The Impact of Cognitive Load Theory on Learning Astronomy

Science.gov (United States)

Foster, Thomas M.

2010-01-01

Every student is different, which is the challenge of astronomy education research (AER) and teaching astronomy. This difference also provides the greatest goal for education researchers - our GUT - we need to be able to quantify these differences and provide explanatory and predictive theories to curriculum developers and teachers. One educational theory that holds promise is Cognitive Load Theory. Cognitive Load Theory begins with the well-established fact that everyone's working memory can hold 7 ± 2 unique items. This quirk of the human brain is why phone numbers are 7 digits long. This quirk is also why we forget peoples’ names after just meeting them, leave the iron on when we leave the house, and become overwhelmed as students of new material. Once the intricacies of Cognitive Load are understood, it becomes possible to design learning environments to marshal the resources students have and guide them to success. Lessons learned from Cognitive Load Theory can and should be applied to learning astronomy. Classroom-ready ideas will be presented.

Learned Predictiveness Influences Rapid Attentional Capture: Evidence from the Dot Probe Task

Science.gov (United States)

Le Pelley, Mike E.; Vadillo, Miguel; Luque, David

2013-01-01

Attentional theories of associative learning and categorization propose that learning about the predictiveness of a stimulus influences the amount of attention that is paid to that stimulus. Three experiments tested this idea by looking at the extent to which stimuli that had previously been experienced as predictive or nonpredictive in a…

Hebbian Learning is about contingency, not contiguity, and explains the emergence of predictive mirror neurons.

Science.gov (United States)

Keysers, Christian; Perrett, David I; Gazzola, Valeria

2014-04-01

Hebbian Learning should not be reduced to contiguity, as it detects contingency and causality. Hebbian Learning accounts of mirror neurons make predictions that differ from associative learning: Through Hebbian Learning, mirror neurons become dynamic networks that calculate predictions and prediction errors and relate to ideomotor theories. The social force of imitation is important for mirror neuron emergence and suggests canalization.

Towards a general theory of neural computation based on prediction by single neurons.

Directory of Open Access Journals (Sweden)

Christopher D Fiorillo

Full Text Available Although there has been tremendous progress in understanding the mechanics of the nervous system, there has not been a general theory of its computational function. Here I present a theory that relates the established biophysical properties of single generic neurons to principles of Bayesian probability theory, reinforcement learning and efficient coding. I suggest that this theory addresses the general computational problem facing the nervous system. Each neuron is proposed to mirror the function of the whole system in learning to predict aspects of the world related to future reward. According to the model, a typical neuron receives current information about the state of the world from a subset of its excitatory synaptic inputs, and prior information from its other inputs. Prior information would be contributed by synaptic inputs representing distinct regions of space, and by different types of non-synaptic, voltage-regulated channels representing distinct periods of the past. The neuron's membrane voltage is proposed to signal the difference between current and prior information ("prediction error" or "surprise". A neuron would apply a Hebbian plasticity rule to select those excitatory inputs that are the most closely correlated with reward but are the least predictable, since unpredictable inputs provide the neuron with the most "new" information about future reward. To minimize the error in its predictions and to respond only when excitation is "new and surprising," the neuron selects amongst its prior information sources through an anti-Hebbian rule. The unique inputs of a mature neuron would therefore result from learning about spatial and temporal patterns in its local environment, and by extension, the external world. Thus the theory describes how the structure of the mature nervous system could reflect the structure of the external world, and how the complexity and intelligence of the system might develop from a population of

Hebbian Learning is about contingency, not contiguity, and explains the emergence of predictive mirror neurons

OpenAIRE

Keysers, C.; Perrett, D.I.; Gazzola, V.

2014-01-01

Hebbian Learning should not be reduced to contiguity, as it detects contingency and causality. Hebbian Learning accounts of mirror neurons make predictions that differ from associative learning: Through Hebbian Learning, mirror neurons become dynamic networks that calculate predictions and prediction errors and relate to ideomotor theories. The social force of imitation is important for mirror neuron emergence and suggests canalization. Publisher PDF Peer reviewed

Theory of Planned Behavior Predicts Graduation Intentions of Canadian and Israeli Postsecondary Students with and without Learning Disabilities/Attention Deficit Hyperactivity Disorder

Science.gov (United States)

Fichten, Catherine S.; Heiman, Tali; Jorgensen, Mary; Nguyen, Mai Nhu; Havel, Alice; King, Laura; Budd, Jillian; Amsel, Rhonda

2016-01-01

We tested the ability of Ajzen's Theory of Planned Behavior (TPB) model to predict intention to graduate among Canadian and Israeli students with and without a learning disability/attention deficit hyperactivity disorder (LD/ADHD). Results based on 1486 postsecondary students show that the model's predictors (i.e., attitude, subjective norms,…

Improving orbit prediction accuracy through supervised machine learning

Science.gov (United States)

Peng, Hao; Bai, Xiaoli

2018-05-01

Due to the lack of information such as the space environment condition and resident space objects' (RSOs') body characteristics, current orbit predictions that are solely grounded on physics-based models may fail to achieve required accuracy for collision avoidance and have led to satellite collisions already. This paper presents a methodology to predict RSOs' trajectories with higher accuracy than that of the current methods. Inspired by the machine learning (ML) theory through which the models are learned based on large amounts of observed data and the prediction is conducted without explicitly modeling space objects and space environment, the proposed ML approach integrates physics-based orbit prediction algorithms with a learning-based process that focuses on reducing the prediction errors. Using a simulation-based space catalog environment as the test bed, the paper demonstrates three types of generalization capability for the proposed ML approach: (1) the ML model can be used to improve the same RSO's orbit information that is not available during the learning process but shares the same time interval as the training data; (2) the ML model can be used to improve predictions of the same RSO at future epochs; and (3) the ML model based on a RSO can be applied to other RSOs that share some common features.

Chinese Learning Through Internet Inspired by Contructivist Learning Theory

OpenAIRE

Yan, Huang

2011-01-01

With the changing concept of education, there is growing emphasis on “student-centered”principle. This is one of the characteristics of Constructivist learning theory. On network teachingChinese, Constructivist learning theory is indispensable. This article is the design of online Chineseteaching which is basic on the Constructivist learning theory.

Opponent appetitive-aversive neural processes underlie predictive learning of pain relief.

Science.gov (United States)

Seymour, Ben; O'Doherty, John P; Koltzenburg, Martin; Wiech, Katja; Frackowiak, Richard; Friston, Karl; Dolan, Raymond

2005-09-01

Termination of a painful or unpleasant event can be rewarding. However, whether the brain treats relief in a similar way as it treats natural reward is unclear, and the neural processes that underlie its representation as a motivational goal remain poorly understood. We used fMRI (functional magnetic resonance imaging) to investigate how humans learn to generate expectations of pain relief. Using a pavlovian conditioning procedure, we show that subjects experiencing prolonged experimentally induced pain can be conditioned to predict pain relief. This proceeds in a manner consistent with contemporary reward-learning theory (average reward/loss reinforcement learning), reflected by neural activity in the amygdala and midbrain. Furthermore, these reward-like learning signals are mirrored by opposite aversion-like signals in lateral orbitofrontal cortex and anterior cingulate cortex. This dual coding has parallels to 'opponent process' theories in psychology and promotes a formal account of prediction and expectation during pain.

Neural coding of basic reward terms of animal learning theory, game theory, microeconomics and behavioural ecology.

Science.gov (United States)

Schultz, Wolfram

2004-04-01

Neurons in a small number of brain structures detect rewards and reward-predicting stimuli and are active during the expectation of predictable food and liquid rewards. These neurons code the reward information according to basic terms of various behavioural theories that seek to explain reward-directed learning, approach behaviour and decision-making. The involved brain structures include groups of dopamine neurons, the striatum including the nucleus accumbens, the orbitofrontal cortex and the amygdala. The reward information is fed to brain structures involved in decision-making and organisation of behaviour, such as the dorsolateral prefrontal cortex and possibly the parietal cortex. The neural coding of basic reward terms derived from formal theories puts the neurophysiological investigation of reward mechanisms on firm conceptual grounds and provides neural correlates for the function of rewards in learning, approach behaviour and decision-making.

Learning to predict chemical reactions.

Science.gov (United States)

Kayala, Matthew A; Azencott, Chloé-Agathe; Chen, Jonathan H; Baldi, Pierre

2011-09-26

Being able to predict the course of arbitrary chemical reactions is essential to the theory and applications of organic chemistry. Approaches to the reaction prediction problems can be organized around three poles corresponding to: (1) physical laws; (2) rule-based expert systems; and (3) inductive machine learning. Previous approaches at these poles, respectively, are not high throughput, are not generalizable or scalable, and lack sufficient data and structure to be implemented. We propose a new approach to reaction prediction utilizing elements from each pole. Using a physically inspired conceptualization, we describe single mechanistic reactions as interactions between coarse approximations of molecular orbitals (MOs) and use topological and physicochemical attributes as descriptors. Using an existing rule-based system (Reaction Explorer), we derive a restricted chemistry data set consisting of 1630 full multistep reactions with 2358 distinct starting materials and intermediates, associated with 2989 productive mechanistic steps and 6.14 million unproductive mechanistic steps. And from machine learning, we pose identifying productive mechanistic steps as a statistical ranking, information retrieval problem: given a set of reactants and a description of conditions, learn a ranking model over potential filled-to-unfilled MO interactions such that the top-ranked mechanistic steps yield the major products. The machine learning implementation follows a two-stage approach, in which we first train atom level reactivity filters to prune 94.00% of nonproductive reactions with a 0.01% error rate. Then, we train an ensemble of ranking models on pairs of interacting MOs to learn a relative productivity function over mechanistic steps in a given system. Without the use of explicit transformation patterns, the ensemble perfectly ranks the productive mechanism at the top 89.05% of the time, rising to 99.86% of the time when the top four are considered. Furthermore, the system

Learning to Predict Chemical Reactions

Science.gov (United States)

Kayala, Matthew A.; Azencott, Chloé-Agathe; Chen, Jonathan H.

2011-01-01

Being able to predict the course of arbitrary chemical reactions is essential to the theory and applications of organic chemistry. Approaches to the reaction prediction problems can be organized around three poles corresponding to: (1) physical laws; (2) rule-based expert systems; and (3) inductive machine learning. Previous approaches at these poles respectively are not high-throughput, are not generalizable or scalable, or lack sufficient data and structure to be implemented. We propose a new approach to reaction prediction utilizing elements from each pole. Using a physically inspired conceptualization, we describe single mechanistic reactions as interactions between coarse approximations of molecular orbitals (MOs) and use topological and physicochemical attributes as descriptors. Using an existing rule-based system (Reaction Explorer), we derive a restricted chemistry dataset consisting of 1630 full multi-step reactions with 2358 distinct starting materials and intermediates, associated with 2989 productive mechanistic steps and 6.14 million unproductive mechanistic steps. And from machine learning, we pose identifying productive mechanistic steps as a statistical ranking, information retrieval, problem: given a set of reactants and a description of conditions, learn a ranking model over potential filled-to-unfilled MO interactions such that the top ranked mechanistic steps yield the major products. The machine learning implementation follows a two-stage approach, in which we first train atom level reactivity filters to prune 94.00% of non-productive reactions with a 0.01% error rate. Then, we train an ensemble of ranking models on pairs of interacting MOs to learn a relative productivity function over mechanistic steps in a given system. Without the use of explicit transformation patterns, the ensemble perfectly ranks the productive mechanism at the top 89.05% of the time, rising to 99.86% of the time when the top four are considered. Furthermore, the system

Combining University Student Self-Regulated Learning Indicators and Engagement with Online Learning Events to Predict Academic Performance

Science.gov (United States)

Pardo, Abelardo; Han, Feifei; Ellis, Robert A.

2017-01-01

Self-regulated learning theories are used to understand the reasons for different levels of university student academic performance. Similarly, learning analytics research proposes the combination of detailed data traces derived from technology-mediated tasks with a variety of algorithms to predict student academic performance. The former approach…

Causal beliefs about depression in different cultural groups – What do cognitive psychological theories of causal learning and reasoning predict?

Directory of Open Access Journals (Sweden)

York eHagmayer

2014-11-01

Full Text Available Cognitive psychological research focusses on causal learning and reasoning while cognitive anthropological and social science research tend to focus on systems of beliefs. Our aim was to explore how these two types of research can inform each other. Cognitive psychological theories (causal model theory and causal Bayes nets were used to derive predictions for systems of causal beliefs. These predictions were then applied to lay theories of depression as a specific test case. A systematic literature review on causal beliefs about depression was conducted, including original, quantitative research. Thirty-six studies investigating 13 non-Western and 32 Western cultural groups were analysed by classifying assumed causes and preferred forms of treatment into common categories. Relations between beliefs and treatment preferences were assessed. Substantial agreement between cultural groups was found with respect to the impact of observable causes. Stress was generally rated as most important. Less agreement resulted for hidden, especially supernatural causes. Causal beliefs were clearly related to treatment preferences in Western groups, while evidence was mostly lacking for non-Western groups. Overall predictions were supported, but there were considerable methodological limitations. Pointers to future research, which may combine studies on causal beliefs with experimental paradigms on causal reasoning, are given.

Some ideas for learning CP-theories

OpenAIRE

Fierens, Daan

2008-01-01

Causal Probabilistic logic (CP-logic) is a language for describing complex probabilistic processes. In this talk we consider the problem of learning CP-theories from data. We briefly discuss three possible approaches. First, we review the existing algorithm by Meert et al. Second, we show how simple CP-theories can be learned by using the learning algorithm for Logical Bayesian Networks and converting the result into a CP-theory. Third, we argue that for learning more complex CP-theories, an ...

Multi-fidelity machine learning models for accurate bandgap predictions of solids

International Nuclear Information System (INIS)

Pilania, Ghanshyam; Gubernatis, James E.; Lookman, Turab

2016-01-01

Here, we present a multi-fidelity co-kriging statistical learning framework that combines variable-fidelity quantum mechanical calculations of bandgaps to generate a machine-learned model that enables low-cost accurate predictions of the bandgaps at the highest fidelity level. Additionally, the adopted Gaussian process regression formulation allows us to predict the underlying uncertainties as a measure of our confidence in the predictions. In using a set of 600 elpasolite compounds as an example dataset and using semi-local and hybrid exchange correlation functionals within density functional theory as two levels of fidelities, we demonstrate the excellent learning performance of the method against actual high fidelity quantum mechanical calculations of the bandgaps. The presented statistical learning method is not restricted to bandgaps or electronic structure methods and extends the utility of high throughput property predictions in a significant way.

Investigating the Learning-Theory Foundations of Game-Based Learning: A Meta-Analysis

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Wu, W-H.; Hsiao, H-C.; Wu, P-L.; Lin, C-H.; Huang, S-H.

2012-01-01

Past studies on the issue of learning-theory foundations in game-based learning stressed the importance of establishing learning-theory foundation and provided an exploratory examination of established learning theories. However, we found research seldom addressed the development of the use or failure to use learning-theory foundations and…

The conditions that promote fear learning: prediction error and Pavlovian fear conditioning.

Science.gov (United States)

Li, Susan Shi Yuan; McNally, Gavan P

2014-02-01

A key insight of associative learning theory is that learning depends on the actions of prediction error: a discrepancy between the actual and expected outcomes of a conditioning trial. When positive, such error causes increments in associative strength and, when negative, such error causes decrements in associative strength. Prediction error can act directly on fear learning by determining the effectiveness of the aversive unconditioned stimulus or indirectly by determining the effectiveness, or associability, of the conditioned stimulus. Evidence from a variety of experimental preparations in human and non-human animals suggest that discrete neural circuits code for these actions of prediction error during fear learning. Here we review the circuits and brain regions contributing to the neural coding of prediction error during fear learning and highlight areas of research (safety learning, extinction, and reconsolidation) that may profit from this approach to understanding learning. Crown Copyright © 2013. Published by Elsevier Inc. All rights reserved.

Learning theory and gestalt therapy.

Science.gov (United States)

Harper, R; Bauer, R; Kannarkat, J

1976-01-01

This article discusses the theory and operations of Gestalt Therapy from the viewpoint of learning theory. General comparative issues are elaborated as well as the concepts of introjection, retroflextion, confluence, and projection. Principles and techniques of Gestalt Therapy are discussed in terms of learning theory paradigm. Practical implications of the various Gestalt techniques are presented.

An integrated theory of prospective time interval estimation : The role of cognition, attention, and learning

NARCIS (Netherlands)

Taatgen, Niels A.; van Rijn, Hedderik; Anderson, John

A theory of prospective time perception is introduced and incorporated as a module in an integrated theory of cognition, thereby extending existing theories and allowing predictions about attention and learning. First, a time perception module is established by fitting existing datasets (interval

General Theory versus ENA Theory: Comparing Their Predictive Accuracy and Scope.

Science.gov (United States)

Ellis, Lee; Hoskin, Anthony; Hartley, Richard; Walsh, Anthony; Widmayer, Alan; Ratnasingam, Malini

2015-12-01

General theory attributes criminal behavior primarily to low self-control, whereas evolutionary neuroandrogenic (ENA) theory envisions criminality as being a crude form of status-striving promoted by high brain exposure to androgens. General theory predicts that self-control will be negatively correlated with risk-taking, while ENA theory implies that these two variables should actually be positively correlated. According to ENA theory, traits such as pain tolerance and muscularity will be positively associated with risk-taking and criminality while general theory makes no predictions concerning these relationships. Data from Malaysia and the United States are used to test 10 hypotheses derived from one or both of these theories. As predicted by both theories, risk-taking was positively correlated with criminality in both countries. However, contrary to general theory and consistent with ENA theory, the correlation between self-control and risk-taking was positive in both countries. General theory's prediction of an inverse correlation between low self-control and criminality was largely supported by the U.S. data but only weakly supported by the Malaysian data. ENA theory's predictions of positive correlations between pain tolerance, muscularity, and offending were largely confirmed. For the 10 hypotheses tested, ENA theory surpassed general theory in predictive scope and accuracy. © The Author(s) 2014.

An Elementary Introduction to Statistical Learning Theory

CERN Document Server

Kulkarni, Sanjeev

2011-01-01

A thought-provoking look at statistical learning theory and its role in understanding human learning and inductive reasoning A joint endeavor from leading researchers in the fields of philosophy and electrical engineering, An Elementary Introduction to Statistical Learning Theory is a comprehensive and accessible primer on the rapidly evolving fields of statistical pattern recognition and statistical learning theory. Explaining these areas at a level and in a way that is not often found in other books on the topic, the authors present the basic theory behind contemporary machine learning and

How we learn to make decisions: rapid propagation of reinforcement learning prediction errors in humans.

Science.gov (United States)

Krigolson, Olav E; Hassall, Cameron D; Handy, Todd C

2014-03-01

Our ability to make decisions is predicated upon our knowledge of the outcomes of the actions available to us. Reinforcement learning theory posits that actions followed by a reward or punishment acquire value through the computation of prediction errors-discrepancies between the predicted and the actual reward. A multitude of neuroimaging studies have demonstrated that rewards and punishments evoke neural responses that appear to reflect reinforcement learning prediction errors [e.g., Krigolson, O. E., Pierce, L. J., Holroyd, C. B., & Tanaka, J. W. Learning to become an expert: Reinforcement learning and the acquisition of perceptual expertise. Journal of Cognitive Neuroscience, 21, 1833-1840, 2009; Bayer, H. M., & Glimcher, P. W. Midbrain dopamine neurons encode a quantitative reward prediction error signal. Neuron, 47, 129-141, 2005; O'Doherty, J. P. Reward representations and reward-related learning in the human brain: Insights from neuroimaging. Current Opinion in Neurobiology, 14, 769-776, 2004; Holroyd, C. B., & Coles, M. G. H. The neural basis of human error processing: Reinforcement learning, dopamine, and the error-related negativity. Psychological Review, 109, 679-709, 2002]. Here, we used the brain ERP technique to demonstrate that not only do rewards elicit a neural response akin to a prediction error but also that this signal rapidly diminished and propagated to the time of choice presentation with learning. Specifically, in a simple, learnable gambling task, we show that novel rewards elicited a feedback error-related negativity that rapidly decreased in amplitude with learning. Furthermore, we demonstrate the existence of a reward positivity at choice presentation, a previously unreported ERP component that has a similar timing and topography as the feedback error-related negativity that increased in amplitude with learning. The pattern of results we observed mirrored the output of a computational model that we implemented to compute reward

The Theory of Linear Prediction

CERN Document Server

Vaidyanathan, PP

2007-01-01

Linear prediction theory has had a profound impact in the field of digital signal processing. Although the theory dates back to the early 1940s, its influence can still be seen in applications today. The theory is based on very elegant mathematics and leads to many beautiful insights into statistical signal processing. Although prediction is only a part of the more general topics of linear estimation, filtering, and smoothing, this book focuses on linear prediction. This has enabled detailed discussion of a number of issues that are normally not found in texts. For example, the theory of vecto

Experiential learning: transforming theory into practice.

Science.gov (United States)

Yardley, Sarah; Teunissen, Pim W; Dornan, Tim

2012-01-01

Whilst much is debated about the importance of experiential learning in curriculum development, the concept only becomes effective if it is applied in an appropriate way. We believe that this effectiveness is directly related to a sound understanding of the theory, supporting the learning. The purpose of this article is to introduce readers to the theories underpinning experiential learning, which are then expanded further in an AMEE Guide, which considers the theoretical basis of experiential learning from a social learning, constructionist perspective and applies it to three stages of medical education: early workplace experience, clerkships and residency. This article argues for the importance and relevance of experiential learning and addresses questions that are commonly asked about it. First, we answer the questions 'what is experiential learning?' and 'how does it relate to social learning theory?' to orientate readers to the principles on which our arguments are based. Then, we consider why those ideas (theories) are relevant to educators--ranging from those with responsibilities for curriculum design to 'hands-on' teachers and workplace supervisors. The remainder of this article discusses how experiential learning theories and a socio-cultural perspective can be applied in practice. We hope that this will give readers a taste for our more detailed AMEE Guide and the further reading recommended at the end of it.

Do Judgments of Learning Predict Automatic Influences of Memory?

Science.gov (United States)

Undorf, Monika; Böhm, Simon; Cüpper, Lutz

2016-01-01

Current memory theories generally assume that memory performance reflects both recollection and automatic influences of memory. Research on people's predictions about the likelihood of remembering recently studied information on a memory test, that is, on judgments of learning (JOLs), suggests that both magnitude and resolution of JOLs are linked…

Learning theories application in nursing education

Science.gov (United States)

Aliakbari, Fatemeh; Parvin, Neda; Heidari, Mohammad; Haghani, Fariba

2015-01-01

Learning theories are the main guide for educational systems planning in the classroom and clinical training included in nursing. The teachers by knowing the general principles of these theories can use their knowledge more effectively according to various learning situations. In this study, Eric, Medline, and Cochrane databases were used for articles in English and for the Persian literature, Magiran, Iran doc, Iran medex, and Sid databases were used with the help of keywords including social cognitive learning, learning theory, behavioral theory, cognitive theory, constructive theory, and nursing education. The search period was considered from 1990 to 2012. Some related books were also studied about each method, its original vision, the founders, practical application of the training theory, especially training of nursing and its strengths and weaknesses. Behaviorists believe that learning is a change in an observable behavior and it happens when the communication occurs between the two events, a stimulus and a response. Among the applications of this approach is the influence on the learner's emotional reactions. Among the theories of this approach, Thorndike and Skinner works are subject to review and critique. Cognitive psychologists unlike the behaviorists believe that learning is an internal process objective and they focus on thinking, understanding, organizing, and consciousness. Fundamentalists believe that learners should be equipped with the skills of inquiry and problem solving in order to learn by the discovery and process of information. Among this group, we will pay attention to analyze Wertheimer, Brunner, Ausubel theories, Ganyeh information processing model, in addition to its applications in nursing education. Humanists in learning pay attention to the feelings and experiences. Carl Rogers support the retention of learning-centered approach and he is believed to a semantic continuum. At the other end of the continuum, experiential learning is

Learning theories application in nursing education.

Science.gov (United States)

Aliakbari, Fatemeh; Parvin, Neda; Heidari, Mohammad; Haghani, Fariba

2015-01-01

Learning theories are the main guide for educational systems planning in the classroom and clinical training included in nursing. The teachers by knowing the general principles of these theories can use their knowledge more effectively according to various learning situations. In this study, Eric, Medline, and Cochrane databases were used for articles in English and for the Persian literature, Magiran, Iran doc, Iran medex, and Sid databases were used with the help of keywords including social cognitive learning, learning theory, behavioral theory, cognitive theory, constructive theory, and nursing education. The search period was considered from 1990 to 2012. Some related books were also studied about each method, its original vision, the founders, practical application of the training theory, especially training of nursing and its strengths and weaknesses. Behaviorists believe that learning is a change in an observable behavior and it happens when the communication occurs between the two events, a stimulus and a response. Among the applications of this approach is the influence on the learner's emotional reactions. Among the theories of this approach, Thorndike and Skinner works are subject to review and critique. Cognitive psychologists unlike the behaviorists believe that learning is an internal process objective and they focus on thinking, understanding, organizing, and consciousness. Fundamentalists believe that learners should be equipped with the skills of inquiry and problem solving in order to learn by the discovery and process of information. Among this group, we will pay attention to analyze Wertheimer, Brunner, Ausubel theories, Ganyeh information processing model, in addition to its applications in nursing education. Humanists in learning pay attention to the feelings and experiences. Carl Rogers support the retention of learning-centered approach and he is believed to a semantic continuum. At the other end of the continuum, experiential learning is

An Integrated Theory of Prospective Time Interval Estimation: The Role of Cognition, Attention, and Learning

Science.gov (United States)

Taatgen, Niels A.; van Rijn, Hedderik; Anderson, John

2007-01-01

A theory of prospective time perception is introduced and incorporated as a module in an integrated theory of cognition, thereby extending existing theories and allowing predictions about attention and learning. First, a time perception module is established by fitting existing datasets (interval estimation and bisection and impact of secondary…

Consideration on Singularities in Learning Theory and the Learning Coefficient

Directory of Open Access Journals (Sweden)

Miki Aoyagi

2013-09-01

Full Text Available We consider the learning coefficients in learning theory and give two new methods for obtaining these coefficients in a homogeneous case: a method for finding a deepest singular point and a method to add variables. In application to Vandermonde matrix-type singularities, we show that these methods are effective. The learning coefficient of the generalization error in Bayesian estimation serves to measure the learning efficiency in singular learning models. Mathematically, the learning coefficient corresponds to a real log canonical threshold of singularities for the Kullback functions (relative entropy in learning theory.

The relationship between epistemological beliefs, implicit theories of intelligence, and self-regulated learning among Norwegian postsecondary students.

Science.gov (United States)

Bråten, Ivar; Strømsø, Helge I

2005-12-01

More empirical work is needed to examine the dimensionality of personal epistemology and relations between those dimensions and motivational and strategic components of self-regulated learning. In particular, there is great need to investigate personal epistemology and its relation to self-regulated learning across cultures and academic contexts. Because the demarcation between personal epistemology and implicit theories of intelligence has been questioned, dimensions of personal epistemology should also be studied in relation to implicit theories of intelligence. The primary aim was to examine the dimensionality of personal epistemology and the relation between those dimensions and implicit theories of intelligence in the cultural context of Norwegian postsecondary education. A secondary aim was to examine the relative contribution of epistemological beliefs and theories of intelligence to motivational and strategic components of self-regulated learning in different academic contexts within that culture. The first sample included 178 business administration students in a traditional transmission-oriented instructional context; the second, 108 student teachers in an innovative pedagogical context. The dimensionality of the Schommer Epistemological Questionnaire was examined through factor analyses, and the resulting dimensions were examined in relation to implicit theories of intelligence. We performed multiple regression analyses, separately for the two academic contexts, to try to predict motivational (i.e. self-efficacy beliefs, mastery goal orientation, and interest) and strategic (i.e. self-regulatory strategy use) components of self-regulated learning with epistemological beliefs and implicit theories of intelligence. Considerable cross-cultural generalizability was found for the dimensionality of personal epistemology. Moreover, the dimensions of personal epistemology seemed to represent constructs separate from the construct of implicit theories of

The sign learning theory

African Journals Online (AJOL)

KING OF DAWN

The sign learning theory also holds secrets that could be exploited in accomplishing motor tasks. ... Introduction ... In his classic work: Cognitive Map in Rats and Men (1948),Tolman talked about five groups of experiments viz: latent learning ...

Help-Seeking Decisions of Battered Women: A Test of Learned Helplessness and Two Stress Theories.

Science.gov (United States)

Wauchope, Barbara A.

This study tested the learned helplessness theory, stress theory, and a modified stress theory to determine the best model for predicting the probability that a woman would seek help when she experienced severe violence from a male partner. The probability was hypothesized to increase as the stress of the violence experienced increased. Data were…

A Dynamic Logic for Learning Theory

DEFF Research Database (Denmark)

Baltag, Alexandru; Gierasimczuk, Nina; Özgün, Aybüke

2017-01-01

Building on previous work that bridged Formal Learning Theory and Dynamic Epistemic Logic in a topological setting, we introduce a Dynamic Logic for Learning Theory (DLLT), extending Subset Space Logics with dynamic observation modalities, as well as with a learning operator, which encodes the le...... the learner’s conjecture after observing a finite sequence of data. We completely axiomatise DLLT, study its expressivity and use it to characterise various notions of knowledge, belief, and learning. ...

What roles do errors serve in motor skill learning? An examination of two theoretical predictions.

Science.gov (United States)

Sanli, Elizabeth A; Lee, Timothy D

2014-01-01

Easy-to-difficult and difficult-to-easy progressions of task difficulty during skill acquisition were examined in 2 experiments that assessed retention, dual-task, and transfer tests of learning. Findings of the first experiment suggest that an easy-to difficult progression did not consistently induce implicit learning processes and was not consistently beneficial to performance under a secondary-task load. The findings of experiment two did not support the predictions made based on schema theory and only partially supported predictions based on reinvestment theory. The authors interpret these findings to suggest that the timing of error in relation to the difficulty of the task (functional task difficulty) plays a role in the transfer of learning to novel versions of a task.

Constructivist learning theories and complex learning environments

NARCIS (Netherlands)

R-J. Simons; Dr. S. Bolhuis

2004-01-01

Learning theories broadly characterised as constructivist, agree on the importance to learning of the environment, but differ on what exactly it is that constitutes this importance. Accordingly, they also differ on the educational consequences to be drawn from the theoretical perspective. Cognitive

Gaming mindsets: implicit theories in serious game learning.

Science.gov (United States)

Lee, Yu-Hao; Heeter, Carrie; Magerko, Brian; Medler, Ben

2012-04-01

Individuals' beliefs about the malleability of their abilities may predict their response and outcome in learning from serious games. Individuals with growth mindsets believe their abilities can develop with practice and effort, whereas individuals with fixed mindsets believe their abilities are static and cannot improve. This study uses survey and gameplay server data to examine the implicit theory of intelligence in the context of serious game learning. The findings show that growth mindset players performed better than fixed mindset players, their mistakes did not affect their attention to the game, and they read more learning feedback than fixed mindset players. In addition, growth mindset players were more likely to actively seek difficult challenges, which is often essential to self-directed learning. General mindset measurements and domain-specific measurements were also compared. These findings suggest that players' psychological attributes should be considered when designing and applying serious games.

Effective Learning Environments in Relation to Different Learning Theories

NARCIS (Netherlands)

Guney, A.; Al, S.

2012-01-01

There are diverse learning theories which explain learning processes which are discussed within this paper, through cognitive structure of learning process. Learning environments are usually described in terms of pedagogical philosophy, curriculum design and social climate. There have been only just

Advanced Learning Theories Applied to Leadership Development

Science.gov (United States)

2006-11-01

Center for Army Leadership Technical Report 2006-2 Advanced Learning Theories Applied to Leadership Development Christina Curnow...2006 5a. CONTRACT NUMBER W91QF4-05-F-0026 5b. GRANT NUMBER 4. TITLE AND SUBTITLE Advanced Learning Theories Applied to Leadership Development 5c...ABSTRACT This report describes the development and implementation of an application of advanced learning theories to leadership development. A

Reconstructing constructivism: causal models, Bayesian learning mechanisms, and the theory theory.

Science.gov (United States)

Gopnik, Alison; Wellman, Henry M

2012-11-01

We propose a new version of the "theory theory" grounded in the computational framework of probabilistic causal models and Bayesian learning. Probabilistic models allow a constructivist but rigorous and detailed approach to cognitive development. They also explain the learning of both more specific causal hypotheses and more abstract framework theories. We outline the new theoretical ideas, explain the computational framework in an intuitive and nontechnical way, and review an extensive but relatively recent body of empirical results that supports these ideas. These include new studies of the mechanisms of learning. Children infer causal structure from statistical information, through their own actions on the world and through observations of the actions of others. Studies demonstrate these learning mechanisms in children from 16 months to 4 years old and include research on causal statistical learning, informal experimentation through play, and imitation and informal pedagogy. They also include studies of the variability and progressive character of intuitive theory change, particularly theory of mind. These studies investigate both the physical and the psychological and social domains. We conclude with suggestions for further collaborative projects between developmental and computational cognitive scientists.

A learning theory account of depression.

Science.gov (United States)

Ramnerö, Jonas; Folke, Fredrik; Kanter, Jonathan W

2015-06-11

Learning theory provides a foundation for understanding and deriving treatment principles for impacting a spectrum of functional processes relevant to the construct of depression. While behavioral interventions have been commonplace in the cognitive behavioral tradition, most often conceptualized within a cognitive theoretical framework, recent years have seen renewed interest in more purely behavioral models. These modern learning theory accounts of depression focus on the interchange between behavior and the environment, mainly in terms of lack of reinforcement, extinction of instrumental behavior, and excesses of aversive control, and include a conceptualization of relevant cognitive and emotional variables. These positions, drawn from extensive basic and applied research, cohere with biological theories on reduced reward learning and reward responsiveness and views of depression as a heterogeneous, complex set of disorders. Treatment techniques based on learning theory, often labeled Behavioral Activation (BA) focus on activating the individual in directions that increase contact with potential reinforcers, as defined ideographically with the client. BA is considered an empirically well-established treatment that generalizes well across diverse contexts and populations. The learning theory account is discussed in terms of being a parsimonious model and ground for treatments highly suitable for large scale dissemination. © 2015 Scandinavian Psychological Associations and John Wiley & Sons Ltd.

EEG Beta power but not background music predicts the recall scores in an foreign-vocobulary learning tast

OpenAIRE

Küssner, M.B.; de Groot, A.M.B.; Hofman, W.F.; Hillen, M.A.

2016-01-01

As tantalizing as the idea that background music beneficially affects foreign vocabulary learning may seem, there is-partly due to a lack of theory-driven research-no consistent evidence to support this notion. We investigated inter-individual differences in the effects of background music on foreign vocabulary learning. Based on Eysenck's theory of personality we predicted that individuals with a high level of cortical arousal should perform worse when learning with background music compared...

Elemental representation and configural mappings: combining elemental and configural theories of associative learning.

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McLaren, I P L; Forrest, C L; McLaren, R P

2012-09-01

In this article, we present our first attempt at combining an elemental theory designed to model representation development in an associative system (based on McLaren, Kaye, & Mackintosh, 1989) with a configural theory that models associative learning and memory (McLaren, 1993). After considering the possible advantages of such a combination (and some possible pitfalls), we offer a hybrid model that allows both components to produce the phenomena that they are capable of without introducing unwanted interactions. We then successfully apply the model to a range of phenomena, including latent inhibition, perceptual learning, the Espinet effect, and first- and second-order retrospective revaluation. In some cases, we present new data for comparison with our model's predictions. In all cases, the model replicates the pattern observed in our experimental results. We conclude that this line of development is a promising one for arriving at general theories of associative learning and memory.

