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Sample records for learning theory predicts

  1. Critical evidence for the prediction error theory in associative learning.

    Science.gov (United States)

    Terao, Kanta; Matsumoto, Yukihisa; Mizunami, Makoto

    2015-03-10

    In associative learning in mammals, it is widely accepted that the discrepancy, or error, between actual and predicted reward determines whether learning occurs. Complete evidence for the prediction error theory, however, has not been obtained in any learning systems: Prediction error theory stems from the finding of a blocking phenomenon, but blocking can also be accounted for by other theories, such as the attentional theory. We demonstrated blocking in classical conditioning in crickets and obtained evidence to reject the attentional theory. To obtain further evidence supporting the prediction error theory and rejecting alternative theories, we constructed a neural model to match the prediction error theory, by modifying our previous model of learning in crickets, and we tested a prediction from the model: the model predicts that pharmacological intervention of octopaminergic transmission during appetitive conditioning impairs learning but not formation of reward prediction itself, and it thus predicts no learning in subsequent training. We observed such an "auto-blocking", which could be accounted for by the prediction error theory but not by other competitive theories to account for blocking. This study unambiguously demonstrates validity of the prediction error theory in associative learning.

  2. Conformal prediction for reliable machine learning theory, adaptations and applications

    CERN Document Server

    Balasubramanian, Vineeth; Vovk, Vladimir

    2014-01-01

    The conformal predictions framework is a recent development in machine learning that can associate a reliable measure of confidence with a prediction in any real-world pattern recognition application, including risk-sensitive applications such as medical diagnosis, face recognition, and financial risk prediction. Conformal Predictions for Reliable Machine Learning: Theory, Adaptations and Applications captures the basic theory of the framework, demonstrates how to apply it to real-world problems, and presents several adaptations, including active learning, change detection, and anomaly detecti

  3. Observational attachment theory-based parenting measures predict children's attachment narratives independently from social learning theory-based measures.

    Science.gov (United States)

    Matias, Carla; O'Connor, Thomas G; Futh, Annabel; Scott, Stephen

    2014-01-01

    Conceptually and methodologically distinct models exist for assessing quality of parent-child relationships, but few studies contrast competing models or assess their overlap in predicting developmental outcomes. Using observational methodology, the current study examined the distinctiveness of attachment theory-based and social learning theory-based measures of parenting in predicting two key measures of child adjustment: security of attachment narratives and social acceptance in peer nominations. A total of 113 5-6-year-old children from ethnically diverse families participated. Parent-child relationships were rated using standard paradigms. Measures derived from attachment theory included sensitive responding and mutuality; measures derived from social learning theory included positive attending, directives, and criticism. Child outcomes were independently-rated attachment narrative representations and peer nominations. Results indicated that Attachment theory-based and Social Learning theory-based measures were modestly correlated; nonetheless, parent-child mutuality predicted secure child attachment narratives independently of social learning theory-based measures; in contrast, criticism predicted peer-nominated fighting independently of attachment theory-based measures. In young children, there is some evidence that attachment theory-based measures may be particularly predictive of attachment narratives; however, no single model of measuring parent-child relationships is likely to best predict multiple developmental outcomes. Assessment in research and applied settings may benefit from integration of different theoretical and methodological paradigms.

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

    Science.gov (United States)

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

    2015-01-01

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

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

    Science.gov (United States)

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

    2015-11-01

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

  6. Pupil dilation indicates the coding of past prediction errors: Evidence for attentional learning theory.

    Science.gov (United States)

    Koenig, Stephan; Uengoer, Metin; Lachnit, Harald

    2018-04-01

    The attentional learning theory of Pearce and Hall () predicts more attention to uncertain cues that have caused a high prediction error in the past. We examined how the cue-elicited pupil dilation during associative learning was linked to such error-driven attentional processes. In three experiments, participants were trained to acquire associations between different cues and their appetitive (Experiment 1), motor (Experiment 2), or aversive (Experiment 3) outcomes. All experiments were designed to examine differences in the processing of continuously reinforced cues (consistently followed by the outcome) versus partially reinforced, uncertain cues (randomly followed by the outcome). We measured the pupil dilation elicited by the cues in anticipation of the outcome and analyzed how this conditioned pupil response changed over the course of learning. In all experiments, changes in pupil size complied with the same basic pattern: During early learning, consistently reinforced cues elicited greater pupil dilation than uncertain, randomly reinforced cues, but this effect gradually reversed to yield a greater pupil dilation for uncertain cues toward the end of learning. The pattern of data accords with the changes in prediction error and error-driven attention formalized by the Pearce-Hall theory. © 2017 The Authors. Psychophysiology published by Wiley Periodicals, Inc. on behalf of Society for Psychophysiological Research.

  7. Large-scale transportation network congestion evolution prediction using deep learning theory.

    Science.gov (United States)

    Ma, Xiaolei; Yu, Haiyang; Wang, Yunpeng; Wang, Yinhai

    2015-01-01

    Understanding how congestion at one location can cause ripples throughout large-scale transportation network is vital for transportation researchers and practitioners to pinpoint traffic bottlenecks for congestion mitigation. Traditional studies rely on either mathematical equations or simulation techniques to model traffic congestion dynamics. However, most of the approaches have limitations, largely due to unrealistic assumptions and cumbersome parameter calibration process. With the development of Intelligent Transportation Systems (ITS) and Internet of Things (IoT), transportation data become more and more ubiquitous. This triggers a series of data-driven research to investigate transportation phenomena. Among them, deep learning theory is considered one of the most promising techniques to tackle tremendous high-dimensional data. This study attempts to extend deep learning theory into large-scale transportation network analysis. A deep Restricted Boltzmann Machine and Recurrent Neural Network architecture is utilized to model and predict traffic congestion evolution based on Global Positioning System (GPS) data from taxi. A numerical study in Ningbo, China is conducted to validate the effectiveness and efficiency of the proposed method. Results show that the prediction accuracy can achieve as high as 88% within less than 6 minutes when the model is implemented in a Graphic Processing Unit (GPU)-based parallel computing environment. The predicted congestion evolution patterns can be visualized temporally and spatially through a map-based platform to identify the vulnerable links for proactive congestion mitigation.

  8. Large-scale transportation network congestion evolution prediction using deep learning theory.

    Directory of Open Access Journals (Sweden)

    Xiaolei Ma

    Full Text Available Understanding how congestion at one location can cause ripples throughout large-scale transportation network is vital for transportation researchers and practitioners to pinpoint traffic bottlenecks for congestion mitigation. Traditional studies rely on either mathematical equations or simulation techniques to model traffic congestion dynamics. However, most of the approaches have limitations, largely due to unrealistic assumptions and cumbersome parameter calibration process. With the development of Intelligent Transportation Systems (ITS and Internet of Things (IoT, transportation data become more and more ubiquitous. This triggers a series of data-driven research to investigate transportation phenomena. Among them, deep learning theory is considered one of the most promising techniques to tackle tremendous high-dimensional data. This study attempts to extend deep learning theory into large-scale transportation network analysis. A deep Restricted Boltzmann Machine and Recurrent Neural Network architecture is utilized to model and predict traffic congestion evolution based on Global Positioning System (GPS data from taxi. A numerical study in Ningbo, China is conducted to validate the effectiveness and efficiency of the proposed method. Results show that the prediction accuracy can achieve as high as 88% within less than 6 minutes when the model is implemented in a Graphic Processing Unit (GPU-based parallel computing environment. The predicted congestion evolution patterns can be visualized temporally and spatially through a map-based platform to identify the vulnerable links for proactive congestion mitigation.

  9. Causal beliefs about depression in different cultural groups—what do cognitive psychological theories of causal learning and reasoning predict?

    OpenAIRE

    Hagmayer, York; Engelmann, Neele

    2014-01-01

    Cognitive psychological research focuses 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 lite...

  10. Predicting the stability of ternary intermetallics with density functional theory and machine learning

    Science.gov (United States)

    Schmidt, Jonathan; Chen, Liming; Botti, Silvana; Marques, Miguel A. L.

    2018-06-01

    We use a combination of machine learning techniques and high-throughput density-functional theory calculations to explore ternary compounds with the AB2C2 composition. We chose the two most common intermetallic prototypes for this composition, namely, the tI10-CeAl2Ga2 and the tP10-FeMo2B2 structures. Our results suggest that there may be ˜10 times more stable compounds in these phases than previously known. These are mostly metallic and non-magnetic. While the use of machine learning reduces the overall calculation cost by around 75%, some limitations of its predictive power still exist, in particular, for compounds involving the second-row of the periodic table or magnetic elements.

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

    Science.gov (United States)

    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

  12. 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…

  13. 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

  14. 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.

  15. 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.

  16. 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 ...

  17. 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.

  18. 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.

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

    NARCIS (Netherlands)

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

    2018-01-01

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

  20. 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,…

  1. 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.

  2. 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

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

    Science.gov (United States)

    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%).

  4. 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.

  5. Towards Predictive Association Theories

    DEFF Research Database (Denmark)

    Kontogeorgis, Georgios; Tsivintzelis, Ioannis; Michelsen, Michael Locht

    2011-01-01

    Association equations of state like SAFT, CPA and NRHB have been previously applied to many complex mixtures. In this work we focus on two of these models, the CPA and the NRHB equations of state and the emphasis is on the analysis of their predictive capabilities for a wide range of applications....... We use the term predictive in two situations: (i) with no use of binary interaction parameters, and (ii) multicomponent calculations using binary interaction parameters based solely on binary data. It is shown that the CPA equation of state can satisfactorily predict CO2–water–glycols–alkanes VLE...

  6. 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.)

  7. Moral learning as intuitive theory revision.

    Science.gov (United States)

    Rhodes, Marjorie; Wellman, Henry

    2017-10-01

    We argue that moral learning, like much of conceptual development more generally, involves development and change in children's intuitive theories of the world. Children's intuitive theories involve coherent and abstract representations of the world, which point to domain-specific, unobservable causal-explanatory entities. From this perspective, children rely on intuitive sociological theories (in particular, an abstract expectation that group memberships constrain people's obligations), and their intuitive psychological theories (including expectations that mental states motivate individual behavior) to predict, explain, and evaluate morally-relevant action. Thus, moral learning involves development and change in each of these theories of the world across childhood, as well as developmental change in how children integrate information from these two intuitive theories. This perspective is supported by a series of research studies on young children's moral reasoning and learning, and compared to other developmental approaches, including more traditional forms of constructivism and more recent nativist perspectives. Copyright © 2016 Elsevier B.V. All rights reserved.

  8. Dynasting Theory: Lessons in learning grounded theory

    Directory of Open Access Journals (Sweden)

    Johnben Teik-Cheok Loy, MBA, MTS, Ph.D.

    2011-06-01

    Full Text Available This article captures the key learning lessons gleaned from the author’s experience learning and developing a grounded theory for his doctoral dissertation using the classic methodology as conceived by Barney Glaser. The theory was developed through data gathered on founders and successors of Malaysian Chinese family-own businesses. The main concern for Malaysian Chinese family businesses emerged as dynasting . the building, maintaining, and growing the power and resources of the business within the family lineage. The core category emerged as dynasting across cultures, where founders and successors struggle to transition from traditional Chinese to hybrid cultural and modernized forms of family business from one generation to the next. The key learning lessons were categorized under five headings: (a sorting through different versions of grounded theory, (b educating and managing research stakeholders, (c embracing experiential learning, (d discovering the core category: grounded intuition, and (e recognizing limitations and possibilities.Keywords: grounded theory, learning, dynasting, family business, Chinese

  9. 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

  10. 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

  11. Comparing theories' performance in predicting violence.

    Science.gov (United States)

    Haas, Henriette; Cusson, Maurice

    2015-01-01

    The stakes of choosing the best theory as a basis for violence prevention and offender rehabilitation are high. However, no single theory of violence has ever been universally accepted by a majority of established researchers. Psychiatry, psychology and sociology are each subdivided into different schools relying upon different premises. All theories can produce empirical evidence for their validity, some of them stating the opposite of each other. Calculating different models with multivariate logistic regression on a dataset of N = 21,312 observations and ninety-two influences allowed a direct comparison of the performance of operationalizations of some of the most important schools. The psychopathology model ranked as the best model in terms of predicting violence right after the comprehensive interdisciplinary model. Next came the rational choice and lifestyle model and third the differential association and learning theory model. Other models namely the control theory model, the childhood-trauma model and the social conflict and reaction model turned out to have low sensitivities for predicting violence. Nevertheless, all models produced acceptable results in predictions of a non-violent outcome. Copyright © 2015. Published by Elsevier Ltd.

  12. 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)

  13. 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.

  14. 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

  15. 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

  16. An instance theory of associative learning.

    Science.gov (United States)

    Jamieson, Randall K; Crump, Matthew J C; Hannah, Samuel D

    2012-03-01

    We present and test an instance model of associative learning. The model, Minerva-AL, treats associative learning as cued recall. Memory preserves the events of individual trials in separate traces. A probe presented to memory contacts all traces in parallel and retrieves a weighted sum of the traces, a structure called the echo. Learning of a cue-outcome relationship is measured by the cue's ability to retrieve a target outcome. The theory predicts a number of associative learning phenomena, including acquisition, extinction, reacquisition, conditioned inhibition, external inhibition, latent inhibition, discrimination, generalization, blocking, overshadowing, overexpectation, superconditioning, recovery from blocking, recovery from overshadowing, recovery from overexpectation, backward blocking, backward conditioned inhibition, and second-order retrospective revaluation. We argue that associative learning is consistent with an instance-based approach to learning and memory.

  17. Theory in learning technology

    Directory of Open Access Journals (Sweden)

    Laura Czerniewicz

    2011-12-01

    Full Text Available This special issue is being published at a significant point in time in relation tosimultaneous changes in higher education, in technology and in the field of learningtechnology itself. As the 2011 ALT C conference themes clearly state, learningtechnology needs to learn to thrive in a colder and more challenging climate. In thisdifficult political and economic environment technological trends continue todevelop in terms of mobility, cloud computing, ubiquity and the emergence of whathas been called big data. E-learning has become mainstream and the field of learningtechnology itself is beginning to stabilise as a profession. Profession here isunderstood as a knowledge-based occupation and a form of cultural work where thetasks addressed are human problems amenable to expert advice and distinguishablefrom other kinds of work by the fact that it is underpinned by abstract knowledge(Macdonald, 1995.

  18. [Linking learning theory with practice].

    Science.gov (United States)

    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.

  19. Learning Theories In Instructional Multimedia For English Learning

    OpenAIRE

    Farani, Rizki

    2016-01-01

    Learning theory is the concept of human learning. This concept is one of the important components in instructional for learning, especially English learning. English subject becomes one of important subjects for students but learning English needs specific strategy since it is not our vernacular. Considering human learning process in English learning is expected to increase students' motivation to understand English better. Nowadays, the application of learning theories in English learning ha...

  20. Early Learning Theories Made Visible

    Science.gov (United States)

    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…

  1. Is quantum theory predictably complete?

    Energy Technology Data Exchange (ETDEWEB)

    Kupczynski, M [Department of Mathematics and Statistics, University of Ottawa, 585 King-Edward Avenue, Ottawa, Ontario K1N 6N5 (Canada); Departement de l' Informatique, UQO, Case postale 1250, succursale Hull, Gatineau, Quebec J8X 3X 7 (Canada)], E-mail: mkupczyn@uottawa.ca

    2009-07-15

    Quantum theory (QT) provides statistical predictions for various physical phenomena. To verify these predictions a considerable amount of data has been accumulated in the 'measurements' performed on the ensembles of identically prepared physical systems or in the repeated 'measurements' on some trapped 'individual physical systems'. The outcomes of these measurements are, in general, some numerical time series registered by some macroscopic instruments. The various empirical probability distributions extracted from these time series were shown to be consistent with the probabilistic predictions of QT. More than 70 years ago the claim was made that QT provided the most complete description of 'individual' physical systems and outcomes of the measurements performed on 'individual' physical systems were obtained in an intrinsically random way. Spin polarization correlation experiments (SPCEs), performed to test the validity of Bell inequalities, clearly demonstrated the existence of strong long-range correlations and confirmed that the beams hitting far away detectors somehow preserve the memory of their common source which would be destroyed if the individual counts of far away detectors were purely random. Since the probabilities describe the random experiments and are not the attributes of the 'individual' physical systems, the claim that QT provides a complete description of 'individual' physical systems seems not only unjustified but also misleading and counter productive. In this paper, we point out that we even do not know whether QT is predictably complete because it has not been tested carefully enough. Namely, it was not proven that the time series of existing experimental data did not contain some stochastic fine structures that could have been averaged out by describing them in terms of the empirical probability distributions. In this paper, we advocate various statistical tests that

  2. 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.

  3. 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.

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

    Science.gov (United States)

    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)

  5. 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.

  6. 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.

  7. 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

  8. The Scientific Status of Learning Styles Theories

    Science.gov (United States)

    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…

  9. 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.)

  10. 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

  11. Learning Theory Foundations of Simulation-Based Mastery Learning.

    Science.gov (United States)

    McGaghie, William C; Harris, Ilene B

    2018-06-01

    Simulation-based mastery learning (SBML), like all education interventions, has learning theory foundations. Recognition and comprehension of SBML learning theory foundations are essential for thoughtful education program development, research, and scholarship. We begin with a description of SBML followed by a section on the importance of learning theory foundations to shape and direct SBML education and research. We then discuss three principal learning theory conceptual frameworks that are associated with SBML-behavioral, constructivist, social cognitive-and their contributions to SBML thought and practice. We then discuss how the three learning theory frameworks converge in the course of planning, conducting, and evaluating SBML education programs in the health professions. Convergence of these learning theory frameworks is illustrated by a description of an SBML education and research program in advanced cardiac life support. We conclude with a brief coda.

  12. 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.

  13. 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

  14. 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 ...

  15. 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. ...

  16. 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.

  17. 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.

  18. 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

  19. 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.

  20. 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...

  1. 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…

  2. 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

  3. 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…

  4. 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)

  5. 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...

  6. 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.

  7. Constructivism, the so-called semantic learning theories, and situated cognition versus the psychological learning theories.

    Science.gov (United States)

    Aparicio, Juan José; Rodríguez Moneo, María

    2005-11-01

    In this paper, the perspective of situated cognition, which gave rise both to the pragmatic theories and the so-called semantic theories of learning and has probably become the most representative standpoint of constructivism, is examined. We consider the claim of situated cognition to provide alternative explanations of the learning phenomenon to those of psychology and, especially, to those of the symbolic perspective, currently predominant in cognitive psychology. The level of analysis of situated cognition (i.e., global interactive systems) is considered an inappropriate approach to the problem of learning. From our analysis, it is concluded that the pragmatic theories and the so-called semantic theories of learning which originated in situated cognition can hardly be considered alternatives to the psychological learning theories, and they are unlikely to add anything of interest to the learning theory or to contribute to the improvement of our knowledge about the learning phenomenon.

  8. 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.

  9. Learned Helplessness: Theory and Evidence

    Science.gov (United States)

    Maier, Steven F.; Seligman, Martin E. P.

    1976-01-01

    Authors believes that three phenomena are all instances of "learned helplessness," instances in which an organism has learned that outcomes are uncontrollable by his responses and is seriously debilitated by this knowledge. This article explores the evidence for the phenomena of learned helplessness, and discussed a variety of theoretical…

  10. 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.

  11. 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...

  12. An Interpretation of Dewey's Experiential Learning Theory.

    Science.gov (United States)

    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…

  13. Jigsaw Cooperative Learning: Acid-Base Theories

    Science.gov (United States)

    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…

  14. Learning automata theory and applications

    CERN Document Server

    Najim, K

    1994-01-01

    Learning systems have made a significant impact on all areas of engineering problems. They are attractive methods for solving many problems which are too complex, highly non-linear, uncertain, incomplete or non-stationary, and have subtle and interactive exchanges with the environment where they operate. The main aim of the book is to give a systematic treatment of learning automata and to produce a guide to a wide variety of ideas and methods that can be used in learning systems, including enough theoretical material to enable the user of the relevant techniques and concepts to understand why

  15. 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

  16. 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

  17. 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...

  18. 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.

  19. 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

  20. 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…

  1. 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)

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

    Science.gov (United States)

    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…

  3. Playing off the curve - testing quantitative predictions of skill acquisition theories in development of chess performance.

    Science.gov (United States)

    Gaschler, Robert; Progscha, Johanna; Smallbone, Kieran; Ram, Nilam; Bilalić, Merim

    2014-01-01

    Learning curves have been proposed as an adequate description of learning processes, no matter whether the processes manifest within minutes or across years. Different mechanisms underlying skill acquisition can lead to differences in the shape of learning curves. In the current study, we analyze the tournament performance data of 1383 chess players who begin competing at young age and play tournaments for at least 10 years. We analyze the performance development with the goal to test the adequacy of learning curves, and the skill acquisition theories they are based on, for describing and predicting expertise acquisition. On the one hand, we show that the skill acquisition theories implying a negative exponential learning curve do a better job in both describing early performance gains and predicting later trajectories of chess performance than those theories implying a power function learning curve. On the other hand, the learning curves of a large proportion of players show systematic qualitative deviations from the predictions of either type of skill acquisition theory. While skill acquisition theories predict larger performance gains in early years and smaller gains in later years, a substantial number of players begin to show substantial improvements with a delay of several years (and no improvement in the first years), deviations not fully accounted for by quantity of practice. The current work adds to the debate on how learning processes on a small time scale combine to large-scale changes.

