules, a mining technique. The association rules were arrived at and filtered through two selection criteria: 1, rules must have an accuracy over 0.8 and 2, they must be present in both sub-samples. Results: The findings of our study highlight the influence of time management in online learning environments, particularly on academic achievement, as there is an association between procrastination variables and student performance. Conclusion: Negative impact of procrastination in learning outcomes has been observed again but in virtual learning environments where practical implications, prevention of, and intervention in, are different from class-based learning. These aspects are discussed to help resolve student difficulties at various ages.

  13. Procrastinating Behavior in Computer-Based Learning Environments to Predict Performance: A Case Study in Moodle

    Directory of Open Access Journals (Sweden)

    Rebeca Cerezo

    2017-08-01

    Full Text Available Introduction: Research about student performance has traditionally considered academic procrastination as a behavior that has negative effects on academic achievement. Although there is much evidence for this in class-based environments, there is a lack of research on Computer-Based Learning Environments (CBLEs. Therefore, the purpose of this study is to evaluate student behavior in a blended learning program and specifically procrastination behavior in relation to performance through Data Mining techniques.Materials and Methods: A sample of 140 undergraduate students participated in a blended learning experience implemented in a Moodle (Modular Object Oriented Developmental Learning Environment Management System. Relevant interaction variables were selected for the study, taking into account student achievement and analyzing data by means of association rules, a mining technique. The association rules were arrived at and filtered through two selection criteria: 1, rules must have an accuracy over 0.8 and 2, they must be present in both sub-samples.Results: The findings of our study highlight the influence of time management in online learning environments, particularly on academic achievement, as there is an association between procrastination variables and student performance.Conclusion: Negative impact of procrastination in learning outcomes has been observed again but in virtual learning environments where practical implications, prevention of, and intervention in, are different from class-based learning. These aspects are discussed to help resolve student difficulties at various ages.

  14. A Review of Empirical Studies Investigating Antecedents and Consequences of Collective Learning Behaviors in Hospitals

    Directory of Open Access Journals (Sweden)

    Florina D. Spânu

    2013-01-01

    Full Text Available This study is a systematic review of the field research conducted in medical settings investigating collective learning behaviors. The review was driven by several research foci. Our main interest was in identifying antecedents and consequences of collective learning in hospitals. We also report results on the types of research questions addressed, research designs used, and types of medical teams investigated. Twelve studies met our inclusion criteria. Our findings revealed that highly contextualized studies that use different ways of measuring learning, different ways of conceptualizing medical teams, and different research methodologies, discuss similar antecedents. Variables like leadership behaviors, unit interpersonal climate, and hierarchical position were found to play a role in explaining organizational learning in hospitals across studies. We also found that despite an intense public discourse on the link between collective learning processes and patients’ safety and medical organizations’ performance, few studies actually report empirical data supporting this relationship.

  15. Learning Behavior Analysis of a Ubiquitous Situated Reflective Learning System with Application to Life Science and Technology Teaching

    Science.gov (United States)

    Hwang, Wu-Yuin; Chen, Hong-Ren; Chen, Nian-Shing; Lin, Li-Kai; Chen, Jin-Wen

    2018-01-01

    Education research has shown that reflective study can efficiently enhance learning, and the acquisition of knowledge and skills from real-life situations has become a focus of interest for scholars. The knowledge-learning model based on verbal instruction, used in traditional classrooms, does not make use of real-life situations that encourage…

  16. The effectiveness of internet-based e-learning on clinician behavior and patient outcomes: a systematic review protocol.

    Science.gov (United States)

    Sinclair, Peter; Kable, Ashley; Levett-Jones, Tracy

    2015-01-01

    research, not least the use of comparative design studies. Comparison between e-learning and traditional teaching methods are illogical and methodologically flawed because comparison groups are heterogeneous, lack uniformity and have multiple confounders that cannot be adjusted for.As early as 1994, researchersin computer-assisted learning were citing these limitations and called for a fresh research agenda in this area. Cookrepeated this call in 2005 and again in 2009 and noted a paucity of research related to patient or clinical practice outcomes. Electronic learning is not an educational panacea and research needs to progress from pre- and post-interventional and comparative designs that evaluate knowledge increases and user satisfaction. It is time to move towards determining whether improved self-efficacy or knowledge gained through e-learning improves patient outcomes or influences clinical behavior change and whether these changes are sustained. In order to develop the empirical evidence base in e-learning, research needs to be guided by established theoretical frameworks and use validated instruments to move from assessing knowledge generation towards improving our understanding of whether e-learning improves HCP behavior and more importantly, patient outcomes.One suitable framework that is congruent with e-learning research is Kirkpatrick'sfour levels of evaluation. Kirkpatrick's model is hierarchically based with level one relating to student reaction and how well the learner is satisfied with the education program. Level two pertains to learning and the evaluation of knowledge, level three expands on this and considers whether the education has influenced behavior. In the context of this review, behavior change is any practice that is intrinsically linked with the outcomes of the e-learning program undertaken. Finally, level four evaluates the impact on outcomes such as cost benefit or quality improvements.The majority of e-learning research has focused on

  17. The Effect of Learning Based on Technology Model and Assessment Technique toward Thermodynamic Learning Achievement

    Science.gov (United States)

    Makahinda, T.

    2018-02-01

    The purpose of this research is to find out the effect of learning model based on technology and assessment technique toward thermodynamic achievement by controlling students intelligence. This research is an experimental research. The sample is taken through cluster random sampling with the total respondent of 80 students. The result of the research shows that the result of learning of thermodynamics of students who taught the learning model of environmental utilization is higher than the learning result of student thermodynamics taught by simulation animation, after controlling student intelligence. There is influence of student interaction, and the subject between models of technology-based learning with assessment technique to student learning result of Thermodynamics, after controlling student intelligence. Based on the finding in the lecture then should be used a thermodynamic model of the learning environment with the use of project assessment technique.

  18. Learning Visual Forward Models to Compensate for Self-Induced Image Motion.

    NARCIS (Netherlands)

    Ghadirzadeh, A.; Kootstra, G.W.; Maki, A.; Björkman, M.

    2014-01-01

    Predicting the sensory consequences of an agent's own actions is considered an important skill for intelligent behavior. In terms of vision, so-called visual forward models can be applied to learn such predictions. This is no trivial task given the high-dimensionality of sensory data and complex

  19. Business Process Elicitation, Modeling, and Reengineering: Teaching and Learning with Simulated Environments

    Science.gov (United States)

    Jeyaraj, Anand

    2010-01-01

    The design of enterprise information systems requires students to master technical skills for elicitation, modeling, and reengineering business processes as well as soft skills for information gathering and communication. These tacit skills and behaviors cannot be effectively taught students but rather experienced and learned by students. This…

  20. User Acceptance of YouTube for Procedural Learning: An Extension of the Technology Acceptance Model

    Science.gov (United States)

    Lee, Doo Young; Lehto, Mark R.

    2013-01-01

    The present study was framed using the Technology Acceptance Model (TAM) to identify determinants affecting behavioral intention to use YouTube. Most importantly, this research emphasizes the motives for using YouTube, which is notable given its extrinsic task goal of being used for procedural learning tasks. Our conceptual framework included two…