The Scientific Status of Learning Styles Theories

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Willingham, Daniel T.; Hughes, Elizabeth M.; Dobolyi, David G.

2015-01-01

Theories of learning styles suggest that individuals think and learn best in different ways. These are not differences of ability but rather preferences for processing certain types of information or for processing information in certain types of way. If accurate, learning styles theories could have important implications for instruction because…

The Activity Theory Approach to Learning

Directory of Open Access Journals (Sweden)

Ritva Engeström

2014-12-01

Full Text Available In this paper the author offers a practical view of the theory-grounded research on education action. She draws on studies carried out at the Center for Research on Activity, Development and Learning (CRADLE at the University of Helsinki in Finland. In its work, the Center draws on cultural-historical activity theory (CHAT and is well-known for the theory of Expansive Learning and its more practical application called Developmental Work Research (DWR. These approaches are widely used to understand professional learning and have served as a theoreticaland methodological foundation for studies examining change and professional development in various human activities.

Learning network theory : its contribution to our understanding of work-based learning projects and learning climate

NARCIS (Netherlands)

Poell, R.F.; Moorsel, M.A.A.H. van

1996-01-01

This paper discusses the relevance of Van der Krogt's learning network theory (1995) for our understanding of the concepts of work-related learning projects and learning climate in organisations. The main assumptions of the learning network theory are presented and transferred to the level of

Learned-Helplessness Theory: Implications for Research in Learning Disabilities.

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Canino, Frank J.

1981-01-01

The application of learned helplessness theory to achievement is discussed within the context of implications for research in learning disabilities. Finally, the similarities between helpless children and learning disabled students in terms of problems solving and attention are discussed. (Author)

A Survey of Quantum Learning Theory

OpenAIRE

Arunachalam, Srinivasan; de Wolf, Ronald

2017-01-01

This paper surveys quantum learning theory: the theoretical aspects of machine learning using quantum computers. We describe the main results known for three models of learning: exact learning from membership queries, and Probably Approximately Correct (PAC) and agnostic learning from classical or quantum examples.

What Learning Systems do Intelligent Agents Need? Complementary Learning Systems Theory Updated.

Science.gov (United States)

Kumaran, Dharshan; Hassabis, Demis; McClelland, James L

2016-07-01

We update complementary learning systems (CLS) theory, which holds that intelligent agents must possess two learning systems, instantiated in mammalians in neocortex and hippocampus. The first gradually acquires structured knowledge representations while the second quickly learns the specifics of individual experiences. We broaden the role of replay of hippocampal memories in the theory, noting that replay allows goal-dependent weighting of experience statistics. We also address recent challenges to the theory and extend it by showing that recurrent activation of hippocampal traces can support some forms of generalization and that neocortical learning can be rapid for information that is consistent with known structure. Finally, we note the relevance of the theory to the design of artificial intelligent agents, highlighting connections between neuroscience and machine learning. Copyright © 2016 Elsevier Ltd. All rights reserved.

Statistical Learning Theory: Models, Concepts, and Results

OpenAIRE

von Luxburg, Ulrike; Schoelkopf, Bernhard

2008-01-01

Statistical learning theory provides the theoretical basis for many of today's machine learning algorithms. In this article we attempt to give a gentle, non-technical overview over the key ideas and insights of statistical learning theory. We target at a broad audience, not necessarily machine learning researchers. This paper can serve as a starting point for people who want to get an overview on the field before diving into technical details.

Reconstructing Constructivism: Causal Models, Bayesian Learning Mechanisms, and the Theory Theory

Science.gov (United States)

Gopnik, Alison; Wellman, Henry M.

2012-01-01

We propose a new version of the "theory theory" grounded in the computational framework of probabilistic causal models and Bayesian learning. Probabilistic models allow a constructivist but rigorous and detailed approach to cognitive development. They also explain the learning of both more specific causal hypotheses and more abstract framework…

Structural reliability analysis under evidence theory using the active learning kriging model

Science.gov (United States)

Yang, Xufeng; Liu, Yongshou; Ma, Panke

2017-11-01

Structural reliability analysis under evidence theory is investigated. It is rigorously proved that a surrogate model providing only correct sign prediction of the performance function can meet the accuracy requirement of evidence-theory-based reliability analysis. Accordingly, a method based on the active learning kriging model which only correctly predicts the sign of the performance function is proposed. Interval Monte Carlo simulation and a modified optimization method based on Karush-Kuhn-Tucker conditions are introduced to make the method more efficient in estimating the bounds of failure probability based on the kriging model. Four examples are investigated to demonstrate the efficiency and accuracy of the proposed method.

Game Engagement Theory and Adult Learning

Science.gov (United States)

Whitton, Nicola

2011-01-01

One of the benefits of computer game-based learning is the ability of certain types of game to engage and motivate learners. However, theories of learning and engagement, particularly in the sphere of higher education, typically fail to consider gaming engagement theory. In this article, the author examines the principles of engagement from games…

Effective Learning Environments in Relation to Different Learning Theories

OpenAIRE

Guney, Ali; Al, Selda

2012-01-01

There are diverse learning theories which explain learning processes which are discussed within this paper, through cognitive structure of learning process. Learning environments are usually described in terms of pedagogical philosophy, curriculum design and social climate. There have been only just a few studies about how physical environment is related to learning process. Many researchers generally consider teaching and learning issues as if independent from physical environment, whereas p...

Learning network theory : its contribution to our understanding of work-based learning projects and learning climate

OpenAIRE

Poell, R.F.; Moorsel, M.A.A.H. van

1996-01-01

This paper discusses the relevance of Van der Krogt's learning network theory (1995) for our understanding of the concepts of work-related learning projects and learning climate in organisations. The main assumptions of the learning network theory are presented and transferred to the level of learning groups in organisations. Four theoretical types of learning projects are distinguished. Four different approaches to the learning climate of work groups are compared to the approach offered by t...

Toward a predictive theory for environmental enrichment.

Science.gov (United States)

Watters, Jason V

2009-11-01

There have been many applications of and successes with environmental enrichment for captive animals. The theoretical spine upon which much enrichment work hangs largely describes why enrichment should work. Yet, there remains no clear understanding of how enrichment should be applied to achieve the most beneficial results. This lack of understanding may stem in part from the assumptions that underlie the application of enrichment by practitioners. These assumptions are derived from an understanding that giving animals choice and control in their environment stimulates their motivation to perform behaviors that may indicate a heightened state of well-being. Learning theory provides a means to question the manner in which these constructs are routinely applied, and converting learning theory's findings to optimality predictions suggests a particularly vexing paradox-that motivation to perform appears to be maintained best when acquiring a payoff for expressing the behavior is uncertain. This effect occurs even when the actual value of the payoff is the same for all schedules of certainty of payoff acquisition. The paradox can be resolved by invoking rewards of an alternative type, such as cognitive rewards, or through an understanding of how the average payoff changes with changes in the probability of reward. This model, with measures of the average change of the payoff, suggests testable scenarios by which practitioners can measure the quality of environmental uncertainty in enrichment programs.

The Relative Effect of Team-Based Learning on Motivation and Learning: A Self-Determination Theory Perspective

Science.gov (United States)

Jeno, Lucas M.; Raaheim, Arild; Kristensen, Sara Madeleine; Kristensen, Kjell Daniel; Hole, Torstein Nielsen; Haugland, Mildrid J.; Mæland, Silje

2017-01-01

We investigate the effects of team-based learning (TBL) on motivation and learning in a quasi-experimental study. The study employs a self-determination theory perspective to investigate the motivational effects of implementing TBL in a physiotherapy course in higher education. We adopted a one-group pretest–posttest design. The results show that the students’ intrinsic motivation, identified regulation, perceived competence, and perceived autonomy support significantly increased going from lectures to TBL. The results further show that students’ engagement and perceived learning significantly increased. Finally, students’ amotivation decreased from pretest to posttest; however, students reported higher external regulation as a function of TBL. Path analysis shows that increases in intrinsic motivation, perceived competence, and external regulation positively predict increases in engagement, which in turn predict increases in perceived learning. We argue that the characteristics of TBL, as opposed to lectures, are likely to engage students and facilitate feelings of competence. TBL is an active-learning approach, as opposed to more passive learning in lectures, which might explain the increase in students’ perception of teachers as autonomy supportive. In contrast, the greater demands TBL puts on students might account for the increase in external regulation. Limitations and practical implications of the results are discussed. PMID:29146665

Predicting Online Learning Success: Applying the Situational Theory of Publics to the Virtual Classroom

Science.gov (United States)

Kruger-Ross, Matthew J.; Waters, Richard D.

2013-01-01

Following the trend of increased interest by students to take online courses and by institutions to offer them, scholars have taken many different approaches to understand what makes one student successful in online learning while another may fail. This study proposes that using the situational theory of publics will provide a better understanding…

Linking theory to practice in learning technology research

Directory of Open Access Journals (Sweden)

Cathy Gunn

2012-03-01

Full Text Available We present a case to reposition theory so that it plays a pivotal role in learning technology research and helps to build an ecology of learning. To support the case, we present a critique of current practice based on a review of articles published in two leading international journals from 2005 to 2010. Our study reveals that theory features only incidentally or not at all in many cases. We propose theory development as a unifying theme for learning technology research study design and reporting. The use of learning design as a strategy to develop and test theories in practice is integral to our argument. We conclude by supporting other researchers who recommend educational design research as a theory focused methodology to move the field forward in productive and consistent ways. The challenge of changing common practice will be involved. However, the potential to raise the profile of learning technology research and improve educational outcomes justifies the effort required.

Practice of Connectivism As Learning Theory: Enhancing Learning Process Through Social Networking Site (Facebook

Directory of Open Access Journals (Sweden)

Fahriye Altınay Aksal

2013-12-01

Full Text Available The impact of the digital age within learning and social interaction has been growing rapidly. The realm of digital age and computer mediated communication requires reconsidering instruction based on collaborative interactive learning process and socio-contextual experience for learning. Social networking sites such as facebook can help create group space for digital dialogue to inform, question and challenge within a frame of connectivism as learning theory within the digital age. The aim of this study is to elaborate the practice of connectivism as learning theory in terms of internship course. Facebook group space provided social learning platform for dialogue and negotiation beside the classroom learning and teaching process in this study. The 35 internship students provided self-reports within a frame of this qualitative research. This showed how principles of theory practiced and how this theory and facebook group space contribute learning, selfleadership, decision making and reflection skills. As the research reflects a practice of new theory based on action research, learning is not individualistic attempt in the digital age as regards the debate on learning in digital age within a frame of connectivism

Learning Theory and Equity Valuation: an Empirical Analysis

Directory of Open Access Journals (Sweden)

Antonio Zoratto Sanvicente

2010-07-01

Full Text Available This paper tested the Pástor and Veronesi (2003 hypothesis that the market-to-book ratio (M/B is negatively related to the number of years (age during which a firm has had its stock traded on an Exchange. The predicted decline takes place as a result of a learning process by investors. The authors tested this implication in the U.S. market using the Fama-MacBeth (1973 methodology. In the present article a more general econometric approach is adopted, with the use of panel data and fixed-factor regressors, with data for stocks traded at the São Paulo Stock Exchange (BOVESPA. The evidence does not reject the Pástor and Veronesi hypothesis. Additional conjectures were tested regarding the learning process. These tests indicate that the greater availability of data on a company amplifies the effect of the age variable on the M/B ratio, implying a more accelerated learning process. This paper concludes that the evidence for the Brazilian market supports the theory that investors learn.

Computer-based teaching module design: principles derived from learning theories.

Science.gov (United States)

Lau, K H Vincent

2014-03-01

The computer-based teaching module (CBTM), which has recently gained prominence in medical education, is a teaching format in which a multimedia program serves as a single source for knowledge acquisition rather than playing an adjunctive role as it does in computer-assisted learning (CAL). Despite empirical validation in the past decade, there is limited research into the optimisation of CBTM design. This review aims to summarise research in classic and modern multimedia-specific learning theories applied to computer learning, and to collapse the findings into a set of design principles to guide the development of CBTMs. Scopus was searched for: (i) studies of classic cognitivism, constructivism and behaviourism theories (search terms: 'cognitive theory' OR 'constructivism theory' OR 'behaviourism theory' AND 'e-learning' OR 'web-based learning') and their sub-theories applied to computer learning, and (ii) recent studies of modern learning theories applied to computer learning (search terms: 'learning theory' AND 'e-learning' OR 'web-based learning') for articles published between 1990 and 2012. The first search identified 29 studies, dominated in topic by the cognitive load, elaboration and scaffolding theories. The second search identified 139 studies, with diverse topics in connectivism, discovery and technical scaffolding. Based on their relative representation in the literature, the applications of these theories were collapsed into a list of CBTM design principles. Ten principles were identified and categorised into three levels of design: the global level (managing objectives, framing, minimising technical load); the rhetoric level (optimising modality, making modality explicit, scaffolding, elaboration, spaced repeating), and the detail level (managing text, managing devices). This review examined the literature in the application of learning theories to CAL to develop a set of principles that guide CBTM design. Further research will enable educators to

Geometrical methods in learning theory

International Nuclear Information System (INIS)

Burdet, G.; Combe, Ph.; Nencka, H.

2001-01-01

The methods of information theory provide natural approaches to learning algorithms in the case of stochastic formal neural networks. Most of the classical techniques are based on some extremization principle. A geometrical interpretation of the associated algorithms provides a powerful tool for understanding the learning process and its stability and offers a framework for discussing possible new learning rules. An illustration is given using sequential and parallel learning in the Boltzmann machine

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)

A Comparative Analysis of Three Unique Theories of Organizational Learning

Science.gov (United States)

Leavitt, Carol C.

2011-01-01

The purpose of this paper is to present three classical theories on organizational learning and conduct a comparative analysis that highlights their strengths, similarities, and differences. Two of the theories -- experiential learning theory and adaptive -- generative learning theory -- represent the thinking of the cognitive perspective, while…

Mobile Affordances and Learning Theories in Supporting and Enhancing Learning

Science.gov (United States)

MacCallum, Kathryn; Day, Stephanie; Skelton, David; Verhaart, Michael

2017-01-01

Mobile technology promises to enhance and better support students' learning. The exploration and adoption of appropriate pedagogies that enhance learning is crucial for the wider adoption of mobile learning. An increasing number of studies have started to address how existing learning theory can be used to underpin and better frame mobile learning…

Mini-review: Prediction errors, attention and associative learning.

Science.gov (United States)

Holland, Peter C; Schiffino, Felipe L

2016-05-01

Most modern theories of associative learning emphasize a critical role for prediction error (PE, the difference between received and expected events). One class of theories, exemplified by the Rescorla-Wagner (1972) model, asserts that PE determines the effectiveness of the reinforcer or unconditioned stimulus (US): surprising reinforcers are more effective than expected ones. A second class, represented by the Pearce-Hall (1980) model, argues that PE determines the associability of conditioned stimuli (CSs), the rate at which they may enter into new learning: the surprising delivery or omission of a reinforcer enhances subsequent processing of the CSs that were present when PE was induced. In this mini-review we describe evidence, mostly from our laboratory, for PE-induced changes in the associability of both CSs and USs, and the brain systems involved in the coding, storage and retrieval of these altered associability values. This evidence favors a number of modifications to behavioral models of how PE influences event processing, and suggests the involvement of widespread brain systems in animals' responses to PE. Copyright © 2016 Elsevier Inc. All rights reserved.

Sex differences in cognitive ageing: testing predictions derived from life-history theory in a dioecious nematode.

Science.gov (United States)

Zwoinska, Martyna K; Kolm, Niclas; Maklakov, Alexei A

2013-12-01

Life-history theory maintains that organisms allocate limited resources to different traits to maximize fitness. Learning ability and memory are costly and known to trade-off with longevity in invertebrates. However, since the relationship between longevity and fitness often differs between the sexes, it is likely that sexes will differentially resolve the trade-off between learning and longevity. We used an established associative learning paradigm in the dioecious nematode Caenorhabditis remanei, which is sexually dimorphic for lifespan, to study age-related learning ability in males and females. In particular, we tested the hypothesis that females (the shorter-lived sex) show higher learning ability than males early in life but senesce faster. Indeed, young females outperformed young males in learning a novel association between an odour (butanone) and food (bacteria). However, while learning ability and offspring production declined rapidly with age in females, males maintained high levels of these traits until mid-age. These results not only demonstrate sexual dimorphism in age-related learning ability but also suggest that it conforms to predictions derived from the life-history theory. © 2013.

Linking theory to practice in learning technology research

OpenAIRE

Cathy Gunn; Caroline Steel

2012-01-01

We present a case to reposition theory so that it plays a pivotal role in learning technology research and helps to build an ecology of learning. To support the case, we present a critique of current practice based on a review of articles published in two leading international journals from 2005 to 2010. Our study reveals that theory features only incidentally or not at all in many cases. We propose theory development as a unifying theme for learning technology research study design and repor...

Predictions of the spontaneous symmetry-breaking theory for visual code completeness and spatial scaling in single-cell learning rules.

Science.gov (United States)

Webber, C J

2001-05-01

This article shows analytically that single-cell learning rules that give rise to oriented and localized receptive fields, when their synaptic weights are randomly and independently initialized according to a plausible assumption of zero prior information, will generate visual codes that are invariant under two-dimensional translations, rotations, and scale magnifications, provided that the statistics of their training images are sufficiently invariant under these transformations. Such codes span different image locations, orientations, and size scales with equal economy. Thus, single-cell rules could account for the spatial scaling property of the cortical simple-cell code. This prediction is tested computationally by training with natural scenes; it is demonstrated that a single-cell learning rule can give rise to simple-cell receptive fields spanning the full range of orientations, image locations, and spatial frequencies (except at the extreme high and low frequencies at which the scale invariance of the statistics of digitally sampled images must ultimately break down, because of the image boundary and the finite pixel resolution). Thus, no constraint on completeness, or any other coupling between cells, is necessary to induce the visual code to span wide ranges of locations, orientations, and size scales. This prediction is made using the theory of spontaneous symmetry breaking, which we have previously shown can also explain the data-driven self-organization of a wide variety of transformation invariances in neurons' responses, such as the translation invariance of complex cell response.

The Attribution Theory of Learning and Advising Students on Academic Probation

Science.gov (United States)

Demetriou, Cynthia

2011-01-01

Academic advisors need to be knowledgeable of the ways students learn. To aid advisors in their exploration of learning theories, I provide an overview of the attribution theory of learning, including recent applications of the theory to research in college student learning. An understanding of this theory may help advisors understand student…

Learning theories application in nursing education

OpenAIRE

Aliakbari, Fatemeh; Parvin, Neda; Heidari, Mohammad; Haghani, Fariba

2015-01-01

Learning theories are the main guide for educational systems planning in the classroom and clinical training included in nursing. The teachers by knowing the general principles of these theories can use their knowledge more effectively according to various learning situations. In this study, Eric, Medline, and Cochrane databases were used for articles in English and for the Persian literature, Magiran, Iran doc, Iran medex, and Sid databases were used with the help of keywords including socia...

Dopamine prediction errors in reward learning and addiction: from theory to neural circuitry

Science.gov (United States)

Keiflin, Ronald; Janak, Patricia H.

2015-01-01

Summary Midbrain dopamine (DA) neurons are proposed to signal reward prediction error (RPE), a fundamental parameter in associative learning models. This RPE hypothesis provides a compelling theoretical framework for understanding DA function in reward learning and addiction. New studies support a causal role for DA-mediated RPE activity in promoting learning about natural reward; however, this question has not been explicitly tested in the context of drug addiction. In this review, we integrate theoretical models with experimental findings on the activity of DA systems, and on the causal role of specific neuronal projections and cell types, to provide a circuit-based framework for probing DA-RPE function in addiction. By examining error-encoding DA neurons in the neural network in which they are embedded, hypotheses regarding circuit-level adaptations that possibly contribute to pathological error-signaling and addiction can be formulated and tested. PMID:26494275

Dopamine Prediction Errors in Reward Learning and Addiction: From Theory to Neural Circuitry.

Science.gov (United States)

Keiflin, Ronald; Janak, Patricia H

2015-10-21

Midbrain dopamine (DA) neurons are proposed to signal reward prediction error (RPE), a fundamental parameter in associative learning models. This RPE hypothesis provides a compelling theoretical framework for understanding DA function in reward learning and addiction. New studies support a causal role for DA-mediated RPE activity in promoting learning about natural reward; however, this question has not been explicitly tested in the context of drug addiction. In this review, we integrate theoretical models with experimental findings on the activity of DA systems, and on the causal role of specific neuronal projections and cell types, to provide a circuit-based framework for probing DA-RPE function in addiction. By examining error-encoding DA neurons in the neural network in which they are embedded, hypotheses regarding circuit-level adaptations that possibly contribute to pathological error signaling and addiction can be formulated and tested. Copyright © 2015 Elsevier Inc. All rights reserved.

Learning theory of distributed spectral algorithms

International Nuclear Information System (INIS)

Guo, Zheng-Chu; Lin, Shao-Bo; Zhou, Ding-Xuan

2017-01-01

Spectral algorithms have been widely used and studied in learning theory and inverse problems. This paper is concerned with distributed spectral algorithms, for handling big data, based on a divide-and-conquer approach. We present a learning theory for these distributed kernel-based learning algorithms in a regression framework including nice error bounds and optimal minimax learning rates achieved by means of a novel integral operator approach and a second order decomposition of inverse operators. Our quantitative estimates are given in terms of regularity of the regression function, effective dimension of the reproducing kernel Hilbert space, and qualification of the filter function of the spectral algorithm. They do not need any eigenfunction or noise conditions and are better than the existing results even for the classical family of spectral algorithms. (paper)

Reconstructing constructivism: Causal models, Bayesian learning mechanisms and the theory theory

OpenAIRE

Gopnik, Alison; Wellman, Henry M.

2012-01-01

We propose a new version of the “theory theory” grounded in the computational framework of probabilistic causal models and Bayesian learning. Probabilistic models allow a constructivist but rigorous and detailed approach to cognitive development. They also explain the learning of both more specific causal hypotheses and more abstract framework theories. We outline the new theoretical ideas, explain the computational framework in an intuitive and non-technical way, and review an extensive but ...

Expectancy-value theory in persistence of learning effects in schizophrenia: role of task value and perceived competency.

Science.gov (United States)

Choi, Jimmy; Fiszdon, Joanna M; Medalia, Alice

2010-09-01

Expectancy-value theory, a widely accepted model of motivation, posits that expectations of success on a learning task and the individual value placed on the task are central determinants of motivation to learn. This is supported by research in healthy controls suggesting that beliefs of self-and-content mastery can be so influential they can predict the degree of improvement on challenging cognitive tasks even more so than general cognitive ability. We examined components of expectancy-value theory (perceived competency and task value), along with baseline arithmetic performance and neuropsychological performance, as possible predictors of learning outcome in a sample of 70 outpatients with schizophrenia randomized to 1 of 2 different arithmetic learning conditions and followed up after 3 months. Results indicated that as with nonpsychiatric samples, perceived self-competency for the learning task was significantly related to perceptions of task value attributed to the learning task. Baseline expectations of success predicted persistence of learning on the task at 3-month follow-up, even after accounting for variance attributable to different arithmetic instruction, baseline arithmetic ability, attention, and self-reports of task interest and task value. We also found that expectation of success is a malleable construct, with posttraining improvements persisting at follow-up. These findings support the notion that expectancy-value theory is operative in schizophrenia. Thus, similar to the nonpsychiatric population, treatment benefits may be enhanced and better maintained if remediation programs also focus on perceptions of self-competency for the training tasks. Treatment issues related to instilling self-efficacy in cognitive recovery programs are discussed.

Mobile Devices in the Classroom: Learning Motivations Predict Specific Types of Multicommunicating Behaviors

Science.gov (United States)

Stephens, Keri K.; Pantoja, Gabriel E.

2016-01-01

This study provides a first look into how learning motivations are associated with different ways that students use mobile devices to carry on multiple conversations--multicommunicate--while in class. We use self-determination theory to make predictions linking intrinsic motivation, extrinsic motivation, and amotivation, to classroom mobile device…

Developing Scale for Assimilate the Integration between Learning Theories and E-learning.

Directory of Open Access Journals (Sweden)

George Maher Iskander

2014-03-01

Full Text Available As e-learning tend to get more and more significant for all kind of universities, researchers and consultants are becoming aware of the fact that a high technology approach and Blackboard do not guarantee successful teaching and learning. Thus, a move to pedagogy-based theories can be observed within the field of e-learning. This study describes the procedure of the development of an empirically-based psychometrically-sound instrument to measure instructional model for e-learning system at Middle East universities. In order to accelerate the acceptance of e-learning and implementation of institution-wide adoption of e-learning, it is important to understand students' perceptions with instructional model for e- learning. The 19-item scale developed shows a high probability of differentiating between positive and negative perceptions and the methods which can be used for embedding the traditional learning theories into e-learning.

PREDICTING ACADEMIC ACHIEVEMENT: THE ROLE OF MOTIVATION AND LEARNING STRATEGIES

Directory of Open Access Journals (Sweden)

Juliana Beatriz Stover

2014-04-01

Full Text Available The aim of this study consists in testing a predictive model of academic achievement including motivation and learning strategies as predictors. Motivation is defined as the energy and the direction of behaviors; it is categorized in three types of motivation –intrinsic, extrinsic and amotivation (Deci & Ryan, 1985. Learning strategies are deliberate operations oriented towards information processing in academic activities (Valle, Barca, González & Núñez, 1999. Several studies analysed the relationship between motivation and learning strategies in high school and college environments. Students with higher academic achievement were intrinsically motivated and used a wider variety of learning strategies more frequently. A non-experimental predictive design was developed. The sample was composed by 459 students (55.2% high-schoolers; 44.8% college students. Data were gathered by means of sociodemographic and academic surveys, and also by the local versions of the Academic Motivation Scale –EMA, Echelle de Motivation en Éducation (Stover, de la Iglesia, Rial Boubeta & Fernández Liporace, 2012; Vallerand, Blais, Briere & Pelletier, 1989 and the Learning and Study Strategies Inventory –LASSI (Stover, Uriel & Fernández Liporace, 2012; Weinstein, Schulte & Palmer, 1987. Several path analyses were carried out to test a hypothetical model to predict academic achievement (Kline, 1998. Results indicated that self-determined motivation explained academic achievement through the use of learning strategies. The final model obtained an excellent fit (χ2=16.523, df= 6, p=0.011; GFI=0.987; AGFI=0.955; SRMR=0.0320; NFI=0.913; IFI=0.943; CFI=0.940. Results are discussed considering Self Determination Theory and previous research.

Unrenormalizable theories can be predictive

CERN Document Server

Kubo, J

2003-01-01

Unrenormalizable theories contain infinitely many free parameters. Considering these theories in terms of the Wilsonian renormalization group (RG), we suggest a method for removing this large ambiguity. Our basic assumption is the existence of a maximal ultraviolet cutoff in a cutoff theory, and we require that the theory be so fine tuned as to reach the maximal cutoff. The theory so obtained behaves as a local continuum theory to the shortest distance. In concrete examples of the scalar theory we find that at least in a certain approximation to the Wilsonian RG, this requirement enables us to make unique predictions in the infrared regime in terms of a finite number of independent parameters. Therefore, this method might provide a way for calculating quantum corrections in a low-energy effective theory of quantum gravity. (orig.)

Learning "in" or "with" Games? Quality Criteria for Digital Learning Games from the Perspectives of Learning, Emotion, and Motivation Theory

Science.gov (United States)

Hense, Jan; Mandl, Heinz

2012-01-01

This conceptual paper aims to clarify the theoretical underpinnings of game based learning (GBL) and learning with digital learning games (DLGs). To do so, it analyses learning of game related skills and contents, which occurs constantly during playing conventional entertainment games, from three perspectives: learning theory, emotion theory, and…

Learning Theory Bases of Communicative Methodology and the Notional/Functional Syllabus

OpenAIRE

Jacqueline D., Beebe

1992-01-01

This paper examines the learning theories that underlie the philosophy and practices known as communicative language teaching methodology. These theories are identified first as a reaction against the behavioristic learning theory of audiolingualism. Approaches to syllabus design based on both the "weak" version of communicative language teaching-learning to use the second language-and the "strong" version-using the second language to learn it-are examined. The application of cognitive theory...

Social Learning Theory, Gender, and Intimate Partner Violent Victimization: A Structural Equations Approach.

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Powers, Ráchael A; Cochran, John K; Maskaly, Jon; Sellers, Christine S

2017-05-01

The purpose of this study is to examine the applicability of Akers's Social Learning Theory (SLT) to explain intimate partner violence (IPV) victimization. In doing so, we draw on the Intergenerational Transmission of Violence Theory (IGT) to extend the scope of SLT to the explanation of victimization and for a consideration of uniquely gendered pathways in its causal structure. Using a structural equation modeling approach with self-report data from a sample of college students, the present study tests the extent to which SLT can effectively explain and predict IPV victimization and the degree, if any, to which the social learning model is gender invariant. Although our findings are largely supportive of SLT and, thus, affirm its extension to victimization as well as perpetration, the findings are also somewhat mixed. More significantly, in line with IGT literature, we find that the social learning process is not gender invariant. The implications of the latter are discussed.

Why formal learning theory matters for cognitive science.

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Fulop, Sean; Chater, Nick

2013-01-01

This article reviews a number of different areas in the foundations of formal learning theory. After outlining the general framework for formal models of learning, the Bayesian approach to learning is summarized. This leads to a discussion of Solomonoff's Universal Prior Distribution for Bayesian learning. Gold's model of identification in the limit is also outlined. We next discuss a number of aspects of learning theory raised in contributed papers, related to both computational and representational complexity. The article concludes with a description of how semi-supervised learning can be applied to the study of cognitive learning models. Throughout this overview, the specific points raised by our contributing authors are connected to the models and methods under review. Copyright © 2013 Cognitive Science Society, Inc.

Introduction to Views of Connectivism Theory of Learning

Directory of Open Access Journals (Sweden)

Sa’adi Sa’adi

2016-07-01

Full Text Available ‘Traditional’ theories of learning as pratical dimensions of psychology majorly tend to focus their interest on humans’ inner factors that influence the process of learning such as intelligences, motivation, interest, attitude, concentration and aptitude. They never connect it with instruments and technological inventions such as multimedia, cyber celluler, internet, even social organization, cultural values, traditions etc., while these are very influential nowdays towards the progress and behaviors of human life. As such the application of connectivism theory of learning which connect those dimensions of life with learning activities, is now and then insparable from any effort to promote the quality of humans’ learning itself, including in teaching and learning languages.

Simulation Methodology in Nursing Education and Adult Learning Theory

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Rutherford-Hemming, Tonya

2012-01-01

Simulation is often used in nursing education as a teaching methodology. Simulation is rooted in adult learning theory. Three learning theories, cognitive, social, and constructivist, explain how learners gain knowledge with simulation experiences. This article takes an in-depth look at each of these three theories as each relates to simulation.…

Statistical predictions from anarchic field theory landscapes

International Nuclear Information System (INIS)

Balasubramanian, Vijay; Boer, Jan de; Naqvi, Asad

2010-01-01

Consistent coupling of effective field theories with a quantum theory of gravity appears to require bounds on the rank of the gauge group and the amount of matter. We consider landscapes of field theories subject to such to boundedness constraints. We argue that appropriately 'coarse-grained' aspects of the randomly chosen field theory in such landscapes, such as the fraction of gauge groups with ranks in a given range, can be statistically predictable. To illustrate our point we show how the uniform measures on simple classes of N=1 quiver gauge theories localize in the vicinity of theories with certain typical structures. Generically, this approach would predict a high energy theory with very many gauge factors, with the high rank factors largely decoupled from the low rank factors if we require asymptotic freedom for the latter.

Predictions of new AB O3 perovskite compounds by combining machine learning and density functional theory

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Balachandran, Prasanna V.; Emery, Antoine A.; Gubernatis, James E.; Lookman, Turab; Wolverton, Chris; Zunger, Alex

2018-04-01

We apply machine learning (ML) methods to a database of 390 experimentally reported A B O3 compounds to construct two statistical models that predict possible new perovskite materials and possible new cubic perovskites. The first ML model classified the 390 compounds into 254 perovskites and 136 that are not perovskites with a 90% average cross-validation (CV) accuracy; the second ML model further classified the perovskites into 22 known cubic perovskites and 232 known noncubic perovskites with a 94% average CV accuracy. We find that the most effective chemical descriptors affecting our classification include largely geometric constructs such as the A and B Shannon ionic radii, the tolerance and octahedral factors, the A -O and B -O bond length, and the A and B Villars' Mendeleev numbers. We then construct an additional list of 625 A B O3 compounds assembled from charge conserving combinations of A and B atoms absent from our list of known compounds. Then, using the two ML models constructed on the known compounds, we predict that 235 of the 625 exist in a perovskite structure with a confidence greater than 50% and among them that 20 exist in the cubic structure (albeit, the latter with only ˜50 % confidence). We find that the new perovskites are most likely to occur when the A and B atoms are a lanthanide or actinide, when the A atom is an alkali, alkali earth, or late transition metal atom, or when the B atom is a p -block atom. We also compare the ML findings with the density functional theory calculations and convex hull analyses in the Open Quantum Materials Database (OQMD), which predicts the T =0 K ground-state stability of all the A B O3 compounds. We find that OQMD predicts 186 of 254 of the perovskites in the experimental database to be thermodynamically stable within 100 meV/atom of the convex hull and predicts 87 of the 235 ML-predicted perovskite compounds to be thermodynamically stable within 100 meV/atom of the convex hull, including 6 of these to

Applying psychological theories to evidence-based clinical practice: identifying factors predictive of placing preventive fissure sealants.

Science.gov (United States)

Bonetti, Debbie; Johnston, Marie; Clarkson, Jan E; Grimshaw, Jeremy; Pitts, Nigel B; Eccles, Martin; Steen, Nick; Thomas, Ruth; Maclennan, Graeme; Glidewell, Liz; Walker, Anne

2010-04-08

Psychological models are used to understand and predict behaviour in a wide range of settings, but have not been consistently applied to health professional behaviours, and the contribution of differing theories is not clear. This study explored the usefulness of a range of models to predict an evidence-based behaviour -- the placing of fissure sealants. Measures were collected by postal questionnaire from a random sample of general dental practitioners (GDPs) in Scotland. Outcomes were behavioural simulation (scenario decision-making), and behavioural intention. Predictor variables were from the Theory of Planned Behaviour (TPB), Social Cognitive Theory (SCT), Common Sense Self-regulation Model (CS-SRM), Operant Learning Theory (OLT), Implementation Intention (II), Stage Model, and knowledge (a non-theoretical construct). Multiple regression analysis was used to examine the predictive value of each theoretical model individually. Significant constructs from all theories were then entered into a 'cross theory' stepwise regression analysis to investigate their combined predictive value. Behavioural simulation - theory level variance explained was: TPB 31%; SCT 29%; II 7%; OLT 30%. Neither CS-SRM nor stage explained significant variance. In the cross theory analysis, habit (OLT), timeline acute (CS-SRM), and outcome expectancy (SCT) entered the equation, together explaining 38% of the variance. Behavioural intention - theory level variance explained was: TPB 30%; SCT 24%; OLT 58%, CS-SRM 27%. GDPs in the action stage had significantly higher intention to place fissure sealants. In the cross theory analysis, habit (OLT) and attitude (TPB) entered the equation, together explaining 68% of the variance in intention. The study provides evidence that psychological models can be useful in understanding and predicting clinical behaviour. Taking a theory-based approach enables the creation of a replicable methodology for identifying factors that may predict clinical behaviour

Reflection of Learning Theories in Iranian ELT Textbooks

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Neghad, Hossein Hashem

2014-01-01

This study was undertaken to evaluate Iranian ELT English textbooks (Senior High school and Pre-University) in the light of three learning theories i.e., behaviourism, cognitivism, and constructivism. Each of these learning theories embedding an array of instructional strategies and techniques acted as evaluation checklist. That is, Iranian ELT…

Jigsaw Cooperative Learning: Acid-Base Theories

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Tarhan, Leman; Sesen, Burcin Acar

2012-01-01

This study focused on investigating the effectiveness of jigsaw cooperative learning instruction on first-year undergraduates' understanding of acid-base theories. Undergraduates' opinions about jigsaw cooperative learning instruction were also investigated. The participants of this study were 38 first-year undergraduates in chemistry education…

Task-Based Language Teaching and Expansive Learning Theory

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Robertson, Margaret

2014-01-01

Task-Based Language Teaching (TBLT) has become increasingly recognized as an effective pedagogy, but its location in generalized sociocultural theories of learning has led to misunderstandings and criticism. The purpose of this article is to explain the congruence between TBLT and Expansive Learning Theory and the benefits of doing so. The merit…

Distinguishing between learning and motivation in behavioral tests of the reinforcement sensitivity theory of personality.