  4. Theories of willpower affect sustained learning.

    Directory of Open Access Journals (Sweden)

    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.

  5. 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.

  6. 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 ...

  7. Game-Based Learning Theory

    Science.gov (United States)

    Laughlin, Daniel

    2008-01-01

    Persistent Immersive Synthetic Environments (PISE) are not just connection points, they are meeting places. They are the new public squares, village centers, malt shops, malls and pubs all rolled into one. They come with a sense of 'thereness" that engages the mind like a real place does. Learning starts as a real code. The code defines "objects." The objects exist in computer space, known as the "grid." The objects and space combine to create a "place." A "world" is created, Before long, the grid and code becomes obscure, and the "world maintains focus.

  8. 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…

  9. 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 ...

  10. 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...

  11. 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;…

  12. 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.

  13. 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

  14. Rolling Bearing Life Prediction, Theory, and Application

    Science.gov (United States)

    Zaretsky, Erwin V.

    2016-01-01

    A tutorial is presented outlining the evolution, theory, and application of rolling-element bearing life prediction from that of A. Palmgren, 1924; W. Weibull, 1939; G. Lundberg and A. Palmgren, 1947 and 1952; E. Ioannides and T. Harris, 1985; and E. Zaretsky, 1987. Comparisons are made between these life models. The Ioannides-Harris model without a fatigue limit is identical to the Lundberg-Palmgren model. The Weibull model is similar to that of Zaretsky if the exponents are chosen to be identical. Both the load-life and Hertz stress-life relations of Weibull, Lundberg and Palmgren, and Ioannides and Harris reflect a strong dependence on the Weibull slope. The Zaretsky model decouples the dependence of the critical shear stress-life relation from the Weibull slope. This results in a nominal variation of the Hertz stress-life exponent. For 9th- and 8th-power Hertz stress-life exponents for ball and roller bearings, respectively, the Lundberg-Palmgren model best predicts life. However, for 12th- and 10th-power relations reflected by modern bearing steels, the Zaretsky model based on the Weibull equation is superior. Under the range of stresses examined, the use of a fatigue limit would suggest that (for most operating conditions under which a rolling-element bearing will operate) the bearing will not fail from classical rolling-element fatigue. Realistically, this is not the case. The use of a fatigue limit will significantly overpredict life over a range of normal operating Hertz stresses. (The use of ISO 281:2007 with a fatigue limit in these calculations would result in a bearing life approaching infinity.) Since the predicted lives of rolling-element bearings are high, the problem can become one of undersizing a bearing for a particular application. Rules had been developed to distinguish and compare predicted lives with those actually obtained. Based upon field and test results of 51 ball and roller bearing sets, 98 percent of these bearing sets had acceptable

  15. 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.

  16. 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…

  17. 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

  18. 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…

  19. 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.

  20. 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.

  1. 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.

  2. Recent Advances in Predictive (Machine) Learning

    Energy Technology Data Exchange (ETDEWEB)

    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.

  3. Predicting Ecosystem Alliances Using Landscape Theory

    Directory of Open Access Journals (Sweden)

    Shruti Satsangi

    2012-08-01

    Full Text Available Previous articles in the TIM Review have covered various aspects of the concept of business ecosystems, from the types of ecosystems to keystone strategy, to different member roles and value co-creation. While there is no dearth of suggested best practices that organizations should follow as ecosystem members, it can be difficult to apply these insights into actionable steps for them to take. This is especially true when the ecosystem members already have a prior history of cooperation or competition with each other, as opposed to where a new ecosystem is created. Landscape theory, a political science approach to predicting coalition formation and strategic alliances, can be a useful complement to ecosystems studies by providing a tool to evaluate the best possible alliance options for an organization, given information about itself and the other companies in the system. As shown in the case study of mobile device manufacturers choosing platform providers in the mobile ecosystem, this tool is highly flexible and customizable, with more data providing a more accurate view of the alliances in the ecosystem. At the same time, with even basic parameters, companies can glean significant information about which coalitions will best serve their interest and overall standing within the ecosystem. This article shows the synergies between landscape theory and an ecosystems approach and offers a practical, actionable way in which to analyze individual member benefits.

  4. 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…

  5. 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

  6. Predicting Learned Helplessness Based on Personality

    Science.gov (United States)

    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…

  7. 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.

  8. 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…

  9. 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.

  10. 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.

  11. Machine learning modelling for predicting soil liquefaction susceptibility

    Directory of Open Access Journals (Sweden)

    P. Samui

    2011-01-01

    Full Text Available This study describes two machine learning techniques applied to predict liquefaction susceptibility of soil based on the standard penetration test (SPT data from the 1999 Chi-Chi, Taiwan earthquake. The first machine learning technique which uses Artificial Neural Network (ANN based on multi-layer perceptions (MLP that are trained with Levenberg-Marquardt backpropagation algorithm. The second machine learning technique uses the Support Vector machine (SVM that is firmly based on the theory of statistical learning theory, uses classification technique. ANN and SVM have been developed to predict liquefaction susceptibility using corrected SPT [(N160] and cyclic stress ratio (CSR. Further, an attempt has been made to simplify the models, requiring only the two parameters [(N160 and peck ground acceleration (amax/g], for the prediction of liquefaction susceptibility. The developed ANN and SVM models have also been applied to different case histories available globally. The paper also highlights the capability of the SVM over the ANN models.

  12. 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.

  13. 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.

  14. 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.

  15. Roles of dopamine neurons in mediating the prediction error in aversive learning in insects.

    Science.gov (United States)

    Terao, Kanta; Mizunami, Makoto

    2017-10-31

    In associative learning in mammals, it is widely accepted that the discrepancy, or error, between actual and predicted reward determines whether learning occurs. The prediction error theory has been proposed to account for the finding of a blocking phenomenon, in which pairing of a stimulus X with an unconditioned stimulus (US) could block subsequent association of a second stimulus Y to the US when the two stimuli were paired in compound with the same US. Evidence for this theory, however, has been imperfect since blocking can also be accounted for by competitive theories. We recently reported blocking in classical conditioning of an odor with water reward in crickets. We also reported an "auto-blocking" phenomenon in appetitive learning, which supported the prediction error theory and rejected alternative theories. The presence of auto-blocking also suggested that octopamine neurons mediate reward prediction error signals. Here we show that blocking and auto-blocking occur in aversive learning to associate an odor with salt water (US) in crickets, and our results suggest that dopamine neurons mediate aversive prediction error signals. We conclude that the prediction error theory is applicable to both appetitive learning and aversive learning in insects.

  16. 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)

  17. 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…

  18. Simulation Methodology in Nursing Education and Adult Learning Theory

    Science.gov (United States)

    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.…

  19. Comparing theories' performance in predicting violence

    OpenAIRE

    Haas, Henriette; Cusson, Maurice

    2015-01-01

    The stakes of choosing the best theory as a basis for violence prevention and offender rehabilitation are high. However, no single theory of violence has ever been universally accepted by a majority of established researchers. Psychiatry, psychology and sociology are each subdivided into different schools relying upon different premises. All theories can produce empirical evidence for their validity, some of them stating the opposite of each other. Calculating different models wit...

  20. Power Load Prediction Based on Fractal Theory

    OpenAIRE

    Jian-Kai, Liang; Cattani, Carlo; Wan-Qing, Song

    2015-01-01

    The basic theories of load forecasting on the power system are summarized. Fractal theory, which is a new algorithm applied to load forecasting, is introduced. Based on the fractal dimension and fractal interpolation function theories, the correlation algorithms are applied to the model of short-term load forecasting. According to the process of load forecasting, the steps of every process are designed, including load data preprocessing, similar day selecting, short-term load forecasting, and...

  1. 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…

  2. 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...

  3. 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…

  4. 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…

  5. 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

  6. 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

  7. 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.

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

    Science.gov (United States)

    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)

  9. Toward an Instructionally Oriented Theory of Example-Based Learning

    Science.gov (United States)

    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…

  10. Proactive Interference in Human Predictive Learning

    OpenAIRE

    Castro, Leyre; Ortega, Nuria; Matute, Helena

    2002-01-01

    The impairment in responding to a secondly trained association because of the prior training of another (i.e., proactive interference) is a well-established effect in human and animal research, and it has been demonstrated in many paradigms. However, learning theories have been concerned with proactive interference only when the competing stimuli have been presented in compound at some moment of the training phase. In this experiment we investigated the possibility of proactive interference b...

  11. 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...

  12. 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.

  13. 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.

  14. Learning Predictive Statistics: Strategies and Brain Mechanisms.

    Science.gov (United States)

    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

  15. 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

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

    Science.gov (United States)

    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…

  17. 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.

  18. Reflection of Learning Theories in Iranian ELT Textbooks

    Science.gov (United States)

    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…

  19. Task-Based Language Teaching and Expansive Learning Theory

    Science.gov (United States)

    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…

  20. 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: ...

  1. Three Theories of Learning and Their Implications for Teachers.

    Science.gov (United States)

    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…

  2. 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…

  3. 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…

  4. Applying Distributed Learning Theory in Online Business Communication Courses.

    Science.gov (United States)

    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)

  5. The utility of theory of planned behavior in predicting consistent ...

    African Journals Online (AJOL)

    admin

    disease. Objective: To examine the utility of theory of planned behavior in predicting consistent condom use intention of HIV .... (24-25), making subjective norms as better predictors of intention ..... Organizational Behavior and Human Decision.

  6. 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

  7. Why hydrological predictions should be evaluated using information theory

    Directory of Open Access Journals (Sweden)

    S. V. Weijs

    2010-12-01

    Full Text Available Probabilistic predictions are becoming increasingly popular in hydrology. Equally important are methods to test such predictions, given the topical debate on uncertainty analysis in hydrology. Also in the special case of hydrological forecasting, there is still discussion about which scores to use for their evaluation. In this paper, we propose to use information theory as the central framework to evaluate predictions. From this perspective, we hope to shed some light on what verification scores measure and should measure. We start from the ''divergence score'', a relative entropy measure that was recently found to be an appropriate measure for forecast quality. An interpretation of a decomposition of this measure provides insight in additive relations between climatological uncertainty, correct information, wrong information and remaining uncertainty. When the score is applied to deterministic forecasts, it follows that these increase uncertainty to infinity. In practice, however, deterministic forecasts tend to be judged far more mildly and are widely used. We resolve this paradoxical result by proposing that deterministic forecasts either are implicitly probabilistic or are implicitly evaluated with an underlying decision problem or utility in mind. We further propose that calibration of models representing a hydrological system should be the based on information-theoretical scores, because this allows extracting all information from the observations and avoids learning from information that is not there. Calibration based on maximizing utility for society trains an implicit decision model rather than the forecasting system itself. This inevitably results in a loss or distortion of information in the data and more risk of overfitting, possibly leading to less valuable and informative forecasts. We also show this in an example. The final conclusion is that models should preferably be explicitly probabilistic and calibrated to maximize the

  8. 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.

  9. 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.

  10. 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...

  11. Learning Theory and the Typewriter Teacher

    Science.gov (United States)

    Wakin, B. Bertha

    1974-01-01

    Eight basic principles of learning are described and discussed in terms of practical learning strategies for typewriting. Described are goal setting, preassessment, active participation, individual differences, reinforcement, practice, transfer of learning, and evaluation. (SC)

  12. Machine learning methods for metabolic pathway prediction

    Directory of Open Access Journals (Sweden)

    Karp Peter D

    2010-01-01

    Full Text Available Abstract Background A key challenge in systems biology is the reconstruction of an organism's metabolic network from its genome sequence. One strategy for addressing this problem is to predict which metabolic pathways, from a reference database of known pathways, are present in the organism, based on the annotated genome of the organism. Results To quantitatively validate methods for pathway prediction, we developed a large "gold standard" dataset of 5,610 pathway instances known to be present or absent in curated metabolic pathway databases for six organisms. We defined a collection of 123 pathway features, whose information content we evaluated with respect to the gold standard. Feature data were used as input to an extensive collection of machine learning (ML methods, including naïve Bayes, decision trees, and logistic regression, together with feature selection and ensemble methods. We compared the ML methods to the previous PathoLogic algorithm for pathway prediction using the gold standard dataset. We found that ML-based prediction methods can match the performance of the PathoLogic algorithm. PathoLogic achieved an accuracy of 91% and an F-measure of 0.786. The ML-based prediction methods achieved accuracy as high as 91.2% and F-measure as high as 0.787. The ML-based methods output a probability for each predicted pathway, whereas PathoLogic does not, which provides more information to the user and facilitates filtering of predicted pathways. Conclusions ML methods for pathway prediction perform as well as existing methods, and have qualitative advantages in terms of extensibility, tunability, and explainability. More advanced prediction methods and/or more sophisticated input features may improve the performance of ML methods. However, pathway prediction performance appears to be limited largely by the ability to correctly match enzymes to the reactions they catalyze based on genome annotations.

  13. Machine learning methods for metabolic pathway prediction

    Science.gov (United States)

    2010-01-01

    Background A key challenge in systems biology is the reconstruction of an organism's metabolic network from its genome sequence. One strategy for addressing this problem is to predict which metabolic pathways, from a reference database of known pathways, are present in the organism, based on the annotated genome of the organism. Results To quantitatively validate methods for pathway prediction, we developed a large "gold standard" dataset of 5,610 pathway instances known to be present or absent in curated metabolic pathway databases for six organisms. We defined a collection of 123 pathway features, whose information content we evaluated with respect to the gold standard. Feature data were used as input to an extensive collection of machine learning (ML) methods, including naïve Bayes, decision trees, and logistic regression, together with feature selection and ensemble methods. We compared the ML methods to the previous PathoLogic algorithm for pathway prediction using the gold standard dataset. We found that ML-based prediction methods can match the performance of the PathoLogic algorithm. PathoLogic achieved an accuracy of 91% and an F-measure of 0.786. The ML-based prediction methods achieved accuracy as high as 91.2% and F-measure as high as 0.787. The ML-based methods output a probability for each predicted pathway, whereas PathoLogic does not, which provides more information to the user and facilitates filtering of predicted pathways. Conclusions ML methods for pathway prediction perform as well as existing methods, and have qualitative advantages in terms of extensibility, tunability, and explainability. More advanced prediction methods and/or more sophisticated input features may improve the performance of ML methods. However, pathway prediction performance appears to be limited largely by the ability to correctly match enzymes to the reactions they catalyze based on genome annotations. PMID:20064214

  14. 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.

  15. 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…

  16. Motivational Classroom Climate for Learning Mathematics: A Reversal Theory Perspective

    Science.gov (United States)

    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…

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

    Science.gov (United States)

    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…

  18. Constructivist Learning Theory and Climate Science Communication

    Science.gov (United States)

    Somerville, R. C.

    2012-12-01

    Communicating climate science is a form of education. A scientist giving a television interview or testifying before Congress is engaged in an educational activity, though one not identical to teaching graduate students. Knowledge, including knowledge about climate science, should never be communicated as a mere catalogue of facts. Science is a process, a way of regarding the natural world, and a fascinating human activity. A great deal is already known about how to do a better job of science communication, but implementing change is not easy. I am confident that improving climate science communication will involve the paradigm of constructivist learning theory, which traces its roots to the 20th-century Swiss epistemologist Jean Piaget, among others. This theory emphasizes the role of the teacher as supportive facilitator rather than didactic lecturer, "a guide on the side, not a sage on the stage." It also stresses the importance of the teacher making a serious effort to understand and appreciate the prior knowledge and viewpoint of the student, recognizing that students' minds are not empty vessels to be filled or blank slates to be written on. Instead, students come to class with a background of life experiences and a body of existing knowledge, of varying degrees of correctness or accuracy, about almost any topic. Effective communication is also usually a conversation rather than a monologue. We know too that for many audiences, the most trusted messengers are those who share the worldview and cultural values of those with whom they are communicating. Constructivist teaching methods stress making use of the parallels between learning and scientific research, such as the analogies between assessing prior knowledge of the audience and surveying scientific literature for a research project. Meanwhile, a well-funded and effective professional disinformation campaign has been successful in sowing confusion, and as a result, many people mistakenly think climate

  19. Learning receptive fields using predictive feedback.

    Science.gov (United States)

    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.

  20. Nonlinear model predictive control theory and algorithms

    CERN Document Server

    Grüne, Lars

    2017-01-01

    This book offers readers a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC schemes with and without stabilizing terminal constraints are detailed, and intuitive examples illustrate the performance of different NMPC variants. NMPC is interpreted as an approximation of infinite-horizon optimal control so that important properties like closed-loop stability, inverse optimality and suboptimality can be derived in a uniform manner. These results are complemented by discussions of feasibility and robustness. An introduction to nonlinear optimal control algorithms yields essential insights into how the nonlinear optimization routine—the core of any nonlinear model predictive controller—works. Accompanying software in MATLAB® and C++ (downloadable from extras.springer.com/), together with an explanatory appendix in the book itself, enables readers to perform computer experiments exploring the possibilities and limitations of NMPC. T...

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

    Science.gov (United States)

    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…

  2. 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…

  3. Goal Setting and Expectancy Theory Predictions of Effort and Performance.

    Science.gov (United States)

    Dossett, Dennis L.; Luce, Helen E.

    Neither expectancy (VIE) theory nor goal setting alone are effective determinants of individual effort and task performance. To test the combined ability of VIE and goal setting to predict effort and performance, 44 real estate agents and their managers completed questionnaires. Quarterly income goals predicted managers' ratings of agents' effort,…

  4. Adaptive vs. eductive learning : Theory and evidence

    NARCIS (Netherlands)

    Bao, T.; Duffy, J.

    2014-01-01

    Adaptive learning and eductive learning are two widely used ways of modeling learning behavior in macroeconomics. Both approaches yield restrictions on model parameters under which agents are able to learn a rational expectation equilibrium (REE) but these restrictions do not always overlap with one

  5. 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...

  6. 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

  7. Physiognomy: Personality Traits Prediction by Learning

    Institute of Scientific and Technical Information of China (English)

    Ting Zhang; Ri-Zhen Qin; Qiu-Lei Dong; Wei Gao; Hua-Rong Xu; Zhan-Yi Hu

    2017-01-01

    Evaluating individuals' personality traits and intelligence from their faces plays a crucial role in interpersonal relationship and important social events such as elections and court sentences.To assess the possible correlations between personality traits (also measured intelligence) and face images,we first construct a dataset consisting of face photographs,personality measurements,and intelligence measurements.Then,we build an end-to-end convolutional neural network for prediction of personality traits and intelligence to investigate whether self-reported personality traits and intelligence can be predicted reliably from a face image.To our knowledge,it is the first work where deep learning is applied to this problem.Experimental results show the following three points:1)"Rule-consciousness" and "Tension" can be reliably predicted from face images.2) It is difficult,if not impossible,to predict intelligence from face images,a finding in accord with previous studies.3) Convolutional neural network (CNN) features outperform traditional handcrafted features in predicting traits.

  8. 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

  9. 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...

  10. 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...

  11. Prediction and theory evaluation: the case of light bending.

    Science.gov (United States)

    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.

  12. A Critical Theory Perspective on Accelerated Learning.

    Science.gov (United States)

    Brookfield, Stephen D.

    2003-01-01

    Critically analyzes accelerated learning using concepts from Herbert Marcuse (rebellious subjectivity) and Erich Fromm (automaton conformity). Concludes that, by providing distance and separation, accelerated learning has more potential to stimulate critical autonomous thought. (SK)

  13. Using Machine Learning to Predict MCNP Bias

    Energy Technology Data Exchange (ETDEWEB)

    Grechanuk, Pavel Aleksandrovi [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2018-01-09

    For many real-world applications in radiation transport where simulations are compared to experimental measurements, like in nuclear criticality safety, the bias (simulated - experimental keff) in the calculation is an extremely important quantity used for code validation. The objective of this project is to accurately predict the bias of MCNP6 [1] criticality calculations using machine learning (ML) algorithms, with the intention of creating a tool that can complement the current nuclear criticality safety methods. In the latest release of MCNP6, the Whisper tool is available for criticality safety analysts and includes a large catalogue of experimental benchmarks, sensitivity profiles, and nuclear data covariance matrices. This data, coming from 1100+ benchmark cases, is used in this study of ML algorithms for criticality safety bias predictions.

  14. Learning theories 101: application to everyday teaching and scholarship.

    Science.gov (United States)

    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.

  15. Predicting radiotherapy outcomes using statistical learning techniques

    International Nuclear Information System (INIS)

    El Naqa, Issam; Bradley, Jeffrey D; Deasy, Joseph O; Lindsay, Patricia E; Hope, Andrew J

    2009-01-01

    Radiotherapy outcomes are determined by complex interactions between treatment, anatomical and patient-related variables. A common obstacle to building maximally predictive outcome models for clinical practice is the failure to capture potential complexity of heterogeneous variable interactions and applicability beyond institutional data. We describe a statistical learning methodology that can automatically screen for nonlinear relations among prognostic variables and generalize to unseen data before. In this work, several types of linear and nonlinear kernels to generate interaction terms and approximate the treatment-response function are evaluated. Examples of institutional datasets of esophagitis, pneumonitis and xerostomia endpoints were used. Furthermore, an independent RTOG dataset was used for 'generalizabilty' validation. We formulated the discrimination between risk groups as a supervised learning problem. The distribution of patient groups was initially analyzed using principle components analysis (PCA) to uncover potential nonlinear behavior. The performance of the different methods was evaluated using bivariate correlations and actuarial analysis. Over-fitting was controlled via cross-validation resampling. Our results suggest that a modified support vector machine (SVM) kernel method provided superior performance on leave-one-out testing compared to logistic regression and neural networks in cases where the data exhibited nonlinear behavior on PCA. For instance, in prediction of esophagitis and pneumonitis endpoints, which exhibited nonlinear behavior on PCA, the method provided 21% and 60% improvements, respectively. Furthermore, evaluation on the independent pneumonitis RTOG dataset demonstrated good generalizabilty beyond institutional data in contrast with other models. This indicates that the prediction of treatment response can be improved by utilizing nonlinear kernel methods for discovering important nonlinear interactions among model

  16. Why formal learning theory matters for cognitive science.

    Science.gov (United States)

    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.