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Smillie, Luke D; Dalgleish, Len I; Jackson, Chris J

2007-04-01

According to Gray's (1973) Reinforcement Sensitivity Theory (RST), a Behavioral Inhibition System (BIS) and a Behavioral Activation System (BAS) mediate effects of goal conflict and reward on behavior. BIS functioning has been linked with individual differences in trait anxiety and BAS functioning with individual differences in trait impulsivity. In this article, it is argued that behavioral outputs of the BIS and BAS can be distinguished in terms of learning and motivation processes and that these can be operationalized using the Signal Detection Theory measures of response-sensitivity and response-bias. In Experiment 1, two measures of BIS-reactivity predicted increased response-sensitivity under goal conflict, whereas one measure of BAS-reactivity predicted increased response-sensitivity under reward. In Experiment 2, two measures of BIS-reactivity predicted response-bias under goal conflict, whereas a measure of BAS-reactivity predicted motivation response-bias under reward. In both experiments, impulsivity measures did not predict criteria for BAS-reactivity as traditionally predicted by RST.

Machine learning landscapes and predictions for patient outcomes

Science.gov (United States)

Das, Ritankar; Wales, David J.

2017-07-01

The theory and computational tools developed to interpret and explore energy landscapes in molecular science are applied to the landscapes defined by local minima for neural networks. These machine learning landscapes correspond to fits of training data, where the inputs are vital signs and laboratory measurements for a database of patients, and the objective is to predict a clinical outcome. In this contribution, we test the predictions obtained by fitting to single measurements, and then to combinations of between 2 and 10 different patient medical data items. The effect of including measurements over different time intervals from the 48 h period in question is analysed, and the most recent values are found to be the most important. We also compare results obtained for neural networks as a function of the number of hidden nodes, and for different values of a regularization parameter. The predictions are compared with an alternative convex fitting function, and a strong correlation is observed. The dependence of these results on the patients randomly selected for training and testing decreases systematically with the size of the database available. The machine learning landscapes defined by neural network fits in this investigation have single-funnel character, which probably explains why it is relatively straightforward to obtain the global minimum solution, or a fit that behaves similarly to this optimal parameterization.

Motivation to learn: an overview of contemporary theories.

Science.gov (United States)

Cook, David A; Artino, Anthony R

2016-10-01

To succinctly summarise five contemporary theories about motivation to learn, articulate key intersections and distinctions among these theories, and identify important considerations for future research. Motivation has been defined as the process whereby goal-directed activities are initiated and sustained. In expectancy-value theory, motivation is a function of the expectation of success and perceived value. Attribution theory focuses on the causal attributions learners create to explain the results of an activity, and classifies these in terms of their locus, stability and controllability. Social- cognitive theory emphasises self-efficacy as the primary driver of motivated action, and also identifies cues that influence future self-efficacy and support self-regulated learning. Goal orientation theory suggests that learners tend to engage in tasks with concerns about mastering the content (mastery goal, arising from a 'growth' mindset regarding intelligence and learning) or about doing better than others or avoiding failure (performance goals, arising from a 'fixed' mindset). Finally, self-determination theory proposes that optimal performance results from actions motivated by intrinsic interests or by extrinsic values that have become integrated and internalised. Satisfying basic psychosocial needs of autonomy, competence and relatedness promotes such motivation. Looking across all five theories, we note recurrent themes of competence, value, attributions, and interactions between individuals and the learning context. To avoid conceptual confusion, and perhaps more importantly to maximise the theory-building potential of their work, researchers must be careful (and precise) in how they define, operationalise and measure different motivational constructs. We suggest that motivation research continue to build theory and extend it to health professions domains, identify key outcomes and outcome measures, and test practical educational applications of the principles

Kolb's Experiential Learning Theory in Athletic Training Education: A Literature Review

Science.gov (United States)

Schellhase, Kristen C.

2008-01-01

Objective: Kolb's Experiential Learning Theory offers insight into the development of learning styles, classification of learning styles, and how students learn through experience. Discussion is presented on the value of Kolb's Experiential Learning Theory for Athletic Training Education. Data Sources: This article reviews research related to…

Constructing a Grounded Theory of E-Learning Assessment

Science.gov (United States)

Alonso-Díaz, Laura; Yuste-Tosina, Rocío

2015-01-01

This study traces the development of a grounded theory of assessment in e-learning environments, a field in need of research to establish the parameters of an assessment that is both reliable and worthy of higher learning accreditation. Using grounded theory as a research method, we studied an e-assessment model that does not require physical…

Developing a Domain Theory Defining and Exemplifying a Learning Theory of Progressive Attainments

Science.gov (United States)

Bunderson, C. Victor

2011-01-01

This article defines the concept of Domain Theory, or, when educational measurement is the goal, one might call it a "Learning Theory of Progressive Attainments in X Domain". The concept of Domain Theory is first shown to be rooted in validity theory, then the concept of domain theory is expanded to amplify its necessary but long neglected…

Relativistic theory of gravitation and nonuniqueness of the predictions of general relativity theory

International Nuclear Information System (INIS)

Logunov, A.A.; Loskutov, Yu.M.

1986-01-01

It is shown that while the predictions of relativistic theory of gravitation (RTG) for the gravitational effects are unique and consistent with the experimental data available, the relevant predictions of general relativity theory are not unique. Therewith the above nonuniqueness manifests itself in some effects in the first order in the gravitational interaction constant in others in the second one. The absence in GRT of the energy-momentum and angular momentum conservation laws for the matter and gravitational field taken together and its inapplicability to give uniquely determined predictions for the gravitational phenomena compel to reject GRT as a physical theory

Predictive Game Theory

Science.gov (United States)

Wolpert, David H.

2005-01-01

Probability theory governs the outcome of a game; there is a distribution over mixed strat.'s, not a single "equilibrium". To predict a single mixed strategy must use our loss function (external to the game's players. Provides a quantification of any strategy's rationality. Prove rationality falls as cost of computation rises (for players who have not previously interacted). All extends to games with varying numbers of players.

Developing the master learner: applying learning theory to the learner, the teacher, and the learning environment.

Science.gov (United States)

Schumacher, Daniel J; Englander, Robert; Carraccio, Carol

2013-11-01

As a result of the paradigm shift to a competency-based framework, both self-directed lifelong learning and learner-centeredness have become essential tenets of medical education. In the competency-based framework, learners drive their own educational process, and both learners and teachers share the responsibility for the path and content of learning. This learner-centered emphasis requires each physician to develop and maintain lifelong learning skills, which the authors propose culminate in becoming a "master leaner." To better understand the development of these skills and the attainment of that goal, the authors explore how learning theories inform the development of master learners and how to translate these theories into practical strategies for the learner, the teacher, and the learning environment so as to optimize this development.The authors begin by exploring self-determination theory, which lays the groundwork for understanding the motivation to learn. They next consider the theories of cognitive load and situated cognition, which inform the optimal context and environment for learning. Building from this foundation, the authors consider key educational theories that affect learners' abilities to serve as primary drivers of their learning, including self-directed learning (SDL); the self-assessment skills necessary for SDL; factors affecting self-assessment (self-concept, self-efficacy, illusory superiority, gap filling); and ways to mitigate the inaccuracies of self-assessment (reflection, self-monitoring, external information seeking, and self-directed assessment seeking).For each theory, they suggest practical action steps for the learner, the teacher, and the learning environment in an effort to provide a road map for developing master learners.

Repositioning Ideology Critique in a Critical Theory of Adult Learning.

Science.gov (United States)

Brookfield, Stephen

2001-01-01

Reexamines critical theory as a response to Marxism and repositions ideology critique as a crucial adult learning process. Argues that a critical theory of adult learning should focus on how adults learn to recognize and challenge ideological domination and manipulation. (Contains 31 references.) (SK)

Learning Theory Applied to the Biology Classroom.

Science.gov (United States)

Novak, Joseph D.

1980-01-01

The material presented in this article is intended to help students learn how to learn. The seven key concepts of David Ausubel's assimilation theory for cognitive learning are discussed with reference to the classroom. Concept mapping is suggested as a tool for demonstrating how the seven key concepts function. (SA)

Identifying predictive features in drug response using machine learning: opportunities and challenges.

Science.gov (United States)

Vidyasagar, Mathukumalli

2015-01-01

This article reviews several techniques from machine learning that can be used to study the problem of identifying a small number of features, from among tens of thousands of measured features, that can accurately predict a drug response. Prediction problems are divided into two categories: sparse classification and sparse regression. In classification, the clinical parameter to be predicted is binary, whereas in regression, the parameter is a real number. Well-known methods for both classes of problems are briefly discussed. These include the SVM (support vector machine) for classification and various algorithms such as ridge regression, LASSO (least absolute shrinkage and selection operator), and EN (elastic net) for regression. In addition, several well-established methods that do not directly fall into machine learning theory are also reviewed, including neural networks, PAM (pattern analysis for microarrays), SAM (significance analysis for microarrays), GSEA (gene set enrichment analysis), and k-means clustering. Several references indicative of the application of these methods to cancer biology are discussed.

On-the-Job Training and Social Learning Theory. A Literature Review

Science.gov (United States)

1980-05-01

and discussed by Albert Bandura (47). The principles of social learning theory and learning from models are first described. Then a series of rules...developed by Bandura and his students (47, 48, 49) to be the most useful theory to account for observational learning and to provide a basis for...Learning Theory and Its Application 47. Bandura , A. Principles of Behavior Modification, New York: Holt, Rinehart & Winston, 1969. 48. Bandura , A

Extending Theory-Based Quantitative Predictions to New Health Behaviors.

Science.gov (United States)

Brick, Leslie Ann D; Velicer, Wayne F; Redding, Colleen A; Rossi, Joseph S; Prochaska, James O

2016-04-01

Traditional null hypothesis significance testing suffers many limitations and is poorly adapted to theory testing. A proposed alternative approach, called Testing Theory-based Quantitative Predictions, uses effect size estimates and confidence intervals to directly test predictions based on theory. This paper replicates findings from previous smoking studies and extends the approach to diet and sun protection behaviors using baseline data from a Transtheoretical Model behavioral intervention (N = 5407). Effect size predictions were developed using two methods: (1) applying refined effect size estimates from previous smoking research or (2) using predictions developed by an expert panel. Thirteen of 15 predictions were confirmed for smoking. For diet, 7 of 14 predictions were confirmed using smoking predictions and 6 of 16 using expert panel predictions. For sun protection, 3 of 11 predictions were confirmed using smoking predictions and 5 of 19 using expert panel predictions. Expert panel predictions and smoking-based predictions poorly predicted effect sizes for diet and sun protection constructs. Future studies should aim to use previous empirical data to generate predictions whenever possible. The best results occur when there have been several iterations of predictions for a behavior, such as with smoking, demonstrating that expected values begin to converge on the population effect size. Overall, the study supports necessity in strengthening and revising theory with empirical data.

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

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

Linking Theory to Practice in Learning Technology Research

Science.gov (United States)

Gunn, Cathy; Steel, Caroline

2012-01-01

We present a case to reposition theory so that it plays a pivotal role in learning technology research and helps to build an ecology of learning. To support the case, we present a critique of current practice based on a review of articles published in two leading international journals from 2005 to 2010. Our study reveals that theory features only…

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.

Optimizing Computer Assisted Instruction By Applying Principles of Learning Theory.

Science.gov (United States)

Edwards, Thomas O.

The development of learning theory and its application to computer-assisted instruction (CAI) are described. Among the early theoretical constructs thought to be important are E. L. Thorndike's concept of learning connectisms, Neal Miller's theory of motivation, and B. F. Skinner's theory of operant conditioning. Early devices incorporating those…

Finite Unification: Theory, Models and Predictions

CERN Document Server

Heinemeyer, S; Zoupanos, G

2011-01-01

All-loop Finite Unified Theories (FUTs) are very interesting N=1 supersymmetric Grand Unified Theories (GUTs) realising an old field theory dream, and moreover have a remarkable predictive power due to the required reduction of couplings. The reduction of the dimensionless couplings in N=1 GUTs is achieved by searching for renormalization group invariant (RGI) relations among them holding beyond the unification scale. Finiteness results from the fact that there exist RGI relations among dimensional couplings that guarantee the vanishing of all beta-functions in certain N=1 GUTs even to all orders. Furthermore developments in the soft supersymmetry breaking sector of N=1 GUTs and FUTs lead to exact RGI relations, i.e. reduction of couplings, in this dimensionful sector of the theory, too. Based on the above theoretical framework phenomenologically consistent FUTs have been constructed. Here we review FUT models based on the SU(5) and SU(3)^3 gauge groups and their predictions. Of particular interest is the Hig...

The application of learning theory in horse training

DEFF Research Database (Denmark)

McLean, Andrew N.; Christensen, Janne Winther

2017-01-01

The millennia-old practices of horse training markedly predate and thus were isolated from the mid-twentieth century revelation of animal learning processes. From this standpoint, the progress made in the application and understanding of learning theory in horse training is reviewed including...... on the correct application of learning theory, and safety and welfare benefits for people and horses would follow. Finally it is also proposed that the term ‘conflict theory’ be taken up in equitation science to facilitate diagnosis of training-related behaviour disorders and thus enable the emergence...

Connectivism: Learning theory of the future or vestige of the past?

Directory of Open Access Journals (Sweden)

Rita Kop

2008-10-01

Full Text Available Siemens and Downes initially received increasing attention in the blogosphere in 2005 when they discussed their ideas concerning distributed knowledge. An extended discourse has ensued in and around the status of ‘connectivism’ as a learning theory for the digital age. This has led to a number of questions in relation to existing learning theories. Do they still meet the needs of today’s learners, and anticipate the needs of learners of the future? Would a new theory that encompasses new developments in digital technology be more appropriate, and would it be suitable for other aspects of learning, including in the traditional class room, in distance education and e-learning? This paper will highlight current theories of learning and critically analyse connectivism within the context of its predecessors, to establish if it has anything new to offer as a learning theory or as an approach to teaching for the 21st Century.

Applying psychological theories to evidence-based clinical practice: identifying factors predictive of placing preventive fissure sealants

Directory of Open Access Journals (Sweden)

Maclennan Graeme

2010-04-01

Full Text Available Abstract Background Psychological models are used to understand and predict behaviour in a wide range of settings, but have not been consistently applied to health professional behaviours, and the contribution of differing theories is not clear. This study explored the usefulness of a range of models to predict an evidence-based behaviour -- the placing of fissure sealants. Methods Measures were collected by postal questionnaire from a random sample of general dental practitioners (GDPs in Scotland. Outcomes were behavioural simulation (scenario decision-making, and behavioural intention. Predictor variables were from the Theory of Planned Behaviour (TPB, Social Cognitive Theory (SCT, Common Sense Self-regulation Model (CS-SRM, Operant Learning Theory (OLT, Implementation Intention (II, Stage Model, and knowledge (a non-theoretical construct. Multiple regression analysis was used to examine the predictive value of each theoretical model individually. Significant constructs from all theories were then entered into a 'cross theory' stepwise regression analysis to investigate their combined predictive value Results Behavioural simulation - theory level variance explained was: TPB 31%; SCT 29%; II 7%; OLT 30%. Neither CS-SRM nor stage explained significant variance. In the cross theory analysis, habit (OLT, timeline acute (CS-SRM, and outcome expectancy (SCT entered the equation, together explaining 38% of the variance. Behavioural intention - theory level variance explained was: TPB 30%; SCT 24%; OLT 58%, CS-SRM 27%. GDPs in the action stage had significantly higher intention to place fissure sealants. In the cross theory analysis, habit (OLT and attitude (TPB entered the equation, together explaining 68% of the variance in intention. Summary The study provides evidence that psychological models can be useful in understanding and predicting clinical behaviour. Taking a theory-based approach enables the creation of a replicable methodology for

Three Theories of Learning and Their Implications for Teachers.

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Ramirez, Aura I.

Currently, three theories of learning dominate classroom practice. First, B.F. Skinner's Theory of Operant Conditioning states that if behavior, including learning behavior, is reinforced, the probability of its being repeated increases strongly. Different types and schedules of reinforcement have been studied, by Skinner and others, and the…

Applying Distributed Learning Theory in Online Business Communication Courses.

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Walker, Kristin

2003-01-01

Focuses on the critical use of technology in online formats that entail relatively new teaching media. Argues that distributed learning theory is valuable for teachers of online business communication courses for several reasons. Discusses the application of distributed learning theory to the teaching of business communication online. (SG)

Predicting Process Behaviour using Deep Learning

OpenAIRE

Evermann, Joerg; Rehse, Jana-Rebecca; Fettke, Peter

2016-01-01

Predicting business process behaviour is an important aspect of business process management. Motivated by research in natural language processing, this paper describes an application of deep learning with recurrent neural networks to the problem of predicting the next event in a business process. This is both a novel method in process prediction, which has largely relied on explicit process models, and also a novel application of deep learning methods. The approach is evaluated on two real da...

[Linking learning theory with practice].

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Ávalos-Carranza, María Teresa; Amador-Olvera, Eric; Zerón-Gutiérrez, Lydia

2016-01-01

It is often said that it is easier to learn what is observed and practiced on a daily basis; to the need to effectively link theory with practice considered in the process of teaching and learning, many strategies have been developed to allow this process to be carried out in a more efficiently maner. It is, therefore, very important to recognize that an appropriate teacher/student relationship is essential for students to acquire the skills and abilities required.

Learning Theories Applied to Teaching Technology: Constructivism versus Behavioral Theory for Instructing Multimedia Software Programs

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Reed, Cajah S.

2012-01-01

This study sought to find evidence for a beneficial learning theory to teach computer software programs. Additionally, software was analyzed for each learning theory's applicability to resolve whether certain software requires a specific method of education. The results are meant to give educators more effective teaching tools, so students…

Predictive Variable Gain Iterative Learning Control for PMSM

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Huimin Xu

2015-01-01

Full Text Available A predictive variable gain strategy in iterative learning control (ILC is introduced. Predictive variable gain iterative learning control is constructed to improve the performance of trajectory tracking. A scheme based on predictive variable gain iterative learning control for eliminating undesirable vibrations of PMSM system is proposed. The basic idea is that undesirable vibrations of PMSM system are eliminated from two aspects of iterative domain and time domain. The predictive method is utilized to determine the learning gain in the ILC algorithm. Compression mapping principle is used to prove the convergence of the algorithm. Simulation results demonstrate that the predictive variable gain is superior to constant gain and other variable gains.

Examining Gender Differences toward the Adoption of Online Learning and Predicting the Readiness of Faculty Members in a Middle-Eastern Recently Established Public University

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Abahussain, Mohammed Mansour

2017-01-01

This quantitative study examined the gender-based difference toward the adoption of online learning based on constructs of the Theory of Planned Behavior (TPB). It is also aimed to predict the Behavioral Intention of the adoption of online learning based on the predicting variables of the TPB, Attitude, Subjective Norm, and Perceived Behavioral…

Predicting behavioural responses to novel organisms: state-dependent detection theory.

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Trimmer, Pete C; Ehlman, Sean M; Sih, Andrew

2017-01-25

Human activity alters natural habitats for many species. Understanding variation in animals' behavioural responses to these changing environments is critical. We show how signal detection theory can be used within a wider framework of state-dependent modelling to predict behavioural responses to a major environmental change: novel, exotic species. We allow thresholds for action to be a function of reserves, and demonstrate how optimal thresholds can be calculated. We term this framework 'state-dependent detection theory' (SDDT). We focus on behavioural and fitness outcomes when animals continue to use formerly adaptive thresholds following environmental change. In a simple example, we show that exposure to novel animals which appear dangerous-but are actually safe-(e.g. ecotourists) can have catastrophic consequences for 'prey' (organisms that respond as if the new organisms are predators), significantly increasing mortality even when the novel species is not predatory. SDDT also reveals that the effect on reproduction can be greater than the effect on lifespan. We investigate factors that influence the effect of novel organisms, and address the potential for behavioural adjustments (via evolution or learning) to recover otherwise reduced fitness. Although effects of environmental change are often difficult to predict, we suggest that SDDT provides a useful route ahead. © 2017 The Author(s).

Dissociation between judgments and outcome-expectancy measures in covariation learning: a signal detection theory approach.

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Perales, José C; Catena, Andrés; Shanks, David R; González, José A

2005-09-01

A number of studies using trial-by-trial learning tasks have shown that judgments of covariation between a cue c and an outcome o deviate from normative metrics. Parameters based on trial-by-trial predictions were estimated from signal detection theory (SDT) in a standard causal learning task. Results showed that manipulations of P(c) when contingency (deltaP) was held constant did not affect participants' ability to predict the appearance of the outcome (d') but had a significant effect on response criterion (c) and numerical causal judgments. The association between criterion c and judgment was further demonstrated in 2 experiments in which the criterion was directly manipulated by linking payoffs to the predictive responses made by learners. In all cases, the more liberal the criterion c was, the higher judgments were. The results imply that the mechanisms underlying the elaboration of judgments and those involved in the elaboration of predictive responses are partially dissociable.

Automatically explaining machine learning prediction results: a demonstration on type 2 diabetes risk prediction.

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Luo, Gang

2016-01-01

Predictive modeling is a key component of solutions to many healthcare problems. Among all predictive modeling approaches, machine learning methods often achieve the highest prediction accuracy, but suffer from a long-standing open problem precluding their widespread use in healthcare. Most machine learning models give no explanation for their prediction results, whereas interpretability is essential for a predictive model to be adopted in typical healthcare settings. This paper presents the first complete method for automatically explaining results for any machine learning predictive model without degrading accuracy. We did a computer coding implementation of the method. Using the electronic medical record data set from the Practice Fusion diabetes classification competition containing patient records from all 50 states in the United States, we demonstrated the method on predicting type 2 diabetes diagnosis within the next year. For the champion machine learning model of the competition, our method explained prediction results for 87.4 % of patients who were correctly predicted by the model to have type 2 diabetes diagnosis within the next year. Our demonstration showed the feasibility of automatically explaining results for any machine learning predictive model without degrading accuracy.

Recent Advances in Predictive (Machine) Learning

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Friedman, J

2004-01-24

Prediction involves estimating the unknown value of an attribute of a system under study given the values of other measured attributes. In prediction (machine) learning the prediction rule is derived from data consisting of previously solved cases. Most methods for predictive learning were originated many years ago at the dawn of the computer age. Recently two new techniques have emerged that have revitalized the field. These are support vector machines and boosted decision trees. This paper provides an introduction to these two new methods tracing their respective ancestral roots to standard kernel methods and ordinary decision trees.

Deep learning methods for protein torsion angle prediction.

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Li, Haiou; Hou, Jie; Adhikari, Badri; Lyu, Qiang; Cheng, Jianlin

2017-09-18

Deep learning is one of the most powerful machine learning methods that has achieved the state-of-the-art performance in many domains. Since deep learning was introduced to the field of bioinformatics in 2012, it has achieved success in a number of areas such as protein residue-residue contact prediction, secondary structure prediction, and fold recognition. In this work, we developed deep learning methods to improve the prediction of torsion (dihedral) angles of proteins. We design four different deep learning architectures to predict protein torsion angles. The architectures including deep neural network (DNN) and deep restricted Boltzmann machine (DRBN), deep recurrent neural network (DRNN) and deep recurrent restricted Boltzmann machine (DReRBM) since the protein torsion angle prediction is a sequence related problem. In addition to existing protein features, two new features (predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments) are used as input to each of the four deep learning architectures to predict phi and psi angles of protein backbone. The mean absolute error (MAE) of phi and psi angles predicted by DRNN, DReRBM, DRBM and DNN is about 20-21° and 29-30° on an independent dataset. The MAE of phi angle is comparable to the existing methods, but the MAE of psi angle is 29°, 2° lower than the existing methods. On the latest CASP12 targets, our methods also achieved the performance better than or comparable to a state-of-the art method. Our experiment demonstrates that deep learning is a valuable method for predicting protein torsion angles. The deep recurrent network architecture performs slightly better than deep feed-forward architecture, and the predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments are useful features for improving prediction accuracy.

Prediction and theory evaluation: the case of light bending.

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Brush, S G

1989-12-01

Is a theory that makes successful predictions of new facts better than one that does not? Does a fact provide better evidence for a theory if it was not known before being deduced from the theory? These questions can be answered by analyzing historical cases. Einstein's successful prediction of gravitational light bending from his general theory of relativity has been presented as an important example of how "real" science works (in contrast to alleged pseudosciences like psychoanalysis). But, while this success gained favorable publicity for the theory, most scientists did not give it any more weight than the deduction of the advance of Mercury's perihelion (a phenomenon known for several decades). The fact that scientists often use the word "prediction" to describe the deduction of such previously known facts suggests that novelty may be of little importance in evaluating theories. It may even detract from the evidential value of a fact, until it is clear that competing theories cannot account for the new fact.

Social cognitive theory, metacognition, and simulation learning in nursing education.

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Burke, Helen; Mancuso, Lorraine

2012-10-01

Simulation learning encompasses simple, introductory scenarios requiring response to patients' needs during basic hygienic care and during situations demanding complex decision making. Simulation integrates principles of social cognitive theory (SCT) into an interactive approach to learning that encompasses the core principles of intentionality, forethought, self-reactiveness, and self-reflectiveness. Effective simulation requires an environment conducive to learning and introduces activities that foster symbolic coding operations and mastery of new skills; debriefing builds self-efficacy and supports self-regulation of behavior. Tailoring the level of difficulty to students' mastery level supports successful outcomes and motivation to set higher standards. Mindful selection of simulation complexity and structure matches course learning objectives and supports progressive development of metacognition. Theory-based facilitation of simulated learning optimizes efficacy of this learning method to foster maturation of cognitive processes of SCT, metacognition, and self-directedness. Examples of metacognition that are supported through mindful, theory-based implementation of simulation learning are provided. Copyright 2012, SLACK Incorporated.

An Interpretation of Dewey's Experiential Learning Theory.

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Roberts, T. Grady

"Experience and Education" (John Dewey, 1938) serves as a foundation piece of literature when discussing experiential learning. To facilitate a better understanding, a conceptual model was developed. In John Dewey's experiential learning theory, everything occurs within a social environment. Knowledge is socially constructed and based on…

Utilizing Kolb’s Experiential Learning Theory to Implement a Golf Scramble

OpenAIRE

Glenna G. Bower

2013-01-01

This study introduced how Kolb’s Experiential Learning Theory was used across the four-mode learning cycle of abstract conceptualization, active experimentation, concrete experience and reflective observation as a pedagogical tool for implementing a golf scramble. The primary research question was to see whether Kolb’s Experiential Learning Theory four-mode learning cycle was an effective means for implementing a the golf scramble. The participants of the experiential learning experience wer...

Gallery Educators as Adult Learners: The Active Application of Adult Learning Theory

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McCray, Kimberly H.

2016-01-01

In order to better understand the importance of adult learning theory to museum educators' work, and that of their profession at large, museum professionals must address the need for more adult learning research and practice in museums--particularly work informed by existing theory and work seeking to generate new theory. Adult learning theory…

Virtual Learning Environments: A View from the Theory of Conceptual Fields

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Iralí Araque

2018-01-01

Full Text Available The inclusion of communication and information technologies for formative purposes has given way to virtual learning environments, which, backed by constructivist theories, provide a theoretical and methodological framework, thus contributing to the cognitive development of students at university level, evidenced in the development of their learning schemes. The theory of conceptual fields offers an analysis of the elements of schemas and the process of knowledge construction. In this sense, the present work had as objective to raise some elements, such as teaching methodology, didactic strategies, materials and resources for learning, teacher and student roles, which should be considered in the design of virtual learning environments, in the light of the theory of conceptual fields, so as to enhance the construction of knowledge and where the emphasis of the educational process lies on learning rather than teaching. The methodology used is a documentary study, type descriptive, based on the review and bibliographical analysis of constructivist theories, as well as researchers related to the design and construction of virtual learning environments. The results show that conceptual fields theory is an excellent option to consider, as a constructivist theory, in order to consolidate the process of knowledge construction, from the individual to the collective.

Cooperative Learning: Improving University Instruction by Basing Practice on Validated Theory

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Johnson, David W.; Johnson, Roger T.; Smith, Karl A.

2014-01-01

Cooperative learning is an example of how theory validated by research may be applied to instructional practice. The major theoretical base for cooperative learning is social interdependence theory. It provides clear definitions of cooperative, competitive, and individualistic learning. Hundreds of research studies have validated its basic…

Sustainability of healthcare improvement: what can we learn from learning theory?

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Hovlid Einar

2012-08-01

Full Text Available Abstract Background Changes that improve the quality of health care should be sustained. Falling back to old, unsatisfactory ways of working is a waste of resources and can in the worst case increase resistance to later initiatives to improve care. Quality improvement relies on changing the clinical system yet factors that influence the sustainability of quality improvements are poorly understood. Theoretical frameworks can guide further research on the sustainability of quality improvements. Theories of organizational learning have contributed to a better understanding of organizational change in other contexts. To identify factors contributing to sustainability of improvements, we use learning theory to explore a case that had displayed sustained improvement. Methods Førde Hospital redesigned the pathway for elective surgery and achieved sustained reduction of cancellation rates. We used a qualitative case study design informed by theory to explore factors that contributed to sustain the improvements at Førde Hospital. The model Evidence in the Learning Organization describes how organizational learning contributes to change in healthcare institutions. This model constituted the framework for data collection and analysis. We interviewed a strategic sample of 20 employees. The in-depth interviews covered themes identified through our theoretical framework. Through a process of coding and condensing, we identified common themes that were interpreted in relation to our theoretical framework. Results Clinicians and leaders shared information about their everyday work and related this knowledge to how the entire clinical pathway could be improved. In this way they developed a revised and deeper understanding of their clinical system and its interdependencies. They became increasingly aware of how different elements needed to interact to enhance the performance and how their own efforts could contribute. Conclusions The improved understanding of

Sustainability of healthcare improvement: what can we learn from learning theory?

Science.gov (United States)

Hovlid, Einar; Bukve, Oddbjørn; Haug, Kjell; Aslaksen, Aslak Bjarne; von Plessen, Christian

2012-08-03

Changes that improve the quality of health care should be sustained. Falling back to old, unsatisfactory ways of working is a waste of resources and can in the worst case increase resistance to later initiatives to improve care. Quality improvement relies on changing the clinical system yet factors that influence the sustainability of quality improvements are poorly understood. Theoretical frameworks can guide further research on the sustainability of quality improvements. Theories of organizational learning have contributed to a better understanding of organizational change in other contexts. To identify factors contributing to sustainability of improvements, we use learning theory to explore a case that had displayed sustained improvement. Førde Hospital redesigned the pathway for elective surgery and achieved sustained reduction of cancellation rates. We used a qualitative case study design informed by theory to explore factors that contributed to sustain the improvements at Førde Hospital. The model Evidence in the Learning Organization describes how organizational learning contributes to change in healthcare institutions. This model constituted the framework for data collection and analysis. We interviewed a strategic sample of 20 employees. The in-depth interviews covered themes identified through our theoretical framework. Through a process of coding and condensing, we identified common themes that were interpreted in relation to our theoretical framework. Clinicians and leaders shared information about their everyday work and related this knowledge to how the entire clinical pathway could be improved. In this way they developed a revised and deeper understanding of their clinical system and its interdependencies. They became increasingly aware of how different elements needed to interact to enhance the performance and how their own efforts could contribute. The improved understanding of the clinical system represented a change in mental models of

Testing the scalar expectancy theory (SET) and the learning-to-time model (LeT) in a double bisection task.

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Machado, Armando; Pata, Paulo

2005-02-01

Two theories of timing, scalar expectancy theory (SET) and learning-to-time (LeT), make substantially different assumptions about what animals learn in temporal tasks. In a test of these assumptions, pigeons learned two temporal discriminations. On Type 1 trials, they learned to choose a red key after a 1-sec signal and a green key after a 4-sec signal; on Type 2 trials, they learned to choose a blue key after a 4-sec signal and a yellow key after either an 8-sec signal (Group 8) or a 16-sec signal (Group 16). Then, the birds were exposed to signals 1 sec, 4 sec, and 16 sec in length and given a choice between novel key combinations (red or green vs. blue or yellow). The choice between the green key and the blue key was of particular significance because both keys were associated with the same 4-sec signal. Whereas SET predicted no effect of the test signal duration on choice, LeT predicted that preference for green would increase monotonically with the length of the signal but would do so faster for Group 8 than for Group 16. The results were consistent with LeT, but not with SET.

Prostate Cancer Probability Prediction By Machine Learning Technique.

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Jović, Srđan; Miljković, Milica; Ivanović, Miljan; Šaranović, Milena; Arsić, Milena

2017-11-26

The main goal of the study was to explore possibility of prostate cancer prediction by machine learning techniques. In order to improve the survival probability of the prostate cancer patients it is essential to make suitable prediction models of the prostate cancer. If one make relevant prediction of the prostate cancer it is easy to create suitable treatment based on the prediction results. Machine learning techniques are the most common techniques for the creation of the predictive models. Therefore in this study several machine techniques were applied and compared. The obtained results were analyzed and discussed. It was concluded that the machine learning techniques could be used for the relevant prediction of prostate cancer.

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

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

2016-01-01

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

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

Directory of Open Access Journals (Sweden)

Mats B Küssner

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

Comparing three attitude-behavior theories for predicting science teachers' intentions

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Zint, Michaela

2002-11-01

Social psychologists' attitude-behavior theories can contribute to understanding science teachers' behaviors. Such understanding can, in turn, be used to improve professional development. This article describes leading attitude-behavior theories and summarizes results from past tests of these theories. A study predicting science teachers' intention to incorporate environmental risk education based on these theories is also reported. Data for that study were collected through a mail questionnaire (n = 1336, radjusted = 80%) and analyzed using confirmatory factor and multiple regression analysis. All determinants of intention to act in the Theory of Reasoned Action and Theory of Planned Behavior and some determinants in the Theory of Trying predicted science teachers' environmental risk education intentions. Given the consistency of results across studies, the Theory of Planned Behavior augmented with past behavior is concluded to provide the best attitude-behavior model for predicting science teachers' intention to act. Thus, science teachers' attitude toward the behavior, perceived behavioral control, and subjective norm need to be enhanced to modify their behavior. Based on the Theory of Trying, improving their attitude toward the process and toward success, and expectations of success may also result in changes. Future research should focus on identifying determinants that can further enhance the ability of these theories to predict and explain science teachers' behaviors.