  17. Predicting Increased Blood Pressure Using Machine Learning

    Science.gov (United States)

    Golino, Hudson Fernandes; Amaral, Liliany Souza de Brito; Duarte, Stenio Fernando Pimentel; Soares, Telma de Jesus; dos Reis, Luciana Araujo

    2014-01-01

    The present study investigates the prediction of increased blood pressure by body mass index (BMI), waist (WC) and hip circumference (HC), and waist hip ratio (WHR) using a machine learning technique named classification tree. Data were collected from 400 college students (56.3% women) from 16 to 63 years old. Fifteen trees were calculated in the training group for each sex, using different numbers and combinations of predictors. The result shows that for women BMI, WC, and WHR are the combination that produces the best prediction, since it has the lowest deviance (87.42), misclassification (.19), and the higher pseudo R 2 (.43). This model presented a sensitivity of 80.86% and specificity of 81.22% in the training set and, respectively, 45.65% and 65.15% in the test sample. For men BMI, WC, HC, and WHC showed the best prediction with the lowest deviance (57.25), misclassification (.16), and the higher pseudo R 2 (.46). This model had a sensitivity of 72% and specificity of 86.25% in the training set and, respectively, 58.38% and 69.70% in the test set. Finally, the result from the classification tree analysis was compared with traditional logistic regression, indicating that the former outperformed the latter in terms of predictive power. PMID:24669313

  18. Predicting increased blood pressure using machine learning.

    Science.gov (United States)

    Golino, Hudson Fernandes; Amaral, Liliany Souza de Brito; Duarte, Stenio Fernando Pimentel; Gomes, Cristiano Mauro Assis; Soares, Telma de Jesus; Dos Reis, Luciana Araujo; Santos, Joselito

    2014-01-01

    The present study investigates the prediction of increased blood pressure by body mass index (BMI), waist (WC) and hip circumference (HC), and waist hip ratio (WHR) using a machine learning technique named classification tree. Data were collected from 400 college students (56.3% women) from 16 to 63 years old. Fifteen trees were calculated in the training group for each sex, using different numbers and combinations of predictors. The result shows that for women BMI, WC, and WHR are the combination that produces the best prediction, since it has the lowest deviance (87.42), misclassification (.19), and the higher pseudo R (2) (.43). This model presented a sensitivity of 80.86% and specificity of 81.22% in the training set and, respectively, 45.65% and 65.15% in the test sample. For men BMI, WC, HC, and WHC showed the best prediction with the lowest deviance (57.25), misclassification (.16), and the higher pseudo R (2) (.46). This model had a sensitivity of 72% and specificity of 86.25% in the training set and, respectively, 58.38% and 69.70% in the test set. Finally, the result from the classification tree analysis was compared with traditional logistic regression, indicating that the former outperformed the latter in terms of predictive power.

  19. Predicting Increased Blood Pressure Using Machine Learning

    Directory of Open Access Journals (Sweden)

    Hudson Fernandes Golino

    2014-01-01

    Full Text Available The present study investigates the prediction of increased blood pressure by body mass index (BMI, waist (WC and hip circumference (HC, and waist hip ratio (WHR using a machine learning technique named classification tree. Data were collected from 400 college students (56.3% women from 16 to 63 years old. Fifteen trees were calculated in the training group for each sex, using different numbers and combinations of predictors. The result shows that for women BMI, WC, and WHR are the combination that produces the best prediction, since it has the lowest deviance (87.42, misclassification (.19, and the higher pseudo R2 (.43. This model presented a sensitivity of 80.86% and specificity of 81.22% in the training set and, respectively, 45.65% and 65.15% in the test sample. For men BMI, WC, HC, and WHC showed the best prediction with the lowest deviance (57.25, misclassification (.16, and the higher pseudo R2 (.46. This model had a sensitivity of 72% and specificity of 86.25% in the training set and, respectively, 58.38% and 69.70% in the test set. Finally, the result from the classification tree analysis was compared with traditional logistic regression, indicating that the former outperformed the latter in terms of predictive power.

  20. 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)

  1. 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.

  2. Predictive Variable Gain Iterative Learning Control for PMSM

    Directory of Open Access Journals (Sweden)

    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.

  3. Learning biases predict a word order universal.

    Science.gov (United States)

    Culbertson, Jennifer; Smolensky, Paul; Legendre, Géraldine

    2012-03-01

    How recurrent typological patterns, or universals, emerge from the extensive diversity found across the world's languages constitutes a central question for linguistics and cognitive science. Recent challenges to a fundamental assumption of generative linguistics-that universal properties of the human language acquisition faculty constrain the types of grammatical systems which can occur-suggest the need for new types of empirical evidence connecting typology to biases of learners. Using an artificial language learning paradigm in which adult subjects are exposed to a mix of grammatical systems (similar to a period of linguistic change), we show that learners' biases mirror a word-order universal, first proposed by Joseph Greenberg, which constrains typological patterns of adjective, numeral, and noun ordering. We briefly summarize the results of a probabilistic model of the hypothesized biases and their effect on learning, and discuss the broader implications of the results for current theories of the origins of cross-linguistic word-order preferences. Copyright © 2011 Elsevier B.V. All rights reserved.

  4. 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…

  5. 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.

  6. 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.

  7. Experiential Learning Theory as a Guide for Effective Teaching.

    Science.gov (United States)

    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…

  8. Supporting Alternative Strategies for Learning Chemical Applications of Group Theory

    Science.gov (United States)

    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…

  9. 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…

  10. 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,…

  11. Life history theory predicts fish assemblage response to hydrologic regimes.

    Science.gov (United States)

    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.

  12. 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.

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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…

  15. 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…

  16. 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…

  17. Randomized Prediction Games for Adversarial Machine Learning.

    Science.gov (United States)

    Rota Bulo, Samuel; Biggio, Battista; Pillai, Ignazio; Pelillo, Marcello; Roli, Fabio

    In spam and malware detection, attackers exploit randomization to obfuscate malicious data and increase their chances of evading detection at test time, e.g., malware code is typically obfuscated using random strings or byte sequences to hide known exploits. Interestingly, randomization has also been proposed to improve security of learning algorithms against evasion attacks, as it results in hiding information about the classifier to the attacker. Recent work has proposed game-theoretical formulations to learn secure classifiers, by simulating different evasion attacks and modifying the classification function accordingly. However, both the classification function and the simulated data manipulations have been modeled in a deterministic manner, without accounting for any form of randomization. In this paper, we overcome this limitation by proposing a randomized prediction game, namely, a noncooperative game-theoretic formulation in which the classifier and the attacker make randomized strategy selections according to some probability distribution defined over the respective strategy set. We show that our approach allows one to improve the tradeoff between attack detection and false alarms with respect to the state-of-the-art secure classifiers, even against attacks that are different from those hypothesized during design, on application examples including handwritten digit recognition, spam, and malware detection.In spam and malware detection, attackers exploit randomization to obfuscate malicious data and increase their chances of evading detection at test time, e.g., malware code is typically obfuscated using random strings or byte sequences to hide known exploits. Interestingly, randomization has also been proposed to improve security of learning algorithms against evasion attacks, as it results in hiding information about the classifier to the attacker. Recent work has proposed game-theoretical formulations to learn secure classifiers, by simulating different

  18. 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.

  19. 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.

  20. 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...

  1. 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

  2. Juggling with Language Learning Theories. [Videotape

    Science.gov (United States)

    Murphey, Tim

    2005-01-01

    Learning to juggle has become popular among corporate training programs because it shows participants how to appreciate mistakes and use "Intelligent Fast Failure" (learning quickly by daring to make a lot of simple mistakes at the beginning of a process). Big business also likes the way juggling can get executives "out of the…

  3. Playing styles based on experiential learning theory

    NARCIS (Netherlands)

    Bontchev, Boyan; Vassileva, Dessislava; Aleksieva-Petrova, Adelina; Petrov, Milen

    2018-01-01

    In recent years, many researchers have reported positive outcomes and effects from applying computer games to the educational process. The main preconditions for an effective game-based learning process include the presence of high learning interest and the desire to study hard. Therefore,

  4. 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...

  5. Why Education Predicts Decreased Belief in Conspiracy Theories

    NARCIS (Netherlands)

    van Prooijen, Jan Willem

    2017-01-01

    People with high education are less likely than people with low education to believe in conspiracy theories. It is yet unclear why these effects occur, however, as education predicts a range of cognitive, emotional, and social outcomes. The present research sought to identify mediators of the

  6. Prediction of Concrete Mix Cost Using Modified Regression Theory ...

    African Journals Online (AJOL)

    The cost of concrete production which largely depends on the cost of the constituent materials, affects the overall cost of construction. In this paper, a model based on modified regression theory is formulated to optimise concrete mix cost (in Naira). Using the model, one can predict the cost per cubic meter of concrete if the ...

  7. 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...

  8. Theory-based Bayesian models of inductive learning and reasoning.

    Science.gov (United States)

    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.

  9. 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.

  10. 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 ...

  11. 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.

  12. 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.

  13. 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…

  14. 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…

  15. Observant, Nonaggressive Temperament Predicts Theory of Mind Development

    Science.gov (United States)

    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

  16. 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.

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

    Science.gov (United States)

    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.

  18. 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.

  19. Exploration of Machine Learning Approaches to Predict Pavement Performance

    Science.gov (United States)

    2018-03-23

    Machine learning (ML) techniques were used to model and predict pavement condition index (PCI) for various pavement types using a variety of input variables. The primary objective of this research was to develop and assess PCI predictive models for t...

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

    Science.gov (United States)

    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…

  1. 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.

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

    Science.gov (United States)

    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.

  3. Hierarchical prediction errors in midbrain and septum during social learning.

    Science.gov (United States)

    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.

  4. Adventure Learning: Theory and Implementation of Hybrid Learning

    Science.gov (United States)

    Doering, A.

    2008-12-01

    Adventure Learning (AL), a hybrid distance education approach, provides students and teachers with the opportunity to learn about authentic curricular content areas while interacting with adventurers, students, and content experts at various locations throughout the world within an online learning environment (Doering, 2006). An AL curriculum and online environment provides collaborative community spaces where traditional hierarchical classroom roles are blurred and learning is transformed. AL has most recently become popular in K-12 classrooms nationally and internationally with millions of students participating online. However, in the literature, the term "adventure learning" many times gets confused with phrases such as "virtual fieldtrip" and activities where someone "exploring" is posting photos and text. This type of "adventure learning" is not "Adventure Learning" (AL), but merely a slideshow of their activities. The learning environment may not have any curricular and/or social goals, and if it does, the environment design many times does not support these objectives. AL, on the other hand, is designed so that both teachers and students understand that their online and curriculum activities are in synch and supportive of the curricular goals. In AL environments, there are no disparate activities as the design considers the educational, social, and technological affordances (Kirschner, Strijbos, Kreijns, & Beers, 2004); in other words, the artifacts of the learning environment encourage and support the instructional goals, social interactions, collaborative efforts, and ultimately learning. AL is grounded in two major theoretical approaches to learning - experiential and inquiry-based learning. As Kolb (1984) noted, in experiential learning, a learner creates meaning from direct experiences and reflections. Such is the goal of AL within the classroom. Additionally, AL affords learners a real-time authentic online learning experience concurrently as they

  5. 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

  6. 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

  7. 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

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

    Science.gov (United States)

    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)

  9. 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.

  10. High school students' implicit theories of what facilitates science learning

    Science.gov (United States)

    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

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

    Science.gov (United States)

    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.

  12. 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.

  13. 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…

  14. 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

  15. Teaching Density Functional Theory Through Experiential Learning

    International Nuclear Information System (INIS)

    Narasimhan, Shobhana

    2015-01-01

    Today, quantum mechanical density functional theory is often the method of choice for performing accurate calculations on atomic, molecular and condensed matter systems. Here, I share some of my experiences in teaching the necessary basics of solid state physics, as well as the theory and practice of density functional theory, in a number of workshops held in developing countries over the past two decades. I discuss the advantages of supplementing the usual mathematically formal teaching methods, characteristic of graduate courses, with the use of visual imagery and analogies. I also describe a successful experiment we carried out, which resulted in a joint publication co-authored by 67 lecturers and students participating in a summer school. (paper)

  16. The Social Life of Learning Theory

    DEFF Research Database (Denmark)

    Ratner, Helene Gad

    2013-01-01

    There is a growing understanding that public institutions need to be proactive in not only developing their welfare services but doing so continually through various concepts of learning. This article presents an ethnographic study of a public school that aims at working strategically...... with organizational learning through Donald Schön's concept of “the reflective practitioner.” Schön's concepts provide the school manager with a vocabulary to criticize the destructive effects of New Public Management's linear steering technologies. The study also illustrates that expectations of reflective...

  17. 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.

  18. 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.

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

    Science.gov (United States)

    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.

  20. Transformative Learning Theory in Gerontology: Nontraditional Students

    Science.gov (United States)

    Brown, Pamela Pitman; Brown, Candace S.

    2015-01-01

    Mezirow (1978) applied and used Transformative Learning Theoretical (TLT) processes while studying women who reentered academics during the 1970s. Similar to Mezirow's original 1975 work, we identify "factors that impeded or facilitated" participants' progress to obtain their undergraduate degree during the traditional student…

  1. The Social Life of Learning Theory

    DEFF Research Database (Denmark)

    Ratner, Helene Gad

    2013-01-01

    with organizational learning through Donald Schön's concept of “the reflective practitioner.” Schön's concepts provide the school manager with a vocabulary to criticize the destructive effects of New Public Management's linear steering technologies. The study also illustrates that expectations of reflective...

  2. Contemporary Privacy Theory Contributions to Learning Analytics

    Science.gov (United States)

    Heath, Jennifer

    2014-01-01

    With the continued adoption of learning analytics in higher education institutions, vast volumes of data are generated and "big data" related issues, including privacy, emerge. Privacy is an ill-defined concept and subject to various interpretations and perspectives, including those of philosophers, lawyers, and information systems…

  3. 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.

  4. 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

  5. Implicit Learning Abilities Predict Treatment Response in Autism Spectrum Disorders

    Science.gov (United States)

    2015-09-01

    early behavioral interventions are the most effective treatment for Autism Spectrum Disorder (ASD), but almost half of the children do not make...behavioral intervention . 2. KEYWORDS Autism Spectrum Disorder , implicit learning, associative learning, individual differences, functional Magnetic...2 AWARD NUMBER: W81XWH-14-1-0261 TITLE: Implicit Learning Abilities Predict Treatment Response in Autism Spectrum Disorders PRINCIPAL

  6. Predicting Student Performance in a Collaborative Learning Environment

    Science.gov (United States)

    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…

  7. 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.

  8. Psychodynamic theory and counseling in predictive testing for Huntington's disease.

    Science.gov (United States)

    Tassicker, Roslyn J

    2005-04-01

    This paper revisits psychodynamic theory, which can be applied in predictive testing counseling for Huntington's Disease (HD). Psychodynamic theory has developed from the work of Freud and places importance on early parent-child experiences. The nature of these relationships, or attachments are reflected in adult expectations and relationships. Two significant concepts, identification and fear of abandonment, have been developed and expounded by the psychodynamic theorist, Melanie Klein. The processes of identification and fear of abandonment can become evident in predictive testing counseling and are colored by the client's experience of growing up with a parent affected by Huntington's Disease. In reflecting on family-of-origin experiences, clients can also express implied expectations of the future, and future relationships. Case examples are given to illustrate the dynamic processes of identification and fear of abandonment which may present in the clinical setting. Counselor recognition of these processes can illuminate and inform counseling practice.

  9. Using Machine Learning to Advance Personality Assessment and Theory.

    Science.gov (United States)

    Bleidorn, Wiebke; Hopwood, Christopher James

    2018-05-01

    Machine learning has led to important advances in society. One of the most exciting applications of machine learning in psychological science has been the development of assessment tools that can powerfully predict human behavior and personality traits. Thus far, machine learning approaches to personality assessment have focused on the associations between social media and other digital records with established personality measures. The goal of this article is to expand the potential of machine learning approaches to personality assessment by embedding it in a more comprehensive construct validation framework. We review recent applications of machine learning to personality assessment, place machine learning research in the broader context of fundamental principles of construct validation, and provide recommendations for how to use machine learning to advance our understanding of personality.

  10. Comparison of Predictive Contract Mechanisms from an Information Theory Perspective

    OpenAIRE

    Zhang, Xin; Ward, Tomas; McLoone, Seamus

    2012-01-01

    Inconsistency arises across a Distributed Virtual Environment due to network latency induced by state changes communications. Predictive Contract Mechanisms (PCMs) combat this problem through reducing the amount of messages transmitted in return for perceptually tolerable inconsistency. To date there are no methods to quantify the efficiency of PCMs in communicating this reduced state information. This article presents an approach derived from concepts in information theory for a dee...

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

    Science.gov (United States)

    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.

  12. Using the theory of reasoned action to predict organizational misbehavior.

    Science.gov (United States)

    Vardi, Yoav; Weitz, Ely

    2002-12-01

    A review of literature on organizational behavior and management on predicting work behavior indicated that most reported studies emphasize positive work outcomes, e.g., attachment, performance, and satisfaction, while job related misbehaviors have received relatively less systematic research attention. Yet, forms of employee misconduct in organizations are pervasive and quite costly for both individuals and organizations. We selected two conceptual frameworks for the present investigation: Vardi and Wiener's model of organizational misbehavior and Fishbein and Ajzen's Theory of Reasoned Action. The latter views individual behavior as intentional, a function of rationally based attitudes toward the behavior, and internalized normative pressures concerning such behavior. The former model posits that different (normative and instrumental) internal forces lead to the intention to engage in job-related misbehavior. In this paper we report a scenario based quasi-experimental study especially designed to test the utility of the Theory of Reasoned Action in predicting employee intentions to engage in self-benefitting (Type S), organization-benefitting (Type O, or damaging (Type D) organizational misbehavior. Results support the Theory of Reasoned Action in predicting negative workplace behaviors. Both attitude and subjective norm are useful in explaining organizational misbehavior. We discuss some theoretical and methodological implications for the study of misbehavior intentions in organizations.

  13. 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.

  14. 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.

  15. 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…

  16. 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

  17. 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...

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

    Science.gov (United States)

    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.

  19. Predicting the Appearance of Materials Using Lorenz-Mie Theory

    DEFF Research Database (Denmark)

    Frisvad, Jeppe Revall; Christensen, Niels Jørgen; Jensen, Henrik Wann

    2012-01-01

    Computer graphics systems today are able to produce highly realistic images. The realism has reached a level where an observer has difficulties telling whether an image is real or synthetic. The exception is when we try to compute a picture of a scene that really exists and compare the result...... in the scene have few geometrical details, a graphics system will still have a hard time predicting the result of taking a picture with a digital camera. The problem here is to model the optical properties of the materials correctly. In this chapter, we show how Lorenz–Mie theory enables us to compute...... the optical properties of turbid materials such that we can predict their appearance. To describe the entire process of predicting the appearance of amaterial, we include a description of the mathematical models used in realistic image synthesis....

  20. 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

  1. Competences, Learning Theories and MOOCs: Recent Developments in Lifelong Learning

    Science.gov (United States)

    Steffens, Karl

    2015-01-01

    Our societies have come to be known as knowledge societies in which lifelong learning is becoming increasingly important. In this context, competences have become a much discussed topic. Many documents were published by international organisations (UNESCO, World Bank, European Commission) which enumerated 21st century key competences. The field of…

  2. Fractal Theory for Permeability Prediction, Venezuelan and USA Wells

    Science.gov (United States)

    Aldana, Milagrosa; Altamiranda, Dignorah; Cabrera, Ana

    2014-05-01

    Inferring petrophysical parameters such as permeability, porosity, water saturation, capillary pressure, etc, from the analysis of well logs or other available core data has always been of critical importance in the oil industry. Permeability in particular, which is considered to be a complex parameter, has been inferred using both empirical and theoretical techniques. The main goal of this work is to predict permeability values on different wells using Fractal Theory, based on a method proposed by Pape et al. (1999). This approach uses the relationship between permeability and the geometric form of the pore space of the rock. This method is based on the modified equation of Kozeny-Carman and a fractal pattern, which allows determining permeability as a function of the cementation exponent, porosity and the fractal dimension. Data from wells located in Venezuela and the United States of America are analyzed. Employing data of porosity and permeability obtained from core samples, and applying the Fractal Theory method, we calculated the prediction equations for each well. At the beginning, this was achieved by training with 50% of the data available for each well. Afterwards, these equations were tested inferring over 100% of the data to analyze possible trends in their distribution. This procedure gave excellent results in all the wells in spite of their geographic distance, generating permeability models with the potential to accurately predict permeability logs in the remaining parts of the well for which there are no core samples, using even porority logs. Additionally, empirical models were used to determine permeability and the results were compared with those obtained by applying the fractal method. The results indicated that, although there are empirical equations that give a proper adjustment, the prediction results obtained using fractal theory give a better fit to the core reference data.

  3. Perceptual learning: toward a comprehensive theory.

    Science.gov (United States)

    Watanabe, Takeo; Sasaki, Yuka

    2015-01-03

    Visual perceptual learning (VPL) is long-term performance increase resulting from visual perceptual experience. Task-relevant VPL of a feature results from training of a task on the feature relevant to the task. Task-irrelevant VPL arises as a result of exposure to the feature irrelevant to the trained task. At least two serious problems exist. First, there is the controversy over which stage of information processing is changed in association with task-relevant VPL. Second, no model has ever explained both task-relevant and task-irrelevant VPL. Here we propose a dual plasticity model in which feature-based plasticity is a change in a representation of the learned feature, and task-based plasticity is a change in processing of the trained task. Although the two types of plasticity underlie task-relevant VPL, only feature-based plasticity underlies task-irrelevant VPL. This model provides a new comprehensive framework in which apparently contradictory results could be explained.

  4. Differential theory of learning for efficient neural network pattern recognition

    Science.gov (United States)

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

    1993-09-01

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

  5. 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.

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

    Science.gov (United States)

    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.

  7. 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.

  8. Constructivist Teaching/Learning Theory and Participatory Teaching Methods

    Science.gov (United States)

    Fernando, Sithara Y. J. N.; Marikar, Faiz M. M. T.

    2017-01-01

    Evidence for the teaching involves transmission of knowledge, superiority of guided transmission is explained in the context of our knowledge, but it is also much more that. In this study we have examined General Sir John Kotelawala Defence University's cadet and civilian students' response to constructivist learning theory and participatory…

  9. 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…

  10. Writing for publication: faculty development initiative using social learning theory.

    Science.gov (United States)

    Sanderson, Bonnie K; Carter, Matt; Schuessler, Jenny B

    2012-01-01

    Demonstrating scholarly competency is an expectation for nurse faculty. However, there is hesitancy among some faculty to fully engage in scholarly activities. To strengthen a school of nursing's culture of scholarship, a faculty development writing initiative based on Social Learning Theory was implemented. The authors discuss this initiative to facilitate writing for publication productivity among faculty and the successful outcomes.

  11. Social Learning Theory: A Vanishing or Expanding Presence?

    Science.gov (United States)

    Stuart, Richard B.

    1989-01-01

    Reviews history and current status of social learning theory (SLT) including present conflict between "cognitive behaviorists" within the movement. Makes suggestions on how to resolve conflict in a way that will further secure the future role of SLT. Offers prescription for adoption of a multifaceted "indirect" approach to…

  12. Adult Basic Skills Instructor Training and Experiential Learning Theory.

    Science.gov (United States)

    Marlowe, Mike; And Others

    1991-01-01

    Competency-based training workshops based on Kolb's experiential learning theory were held for North Carolina adult basic education teachers; 251 attended 1-day sessions and 91 a week-long summer institute. Topics included interpersonal communication, reading, numeracy, language arts, math, assessment, and program evaluation. (SK)

  13. Gestalt-A Learning Theory for Graphic Design Education

    Science.gov (United States)

    Jackson, Ian

    2008-01-01

    This article will begin by seeking to define the notion of learning "by, through" and "from" experience. A linkage will then be established between these notions of experiences and gestalt theory. This will be explored within a subject specific context of graphic design. Links will be highlighted between the inherent nature of graphic design and…

  14. The Interdependence of Pedagogy, Learning Theory, Morality and Metaphysics.

    Science.gov (United States)

    Blunden, Ralph

    1997-01-01

    Explores the incompatibility between constructivist theories of learning and realist metaphysics (belief that knowledge and skills exist in mind-independent workplace practices). Shows how this results in conflict between constructivist teaching approaches and the transmission or banking mode favored by realist metaphysics. (SK)

  15. A Lifespan Perspective on Cooperative Education Learning: A Grounded Theory

    Science.gov (United States)

    Linn, Patricia

    2015-01-01

    This qualitative study sits at the intersection of two trends in vocational education. The first trend is a narrative approach to understanding cooperative education learning; the second is a movement away from career development theories toward the view that individuals use work experiences to help construct their lives. Both trends view learning…

  16. Children balance theories and evidence in exploration, explanation, and learning

    NARCIS (Netherlands)

    Bonawitz, E.B.; van Schijndel, T.J.P.; Friel, D.; Schulz, L.

    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,

  17. Designing Opportunities to Learn Mathematics Theory-Building Practices

    Science.gov (United States)

    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…

  18. 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.

  19. Amidst Multiple Theories of Learning in Mathematics Education

    Science.gov (United States)

    Simon, Martin A.

    2009-01-01

    Currently, there are more theories of learning in use in mathematics education research than ever before (Lerman & Tsatsaroni, 2004). Although this is a positive sign for the field, it also has brought with it a set of challenges. In this article, I identify some of these challenges and consider how mathematics education researchers might think…

  20. Conversations, Individuals and Knowables: Toward a Theory of Learning

    Science.gov (United States)

    Daniel, John S.

    1975-01-01

    Presents a learning theory in the language of cybernetics based on the tenet that the minimal experimental situation for making psychological observations is a conversation. The account is directed at generating interest in the original work by G. Pask, et al. (GS)

  1. Learning and Emotion: Perspectives for Theory and Research

    Science.gov (United States)

    Hascher, Tina

    2010-01-01

    There is growing interest in and knowledge about the interplay of learning and emotion. However, the different approaches and empirical studies correspond to each other only to a low extent. To prevent this research field from increasing fragmentation, a shared basis of theory and research is needed. The presentation aims at giving an overview of…

  2. 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

  3. 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…

  4. Predictive microbiology in a dynamic environment: a system theory approach.

    Science.gov (United States)

    Van Impe, J F; Nicolaï, B M; Schellekens, M; Martens, T; De Baerdemaeker, J

    1995-05-01

    The main factors influencing the microbial stability of chilled prepared food products for which there is an increased consumer interest-are temperature, pH, and water activity. Unlike the pH and the water activity, the temperature may vary extensively throughout the complete production and distribution chain. The shelf life of this kind of foods is usually limited due to spoilage by common microorganisms, and the increased risk for food pathogens. In predicting the shelf life, mathematical models are a powerful tool to increase the insight in the different subprocesses and their interactions. However, the predictive value of the sigmoidal functions reported in the literature to describe a bacterial growth curve as an explicit function of time is only guaranteed at a constant temperature within the temperature range of microbial growth. As a result, they are less appropriate in optimization studies of a whole production and distribution chain. In this paper a more general modeling approach, inspired by system theory concepts, is presented if for instance time varying temperature profiles are to be taken into account. As a case study, we discuss a recently proposed dynamic model to predict microbial growth and inactivation under time varying temperature conditions from a system theory point of view. Further, the validity of this methodology is illustrated with experimental data of Brochothrix thermosphacta and Lactobacillus plantarum. Finally, we propose some possible refinements of this model inspired by experimental results.

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

    Science.gov (United States)

    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…

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

    Science.gov (United States)

    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…

  7. 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…

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

    NARCIS (Netherlands)

    Keysers, C.; Perrett, David I; Gazzola, Valeria

    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

  9. Deep learning methods for protein torsion angle prediction.

    Science.gov (United States)

    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.

  10. Predictable Locations Aid Early Object Name Learning

    Science.gov (United States)

    Benitez, Viridiana L.; Smith, Linda B.

    2012-01-01

    Expectancy-based localized attention has been shown to promote the formation and retrieval of multisensory memories in adults. Three experiments show that these processes also characterize attention and learning in 16- to 18-month old infants and, moreover, that these processes may play a critical role in supporting early object name learning. The…

  11. Prostate Cancer Probability Prediction By Machine Learning Technique.

    Science.gov (United States)

    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.

  12. 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.

  13. Predicting Malaysian palm oil price using Extreme Value Theory

    OpenAIRE

    Chuangchid, K; Sriboonchitta, S; Rahman, S; Wiboonpongse, A

    2013-01-01

    This paper uses the extreme value theory (EVT) to predict extreme price events of Malaysian palm oil in the future, based on monthly futures price data for a 25 year period (mid-1986 to mid-2011). Model diagnostic has confirmed non-normal distribution of palm oil price data, thereby justifying the use of EVT. Two principal approaches to model extreme values – the Block Maxima (BM) and Peak-Over- Threshold (POT) models – were used. Both models revealed that the palm oil price will peak at ...

  14. What propels sexual murderers: a proposed integrated theory of social learning and routine activities theories.

    Science.gov (United States)

    Chan, Heng Choon Oliver; Heide, Kathleen M; Beauregard, Eric

    2011-04-01

    Despite the great interest in the study of sexual homicide, little is known about the processes involved in an individual's becoming motivated to sexually kill, deciding to sexually kill, and acting on that desire, intention, and opportunity. To date, no comprehensive model of sexual murdering from the offending perspective has been proposed in the criminological literature. This article incorporates the works of Akers and Cohen and Felson regarding their social learning theory and routine activities theory, respectively, to construct an integrated conceptual offending framework in sexual homicide. This integrated model produces a stronger and more comprehensive explanation of sexual murder than any single theory currently available.

  15. 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,…

  16. Plant interactions alter the predictions of metabolic scaling theory

    DEFF Research Database (Denmark)

    Lin, Yue; Berger, Uta; Grimm, Volker

    2013-01-01

    Metabolic scaling theory (MST) is an attempt to link physiological processes of individual organisms with macroecology. It predicts a power law relationship with an exponent of 24/3 between mean individual biomass and density during densitydependent mortality (self-thinning). Empirical tests have...... processes can scale up to the population level. MST, like thermodynamics or biomechanics, sets limits within which organisms can live and function, but there may be stronger limits determined by ecological interactions. In such cases MST will not be predictive....... of plant stand development that includes three elements: a model of individual plant growth based on MST, different modes of local competition (size-symmetric vs. -asymmetric), and different resource levels. Our model is consistent with the observed variation in the slopes of self-thinning trajectories...

  17. 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).

  18. 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

  19. Using machine learning, neural networks and statistics to predict bankruptcy

    NARCIS (Netherlands)

    Pompe, P.P.M.; Feelders, A.J.; Feelders, A.J.

    1997-01-01

    Recent literature strongly suggests that machine learning approaches to classification outperform "classical" statistical methods. We make a comparison between the performance of linear discriminant analysis, classification trees, and neural networks in predicting corporate bankruptcy. Linear

  20. Resting alpha activity predicts learning ability in alpha neurofeedback

    Directory of Open Access Journals (Sweden)

    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.

  1. DIALOGIC LEARNING AND ITS CONTRIBUTIONS TO EDUCATIONAL THEORY

    Directory of Open Access Journals (Sweden)

    Óscar Prieto

    2009-11-01

    Full Text Available This article highlights the contributions of the dialogic learning approach toeducational theory, with the aim of providing some orientations in order to promoteegalitarian and scientific educational practice. The seven principles of dialogic learningare discussed, along with other reproductionist theories and practices from the educationalfield, demonstrating how the former both surpass the latter. The article also reflectsopen dialogue with the critical theories of education which the dialogic learningtheory is based on. These basic theories are, on the one hand, by authors who are distantin time but very close in their educational approach, such as Ferrer i Guàrdia, Vygotsky,or Paulo Freire, and, on the other hand, by other contemporary authors in critical pedagogy.Each of the seven principles presented are provided along with a critical examinationof a specific educational practice. The consequences of the implementation of dialogiclearning are underlined here through an analysis of innovative and critical educationalprojects which are academically successful.

  2. 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.

  3. 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...

  4. Machine learning applied to crime prediction

    OpenAIRE

    Vaquero Barnadas, Miquel

    2016-01-01

    Machine Learning is a cornerstone when it comes to artificial intelligence and big data analysis. It provides powerful algorithms that are capable of recognizing patterns, classifying data, and, basically, learn by themselves to perform a specific task. This field has incredibly grown in popularity these days, however, it still remains unknown for the majority of people, and even for most professionals. This project intends to provide an understandable explanation of what is it, what types ar...

  5. Learning Behavior Models for Interpreting and Predicting Traffic Situations

    OpenAIRE

    Gindele, Tobias

    2014-01-01

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

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

    Science.gov (United States)

    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.

  7. Predicting Student Success from the "LASSI for Learning Online" (LLO)

    Science.gov (United States)

    Carson, Andrew D.

    2011-01-01

    This study tested the degree to which subscales of the "LASSI for Learning Online" (LLO) (Weinstein & Palmer, 2006), a measure of learning strategies and study skills, predict student success in the form of passing grades, using a combination of large training (N = 4,409) and cross-validation (N = 3,203) samples. Discriminant function analysis…

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

    CERN Document Server

    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 ...

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

    Science.gov (United States)

    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.

  10. 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.

  11. 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.

  12. 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.

  13. 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...

  14. 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/.

  15. WORMHOLE: Novel Least Diverged Ortholog Prediction through Machine Learning

    Science.gov (United States)

    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

  16. 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...

  17. Theory-generating practice. Proposing a principle for learning design

    DEFF Research Database (Denmark)

    Buhl, Mie

    2016-01-01

    This contribution proposes a principle for learning design – Theory-Generating Practice (TGP) – as an alternative to the way university courses are traditionally taught and structured, with a series of theoretical lectures isolated from practical experience and concluding with an exam or a project...... building, and takes tacit knowledge into account. The article introduces TGP, contextualizes it to a Danish tradition of didactics, and discusses it in relation to contemporary conceptual currents of didactic design and learning design. This is followed by a theoretical framing of TGP. Finally, three...

  18. 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.

  19. 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

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

    Science.gov (United States)

    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…

  1. 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

  2. Universal LD50 predictions using deep learning

    Science.gov (United States)

    NICEATM Predictive Models for Acute Oral Systemic Toxicity LD50 entry Risa R. Sayre (sayre.risa@epa.gov) & Christopher M. Grulke Our approach uses an ensemble of multilayer perceptron regressions to predict rat acute oral LD50 values from chemical features. Features were genera...

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

    Science.gov (United States)

    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…

  4. 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

  5. Plant interactions alter the predictions of metabolic scaling theory.

    Directory of Open Access Journals (Sweden)

    Yue Lin

    Full Text Available Metabolic scaling theory (MST is an attempt to link physiological processes of individual organisms with macroecology. It predicts a power law relationship with an exponent of -4/3 between mean individual biomass and density during density-dependent mortality (self-thinning. Empirical tests have produced variable results, and the validity of MST is intensely debated. MST focuses on organisms' internal physiological mechanisms but we hypothesize that ecological interactions can be more important in determining plant mass-density relationships induced by density. We employ an individual-based model of plant stand development that includes three elements: a model of individual plant growth based on MST, different modes of local competition (size-symmetric vs. -asymmetric, and different resource levels. Our model is consistent with the observed variation in the slopes of self-thinning trajectories. Slopes were significantly shallower than -4/3 if competition was size-symmetric. We conclude that when the size of survivors is influenced by strong ecological interactions, these can override predictions of MST, whereas when surviving plants are less affected by interactions, individual-level metabolic processes can scale up to the population level. MST, like thermodynamics or biomechanics, sets limits within which organisms can live and function, but there may be stronger limits determined by ecological interactions. In such cases MST will not be predictive.

  6. UNIVERSITY TEACHING-LEARNING PROCESS: REFLECTIONS THROUGHOUT THE AGENCY THEORY

    Directory of Open Access Journals (Sweden)

    Víctor Jacques Parraguez

    2011-04-01

    Full Text Available This work analyses some reasons that might explain the insufficient academic level which is perceived in universities of developing countries. The discussion element is the teacher-student relationship which is studied under the perspective of the agency theory. It is concluded that in absence of efficient monitoring mechanisms of the teacher and student’s behavior might proliferate gaps of due diligence which attempts against the quality of the teaching-learning process.

  7. 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.

  8. Robust portfolio choice with ambiguity and learning about return predictability

    DEFF Research Database (Denmark)

    Larsen, Linda Sandris; Branger, Nicole; Munk, Claus

    2013-01-01

    We analyze the optimal stock-bond portfolio under both learning and ambiguity aversion. Stock returns are predictable by an observable and an unobservable predictor, and the investor has to learn about the latter. Furthermore, the investor is ambiguity-averse and has a preference for investment...... strategies that are robust to model misspecifications. We derive a closed-form solution for the optimal robust investment strategy. We find that both learning and ambiguity aversion impact the level and structure of the optimal stock investment. Suboptimal strategies resulting either from not learning...... or from not considering ambiguity can lead to economically significant losses....

  9. Predicting Contextual Informativeness for Vocabulary Learning

    Science.gov (United States)

    Kapelner, Adam; Soterwood, Jeanine; Nessaiver, Shalev; Adlof, Suzanne

    2018-01-01

    Vocabulary knowledge is essential to educational progress. High quality vocabulary instruction requires supportive contextual examples to teach word meaning and proper usage. Identifying such contexts by hand for a large number of words can be difficult. In this work, we take a statistical learning approach to engineer a system that predicts…

  10. Improving sequence segmentation learning by predicting trigrams

    NARCIS (Netherlands)

    van den Bosch, A.; Daelemans, W.; Dagan, I.; Gildea, D.

    2005-01-01

    Symbolic machine-learning classifiers are known to suffer from near-sightedness when performing sequence segmentation (chunking) tasks in natural language processing: without special architectural additions they are oblivious of the decisions they made earlier when making new ones. We introduce a

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

    Science.gov (United States)

    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…

  12. 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.

  13. Rutting Prediction in Asphalt Pavement Based on Viscoelastic Theory

    Directory of Open Access Journals (Sweden)

    Nahi Mohammed Hadi

    2016-01-01

    Full Text Available Rutting is one of the most disturbing failures on the asphalt roads due to the interrupting it is caused to the drivers. Predicting of asphalt pavement rutting is essential tool leads to better asphalt mixture design. This work describes a method of predicting the behaviour of various asphalt pavement mixes and linking these to an accelerated performance testing. The objective of this study is to develop a finite element model based on viscoplastic theory for simulating the laboratory testing of asphalt mixes in Hamburg Wheel Rut Tester (HWRT for rutting. The creep parameters C1, C2 and C3 are developed from the triaxial repeated load creep test at 50°C and at a frequency of 1 Hz and the modulus of elasticity and Poisson’ s ratio determined at the same temperature. Viscoelastic model (creep model is adopted using a FE simulator (ANSYS in order to calculate the rutting for various mixes under a uniform loading pressure of 500 kPa. An eight-node with a three Degrees of Freedom (UX, UY, and UZ Element is used for the simulation. The creep model developed for HWRT tester was verified by comparing the predicted rut depths with the measured one and by comparing the rut depth with ABAQUS result from literature. Reasonable agreement can be obtained between the predicted rut depths and the measured one. Moreover, it is found that creep model parameter C1 and C3 have a strong relationship with rutting. It was clear that the parameter C1 strongly influences rutting than the parameter C3. Finally, it can be concluded that creep model based on finite element method can be used as an effective tool to analyse rutting of asphalt pavements.

  14. Theory-Generating Practice: Proposing a principle for learning design

    Directory of Open Access Journals (Sweden)

    Mie Buhl

    2016-06-01

    Full Text Available This contribution proposes a principle for learning design: Theory-Generating Practice (TGP as an alternative to the way university courses often are taught and structured with a series of theoretical lectures separate from practical experience and concluding with an exam or a project. The aim is to contribute to a development of theoretical frameworks for learning designs by suggesting TGP which may lead to new practices and turn the traditional dramaturgy for teaching upside down. TGP focuses on embodied experience prior to text reading and lectures to enhance theoretical knowledge building and takes tacit knowledge into account. The article introduces TGP and contextualizes it to a Danish tradition of didactics as well as discusses it in relation to contemporary conceptual currents of didactic design and learning design. This is followed by a theoretical framing of TGP, and is discussed through three empirical examples from bachelor and master programs involving technology, and showing three ways of practicing it.

  15. Theory-Generating Practice: Proposing a principle for learning design

    Directory of Open Access Journals (Sweden)

    Mie Buhl

    2016-05-01

    Full Text Available This contribution proposes a principle for learning design: Theory-Generating Practice (TGP as an alternative to the way university courses often are taught and structured with a series of theoretical lectures separate from practical experience and concluding with an exam or a project. The aim is to contribute to a development of theoretical frameworks for learning designs by suggesting TGP which may lead to new practices and turn the traditional dramaturgy for teaching upside down. TGP focuses on embodied experience prior to text reading and lectures to enhance theoretical knowledge building and takes tacit knowledge into account. The article introduces TGP and contextualizes it to a Danish tradition of didactics as well as discusses it in relation to contemporary conceptual currents of didactic design and learning design. This is followed by a theoretical framing of TGP, and is discussed through three empirical examples from bachelor and master programs involving technology, and showing three ways of practicing it.

  16. 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...