Concept-Based Learning in Clinical Experiences: Bringing Theory to Clinical Education for Deep Learning.

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Nielsen, Ann

2016-07-01

Concept-based learning is used increasingly in nursing education to support the organization, transfer, and retention of knowledge. Concept-based learning activities (CBLAs) have been used in clinical education to explore key aspects of the patient situation and principles of nursing care, without responsibility for total patient care. The nature of best practices in teaching and the resultant learning are not well understood. The purpose of this multiple-case study research was to explore and describe concept-based learning in the context of clinical education in inpatient settings. Four clinical groups (each a case) were observed while they used CBLAs in the clinical setting. Major findings include that concept-based learning fosters deep learning, connection of theory with practice, and clinical judgment. Strategies used to support learning, major teaching-learning foci, and preconditions for concept-based teaching and learning will be described. Concept-based learning is promising to support integration of theory with practice and clinical judgment through application experiences with patients. [J Nurs Educ. 2016;55(7):365-371.]. Copyright 2016, SLACK Incorporated.

The Prediction of Item Parameters Based on Classical Test Theory and Latent Trait Theory

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Anil, Duygu

2008-01-01

In this study, the prediction power of the item characteristics based on the experts' predictions on conditions try-out practices cannot be applied was examined for item characteristics computed depending on classical test theory and two-parameters logistic model of latent trait theory. The study was carried out on 9914 randomly selected students…

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

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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.

Learning theories 101: application to everyday teaching and scholarship.

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Kay, Denise; Kibble, Jonathan

2016-03-01

Shifts in educational research, in how scholarship in higher education is defined, and in how funding is appropriated suggest that educators within basic science fields can benefit from increased understanding of learning theory and how it applies to classroom practice. This article uses a mock curriculum design scenario as a framework for the introduction of five major learning theories. Foundational constructs and principles from each theory and how they apply to the proposed curriculum designs are described. A summative table that includes basic principles, constructs, and classroom applications as well as the role of the teacher and learner is also provided for each theory. Copyright © 2016 The American Physiological Society.

A Critical Comparison of Transformation and Deep Approach Theories of Learning

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Howie, Peter; Bagnall, Richard

2015-01-01

This paper reports a critical comparative analysis of two popular and significant theories of adult learning: the transformation and the deep approach theories of learning. These theories are operative in different educational sectors, are significant, respectively, in each, and they may be seen as both touching on similar concerns with learning…

Expectancy-Value Theory in Persistence of Learning Effects in Schizophrenia: Role of Task Value and Perceived Competency

OpenAIRE

Choi, Jimmy; Fiszdon, Joanna M.; Medalia, Alice

2010-01-01

Expectancy-value theory, a widely accepted model of motivation, posits that expectations of success on a learning task and the individual value placed on the task are central determinants of motivation to learn. This is supported by research in healthy controls suggesting that beliefs of self-and-content mastery can be so influential they can predict the degree of improvement on challenging cognitive tasks even more so than general cognitive ability. We examined components of expectancy-value...

Using Machine Learning to Predict Student Performance

OpenAIRE

Pojon, Murat

2017-01-01

This thesis examines the application of machine learning algorithms to predict whether a student will be successful or not. The specific focus of the thesis is the comparison of machine learning methods and feature engineering techniques in terms of how much they improve the prediction performance. Three different machine learning methods were used in this thesis. They are linear regression, decision trees, and naïve Bayes classification. Feature engineering, the process of modification ...

High school students' implicit theories of what facilitates science learning

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Carlton Parsons, Eileen; Miles, Rhea; Petersen, Michael

2011-11-01

Background: Research has primarily concentrated on adults' implicit theories about high quality science education for all students. Little work has considered the students' perspective. This study investigated high school students' implicit theories about what helped them learn science. Purpose: This study addressed (1) What characterizes high school students' implicit theories of what facilitates their learning of science?; (2) With respect to students' self-classifications as African American or European American and female or male, do differences exist in the students' implicit theories? Sample, design and methods: Students in an urban high school located in south-eastern United States were surveyed in 2006 about their thoughts on what helps them learn science. To confirm or disconfirm any differences, data from two different samples were analyzed. Responses of 112 African American and 118 European American students and responses from 297 European American students comprised the data for sample one and two, respectively. Results: Seven categories emerged from the deductive and inductive analyses of data: personal responsibility, learning arrangements, interest and knowledge, communication, student mastery, environmental responsiveness, and instructional strategies. Instructional strategies captured 82% and 80% of the data from sample one and two, respectively; consequently, this category was further subjected to Mann-Whitney statistical analysis at p ethnic differences. Significant differences did not exist for ethnicity but differences between females and males in sample one and sample two emerged. Conclusions: African American and European American students' implicit theories about instructional strategies that facilitated their science learning did not significantly differ but female and male students' implicit theories about instructional strategies that helped them learn science significantly differed. Because students attend and respond to what they think

Theory-based Bayesian models of inductive learning and reasoning.

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Tenenbaum, Joshua B; Griffiths, Thomas L; Kemp, Charles

2006-07-01

Inductive inference allows humans to make powerful generalizations from sparse data when learning about word meanings, unobserved properties, causal relationships, and many other aspects of the world. Traditional accounts of induction emphasize either the power of statistical learning, or the importance of strong constraints from structured domain knowledge, intuitive theories or schemas. We argue that both components are necessary to explain the nature, use and acquisition of human knowledge, and we introduce a theory-based Bayesian framework for modeling inductive learning and reasoning as statistical inferences over structured knowledge representations.

Understanding Self-Controlled Motor Learning Protocols through the Self-Determination Theory.

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Sanli, Elizabeth A; Patterson, Jae T; Bray, Steven R; Lee, Timothy D

2012-01-01

The purpose of the present review was to provide a theoretical understanding of the learning advantages underlying a self-controlled practice context through the tenets of the self-determination theory (SDT). Three micro-theories within the macro-theory of SDT (Basic psychological needs theory, Cognitive Evaluation Theory, and Organismic Integration Theory) are used as a framework for examining the current self-controlled motor learning literature. A review of 26 peer-reviewed, empirical studies from the motor learning and medical training literature revealed an important limitation of the self-controlled research in motor learning: that the effects of motivation have been assumed rather than quantified. The SDT offers a basis from which to include measurements of motivation into explanations of changes in behavior. This review suggests that a self-controlled practice context can facilitate such factors as feelings of autonomy and competence of the learner, thereby supporting the psychological needs of the learner, leading to long term changes to behavior. Possible tools for the measurement of motivation and regulation in future studies are discussed. The SDT not only allows for a theoretical reinterpretation of the extant motor learning research supporting self-control as a learning variable, but also can help to better understand and measure the changes occurring between the practice environment and the observed behavioral outcomes.

Understanding self-controlled motor learning protocols through the self determination theory

Directory of Open Access Journals (Sweden)

Elizabeth Ann Sanli

2013-01-01

Full Text Available The purpose of the present review was to provide a theoretical understanding of the learning advantages underlying a self-controlled practice context through the tenets of the self-determination theory (SDT. Three micro theories within the macro theory of SDT (Basic psychological needs theory, Cognitive Evaluation Theory & Organismic Integration Theory are used as a framework for examining the current self-controlled motor learning literature. A review of 26 peer-reviewed, empirical studies from the motor learning and medical training literature revealed an important limitation of the self-controlled research in motor learning: that the effects of motivation have been assumed rather than quantified. The SDT offers a basis from which to include measurements of motivation into explanations of changes in behavior. This review suggests that a self-controlled practice context can facilitate such factors as feelings of autonomy and competence of the learner, thereby supporting the psychological needs of the learner, leading to long term changes to behavior. Possible tools for the measurement of motivation and regulation in future studies are discussed. The SDT not only allows for a theoretical reinterpretation of the extant motor learning research supporting self-control as a learning variable, but also can help to better understand and measure the changes occurring between the practice environment and the observed behavioral outcomes.

Cognitive Theory of Multimedia Learning, Instructional Design Principles, and Students with Learning Disabilities in Computer-Based and Online Learning Environments

Science.gov (United States)

Greer, Diana L.; Crutchfield, Stephen A.; Woods, Kari L.

2013-01-01

Struggling learners and students with Learning Disabilities often exhibit unique cognitive processing and working memory characteristics that may not align with instructional design principles developed with typically developing learners. This paper explains the Cognitive Theory of Multimedia Learning and underlying Cognitive Load Theory, and…

Learned predictiveness and outcome predictability effects are not simply two sides of the same coin.

Science.gov (United States)

Thorwart, Anna; Livesey, Evan J; Wilhelm, Francisco; Liu, Wei; Lachnit, Harald

2017-10-01

The Learned Predictiveness effect refers to the observation that learning about the relationship between a cue and an outcome is influenced by the predictive relevance of the cue for other outcomes. Similarly, the Outcome Predictability effect refers to a recent observation that the previous predictability of an outcome affects learning about this outcome in new situations, too. We hypothesize that both effects may be two manifestations of the same phenomenon and stimuli that have been involved in highly predictive relationships may be learned about faster when they are involved in new relationships regardless of their functional role in predictive learning as cues and outcomes. Four experiments manipulated both the relationships and the function of the stimuli. While we were able to replicate the standard effects, they did not survive a transfer to situations where the functional role of the stimuli changed, that is the outcome of the first phase becomes a cue in the second learning phase or the cue of the first phase becomes the outcome of the second phase. Furthermore, unlike learned predictiveness, there was little indication that the distribution of overt attention in the second phase was influenced by previous predictability. The results suggest that these 2 very similar effects are not manifestations of a more general phenomenon but rather independent from each other. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Finite and Gauge-Yukawa unified theories: Theory and predictions

International Nuclear Information System (INIS)

Kobayashi, T.; Kubo, J.; Mondragon, M.; Zoupanos, G.

1999-01-01

All-loop Finite Unified Theories (FUTs) are very interesting N=1 GUTs in which a complete reduction of couplings has been achieved. FUTs realize an old field theoretical dream and have remarkable predictive power. Reduction of dimensionless couplings in N=1 GUTs is achieved by searching for renormalization group invariant (RGI) relations among them holding beyond the unification scale. Finiteness results from the fact that there exists RGI relations among dimensionless couplings that guarantee the vanishing of the β- functions in certain N=1 supersymmetric GUTS even to all orders. Recent developments in the soft supersymmetry breaking (SSB) sector of N=1 GUTs and FUTs lead to exact RGI relations also in this sector of the theories. Of particular interest is a RGI sum rule for the soft scalar masses holding to all orders. The characteristic features of SU(5) models that have been constructed based on the above tools are: a) the old agreement of the top quark prediction with the measured value remains unchanged, b) the lightest Higgs boson is predicted to be around 120 GeV, c) the s-spectrum starts above several hundreds of GeV

Implications of learning theory for developing programs to decrease overeating.

Science.gov (United States)

Boutelle, Kerri N; Bouton, Mark E

2015-10-01

Childhood obesity is associated with medical and psychological comorbidities, and interventions targeting overeating could be pragmatic and have a significant impact on weight. Calorically dense foods are easily available, variable, and tasty which allows for effective opportunities to learn to associate behaviors and cues in the environment with food through fundamental conditioning processes, resulting in measurable psychological and physiological food cue reactivity in vulnerable children. Basic research suggests that initial learning is difficult to erase, and that it is vulnerable to a number of phenomena that will allow the original learning to re-emerge after it is suppressed or replaced. These processes may help explain why it may be difficult to change food cue reactivity and overeating over the long term. Extinction theory may be used to develop effective cue-exposure treatments to decrease food cue reactivity through inhibitory learning, although these processes are complex and require an integral understanding of the theory and individual differences. Additionally, learning theory can be used to develop other interventions that may prove to be useful. Through an integration of learning theory, basic and translational research, it may be possible to develop interventions that can decrease the urges to overeat, and improve the weight status of children. Copyright © 2015 Elsevier Ltd. All rights reserved.

Evaluating theories of bird song learning: implications for future directions.

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Margoliash, D

2002-12-01

Studies of birdsong learning have stimulated extensive hypotheses at all levels of behavioral and physiological organization. This hypothesis building is valuable for the field and is consistent with the remarkable range of issues that can be rigorously addressed in this system. The traditional instructional (template) theory of song learning has been challenged on multiple fronts, especially at a behavioral level by evidence consistent with selectional hypotheses. In this review I highlight the caveats associated with these theories to better define the limits of our knowledge and identify important experiments for the future. The sites and representational forms of the various conceptual entities posited by the template theory are unknown. The distinction between instruction and selection in vocal learning is not well established at a mechanistic level. There is as yet insufficient neurophysiological data to choose between competing mechanisms of error-driven learning and reinforcement learning. Both may obtain for vocal learning. The possible role of sleep in acoustic or procedural memory consolidation, while supported by some physiological observations, does not yet have support in the behavioral literature. The remarkable expansion of knowledge in the past 20 years and the recent development of new technologies for physiological and behavioral experiments should permit direct tests of these theories in the coming decade.

Fuzzy-logic based learning style prediction in e-learning using web ...

Indian Academy of Sciences (India)

tion, especially in web environments and proposes to use Fuzzy rules to handle the uncertainty in .... learning in safe and supportive environment ... working of the proposed Fuzzy-logic based learning style prediction in e-learning. Section 4.

Predictions of a theory of quark confinement

International Nuclear Information System (INIS)

Mack, G.

1980-03-01

We propose a theory of quark confinement which uses only the simplest of approximations. It explains persistence of quark confinement in Yang Mills theories with gauge group SU(2) or SU(3) as a consequence of asymptotic freedom in perturbation theory and of the known phase structure of Z(2) resp. Z(3) lattice gauge theory. Predictions are derived which can in principle be tested by computer simulation. Some are already tested by results of Creutz. They are in good agreement. (orig.)

Predictions of a theory of quark confinement

International Nuclear Information System (INIS)

Mack, G.

1980-01-01

A theory of quark confinement is proposed which uses only the simplest of approximations. It explains persistence of quark confinement in Yang-Mills theories with gauge group SU(2) or SU(3) as a consequence of asymptotic freedom in perturbation theory and of the known phase structure of Z(2) and Z(3) lattice gauge theory. Predictions are derived which can in principle be tested by computer simulation. Some are are already tested by results of Creutz. They are in good agreement

Toward an Instructionally Oriented Theory of Example-Based Learning

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Renkl, Alexander

2014-01-01

Learning from examples is a very effective means of initial cognitive skill acquisition. There is an enormous body of research on the specifics of this learning method. This article presents an instructionally oriented theory of example-based learning that integrates theoretical assumptions and findings from three research areas: learning from…

Personalised Learning Object System Based on Self-Regulated Learning Theories

Directory of Open Access Journals (Sweden)

Ali Alharbi

2014-06-01

Full Text Available Self-regulated learning has become an important construct in education research in the last few years. Selfregulated learning in its simple form is the learner’s ability to monitor and control the learning process. There is increasing research in the literature on how to support students become more self-regulated learners. However, the advancement in the information technology has led to paradigm changes in the design and development of educational content. The concept of learning object instructional technology has emerged as a result of this shift in educational technology paradigms. This paper presents the results of a study that investigated the potential educational effectiveness of a pedagogical framework based on the self-regulated learning theories to support the design of learning object systems to help computer science students. A prototype learning object system was developed based on the contemporary research on self-regulated learning. The system was educationally evaluated in a quasi-experimental study over two semesters in a core programming languages concepts course. The evaluation revealed that a learning object system that takes into consideration contemporary research on self-regulated learning can be an effective learning environment to support computer science education.

Theories and control models and motor learning: clinical applications in neuro-rehabilitation.

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Cano-de-la-Cuerda, R; Molero-Sánchez, A; Carratalá-Tejada, M; Alguacil-Diego, I M; Molina-Rueda, F; Miangolarra-Page, J C; Torricelli, D

2015-01-01

In recent decades there has been a special interest in theories that could explain the regulation of motor control, and their applications. These theories are often based on models of brain function, philosophically reflecting different criteria on how movement is controlled by the brain, each being emphasised in different neural components of the movement. The concept of motor learning, regarded as the set of internal processes associated with practice and experience that produce relatively permanent changes in the ability to produce motor activities through a specific skill, is also relevant in the context of neuroscience. Thus, both motor control and learning are seen as key fields of study for health professionals in the field of neuro-rehabilitation. The major theories of motor control are described, which include, motor programming theory, systems theory, the theory of dynamic action, and the theory of parallel distributed processing, as well as the factors that influence motor learning and its applications in neuro-rehabilitation. At present there is no consensus on which theory or model defines the regulations to explain motor control. Theories of motor learning should be the basis for motor rehabilitation. The new research should apply the knowledge generated in the fields of control and motor learning in neuro-rehabilitation. Copyright © 2011 Sociedad Española de Neurología. Published by Elsevier Espana. All rights reserved.

Early Learning Theories Made Visible

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Beloglovsky, Miriam; Daly, Lisa

2015-01-01

Go beyond reading about early learning theories and see what they look like in action in modern programs and teacher practices. With classroom vignettes and colorful photographs, this book makes the works of Jean Piaget, Erik Erikson, Lev Vygotsky, Abraham Maslow, John Dewey, Howard Gardner, and Louise Derman-Sparks visible, accessible, and easier…

Boosting compound-protein interaction prediction by deep learning.

Science.gov (United States)

Tian, Kai; Shao, Mingyu; Wang, Yang; Guan, Jihong; Zhou, Shuigeng

2016-11-01

The identification of interactions between compounds and proteins plays an important role in network pharmacology and drug discovery. However, experimentally identifying compound-protein interactions (CPIs) is generally expensive and time-consuming, computational approaches are thus introduced. Among these, machine-learning based methods have achieved a considerable success. However, due to the nonlinear and imbalanced nature of biological data, many machine learning approaches have their own limitations. Recently, deep learning techniques show advantages over many state-of-the-art machine learning methods in some applications. In this study, we aim at improving the performance of CPI prediction based on deep learning, and propose a method called DL-CPI (the abbreviation of Deep Learning for Compound-Protein Interactions prediction), which employs deep neural network (DNN) to effectively learn the representations of compound-protein pairs. Extensive experiments show that DL-CPI can learn useful features of compound-protein pairs by a layerwise abstraction, and thus achieves better prediction performance than existing methods on both balanced and imbalanced datasets. Copyright © 2016 Elsevier Inc. All rights reserved.

An applied test of the social learning theory of deviance to college alcohol use.

Science.gov (United States)

DeMartino, Cynthia H; Rice, Ronald E; Saltz, Robert

2015-04-01

Several hypotheses about influences on college drinking derived from the social learning theory of deviance were tested and confirmed. The effect of ethnicity on alcohol use was completely mediated by differential association and differential reinforcement, whereas the effect of biological sex on alcohol use was partially mediated. Higher net positive reinforcements to costs for alcohol use predicted increased general use, more underage use, and more frequent binge drinking. Two unexpected finding were the negative relationship between negative expectations and negative experiences, and the substantive difference between nondrinkers and general drinkers compared with illegal or binge drinkers. The discussion considers implications for future campaigns based on Akers's deterrence theory.

An Overview of the History of Learning Theory

Science.gov (United States)

Illeris, Knud

2018-01-01

This article is an account of the history of learning theory as the author has come to know and interpret it by dealing with this subject for almost half a century during which he has also himself gradually developed the broad understanding of human learning which is presented in his well known books on "How We Learn" and a lot of other…

Learning Styles of Baccalaureate Nursing Students and Attitudes toward Theory-Based Nursing.

Science.gov (United States)

Laschinger, Heather K.; Boss, Marvin K.

1989-01-01

The personal and environmental factors related to undergraduate and post-RN nursing students' attitudes toward theory-based nursing from Kolb's experiential learning theory perspective were investigated. Learning style and environmental press perceptions were found to be related to attitudes toward theory-based nursing. (Author/MLW)

Comparison of Scalar Expectancy Theory (SET) and the Learning-to-Time (LeT) model in a successive temporal bisection task.

Science.gov (United States)

Arantes, Joana

2008-06-01

The present research tested the generality of the "context effect" previously reported in experiments using temporal double bisection tasks [e.g., Arantes, J., Machado, A. Context effects in a temporal discrimination task: Further tests of the Scalar Expectancy Theory and Learning-to-Time models. J. Exp. Anal. Behav., in press]. Pigeons learned two temporal discriminations in which all the stimuli appear successively: 1s (red) vs. 4s (green) and 4s (blue) vs. 16s (yellow). Then, two tests were conducted to compare predictions of two timing models, Scalar Expectancy Theory (SET) and the Learning-to-Time (LeT) model. In one test, two psychometric functions were obtained by presenting pigeons with intermediate signal durations (1-4s and 4-16s). Results were mixed. In the critical test, pigeons were exposed to signals ranging from 1 to 16s and followed by the green or the blue key. Whereas SET predicted that the relative response rate to each of these keys should be independent of the signal duration, LeT predicted that the relative response rate to the green key (compared with the blue key) should increase with the signal duration. Results were consistent with LeT's predictions, showing that the context effect is obtained even when subjects do not need to make a choice between two keys presented simultaneously.

The Development of a Comprehensive and Coherent Theory of Learning

Science.gov (United States)

Illeris, Knud

2015-01-01

This article is an account of how the author developed a comprehensive understanding of human learning over a period of almost 50 years. The learning theory includes the structure of learning, different types of learning, barriers of learning as well as how individual dispositions, age, the learning environment and general social and societal…

Strategies for application of learning theories in art studio practices ...

African Journals Online (AJOL)

This study highlights the link between learning theories and art studio practices. The paper is of the opinion that if these theories are critically understood and applied to the practical aspect of fine and applied arts then learning will be more functional. Nigerian Journal of Technology and Education in Nigeria Vol. 8(1) 2003: ...

Age-Related Differences in Goals: Testing Predictions from Selection, Optimization, and Compensation Theory and Socioemotional Selectivity Theory

Science.gov (United States)

Penningroth, Suzanna L.; Scott, Walter D.

2012-01-01

Two prominent theories of lifespan development, socioemotional selectivity theory and selection, optimization, and compensation theory, make similar predictions for differences in the goal representations of younger and older adults. Our purpose was to test whether the goals of younger and older adults differed in ways predicted by these two…

From Theory Use to Theory Building in Learning Analytics: A Commentary on "Learning Analytics to Support Teachers during Synchronous CSCL"

Science.gov (United States)

Chen, Bodong

2015-01-01

In this commentary on Van Leeuwen (2015, this issue), I explore the relation between theory and practice in learning analytics. Specifically, I caution against adhering to one specific theoretical doctrine while ignoring others, suggest deeper applications of cognitive load theory to understanding teaching with analytics tools, and comment on…

Complexity Theory and CALL Curriculum in Foreign Language Learning

Directory of Open Access Journals (Sweden)

Hassan Soleimani

2014-05-01

Full Text Available Complexity theory literally indicates the complexity of a system, behavior, or a process. Its connotative meaning, while, implies dynamism, openness, sensitivity to initial conditions and feedback, and adaptation properties of a system. Regarding English as a Foreign/ Second Language (EFL/ESL this theory emphasizes on the complexity of the process of teaching and learning, including all the properties of a complex system. The purpose of the current study is to discuss the role of CALL as a modern technology in simplifying the process of teaching and learning a new language while integrating into the complexity theory. Nonetheless, the findings obtained from reviewing previously conducted studies in this field confirmed the usefulness of CALL curriculum in EFL/ESL contexts. These findings can also provide pedagogical implications for employing computer as an effective teaching and learning tool.

Combining Vision with Voice: A Learning and Implementation Structure Promoting Teachers' Internalization of Practices Based on Self-Determination Theory

Science.gov (United States)

Assor, Avi; Kaplan, Haya; Feinberg, Ofra; Tal, Karen

2009-01-01

We propose that self-determination theory's conceptualization of internalization may help school reformers overcome the recurrent problem of "the predictable failure of educational reform" (Sarason, 1993). Accordingly, we present a detailed learning and implementation structure to promote teachers' internalization and application of ideas and…

Inference algorithms and learning theory for Bayesian sparse factor analysis

International Nuclear Information System (INIS)

Rattray, Magnus; Sharp, Kevin; Stegle, Oliver; Winn, John

2009-01-01

Bayesian sparse factor analysis has many applications; for example, it has been applied to the problem of inferring a sparse regulatory network from gene expression data. We describe a number of inference algorithms for Bayesian sparse factor analysis using a slab and spike mixture prior. These include well-established Markov chain Monte Carlo (MCMC) and variational Bayes (VB) algorithms as well as a novel hybrid of VB and Expectation Propagation (EP). For the case of a single latent factor we derive a theory for learning performance using the replica method. We compare the MCMC and VB/EP algorithm results with simulated data to the theoretical prediction. The results for MCMC agree closely with the theory as expected. Results for VB/EP are slightly sub-optimal but show that the new algorithm is effective for sparse inference. In large-scale problems MCMC is infeasible due to computational limitations and the VB/EP algorithm then provides a very useful computationally efficient alternative.

Inference algorithms and learning theory for Bayesian sparse factor analysis

Energy Technology Data Exchange (ETDEWEB)

Rattray, Magnus; Sharp, Kevin [School of Computer Science, University of Manchester, Manchester M13 9PL (United Kingdom); Stegle, Oliver [Max-Planck-Institute for Biological Cybernetics, Tuebingen (Germany); Winn, John, E-mail: magnus.rattray@manchester.ac.u [Microsoft Research Cambridge, Roger Needham Building, Cambridge, CB3 0FB (United Kingdom)

2009-12-01

Bayesian sparse factor analysis has many applications; for example, it has been applied to the problem of inferring a sparse regulatory network from gene expression data. We describe a number of inference algorithms for Bayesian sparse factor analysis using a slab and spike mixture prior. These include well-established Markov chain Monte Carlo (MCMC) and variational Bayes (VB) algorithms as well as a novel hybrid of VB and Expectation Propagation (EP). For the case of a single latent factor we derive a theory for learning performance using the replica method. We compare the MCMC and VB/EP algorithm results with simulated data to the theoretical prediction. The results for MCMC agree closely with the theory as expected. Results for VB/EP are slightly sub-optimal but show that the new algorithm is effective for sparse inference. In large-scale problems MCMC is infeasible due to computational limitations and the VB/EP algorithm then provides a very useful computationally efficient alternative.

Shaping a valued learning journey: Student satisfaction with learning in undergraduate nursing programs, a grounded theory study.

Science.gov (United States)

Smith, Morgan R; Grealish, Laurie; Henderson, Saras

2018-05-01

Student satisfaction is a quality measure of increasing importance in undergraduate programs, including nursing programs. To date theories of student satisfaction have focused primarily on students' perceptions of the educational environment rather than their perceptions of learning. Understanding how students determine satisfaction with learning is necessary to facilitate student learning across a range of educational contexts and meet the expectations of diverse stakeholders. To understand undergraduate nursing students' satisfaction with learning. Constructivist grounded theory methodology was used to identify how nursing students determined satisfaction with learning. Two large, multi-campus, nursing schools in Australia. Seventeen demographically diverse undergraduate nursing students studying different stages of a three year program participated in the study. Twenty nine semi-structured interviews were conducted. Students were invited to describe situations where they had been satisfied or dissatisfied with their learning. A constructivist grounded theory approach was used to analyse the data. Students are satisfied with learning when they shape a valued learning journey that accommodates social contexts of self, university and nursing workplace. The theory has three phases. Phase 1 - orienting self to valued learning in the pedagogical landscape; phase 2 - engaging with valued learning experiences across diverse pedagogical terrain; and phase 3 - recognising valued achievement along the way. When students experience a valued learning journey they are satisfied with their learning. Student satisfaction with learning is unique to the individual, changes over time and maybe transient or sustained, mild or intense. Finding from the research indicate areas where nurse academics may facilitate satisfaction with learning in undergraduate nursing programs while mindful of the expectations of other stakeholders such as the university, nurse registering authorities

Employee Learning Theories and Their Organizational Applications

Directory of Open Access Journals (Sweden)

Abdussalaam Iyanda Ismail

2017-12-01

Full Text Available Empirical evidence identifies that organizational success hinges on employees with the required knowledge, skills, and abilities and that employees’ effectiveness at learning new skills and knowledge is connected with the kind of learning technique the organization adopts. Given this, this work explored employee learning theories and their organizational applications. Using far reaching literature survey and extensive theoretical and logical argument and exposition. This paper revealed that cognitive-based approaches, non-cognitive approach and need-based approaches play vital roles in shrinking the occurrence of unwanted behaviors and upturning the occurrence of desired behaviors in the organization. Proper application of the theories can induce positive employee behaviors such as task performance and organizational citizenship behavior and consequently enhance both individual and organizational performance. This work has hopefully contributed to the enrichment of the existing relevant literature and served as a useful guide for stakeholders on how they can stimulate positive employee behaviors and the consequent enhanced organizational performance.

Reinforcement Learning Based Novel Adaptive Learning Framework for Smart Grid Prediction

Directory of Open Access Journals (Sweden)

Tian Li

2017-01-01

Full Text Available Smart grid is a potential infrastructure to supply electricity demand for end users in a safe and reliable manner. With the rapid increase of the share of renewable energy and controllable loads in smart grid, the operation uncertainty of smart grid has increased briskly during recent years. The forecast is responsible for the safety and economic operation of the smart grid. However, most existing forecast methods cannot account for the smart grid due to the disabilities to adapt to the varying operational conditions. In this paper, reinforcement learning is firstly exploited to develop an online learning framework for the smart grid. With the capability of multitime scale resolution, wavelet neural network has been adopted in the online learning framework to yield reinforcement learning and wavelet neural network (RLWNN based adaptive learning scheme. The simulations on two typical prediction problems in smart grid, including wind power prediction and load forecast, validate the effectiveness and the scalability of the proposed RLWNN based learning framework and algorithm.

Applying psychological theories to evidence-based clinical practice: Identifying factors predictive of managing upper respiratory tract infections without antibiotics

Directory of Open Access Journals (Sweden)

Glidewell Elizabeth

2007-08-01

Full Text Available Abstract Background Psychological models can be used to understand and predict behaviour in a wide range of settings. However, they have not been consistently applied to health professional behaviours, and the contribution of differing theories is not clear. The aim of this study was to explore the usefulness of a range of psychological theories to predict health professional behaviour relating to management of upper respiratory tract infections (URTIs without antibiotics. Methods Psychological measures were collected by postal questionnaire survey from a random sample of general practitioners (GPs in Scotland. The outcome measures were clinical behaviour (using antibiotic prescription rates as a proxy indicator, behavioural simulation (scenario-based decisions to managing URTI with or without antibiotics and behavioural intention (general intention to managing URTI without antibiotics. Explanatory variables were the constructs within the following theories: Theory of Planned Behaviour (TPB, Social Cognitive Theory (SCT, Common Sense Self-Regulation Model (CS-SRM, Operant Learning Theory (OLT, Implementation Intention (II, Stage Model (SM, and knowledge (a non-theoretical construct. For each outcome measure, multiple regression analysis was used to examine the predictive value of each theoretical model individually. Following this 'theory level' analysis, a 'cross theory' analysis was conducted to investigate the combined predictive value of all significant individual constructs across theories. Results All theories were tested, but only significant results are presented. When predicting behaviour, at the theory level, OLT explained 6% of the variance and, in a cross theory analysis, OLT 'evidence of habitual behaviour' also explained 6%. When predicting behavioural simulation, at the theory level, the proportion of variance explained was: TPB, 31%; SCT, 26%; II, 6%; OLT, 24%. GPs who reported having already decided to change their management to

Mean-field theory of meta-learning

International Nuclear Information System (INIS)

Plewczynski, Dariusz

2009-01-01

We discuss here the mean-field theory for a cellular automata model of meta-learning. Meta-learning is the process of combining outcomes of individual learning procedures in order to determine the final decision with higher accuracy than any single learning method. Our method is constructed from an ensemble of interacting, learning agents that acquire and process incoming information using various types, or different versions, of machine learning algorithms. The abstract learning space, where all agents are located, is constructed here using a fully connected model that couples all agents with random strength values. The cellular automata network simulates the higher level integration of information acquired from the independent learning trials. The final classification of incoming input data is therefore defined as the stationary state of the meta-learning system using simple majority rule, yet the minority clusters that share the opposite classification outcome can be observed in the system. Therefore, the probability of selecting a proper class for a given input data, can be estimated even without the prior knowledge of its affiliation. The fuzzy logic can be easily introduced into the system, even if learning agents are built from simple binary classification machine learning algorithms by calculating the percentage of agreeing agents

Structural Learning Theory: Current Status and New Perspectives.

Science.gov (United States)

Scandura, Joseph M.

2001-01-01

Presents the current status and new perspectives on the Structured Learning Theory (SLT), with special consideration given to how SLT has been influenced by recent research in software engineering. Topics include theoretical constructs; content domains; structural analysis; cognition; assessing behavior potential; and teaching and learning issues,…

Entity versus incremental theories predict older adults' memory performance.

Science.gov (United States)

Plaks, Jason E; Chasteen, Alison L

2013-12-01

The authors examined whether older adults' implicit theories regarding the modifiability of memory in particular (Studies 1 and 3) and abilities in general (Study 2) would predict memory performance. In Study 1, individual differences in older adults' endorsement of the "entity theory" (a belief that one's ability is fixed) or "incremental theory" (a belief that one's ability is malleable) of memory were measured using a version of the Implicit Theories Measure (Dweck, 1999). Memory performance was assessed with a free-recall task. Results indicated that the higher the endorsement of the incremental theory, the better the free recall. In Study 2, older and younger adults' theories were measured using a more general version of the Implicit Theories Measure that focused on the modifiability of abilities in general. Again, for older adults, the higher the incremental endorsement, the better the free recall. Moreover, as predicted, implicit theories did not predict younger adults' memory performance. In Study 3, participants read mock news articles reporting evidence in favor of either the entity or incremental theory. Those in the incremental condition outperformed those in the entity condition on reading span and free-recall tasks. These effects were mediated by pretask worry such that, for those in the entity condition, higher worry was associated with lower performance. Taken together, these studies suggest that variation in entity versus incremental endorsement represents a key predictor of older adults' memory performance. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Rhetorical ways of thinking Vygotskian theory and mathematical learning

CERN Document Server

Albert, Lillie R; Macadino, Vittoria

2012-01-01

Combining Vygotskian theory with current teaching and learning practices, this volume focuses on how the co-construction of learning models the interpretation of a mathematical situation, providing educationalists with a valuable practical methodology.

Social Learning, Social Control, and Strain Theories: A Formalization of Micro-level Criminological Theories

OpenAIRE

Proctor, Kristopher Ryan

2010-01-01

This dissertation proposes theoretical formalization as a way of enhancing theory development within criminology. Differential association, social learning, social control, and general strain theories are formalized in order to identify assumptions of human nature, key theoretical concepts, theoretical knowledge claims, and scope conditions. The resulting formalization allows greater comparability between theories in terms of explanatory power, and additionally provides insights into integrat...

Machine learning in radiation oncology theory and applications

CERN Document Server

El Naqa, Issam; Murphy, Martin J

2015-01-01

​This book provides a complete overview of the role of machine learning in radiation oncology and medical physics, covering basic theory, methods, and a variety of applications in medical physics and radiotherapy. An introductory section explains machine learning, reviews supervised and unsupervised learning methods, discusses performance evaluation, and summarizes potential applications in radiation oncology. Detailed individual sections are then devoted to the use of machine learning in quality assurance; computer-aided detection, including treatment planning and contouring; image-guided rad

Theories of willpower affect sustained learning.

Science.gov (United States)

Miller, Eric M; Walton, Gregory M; Dweck, Carol S; Job, Veronika; Trzesniewski, Kali H; McClure, Samuel M

2012-01-01

Building cognitive abilities often requires sustained engagement with effortful tasks. We demonstrate that beliefs about willpower-whether willpower is viewed as a limited or non-limited resource-impact sustained learning on a strenuous mental task. As predicted, beliefs about willpower did not affect accuracy or improvement during the initial phases of learning; however, participants who were led to view willpower as non-limited showed greater sustained learning over the full duration of the task. These findings highlight the interactive nature of motivational and cognitive processes: motivational factors can substantially affect people's ability to recruit their cognitive resources to sustain learning over time.

Predicting Solar Activity Using Machine-Learning Methods

Science.gov (United States)

Bobra, M.

2017-12-01

Of all the activity observed on the Sun, two of the most energetic events are flares and coronal mass ejections. However, we do not, as of yet, fully understand the physical mechanism that triggers solar eruptions. A machine-learning algorithm, which is favorable in cases where the amount of data is large, is one way to [1] empirically determine the signatures of this mechanism in solar image data and [2] use them to predict solar activity. In this talk, we discuss the application of various machine learning algorithms - specifically, a Support Vector Machine, a sparse linear regression (Lasso), and Convolutional Neural Network - to image data from the photosphere, chromosphere, transition region, and corona taken by instruments aboard the Solar Dynamics Observatory in order to predict solar activity on a variety of time scales. Such an approach may be useful since, at the present time, there are no physical models of flares available for real-time prediction. We discuss our results (Bobra and Couvidat, 2015; Bobra and Ilonidis, 2016; Jonas et al., 2017) as well as other attempts to predict flares using machine-learning (e.g. Ahmed et al., 2013; Nishizuka et al. 2017) and compare these results with the more traditional techniques used by the NOAA Space Weather Prediction Center (Crown, 2012). We also discuss some of the challenges in using machine-learning algorithms for space science applications.

Understanding feedback: A learning theory perspective

NARCIS (Netherlands)

Thurlings, Marieke; Vermeulen, Marjan; Bastiaens, Theo; Stijnen, Sjef

2018-01-01

This article aims to review literature on feedback to teachers. Because research has hardly focused on feedback among teachers, the review’s scope also includes feedback in class- rooms. The review proposes that the effectiveness of feedback and feedback processes depend on the learning theory

The right time to learn: mechanisms and optimization of spaced learning

Science.gov (United States)

Smolen, Paul; Zhang, Yili; Byrne, John H.

2016-01-01

For many types of learning, spaced training, which involves repeated long inter-trial intervals, leads to more robust memory formation than does massed training, which involves short or no intervals. Several cognitive theories have been proposed to explain this superiority, but only recently have data begun to delineate the underlying cellular and molecular mechanisms of spaced training, and we review these theories and data here. Computational models of the implicated signalling cascades have predicted that spaced training with irregular inter-trial intervals can enhance learning. This strategy of using models to predict optimal spaced training protocols, combined with pharmacotherapy, suggests novel ways to rescue impaired synaptic plasticity and learning. PMID:26806627

Viscosity Prediction of Hydrocarbon Mixtures Based on the Friction Theory

DEFF Research Database (Denmark)

Zeberg-Mikkelsen, Claus Kjær; Cisneros, Sergio; Stenby, Erling Halfdan

2001-01-01

The application and capability of the friction theory (f-theory) for viscosity predictions of hydrocarbon fluids is further illustrated by predicting the viscosity of binary and ternary liquid mixtures composed of n-alkanes ranging from n-pentane to n-decane for wide ranges of temperature and from...

When theory and biology differ: The relationship between reward prediction errors and expectancy.

Science.gov (United States)

Williams, Chad C; Hassall, Cameron D; Trska, Robert; Holroyd, Clay B; Krigolson, Olave E

2017-10-01

Comparisons between expectations and outcomes are critical for learning. Termed prediction errors, the violations of expectancy that occur when outcomes differ from expectations are used to modify value and shape behaviour. In the present study, we examined how a wide range of expectancy violations impacted neural signals associated with feedback processing. Participants performed a time estimation task in which they had to guess the duration of one second while their electroencephalogram was recorded. In a key manipulation, we varied task difficulty across the experiment to create a range of different feedback expectancies - reward feedback was either very expected, expected, 50/50, unexpected, or very unexpected. As predicted, the amplitude of the reward positivity, a component of the human event-related brain potential associated with feedback processing, scaled inversely with expectancy (e.g., unexpected feedback yielded a larger reward positivity than expected feedback). Interestingly, the scaling of the reward positivity to outcome expectancy was not linear as would be predicted by some theoretical models. Specifically, we found that the amplitude of the reward positivity was about equivalent for very expected and expected feedback, and for very unexpected and unexpected feedback. As such, our results demonstrate a sigmoidal relationship between reward expectancy and the amplitude of the reward positivity, with interesting implications for theories of reinforcement learning. Copyright © 2017 Elsevier B.V. All rights reserved.

A causal link between prediction errors, dopamine neurons and learning.

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Steinberg, Elizabeth E; Keiflin, Ronald; Boivin, Josiah R; Witten, Ilana B; Deisseroth, Karl; Janak, Patricia H

2013-07-01

Situations in which rewards are unexpectedly obtained or withheld represent opportunities for new learning. Often, this learning includes identifying cues that predict reward availability. Unexpected rewards strongly activate midbrain dopamine neurons. This phasic signal is proposed to support learning about antecedent cues by signaling discrepancies between actual and expected outcomes, termed a reward prediction error. However, it is unknown whether dopamine neuron prediction error signaling and cue-reward learning are causally linked. To test this hypothesis, we manipulated dopamine neuron activity in rats in two behavioral procedures, associative blocking and extinction, that illustrate the essential function of prediction errors in learning. We observed that optogenetic activation of dopamine neurons concurrent with reward delivery, mimicking a prediction error, was sufficient to cause long-lasting increases in cue-elicited reward-seeking behavior. Our findings establish a causal role for temporally precise dopamine neuron signaling in cue-reward learning, bridging a critical gap between experimental evidence and influential theoretical frameworks.

Informal Workplace Learning among Nurses: Organisational Learning Conditions and Personal Characteristics That Predict Learning Outcomes

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Kyndt, Eva; Vermeire, Eva; Cabus, Shana

2016-01-01

Purpose: This paper aims to examine which organisational learning conditions and individual characteristics predict the learning outcomes nurses achieve through informal learning activities. There is specific relevance for the nursing profession because of the rapidly changing healthcare systems. Design/Methodology/Approach: In total, 203 nurses…

Learning theories in computer-assisted foreign language acquisition

OpenAIRE

Baeva, D.

2013-01-01

This paper reviews the learning theories, focusing to the strong interest in technology use for language learning. It is important to look at how technology has been used in the field thus far. The goals of this review are to understand how computers have been used in the past years to support foreign language learning, and to explore any research evidence with regards to how computer technology can enhance language skills acquisition

Locus of Control and Academic Achievement: Integrating Social Learning Theory and Expectancy-Value Theory

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Youse, Keith Edward

2012-01-01

The current study examines predictors of math achievement and college graduation by integrating social learning theory and expectancy-value theory. Data came from a nationally-representative longitudinal database tracking 12,144 students over twelve years from 8th grade forward. Models for math achievement and college graduation were tested…

Predicting genome-wide redundancy using machine learning

Directory of Open Access Journals (Sweden)

Shasha Dennis E

2010-11-01

Full Text Available Abstract Background Gene duplication can lead to genetic redundancy, which masks the function of mutated genes in genetic analyses. Methods to increase sensitivity in identifying genetic redundancy can improve the efficiency of reverse genetics and lend insights into the evolutionary outcomes of gene duplication. Machine learning techniques are well suited to classifying gene family members into redundant and non-redundant gene pairs in model species where sufficient genetic and genomic data is available, such as Arabidopsis thaliana, the test case used here. Results Machine learning techniques that combine multiple attributes led to a dramatic improvement in predicting genetic redundancy over single trait classifiers alone, such as BLAST E-values or expression correlation. In withholding analysis, one of the methods used here, Support Vector Machines, was two-fold more precise than single attribute classifiers, reaching a level where the majority of redundant calls were correctly labeled. Using this higher confidence in identifying redundancy, machine learning predicts that about half of all genes in Arabidopsis showed the signature of predicted redundancy with at least one but typically less than three other family members. Interestingly, a large proportion of predicted redundant gene pairs were relatively old duplications (e.g., Ks > 1, suggesting that redundancy is stable over long evolutionary periods. Conclusions Machine learning predicts that most genes will have a functionally redundant paralog but will exhibit redundancy with relatively few genes within a family. The predictions and gene pair attributes for Arabidopsis provide a new resource for research in genetics and genome evolution. These techniques can now be applied to other organisms.

Deep learning versus traditional machine learning methods for aggregated energy demand prediction

NARCIS (Netherlands)

Paterakis, N.G.; Mocanu, E.; Gibescu, M.; Stappers, B.; van Alst, W.

2018-01-01

In this paper the more advanced, in comparison with traditional machine learning approaches, deep learning methods are explored with the purpose of accurately predicting the aggregated energy consumption. Despite the fact that a wide range of machine learning methods have been applied to

Theories of willpower affect sustained learning.

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Eric M Miller

Full Text Available Building cognitive abilities often requires sustained engagement with effortful tasks. We demonstrate that beliefs about willpower-whether willpower is viewed as a limited or non-limited resource-impact sustained learning on a strenuous mental task. As predicted, beliefs about willpower did not affect accuracy or improvement during the initial phases of learning; however, participants who were led to view willpower as non-limited showed greater sustained learning over the full duration of the task. These findings highlight the interactive nature of motivational and cognitive processes: motivational factors can substantially affect people's ability to recruit their cognitive resources to sustain learning over time.

Viscosity Prediction of Natural Gas Using the Friction Theory

DEFF Research Database (Denmark)

Zeberg-Mikkelsen, Claus Kjær; Cisneros, Sergio; Stenby, Erling Halfdan

2002-01-01

Based on the concepts of the friction theory (f-theory) for viscosity modeling, a procedure is introduced for predicting the viscosity of hydrocarbon mixtures rich in one component, which is the case for natural gases. In this procedure, the mixture friction coefficients are estimated with mixing...... rules based on the values of the pure component friction coefficients. Since natural gases contain mainly methane, two f-theory models are combined, where the friction coefficients of methane are estimated by a seven-constant f-theory model directly fitted to methane viscosities, and the friction...... coefficients of the other components are estimated by the one-parameter general f-theory model. The viscosity predictions are performed with the SRK, the PR, and the PRSV equations of state, respectively. For recently measured viscosities of natural gases, the resultant AAD (0.5 to 0.8%) is in excellent...

Towards a Semantic E-Learning Theory by Using a Modelling Approach

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Yli-Luoma, Pertti V. J.; Naeve, Ambjorn

2006-01-01

In the present study, a semantic perspective on e-learning theory is advanced and a modelling approach is used. This modelling approach towards the new learning theory is based on the four SECI phases of knowledge conversion: Socialisation, Externalisation, Combination and Internalisation, introduced by Nonaka in 1994, and involving two levels of…

Predicting Learned Helplessness Based on Personality

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Maadikhah, Elham; Erfani, Nasrollah

2014-01-01

Learned helplessness as a negative motivational state can latently underlie repeated failures and create negative feelings toward the education as well as depression in students and other members of a society. The purpose of this paper is to predict learned helplessness based on students' personality traits. The research is a predictive…

Video Scene Parsing with Predictive Feature Learning

OpenAIRE

Jin, Xiaojie; Li, Xin; Xiao, Huaxin; Shen, Xiaohui; Lin, Zhe; Yang, Jimei; Chen, Yunpeng; Dong, Jian; Liu, Luoqi; Jie, Zequn; Feng, Jiashi; Yan, Shuicheng

2016-01-01

In this work, we address the challenging video scene parsing problem by developing effective representation learning methods given limited parsing annotations. In particular, we contribute two novel methods that constitute a unified parsing framework. (1) \\textbf{Predictive feature learning}} from nearly unlimited unlabeled video data. Different from existing methods learning features from single frame parsing, we learn spatiotemporal discriminative features by enforcing a parsing network to ...

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

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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.

Health education and multimedia learning: educational psychology and health behavior theory (Part 1).

Science.gov (United States)

Mas, Francisco G Soto; Plass, Jan; Kane, William M; Papenfuss, Richard L

2003-07-01

When health education researchers began to investigate how individuals make decisions related to health and the factors that influence health behaviors, they referred to frameworks shared by educational and learning research. Health education adopted the basic principles of the cognitive revolution, which were instrumental in advancing the field. There is currently a new challenge to confront: the widespread use of new technologies for health education. To better overcome this challenge, educational psychology and instructional technology theory should be considered. Unfortunately, the passion to incorporate new technologies too often overshadows how people learn or, in particular, how people learn through computer technologies. This two-part article explains how educational theory contributed to the early development of health behavior theory, describes the most relevant multimedia learning theories and constructs, and provides recommendations for developing multimedia health education programs and connecting theory and practice.

En retorisk forståelsesramme for Computer Supported Collaborative Learning (A Rhetorical Theory on Computer Supported Collaborative Learning)

DEFF Research Database (Denmark)

Harlung, Asger

2003-01-01

The dissertation explores the potential of rhetorical theories for understanding, analyzing, or planning communication and learning processes, and for integrating the digitized contexts and human interaction and communication proccesses in a single theoretical framework. Based on Cicero's rhetori...... applied to two empirical case studies of Master programs, the dissertation develops and presents a new theory on Computer Supported Collaborative Learning (CSCL).......The dissertation explores the potential of rhetorical theories for understanding, analyzing, or planning communication and learning processes, and for integrating the digitized contexts and human interaction and communication proccesses in a single theoretical framework. Based on Cicero's rhetoric...

Evaluating clinical simulations for learning procedural skills: a theory-based approach.

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Kneebone, Roger

2005-06-01

Simulation-based learning is becoming widely established within medical education. It offers obvious benefits to novices learning invasive procedural skills, especially in a climate of decreasing clinical exposure. However, simulations are often accepted uncritically, with undue emphasis being placed on technological sophistication at the expense of theory-based design. The author proposes four key areas that underpin simulation-based learning, and summarizes the theoretical grounding for each. These are (1) gaining technical proficiency (psychomotor skills and learning theory, the importance of repeated practice and regular reinforcement), (2) the place of expert assistance (a Vygotskian interpretation of tutor support, where assistance is tailored to each learner's needs), (3) learning within a professional context (situated learning and contemporary apprenticeship theory), and (4) the affective component of learning (the effect of emotion on learning). The author then offers four criteria for critically evaluating new or existing simulations, based on the theoretical framework outlined above. These are: (1) Simulations should allow for sustained, deliberate practice within a safe environment, ensuring that recently-acquired skills are consolidated within a defined curriculum which assures regular reinforcement; (2) simulations should provide access to expert tutors when appropriate, ensuring that such support fades when no longer needed; (3) simulations should map onto real-life clinical experience, ensuring that learning supports the experience gained within communities of actual practice; and (4) simulation-based learning environments should provide a supportive, motivational, and learner-centered milieu which is conducive to learning.

Use of the Learning together technique associated to the theory of significative learning

Directory of Open Access Journals (Sweden)

Ester López Donoso

2008-09-01

Full Text Available This article deals with an experimental research, regarding a qualitative and quantitative design, applied to a group of students of General Physics course during the first semester of the university career of Engineering. Historically, students of this course present learning difficulties that directly affect their performance, conceptualization and permanence in the university. The present methodology integrates the collaborative learning, denominated Learning Together", with the theory of significant learning to avoid the above-written difficulties. Results of this research show that the proposed methodology works properly, especially to improve the conceptualization.

Learning and Retention through Predictive Inference and Classification

Science.gov (United States)

Sakamoto, Yasuaki; Love, Bradley C.

2010-01-01

Work in category learning addresses how humans acquire knowledge and, thus, should inform classroom practices. In two experiments, we apply and evaluate intuitions garnered from laboratory-based research in category learning to learning tasks situated in an educational context. In Experiment 1, learning through predictive inference and…

Experiential Learning Theory as a Guide for Effective Teaching.

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Murrell, Patricia H.; Claxton, Charles S.

1987-01-01

David Kolb's experiential learning theory involves a framework useful in designing courses that meet needs of diverse learners. Course designs providing systematic activities in concrete experience, reflective observations, abstract conceptualization, and active experimentation will be sensitive to students' learning styles while challenging…

Early hospital mortality prediction of intensive care unit patients using an ensemble learning approach.

Science.gov (United States)

Awad, Aya; Bader-El-Den, Mohamed; McNicholas, James; Briggs, Jim

2017-12-01

Mortality prediction of hospitalized patients is an important problem. Over the past few decades, several severity scoring systems and machine learning mortality prediction models have been developed for predicting hospital mortality. By contrast, early mortality prediction for intensive care unit patients remains an open challenge. Most research has focused on severity of illness scoring systems or data mining (DM) models designed for risk estimation at least 24 or 48h after ICU admission. This study highlights the main data challenges in early mortality prediction in ICU patients and introduces a new machine learning based framework for Early Mortality Prediction for Intensive Care Unit patients (EMPICU). The proposed method is evaluated on the Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II) database. Mortality prediction models are developed for patients at the age of 16 or above in Medical ICU (MICU), Surgical ICU (SICU) or Cardiac Surgery Recovery Unit (CSRU). We employ the ensemble learning Random Forest (RF), the predictive Decision Trees (DT), the probabilistic Naive Bayes (NB) and the rule-based Projective Adaptive Resonance Theory (PART) models. The primary outcome was hospital mortality. The explanatory variables included demographic, physiological, vital signs and laboratory test variables. Performance measures were calculated using cross-validated area under the receiver operating characteristic curve (AUROC) to minimize bias. 11,722 patients with single ICU stays are considered. Only patients at the age of 16 years old and above in Medical ICU (MICU), Surgical ICU (SICU) or Cardiac Surgery Recovery Unit (CSRU) are considered in this study. The proposed EMPICU framework outperformed standard scoring systems (SOFA, SAPS-I, APACHE-II, NEWS and qSOFA) in terms of AUROC and time (i.e. at 6h compared to 48h or more after admission). The results show that although there are many values missing in the first few hour of ICU admission

The (kinetic) theory of active particles applied to learning dynamics. Comment on "Collective learning modeling based on the kinetic theory of active particles" by D. Burini et al.

Science.gov (United States)

Nieto, J.

2016-03-01

The learning phenomena, their complexity, concepts, structure, suitable theories and models, have been extensively treated in the mathematical literature in the last century, and [4] contains a very good introduction to the literature describing the many approaches and lines of research developed about them. Two main schools have to be pointed out [5] in order to understand the two -not exclusive- kinds of existing models: the stimulus sampling models and the stochastic learning models. Also [6] should be mentioned as a survey where two methods of learning are pointed out, the cognitive and the social, and where the knowledge looks like a mathematical unknown. Finally, as the authors do, we refer to the works [9,10], where the concept of population thinking was introduced and which motivate the game theory rules as a tool (both included in [4] to develop their theory) and [7], where the ideas of developing a mathematical kinetic theory of perception and learning were proposed.

Scaling prediction errors to reward variability benefits error-driven learning in humans.

Science.gov (United States)

Diederen, Kelly M J; Schultz, Wolfram

2015-09-01

Effective error-driven learning requires individuals to adapt learning to environmental reward variability. The adaptive mechanism may involve decays in learning rate across subsequent trials, as shown previously, and rescaling of reward prediction errors. The present study investigated the influence of prediction error scaling and, in particular, the consequences for learning performance. Participants explicitly predicted reward magnitudes that were drawn from different probability distributions with specific standard deviations. By fitting the data with reinforcement learning models, we found scaling of prediction errors, in addition to the learning rate decay shown previously. Importantly, the prediction error scaling was closely related to learning performance, defined as accuracy in predicting the mean of reward distributions, across individual participants. In addition, participants who scaled prediction errors relative to standard deviation also presented with more similar performance for different standard deviations, indicating that increases in standard deviation did not substantially decrease "adapters'" accuracy in predicting the means of reward distributions. However, exaggerated scaling beyond the standard deviation resulted in impaired performance. Thus efficient adaptation makes learning more robust to changing variability. Copyright © 2015 the American Physiological Society.

The Last Planner System Style of Planning: Its Basis in Learning Theory

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Bo Terje Kalsaas

2012-07-01

Full Text Available The objective of this article is to contribute to creating a better understanding of the Last Planner System (LPS – which is associated with Lean Construction – in the light of the learning processes at the basis of knowledge development, and of change and innovation. Founded on a theoretical discussion, three research questions are asked, namely: In what ways can the LPS be expected to alter the learning arenas compared to conventional project management in construction; according to learning theory, what are the main challenges associated with implementing the LPS; and, finally, what kind of learning can be linked to an implemented LPS that functions as intended? The implementation of the LPS is shown to require substantial changes to the technical-organisational learning arena. In order for the implementation to be successful, the work identity has to alter on the individual level so that an overlap occurs with the new work practices prescribed by the LPS. The LPS has an inbuilt experiential learning cycle, and provides a good starting point for single-loop learning, as well as for simple forms of double-loop learning (“routinized learning capability”. However, it is argued that the LPS understood as experiential learning has clear limitations with regard to “evolutionary learning capability”. This is amplified by the context project organisation provides. In terms of theoretical implications, this article promotes an understanding of the planning process informed by the theory describing it as an experiential learning cycle. The conceptualisation which separates the LPS from conventional production control theory is critiqued. Finally, it is argued that an understanding of the LPS grounded in learning theory will improve the possibilities for successful implementation and maximise the learning effects.

Collective learning modeling based on the kinetic theory of active particles

Science.gov (United States)

Burini, D.; De Lillo, S.; Gibelli, L.

2016-03-01

This paper proposes a systems approach to the theory of perception and learning in populations composed of many living entities. Starting from a phenomenological description of these processes, a mathematical structure is derived which is deemed to incorporate their complexity features. The modeling is based on a generalization of kinetic theory methods where interactions are described by theoretical tools of game theory. As an application, the proposed approach is used to model the learning processes that take place in a classroom.

Networks and learning in game theory

NARCIS (Netherlands)

Kets, W.

2008-01-01

This work concentrates on two topics, networks and game theory, and learning in games. The first part of this thesis looks at network games and the role of incomplete information in such games. It is assumed that players are located on a network and interact with their neighbors in the network.

A Bayesian Theory of Sequential Causal Learning and Abstract Transfer.

Science.gov (United States)

Lu, Hongjing; Rojas, Randall R; Beckers, Tom; Yuille, Alan L

2016-03-01

Two key research issues in the field of causal learning are how people acquire causal knowledge when observing data that are presented sequentially, and the level of abstraction at which learning takes place. Does sequential causal learning solely involve the acquisition of specific cause-effect links, or do learners also acquire knowledge about abstract causal constraints? Recent empirical studies have revealed that experience with one set of causal cues can dramatically alter subsequent learning and performance with entirely different cues, suggesting that learning involves abstract transfer, and such transfer effects involve sequential presentation of distinct sets of causal cues. It has been demonstrated that pre-training (or even post-training) can modulate classic causal learning phenomena such as forward and backward blocking. To account for these effects, we propose a Bayesian theory of sequential causal learning. The theory assumes that humans are able to consider and use several alternative causal generative models, each instantiating a different causal integration rule. Model selection is used to decide which integration rule to use in a given learning environment in order to infer causal knowledge from sequential data. Detailed computer simulations demonstrate that humans rely on the abstract characteristics of outcome variables (e.g., binary vs. continuous) to select a causal integration rule, which in turn alters causal learning in a variety of blocking and overshadowing paradigms. When the nature of the outcome variable is ambiguous, humans select the model that yields the best fit with the recent environment, and then apply it to subsequent learning tasks. Based on sequential patterns of cue-outcome co-occurrence, the theory can account for a range of phenomena in sequential causal learning, including various blocking effects, primacy effects in some experimental conditions, and apparently abstract transfer of causal knowledge. Copyright © 2015

Role of Adult Learning Theories in the Development of Corporate Training in the USA

Directory of Open Access Journals (Sweden)

Iryna Lytovchenko

2016-07-01

Full Text Available The article presents the analysis of the role of adult learning theories in the development of corporate training in the USA. Considering that corporate education is part of the adult education system in this country, the author examines theories of organizational learning in the context of adult learning. The results of the study have revealed that adult education in the US is based on dif erent learning theories which should be viewed from the perspective of several main orientations: behaviorism, cognitivism, humanism, developmental theories, social learning, constructivism, which have dif erent philosophical background and, accordingly, different understanding of the nature and methodology of adult learning. Based on the results of the study it has been concluded that theories of organizational learning which explain motivation of students, their needs and goals, cognitive processes and other aspects of the learning in organizations and have had the main influence on the development of corporate education in the United States should be viewed in the context of the above-mentioned basic orientations to learning, too. From the methodological perspective, the research was based on interdisciplinary and systemic approaches. Thus, we used a set of interrelated research methods: comparative, structural, systemic-functional analyses, comparison and synthesis.

Connectivism: A knowledge learning theory for the digital age?

Science.gov (United States)

Goldie, John Gerard Scott

2016-10-01

The emergence of the internet, particularly Web 2.0 has provided access to the views and opinions of a wide range of individuals opening up opportunities for new forms of communication and knowledge formation. Previous ways of navigating and filtering available information are likely to prove ineffective in these new contexts. Connectivism is one of the most prominent of the network learning theories which have been developed for e-learning environments. It is beginning to be recognized by medical educators. This article aims to examine connectivism and its potential application. The conceptual framework and application of connectivism are presented along with an outline of the main criticisms. Its potential application in medical education is then considered. While connectivism provides a useful lens through which teaching and learning using digital technologies can be better understood and managed, further development and testing is required. There is unlikely to be a single theory that will explain learning in technological enabled networks. Educators have an important role to play in online network learning.

Aligning Coordination Class Theory with a New Context: Applying a Theory of Individual Learning to Group Learning

Science.gov (United States)

Barth-Cohen, Lauren A.; Wittmann, Michael C.

2017-01-01

This article presents an empirical analysis of conceptual difficulties encountered and ways students made progress in learning at both individual and group levels in a classroom environment in which the students used an embodied modeling activity to make sense of a specific scientific scenario. The theoretical framework, coordination class theory,…

Development and application of social learning theory.

Science.gov (United States)

Price, V; Archbold, J

This article traces the development of social learning theory over the last 30 years, relating the developments to clinical nursing practice. Particular attention is focused on the contribution of Albert Bandura, the American psychologist, and his work on modelling.

Social learning through prediction error in the brain

Science.gov (United States)

Joiner, Jessica; Piva, Matthew; Turrin, Courtney; Chang, Steve W. C.

2017-06-01

Learning about the world is critical to survival and success. In social animals, learning about others is a necessary component of navigating the social world, ultimately contributing to increasing evolutionary fitness. How humans and nonhuman animals represent the internal states and experiences of others has long been a subject of intense interest in the developmental psychology tradition, and, more recently, in studies of learning and decision making involving self and other. In this review, we explore how psychology conceptualizes the process of representing others, and how neuroscience has uncovered correlates of reinforcement learning signals to explore the neural mechanisms underlying social learning from the perspective of representing reward-related information about self and other. In particular, we discuss self-referenced and other-referenced types of reward prediction errors across multiple brain structures that effectively allow reinforcement learning algorithms to mediate social learning. Prediction-based computational principles in the brain may be strikingly conserved between self-referenced and other-referenced information.

Talking back to theory: the missed opportunities in learning technology research

Directory of Open Access Journals (Sweden)

Martin Oliver

2011-12-01

Full Text Available Research into learning technology has developed a reputation for being drivenby rhetoric about the revolutionary nature of new developments, for payingscant attention to theories that might be used to frame and inform research, andfor producing shallow analyses that do little to inform the practice of education.Although there is theoretically-informed research in learning technology, this isin the minority, and has been actively marginalised by calls for applied designwork. This limits opportunities to advance knowledge in the field. Using threeexamples, alternative ways to engage with theory are identified. The paper concludesby calling for greater engagement with theory, and the development of ascholarship of learning technology, in order to enrich practice within the fieldand demonstrate its relevance to other fields of work.

Doping Among Professional Athletes in Iran: A Test of Akers's Social Learning Theory.

Science.gov (United States)

Kabiri, Saeed; Cochran, John K; Stewart, Bernadette J; Sharepour, Mahmoud; Rahmati, Mohammad Mahdi; Shadmanfaat, Syede Massomeh

2018-04-01

The use of performance-enhancing drugs (PED) is common among Iranian professional athletes. As this phenomenon is a social problem, the main purpose of this research is to explain why athletes engage in "doping" activity, using social learning theory. For this purpose, a sample of 589 professional athletes from Rasht, Iran, was used to test assumptions related to social learning theory. The results showed that there are positive and significant relationships between the components of social learning theory (differential association, differential reinforcement, imitation, and definitions) and doping behavior (past, present, and future use of PED). The structural modeling analysis indicated that the components of social learning theory accounts for 36% of the variance in past doping behavior, 35% of the variance in current doping behavior, and 32% of the variance in future use of PED.

Modelling the Factors that Affect Individuals' Utilisation of Online Learning Systems: An Empirical Study Combining the Task Technology Fit Model with the Theory of Planned Behaviour

Science.gov (United States)

Yu, Tai-Kuei; Yu, Tai-Yi

2010-01-01

Understanding learners' behaviour, perceptions and influence in terms of learner performance is crucial to predict the use of electronic learning systems. By integrating the task-technology fit (TTF) model and the theory of planned behaviour (TPB), this paper investigates the online learning utilisation of Taiwanese students. This paper provides a…

A novel time series link prediction method: Learning automata approach

Science.gov (United States)

Moradabadi, Behnaz; Meybodi, Mohammad Reza

2017-09-01

Link prediction is a main social network challenge that uses the network structure to predict future links. The common link prediction approaches to predict hidden links use a static graph representation where a snapshot of the network is analyzed to find hidden or future links. For example, similarity metric based link predictions are a common traditional approach that calculates the similarity metric for each non-connected link and sort the links based on their similarity metrics and label the links with higher similarity scores as the future links. Because people activities in social networks are dynamic and uncertainty, and the structure of the networks changes over time, using deterministic graphs for modeling and analysis of the social network may not be appropriate. In the time-series link prediction problem, the time series link occurrences are used to predict the future links In this paper, we propose a new time series link prediction based on learning automata. In the proposed algorithm for each link that must be predicted there is one learning automaton and each learning automaton tries to predict the existence or non-existence of the corresponding link. To predict the link occurrence in time T, there is a chain consists of stages 1 through T - 1 and the learning automaton passes from these stages to learn the existence or non-existence of the corresponding link. Our preliminary link prediction experiments with co-authorship and email networks have provided satisfactory results when time series link occurrences are considered.

Signed reward prediction errors drive declarative learning

NARCIS (Netherlands)

De Loof, E.; Ergo, K.; Naert, L.; Janssens, C.; Talsma, D.; van Opstal, F.; Verguts, T.

2018-01-01

Reward prediction errors (RPEs) are thought to drive learning. This has been established in procedural learning (e.g., classical and operant conditioning). However, empirical evidence on whether RPEs drive declarative learning–a quintessentially human form of learning–remains surprisingly absent. We

Paired-Associate and Feedback-Based Weather Prediction Tasks Support Multiple Category Learning Systems.

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Li, Kaiyun; Fu, Qiufang; Sun, Xunwei; Zhou, Xiaoyan; Fu, Xiaolan

2016-01-01

It remains unclear whether probabilistic category learning in the feedback-based weather prediction task (FB-WPT) can be mediated by a non-declarative or procedural learning system. To address this issue, we compared the effects of training time and verbal working memory, which influence the declarative learning system but not the non-declarative learning system, in the FB and paired-associate (PA) WPTs, as the PA task recruits a declarative learning system. The results of Experiment 1 showed that the optimal accuracy in the PA condition was significantly decreased when the training time was reduced from 7 to 3 s, but this did not occur in the FB condition, although shortened training time impaired the acquisition of explicit knowledge in both conditions. The results of Experiment 2 showed that the concurrent working memory task impaired the optimal accuracy and the acquisition of explicit knowledge in the PA condition but did not influence the optimal accuracy or the acquisition of self-insight knowledge in the FB condition. The apparent dissociation results between the FB and PA conditions suggested that a non-declarative or procedural learning system is involved in the FB-WPT and provided new evidence for the multiple-systems theory of human category learning.

Statistical and Machine Learning Models to Predict Programming Performance

OpenAIRE

Bergin, Susan

2006-01-01

This thesis details a longitudinal study on factors that influence introductory programming success and on the development of machine learning models to predict incoming student performance. Although numerous studies have developed models to predict programming success, the models struggled to achieve high accuracy in predicting the likely performance of incoming students. Our approach overcomes this by providing a machine learning technique, using a set of three significant...

E-Learning Content Design Standards Based on Interactive Digital Concepts Maps in the Light of Meaningful and Constructivist Learning Theory

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Afify, Mohammed Kamal

2018-01-01

The present study aims to identify standards of interactive digital concepts maps design and their measurement indicators as a tool to develop, organize and administer e-learning content in the light of Meaningful Learning Theory and Constructivist Learning Theory. To achieve the objective of the research, the author prepared a list of E-learning…

The Application of Carl Rogers' Person-Centered Learning Theory to Web-Based Instruction.

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Miller, Christopher T.

This paper provides a review of literature that relates research on Carl Rogers' person-centered learning theory to Web-based learning. Based on the review of the literature, a set of criteria is described that can be used to determine how closely a Web-based course matches the different components of Rogers' person-centered learning theory. Using…

Collective learning modeling based on the kinetic theory of active particles.

Science.gov (United States)

Burini, D; De Lillo, S; Gibelli, L

2016-03-01

This paper proposes a systems approach to the theory of perception and learning in populations composed of many living entities. Starting from a phenomenological description of these processes, a mathematical structure is derived which is deemed to incorporate their complexity features. The modeling is based on a generalization of kinetic theory methods where interactions are described by theoretical tools of game theory. As an application, the proposed approach is used to model the learning processes that take place in a classroom. Copyright © 2015 Elsevier B.V. All rights reserved.

Social Capital Theory: Implications for Women's Networking and Learning

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Alfred, Mary V.

2009-01-01

This chapter describes social capital theory as a framework for exploring women's networking and social capital resources. It presents the foundational assumptions of the theory, the benefits and risks of social capital engagement, a feminist critique of social capital, and the role of social capital in adult learning.

Predicting heavy episodic drinking using an extended temporal self-regulation theory.

Science.gov (United States)

Black, Nicola; Mullan, Barbara; Sharpe, Louise

2017-10-01

Alcohol consumption contributes significantly to the global burden from disease and injury, and specific patterns of heavy episodic drinking contribute uniquely to this burden. Temporal self-regulation theory and the dual-process model describe similar theoretical constructs that might predict heavy episodic drinking. The aims of this study were to test the utility of temporal self-regulation theory in predicting heavy episodic drinking, and examine whether the theoretical relationships suggested by the dual-process model significantly extend temporal self-regulation theory. This was a predictive study with 149 Australian adults. Measures were questionnaires (self-report habit index, cues to action scale, purpose-made intention questionnaire, timeline follow-back questionnaire) and executive function tasks (Stroop, Tower of London, operation span). Participants completed measures of theoretical constructs at baseline and reported their alcohol consumption two weeks later. Data were analysed using hierarchical multiple linear regression. Temporal self-regulation theory significantly predicted heavy episodic drinking (R 2 =48.0-54.8%, ptheory and the extended temporal self-regulation theory provide good prediction of heavy episodic drinking. Intention, behavioural prepotency, planning ability and inhibitory control may be good targets for interventions designed to decrease heavy episodic drinking. Copyright © 2017 Elsevier Ltd. All rights reserved.