  17. Biorhythm theory does not predict admission for acute myocardial infarction.

    Science.gov (United States)

    Joncas, Sébastien X; Carrier, Nathalie; Nguyen, Michel; Farand, Paul

    2011-02-01

    Temporal variations in the incidence of acute myocardial infarction (AMI) have been described. However, AMI occurrence and biorhythm theory, which proposes the existence of three endogenous independent infradian cycles and AMI occurrence, has not been well studied. The purpose of this study is to determine whether specific days in the biorhythm cycles are related to AMI incidence. Patients (40-85 years old) admitted for AMI at the Sherbrooke University Hospital Center, 1993-2008 were subjects of this study. Potential vulnerable days and performance days of the biorhythm cycles were calculated using birth and admission dates from the warehouse database. Observed AMI frequencies were compared to those expected using χ² tests. There were 11,395 admissions for AMI. No relation was noted between single, double, or triple critical or noncritical days and AMI (χ² = 3.78; p > 0.05). Observed and expected AMI frequencies for maximal and minimal performance days were similar (χ² = 15.06; p > 0.05). We found no evidence for a possible relationship between the date of AMI and critical maximum and minimum performance days of an individual's physical, emotional, or intellectual biorhythm cycles. We conclude that biorhythm theory does not predict admission for AMI.

  18. Understanding predicted shifts in diazotroph biogeography using resource competition theory

    Directory of Open Access Journals (Sweden)

    S. Dutkiewicz

    2014-10-01

    Full Text Available We examine the sensitivity of the biogeography of nitrogen fixers to a warming climate and increased aeolian iron deposition in the context of a global earth system model. We employ concepts from the resource-ratio theory to provide a simplifying and transparent interpretation of the results. First we demonstrate that a set of clearly defined, easily diagnosed provinces are consistent with the theory. Using this framework we show that the regions most vulnerable to province shifts and changes in diazotroph biogeography are the equatorial and South Pacific, and central Atlantic. Warmer and dustier climates favor diazotrophs due to an increase in the ratio of supply rate of iron to fixed nitrogen. We suggest that the emergent provinces could be a standard diagnostic for global change models, allowing for rapid and transparent interpretation and comparison of model predictions and the underlying mechanisms. The analysis suggests that monitoring of real world province boundaries, indicated by transitions in surface nutrient concentrations, would provide a clear and easily interpreted indicator of ongoing global change.

  19. Theory of Reasoned Action predicts milk consumption in women.

    Science.gov (United States)

    Brewer, J L; Blake, A J; Rankin, S A; Douglass, L W

    1999-01-01

    To determine the factors influencing the consumption or avoidance of milk in women. One hundred women completed food frequency questionnaires and a milk attitudes questionnaire framed within the Theory of Reasoned Action and performed sensory evaluations of different milk samples. Differences among milk types were assessed using 2-way analysis of variance and least-significant-difference mean comparison procedures. Correlation and multiple regression analyses, and standardized partial regression coefficients, were used to determine the contribution of each component of the model in predicting behavior. Mean age of the 100 subjects was 39 years (range = 20-70 years). Milk consumption among subjects was low; 23 subjects indicated that they seldom or never drank milk. Data from the dairy frequency questionnaire showed that the primary milk for 42%, 36%, 27%, and 18% of the milk drinkers was skim, 2%, 1%, and whole, respectively (subjects could indicate more than 1 type of milk consumed). The Theory of Reasoned Action indicated that health and familiarity belief items were most associated with attitudes toward milk consumption. Skim milk had significantly lower scores for taste and texture belief items than 1%, 2%, and whole milk (P reasons other than beliefs about taste and texture or actual sensory preference. This study identifies important factors contributing to milk consumption such as beliefs, attitudes, and sensory evaluation, which can be used to develop a specific framework in which to examine other components of milk consumption behavior.

  20. Transition-state theory predicts clogging at the microscale

    Science.gov (United States)

    Laar, T. Van De; Klooster, S. Ten; Schroën, K.; Sprakel, J.

    2016-06-01

    Clogging is one of the main failure mechanisms encountered in industrial processes such as membrane filtration. Our understanding of the factors that govern the build-up of fouling layers and the emergence of clogs is largely incomplete, so that prevention of clogging remains an immense and costly challenge. In this paper we use a microfluidic model combined with quantitative real-time imaging to explore the influence of pore geometry and particle interactions on suspension clogging in constrictions, two crucial factors which remain relatively unexplored. We find a distinct dependence of the clogging rate on the entrance angle to a membrane pore which we explain quantitatively by deriving a model, based on transition-state theory, which describes the effect of viscous forces on the rate with which particles accumulate at the channel walls. With the same model we can also predict the effect of the particle interaction potential on the clogging rate. In both cases we find excellent agreement between our experimental data and theory. A better understanding of these clogging mechanisms and the influence of design parameters could form a stepping stone to delay or prevent clogging by rational membrane design.

  1. 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.

  2. Why (we think) facilitation works: insights from organizational learning theory.

    Science.gov (United States)

    Berta, Whitney; Cranley, Lisa; Dearing, James W; Dogherty, Elizabeth J; Squires, Janet E; Estabrooks, Carole A

    2015-10-06

    Facilitation is a guided interactional process that has been popularized in health care. Its popularity arises from its potential to support uptake and application of scientific knowledge that stands to improve clinical and managerial decision-making, practice, and ultimately patient outcomes and organizational performance. While this popular concept has garnered attention in health services research, we know that both the content of facilitation and its impact on knowledge implementation vary. The basis of this variation is poorly understood, and understanding is hampered by a lack of conceptual clarity. In this paper, we argue that our understanding of facilitation and its effects is limited in part by a lack of clear theoretical grounding. We propose a theoretical home for facilitation in organizational learning theory. Referring to extant literature on facilitation and drawing on theoretical literature, we discuss the features of facilitation that suggest its role in contributing to learning capacity. We describe how facilitation may contribute to generating knowledge about the application of new scientific knowledge in health-care organizations. Facilitation's promise, we suggest, lies in its potential to stimulate higher-order learning in organizations through experimenting with, generating learning about, and sustaining small-scale adaptations to organizational processes and work routines. The varied effectiveness of facilitation observed in the literature is associated with the presence or absence of factors known to influence organizational learning, since facilitation itself appears to act as a learning mechanism. We offer propositions regarding the relationships between facilitation processes and key organizational learning concepts that have the potential to guide future work to further our understanding of the role that facilitation plays in learning and knowledge generation.

  3. Learning to forget: continual prediction with LSTM.

    Science.gov (United States)

    Gers, F A; Schmidhuber, J; Cummins, F

    2000-10-01

    Long short-term memory (LSTM; Hochreiter & Schmidhuber, 1997) can solve numerous tasks not solvable by previous learning algorithms for recurrent neural networks (RNNs). We identify a weakness of LSTM networks processing continual input streams that are not a priori segmented into subsequences with explicitly marked ends at which the network's internal state could be reset. Without resets, the state may grow indefinitely and eventually cause the network to break down. Our remedy is a novel, adaptive "forget gate" that enables an LSTM cell to learn to reset itself at appropriate times, thus releasing internal resources. We review illustrative benchmark problems on which standard LSTM outperforms other RNN algorithms. All algorithms (including LSTM) fail to solve continual versions of these problems. LSTM with forget gates, however, easily solves them, and in an elegant way.

  4. 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…

  5. 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...

  6. 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…

  7. Visual Descriptor Learning for Predicting Grasping Affordances

    DEFF Research Database (Denmark)

    Thomsen, Mikkel Tang

    2016-01-01

    by the task of grasping unknown objects given visual sensor information. The contributions from this thesis stem from three works that all relate to the task of grasping unknown objects but with particular focus on the visual representation part of the problem. First an investigation of a visual feature space...... consisting of surface features was performed. Dimensions in the visual space were varied and the effects were evaluated with the task of grasping unknown object. The evaluation was performed using a novel probabilistic grasp prediction approach based on neighbourhood analysis. The resulting success......-rates for predicting grasps were between 75% and 90% depending on the object class. The investigations also provided insights into the importance of selecting a proper visual feature space when utilising it for predicting affordances. As a consequence of the gained insights, a semi-local surface feature, the Sliced...

  8. Strength of Temporal White Matter Pathways Predicts Semantic Learning.

    Science.gov (United States)

    Ripollés, Pablo; Biel, Davina; Peñaloza, Claudia; Kaufmann, Jörn; Marco-Pallarés, Josep; Noesselt, Toemme; Rodríguez-Fornells, Antoni

    2017-11-15

    Learning the associations between words and meanings is a fundamental human ability. Although the language network is cortically well defined, the role of the white matter pathways supporting novel word-to-meaning mappings remains unclear. Here, by using contextual and cross-situational word learning, we tested whether learning the meaning of a new word is related to the integrity of the language-related white matter pathways in 40 adults (18 women). The arcuate, uncinate, inferior-fronto-occipital and inferior-longitudinal fasciculi were virtually dissected using manual and automatic deterministic fiber tracking. Critically, the automatic method allowed assessing the white matter microstructure along the tract. Results demonstrate that the microstructural properties of the left inferior-longitudinal fasciculus predict contextual learning, whereas the left uncinate was associated with cross-situational learning. In addition, we identified regions of special importance within these pathways: the posterior middle temporal gyrus, thought to serve as a lexical interface and specifically related to contextual learning; the anterior temporal lobe, known to be an amodal hub for semantic processing and related to cross-situational learning; and the white matter near the hippocampus, a structure fundamental for the initial stages of new-word learning and, remarkably, related to both types of word learning. No significant associations were found for the inferior-fronto-occipital fasciculus or the arcuate. While previous results suggest that learning new phonological word forms is mediated by the arcuate fasciculus, these findings show that the temporal pathways are the crucial neural substrate supporting one of the most striking human abilities: our capacity to identify correct associations between words and meanings under referential indeterminacy. SIGNIFICANCE STATEMENT The language-processing network is cortically (i.e., gray matter) well defined. However, the role of the

  9. Learning from sensory and reward prediction errors during motor adaptation.

    Science.gov (United States)

    Izawa, Jun; Shadmehr, Reza

    2011-03-01

    Voluntary motor commands produce two kinds of consequences. Initially, a sensory consequence is observed in terms of activity in our primary sensory organs (e.g., vision, proprioception). Subsequently, the brain evaluates the sensory feedback and produces a subjective measure of utility or usefulness of the motor commands (e.g., reward). As a result, comparisons between predicted and observed consequences of motor commands produce two forms of prediction error. How do these errors contribute to changes in motor commands? Here, we considered a reach adaptation protocol and found that when high quality sensory feedback was available, adaptation of motor commands was driven almost exclusively by sensory prediction errors. This form of learning had a distinct signature: as motor commands adapted, the subjects altered their predictions regarding sensory consequences of motor commands, and generalized this learning broadly to neighboring motor commands. In contrast, as the quality of the sensory feedback degraded, adaptation of motor commands became more dependent on reward prediction errors. Reward prediction errors produced comparable changes in the motor commands, but produced no change in the predicted sensory consequences of motor commands, and generalized only locally. Because we found that there was a within subject correlation between generalization patterns and sensory remapping, it is plausible that during adaptation an individual's relative reliance on sensory vs. reward prediction errors could be inferred. We suggest that while motor commands change because of sensory and reward prediction errors, only sensory prediction errors produce a change in the neural system that predicts sensory consequences of motor commands.

  10. 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.

  11. 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.

  12. 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.

  13. Deep-Learning-Based Approach for Prediction of Algal Blooms

    Directory of Open Access Journals (Sweden)

    Feng Zhang

    2016-10-01

    Full Text Available Algal blooms have recently become a critical global environmental concern which might put economic development and sustainability at risk. However, the accurate prediction of algal blooms remains a challenging scientific problem. In this study, a novel prediction approach for algal blooms based on deep learning is presented—a powerful tool to represent and predict highly dynamic and complex phenomena. The proposed approach constructs a five-layered model to extract detailed relationships between the density of phytoplankton cells and various environmental parameters. The algal blooms can be predicted by the phytoplankton density obtained from the output layer. A case study is conducted in coastal waters of East China using both our model and a traditional back-propagation neural network for comparison. The results show that the deep-learning-based model yields better generalization and greater accuracy in predicting algal blooms than a traditional shallow neural network does.

  14. Economics of Distance and Online Learning Theory, Practice and Research

    Directory of Open Access Journals (Sweden)

    reviewed by TOJDE

    2009-10-01

    Full Text Available Economics of Distance and Online LearningTheory, Practice and ResearchBy William Bramble & Santosh PandaPrice: $125.00ISBN: 978-0-415-96388-6, Binding: Hardback, Publishedby: Routledge, New York, Publication Date: March 2008, Pages: 312TOJDEABOUT THE BOOKThis book provides a comprehensive overview of theorganizational models of distance and online learning froman international perspective and from the point of view ofeconomic planning, costing and management decisionmaking.The book points to directions for the further research anddevelopment in this area, and will promote furtherunderstanding and critical reflection on the part ofadministrators, practitioners and researchers of distanceeducation.The experiences and perspectives in distance education inthe US are balanced with those in other areas of the world.Table of ContentsPrefaceSECTION ONE: INTRODUCTIONChapter 1: Organizational and Cost Structures for Distanceand Online Learning, William J. Bramble and Santosh PandaSECTION TWO: PLANNING AND MANAGEMENTChapter 2: Changing Distance Education andChanging Organizational Issues, D. Randy Garrison and Heather KanukaChapter 3: Online Learning and the University, Chris Curran217Chapter 4: Virtual Schooling and Basic Education, Thomas ClarkChapter 5: Historical Perspectives on Distance Education in the United States, Paul J.Edelson and Von PittmanSECTION THREE: FUNDINGChapter 6: Funding of Distance and Online Learning in the United States, Mark J. Smithand William J. BrambleChapter 7: Funding Distance Education: A Regional Perspective, Santosh Panda andAshok GabaSECTION FOUR: COST STRUCTURES AND MODELSChapter 8: Costs and Quality of Online Learning, Alistair InglisChapter 9: Costing Virtual University Education, Insung JungChapter 10: Cost-Benefit of Student Retention Policies and Practices, Ormond SimpsonSECTION FIVE: DISTANCE TRAININGChapter 11: Cost Benefit of Online Learning, Zane Berge and Charlotte DonaldsonChapter 12: Transforming Workplace

  15. Academic learning for specialist nurses: a grounded theory study.

    Science.gov (United States)

    Millberg, Lena German; Berg, Linda; Brämberg, Elisabeth Björk; Nordström, Gun; Ohlén, Joakim

    2014-11-01

    The aim was to explore the major concerns of specialist nurses pertaining to academic learning during their education and initial professional career. Specialist nursing education changed in tandem with the European educational reform in 2007. At the same time, greater demands were made on the healthcare services to provide evidence-based and safe patient-care. These changes have influenced specialist nursing programmes and consequently the profession. Grounded Theory guided the study. Data were collected by means of a questionnaire with open-ended questions distributed at the end of specialist nursing programmes in 2009 and 2010. Five universities were included. Further, individual, pair and group interviews were used to collect data from 12 specialist nurses, 5-14 months after graduation. A major concern for specialist nurses was that academic learning should be "meaningful" for their professional future. The specialist nurses' "meaningful academic learning process" was characterised by an ambivalence of partly believing in and partly being hesitant about the significance of academic learning and partly receiving but also lacking support. Specialist nurses were influenced by factors in two areas: curriculum and healthcare context. They felt that the outcome of contribution to professional confidence was critical in making academic learning meaningful. Copyright © 2014 Elsevier Ltd. All rights reserved.

  16. Psychological theory and pedagogical effectiveness: the learning promotion potential framework.

    Science.gov (United States)

    Tomlinson, Peter

    2008-12-01

    After a century of educational psychology, eminent commentators are still lamenting problems besetting the appropriate relating of psychological insights to teaching design, a situation not helped by the persistence of crude assumptions concerning the nature of pedagogical effectiveness. To propose an analytical or meta-theoretical framework based on the concept of learning promotion potential (LPP) as a basis for understanding the basic relationship between psychological insights and teaching strategies, and to draw out implications for psychology-based pedagogical design, development and research. This is a theoretical and meta-theoretical paper relying mainly on conceptual analysis, though also calling on psychological theory and research. Since teaching consists essentially in activity designed to promote learning, it follows that a teaching strategy has the potential in principle to achieve particular kinds of learning gains (LPP) to the extent that it embodies or stimulates the relevant learning processes on the part of learners and enables the teacher's functions of on-line monitoring and assistance for such learning processes. Whether a teaching strategy actually does realize its LPP by way of achieving its intended learning goals depends also on the quality of its implementation, in conjunction with other factors in the situated interaction that teaching always involves. The core role of psychology is to provide well-grounded indication of the nature of such learning processes and the teaching functions that support them, rather than to directly generate particular ways of teaching. A critically eclectic stance towards potential sources of psychological insight is argued for. Applying this framework, the paper proposes five kinds of issue to be attended to in the design and evaluation of psychology-based pedagogy. Other work proposing comparable ideas is briefly reviewed, with particular attention to similarities and a key difference with the ideas of Oser

  17. 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

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

    Science.gov (United States)

    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.

  19. Predicting Market Impact Costs Using Nonparametric Machine Learning Models.

    Directory of Open Access Journals (Sweden)

    Saerom Park

    Full Text Available Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to provide the predictive model that is versatile in the number of variables. We collected a large amount of real single transaction data of US stock market from Bloomberg Terminal and generated three independent input variables. As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures. Although these models encounter certain difficulties in separating the permanent and temporary cost directly, nonparametric machine learning models can be good alternatives in reducing transaction costs by considerably improving in prediction performance.

  20. Predicting Market Impact Costs Using Nonparametric Machine Learning Models.

    Science.gov (United States)

    Park, Saerom; Lee, Jaewook; Son, Youngdoo

    2016-01-01

    Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to provide the predictive model that is versatile in the number of variables. We collected a large amount of real single transaction data of US stock market from Bloomberg Terminal and generated three independent input variables. As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures. Although these models encounter certain difficulties in separating the permanent and temporary cost directly, nonparametric machine learning models can be good alternatives in reducing transaction costs by considerably improving in prediction performance.

  1. Predicting the dissolution kinetics of silicate glasses using machine learning

    Science.gov (United States)

    Anoop Krishnan, N. M.; Mangalathu, Sujith; Smedskjaer, Morten M.; Tandia, Adama; Burton, Henry; Bauchy, Mathieu

    2018-05-01

    Predicting the dissolution rates of silicate glasses in aqueous conditions is a complex task as the underlying mechanism(s) remain poorly understood and the dissolution kinetics can depend on a large number of intrinsic and extrinsic factors. Here, we assess the potential of data-driven models based on machine learning to predict the dissolution rates of various aluminosilicate glasses exposed to a wide range of solution pH values, from acidic to caustic conditions. Four classes of machine learning methods are investigated, namely, linear regression, support vector machine regression, random forest, and artificial neural network. We observe that, although linear methods all fail to describe the dissolution kinetics, the artificial neural network approach offers excellent predictions, thanks to its inherent ability to handle non-linear data. Overall, we suggest that a more extensive use of machine learning approaches could significantly accelerate the design of novel glasses with tailored properties.

  2. Machine learning algorithms for datasets popularity prediction

    CERN Document Server

    Kancys, Kipras

    2016-01-01

    This report represents continued study where ML algorithms were used to predict databases popularity. Three topics were covered. First of all, there was a discrepancy between old and new meta-data collection procedures, so a reason for that had to be found. Secondly, different parameters were analysed and dropped to make algorithms perform better. And third, it was decided to move modelling part on Spark.

  3. Teaching Theory Construction With Initial Grounded Theory Tools: A Reflection on Lessons and Learning.

    Science.gov (United States)

    Charmaz, Kathy

    2015-12-01

    This article addresses criticisms of qualitative research for spawning studies that lack analytic development and theoretical import. It focuses on teaching initial grounded theory tools while interviewing, coding, and writing memos for the purpose of scaling up the analytic level of students' research and advancing theory construction. Adopting these tools can improve teaching qualitative methods at all levels although doctoral education is emphasized here. What teachers cover in qualitative methods courses matters. The pedagogy presented here requires a supportive environment and relies on demonstration, collective participation, measured tasks, progressive analytic complexity, and accountability. Lessons learned from using initial grounded theory tools are exemplified in a doctoral student's coding and memo-writing excerpts that demonstrate progressive analytic development. The conclusion calls for increasing the number and depth of qualitative methods courses and for creating a cadre of expert qualitative methodologists. © The Author(s) 2015.

  4. Transitional clerkship: an experiential course based on workplace learning theory.

    Science.gov (United States)

    Chittenden, Eva H; Henry, Duncan; Saxena, Varun; Loeser, Helen; O'Sullivan, Patricia S

    2009-07-01

    Starting clerkships is anxiety provoking for medical students. To ease the transition from preclerkship to clerkship curricula, schools offer classroom-based courses which may not be the best model for preparing learners. Drawing from workplace learning theory, the authors developed a seven-day transitional clerkship (TC) in 2007 at the University of California, San Francisco School of Medicine in which students spent half of the course in the hospital, learning routines and logistics of the wards along with their roles and responsibilities as members of ward teams. Twice, they admitted and followed a patient into the next day as part of a shadow team that had no patient-care responsibilities. Dedicated preceptors gave feedback on oral presentations and patient write-ups. Satisfaction with the TC was higher than with the previous year's classroom-based course. TC students felt clearer about their roles and more confident in their abilities as third-year students compared with previous students. TC students continued to rate the transitional course highly after their first clinical rotation. Preceptors were enthusiastic about the course and expressed willingness to commit to future TC preceptorships. The transitional course models an approach to translating workplace learning theory into practice and demonstrates improved satisfaction, better understanding of roles, and increased confidence among new third-year students.