Analysis of ensemble learning using simple perceptrons based on online learning theory

Science.gov (United States)

Miyoshi, Seiji; Hara, Kazuyuki; Okada, Masato

2005-03-01

Ensemble learning of K nonlinear perceptrons, which determine their outputs by sign functions, is discussed within the framework of online learning and statistical mechanics. One purpose of statistical learning theory is to theoretically obtain the generalization error. This paper shows that ensemble generalization error can be calculated by using two order parameters, that is, the similarity between a teacher and a student, and the similarity among students. The differential equations that describe the dynamical behaviors of these order parameters are derived in the case of general learning rules. The concrete forms of these differential equations are derived analytically in the cases of three well-known rules: Hebbian learning, perceptron learning, and AdaTron (adaptive perceptron) learning. Ensemble generalization errors of these three rules are calculated by using the results determined by solving their differential equations. As a result, these three rules show different characteristics in their affinity for ensemble learning, that is “maintaining variety among students.” Results show that AdaTron learning is superior to the other two rules with respect to that affinity.

Machine Learning Methods to Predict Density Functional Theory B3LYP Energies of HOMO and LUMO Orbitals.

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Pereira, Florbela; Xiao, Kaixia; Latino, Diogo A R S; Wu, Chengcheng; Zhang, Qingyou; Aires-de-Sousa, Joao

2017-01-23

Machine learning algorithms were explored for the fast estimation of HOMO and LUMO orbital energies calculated by DFT B3LYP, on the basis of molecular descriptors exclusively based on connectivity. The whole project involved the retrieval and generation of molecular structures, quantum chemical calculations for a database with >111 000 structures, development of new molecular descriptors, and training/validation of machine learning models. Several machine learning algorithms were screened, and an applicability domain was defined based on Euclidean distances to the training set. Random forest models predicted an external test set of 9989 compounds achieving mean absolute error (MAE) up to 0.15 and 0.16 eV for the HOMO and LUMO orbitals, respectively. The impact of the quantum chemical calculation protocol was assessed with a subset of compounds. Inclusion of the orbital energy calculated by PM7 as an additional descriptor significantly improved the quality of estimations (reducing the MAE in >30%).

Action Learning and Constructivist Grounded Theory: Powerfully Overlapping Fields of Practice

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Rand, Jane

2013-01-01

This paper considers the shared characteristics between action learning (AL) and the research methodology constructivist grounded theory (CGT). Mirroring Edmonstone's [2011. "Action Learning and Organisation Development: Overlapping Fields of Practice." "Action Learning: Research and Practice" 8 (2): 93-102] article, which…

Motivational Classroom Climate for Learning Mathematics: A Reversal Theory Perspective

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Lewis, Gareth

2015-01-01

In this article, a case is made that affect is central in determining students' experience of learning or not learning mathematics. I show how reversal theory (Apter, 2001), and particularly its taxonomy of motivations and emotions, provides a basis for a thick description of students' experiences of learning in a mathematics classroom. Using data…

Learning receptive fields using predictive feedback.

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Jehee, Janneke F M; Rothkopf, Constantin; Beck, Jeffrey M; Ballard, Dana H

2006-01-01

Previously, it was suggested that feedback connections from higher- to lower-level areas carry predictions of lower-level neural activities, whereas feedforward connections carry the residual error between the predictions and the actual lower-level activities [Rao, R.P.N., Ballard, D.H., 1999. Nature Neuroscience 2, 79-87.]. A computational model implementing the hypothesis learned simple cell receptive fields when exposed to natural images. Here, we use predictive feedback to explain tuning properties in medial superior temporal area (MST). We implement the hypothesis using a new, biologically plausible, algorithm based on matching pursuit, which retains all the features of the previous implementation, including its ability to efficiently encode input. When presented with natural images, the model developed receptive field properties as found in primary visual cortex. In addition, when exposed to visual motion input resulting from movements through space, the model learned receptive field properties resembling those in MST. These results corroborate the idea that predictive feedback is a general principle used by the visual system to efficiently encode natural input.

The Implementation of Cumulative Learning Theory in Calculating Triangular Prism and Tube Volumes

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Muklis, M.; Abidin, C.; Pamungkas, M. D.; Masriyah

2018-01-01

This study aims at describing the application of cumulative learning theory in calculating the volume of a triangular prism and a tube as well as revealing the students’ responses toward the learning. The research method used was descriptive qualitative with elementary school students as the subjects of the research. Data obtained through observation, field notes, questionnaire, tests, and interviews. The results from the application of cumulative learning theory obtained positive students’ responses in following the learning and students’ learning outcomes was dominantly above the average. This showed that cumulative learning could be used as a reference to be implemented in learning, so as to improve the students’ achievement.

Distributed Learning, Recognition, and Prediction by ART and ARTMAP Neural Networks.

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Carpenter, Gail A.

1997-11-01

A class of adaptive resonance theory (ART) models for learning, recognition, and prediction with arbitrarily distributed code representations is introduced. Distributed ART neural networks combine the stable fast learning capabilities of winner-take-all ART systems with the noise tolerance and code compression capabilities of multilayer perceptrons. With a winner-take-all code, the unsupervised model dART reduces to fuzzy ART and the supervised model dARTMAP reduces to fuzzy ARTMAP. With a distributed code, these networks automatically apportion learned changes according to the degree of activation of each coding node, which permits fast as well as slow learning without catastrophic forgetting. Distributed ART models replace the traditional neural network path weight with a dynamic weight equal to the rectified difference between coding node activation and an adaptive threshold. Thresholds increase monotonically during learning according to a principle of atrophy due to disuse. However, monotonic change at the synaptic level manifests itself as bidirectional change at the dynamic level, where the result of adaptation resembles long-term potentiation (LTP) for single-pulse or low frequency test inputs but can resemble long-term depression (LTD) for higher frequency test inputs. This paradoxical behavior is traced to dual computational properties of phasic and tonic coding signal components. A parallel distributed match-reset-search process also helps stabilize memory. Without the match-reset-search system, dART becomes a type of distributed competitive learning network.

A Visual Encapsulation of Adlerian Theory: A Tool for Teaching and Learning.

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Osborn, Cynthia J.

2001-01-01

A visual diagram is presented in this article to illustrate 6 key concepts of Adlerian theory discussed in corresponding narrative format. It is proposed that in an age of multimedia learning, a pictorial reference can enhance the teaching and learning of Adlerian theory, representing a commitment to humanistic education. (Contains 18 references.)…

Finding the Right Fit: Helping Students Apply Theory to Service-Learning Contexts

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Ricke, Audrey

2018-01-01

Background: Although past studies of service-learning focus on assessing student growth, few studies address how to support students in applying theory to their service-learning experiences. Yet, the task of applying theory is a central component of critical reflections within the social sciences in higher education and often causes anxiety among…

Vaccination learning experiences of nursing students: a grounded theory study.

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Ildarabadi, Eshagh; Karimi Moonaghi, Hossein; Heydari, Abbas; Taghipour, Ali; Abdollahimohammad, Abdolghani

2015-01-01

This study aimed to explore the experiences of nursing students being trained to perform vaccinations. The grounded theory method was applied to gather information through semi-structured interviews. The participants included 14 undergraduate nursing students in their fifth and eighth semesters of study in a nursing school in Iran. The information was analyzed according to Strauss and Corbin's method of grounded theory. A core category of experiential learning was identified, and the following eight subcategories were extracted: students' enthusiasm, vaccination sensitivity, stress, proper educational environment, absence of prerequisites, students' responsibility for learning, providing services, and learning outcomes. The vaccination training of nursing students was found to be in an acceptable state. However, some barriers to effective learning were identified. As such, the results of this study may provide empirical support for attempts to reform vaccination education by removing these barriers.

Applications of machine learning in cancer prediction and prognosis.

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Cruz, Joseph A; Wishart, David S

2007-02-11

Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to "learn" from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. This capability is particularly well-suited to medical applications, especially those that depend on complex proteomic and genomic measurements. As a result, machine learning is frequently used in cancer diagnosis and detection. More recently machine learning has been applied to cancer prognosis and prediction. This latter approach is particularly interesting as it is part of a growing trend towards personalized, predictive medicine. In assembling this review we conducted a broad survey of the different types of machine learning methods being used, the types of data being integrated and the performance of these methods in cancer prediction and prognosis. A number of trends are noted, including a growing dependence on protein biomarkers and microarray data, a strong bias towards applications in prostate and breast cancer, and a heavy reliance on "older" technologies such artificial neural networks (ANNs) instead of more recently developed or more easily interpretable machine learning methods. A number of published studies also appear to lack an appropriate level of validation or testing. Among the better designed and validated studies it is clear that machine learning methods can be used to substantially (15-25%) improve the accuracy of predicting cancer susceptibility, recurrence and mortality. At a more fundamental level, it is also evident that machine learning is also helping to improve our basic understanding of cancer development and progression.

Andragogy And Pedagogy Theories Of Learning In Joint Professional Military Education

Science.gov (United States)

2015-09-27

needs of joint military leaders. This research examines each theory and its fundamental design in an attempt to determine if pedagogy alone can meet... Abraham H. Maslow , known largely for his studies in motivation and personality, saw the goal of learning to be self-actualization, or a person’s...AU/ACSC/MCMAHON, S/AY16 AIR COMMAND AND STAFF COLLEGE AIR UNIVERSITY ANDRAGOGY AND PEDAGOGY THEORIES OF LEARNING IN JOINT PROFESSIONAL

The Effects of Reflective Activities on Skill Adaptation in a Work-Related Instrumental Learning Setting

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Roessger, Kevin M.

2014-01-01

In work-related instrumental learning contexts, the role of reflective activities is unclear. Kolb's experiential learning theory and Mezirow's transformative learning theory predict skill adaptation as an outcome. This prediction was tested by manipulating reflective activities and assessing participants' response and error rates during novel…

Predicting Student Performance in a Collaborative Learning Environment

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Olsen, Jennifer K.; Aleven, Vincent; Rummel, Nikol

2015-01-01

Student models for adaptive systems may not model collaborative learning optimally. Past research has either focused on modeling individual learning or for collaboration, has focused on group dynamics or group processes without predicting learning. In the current paper, we adjust the Additive Factors Model (AFM), a standard logistic regression…

Learning a commonsense moral theory.

Science.gov (United States)

Kleiman-Weiner, Max; Saxe, Rebecca; Tenenbaum, Joshua B

2017-10-01

We introduce a computational framework for understanding the structure and dynamics of moral learning, with a focus on how people learn to trade off the interests and welfare of different individuals in their social groups and the larger society. We posit a minimal set of cognitive capacities that together can solve this learning problem: (1) an abstract and recursive utility calculus to quantitatively represent welfare trade-offs; (2) hierarchical Bayesian inference to understand the actions and judgments of others; and (3) meta-values for learning by value alignment both externally to the values of others and internally to make moral theories consistent with one's own attachments and feelings. Our model explains how children can build from sparse noisy observations of how a small set of individuals make moral decisions to a broad moral competence, able to support an infinite range of judgments and decisions that generalizes even to people they have never met and situations they have not been in or observed. It also provides insight into the causes and dynamics of moral change across time, including cases when moral change can be rapidly progressive, changing values significantly in just a few generations, and cases when it is likely to move more slowly. Copyright © 2017 Elsevier B.V. All rights reserved.

Using David Kolb's Experiential Learning Theory in Portfolio Development Courses.

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Mark, Michael; Menson, Betty

1982-01-01

As personal portfolio assessment matures, practitioners continue to look for techniques that enhance both personal development and the process of seeking academic credit through assessment. Kolb's experiential learning theory and learning style inventory may have applications in this search. (Author)

Using Active-Learning Pedagogy to Develop Essay-Writing Skills in Introductory Political Theory Tutorials

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Murphy, Michael P. A.

2017-01-01

Building on prior research into active learning pedagogy in political science, I discuss the development of a new active learning strategy called the "thesis-building carousel," designed for use in political theory tutorials. This use of active learning pedagogy in a graduate student-led political theory tutorial represents the overlap…

Observant, Nonaggressive Temperament Predicts Theory of Mind Development

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Wellman, Henry M.; Lane, Jonathan D.; LaBounty, Jennifer; Olson, Sheryl L.

2010-01-01

Temperament dimensions influence children’s approach to and participation in social interactive experiences which reflect and impact children’s social understandings. Therefore, temperament differences might substantially impact theory of mind development in early childhood. Using longitudinal data, we report that certain early temperament characteristics (at age 3) – lack of aggressiveness, a shy-withdrawn stance to social interaction, and social-perceptual sensitivity – predict children’s more advanced theory-of-mind understanding two years later. The findings contribute to our understanding of how theory of mind develops in the formative preschool period; they may also inform debates as to the evolutionary origins of theory of mind. PMID:21499499

Prediction of Students’ Use and Acceptance of Clickers by Learning Approaches: A Cross-Sectional Observational Study

Directory of Open Access Journals (Sweden)

Kelvin Wan

2017-12-01

Full Text Available The student response system (a.k.a clickers had been widely used in classrooms for various pedagogical purposes these years. However, few of the studies examine students learning approaches toward both technology and engagement. The present study adopted a cross-sectional study method to investigate the relationship between students’ user acceptance of clickers, learning approaches, and general engagement in the clicker classes. A group of 3371 university students were investigated by an online questionnaire that contained with Unified Theory of Use and Acceptance of Technology, Study Process Questionnaire, and National Survey of Student Engagement across a two-semester span in 2015 and 2016. A regression analysis had been adopted to examine the relationship between those variables. Results indicated that a deep learning approach significantly predicted all user acceptance domains towards using clickers and significantly predicted several engagement domains such as collaborative learning and reflective and integrative learning. We concluded that deep learners tend to share a constructive attitude toward using clickers, especially when their peers are also using the clickers. While deep learners prefer integration of knowledge and skills from various sources and experiences, we hypothesize that their willingness to integrate clicker activities in their learning process stems from seeing clickers as a medium for consolidation in the learning process. Future research is, therefore, necessary to provide more detailed evidence of the characteristic of deep learners on the qualitative arm or in a way of mixed research method.

Prior Knowledge and the Learning of Science. A Review of Ausubel's Theory of This Process

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West, L. H. T.; Fensham, P. J.

1974-01-01

Examines Ausubel's theory of learning as a model of the role concerning the influence of prior knowledge on how learning occurs. Research evidence for Ausubel's theory is presented and discussed. Implications of Ausubel's theory for teaching are summarized. (PEB)

Dropout Prediction in E-Learning Courses through the Combination of Machine Learning Techniques

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Lykourentzou, Ioanna; Giannoukos, Ioannis; Nikolopoulos, Vassilis; Mpardis, George; Loumos, Vassili

2009-01-01

In this paper, a dropout prediction method for e-learning courses, based on three popular machine learning techniques and detailed student data, is proposed. The machine learning techniques used are feed-forward neural networks, support vector machines and probabilistic ensemble simplified fuzzy ARTMAP. Since a single technique may fail to…

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

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

2012-11-01

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

The Self-Perception Theory vs. a Dynamic Learning Model

OpenAIRE

Swank, Otto H.

2006-01-01

Several economists have directed our attention to a finding in the social psychological literature that extrinsic motivation may undermine intrinsic motivation. The self-perception (SP) theory developed by Bem (1972) explains this finding. The crux of this theory is that people remember their past decisions and the extrinsic rewards they received, but they do not recall their intrinsic motives. In this paper I show that the SP theory can be modeled as a variant of a conventional dynamic learn...

Presentation-Practice-Production and Task-Based Learning in the Light of Second Language Learning Theories.

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Ritchie, Graeme

2003-01-01

Features of presentation-practice-production (PPP) and task-based learning (TBL) models for language teaching are discussed with reference to language learning theories. Pre-selection of target structures, use of controlled repetition, and explicit grammar instruction in a PPP lesson are given. Suggests TBL approaches afford greater learning…

Sociocultural Theory Applied to Second Language Learning: Collaborative Learning with Reference to the Chinese Context

Science.gov (United States)

Dongyu, Zhang; Fanyu, B.; Wanyi, Du

2013-01-01

This paper discusses the sociocultural theory (SCT). In particular, three significant concepts of Vyogtsky's theory: self-regulation, the Zone of Proximal Development (ZPD), and scaffolding all of which have been discussed in numerous second language acquisition (SLA) and second language learning (SLL) research papers. These concepts lay the…

Signed reward prediction errors drive declarative learning.

Directory of Open Access Journals (Sweden)

Esther De Loof

Full Text Available Reward prediction errors (RPEs are thought to drive learning. This has been established in procedural learning (e.g., classical and operant conditioning. However, empirical evidence on whether RPEs drive declarative learning-a quintessentially human form of learning-remains surprisingly absent. We therefore coupled RPEs to the acquisition of Dutch-Swahili word pairs in a declarative learning paradigm. Signed RPEs (SRPEs; "better-than-expected" signals during declarative learning improved recognition in a follow-up test, with increasingly positive RPEs leading to better recognition. In addition, classic declarative memory mechanisms such as time-on-task failed to explain recognition performance. The beneficial effect of SRPEs on recognition was subsequently affirmed in a replication study with visual stimuli.

Signed reward prediction errors drive declarative learning.

Science.gov (United States)

De Loof, Esther; Ergo, Kate; Naert, Lien; Janssens, Clio; Talsma, Durk; Van Opstal, Filip; Verguts, Tom

2018-01-01

Reward prediction errors (RPEs) are thought to drive learning. This has been established in procedural learning (e.g., classical and operant conditioning). However, empirical evidence on whether RPEs drive declarative learning-a quintessentially human form of learning-remains surprisingly absent. We therefore coupled RPEs to the acquisition of Dutch-Swahili word pairs in a declarative learning paradigm. Signed RPEs (SRPEs; "better-than-expected" signals) during declarative learning improved recognition in a follow-up test, with increasingly positive RPEs leading to better recognition. In addition, classic declarative memory mechanisms such as time-on-task failed to explain recognition performance. The beneficial effect of SRPEs on recognition was subsequently affirmed in a replication study with visual stimuli.

Learning predictive statistics from temporal sequences: Dynamics and strategies.

Science.gov (United States)

Wang, Rui; Shen, Yuan; Tino, Peter; Welchman, Andrew E; Kourtzi, Zoe

2017-10-01

Human behavior is guided by our expectations about the future. Often, we make predictions by monitoring how event sequences unfold, even though such sequences may appear incomprehensible. Event structures in the natural environment typically vary in complexity, from simple repetition to complex probabilistic combinations. How do we learn these structures? Here we investigate the dynamics of structure learning by tracking human responses to temporal sequences that change in structure unbeknownst to the participants. Participants were asked to predict the upcoming item following a probabilistic sequence of symbols. Using a Markov process, we created a family of sequences, from simple frequency statistics (e.g., some symbols are more probable than others) to context-based statistics (e.g., symbol probability is contingent on preceding symbols). We demonstrate the dynamics with which individuals adapt to changes in the environment's statistics-that is, they extract the behaviorally relevant structures to make predictions about upcoming events. Further, we show that this structure learning relates to individual decision strategy; faster learning of complex structures relates to selection of the most probable outcome in a given context (maximizing) rather than matching of the exact sequence statistics. Our findings provide evidence for alternate routes to learning of behaviorally relevant statistics that facilitate our ability to predict future events in variable environments.

Different protein-protein interface patterns predicted by different machine learning methods.

Science.gov (United States)

Wang, Wei; Yang, Yongxiao; Yin, Jianxin; Gong, Xinqi

2017-11-22

Different types of protein-protein interactions make different protein-protein interface patterns. Different machine learning methods are suitable to deal with different types of data. Then, is it the same situation that different interface patterns are preferred for prediction by different machine learning methods? Here, four different machine learning methods were employed to predict protein-protein interface residue pairs on different interface patterns. The performances of the methods for different types of proteins are different, which suggest that different machine learning methods tend to predict different protein-protein interface patterns. We made use of ANOVA and variable selection to prove our result. Our proposed methods taking advantages of different single methods also got a good prediction result compared to single methods. In addition to the prediction of protein-protein interactions, this idea can be extended to other research areas such as protein structure prediction and design.

Learning theory and its application to the use of social media in medical education.

Science.gov (United States)

Flynn, Leslie; Jalali, Alireza; Moreau, Katherine A

2015-10-01

There is rapidly increasing pressure to employ social media in medical education, but a review of the literature demonstrates that its value and role are uncertain. To determine if medical educators have a conceptual framework that informs their use of social media and whether this framework can be mapped to learning theory. Thirty-six participants engaged in an iterative, consensus building process that identified their conceptual framework and determined if it aligned with one or more learning theories. The results show that the use of social media by the participants could be traced to two dominant theories-Connectivism and Constructivism. They also suggest that many medical educators may not be fully informed of these theories. Medical educators' use of social media can be traced to learning theories, but these theories may not be explicitly utilised in instructional design. It is recommended that formal education (faculty development) around learning theory would further enhance the use of social media in medical education. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Midwifery students learning experiences in labor wards: a grounded theory.

Science.gov (United States)

Brunstad, Anne; Hjälmhult, Esther

2014-12-01

The labor ward is an important and challenging learning area for midwifery students. It is there the students learn in authentic complex situations, in intimate situations, with potential risk for the life and health of mothers and their babies. The aim of this study was to explore the main concern expressed by midwifery students in labor wards and how they handled this concern. A longitudinal study based on grounded theory methodology was used. The participants were 10 postgraduate midwifery students, from a University College in Norway. Data were gathered and analyzed throughout the 2-year postgraduate program, in the students first, third and fourth semesters. Every student was interviewed three times in a total of 15 single and three focus-group sessions. The grounded theory of "building relationships" explains how students dealt with their main concern: "how to gain access to learning experiences". This theory consisted of three strategies; a) controlling vulnerability, b) cultivating trust and c) obtaining acceptance. Clarifying discussions involving midwives and students may facilitate the process of building relationships and contribute to confident learning. Students appreciate it when the midwives initiate discussions about acute situations and state that a novice may perceive labor and childbirth as more frightening than an experienced midwife would. Copyright © 2014 Elsevier Ltd. All rights reserved.

What learning theories can teach us in designing neurofeedback treatments

Directory of Open Access Journals (Sweden)

Ute eStrehl

2014-11-01

Full Text Available Popular definitions of neurofeedback point out that neurofeedback is a process of operant conditioning which leads to self-regulation of brain activity. Self-regulation of brain activity is considered to be a skill. The aim of this paper is to clarify that not only operant conditioning plays a role in the acquisition of this skill. In order to design the learning process additional references have to be derived from classical conditioning, two-process-theory and in particular from skill learning and research into motivational aspects. The impact of learning by trial and error, cueing of behavior, feedback, reinforcement, and knowledge of results as well as transfer of self-regulation skills into everyday life will be analyzed in this paper. In addition to these learning theory basics this paper tries to summarize the knowledge about acquisition of self-regulation from neurofeedback studies with a main emphasis on clinical populations. As a conclusion it is hypothesized that learning to self-regulate has to be offered in a psychotherapeutic, i.e. behavior therapy framework.

What learning theories can teach us in designing neurofeedback treatments.

Science.gov (United States)

Strehl, Ute

2014-01-01

Popular definitions of neurofeedback point out that neurofeedback is a process of operant conditioning which leads to self-regulation of brain activity. Self-regulation of brain activity is considered to be a skill. The aim of this paper is to clarify that not only operant conditioning plays a role in the acquisition of this skill. In order to design the learning process additional references have to be derived from classical conditioning, two-process-theory and in particular from skill learning and research into motivational aspects. The impact of learning by trial and error, cueing of behavior, feedback, reinforcement, and knowledge of results as well as transfer of self-regulation skills into everyday life will be analyzed in this paper. In addition to these learning theory basics this paper tries to summarize the knowledge about acquisition of self-regulation from neurofeedback studies with a main emphasis on clinical populations. As a conclusion it is hypothesized that learning to self-regulate has to be offered in a psychotherapeutic, i.e., behavior therapy framework.

Predicting breast screening attendance using machine learning techniques.

Science.gov (United States)

Baskaran, Vikraman; Guergachi, Aziz; Bali, Rajeev K; Naguib, Raouf N G

2011-03-01

Machine learning-based prediction has been effectively applied for many healthcare applications. Predicting breast screening attendance using machine learning (prior to the actual mammogram) is a new field. This paper presents new predictor attributes for such an algorithm. It describes a new hybrid algorithm that relies on back-propagation and radial basis function-based neural networks for prediction. The algorithm has been developed in an open source-based environment. The algorithm was tested on a 13-year dataset (1995-2008). This paper compares the algorithm and validates its accuracy and efficiency with different platforms. Nearly 80% accuracy and 88% positive predictive value and sensitivity were recorded for the algorithm. The results were encouraging; 40-50% of negative predictive value and specificity warrant further work. Preliminary results were promising and provided ample amount of reasons for testing the algorithm on a larger scale.

Prediction of attendance at fitness center: a comparison between the theory of planned behavior, the social cognitive theory, and the physical activity maintenance theory

OpenAIRE

Jekauc, Darko; Völkle, Manuel; Wagner, Matthias O.; Mess, Filip; Reiner, Miriam; Renner, Britta

2015-01-01

In the processes of physical activity (PA) maintenance specific predictors are effective, which differ from other stages of PA development. Recently, Physical Activity Maintenance Theory (PAMT) was specifically developed for prediction of PA maintenance. The aim of the present study was to evaluate the predictability of the future behavior by the PAMT and compare it with the Theory of Planned Behavior (TPB) and Social Cognitive Theory (SCT). Participation rate in a fitness center was observed...

Higgs, Top, and Bottom Mass Predictions in Finite Unified Theories

CERN Document Server

Heinemeyer, Sven; Zoupanos, George

2014-01-01

All-loop Finite Unified Theories (FUTs) are N = 1 supersymmetric Grand Unified Theories (GUTs) based on the principle of reduction of couplings, which have a remarkable predictive power. The reduction of couplings implies the existence of renormalization group invariant relations among them, which guarantee the vanishing of the beta functions at all orders in perturbation theory in particular N = 1 GUTs. In the soft breaking sector these relations imply the existence of a sum rule among the soft scalar masses. The confrontation of the predictions of a SU(5) FUT model with the top and bottom quark masses and other low-energy experimental constraints leads to a prediction of the light Higgs-boson mass in the rangeMh ∼ 121−126 GeV, in remarkable agreement with the discovery of the Higgs boson with a mass around ∼ 125.7 GeV. Also a relatively heavy spectrum with coloured supersymmetric particles above ∼ 1.5 TeV is predicted, consistent with the non-observation of those particles at the LHC.

Processes of Self-Regulated Learning in Music Theory in Elementary Music Schools in Slovenia

Science.gov (United States)

Fritz, Barbara Smolej; Peklaj, Cirila

2011-01-01

The aim of our study was determine how students regulate their learning in music theory (MT). The research is based on the socio-cognitive theory of learning. The aim of our study was twofold: first, to design the instruments for measuring (meta)cognitive and affective-motivational processes in learning MT, and, second, to examine the relationship…

Theory of mind and switching predict prospective memory performance in adolescents.

Science.gov (United States)

Altgassen, Mareike; Vetter, Nora C; Phillips, Louise H; Akgün, Canan; Kliegel, Matthias

2014-11-01

Research indicates ongoing development of prospective memory as well as theory of mind and executive functions across late childhood and adolescence. However, so far the interplay of these processes has not been investigated. Therefore, the purpose of the current study was to investigate whether theory of mind and executive control processes (specifically updating, switching, and inhibition) predict prospective memory development across adolescence. In total, 42 adolescents and 41 young adults participated in this study. Young adults outperformed adolescents on tasks of prospective memory, theory of mind, and executive functions. Switching and theory of mind predicted prospective memory performance in adolescents. Copyright © 2014 Elsevier Inc. All rights reserved.

Occupational therapy students in the process of interprofessional collaborative learning: a grounded theory study.

Science.gov (United States)

Howell, Dana

2009-01-01

The purpose of this grounded theory study was to generate a theory of the interprofessional collaborative learning process of occupational therapy (OT) students who were engaged in a collaborative learning experience with students from other allied health disciplines. Data consisted of semi-structured interviews with nine OT students from four different interprofessional collaborative learning experiences at three universities. The emergent theory explained OT students' need to build a culture of mutual respect among disciplines in order to facilitate interprofessional collaborative learning. Occupational therapy students went through a progression of learned skills that included learning how to represent the profession of OT, hold their weight within a team situation, solve problems collaboratively, work as a team, and ultimately, to work in an actual team in practice. This learning process occurred simultaneously as students also learned course content. The students had to contend with barriers and facilitators that influenced their participation and the success of their collaboration. Understanding the interprofessional learning process of OT students will help allied health faculty to design more effective, inclusive interprofessional courses.

Not that Different in Theory: Discussing the Control-Value Theory of Emotions in Online Learning Environments

Science.gov (United States)

Daniels, Lia M.; Stupnisky, Robert H.

2012-01-01

This commentary investigates the extent to which the control-value theory of emotions (Pekrun, 2006) is applicable in online learning environments. Four empirical studies in this special issue of "The Internet and Higher Education" explicitly used the control-value theory as their theoretical framework and several others have components of the…

Predicting behavioural responses to novel organisms: state-dependent detection theory

Science.gov (United States)

Sih, Andrew

2017-01-01

Human activity alters natural habitats for many species. Understanding variation in animals' behavioural responses to these changing environments is critical. We show how signal detection theory can be used within a wider framework of state-dependent modelling to predict behavioural responses to a major environmental change: novel, exotic species. We allow thresholds for action to be a function of reserves, and demonstrate how optimal thresholds can be calculated. We term this framework ‘state-dependent detection theory’ (SDDT). We focus on behavioural and fitness outcomes when animals continue to use formerly adaptive thresholds following environmental change. In a simple example, we show that exposure to novel animals which appear dangerous—but are actually safe—(e.g. ecotourists) can have catastrophic consequences for ‘prey’ (organisms that respond as if the new organisms are predators), significantly increasing mortality even when the novel species is not predatory. SDDT also reveals that the effect on reproduction can be greater than the effect on lifespan. We investigate factors that influence the effect of novel organisms, and address the potential for behavioural adjustments (via evolution or learning) to recover otherwise reduced fitness. Although effects of environmental change are often difficult to predict, we suggest that SDDT provides a useful route ahead. PMID:28100814

Can Learning Motivation Predict Learning Achievement? A Case Study of a Mobile Game-Based English Learning Approach

Science.gov (United States)

Tsai, Chia-Hui; Cheng, Ching-Hsue; Yeh, Duen-Yian; Lin, Shih-Yun

2017-01-01

This study applied a quasi-experimental design to investigate the influence and predictive power of learner motivation for achievement, employing a mobile game-based English learning approach. A system called the Happy English Learning System, integrating learning material into a game-based context, was constructed and installed on mobile devices…

Hierarchical prediction errors in midbrain and basal forebrain during sensory learning.

Science.gov (United States)

Iglesias, Sandra; Mathys, Christoph; Brodersen, Kay H; Kasper, Lars; Piccirelli, Marco; den Ouden, Hanneke E M; Stephan, Klaas E

2013-10-16

In Bayesian brain theories, hierarchically related prediction errors (PEs) play a central role for predicting sensory inputs and inferring their underlying causes, e.g., the probabilistic structure of the environment and its volatility. Notably, PEs at different hierarchical levels may be encoded by different neuromodulatory transmitters. Here, we tested this possibility in computational fMRI studies of audio-visual learning. Using a hierarchical Bayesian model, we found that low-level PEs about visual stimulus outcome were reflected by widespread activity in visual and supramodal areas but also in the midbrain. In contrast, high-level PEs about stimulus probabilities were encoded by the basal forebrain. These findings were replicated in two groups of healthy volunteers. While our fMRI measures do not reveal the exact neuron types activated in midbrain and basal forebrain, they suggest a dichotomy between neuromodulatory systems, linking dopamine to low-level PEs about stimulus outcome and acetylcholine to more abstract PEs about stimulus probabilities. Copyright © 2013 Elsevier Inc. All rights reserved.

Kolb's Experiential Learning Theory: A Meta-Model for Career Exploration.

Science.gov (United States)

Atkinson, George, Jr.; Murrell, Patricia H.

1988-01-01

Kolb's experiential learning theory offers the career counselor a meta-model with which to structure career exploration exercises and ensure a thorough investigation of self and the world of work in a manner that provides the client with an optimal amount of learning and personal development. (Author)

Assessing Student Learning in Academic Advising Using Social Cognitive Theory

Science.gov (United States)

Erlich, Richard J.; Russ-Eft, Darlene F.

2013-01-01

We investigated whether the social cognitive theory constructs of self-efficacy and self-regulated learning apply to academic advising for measuring student learning outcomes. Community college students (N = 120) participated in an individual academic-advising session. We assessed students' post-intervention self-efficacy in academic planning and…

Predicting health-promoting self-care behaviors in people with pre-diabetes by applying Bandura social learning theory.

Science.gov (United States)

Chen, Mei-Fang; Wang, Ruey-Hsia; Hung, Shu-Ling

2015-11-01

The aim of this study was to apply Bandura social learning theory in a model for identifying personal and environmental factors that predict health-promoting self-care behaviors in people with pre-diabetes. The theoretical basis of health-promoting self-care behaviors must be examined to obtain evidence-based knowledge that can help improve the effectiveness of pre-diabetes care. However, such behaviors are rarely studied in people with pre-diabetes. This quantitative, cross-sectional survey study was performed in a convenience sample of two hospitals in southern Taiwan. Two hundred people diagnosed with pre-diabetes at a single health examination center were recruited. A questionnaire survey was performed to collect data regarding personal factors (i.e., participant characteristics, pre-diabetes knowledge, and self-efficacy) and data regarding environmental factors (i.e., social support and perceptions of empowerment process) that may have associations with health-promoting self-care behaviors in people with pre-diabetes. Multiple linear regression showed that the factors that had the largest influence on the practice of health-promoting self-care behaviors were self-efficacy, diabetes history, perceptions of empowerment process, and pre-diabetes knowledge. These factors explained 59.3% of the variance in health-promoting self-care behaviors. To prevent the development of diabetes in people with pre-diabetes, healthcare professionals should consider both the personal and the environmental factors identified in this study when assessing health promoting self-care behaviors in patients with pre-diabetes and when selecting the appropriate interventions. Copyright © 2015 Elsevier Inc. All rights reserved.

Using theories of learning in workplaces to enhance physiotherapy clinical education.

Science.gov (United States)

Patton, Narelle; Higgs, Joy; Smith, Megan

2013-10-01

Clinical education has long been accepted as integral to the education of physiotherapy students and their preparation for professional practice. The clinical environment, through practice immersion, situates students in a powerful learning context and plays a critical role in students' construction of professional knowledge. Despite this acknowledged centrality of practice and clinical environments to the students' experiential construction of professional knowledge, there has been limited exploration of learning theories underpinning clinical education in the literature. In this paper, we explore a selection of learning theories underpinning physiotherapy clinical education with a view to providing clinical educators with a firm foundation on which to base wise educational practices and potentially enhance physiotherapy students' clinical learning experiences. This exploration has drawn from leading thinkers in the field of education over the past century.

From the social learning theory to a social learning algorithm for global optimization

OpenAIRE

Gong, Yue-Jiao; Zhang, Jun; Li, Yun

2014-01-01

Traditionally, the Evolutionary Computation (EC) paradigm is inspired by Darwinian evolution or the swarm intelligence of animals. Bandura's Social Learning Theory pointed out that the social learning behavior of humans indicates a high level of intelligence in nature. We found that such intelligence of human society can be implemented by numerical computing and be utilized in computational algorithms for solving optimization problems. In this paper, we design a novel and generic optimization...

High School Students' Implicit Theories of What Facilitates Science Learning

Science.gov (United States)

Parsons, Eileen Carlton; Miles, Rhea; Petersen, Michael

2011-01-01

Background: Research has primarily concentrated on adults' implicit theories about high quality science education for all students. Little work has considered the students' perspective. This study investigated high school students' implicit theories about what helped them learn science. Purpose: This study addressed (1) What characterizes high…

Context effects in a temporal discrimination task" further tests of the Scalar Expectancy Theory and Learning-to-Time models.

Science.gov (United States)

Arantes, Joana; Machado, Armando

2008-07-01

Pigeons were trained on two temporal bisection tasks, which alternated every two sessions. In the first task, they learned to choose a red key after a 1-s signal and a green key after a 4-s signal; in the second task, they learned to choose a blue key after a 4-s signal and a yellow key after a 16-s signal. Then the pigeons were exposed to a series of test trials in order to contrast two timing models, Learning-to-Time (LeT) and Scalar Expectancy Theory (SET). The models made substantially different predictions particularly for the test trials in which the sample duration ranged from 1 s to 16 s and the choice keys were Green and Blue, the keys associated with the same 4-s samples: LeT predicted that preference for Green should increase with sample duration, a context effect, but SET predicted that preference for Green should not vary with sample duration. The results were consistent with LeT. The present study adds to the literature the finding that the context effect occurs even when the two basic discriminations are never combined in the same session.

Machine learning in Python essential techniques for predictive analysis

CERN Document Server

Bowles, Michael

2015-01-01

Learn a simpler and more effective way to analyze data and predict outcomes with Python Machine Learning in Python shows you how to successfully analyze data using only two core machine learning algorithms, and how to apply them using Python. By focusing on two algorithm families that effectively predict outcomes, this book is able to provide full descriptions of the mechanisms at work, and the examples that illustrate the machinery with specific, hackable code. The algorithms are explained in simple terms with no complex math and applied using Python, with guidance on algorithm selection, d

Collaborative Learning in an Undergraduate Theory Course: An Assessment of Goals and Outcomes

Science.gov (United States)

McDuff, Elaine

2012-01-01

This project was designed to assess whether a collaborative learning approach to teaching sociological theory would be a successful means of improving student engagement in learning theory and of increasing both the depth of students' understanding of theoretical arguments and concepts and the ability of students to theorize for themselves. A…

Implications of Bandura's Observational Learning Theory for a Competency Based Teacher Education Model.

Science.gov (United States)

Hartjen, Raymond H.

Albert Bandura of Stanford University has proposed four component processes to his theory of observational learning: a) attention, b) retention, c) motor reproduction, and d) reinforcement and motivation. This study represents one phase of an effort to relate modeling and observational learning theory to teacher training. The problem of this study…

Chimpanzee choice rates in competitive games match equilibrium game theory predictions.

Science.gov (United States)

Martin, Christopher Flynn; Bhui, Rahul; Bossaerts, Peter; Matsuzawa, Tetsuro; Camerer, Colin

2014-06-05

The capacity for strategic thinking about the payoff-relevant actions of conspecifics is not well understood across species. We use game theory to make predictions about choices and temporal dynamics in three abstract competitive situations with chimpanzee participants. Frequencies of chimpanzee choices are extremely close to equilibrium (accurate-guessing) predictions, and shift as payoffs change, just as equilibrium theory predicts. The chimpanzee choices are also closer to the equilibrium prediction, and more responsive to past history and payoff changes, than two samples of human choices from experiments in which humans were also initially uninformed about opponent payoffs and could not communicate verbally. The results are consistent with a tentative interpretation of game theory as explaining evolved behavior, with the additional hypothesis that chimpanzees may retain or practice a specialized capacity to adjust strategy choice during competition to perform at least as well as, or better than, humans have.

Investigating the Impact of Formal Reflective Activities on Skill Adaptation in a Work-Related Instrumental Learning Setting

Science.gov (United States)

Roessger, Kevin M.

2013-01-01

In work-related, instrumental learning contexts the role of reflective activities is unclear. Kolb's (1985) experiential learning theory and Mezirow's transformative learning theory (2000) predict skill-adaptation as a possible outcome. This prediction was experimentally explored by manipulating reflective activities and assessing participants'…

Machine Learning Methods to Predict Diabetes Complications.

Science.gov (United States)

Dagliati, Arianna; Marini, Simone; Sacchi, Lucia; Cogni, Giulia; Teliti, Marsida; Tibollo, Valentina; De Cata, Pasquale; Chiovato, Luca; Bellazzi, Riccardo

2018-03-01

One of the areas where Artificial Intelligence is having more impact is machine learning, which develops algorithms able to learn patterns and decision rules from data. Machine learning algorithms have been embedded into data mining pipelines, which can combine them with classical statistical strategies, to extract knowledge from data. Within the EU-funded MOSAIC project, a data mining pipeline has been used to derive a set of predictive models of type 2 diabetes mellitus (T2DM) complications based on electronic health record data of nearly one thousand patients. Such pipeline comprises clinical center profiling, predictive model targeting, predictive model construction and model validation. After having dealt with missing data by means of random forest (RF) and having applied suitable strategies to handle class imbalance, we have used Logistic Regression with stepwise feature selection to predict the onset of retinopathy, neuropathy, or nephropathy, at different time scenarios, at 3, 5, and 7 years from the first visit at the Hospital Center for Diabetes (not from the diagnosis). Considered variables are gender, age, time from diagnosis, body mass index (BMI), glycated hemoglobin (HbA1c), hypertension, and smoking habit. Final models, tailored in accordance with the complications, provided an accuracy up to 0.838. Different variables were selected for each complication and time scenario, leading to specialized models easy to translate to the clinical practice.

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;…

Learning Organisation Review--A "Good" Theory Perspective

Science.gov (United States)

Santa, Mijalce

2015-01-01

Purpose: The purpose of this paper is to perform integrative literature review of the learning organisation (LO) concept, on the basis of the results of the literature review to assess the concept on the principles of "good" theory, and provide future avenues for LO concept clarification and development. Design/methodology/approach: The…

Contextual learning theory: Concrete form and a software prototype to improve early education.

NARCIS (Netherlands)

Mooij, Ton

2016-01-01

In 'contextual learning theory' three types of contextual conditions (differentiation of learning procedures and materials, integrated ICT support, and improvement of development and learning progress) are related to four aspects of the learning process (diagnostic, instructional, managerial, and

Kolb's Experiential Learning Theory and Its Application in Geography in Higher Education.

Science.gov (United States)

Healey, Mick; Jenkins, Alan

2000-01-01

Describes David Kolb's experiential learning theory focusing on the main features of his theory. Applies Kolb's theory to the teaching of geography addressing ideas such as teaching how theories of gender explain aspects of suburbia, teaching a field course, and encouraging staff to rethink their teaching style. Include references. (CMK)

Spontaneous brain activity predicts learning ability of foreign sounds.

Science.gov (United States)

Ventura-Campos, Noelia; Sanjuán, Ana; González, Julio; Palomar-García, María-Ángeles; Rodríguez-Pujadas, Aina; Sebastián-Gallés, Núria; Deco, Gustavo; Ávila, César

2013-05-29

Can learning capacity of the human brain be predicted from initial spontaneous functional connectivity (FC) between brain areas involved in a task? We combined task-related functional magnetic resonance imaging (fMRI) and resting-state fMRI (rs-fMRI) before and after training with a Hindi dental-retroflex nonnative contrast. Previous fMRI results were replicated, demonstrating that this learning recruited the left insula/frontal operculum and the left superior parietal lobe, among other areas of the brain. Crucially, resting-state FC (rs-FC) between these two areas at pretraining predicted individual differences in learning outcomes after distributed (Experiment 1) and intensive training (Experiment 2). Furthermore, this rs-FC was reduced at posttraining, a change that may also account for learning. Finally, resting-state network analyses showed that the mechanism underlying this reduction of rs-FC was mainly a transfer in intrinsic activity of the left frontal operculum/anterior insula from the left frontoparietal network to the salience network. Thus, rs-FC may contribute to predict learning ability and to understand how learning modifies the functioning of the brain. The discovery of this correspondence between initial spontaneous brain activity in task-related areas and posttraining performance opens new avenues to find predictors of learning capacities in the brain using task-related fMRI and rs-fMRI combined.

Applications of Machine Learning in Cancer Prediction and Prognosis

Directory of Open Access Journals (Sweden)

Joseph A. Cruz

2006-01-01

Full Text Available Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to “learn” from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. This capability is particularly well-suited to medical applications, especially those that depend on complex proteomic and genomic measurements. As a result, machine learning is frequently used in cancer diagnosis and detection. More recently machine learning has been applied to cancer prognosis and prediction. This latter approach is particularly interesting as it is part of a growing trend towards personalized, predictive medicine. In assembling this review we conducted a broad survey of the different types of machine learning methods being used, the types of data being integrated and the performance of these methods in cancer prediction and prognosis. A number of trends are noted, including a growing dependence on protein biomarkers and microarray data, a strong bias towards applications in prostate and breast cancer, and a heavy reliance on “older” technologies such artificial neural networks (ANNs instead of more recently developed or more easily interpretable machine learning methods. A number of published studies also appear to lack an appropriate level of validation or testing. Among the better designed and validated studies it is clear that machine learning methods can be used to substantially (15-25% improve the accuracy of predicting cancer susceptibility, recurrence and mortality. At a more fundamental level, it is also evident that machine learning is also helping to improve our basic understanding of cancer development and progression.

Contrasting cue-density effects in causal and prediction judgments.

Science.gov (United States)

Vadillo, Miguel A; Musca, Serban C; Blanco, Fernando; Matute, Helena

2011-02-01

Many theories of contingency learning assume (either explicitly or implicitly) that predicting whether an outcome will occur should be easier than making a causal judgment. Previous research suggests that outcome predictions would depart from normative standards less often than causal judgments, which is consistent with the idea that the latter are based on more numerous and complex processes. However, only indirect evidence exists for this view. The experiment presented here specifically addresses this issue by allowing for a fair comparison of causal judgments and outcome predictions, both collected at the same stage with identical rating scales. Cue density, a parameter known to affect judgments, is manipulated in a contingency learning paradigm. The results show that, if anything, the cue-density bias is stronger in outcome predictions than in causal judgments. These results contradict key assumptions of many influential theories of contingency learning.

DNA Sequencing and Predictions of the Cosmic Theory of Life

Science.gov (United States)

Wickramasinghe, N. Chandra

The theory of cometary panspermia, developed by the late Sir Fred Hoyle and the present author argues that life originated cosmically as a unique event in one of a great multitude of comets or planetary bodies in the Universe. Life on Earth did not originate here but was introduced by impacting comets, and its further evolution was driven by the subsequent acquisition of cosmically derived genes. Explicit predictions of this theory published in 1979-1981, stating how the acquisition of new genes drives evolution, are compared with recent developments in relation to horizontal gene transfer, and the role of retroviruses in evolution. Precisely-stated predictions of the theory of cometary panspermia are shown to have been verified.

An approach to children's smoking behavior using social cognitive learning theory.

Science.gov (United States)

Bektas, Murat; Ozturk, Candan; Armstrong, Merry

2010-01-01

This review article discusses the theoretical principles of social cognitive learning theory and children's risk-taking behavior of cigarette smoking, along with preventive initiatives. Social cognitive learning theorists examine the behavior of initiating and sustained smoking using a social systems approach. The authors discuss the reciprocal determinism aspect of the theory as applied to the importance of individual factors, and environment and behavioral interactions that influence smoking behavior. Included is the concept of vicarious capability that suggests that smoking behavior is determined in response to and interaction with feedback provided by the environment. The principle of self-regulatory capability asserts that people have control over their own behavior and thus that behavior change is possible. The principle of self-efficacy proposes that high level of self-efficacy of an individual may decrease the behavior of attempting to or continuing to smoke. Examples of initiatives to be undertaken in order to prevent smoking in accordance with social cognitive learning theory are presented at the end of each principle.

Designing the Electronic Classroom: Applying Learning Theory and Ergonomic Design Principles.

Science.gov (United States)

Emmons, Mark; Wilkinson, Frances C.

2001-01-01

Applies learning theory and ergonomic principles to the design of effective learning environments for library instruction. Discusses features of electronic classroom ergonomics, including the ergonomics of physical space, environmental factors, and workstations; and includes classroom layouts. (Author/LRW)

Learning theories and tools for the assessment of core nursing competencies in simulation: A theoretical review.

Science.gov (United States)

Lavoie, Patrick; Michaud, Cécile; Bélisle, Marilou; Boyer, Louise; Gosselin, Émilie; Grondin, Myrian; Larue, Caroline; Lavoie, Stéphan; Pepin, Jacinthe

2018-02-01

To identify the theories used to explain learning in simulation and to examine how these theories guided the assessment of learning outcomes related to core competencies in undergraduate nursing students. Nurse educators face the challenge of making explicit the outcomes of competency-based education, especially when competencies are conceptualized as holistic and context dependent. Theoretical review. Research papers (N = 182) published between 1999-2015 describing simulation in nursing education. Two members of the research team extracted data from the papers, including theories used to explain how simulation could engender learning and tools used to assess simulation outcomes. Contingency tables were created to examine the associations between theories, outcomes and tools. Some papers (N = 79) did not provide an explicit theory. The 103 remaining papers identified one or more learning or teaching theories; the most frequent were the National League for Nursing/Jeffries Simulation Framework, Kolb's theory of experiential learning and Bandura's social cognitive theory and concept of self-efficacy. Students' perceptions of simulation, knowledge and self-confidence were the most frequently assessed, mainly via scales designed for the study where they were used. Core competencies were mostly assessed with an observational approach. This review highlighted the fact that few studies examined the use of simulation in nursing education through learning theories and via assessment of core competencies. It also identified observational tools used to assess competencies in action, as holistic and context-dependent constructs. © 2017 John Wiley & Sons Ltd.

An Analysis of Stochastic Game Theory for Multiagent Reinforcement Learning

National Research Council Canada - National Science Library

Bowling, Michael

2000-01-01

.... In this paper we contribute a comprehensive presentation of the relevant techniques for solving stochastic games from both the game theory community and reinforcement learning communities. We examine the assumptions and limitations of these algorithms, and identify similarities between these algorithms, single agent reinforcement learners, and basic game theory techniques.

Some Consequences of Learning Theory Applied to Division of Fractions

Science.gov (United States)

Bidwell, James K.

1971-01-01

Reviews the learning theories of Robert Gagne and David Ausubel, and applies these theories to the three most common approaches to teaching division of fractions: common denominator, complex fraction, and inverse operation methods. Such analysis indicates the inverse approach should be most effective for meaningful teaching, as is verified by…

Learning Theories Applied to the Teaching of Business Communication.

Science.gov (United States)

Hart, Maxine Barton

1980-01-01

Reviews major learning theories that can be followed by business communication instructors, including those by David Ausubel, Albert Bandura, Kurt Lewin, Edward Thorndike, B.F. Skinner, and Robert Gagne. (LRA)

Predicting Virtual Learning Environment Adoption

DEFF Research Database (Denmark)

Penjor, Sonam; Zander, Pär-Ola Mikael

2016-01-01

This study investigates the significance of Rogers’ Diffusion of Innovations (DOI) theory with regard to the use of a Virtual Learning Environment (VLE) at the Royal University of Bhutan (RUB). The focus is on different adoption types and characteristics of users. Rogers’ DOI theory is applied...... to investigate the influence of five predictors (relative advantage, complexity, compatibility, trialability and observability) and their significance in the perception of academic staff at the RUB in relation to the probability of VLE adoption. These predictors are attributes of the VLE that determine the rate...... of adoption by various adopter group memberships (Innovators, Early Adopters, Early Majority, Late Majority, Laggards). Descriptive statistics and regression analysis were deployed to analyse adopter group memberships and predictor significance in VLE adoption and use. The results revealed varying attitudes...

GP and pharmacist inter-professional learning - a grounded theory study.

Science.gov (United States)

Cunningham, David E; Ferguson, Julie; Wakeling, Judy; Zlotos, Leon; Power, Ailsa

2016-05-01

Practice Based Small Group Learning (PBSGL) is an established learning resource for primary care clinicians in Scotland and is used by one-third of general practitioners (GPs). Scottish Government and UK professional bodies have called for GPs and pharmacists to work more closely together to improve care. To gain GPs' and pharmacists' perceptions and experiences of learning together in an inter-professional PBSGL pilot. Qualitative research methods involving established GP PBSGL groups in NHS Scotland recruiting one or two pharmacists to join them. A grounded theory method was used. GPs were interviewed in focus groups by a fellow GP, and pharmacists were interviewed individually by two researchers, neither being a GP or a pharmacist. Interviews were audio-recorded, transcribed and analysed using grounded theory methods. Data saturation was achieved and confirmed. Three themes were identified: GPs' and pharmacists' perceptions and experiences of inter-professional learning; Inter-professional relationships and team-working; Group identity and purpose of existing GP groups. Pharmacists were welcomed into GP groups and both professions valued inter-professional PBSGL learning. Participants learned from each other and both professions gained a wider perspective of the NHS and of each others' roles in the organisation. Inter-professional relationships, communication and team-working were strengthened and professionals regarded each other as peers and friends.

Theory of mind predicts severity level in autism.

Science.gov (United States)

Hoogenhout, Michelle; Malcolm-Smith, Susan

2017-02-01

We investigated whether theory of mind skills can indicate autism spectrum disorder severity. In all, 62 children with autism spectrum disorder completed a developmentally sensitive theory of mind battery. We used intelligence quotient, Diagnostic and Statistical Manual of Mental Disorders (4th ed.) diagnosis and level of support needed as indicators of severity level. Using hierarchical cluster analysis, we found three distinct clusters of theory of mind ability: early-developing theory of mind (Cluster 1), false-belief reasoning (Cluster 2) and sophisticated theory of mind understanding (Cluster 3). The clusters corresponded to severe, moderate and mild autism spectrum disorder. As an indicator of level of support needed, cluster grouping predicted the type of school children attended. All Cluster 1 children attended autism-specific schools; Cluster 2 was divided between autism-specific and special needs schools and nearly all Cluster 3 children attended general special needs and mainstream schools. Assessing theory of mind skills can reliably discriminate severity levels within autism spectrum disorder.

Using Expectancy Value Theory as a Framework to Reduce Student Resistance to Active Learning: A Proof of Concept.

Science.gov (United States)

Cooper, Katelyn M; Ashley, Michael; Brownell, Sara E

2017-01-01

There has been a national movement to transition college science courses from passive lectures to active learning environments. Active learning has been shown to be a more effective way for students to learn, yet there is concern that some students are resistant to active learning approaches. Although there is much discussion about student resistance to active learning, few studies have explored this topic. Furthermore, a limited number of studies have applied theoretical frameworks to student engagement in active learning. We propose using a theoretical lens of expectancy value theory to understand student resistance to active learning. In this study, we examined student perceptions of active learning after participating in 40 hours of active learning. We used the principal components of expectancy value theory to probe student experience in active learning: student perceived self-efficacy in active learning, value of active learning, and potential cost of participating in active learning. We found that students showed positive changes in the components of expectancy value theory and reported high levels of engagement in active learning, which provide proof of concept that expectancy value theory can be used to boost student perceptions of active learning and their engagement in active learning classrooms. From these findings, we have built a theoretical framework of expectancy value theory applied to active learning.

Applying Social Cognitive Theory to Academic Advising to Assess Student Learning Outcomes

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Erlich, Richard J.; Russ-Eft, Darlene

2011-01-01

Review of social cognitive theory constructs of self-efficacy and self-regulated learning is applied to academic advising for the purposes of assessing student learning. A brief overview of the history of student learning outcomes in higher education is followed by an explanation of self-efficacy and self-regulated learning constructs and how they…

Towards a predictive theory for genetic regulatory networks

Science.gov (United States)

Tkacik, Gasper

When cells respond to changes in the environment by regulating the expression levels of their genes, we often draw parallels between these biological processes and engineered information processing systems. One can go beyond this qualitative analogy, however, by analyzing information transmission in biochemical ``hardware'' using Shannon's information theory. Here, gene regulation is viewed as a transmission channel operating under restrictive constraints set by the resource costs and intracellular noise. We present a series of results demonstrating that a theory of information transmission in genetic regulatory circuits feasibly yields non-trivial, testable predictions. These predictions concern strategies by which individual gene regulatory elements, e.g., promoters or enhancers, read out their signals; as well as strategies by which small networks of genes, independently or in spatially coupled settings, respond to their inputs. These predictions can be quantitatively compared to the known regulatory networks and their function, and can elucidate how reproducible biological processes, such as embryonic development, can be orchestrated by networks built out of noisy components. Preliminary successes in the gap gene network of the fruit fly Drosophila indicate that a full ab initio theoretical prediction of a regulatory network is possible, a feat that has not yet been achieved for any real regulatory network. We end by describing open challenges on the path towards such a prediction.

WORMHOLE: Novel Least Diverged Ortholog Prediction through Machine Learning

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Sutphin, George L.; Mahoney, J. Matthew; Sheppard, Keith; Walton, David O.; Korstanje, Ron

2016-01-01

The rapid advancement of technology in genomics and targeted genetic manipulation has made comparative biology an increasingly prominent strategy to model human disease processes. Predicting orthology relationships between species is a vital component of comparative biology. Dozens of strategies for predicting orthologs have been developed using combinations of gene and protein sequence, phylogenetic history, and functional interaction with progressively increasing accuracy. A relatively new class of orthology prediction strategies combines aspects of multiple methods into meta-tools, resulting in improved prediction performance. Here we present WORMHOLE, a novel ortholog prediction meta-tool that applies machine learning to integrate 17 distinct ortholog prediction algorithms to identify novel least diverged orthologs (LDOs) between 6 eukaryotic species—humans, mice, zebrafish, fruit flies, nematodes, and budding yeast. Machine learning allows WORMHOLE to intelligently incorporate predictions from a wide-spectrum of strategies in order to form aggregate predictions of LDOs with high confidence. In this study we demonstrate the performance of WORMHOLE across each combination of query and target species. We show that WORMHOLE is particularly adept at improving LDO prediction performance between distantly related species, expanding the pool of LDOs while maintaining low evolutionary distance and a high level of functional relatedness between genes in LDO pairs. We present extensive validation, including cross-validated prediction of PANTHER LDOs and evaluation of evolutionary divergence and functional similarity, and discuss future applications of machine learning in ortholog prediction. A WORMHOLE web tool has been developed and is available at http://wormhole.jax.org/. PMID:27812085

WORMHOLE: Novel Least Diverged Ortholog Prediction through Machine Learning.

Directory of Open Access Journals (Sweden)

George L Sutphin

2016-11-01

Full Text Available The rapid advancement of technology in genomics and targeted genetic manipulation has made comparative biology an increasingly prominent strategy to model human disease processes. Predicting orthology relationships between species is a vital component of comparative biology. Dozens of strategies for predicting orthologs have been developed using combinations of gene and protein sequence, phylogenetic history, and functional interaction with progressively increasing accuracy. A relatively new class of orthology prediction strategies combines aspects of multiple methods into meta-tools, resulting in improved prediction performance. Here we present WORMHOLE, a novel ortholog prediction meta-tool that applies machine learning to integrate 17 distinct ortholog prediction algorithms to identify novel least diverged orthologs (LDOs between 6 eukaryotic species-humans, mice, zebrafish, fruit flies, nematodes, and budding yeast. Machine learning allows WORMHOLE to intelligently incorporate predictions from a wide-spectrum of strategies in order to form aggregate predictions of LDOs with high confidence. In this study we demonstrate the performance of WORMHOLE across each combination of query and target species. We show that WORMHOLE is particularly adept at improving LDO prediction performance between distantly related species, expanding the pool of LDOs while maintaining low evolutionary distance and a high level of functional relatedness between genes in LDO pairs. We present extensive validation, including cross-validated prediction of PANTHER LDOs and evaluation of evolutionary divergence and functional similarity, and discuss future applications of machine learning in ortholog prediction. A WORMHOLE web tool has been developed and is available at http://wormhole.jax.org/.

Social Learning Theory: its application in the context of nurse education.

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Bahn, D

2001-02-01

Cognitive theories are fundamental to enable problem solving and the ability to understand and apply principles in a variety of situations. This article looks at Social Learning Theory, critically analysing its principles, which are based on observational learning and modelling, and considering its value and application in the context of nurse education. It also considers the component processes that will determine the outcome of observed behaviour, other than reinforcement, as identified by Bandura, namely: attention, retention, motor reproduction, and motivation. Copyright 2001 Harcourt Publishers Ltd.

Hierarchical prediction errors in midbrain and septum during social learning.

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Diaconescu, Andreea O; Mathys, Christoph; Weber, Lilian A E; Kasper, Lars; Mauer, Jan; Stephan, Klaas E

2017-04-01

Social learning is fundamental to human interactions, yet its computational and physiological mechanisms are not well understood. One prominent open question concerns the role of neuromodulatory transmitters. We combined fMRI, computational modelling and genetics to address this question in two separate samples (N = 35, N = 47). Participants played a game requiring inference on an adviser's intentions whose motivation to help or mislead changed over time. Our analyses suggest that hierarchically structured belief updates about current advice validity and the adviser's trustworthiness, respectively, depend on different neuromodulatory systems. Low-level prediction errors (PEs) about advice accuracy not only activated regions known to support 'theory of mind', but also the dopaminergic midbrain. Furthermore, PE responses in ventral striatum were influenced by the Met/Val polymorphism of the Catechol-O-Methyltransferase (COMT) gene. By contrast, high-level PEs ('expected uncertainty') about the adviser's fidelity activated the cholinergic septum. These findings, replicated in both samples, have important implications: They suggest that social learning rests on hierarchically related PEs encoded by midbrain and septum activity, respectively, in the same manner as other forms of learning under volatility. Furthermore, these hierarchical PEs may be broadcast by dopaminergic and cholinergic projections to induce plasticity specifically in cortical areas known to represent beliefs about others. © The Author (2017). Published by Oxford University Press.

Designing Opportunities to Learn Mathematics Theory-Building Practices

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Bass, Hyman

2017-01-01

Mathematicians commonly distinguish two modes of work in the discipline: "Problem solving," and "theory building." Mathematics education offers many opportunities to learn problem solving. This paper explores the possibility, and value, of designing instructional activities that provide supported opportunities for students to…

IMPLIKASI TEORI BELAJAR SOSIAL (SOCIAL LEARNING THEORY DARI ALBERT BANDURA DALAM BIMBINGAN DAN KONSELING

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Tarsono Tarsono

2018-02-01

Full Text Available One of many ways of conceiving individual personality is by observing and analyzing his or her behavior. Bandura said that we need to know how people interact with the world around them. Sometimes any actions and his or her interaction with environment can be contrary to values that holds by their society. However—Bandura was also point out and human behavior can be predicted and modified by altering their behavior through learning. This learning processes must also considering the capacity of each person according to each own ability to think and how they interact with their surrounding environment. Based on social learning theory, behavior can be altered and modified through modeling, which is used to shape and molding a new behavior that can be approved by society and eliminating any unwanted behavior. Basic principle from this modeling is a goal that client can create, shape and mold new behavior through imitation or copying someone else’s behavior or people that become a model figure for them.

Transformative Learning: A Case for Using Grounded Theory as an Assessment Analytic

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Patterson, Barbara A. B.; Munoz, Leslie; Abrams, Leah; Bass, Caroline

2015-01-01

Transformative Learning Theory and pedagogies leverage disruptive experiences as catalysts for learning and teaching. By facilitating processes of critical analysis and reflection that challenge assumptions, transformative learning reframes what counts as knowledge and the sources and processes for gaining and producing it. Students develop a…

Machine Learning Principles Can Improve Hip Fracture Prediction

DEFF Research Database (Denmark)

Kruse, Christian; Eiken, Pia; Vestergaard, Peter

2017-01-01

Apply machine learning principles to predict hip fractures and estimate predictor importance in Dual-energy X-ray absorptiometry (DXA)-scanned men and women. Dual-energy X-ray absorptiometry data from two Danish regions between 1996 and 2006 were combined with national Danish patient data.......89 [0.82; 0.95], but with poor calibration in higher probabilities. A ten predictor subset (BMD, biochemical cholesterol and liver function tests, penicillin use and osteoarthritis diagnoses) achieved a test AUC of 0.86 [0.78; 0.94] using an “xgbTree” model. Machine learning can improve hip fracture...... prediction beyond logistic regression using ensemble models. Compiling data from international cohorts of longer follow-up and performing similar machine learning procedures has the potential to further improve discrimination and calibration....

Resting alpha activity predicts learning ability in alpha neurofeedback

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Wenya eNan

2014-07-01

Full Text Available Individuals differ in their ability to learn how to regulate the alpha activity by neurofeedback. This study aimed to investigate whether the resting alpha activity is related to the learning ability of alpha enhancement in neurofeedback and could be used as a predictor. A total of 25 subjects performed 20 sessions of individualized alpha neurofeedback in order to learn how to enhance activity in the alpha frequency band. The learning ability was assessed by three indices respectively: the training parameter changes between two periods, within a short period and across the whole training time. It was found that the resting alpha amplitude measured before training had significant positive correlations with all learning indices and could be used as a predictor for the learning ability prediction. This finding would help the researchers in not only predicting the training efficacy in individuals but also gaining further insight into the mechanisms of alpha neurofeedback.

Machine Learning and Conflict Prediction: A Use Case

Directory of Open Access Journals (Sweden)

Chris Perry

2013-10-01

Full Text Available For at least the last two decades, the international community in general and the United Nations specifically have attempted to develop robust, accurate and effective conflict early warning system for conflict prevention. One potential and promising component of integrated early warning systems lies in the field of machine learning. This paper aims at giving conflict analysis a basic understanding of machine learning methodology as well as to test the feasibility and added value of such an approach. The paper finds that the selection of appropriate machine learning methodologies can offer substantial improvements in accuracy and performance. It also finds that even at this early stage in testing machine learning on conflict prediction, full models offer more predictive power than simply using a prior outbreak of violence as the leading indicator of current violence. This suggests that a refined data selection methodology combined with strategic use of machine learning algorithms could indeed offer a significant addition to the early warning toolkit. Finally, the paper suggests a number of steps moving forward to improve upon this initial test methodology.

Neurocomputational mechanisms of prosocial learning and links to empathy.

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Lockwood, Patricia L; Apps, Matthew A J; Valton, Vincent; Viding, Essi; Roiser, Jonathan P

2016-08-30

Reinforcement learning theory powerfully characterizes how we learn to benefit ourselves. In this theory, prediction errors-the difference between a predicted and actual outcome of a choice-drive learning. However, we do not operate in a social vacuum. To behave prosocially we must learn the consequences of our actions for other people. Empathy, the ability to vicariously experience and understand the affect of others, is hypothesized to be a critical facilitator of prosocial behaviors, but the link between empathy and prosocial behavior is still unclear. During functional magnetic resonance imaging (fMRI) participants chose between different stimuli that were probabilistically associated with rewards for themselves (self), another person (prosocial), or no one (control). Using computational modeling, we show that people can learn to obtain rewards for others but do so more slowly than when learning to obtain rewards for themselves. fMRI revealed that activity in a posterior portion of the subgenual anterior cingulate cortex/basal forebrain (sgACC) drives learning only when we are acting in a prosocial context and signals a prosocial prediction error conforming to classical principles of reinforcement learning theory. However, there is also substantial variability in the neural and behavioral efficiency of prosocial learning, which is predicted by trait empathy. More empathic people learn more quickly when benefitting others, and their sgACC response is the most selective for prosocial learning. We thus reveal a computational mechanism driving prosocial learning in humans. This framework could provide insights into atypical prosocial behavior in those with disorders of social cognition.

Teaching and Learning of Knot Theory in School Mathematics

CERN Document Server

Kawauchi, Akio

2012-01-01

This book is the result of a joint venture between Professor Akio Kawauchi, Osaka City University, well-known for his research in knot theory, and the Osaka study group of mathematics education, founded by Professor Hirokazu Okamori and now chaired by his successor Professor Tomoko Yanagimoto, Osaka Kyoiku University. The seven chapters address the teaching and learning of knot theory from several perspectives. Readers will find an extremely clear and concise introduction to the fundamentals of knot theory, an overview of curricular developments in Japan, and in particular a series of teaching

Practical skills teaching in contemporary surgical education: how can educational theory be applied to promote effective learning?

Science.gov (United States)

Sadideen, Hazim; Kneebone, Roger

2012-09-01

Teaching practical skills is a core component of undergraduate and postgraduate surgical education. It is crucial to optimize our current learning and teaching models, particularly in a climate of decreased clinical exposure. This review explores the role of educational theory in promoting effective learning in practical skills teaching. Peer-reviewed publications, books, and online resources from national bodies (eg, the UK General Medical Council) were reviewed. This review highlights several aspects of surgical education, modeling them on current educational theory. These include the following: (1) acquisition and retention of motor skills (Miller's triangle; Fitts' and Posner's theory), (2) development of expertise after repeated practice and regular reinforcement (Ericsson's theory), (3) importance of the availability of expert assistance (Vygotsky's theory), (4) learning within communities of practice (Lave and Wenger's theory), (5) importance of feedback in learning practical skills (Boud, Schon, and Endes' theories), and (6) affective component of learning. It is hoped that new approaches to practical skills teaching are designed in light of our understanding of educational theory. Copyright © 2012 Elsevier Inc. All rights reserved.

Application of Ausubel's Theory of Meaningful Verbal Learning to Curriculum, Teaching and Learning of Deaf Students.

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Biser, Eileen

Implications of D. Ausubel's Theory of Meaningful Verbal Learning and its derivative, the Advance Organizer Model of Teaching, for deaf students are examined. Ausubel believes that complex intellectual processes (thinking, language, problem-solving, concept formation) are the major aspects of learning, and that primary emphasis should be placed on…

Learning Predictive Statistics: Strategies and Brain Mechanisms.

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Wang, Rui; Shen, Yuan; Tino, Peter; Welchman, Andrew E; Kourtzi, Zoe

2017-08-30

When immersed in a new environment, we are challenged to decipher initially incomprehensible streams of sensory information. However, quite rapidly, the brain finds structure and meaning in these incoming signals, helping us to predict and prepare ourselves for future actions. This skill relies on extracting the statistics of event streams in the environment that contain regularities of variable complexity from simple repetitive patterns to complex probabilistic combinations. Here, we test the brain mechanisms that mediate our ability to adapt to the environment's statistics and predict upcoming events. By combining behavioral training and multisession fMRI in human participants (male and female), we track the corticostriatal mechanisms that mediate learning of temporal sequences as they change in structure complexity. We show that learning of predictive structures relates to individual decision strategy; that is, selecting the most probable outcome in a given context (maximizing) versus matching the exact sequence statistics. These strategies engage distinct human brain regions: maximizing engages dorsolateral prefrontal, cingulate, sensory-motor regions, and basal ganglia (dorsal caudate, putamen), whereas matching engages occipitotemporal regions (including the hippocampus) and basal ganglia (ventral caudate). Our findings provide evidence for distinct corticostriatal mechanisms that facilitate our ability to extract behaviorally relevant statistics to make predictions. SIGNIFICANCE STATEMENT Making predictions about future events relies on interpreting streams of information that may initially appear incomprehensible. Past work has studied how humans identify repetitive patterns and associative pairings. However, the natural environment contains regularities that vary in complexity from simple repetition to complex probabilistic combinations. Here, we combine behavior and multisession fMRI to track the brain mechanisms that mediate our ability to adapt to

Social Learning Theories--An Important Design Consideration for Geoscience Fieldwork

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Streule, M. J.; Craig, L. E.