  5. Auditory working memory predicts individual differences in absolute pitch learning.

    Science.gov (United States)

    Van Hedger, Stephen C; Heald, Shannon L M; Koch, Rachelle; Nusbaum, Howard C

    2015-07-01

    Absolute pitch (AP) is typically defined as the ability to label an isolated tone as a musical note in the absence of a reference tone. At first glance the acquisition of AP note categories seems like a perceptual learning task, since individuals must assign a category label to a stimulus based on a single perceptual dimension (pitch) while ignoring other perceptual dimensions (e.g., loudness, octave, instrument). AP, however, is rarely discussed in terms of domain-general perceptual learning mechanisms. This is because AP is typically assumed to depend on a critical period of development, in which early exposure to pitches and musical labels is thought to be necessary for the development of AP precluding the possibility of adult acquisition of AP. Despite this view of AP, several previous studies have found evidence that absolute pitch category learning is, to an extent, trainable in a post-critical period adult population, even if the performance typically achieved by this population is below the performance of a "true" AP possessor. The current studies attempt to understand the individual differences in learning to categorize notes using absolute pitch cues by testing a specific prediction regarding cognitive capacity related to categorization - to what extent does an individual's general auditory working memory capacity (WMC) predict the success of absolute pitch category acquisition. Since WMC has been shown to predict performance on a wide variety of other perceptual and category learning tasks, we predict that individuals with higher WMC should be better at learning absolute pitch note categories than individuals with lower WMC. Across two studies, we demonstrate that auditory WMC predicts the efficacy of learning absolute pitch note categories. These results suggest that a higher general auditory WMC might underlie the formation of absolute pitch categories for post-critical period adults. Implications for understanding the mechanisms that underlie the

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

    Science.gov (United States)

    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…

  7. Applications of machine learning in cancer prediction and prognosis.

    Science.gov (United States)

    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.

  8. 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.

  9. Learning in Context: Technology Integration in a Teacher Preparation Program Informed by Situated Learning Theory

    Science.gov (United States)

    Bell, Randy L.; Maeng, Jennifer L.; Binns, Ian C.

    2013-01-01

    This investigation explores the effectiveness of a teacher preparation program aligned with situated learning theory on preservice science teachers' use of technology during their student teaching experiences. Participants included 26 preservice science teachers enrolled in a 2-year Master of Teaching program. A specific program goal was to…

  10. 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.

  11. 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...

  12. Just do it: action-dependent learning allows sensory prediction.

    Directory of Open Access Journals (Sweden)

    Itai Novick

    Full Text Available Sensory-motor learning is commonly considered as a mapping process, whereby sensory information is transformed into the motor commands that drive actions. However, this directional mapping, from inputs to outputs, is part of a loop; sensory stimuli cause actions and vice versa. Here, we explore whether actions affect the understanding of the sensory input that they cause. Using a visuo-motor task in humans, we demonstrate two types of learning-related behavioral effects. Stimulus-dependent effects reflect stimulus-response learning, while action-dependent effects reflect a distinct learning component, allowing the brain to predict the forthcoming sensory outcome of actions. Together, the stimulus-dependent and the action-dependent learning components allow the brain to construct a complete internal representation of the sensory-motor loop.

  13. 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...

  14. 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.

  15. 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.

  16. [Role of the implicit theories of intelligence in learning situations].

    Science.gov (United States)

    Da Fonseca, D; Cury, F; Bailly, D; Rufo, M

    2004-01-01

    Most studies have tried to explain the school difficulties by analysing the intellectual factors that lead to school failure. However in addition to the instrumental capacities, authors also recognize the role played by other factors such as motivation. More specifically, the theory of achievement motivation aims to determine motivational factors involved in achievement situations when the students have to demonstrate their competencies. This paradigm attributes a central place to beliefs in order to explain children's behavior in academic situations. According to Dweck, it seems that beliefs about the nature of intelligence have a very powerful impact on behavior. These implicit theories of intelligence create a meaning system or conceptual framework that influences the individual interpretation of school situations. Thus, an entity theory of intelligence is the belief that intelligence is a fixed trait, a personal quality that cannot be changed. Students who subscribe to this theory believe that although people can learn new things, their underlying intelligence remains the same. In contrast, an incremental theory of intelligence is the belief that intelligence is a malleable quality that can increase through efforts. The identification of these two theories allows us to understand the cognition and behavior of individuals in achievement situations. Many studies carried out in the academic area show that students who hold an entity theory of intelligence (ie they consider intelligence like a stable quality) have a strong tendency to attribute their failures to a fixed trait. They are more likely to blame their intelligence for ne-gative outcomes and to attribute failures to their bad intellectual ability. In contrast, students who hold an incremental theory of intelligence (ie they consider intelligence as a malleable quality) are more likely to understand the same ne-gative outcomes in terms of specific factors: they attribute them to a lack of effort. This

  17. Action learning in virtual higher education: applying leadership theory.

    Science.gov (United States)

    Curtin, Joseph

    2016-05-03

    This paper reports the historical foundation of Northeastern University's course, LDR 6100: Developing Your Leadership Capability, a partial literature review of action learning (AL) and virtual action learning (VAL), a course methodology of LDR 6100 requiring students to apply leadership perspectives using VAL as instructed by the author, questionnaire and survey results of students who evaluated the effectiveness of their application of leadership theories using VAL and insights believed to have been gained by the author administering VAL. Findings indicate most students thought applying leadership perspectives using AL was better than considering leadership perspectives not using AL. In addition as implemented in LDR 6100, more students evaluated VAL positively than did those who assessed VAL negatively.

  18. Application of Learning Theories on Medical Imaging Education

    Directory of Open Access Journals (Sweden)

    Osama A. Mabrouk Kheiralla

    2018-05-01

    Full Text Available The main objective of the education process is that student must learn well rather than the educators to teach well. If radiologists get involved in the process of medical education, it is important for them to do it through sound knowledge of how students learn. Researches have proved that most of the teachers in the field of medical education including diagnostic imaging are actually doctors or technicians, who didn’t have an opportunity to study the basics of learning. Mostly they have gained their knowledge through watching other educators, and they mostly rely on their personal skills and experience in doing their job. This will hinder them from conveying knowledge in an effective and scientific way, and they will find themselves lagging away behind the latest advances in the field of medical education and educational research, which will lead to negative cognitive outcomes among learners. This article presents an overview of three of the most influential basic theories of learning, upon which many teachers rely in their practical applications, which must be considered by radiologist who act as medical educators.

  19. 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

  20. Artificial grammar learning meets formal language theory: an overview

    Science.gov (United States)

    Fitch, W. Tecumseh; Friederici, Angela D.

    2012-01-01

    Formal language theory (FLT), part of the broader mathematical theory of computation, provides a systematic terminology and set of conventions for describing rules and the structures they generate, along with a rich body of discoveries and theorems concerning generative rule systems. Despite its name, FLT is not limited to human language, but is equally applicable to computer programs, music, visual patterns, animal vocalizations, RNA structure and even dance. In the last decade, this theory has been profitably used to frame hypotheses and to design brain imaging and animal-learning experiments, mostly using the ‘artificial grammar-learning’ paradigm. We offer a brief, non-technical introduction to FLT and then a more detailed analysis of empirical research based on this theory. We suggest that progress has been hampered by a pervasive conflation of distinct issues, including hierarchy, dependency, complexity and recursion. We offer clarifications of several relevant hypotheses and the experimental designs necessary to test them. We finally review the recent brain imaging literature, using formal languages, identifying areas of convergence and outstanding debates. We conclude that FLT has much to offer scientists who are interested in rigorous empirical investigations of human cognition from a neuroscientific and comparative perspective. PMID:22688631

  1. Time-sensitive Customer Churn Prediction based on PU Learning

    OpenAIRE

    Wang, Li; Chen, Chaochao; Zhou, Jun; Li, Xiaolong

    2018-01-01

    With the fast development of Internet companies throughout the world, customer churn has become a serious concern. To better help the companies retain their customers, it is important to build a customer churn prediction model to identify the customers who are most likely to churn ahead of time. In this paper, we propose a Time-sensitive Customer Churn Prediction (TCCP) framework based on Positive and Unlabeled (PU) learning technique. Specifically, we obtain the recent data by shortening the...

  2. Prediction of Employee Turnover in Organizations using Machine Learning Algorithms

    OpenAIRE

    Rohit Punnoose; Pankaj Ajit

    2016-01-01

    Employee turnover has been identified as a key issue for organizations because of its adverse impact on work place productivity and long term growth strategies. To solve this problem, organizations use machine learning techniques to predict employee turnover. Accurate predictions enable organizations to take action for retention or succession planning of employees. However, the data for this modeling problem comes from HR Information Systems (HRIS); these are typically under-funded compared t...

  3. 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.

  4. Predicting Knowledge Workers' Participation in Voluntary Learning with Employee Characteristics and Online Learning Tools

    Science.gov (United States)

    Hicks, Catherine

    2018-01-01

    Purpose: This paper aims to explore predicting employee learning activity via employee characteristics and usage for two online learning tools. Design/methodology/approach: Statistical analysis focused on observational data collected from user logs. Data are analyzed via regression models. Findings: Findings are presented for over 40,000…

  5. 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…

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

    Science.gov (United States)

    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…

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

    Science.gov (United States)

    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…

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

    Science.gov (United States)

    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…

  9. Learning Study: Helping Teachers to Use Theory, Develop Professionally, and Produce New Knowledge to Be Shared

    Science.gov (United States)

    Pang, Ming Fai; Ling, Lo Mun

    2012-01-01

    The lesson study approach is a systematic process for producing professional knowledge about teaching by teachers, and has spread rapidly and extensively in the United States. The learning study approach is essentially a kind of lesson study with an explicit learning theory--the variation theory of learning. In this paper, we argue that having an…

  10. 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)

  11. A Critical Comparison of Transformation and Deep Approach Theories of Learning

    Science.gov (United States)

    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…

  12. 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…

  13. Quicksilver: Fast predictive image registration - A deep learning approach.

    Science.gov (United States)

    Yang, Xiao; Kwitt, Roland; Styner, Martin; Niethammer, Marc

    2017-09-01

    This paper introduces Quicksilver, a fast deformable image registration method. Quicksilver registration for image-pairs works by patch-wise prediction of a deformation model based directly on image appearance. A deep encoder-decoder network is used as the prediction model. While the prediction strategy is general, we focus on predictions for the Large Deformation Diffeomorphic Metric Mapping (LDDMM) model. Specifically, we predict the momentum-parameterization of LDDMM, which facilitates a patch-wise prediction strategy while maintaining the theoretical properties of LDDMM, such as guaranteed diffeomorphic mappings for sufficiently strong regularization. We also provide a probabilistic version of our prediction network which can be sampled during the testing time to calculate uncertainties in the predicted deformations. Finally, we introduce a new correction network which greatly increases the prediction accuracy of an already existing prediction network. We show experimental results for uni-modal atlas-to-image as well as uni-/multi-modal image-to-image registrations. These experiments demonstrate that our method accurately predicts registrations obtained by numerical optimization, is very fast, achieves state-of-the-art registration results on four standard validation datasets, and can jointly learn an image similarity measure. Quicksilver is freely available as an open-source software. Copyright © 2017 Elsevier Inc. All rights reserved.

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

    Science.gov (United States)

    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).

  15. 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

  16. Reward and Cognition: Integrating Reinforcement Sensitivity Theory and Social Cognitive Theory to Predict Drinking Behavior.

    Science.gov (United States)

    Hasking, Penelope; Boyes, Mark; Mullan, Barbara

    2015-01-01

    Both Reinforcement Sensitivity Theory and Social Cognitive Theory have been applied to understanding drinking behavior. We propose that theoretical relationships between these models support an integrated approach to understanding alcohol use and misuse. We aimed to test an integrated model in which the relationships between reward sensitivity and drinking behavior (alcohol consumption, alcohol-related problems, and symptoms of dependence) were mediated by alcohol expectancies and drinking refusal self-efficacy. Online questionnaires assessing the constructs of interest were completed by 443 Australian adults (M age = 26.40, sd = 1.83) in 2013 and 2014. Path analysis revealed both direct and indirect effects and implicated two pathways to drinking behavior with differential outcomes. Drinking refusal self-efficacy both in social situations and for emotional relief was related to alcohol consumption. Sensitivity to reward was associated with alcohol-related problems, but operated through expectations of increased confidence and personal belief in the ability to limit drinking in social situations. Conversely, sensitivity to punishment operated through negative expectancies and drinking refusal self-efficacy for emotional relief to predict symptoms of dependence. Two pathways relating reward sensitivity, alcohol expectancies, and drinking refusal self-efficacy may underlie social and dependent drinking, which has implications for development of intervention to limit harmful drinking.

  17. Applicability of the theory of thermodynamic similarity to predict the enthalpies of vaporization of aliphatic aldehydes

    Science.gov (United States)

    Esina, Z. N.; Korchuganova, M. R.

    2015-06-01

    The theory of thermodynamic similarity is used to predict the enthalpies of vaporization of aliphatic aldehydes. The predicted data allow us to calculate the phase diagrams of liquid-vapor equilibrium in a binary water-aliphatic aldehyde system.

  18. Learning Political Science with Prediction Markets: An Experimental Study

    Science.gov (United States)

    Ellis, Cali Mortenson; Sami, Rahul

    2012-01-01

    Prediction markets are designed to aggregate the information of many individuals to forecast future events. These markets provide participants with an incentive to seek information and a forum for interaction, making markets a promising tool to motivate student learning. We carried out a quasi-experiment in an introductory political science class…

  19. 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....

  20. Ensemble learned vaccination uptake prediction using web search queries

    DEFF Research Database (Denmark)

    Hansen, Niels Dalum; Lioma, Christina; Mølbak, Kåre

    2016-01-01

    We present a method that uses ensemble learning to combine clinical and web-mined time-series data in order to predict future vaccination uptake. The clinical data is official vaccination registries, and the web data is query frequencies collected from Google Trends. Experiments with official...... vaccine records show that our method predicts vaccination uptake eff?ectively (4.7 Root Mean Squared Error). Whereas performance is best when combining clinical and web data, using solely web data yields comparative performance. To our knowledge, this is the ?first study to predict vaccination uptake...

  1. What Predicts Use of Learning-Centered, Interactive Engagement Methods?

    Science.gov (United States)

    Madson, Laura; Trafimow, David; Gray, Tara; Gutowitz, Michael

    2014-01-01

    What makes some faculty members more likely to use interactive engagement methods than others? We use the theory of reasoned action to predict faculty members' use of interactive engagement methods. Results indicate that faculty members' beliefs about the personal positive consequences of using these methods (e.g., "Using interactive…

  2. 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

  3. 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

  4. Prediction of length-of-day using extreme learning machine

    Directory of Open Access Journals (Sweden)

    Yu Lei

    2015-03-01

    Full Text Available Traditional artificial neural networks (ANN such as back-propagation neural networks (BPNN provide good predictions of length-of-day (LOD. However, the determination of network topology is difficult and time consuming. Therefore, we propose a new type of neural network, extreme learning machine (ELM, to improve the efficiency of LOD predictions. Earth orientation parameters (EOP C04 time-series provides daily values from International Earth Rotation and Reference Systems Service (IERS, which serves as our database. First, the known predictable effects that can be described by functional models—such as the effects of solid earth, ocean tides, or seasonal atmospheric variations—are removed a priori from the C04 time-series. Only the residuals after the subtraction of a priori model from the observed LOD data (i.e., the irregular and quasi-periodic variations are employed for training and predictions. The predicted LOD is the sum of a prior extrapolation model and the ELM predictions of the residuals. Different input patterns are discussed and compared to optimize the network solution. The prediction results are analyzed and compared with those obtained by other machine learning-based prediction methods, including BPNN, generalization regression neural networks (GRNN, and adaptive network-based fuzzy inference systems (ANFIS. It is shown that while achieving similar prediction accuracy, the developed method uses much less training time than other methods. Furthermore, to conduct a direct comparison with the existing prediction techniques, the mean-absolute-error (MAE from the proposed method is compared with that from the EOP prediction comparison campaign (EOP PCC. The results indicate that the accuracy of the proposed method is comparable with that of the former techniques. The implementation of the proposed method is simple.

  5. 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

  6. Implicit and explicit theories in the teaching and learning processes of music theory

    Directory of Open Access Journals (Sweden)

    Henry Roa Ordoñez

    2014-06-01

    Full Text Available This study explores the characteristics of similarity and divergence between the pedagogical discourse of teachers and their performance in the classroom, from the different educational paradigms that guide, today, the educational events. The teaching and learning of music theory constitute the backbone of the proposed curriculum of the Department of Music, which has implications in the other musical areas and, therefore, the training program that orients the area of music theory, requires an assessment of the impacts and effects caused by the performance of the teacher in charge of running this course as an essential condition to establish elements of building and transfer of knowledge in each of the disciplines that make up the curricular structure of the Department of Music.

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

    Science.gov (United States)

    Wood, Rodger Ll; Alderman, Nick

    2011-01-01

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

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

    Science.gov (United States)

    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.

  9. Toward a theory of childhood learning disorders, hyperactivity, and aggression.

    Science.gov (United States)

    Mawson, Anthony R

    2012-01-01

    Learning disorders are often associated with persistent hyperactivity and aggression and are part of a spectrum of neurodevelopmental disorders. A potential clue to understanding these linked phenomena is that physical exercise and passive forms of stimulation are calming, enhance cognitive functions and learning, and are recommended as complementary treatments for these problems. The theory is proposed that hyperactivity and aggression are intense stimulation-seeking behaviors (SSBs) driven by increased brain retinergic activity, and the stimulation thus obtained activates opposing nitrergic systems which inhibit retinergic activity, induce a state of calm, and enhance cognition and learning. In persons with cognitive deficits and associated behavioral disorders, the retinergic system may be chronically overactivated and the nitrergic system chronically underactivated due to environmental exposures occurring pre- and/or postnatally that affect retinoid metabolism or expression. For such individuals, the intensity of stimulation generated by SSB may be insufficient to activate the inhibitory nitrergic system. A multidisciplinary research program is needed to test the model and, in particular, to determine the extent to which applied physical treatments can activate the nitrergic system directly, providing the necessary level of intensity of sensory stimulation to substitute for that obtained in maladaptive and harmful ways by SSB, thereby reducing SSB and enhancing cognitive skills and performance.

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

    Science.gov (United States)

    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.

  11. 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.

  12. 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)

  13. IMPROVING TRUST THROUGH ETHICAL LEADERSHIP: MOVING BEYOND THE SOCIAL LEARNING THEORY TO A HISTORICAL LEARNING APPROACH

    Directory of Open Access Journals (Sweden)

    Omoregie Charles Osifo

    2016-12-01

    Full Text Available The complex nature of trust and its evolving relative concepts require a more idealistic and simpler review. Ethical leadership is related to trust, honesty, transparency, compassion, empathy, results-orientedness, and many other behavioral attributes. Ethical leadership and good leadership are the same, because they represent practicing what one preaches or showing a way to the accomplishment of set goals. The outcomes and findings of many research papers on trust and ethical leadership report positive correlations between ethical leadership and trust. Improving trust from different rational standpoints requires moving and looking beyond the popular theoretical framework through which most results are derived in order to create a new thinking perspective. Social learning theory strongly emphasizes modelling while the new historical learning approach, proposed by the author, is defined as an approach that creates unique historical awareness among individuals, groups, institutions, societies, and nations to use previous experience(s or occurrence(s as a guide in developing positive opinion(s and framework(s in order to tackle the problems and issues of today and tomorrow. Social learning theory is seen as limited from the perspectives of balancing the equation between leadership and trust, the non-compatibility of the values of different generations at work, and other approaches and methods that support the historical approach. This paper is argumentative, adopts a writer´s perspective, and employs a logical analysis of the literature. The main contention is that a historical learning approach can inform an independent-learning to improve trust and its relatives (e.g. motivation and performance, because independent learning can positively shape the value of integrity, which is an integral part of ethical leadership. Historical learning can positively shape leadership in every perspective, because good leadership can develop based on history and

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

    Science.gov (United States)

    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…

  15. Using Game Theory and Competition-Based Learning to Stimulate Student Motivation and Performance

    Science.gov (United States)

    Burguillo, Juan C.

    2010-01-01

    This paper introduces a framework for using Game Theory tournaments as a base to implement Competition-based Learning (CnBL), together with other classical learning techniques, to motivate the students and increase their learning performance. The paper also presents a description of the learning activities performed along the past ten years of a…

  16. Information theory and artificial grammar learning: inferring grammaticality from redundancy.

    Science.gov (United States)

    Jamieson, Randall K; Nevzorova, Uliana; Lee, Graham; Mewhort, D J K

    2016-03-01

    In artificial grammar learning experiments, participants study strings of letters constructed using a grammar and then sort novel grammatical test exemplars from novel ungrammatical ones. The ability to distinguish grammatical from ungrammatical strings is often taken as evidence that the participants have induced the rules of the grammar. We show that judgements of grammaticality are predicted by the local redundancy of the test strings, not by grammaticality itself. The prediction holds in a transfer test in which test strings involve different letters than the training strings. Local redundancy is usually confounded with grammaticality in stimuli widely used in the literature. The confounding explains why the ability to distinguish grammatical from ungrammatical strings has popularized the idea that participants have induced the rules of the grammar, when they have not. We discuss the judgement of grammaticality task in terms of attribute substitution and pattern goodness. When asked to judge grammaticality (an inaccessible attribute), participants answer an easier question about pattern goodness (an accessible attribute).