2016-01-01

The nature of field trips in geoscience lends them to the application of social learning theories for three key reasons. First, they provide opportunity for meaningful practical experience and promote effective learning afforded by no other educational vehicle in the subject. Second, they are integral for students creating a strong but changing…

Supporting Alternative Strategies for Learning Chemical Applications of Group Theory

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Southam, Daniel C.; Lewis, Jennifer E.

2013-01-01

A group theory course for chemists was taught entirely with process oriented guided inquiry learning (POGIL) to facilitate alternative strategies for learning. Students completed a test of one aspect of visuospatial aptitude to determine their individual approaches to solving spatial tasks, and were sorted into groups for analysis on the basis of…

Social Learning Theory and Developmental Psychology: The Legacies of Robert Sears and Albert Bandura.

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Grusec, Joan E.

1992-01-01

Social learning theory is evaluated from a historical perspective that goes up to the present. Sears and others melded psychoanalytic and stimulus-response learning theory into a comprehensive explanation of human behavior. Bandura emphasized cognitive and information-processing capacities that mediate social behavior. (LB)

Children Balance Theories and Evidence in Exploration, Explanation, and Learning

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Bonawitz, Elizabeth Baraff; van Schijndel, Tessa J. P.; Friel, Daniel; Schulz, Laura

2012-01-01

We look at the effect of evidence and prior beliefs on exploration, explanation and learning. In Experiment 1, we tested children both with and without differential prior beliefs about balance relationships (Center Theorists, mean: 82 months; Mass Theorists, mean: 89 months; No Theory children, mean: 62 months). Center and Mass Theory children who…

The Role of the Constructivist Learning Theory and Collaborative Learning Environment on Wiki Classroom, and the Relationship between Them

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Alzahrani, Ibraheem; Woollard, John

2013-01-01

This paper seeks to discover the relationship between both the social constructivist learning theory and the collaborative learning environment. This relationship can be identified by giving an example of the learning environment. Due to wiki characteristics, Wiki technology is one of the most famous learning environments that can show the…

Further tests of the Scalar Expectancy Theory (SET) and the Learning-to-Time (LeT) model in a temporal bisection task.

Science.gov (United States)

Machado, Armando; Arantes, Joana

2006-06-01

To contrast two models of timing, Scalar Expectancy Theory (SET) and Learning to Time (LeT), pigeons were exposed to a double temporal bisection procedure. On half of the trials, they learned to choose a red key after a 1s signal and a green key after a 4s signal; on the other half of the trials, they learned to choose a blue key after a 4-s signal and a yellow key after a 16-s signal. This was Phase A of an ABA design. On Phase B, the pigeons were divided into two groups and exposed to a new bisection task in which the signals ranged from 1 to 16s and the choice keys were blue and green. One group was reinforced for choosing blue after 1-s signals and green after 16-s signals and the other group was reinforced for the opposite mapping (green after 1-s signals and blue after 16-s signals). Whereas SET predicted no differences between the groups, LeT predicted that the former group would learn the new discrimination faster than the latter group. The results were consistent with LeT. Finally, the pigeons returned to Phase A. Only LeT made specific predictions regarding the reacquisition of the four temporal discriminations. These predictions were only partly consistent with the results.

Machine learning applied to the prediction of citrus production

OpenAIRE

Díaz Rodríguez, Susana Irene; Mazza, Silvia M.; Fernández-Combarro Álvarez, Elías; Giménez, Laura I.; Gaiad, José E.

2017-01-01

An in-depth knowledge about variables affecting production is required in order to predict global production and take decisions in agriculture. Machine learning is a technique used in agricultural planning and precision agriculture. This work (i) studies the effectiveness of machine learning techniques for predicting orchards production; and (ii) variables affecting this production were also identified. Data from 964 orchards of lemon, mandarin, and orange in Corrientes, Argentina are analyse...

The Learning Organization and Some Other Modern Theories of Management

International Nuclear Information System (INIS)

Sugarman, Barry

2003-01-01

In the first part, Dr Sugarman reviewed several recent theories of management and their relevance to NPP management. These theories encompass basic aspects like bureaucracy, un-bureaucracy, quality and excellence, re-engineering, knowledge management, emotional intelligence and learning organisation. Dr Sugarman discussed evolution from the Old Paradigm (Bureaucracy) to the New One (Learning Organization), defining the main aspects of both models, that can be summarises primarily in regards to strategy and structure. In terms of strategy, the new paradigm moves away from being largely inflexible to a much more dynamic environment whereby innovation is encouraged as opposed to performing in the prescribed manner whether suitable or not. The structural changes in the new model are also evident. There has been a marked move away from a hierarchical 'top down' approach to a much flatter structure that encourages less empire building and more openness between teams of various. This ensures a much greater understanding by the whole organisation of what is happening. Dr Sugarman centred his talk on the new model and how this has affected the development of the current situation. The Learning Organisation, according to the definition of Peter Senge in The Fifth Discipline (1990), is an organisation where people continually expand their capacity to create results they truly desire, and where the people are continually learning how to learn together. The movements behind increasing quality introduced a new model into industry based on Production, where workers became responsible for quality assurance instead of quality controls being reviewed by inspectors. All the employees share responsibility for learning how to improve continuously. That means, complete involvement of all staff. Following on from this, the Quality Revolution made appearance and was driven by extra attention to the customer. Out-sourcing became common and the 'Internal customer' became more common. All

Research Notes ~ Second Language Acquisition Theories as a Framework for Creating Distance Learning Courses

Directory of Open Access Journals (Sweden)

Eileen N. Ariza

2003-10-01

Full Text Available Moore and Kearsley (1996 maintain distance educators should provide for three types of interaction: a learner-content; b learner-instructor; and c learner-learner. According to interactionist second language acquisition (SLA theories that reflect Krashen’s theory (1994 that comprehensible input is critical for second language acquisition, interaction can enhance second language acquisition and fluency. Effective output is necessary as well. We reviewed the research on distance learning for second language learners and concluded that SLA theories can, and should, be the framework that drives the development of courses for students seeking to learn languages by distance technology. This article delineates issues to consider in support of combining SLA theories and research literature as a guide in creating distance language learning courses.

Life history theory predicts fish assemblage response to hydrologic regimes.

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Mims, Meryl C; Olden, Julian D

2012-01-01

The hydrologic regime is regarded as the primary driver of freshwater ecosystems, structuring the physical habitat template, providing connectivity, framing biotic interactions, and ultimately selecting for specific life histories of aquatic organisms. In the present study, we tested ecological theory predicting directional relationships between major dimensions of the flow regime and life history composition of fish assemblages in perennial free-flowing rivers throughout the continental United States. Using long-term discharge records and fish trait and survey data for 109 stream locations, we found that 11 out of 18 relationships (61%) tested between the three life history strategies (opportunistic, periodic, and equilibrium) and six hydrologic metrics (two each describing flow variability, predictability, and seasonality) were statistically significant (P history strategies, with 82% of all significant relationships observed supporting predictions from life history theory. Specifically, we found that (1) opportunistic strategists were positively related to measures of flow variability and negatively related to predictability and seasonality, (2) periodic strategists were positively related to high flow seasonality and negatively related to variability, and (3) the equilibrium strategists were negatively related to flow variability and positively related to predictability. Our study provides important empirical evidence illustrating the value of using life history theory to understand both the patterns and processes by which fish assemblage structure is shaped by adaptation to natural regimes of variability, predictability, and seasonality of critical flow events over broad biogeographic scales.

The cross-national pattern of happiness. Test of predictions implied in three theories of happiness

NARCIS (Netherlands)

R. Veenhoven (Ruut); J.J. Ehrhardt (Joop)

1995-01-01

textabstractABSTRACT. Predictions about level and dispersion of happiness in nations are derived from three theories of happiness: comparison-theory, folklore-theory and livability-theory. The predictions are tested on two cross national data-sets: a comparative survey among university students in

Executive functioning predicts reading, mathematics, and theory of mind during the elementary years.

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Cantin, Rachelle H; Gnaedinger, Emily K; Gallaway, Kristin C; Hesson-McInnis, Matthew S; Hund, Alycia M

2016-06-01

The goal of this study was to specify how executive functioning components predict reading, mathematics, and theory of mind performance during the elementary years. A sample of 93 7- to 10-year-old children completed measures of working memory, inhibition, flexibility, reading, mathematics, and theory of mind. Path analysis revealed that all three executive functioning components (working memory, inhibition, and flexibility) mediated age differences in reading comprehension, whereas age predicted mathematics and theory of mind directly. In addition, reading mediated the influence of executive functioning components on mathematics and theory of mind, except that flexibility also predicted mathematics directly. These findings provide important details about the development of executive functioning, reading, mathematics, and theory of mind during the elementary years. Copyright © 2016 Elsevier Inc. All rights reserved.

Can machine-learning improve cardiovascular risk prediction using routine clinical data?

Science.gov (United States)

Kai, Joe; Garibaldi, Jonathan M.; Qureshi, Nadeem

2017-01-01

Background Current approaches to predict cardiovascular risk fail to identify many people who would benefit from preventive treatment, while others receive unnecessary intervention. Machine-learning offers opportunity to improve accuracy by exploiting complex interactions between risk factors. We assessed whether machine-learning can improve cardiovascular risk prediction. Methods Prospective cohort study using routine clinical data of 378,256 patients from UK family practices, free from cardiovascular disease at outset. Four machine-learning algorithms (random forest, logistic regression, gradient boosting machines, neural networks) were compared to an established algorithm (American College of Cardiology guidelines) to predict first cardiovascular event over 10-years. Predictive accuracy was assessed by area under the ‘receiver operating curve’ (AUC); and sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) to predict 7.5% cardiovascular risk (threshold for initiating statins). Findings 24,970 incident cardiovascular events (6.6%) occurred. Compared to the established risk prediction algorithm (AUC 0.728, 95% CI 0.723–0.735), machine-learning algorithms improved prediction: random forest +1.7% (AUC 0.745, 95% CI 0.739–0.750), logistic regression +3.2% (AUC 0.760, 95% CI 0.755–0.766), gradient boosting +3.3% (AUC 0.761, 95% CI 0.755–0.766), neural networks +3.6% (AUC 0.764, 95% CI 0.759–0.769). The highest achieving (neural networks) algorithm predicted 4,998/7,404 cases (sensitivity 67.5%, PPV 18.4%) and 53,458/75,585 non-cases (specificity 70.7%, NPV 95.7%), correctly predicting 355 (+7.6%) more patients who developed cardiovascular disease compared to the established algorithm. Conclusions Machine-learning significantly improves accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment, while avoiding unnecessary treatment of others

Deep learning predictions of survival based on MRI in amyotrophic lateral sclerosis.

Science.gov (United States)

van der Burgh, Hannelore K; Schmidt, Ruben; Westeneng, Henk-Jan; de Reus, Marcel A; van den Berg, Leonard H; van den Heuvel, Martijn P

2017-01-01

Amyotrophic lateral sclerosis (ALS) is a progressive neuromuscular disease, with large variation in survival between patients. Currently, it remains rather difficult to predict survival based on clinical parameters alone. Here, we set out to use clinical characteristics in combination with MRI data to predict survival of ALS patients using deep learning, a machine learning technique highly effective in a broad range of big-data analyses. A group of 135 ALS patients was included from whom high-resolution diffusion-weighted and T1-weighted images were acquired at the first visit to the outpatient clinic. Next, each of the patients was monitored carefully and survival time to death was recorded. Patients were labeled as short, medium or long survivors, based on their recorded time to death as measured from the time of disease onset. In the deep learning procedure, the total group of 135 patients was split into a training set for deep learning (n = 83 patients), a validation set (n = 20) and an independent evaluation set (n = 32) to evaluate the performance of the obtained deep learning networks. Deep learning based on clinical characteristics predicted survival category correctly in 68.8% of the cases. Deep learning based on MRI predicted 62.5% correctly using structural connectivity and 62.5% using brain morphology data. Notably, when we combined the three sources of information, deep learning prediction accuracy increased to 84.4%. Taken together, our findings show the added value of MRI with respect to predicting survival in ALS, demonstrating the advantage of deep learning in disease prognostication.

SU-E-P-04: Transport Theory Learning Module in the Maple Environment

Energy Technology Data Exchange (ETDEWEB)

Both, J [University of Miami, Miller School of Medicine, Department of Radiation Oncology (United States)

2014-06-01

Purpose: The medical physics graduate program at the University of Miami is developing a computerized instructional module which provides an interactive mechanism for students to learn transport theory. While not essential in the medical physics curriculum, transport theory should be taught because the conceptual level of transport theory is fundamental, a substantial literature exists and ought to be accessible, and students should understand commercial software which solves the Boltzmann equation.But conventional teaching and learning of transport theory is challenging. Students may be under prepared to appreciate its methods, results, and relevance, and it is not substantially addressed in textbooks for the medical physicists. Other resources an instructor might reasonably use, while excellent, may be too briskly paced for beginning students. The purpose of this work is to render teaching of transport theory more tractable by making learning highly interactive. Methods: The module is being developed in the Maple mathematics environment by instructors and graduate students. It will refresh the students' knowledge of vector calculus and differential equations, and will develop users' intuition for phase space concepts. Scattering concepts will be developed with animated simulations using tunable parameters characterizing interactions, so that students may develop a “feel” for cross section. Transport equations for one and multiple types of radiation will be illustrated with phase space animations. Numerical methods of solution will be illustrated. Results: Attempts to teach rudiments of transport theory in radiation physics and dosimetry courses using conventional classroom techniques at the University of Miami have had small success, because classroom time is limited and the material has been hard for our students to appreciate intuitively. Conclusion: A joint effort of instructor and students to teach and learn transport theory by building an

SU-E-P-04: Transport Theory Learning Module in the Maple Environment

International Nuclear Information System (INIS)

Both, J

2014-01-01

Purpose: The medical physics graduate program at the University of Miami is developing a computerized instructional module which provides an interactive mechanism for students to learn transport theory. While not essential in the medical physics curriculum, transport theory should be taught because the conceptual level of transport theory is fundamental, a substantial literature exists and ought to be accessible, and students should understand commercial software which solves the Boltzmann equation.But conventional teaching and learning of transport theory is challenging. Students may be under prepared to appreciate its methods, results, and relevance, and it is not substantially addressed in textbooks for the medical physicists. Other resources an instructor might reasonably use, while excellent, may be too briskly paced for beginning students. The purpose of this work is to render teaching of transport theory more tractable by making learning highly interactive. Methods: The module is being developed in the Maple mathematics environment by instructors and graduate students. It will refresh the students' knowledge of vector calculus and differential equations, and will develop users' intuition for phase space concepts. Scattering concepts will be developed with animated simulations using tunable parameters characterizing interactions, so that students may develop a “feel” for cross section. Transport equations for one and multiple types of radiation will be illustrated with phase space animations. Numerical methods of solution will be illustrated. Results: Attempts to teach rudiments of transport theory in radiation physics and dosimetry courses using conventional classroom techniques at the University of Miami have had small success, because classroom time is limited and the material has been hard for our students to appreciate intuitively. Conclusion: A joint effort of instructor and students to teach and learn transport theory by building an interactive

Reexamining Theories of Adult Learning and Adult Development through the Lenses of Public Pedagogy

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Sandlin, Jennifer A.; Wright, Robin Redmon; Clark, Carolyn

2013-01-01

The authors examine the modernist underpinnings of traditional adult learning and development theories and evaluate elements of those theories through more contemporary lenses. Drawing on recent literature focused on "public pedagogy," the authors argue that much learning takes place outside of formal educational institutions. They look beyond…

Cultural Historical Activity Theory, Expansive Learning and Agency ...

African Journals Online (AJOL)

The paper focuses on how contradictions were used as sources of learning and development leading to 'real life expansions'. This demonstrates and reflects on the value of an interventionist research theory and methodology employed in the study to enhance participants' agency in sustainable agriculture workplaces.

Test Framing Generates a Stability Bias for Predictions of Learning by Causing People to Discount their Learning Beliefs

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Ariel, Robert; Hines, Jarrod C.; Hertzog, Christopher

2014-01-01

People estimate minimal changes in learning when making predictions of learning (POLs) for future study opportunities despite later showing increased performance and an awareness of that increase (Kornell & Bjork, 2009). This phenomenon is conceptualized as a stability bias in judgments about learning. We investigated the malleability of this effect, and whether it reflected people’s underlying beliefs about learning. We manipulated prediction framing to emphasize the role of testing vs. studying on memory and directly measured beliefs about multi-trial study effects on learning by having participants construct predicted learning curves before and after the experiment. Mean POLs were more sensitive to the number of study-test opportunities when performance was framed in terms of study benefits rather than testing benefits and POLs reflected pre-existing beliefs about learning. The stability bias is partially due to framing and reflects discounted beliefs about learning benefits rather than inherent belief in the stability of performance. PMID:25067885

Learning to perceive in the sensorimotor approach: Piaget's theory of equilibration interpreted dynamically.

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Di Paolo, Ezequiel Alejandro; Barandiaran, Xabier E; Beaton, Michael; Buhrmann, Thomas

2014-01-01

if understanding is required for perception, how can we learn to perceive something new, something we do not yet understand? According to the sensorimotor approach, perception involves mastery of regular sensorimotor co-variations that depend on the agent and the environment, also known as the "laws" of sensorimotor contingencies (SMCs). In this sense, perception involves enacting relevant sensorimotor skills in each situation. It is important for this proposal that such skills can be learned and refined with experience and yet up to this date, the sensorimotor approach has had no explicit theory of perceptual learning. The situation is made more complex if we acknowledge the open-ended nature of human learning. In this paper we propose Piaget's theory of equilibration as a potential candidate to fulfill this role. This theory highlights the importance of intrinsic sensorimotor norms, in terms of the closure of sensorimotor schemes. It also explains how the equilibration of a sensorimotor organization faced with novelty or breakdowns proceeds by re-shaping pre-existing structures in coupling with dynamical regularities of the world. This way learning to perceive is guided by the equilibration of emerging forms of skillful coping with the world. We demonstrate the compatibility between Piaget's theory and the sensorimotor approach by providing a dynamical formalization of equilibration to give an explicit micro-genetic account of sensorimotor learning and, by extension, of how we learn to perceive. This allows us to draw important lessons in the form of general principles for open-ended sensorimotor learning, including the need for an intrinsic normative evaluation by the agent itself. We also explore implications of our micro-genetic account at the personal level.

Curiosity and reward: Valence predicts choice and information prediction errors enhance learning.

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Marvin, Caroline B; Shohamy, Daphna

2016-03-01

Curiosity drives many of our daily pursuits and interactions; yet, we know surprisingly little about how it works. Here, we harness an idea implied in many conceptualizations of curiosity: that information has value in and of itself. Reframing curiosity as the motivation to obtain reward-where the reward is information-allows one to leverage major advances in theoretical and computational mechanisms of reward-motivated learning. We provide new evidence supporting 2 predictions that emerge from this framework. First, we find an asymmetric effect of positive versus negative information, with positive information enhancing both curiosity and long-term memory for information. Second, we find that it is not the absolute value of information that drives learning but, rather, the gap between the reward expected and reward received, an "information prediction error." These results support the idea that information functions as a reward, much like money or food, guiding choices and driving learning in systematic ways. (c) 2016 APA, all rights reserved).

Learning to perceive in the sensorimotor approach: Piaget's theory of equilibration interpreted dynamically

Directory of Open Access Journals (Sweden)

Ezequiel Alejandro Di Paolo

2014-07-01

Full Text Available Learning to perceive faces a classical paradox: if understanding is required for perception, how can we learn to perceive something new, something we do not yet understand? According to the sensorimotor approach, perception involves mastery of regular sensorimotor co-variations that depend on the agent and the environment, also known as the ‘laws’ of sensorimotor contingencies. In this sense, perception involves enacting relevant sensorimotor skills in each situation. It is important for this proposal that such skills can be learned and refined with experience and yet up to this date, the sensorimotor approach has had no explicit theory of perceptual learning. The situation is made more complex if we acknowledge the open-ended nature of human learning. In this paper we propose Piaget’s theory of equilibration as a potential candidate to fulfill this role. This theory highlights the importance of intrinsic sensorimotor norms, in terms of the closure of sensorimotor schemes. It also explains how the equilibration of a sensorimotor organization faced with novelty or breakdowns proceeds by re-shaping pre-existing structures in coupling with dynamical regularities of the world. This way learning to perceive is guided by the equilibration of emerging forms of skillful coping with the world. We demonstrate the compatibility between Piaget’s theory and the sensorimotor approach by providing a dynamical formalization of equilibration to give an explicit micro-genetic account of sensorimotor learning and, by extension, of how we learn to perceive. This allows us to draw important lessons in the form of general principles for open-ended sensorimotor learning, including the need for an intrinsic normative evaluation by the agent itself. We also explore implications of our micro-genetic account at the personal level.

A model of e-learning uptake and continuance in Higher Educational Institutions

OpenAIRE

Pinpathomrat, Nakarin

2015-01-01

To predict and explain E-learning usage in higher educational institutes (HEIs) better, this research conceptualized E-learning usage as two steps, E-learning uptake and continuance. The aim was to build a model of effective uptake and continuance of E-learning in HEIs, or ‘EUCH’.The EUCH model was constructed by applying five grounded theories: Unified Theory of Acceptance and Use of Technology (UTAUT); Keller’s ARCS model; Theory of Reasoned Action (TRA); Cognitive Dissonance Theory (CDT); ...

A BCM theory of meta-plasticity for online self-reorganizing fuzzy-associative learning.

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Tan, Javan; Quek, Chai

2010-06-01

Self-organizing neurofuzzy approaches have matured in their online learning of fuzzy-associative structures under time-invariant conditions. To maximize their operative value for online reasoning, these self-sustaining mechanisms must also be able to reorganize fuzzy-associative knowledge in real-time dynamic environments. Hence, it is critical to recognize that they would require self-reorganizational skills to rebuild fluid associative structures when their existing organizations fail to respond well to changing circumstances. In this light, while Hebbian theory (Hebb, 1949) is the basic computational framework for associative learning, it is less attractive for time-variant online learning because it suffers from stability limitations that impedes unlearning. Instead, this paper adopts the Bienenstock-Cooper-Munro (BCM) theory of neurological learning via meta-plasticity principles (Bienenstock et al., 1982) that provides for both online associative and dissociative learning. For almost three decades, BCM theory has been shown to effectively brace physiological evidence of synaptic potentiation (association) and depression (dissociation) into a sound mathematical framework for computational learning. This paper proposes an interpretation of the BCM theory of meta-plasticity for an online self-reorganizing fuzzy-associative learning system to realize online-reasoning capabilities. Experimental findings are twofold: 1) the analysis using S&P-500 stock index illustrated that the self-reorganizing approach could follow the trajectory shifts in the time-variant S&P-500 index for about 60 years, and 2) the benchmark profiles showed that the fuzzy-associative approach yielded comparable results with other fuzzy-precision models with similar online objectives.

Evolutionary game theory and social learning can determine how vaccine scares unfold.

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Bauch, Chris T; Bhattacharyya, Samit

2012-01-01

Immunization programs have often been impeded by vaccine scares, as evidenced by the measles-mumps-rubella (MMR) autism vaccine scare in Britain. A "free rider" effect may be partly responsible: vaccine-generated herd immunity can reduce disease incidence to such low levels that real or imagined vaccine risks appear large in comparison, causing individuals to cease vaccinating. This implies a feedback loop between disease prevalence and strategic individual vaccinating behavior. Here, we analyze a model based on evolutionary game theory that captures this feedback in the context of vaccine scares, and that also includes social learning. Vaccine risk perception evolves over time according to an exogenously imposed curve. We test the model against vaccine coverage data and disease incidence data from two vaccine scares in England & Wales: the whole cell pertussis vaccine scare and the MMR vaccine scare. The model fits vaccine coverage data from both vaccine scares relatively well. Moreover, the model can explain the vaccine coverage data more parsimoniously than most competing models without social learning and/or feedback (hence, adding social learning and feedback to a vaccine scare model improves model fit with little or no parsimony penalty). Under some circumstances, the model can predict future vaccine coverage and disease incidence--up to 10 years in advance in the case of pertussis--including specific qualitative features of the dynamics, such as future incidence peaks and undulations in vaccine coverage due to the population's response to changing disease incidence. Vaccine scares could become more common as eradication goals are approached for more vaccine-preventable diseases. Such models could help us predict how vaccine scares might unfold and assist mitigation efforts.

Spontaneous eye movements and trait empathy predict vicarious learning of fear.

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Kleberg, Johan L; Selbing, Ida; Lundqvist, Daniel; Hofvander, Björn; Olsson, Andreas

2015-12-01

Learning to predict dangerous outcomes is important to survival. In humans, this kind of learning is often transmitted through the observation of others' emotional responses. We analyzed eye movements during an observational/vicarious fear learning procedure, in which healthy participants (N=33) watched another individual ('learning model') receiving aversive treatment (shocks) paired with a predictive conditioned stimulus (CS+), but not a control stimulus (CS-). Participants' gaze pattern towards the model differentiated as a function of whether the CS was predictive or not of a shock to the model. Consistent with our hypothesis that the face of a conspecific in distress can act as an unconditioned stimulus (US), we found that the total fixation time at a learning model's face increased when the CS+ was shown. Furthermore, we found that the total fixation time at the CS+ during learning predicted participants' conditioned responses (CRs) at a later test in the absence of the model. We also demonstrated that trait empathy was associated with stronger CRs, and that autistic traits were positively related to autonomic reactions to watching the model receiving the aversive treatment. Our results have implications for both healthy and dysfunctional socio-emotional learning. Copyright © 2015 Elsevier B.V. All rights reserved.

Application of Machine Learning Approaches for Protein-protein Interactions Prediction.

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Zhang, Mengying; Su, Qiang; Lu, Yi; Zhao, Manman; Niu, Bing

2017-01-01

Proteomics endeavors to study the structures, functions and interactions of proteins. Information of the protein-protein interactions (PPIs) helps to improve our knowledge of the functions and the 3D structures of proteins. Thus determining the PPIs is essential for the study of the proteomics. In this review, in order to study the application of machine learning in predicting PPI, some machine learning approaches such as support vector machine (SVM), artificial neural networks (ANNs) and random forest (RF) were selected, and the examples of its applications in PPIs were listed. SVM and RF are two commonly used methods. Nowadays, more researchers predict PPIs by combining more than two methods. This review presents the application of machine learning approaches in predicting PPI. Many examples of success in identification and prediction in the area of PPI prediction have been discussed, and the PPIs research is still in progress. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Machine learning models in breast cancer survival prediction.

Science.gov (United States)

Montazeri, Mitra; Montazeri, Mohadeseh; Montazeri, Mahdieh; Beigzadeh, Amin

2016-01-01

Breast cancer is one of the most common cancers with a high mortality rate among women. With the early diagnosis of breast cancer survival will increase from 56% to more than 86%. Therefore, an accurate and reliable system is necessary for the early diagnosis of this cancer. The proposed model is the combination of rules and different machine learning techniques. Machine learning models can help physicians to reduce the number of false decisions. They try to exploit patterns and relationships among a large number of cases and predict the outcome of a disease using historical cases stored in datasets. The objective of this study is to propose a rule-based classification method with machine learning techniques for the prediction of different types of Breast cancer survival. We use a dataset with eight attributes that include the records of 900 patients in which 876 patients (97.3%) and 24 (2.7%) patients were females and males respectively. Naive Bayes (NB), Trees Random Forest (TRF), 1-Nearest Neighbor (1NN), AdaBoost (AD), Support Vector Machine (SVM), RBF Network (RBFN), and Multilayer Perceptron (MLP) machine learning techniques with 10-cross fold technique were used with the proposed model for the prediction of breast cancer survival. The performance of machine learning techniques were evaluated with accuracy, precision, sensitivity, specificity, and area under ROC curve. Out of 900 patients, 803 patients and 97 patients were alive and dead, respectively. In this study, Trees Random Forest (TRF) technique showed better results in comparison to other techniques (NB, 1NN, AD, SVM and RBFN, MLP). The accuracy, sensitivity and the area under ROC curve of TRF are 96%, 96%, 93%, respectively. However, 1NN machine learning technique provided poor performance (accuracy 91%, sensitivity 91% and area under ROC curve 78%). This study demonstrates that Trees Random Forest model (TRF) which is a rule-based classification model was the best model with the highest level of

Learning relationships from theory to design

Directory of Open Access Journals (Sweden)

C. J.H. Fowler

1999-12-01

Full Text Available Over the last five years we have seen a very significant increase in the use of Information Communication Technologies (ICT in schools, colleges and university. For example in 1998, there were over 195 accredited US universities offering a thousand or more distance learning courses (Philips and Yager, 1998. By no means were all of these new courses associated with educational innovation. The speed and ease of implementation of Webbased approaches, in particular, is resulting in design by imitation of current courses and methods, with a real lack of innovation or utilization of the power inherent in technologybased learning. Although matters are improving (see for example Brown, 1999, part of the reason for this failure to innovate is, we argue, because of the large gap between theory and practice.

The Role of Spirituality in Transition to Parenthood: Qualitative Research Using Transformative Learning Theory.

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Klobučar, Nataša Rijavec

2016-08-01

This article presents results of a qualitative study of 12 adult couples making transition to parenthood. The aim of the study was to research the meaning of transition to parenthood through the lens of transformative learning theory. Transformative learning theory explains learning through meaning-making of that life experience. In this paper, the spiritual dimension of learning is emphasized. An important part of research methodology included biographical method, using semi-structured interviews before and after the birth of the first child. The research showed that transformative learning occurs in different spheres of life during transition to parenthood. This paper discusses the spiritual dimension of learning, meaning-making and presents results of the research.

Capabilities and limitations of predictive engineering theories for multicomponent adsorption

DEFF Research Database (Denmark)

Bartholdy, Sofie; Bjørner, Martin Gamel; Solbraa, Even

2013-01-01

for the prediction of multicomponent adsorption with parameters obtained solely from correlating single gas/solid data. We have tested them over an extensive database with emphasis on polar systems (both gases and solids). The three theories are the multicomponent Langmuir, the ideal adsorbed solution theory (IAST...

The Relative Effect of Team-Based Learning on Motivation and Learning: A Self-Determination Theory Perspective

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Jeno, Lucas M.; Raaheim, Arild; Kristensen, Sara Madeleine; Kristensen, Kjell Daniel; Hole, Torstein Nielsen; Haugland, Mildrid J.; Mæland, Silje

2017-01-01

We investigate the effects of team-based learning (TBL) on motivation and learning in a quasi-experimental study. The study employs a self-determination theory perspective to investigate the motivational effects of implementing TBL in a physiotherapy course in higher education. We adopted a one-group pretest-posttest design. The results show that…

Learning Theory Expertise in the Design of Learning Spaces: Who Needs a Seat at the Table?

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Rook, Michael M.; Choi, Koun; McDonald, Scott P.

2015-01-01

This study highlights the impact of including stakeholders with expertise in learning theory in a learning space design process. We present the decision-making process during the design of the Krause Innovation Studio on the campus of the Pennsylvania State University to draw a distinction between the architect and faculty member's decision-making…

PAL driven organizational learning theory and practices a light on learning journey of organizations

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Chuah, Kong

2015-01-01

Presenting an innovative concept and approach for organization management, this book serves to document an organization’s journey towards the ultimate goal of learning organization. This book also shares the experience on how a OL framework built on established learning theories, could be used effectively, overcoming many of the barriers in a real industrial setting. Utilizing a ready-to-use tool called Project Action Learning (PAL) to analyze real life case studies, the authors introduce a framework that allows teams of people to work and learn over the course of business projects. Equal emphasis is placed on the achievement of pre-set project outcomes and the learning objectives of the participants. In addition, a long term organizational learning strategy is put forward and the necessary supporting infrastructure, in the form of four ‘PAL Pillars’, is described. The concepts and development of the PAL driven Organizational Learning model are inspired by, and grounded in, Western and Eastern business ...

A predictive validity study of the Learning Style Questionnaire (LSQ) using multiple, specific learning criteria

NARCIS (Netherlands)

Kappe, F.R.; Boekholt, L.; den Rooyen, C.; van der Flier, H.

2009-01-01

Multiple and specific learning criteria were used to examine the predictive validity of the Learning Style Questionnaire (LSQ). Ninety-nine students in a college of higher learning in The Netherlands participated in a naturally occurring field study. The students were categorized into one of four

Aligning Kolb's Experiential Learning Theory with a Comprehensive Agricultural Education Model

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Baker, Marshall A.; Robinson, J. Shane; Kolb, David A.

2012-01-01

Experiential learning has been a foundational tenant of agricultural education since its inception. However, the theory of experiential education has received limited attention in the permanent agricultural education literature base. As such, this philosophical manuscript examined Kolb's experiential learning process further, and considered the…

Enhancing Student Learning in Knowledge-Based Courses: Integrating Team-Based Learning in Mass Communication Theory Classes

Science.gov (United States)

Han, Gang; Newell, Jay

2014-01-01

This study explores the adoption of the team-based learning (TBL) method in knowledge-based and theory-oriented journalism and mass communication (J&MC) courses. It first reviews the origin and concept of TBL, the relevant theories, and then introduces the TBL method and implementation, including procedures and assessments, employed in an…

Unsupervised Learning and Pattern Recognition of Biological Data Structures with Density Functional Theory and Machine Learning.

Science.gov (United States)

Chen, Chien-Chang; Juan, Hung-Hui; Tsai, Meng-Yuan; Lu, Henry Horng-Shing

2018-01-11

By introducing the methods of machine learning into the density functional theory, we made a detour for the construction of the most probable density function, which can be estimated by learning relevant features from the system of interest. Using the properties of universal functional, the vital core of density functional theory, the most probable cluster numbers and the corresponding cluster boundaries in a studying system can be simultaneously and automatically determined and the plausibility is erected on the Hohenberg-Kohn theorems. For the method validation and pragmatic applications, interdisciplinary problems from physical to biological systems were enumerated. The amalgamation of uncharged atomic clusters validated the unsupervised searching process of the cluster numbers and the corresponding cluster boundaries were exhibited likewise. High accurate clustering results of the Fisher's iris dataset showed the feasibility and the flexibility of the proposed scheme. Brain tumor detections from low-dimensional magnetic resonance imaging datasets and segmentations of high-dimensional neural network imageries in the Brainbow system were also used to inspect the method practicality. The experimental results exhibit the successful connection between the physical theory and the machine learning methods and will benefit the clinical diagnoses.

The application of learning theory in horse training

DEFF Research Database (Denmark)

McLean, Andrew N.; Christensen, Janne Winther

2017-01-01

additional techniques (approach conditioning and stimulus blending). The salience of different types of cues, the interaction of operant and classical conditioning and the impact of stress are also discussed. This paper also exposes the inflexibility and occasional inadequacy of the terminology of learning...... on the correct application of learning theory, and safety and welfare benefits for people and horses would follow. Finally it is also proposed that the term ‘conflict theory’ be taken up in equitation science to facilitate diagnosis of training-related behaviour disorders and thus enable the emergence...

A Reflective Journey through Theory and Research in Mathematical Learning and Development

Science.gov (United States)

Belbase, Shashidhar

2010-01-01

This paper is an attempt to reflect on class sessions during the fall 2010 in a course "Theory and Research in Mathematical Learning and Development". This reflection as a learning journey portrays discussions based on foundational perspectives (FP), historical highlights (HH), and guiding questions (GQ) related to mathematics learning and…

Successful online learning the five Ps

OpenAIRE

Jim FLOOD

2004-01-01

Successful online learning the five Ps Jim FLOOD E-learning Consultant-UK Key learning points An important aspect of design for online learning is visual ergonomics. Learning theories offer poor predictive power in terms of how learners work and learn. Success at learning is closely related to emotional engagementand learning designers tend to ignore this aspect. Online learning poses a challenging experience for learnersand they need support t...

Predicting short-term weight loss using four leading health behavior change theories