  17. The Place of Learning Quantum Theory in Physics Teacher Education: Motivational Elements Arising from the Context

    Science.gov (United States)

    Körhasan, Nilüfer Didis

    2015-01-01

    Quantum theory is one of the most successful theories in physics. Because of its abstract, mathematical, and counter-intuitive nature, many students have problems learning the theory, just as teachers experience difficulty in teaching it. Pedagogical research on quantum theory has mainly focused on cognitive issues. However, affective issues about…

  18. 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)

  19. Deep Learning in Intermediate Microeconomics: Using Scaffolding Assignments to Teach Theory and Promote Transfer

    Science.gov (United States)

    Green, Gareth P.; Bean, John C.; Peterson, Dean J.

    2013-01-01

    Intermediate microeconomics is typically viewed as a theory and tools course that relies on algorithmic problems to help students learn and apply economic theory. However, the authors' assessment research suggests that algorithmic problems by themselves do not encourage students to think about where the theory comes from, why the theory is…

  20. 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.

  1. Learning about Learning: The Contributions of Ausubel's Assimilation Theory to a Teacher Education Program at the University of Vermont.

    Science.gov (United States)

    Smith, Markley; Stowell, Mary Ellen

    An experiment employed cognitive based teaching and learning procedures in an undergraduate educational psychology course. The procedures were strongly influenced by David Ausubel's theory on learning and related skills. Ausubel defines effective learning as a process by which humans understand the structure of knowledge and consciously make…

  2. 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

  3. Testing Theories of Recognition Memory by Predicting Performance Across Paradigms

    Science.gov (United States)

    Smith, David G.; Duncan, Matthew J. J.

    2004-01-01

    Signal-detection theory (SDT) accounts of recognition judgments depend on the assumption that recognition decisions result from a single familiarity-based process. However, fits of a hybrid SDT model, called dual-process theory (DPT), have provided evidence for the existence of a second, recollection-based process. In 2 experiments, the authors…

  4. Theory of Mind Predicts Severity Level in Autism

    Science.gov (United States)

    Hoogenhout, Michelle; Malcolm-Smith, Susan

    2017-01-01

    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…

  5. 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.

  6. Optimizing performance through intrinsic motivation and attention for learning: The OPTIMAL theory of motor learning.

    Science.gov (United States)

    Wulf, Gabriele; Lewthwaite, Rebecca

    2016-10-01

    Effective motor performance is important for surviving and thriving, and skilled movement is critical in many activities. Much theorizing over the past few decades has focused on how certain practice conditions affect the processing of task-related information to affect learning. Yet, existing theoretical perspectives do not accommodate significant recent lines of evidence demonstrating motivational and attentional effects on performance and learning. These include research on (a) conditions that enhance expectancies for future performance, (b) variables that influence learners' autonomy, and (c) an external focus of attention on the intended movement effect. We propose the OPTIMAL (Optimizing Performance through Intrinsic Motivation and Attention for Learning) theory of motor learning. We suggest that motivational and attentional factors contribute to performance and learning by strengthening the coupling of goals to actions. We provide explanations for the performance and learning advantages of these variables on psychological and neuroscientific grounds. We describe a plausible mechanism for expectancy effects rooted in responses of dopamine to the anticipation of positive experience and temporally associated with skill practice. Learner autonomy acts perhaps largely through an enhanced expectancy pathway. Furthermore, we consider the influence of an external focus for the establishment of efficient functional connections across brain networks that subserve skilled movement. We speculate that enhanced expectancies and an external focus propel performers' cognitive and motor systems in productive "forward" directions and prevent "backsliding" into self- and non-task focused states. Expected success presumably breeds further success and helps consolidate memories. We discuss practical implications and future research directions.

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

    Directory of Open Access Journals (Sweden)

    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

  8. 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

  9. 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.

  10. 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…

  11. 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.

  12. ERPs recorded during early second language exposure predict syntactic learning.

    Science.gov (United States)

    Batterink, Laura; Neville, Helen J

    2014-09-01

    Millions of adults worldwide are faced with the task of learning a second language (L2). Understanding the neural mechanisms that support this learning process is an important area of scientific inquiry. However, most previous studies on the neural mechanisms underlying L2 acquisition have focused on characterizing the results of learning, relying upon end-state outcome measures in which learning is assessed after it has occurred, rather than on the learning process itself. In this study, we adopted a novel and more direct approach to investigate neural mechanisms engaged during L2 learning, in which we recorded ERPs from beginning adult learners as they were exposed to an unfamiliar L2 for the first time. Learners' proficiency in the L2 was then assessed behaviorally using a grammaticality judgment task, and ERP data acquired during initial L2 exposure were sorted as a function of performance on this task. High-proficiency learners showed a larger N100 effect to open-class content words compared with closed-class function words, whereas low-proficiency learners did not show a significant N100 difference between open- and closed-class words. In contrast, amplitude of the N400 word category effect correlated with learners' L2 comprehension, rather than predicting syntactic learning. Taken together, these results indicate that learners who spontaneously direct greater attention to open- rather than closed-class words when processing L2 input show better syntactic learning, suggesting a link between selective attention to open-class content words and acquisition of basic morphosyntactic rules. These findings highlight the importance of selective attention mechanisms for L2 acquisition.

  13. Machine learning methods in predicting the student academic motivation

    Directory of Open Access Journals (Sweden)

    Ivana Đurđević Babić

    2017-01-01

    Full Text Available Academic motivation is closely related to academic performance. For educators, it is equally important to detect early students with a lack of academic motivation as it is to detect those with a high level of academic motivation. In endeavouring to develop a classification model for predicting student academic motivation based on their behaviour in learning management system (LMS courses, this paper intends to establish links between the predicted student academic motivation and their behaviour in the LMS course. Students from all years at the Faculty of Education in Osijek participated in this research. Three machine learning classifiers (neural networks, decision trees, and support vector machines were used. To establish whether a significant difference in the performance of models exists, a t-test of the difference in proportions was used. Although, all classifiers were successful, the neural network model was shown to be the most successful in detecting the student academic motivation based on their behaviour in LMS course.

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

    Science.gov (United States)

    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.

  15. Learning circumference concepts from the didactical situations theory perspective

    Directory of Open Access Journals (Sweden)

    Valdir de Sousa Cavalcanti

    2013-08-01

    Full Text Available The circumference study, as its importance, it is one of the most relevant contents in the Analytical Geometry curriculum. However, the complexity of related concepts to this theme linked to the content fragmentation, it difficulties the students thinking of transforming geometrical problems into equations solution, systems or inequations. Within, in this article we present a partial report of a master research work, of qualitative mode, which aimed to develop and to evaluate an alternative methodology by using musical parody composition to the teaching of Mathematics in trying to contribute to the circumference concepts learning process. For that, we carried out a case study with 36 third year high school students of a public school from the city of Campina Grande, Paraíba. The research work was based and discussed on Brousseau Didactical Situation Theory. It was chosen triangulation technique for the data analyses, collected from interviews, questionnaires and a list of mathematical exercises. We concluded that the parody composition resource allowed the students better understand the concepts of center, ratio, cord and the definition of the general circumference equation, as they were capable to identify the relative positions which a circumference assumes in relation to an equation of a straight line and between two circumferences in the various concepts that differentiated them. Thus, we can state that the musical parody composition as a didactical resource can contribute to the learning of mathematical contents.

  16. Using Item Response Theory to Evaluate LSCI Learning Gains

    Science.gov (United States)

    Schlingman, Wayne M.; Prather, E. E.; Collaboration of Astronomy Teaching Scholars CATS

    2012-01-01

    Analyzing the data from the recent national study using the Light and Spectroscopy Concept Inventory (LSCI), this project uses Item Response Theory (IRT) to investigate the learning gains of students as measured by the LSCI. IRT provides a theoretical model to generate parameters accounting for students’ abilities. We use IRT to measure changes in students’ abilities to reason about light from pre- to post-instruction. Changes in students’ abilities are compared by classroom to better understand the learning that is taking place in classrooms across the country. We compare the average change in ability for each classroom to the Interactivity Assessment Score (IAS) to provide further insight into the prior results presented from this data set. This material is based upon work supported by the National Science Foundation under Grant No. 0715517, a CCLI Phase III Grant for the Collaboration of Astronomy Teaching Scholars (CATS). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

  17. Transformative learning theory: facilitating mammography screening in rural women.

    Science.gov (United States)

    Purtzer, Mary Anne; Overstreet, Lindsey

    2014-03-01

    To use transformative learning to investigate what experiences serve as catalysts for mammography screening, the cognitive and affective responses that result from the catalyst, and how screening behavior is impacted. A descriptive qualitative study. Southeastern Wyoming. 25 low-income, rural women aged 40 years and older. Four focus group interviews. Cancer experiences triggered universal responses of fear by screeners and nonscreeners. The manner in which that fear response was interpreted was a critical factor in the facilitation of, or impedance to, screening. Dichotomous interpretations of fear responses provided the context for screening behavior. Immobilizing and isolating experiences were associated with nonscreening behavior, whereas motivation and self-efficacy were associated with screening behavior. Transformative learning theory is a useful framework from which to explain differences in mammography screening behavior. Creating opportunities that facilitate dialogue and critical reflection hold the potential to change immobilizing and isolating frames of reference in nonscreening women. To help women transcend their fear and become self-efficacious, nurses can assess how cancer and the screening experience is viewed and, if indicated, move beyond standard education and offer opportunities for dialogue and critical reflection.

  18. Teaching organization theory for healthcare management: three applied learning methods.

    Science.gov (United States)

    Olden, Peter C

    2006-01-01

    Organization theory (OT) provides a way of seeing, describing, analyzing, understanding, and improving organizations based on patterns of organizational design and behavior (Daft 2004). It gives managers models, principles, and methods with which to diagnose and fix organization structure, design, and process problems. Health care organizations (HCOs) face serious problems such as fatal medical errors, harmful treatment delays, misuse of scarce nurses, costly inefficiency, and service failures. Some of health care managers' most critical work involves designing and structuring their organizations so their missions, visions, and goals can be achieved-and in some cases so their organizations can survive. Thus, it is imperative that graduate healthcare management programs develop effective approaches for teaching OT to students who will manage HCOs. Guided by principles of education, three applied teaching/learning activities/assignments were created to teach OT in a graduate healthcare management program. These educationalmethods develop students' competency with OT applied to HCOs. The teaching techniques in this article may be useful to faculty teaching graduate courses in organization theory and related subjects such as leadership, quality, and operation management.

  19. Machine learning application in online lending risk prediction

    OpenAIRE

    Yu, Xiaojiao

    2017-01-01

    Online leading has disrupted the traditional consumer banking sector with more effective loan processing. Risk prediction and monitoring is critical for the success of the business model. Traditional credit score models fall short in applying big data technology in building risk model. In this manuscript, data with various format and size were collected from public website, third-parties and assembled with client's loan application information data. Ensemble machine learning models, random fo...

  20. Pileup Subtraction and Jet Energy Prediction Using Machine Learning

    OpenAIRE

    Kong, Vein S; Li, Jiakun; Zhang, Yujia

    2015-01-01

    In the Large Hardron Collider (LHC), multiple proton-proton collisions cause pileup in reconstructing energy information for a single primary collision (jet). This project aims to select the most important features and create a model to accurately estimate jet energy. Different machine learning methods were explored, including linear regression, support vector regression and decision tree. The best result is obtained by linear regression with predictive features and the performance is improve...

  1. Group-regularized individual prediction: theory and application to pain.

    Science.gov (United States)

    Lindquist, Martin A; Krishnan, Anjali; López-Solà, Marina; Jepma, Marieke; Woo, Choong-Wan; Koban, Leonie; Roy, Mathieu; Atlas, Lauren Y; Schmidt, Liane; Chang, Luke J; Reynolds Losin, Elizabeth A; Eisenbarth, Hedwig; Ashar, Yoni K; Delk, Elizabeth; Wager, Tor D

    2017-01-15

    Multivariate pattern analysis (MVPA) has become an important tool for identifying brain representations of psychological processes and clinical outcomes using fMRI and related methods. Such methods can be used to predict or 'decode' psychological states in individual subjects. Single-subject MVPA approaches, however, are limited by the amount and quality of individual-subject data. In spite of higher spatial resolution, predictive accuracy from single-subject data often does not exceed what can be accomplished using coarser, group-level maps, because single-subject patterns are trained on limited amounts of often-noisy data. Here, we present a method that combines population-level priors, in the form of biomarker patterns developed on prior samples, with single-subject MVPA maps to improve single-subject prediction. Theoretical results and simulations motivate a weighting based on the relative variances of biomarker-based prediction-based on population-level predictive maps from prior groups-and individual-subject, cross-validated prediction. Empirical results predicting pain using brain activity on a trial-by-trial basis (single-trial prediction) across 6 studies (N=180 participants) confirm the theoretical predictions. Regularization based on a population-level biomarker-in this case, the Neurologic Pain Signature (NPS)-improved single-subject prediction accuracy compared with idiographic maps based on the individuals' data alone. The regularization scheme that we propose, which we term group-regularized individual prediction (GRIP), can be applied broadly to within-person MVPA-based prediction. We also show how GRIP can be used to evaluate data quality and provide benchmarks for the appropriateness of population-level maps like the NPS for a given individual or study. Copyright © 2015 Elsevier Inc. All rights reserved.

  2. Machine learning derived risk prediction of anorexia nervosa.

    Science.gov (United States)

    Guo, Yiran; Wei, Zhi; Keating, Brendan J; Hakonarson, Hakon

    2016-01-20

    Anorexia nervosa (AN) is a complex psychiatric disease with a moderate to strong genetic contribution. In addition to conventional genome wide association (GWA) studies, researchers have been using machine learning methods in conjunction with genomic data to predict risk of diseases in which genetics play an important role. In this study, we collected whole genome genotyping data on 3940 AN cases and 9266 controls from the Genetic Consortium for Anorexia Nervosa (GCAN), the Wellcome Trust Case Control Consortium 3 (WTCCC3), Price Foundation Collaborative Group and the Children's Hospital of Philadelphia (CHOP), and applied machine learning methods for predicting AN disease risk. The prediction performance is measured by area under the receiver operating characteristic curve (AUC), indicating how well the model distinguishes cases from unaffected control subjects. Logistic regression model with the lasso penalty technique generated an AUC of 0.693, while Support Vector Machines and Gradient Boosted Trees reached AUC's of 0.691 and 0.623, respectively. Using different sample sizes, our results suggest that larger datasets are required to optimize the machine learning models and achieve higher AUC values. To our knowledge, this is the first attempt to assess AN risk based on genome wide genotype level data. Future integration of genomic, environmental and family-based information is likely to improve the AN risk evaluation process, eventually benefitting AN patients and families in the clinical setting.

  3. Using Deep Learning Model for Meteorological Satellite Cloud Image Prediction

    Science.gov (United States)

    Su, X.

    2017-12-01

    A satellite cloud image contains much weather information such as precipitation information. Short-time cloud movement forecast is important for precipitation forecast and is the primary means for typhoon monitoring. The traditional methods are mostly using the cloud feature matching and linear extrapolation to predict the cloud movement, which makes that the nonstationary process such as inversion and deformation during the movement of the cloud is basically not considered. It is still a hard task to predict cloud movement timely and correctly. As deep learning model could perform well in learning spatiotemporal features, to meet this challenge, we could regard cloud image prediction as a spatiotemporal sequence forecasting problem and introduce deep learning model to solve this problem. In this research, we use a variant of Gated-Recurrent-Unit(GRU) that has convolutional structures to deal with spatiotemporal features and build an end-to-end model to solve this forecast problem. In this model, both the input and output are spatiotemporal sequences. Compared to Convolutional LSTM(ConvLSTM) model, this model has lower amount of parameters. We imply this model on GOES satellite data and the model perform well.

  4. Human medial frontal cortex activity predicts learning from errors.

    Science.gov (United States)

    Hester, Robert; Barre, Natalie; Murphy, Kevin; Silk, Tim J; Mattingley, Jason B

    2008-08-01

    Learning from errors is a critical feature of human cognition. It underlies our ability to adapt to changing environmental demands and to tune behavior for optimal performance. The posterior medial frontal cortex (pMFC) has been implicated in the evaluation of errors to control behavior, although it has not previously been shown that activity in this region predicts learning from errors. Using functional magnetic resonance imaging, we examined activity in the pMFC during an associative learning task in which participants had to recall the spatial locations of 2-digit targets and were provided with immediate feedback regarding accuracy. Activity within the pMFC was significantly greater for errors that were subsequently corrected than for errors that were repeated. Moreover, pMFC activity during recall errors predicted future responses (correct vs. incorrect), despite a sizeable interval (on average 70 s) between an error and the next presentation of the same recall probe. Activity within the hippocampus also predicted future performance and correlated with error-feedback-related pMFC activity. A relationship between performance expectations and pMFC activity, in the absence of differing reinforcement value for errors, is consistent with the idea that error-related pMFC activity reflects the extent to which an outcome is "worse than expected."

  5. Sleep Quality Prediction From Wearable Data Using Deep Learning.

    Science.gov (United States)

    Sathyanarayana, Aarti; Joty, Shafiq; Fernandez-Luque, Luis; Ofli, Ferda; Srivastava, Jaideep; Elmagarmid, Ahmed; Arora, Teresa; Taheri, Shahrad

    2016-11-04

    The importance of sleep is paramount to health. Insufficient sleep can reduce physical, emotional, and mental well-being and can lead to a multitude of health complications among people with chronic conditions. Physical activity and sleep are highly interrelated health behaviors. Our physical activity during the day (ie, awake time) influences our quality of sleep, and vice versa. The current popularity of wearables for tracking physical activity and sleep, including actigraphy devices, can foster the development of new advanced data analytics. This can help to develop new electronic health (eHealth) applications and provide more insights into sleep science. The objective of this study was to evaluate the feasibility of predicting sleep quality (ie, poor or adequate sleep efficiency) given the physical activity wearable data during awake time. In this study, we focused on predicting good or poor sleep efficiency as an indicator of sleep quality. Actigraphy sensors are wearable medical devices used to study sleep and physical activity patterns. The dataset used in our experiments contained the complete actigraphy data from a subset of 92 adolescents over 1 full week. Physical activity data during awake time was used to create predictive models for sleep quality, in particular, poor or good sleep efficiency. The physical activity data from sleep time was used for the evaluation. We compared the predictive performance of traditional logistic regression with more advanced deep learning methods: multilayer perceptron (MLP), convolutional neural network (CNN), simple Elman-type recurrent neural network (RNN), long short-term memory (LSTM-RNN), and a time-batched version of LSTM-RNN (TB-LSTM). Deep learning models were able to predict the quality of sleep (ie, poor or good sleep efficiency) based on wearable data from awake periods. More specifically, the deep learning methods performed better than traditional logistic regression. “CNN had the highest specificity and

  6. A Neo-Piagetian Theory of Constructive Operators: Applications to Perceptual-Motor Development and Learning.

    Science.gov (United States)

    Todor, John I.

    The author presents an overview of Pascual-Leone's Theory of Constructive Operators, a process-structural theory based upon Piagetian constructs which has evolved to both explain and predict the temporal unfolding of behavior. An application is made of the theory to the demands of a discrete motor task and prediction of (a) the minimal age…

  7. 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...

  8. Prediction of sodium critical heat flux (CHF) in annular channel using grey systems theory

    International Nuclear Information System (INIS)

    Zhou Tao; Su Guanghui; Zhang Weizhong; Qiu Suizheng; Jia Dounan

    2001-01-01

    Using grey systems theory and experimental data obtained from sodium boiling test loop in China, the grey mutual analysis of some parameters influencing sodium CHF is carried out, and the CHF values are predicted by GM(1, 1) model. The GM(1, h) model is established for CHF prediction, and the predicted CHF values are good agreement with the experimental data

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

    Science.gov (United States)

    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.

  10. Incorporating rapid neocortical learning of new schema-consistent information into complementary learning systems theory.

    Science.gov (United States)

    McClelland, James L

    2013-11-01

    The complementary learning systems theory of the roles of hippocampus and neocortex (McClelland, McNaughton, & O'Reilly, 1995) holds that the rapid integration of arbitrary new information into neocortical structures is avoided to prevent catastrophic interference with structured knowledge representations stored in synaptic connections among neocortical neurons. Recent studies (Tse et al., 2007, 2011) showed that neocortical circuits can rapidly acquire new associations that are consistent with prior knowledge. The findings challenge the complementary learning systems theory as previously presented. However, new simulations extending those reported in McClelland et al. (1995) show that new information that is consistent with knowledge previously acquired by a putatively cortexlike artificial neural network can be learned rapidly and without interfering with existing knowledge; it is when inconsistent new knowledge is acquired quickly that catastrophic interference ensues. Several important features of the findings of Tse et al. (2007, 2011) are captured in these simulations, indicating that the neural network model used in McClelland et al. has characteristics in common with neocortical learning mechanisms. An additional simulation generalizes beyond the network model previously used, showing how the rate of change of cortical connections can depend on prior knowledge in an arguably more biologically plausible network architecture. In sum, the findings of Tse et al. are fully consistent with the idea that hippocampus and neocortex are complementary learning systems. Taken together, these findings and the simulations reported here advance our knowledge by bringing out the role of consistency of new experience with existing knowledge and demonstrating that the rate of change of connections in real and artificial neural networks can be strongly prior-knowledge dependent. PsycINFO Database Record (c) 2013 APA, all rights reserved.

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

    Science.gov (United States)

    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…

  12. Social Learning Theory Parenting Intervention Promotes Attachment-Based Caregiving in Young Children: Randomized Clinical Trial

    Science.gov (United States)

    O'Connor, Thomas G.; Matias, Carla; Futh, Annabel; Tantam, Grace; Scott, Stephen

    2013-01-01

    Parenting programs for school-aged children are typically based on behavioral principles as applied in social learning theory. It is not yet clear if the benefits of these interventions extend beyond aspects of the parent-child relationship quality conceptualized by social learning theory. The current study examined the extent to which a social…

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

    Science.gov (United States)

    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)

  14. 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…

  15. 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…

  16. Instructional Theory for Using a Class Wiki to Support Collaborative Learning in Higher Education

    Science.gov (United States)

    Lin, Chun-Yi

    2013-01-01

    The purpose of this study was to develop an instructional theory for using a class wiki to support collaborative learning in higher education. Although wikis have been identified in theory as one of the most powerful emerging technologies to support collaborative learning, challenges have been revealed in a number of studies regarding student…

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

    Science.gov (United States)

    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.)…

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

    Science.gov (United States)

    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…

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

    Science.gov (United States)

    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…

  20. 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

  1. An Information Theory Account of Preference Prediction Accuracy

    NARCIS (Netherlands)

    Pollmann, Monique; Scheibehenne, Benjamin

    2015-01-01

    Knowledge about other people's preferences is essential for successful social interactions, but what exactly are the driving factors that determine how well we can predict the likes and dislikes of people around us? To investigate the accuracy of couples’ preference predictions we outline and

  2. 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

  3. Machine Learning Techniques for Prediction of Early Childhood Obesity.

    Science.gov (United States)

    Dugan, T M; Mukhopadhyay, S; Carroll, A; Downs, S

    2015-01-01

    This paper aims to predict childhood obesity after age two, using only data collected prior to the second birthday by a clinical decision support system called CHICA. Analyses of six different machine learning methods: RandomTree, RandomForest, J48, ID3, Naïve Bayes, and Bayes trained on CHICA data show that an accurate, sensitive model can be created. Of the methods analyzed, the ID3 model trained on the CHICA dataset proved the best overall performance with accuracy of 85% and sensitivity of 89%. Additionally, the ID3 model had a positive predictive value of 84% and a negative predictive value of 88%. The structure of the tree also gives insight into the strongest predictors of future obesity in children. Many of the strongest predictors seen in the ID3 modeling of the CHICA dataset have been independently validated in the literature as correlated with obesity, thereby supporting the validity of the model. This study demonstrated that data from a production clinical decision support system can be used to build an accurate machine learning model to predict obesity in children after age two.

  4. Machine learning applied to the prediction of citrus production

    Energy Technology Data Exchange (ETDEWEB)

    Díaz, I.; Mazza, S.M.; Combarro, E.F.; Giménez, L.I.; Gaiad, J.E.

    2017-07-01

    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 analysed. Graphic and analytical descriptive statistics, correlation coefficients, principal component analysis and Biplot were performed. Production was predicted via M5-Prime, a model regression tree constructor which produces a classification based on piecewise linear functions. For all the species studied, the most informative variable was the trees' age; in mandarin and orange orchards, age was followed by between and within row distances; irrigation also affected mandarin production. Also, the performance of M5-Prime in the prediction of production is adequate, as shown when measured with correlation coefficients (~0.8) and relative mean absolute error (~0.1). These results show that M5-Prime is an appropriate method to classify citrus orchards according to production and, in addition, it allows for identifying the most informative variables affecting production by tree.

  5. Machine learning applied to the prediction of citrus production

    International Nuclear Information System (INIS)

    Díaz, I.; Mazza, S.M.; Combarro, E.F.; Giménez, L.I.; Gaiad, J.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 analysed. Graphic and analytical descriptive statistics, correlation coefficients, principal component analysis and Biplot were performed. Production was predicted via M5-Prime, a model regression tree constructor which produces a classification based on piecewise linear functions. For all the species studied, the most informative variable was the trees' age; in mandarin and orange orchards, age was followed by between and within row distances; irrigation also affected mandarin production. Also, the performance of M5-Prime in the prediction of production is adequate, as shown when measured with correlation coefficients (~0.8) and relative mean absolute error (~0.1). These results show that M5-Prime is an appropriate method to classify citrus orchards according to production and, in addition, it allows for identifying the most informative variables affecting production by tree.

  6. Machine learning applied to the prediction of citrus production

    Directory of Open Access Journals (Sweden)

    Irene Díaz

    2017-07-01

    Full Text Available 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 analysed. Graphic and analytical descriptive statistics, correlation coefficients, principal component analysis and Biplot were performed. Production was predicted via M5-Prime, a model regression tree constructor which produces a classification based on piecewise linear functions. For all the species studied, the most informative variable was the trees’ age; in mandarin and orange orchards, age was followed by between and within row distances; irrigation also affected mandarin production. Also, the performance of M5-Prime in the prediction of production is adequate, as shown when measured with correlation coefficients (~0.8 and relative mean absolute error (~0.1. These results show that M5-Prime is an appropriate method to classify citrus orchards according to production and, in addition, it allows for identifying the most informative variables affecting production by tree.

  7. 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

  8. eLearning--Theories, Design, Software and Applications

    Science.gov (United States)

    Ghislandi, Patrizia, Ed.

    2012-01-01

    Chapters in this book include: (1) New e-Learning Environments: e-Merging Networks in the Relational Society (Blanca C. Garcia); (2) Knowledge Building in E-Learning (Xinyu Zhang and Lu Yuhao); (3) E-Learning and Desired Learning Outcomes (Ralph Palliam); (4) Innovative E-Learning Solutions and Environments for Small and Medium Sized Companies…

  9. 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.

  10. Prediction of Student Dropout in E-Learning Program Through the Use of Machine Learning Method

    OpenAIRE

    Mingjie Tan; Peiji Shao

    2015-01-01

    The high rate of dropout is a serious problem in E-learning program. Thus it has received extensive concern from the education administrators and researchers. Predicting the potential dropout students is a workable solution to prevent dropout. Based on the analysis of related literature, this study selected student’s personal characteristic and academic performance as input attributions. Prediction models were developed using Artificial Neural Network (ANN), Decision Tree (DT) and Bayesian Ne...

  11. Learning and Prediction of Slip from Visual Information

    Science.gov (United States)

    Angelova, Anelia; Matthies, Larry; Helmick, Daniel; Perona, Pietro

    2007-01-01

    This paper presents an approach for slip prediction from a distance for wheeled ground robots using visual information as input. Large amounts of slippage which can occur on certain surfaces, such as sandy slopes, will negatively affect rover mobility. Therefore, obtaining information about slip before entering such terrain can be very useful for better planning and avoiding these areas. To address this problem, terrain appearance and geometry information about map cells are correlated to the slip measured by the rover while traversing each cell. This relationship is learned from previous experience, so slip can be predicted remotely from visual information only. The proposed method consists of terrain type recognition and nonlinear regression modeling. The method has been implemented and tested offline on several off-road terrains including: soil, sand, gravel, and woodchips. The final slip prediction error is about 20%. The system is intended for improved navigation on steep slopes and rough terrain for Mars rovers.

  12. Prediction on corrosion rate of pipe in nuclear power system based on optimized grey theory

    International Nuclear Information System (INIS)

    Chen Yonghong; Zhang Dafa; Chen Dengke; Jiang Wei

    2007-01-01

    For the prediction of corrosion rate of pipe in nuclear power system, the pre- diction error from the grey theory is greater, so a new method, optimized grey theory was presented in the paper. A comparison among predicted results from present and other methods was carried out, and it is seem that optimized grey theory is correct and effective for the prediction of corrosion rate of pipe in nuclear power system, and it provides a fundamental basis for the maintenance of pipe in nuclear power system. (authors)

  13. Developing a Model of Theory-to-Practice-to-Theory in Student Affairs: An Extended Case Analysis of Theories of Student Learning and Development

    Science.gov (United States)

    Kimball, Ezekiel W.

    2012-01-01

    Recent literature suggests a problematic connection between theory and practice in higher education scholarship generally and the study of student learning and development specifically (e.g. Bensimon, 2007; Kezar, 2000; Love, 2012). Much of this disconnect stems from a lack of differentiation between various types of theory used in student affairs…

  14. 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…

  15. Prediction of skin sensitization potency using machine learning approaches.

    Science.gov (United States)

    Zang, Qingda; Paris, Michael; Lehmann, David M; Bell, Shannon; Kleinstreuer, Nicole; Allen, David; Matheson, Joanna; Jacobs, Abigail; Casey, Warren; Strickland, Judy

    2017-07-01

    The replacement of animal use in testing for regulatory classification of skin sensitizers is a priority for US federal agencies that use data from such testing. Machine learning models that classify substances as sensitizers or non-sensitizers without using animal data have been developed and evaluated. Because some regulatory agencies require that sensitizers be further classified into potency categories, we developed statistical models to predict skin sensitization potency for murine local lymph node assay (LLNA) and human outcomes. Input variables for our models included six physicochemical properties and data from three non-animal test methods: direct peptide reactivity assay; human cell line activation test; and KeratinoSens™ assay. Models were built to predict three potency categories using four machine learning approaches and were validated using external test sets and leave-one-out cross-validation. A one-tiered strategy modeled all three categories of response together while a two-tiered strategy modeled sensitizer/non-sensitizer responses and then classified the sensitizers as strong or weak sensitizers. The two-tiered model using the support vector machine with all assay and physicochemical data inputs provided the best performance, yielding accuracy of 88% for prediction of LLNA outcomes (120 substances) and 81% for prediction of human test outcomes (87 substances). The best one-tiered model predicted LLNA outcomes with 78% accuracy and human outcomes with 75% accuracy. By comparison, the LLNA predicts human potency categories with 69% accuracy (60 of 87 substances correctly categorized). These results suggest that computational models using non-animal methods may provide valuable information for assessing skin sensitization potency. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

  16. Machine Learning and Neurosurgical Outcome Prediction: A Systematic Review.

    Science.gov (United States)

    Senders, Joeky T; Staples, Patrick C; Karhade, Aditya V; Zaki, Mark M; Gormley, William B; Broekman, Marike L D; Smith, Timothy R; Arnaout, Omar

    2018-01-01

    Accurate measurement of surgical outcomes is highly desirable to optimize surgical decision-making. An important element of surgical decision making is identification of the patient cohort that will benefit from surgery before the intervention. Machine learning (ML) enables computers to learn from previous data to make accurate predictions on new data. In this systematic review, we evaluate the potential of ML for neurosurgical outcome prediction. A systematic search in the PubMed and Embase databases was performed to identify all potential relevant studies up to January 1, 2017. Thirty studies were identified that evaluated ML algorithms used as prediction models for survival, recurrence, symptom improvement, and adverse events in patients undergoing surgery for epilepsy, brain tumor, spinal lesions, neurovascular disease, movement disorders, traumatic brain injury, and hydrocephalus. Depending on the specific prediction task evaluated and the type of input features included, ML models predicted outcomes after neurosurgery with a median accuracy and area under the receiver operating curve of 94.5% and 0.83, respectively. Compared with logistic regression, ML models performed significantly better and showed a median absolute improvement in accuracy and area under the receiver operating curve of 15% and 0.06, respectively. Some studies also demonstrated a better performance in ML models compared with established prognostic indices and clinical experts. In the research setting, ML has been studied extensively, demonstrating an excellent performance in outcome prediction for a wide range of neurosurgical conditions. However, future studies should investigate how ML can be implemented as a practical tool supporting neurosurgical care. Copyright © 2017 Elsevier Inc. All rights reserved.

  17. 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.

  18. The theory of planned behaviour as a framework for predicting ...

    African Journals Online (AJOL)

    With data comparing favourably to those obtained in the international literature, these studies indicate that the TPB can be used to study sexual risk intentions and behaviour in sub-Saharan African youth, and question arguments against the theory's use in non-Western settings. Journal of Child & Adolescent Mental Health ...

  19. Observant, Nonaggressive Temperament Predicts Theory-of-Mind Development

    Science.gov (United States)

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

    2011-01-01

    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…

  20. 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

  1. Processes of self-regulated learning in music theory in elementary music schools in Slovenia

    OpenAIRE

    Peklaj, Cirila; Smolej-Fritz, Barbara

    2015-01-01

    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 between these processes. A total of 457 fifth- and sixth- grade students from 10 different elementary music schools in Slovenia participated in the...

  2. 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.

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

    Science.gov (United States)

    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

  4. 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

  5. Reminder cues modulate the renewal effect in human predictive learning

    Directory of Open Access Journals (Sweden)

    Javier Bustamante

    2016-12-01

    Full Text Available Associative learning refers to our ability to learn about regularities in our environment. When a stimulus is repeatedly followed by a specific outcome, we learn to expect the outcome in the presence of the stimulus. We are also able to modify established expectations in the face of disconfirming information (the stimulus is no longer followed by the outcome. Both the change of environmental regularities and the related processes of adaptation are referred to as extinction. However, extinction does not erase the initially acquired expectations. For instance, following successful extinction, the initially learned expectations can recover when there is a context change – a phenomenon called the renewal effect, which is considered as a model for relapse after exposure therapy. Renewal was found to be modulated by reminder cues of acquisition and extinction. However, the mechanisms underlying the effectiveness of reminder cues are not well understood. The aim of the present study was to investigate the impact of reminder cues on renewal in the field of human predictive learning. Experiment I demonstrated that renewal in human predictive learning is modulated by cues related to acquisition or extinction. Initially, participants received pairings of a stimulus and an outcome in one context. These stimulus-outcome pairings were preceded by presentations of a reminder cue (acquisition cue. Then, participants received extinction in a different context in which presentations of the stimulus were no longer followed by the outcome. These extinction trials were preceded by a second reminder cue (extinction cue. During a final phase conducted in a third context, participants showed stronger expectations of the outcome in the presence of the stimulus when testing was accompanied by the acquisition cue compared to the extinction cue. Experiment II tested an explanation of the reminder cue effect in terms of simple cue-outcome associations. Therefore

  6. 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…

  7. Combining theories to reach multi-faceted insights into learning opportunities in doctoral supervision

    DEFF Research Database (Denmark)

    Kobayashi, Sofie; Rump, Camilla Østerberg

    The aim of this paper is to illustrate how theories can be combined to explore opportunities for learning in doctoral supervision. While our earlier research into learning dynamics in doctoral supervision in life science research (Kobayashi, 2014) has focused on illustrating learning opportunitie...

  8. Constructivism and Reflectivism as the Logical Counterparts in TESOL: Learning Theory versus Teaching Methodology

    Science.gov (United States)

    al Mahmud, Abdullah

    2013-01-01

    The gist of the entire constructivist learning theory is that learners are self-builders of their learning that occurs through a mental process in a social context or communication setting, and teachers as facilitators generate learning by creating the expected environment and/or utilizing the process. This article theoretically proves…

  9. Connected and Ubiquitous: A Discussion of Two Theories That Impact Future Learning Applications

    Science.gov (United States)

    Bair, Richard A.; Stafford, Timothy

    2016-01-01

    Mobile media break down traditional barriers that have defined learning in schools because they enable constant, personalized access to media. This information-rich environment could dramatically expand learning opportunities. This article identifies and discusses two instructional design theories for mobile learning including the major…

  10. Pedagogical Distance: Explaining Misalignment in Student-Driven Online Learning Activities Using Activity Theory

    Science.gov (United States)

    Westberry, Nicola; Franken, Margaret

    2015-01-01

    This paper provides an Activity Theory analysis of two online student-driven interactive learning activities to interrogate assumptions that such groups can effectively learn in the absence of the teacher. Such an analysis conceptualises learning tasks as constructed objects that drive pedagogical activity. The analysis shows a disconnect between…

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

    Science.gov (United States)

    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…

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

    Science.gov (United States)

    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…

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

    Science.gov (United States)

    Yuan, Yu; McKelvey, Bill

    2004-01-01

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

  14. Evaluation of Deep Learning Models for Predicting CO2 Flux

    Science.gov (United States)

    Halem, M.; Nguyen, P.; Frankel, D.

    2017-12-01

    Artificial neural networks have been employed to calculate surface flux measurements from station data because they are able to fit highly nonlinear relations between input and output variables without knowing the detail relationships between the variables. However, the accuracy in performing neural net estimates of CO2 flux from observations of CO2 and other atmospheric variables is influenced by the architecture of the neural model, the availability, and complexity of interactions between physical variables such as wind, temperature, and indirect variables like latent heat, and sensible heat, etc. We evaluate two deep learning models, feed forward and recurrent neural network models to learn how they each respond to the physical measurements, time dependency of the measurements of CO2 concentration, humidity, pressure, temperature, wind speed etc. for predicting the CO2 flux. In this paper, we focus on a) building neural network models for estimating CO2 flux based on DOE data from tower Atmospheric Radiation Measurement data; b) evaluating the impact of choosing the surface variables and model hyper-parameters on the accuracy and predictions of surface flux; c) assessing the applicability of the neural network models on estimate CO2 flux by using OCO-2 satellite data; d) studying the efficiency of using GPU-acceleration for neural network performance using IBM Power AI deep learning software and packages on IBM Minsky system.

  15. Group processes in medical education: learning from social identity theory.

    Science.gov (United States)

    Burford, Bryan

    2012-02-01

    The clinical workplace in which doctors learn involves many social groups, including representatives of different professions, clinical specialties and workplace teams. This paper suggests that medical education research does not currently take full account of the effects of group membership, and describes a theoretical approach from social psychology, the social identity approach, which allows those effects to be explored. The social identity approach has a long history in social psychology and provides an integrated account of group processes, from the adoption of group identity through a process of self-categorisation, to the biases and conflicts between groups. This paper outlines key elements of this theoretical approach and illustrates their relevance to medical education. The relevance of the social identity approach is illustrated with reference to a number of areas of medical education. The paper shows how research questions in medical education may be usefully reframed in terms of social identity in ways that allow a deeper exploration of the psychological processes involved. Professional identity and professionalism may be viewed in terms of self-categorisation rather than simply attainment; the salience of different identities may be considered as influences on teamwork and interprofessional learning, and issues in communication and assessment may be considered in terms of intergroup biases. Social identity theory provides a powerful framework with which to consider many areas of medical education. It allows disparate influences on, and consequences of, group membership to be considered as part of an integrated system, and allows assumptions, such as about the nature of professional identity and interprofessional tensions, to be made explicit in the design of research studies. This power to question assumptions and develop deeper and more meaningful research questions may be increasingly relevant as the nature and role of the medical profession change

  16. 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.

  17. 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.

  18. Predictive modeling of coupled multi-physics systems: I. Theory

    International Nuclear Information System (INIS)

    Cacuci, Dan Gabriel

    2014-01-01

    Highlights: • We developed “predictive modeling of coupled multi-physics systems (PMCMPS)”. • PMCMPS reduces predicted uncertainties in predicted model responses and parameters. • PMCMPS treats efficiently very large coupled systems. - Abstract: This work presents an innovative mathematical methodology for “predictive modeling of coupled multi-physics systems (PMCMPS).” This methodology takes into account fully the coupling terms between the systems but requires only the computational resources that would be needed to perform predictive modeling on each system separately. The PMCMPS methodology uses the maximum entropy principle to construct an optimal approximation of the unknown a priori distribution based on a priori known mean values and uncertainties characterizing the parameters and responses for both multi-physics models. This “maximum entropy”-approximate a priori distribution is combined, using Bayes’ theorem, with the “likelihood” provided by the multi-physics simulation models. Subsequently, the posterior distribution thus obtained is evaluated using the saddle-point method to obtain analytical expressions for the optimally predicted values for the multi-physics models parameters and responses along with corresponding reduced uncertainties. Noteworthy, the predictive modeling methodology for the coupled systems is constructed such that the systems can be considered sequentially rather than simultaneously, while preserving exactly the same results as if the systems were treated simultaneously. Consequently, very large coupled systems, which could perhaps exceed available computational resources if treated simultaneously, can be treated with the PMCMPS methodology presented in this work sequentially and without any loss of generality or information, requiring just the resources that would be needed if the systems were treated sequentially

  19. Attachment Theory and Theory of Planned Behavior: An Integrative Model Predicting Underage Drinking

    Science.gov (United States)

    Lac, Andrew; Crano, William D.; Berger, Dale E.; Alvaro, Eusebio M.

    2013-01-01

    Research indicates that peer and maternal bonds play important but sometimes contrasting roles in the outcomes of children. Less is known about attachment bonds to these 2 reference groups in young adults. Using a sample of 351 participants (18 to 20 years of age), the research integrated two theoretical traditions: attachment theory and theory of…

  20. Maximal locality and predictive power in higher-dimensional, compactified field theories

    International Nuclear Information System (INIS)

    Kubo, Jisuke; Nunami, Masanori

    2004-01-01

    To realize maximal locality in a trivial field theory, we maximize the ultraviolet cutoff of the theory by fine tuning the infrared values of the parameters. This optimization procedure is applied to the scalar theory in D + 1 dimensional (D ≥ 4) with one extra dimension compactified on a circle of radius R. The optimized, infrared values of the parameters are then compared with the corresponding ones of the uncompactified theory in D dimensions, which is assumed to be the low-energy effective theory. We find that these values approximately agree with each other as long as R -1 > approx sM is satisfied, where s ≅ 10, 50, 50, 100 for D = 4,5,6,7, and M is a typical scale of the D-dimensional theory. This result supports the previously made claim that the maximization of the ultraviolet cutoff in a nonrenormalizable field theory can give the theory more predictive power. (author)