Examination of the Teaching Styles of Nursing Professional Development Specialists, Part II: Correlational Study on Teaching Styles and Use of Adult Learning Theory.

PubMed

Curran, Mary K

2014-07-16

This article, the second in a two-part series, details a correlational study that examined the effects of four variables (graduate degrees in nursing education, professional development training in adult learning theory, nursing professional development [NPD] certification, and NPD specialist experience) on the use of adult learning theory to guide curriculum development. Using the Principles of Adult Learning Scale, 114 NPD specialists tested the hypothesis that NPD specialists with graduate degrees in nursing education, professional development training in adult learning theory, NPD certification, and NPD experience would use higher levels of adult learning theory in their teaching practices to guide curriculum development than those without these attributes. This hypothesis was rejected as regression analysis revealed only one statistically significant predictor variable, NPD certification, influenced the use of adult learning theory. In addition, analysis revealed NPD specialists tended to support a teacher-centered rather than a learner-centered teaching style, indicating NPD educators are not using adult learning theory to guide teaching practices and curriculum development. J Contin Educ Nurs. 2014;45(8):xxx-xxx. Copyright 2014, SLACK Incorporated.

Teaching Real-World Applications of Business Statistics Using Communication to Scaffold Learning

ERIC Educational Resources Information Center

Green, Gareth P.; Jones, Stacey; Bean, John C.

2015-01-01

Our assessment research suggests that quantitative business courses that rely primarily on algorithmic problem solving may not produce the deep learning required for addressing real-world business problems. This article illustrates a strategy, supported by recent learning theory, for promoting deep learning by moving students gradually from…

An Application of Extreme Value Theory to Learning Analytics: Predicting Collaboration Outcome from Eye-Tracking Data

ERIC Educational Resources Information Center

Sharma, Kshitij; Chavez-Demoulin, Valérie; Dillenbourg, Pierre

2017-01-01

The statistics used in education research are based on central trends such as the mean or standard deviation, discarding outliers. This paper adopts another viewpoint that has emerged in statistics, called extreme value theory (EVT). EVT claims that the bulk of normal distribution is comprised mainly of uninteresting variations while the most…

Theory-Based Causal Induction

ERIC Educational Resources Information Center

Griffiths, Thomas L.; Tenenbaum, Joshua B.

2009-01-01

Inducing causal relationships from observations is a classic problem in scientific inference, statistics, and machine learning. It is also a central part of human learning, and a task that people perform remarkably well given its notorious difficulties. People can learn causal structure in various settings, from diverse forms of data: observations…

Comparing associative, statistical, and inferential reasoning accounts of human contingency learning.

PubMed

Pineño, Oskar; Miller, Ralph R

2007-03-01

For more than two decades, researchers have contrasted the relative merits of associative and statistical theories as accounts of human contingency learning. This debate, still far from resolution, has led to further refinement of models within each family of theories. More recently, a third theoretical view has joined the debate: the inferential reasoning account. The explanations of these three accounts differ critically in many aspects, such as level of analysis and their emphasis on different steps within the information-processing sequence. Also, each account has important advantages (as well as critical flaws) and emphasizes experimental evidence that poses problems to the others. Some hybrid models of human contingency learning have attempted to reconcile certain features of these accounts, thereby benefiting from some of the unique advantages of different families of accounts. A comparison of these families of accounts will help us appreciate the challenges that research on human contingency learning will face over the coming years.

Probabilistic models in human sensorimotor control

PubMed Central

Wolpert, Daniel M.

2009-01-01

Sensory and motor uncertainty form a fundamental constraint on human sensorimotor control. Bayesian decision theory (BDT) has emerged as a unifying framework to understand how the central nervous system performs optimal estimation and control in the face of such uncertainty. BDT has two components: Bayesian statistics and decision theory. Here we review Bayesian statistics and show how it applies to estimating the state of the world and our own body. Recent results suggest that when learning novel tasks we are able to learn the statistical properties of both the world and our own sensory apparatus so as to perform estimation using Bayesian statistics. We review studies which suggest that humans can combine multiple sources of information to form maximum likelihood estimates, can incorporate prior beliefs about possible states of the world so as to generate maximum a posteriori estimates and can use Kalman filter-based processes to estimate time-varying states. Finally, we review Bayesian decision theory in motor control and how the central nervous system processes errors to determine loss functions and optimal actions. We review results that suggest we plan movements based on statistics of our actions that result from signal-dependent noise on our motor outputs. Taken together these studies provide a statistical framework for how the motor system performs in the presence of uncertainty. PMID:17628731

Statistical learning of novel graphotactic constraints in children and adults.

PubMed

Samara, Anna; Caravolas, Markéta

2014-05-01

The current study explored statistical learning processes in the acquisition of orthographic knowledge in school-aged children and skilled adults. Learning of novel graphotactic constraints on the position and context of letter distributions was induced by means of a two-phase learning task adapted from Onishi, Chambers, and Fisher (Cognition, 83 (2002) B13-B23). Following incidental exposure to pattern-embedding stimuli in Phase 1, participants' learning generalization was tested in Phase 2 with legality judgments about novel conforming/nonconforming word-like strings. Test phase performance was above chance, suggesting that both types of constraints were reliably learned even after relatively brief exposure. As hypothesized, signal detection theory d' analyses confirmed that learning permissible letter positions (d'=0.97) was easier than permissible neighboring letter contexts (d'=0.19). Adults were more accurate than children in all but a strict analysis of the contextual constraints condition. Consistent with the statistical learning perspective in literacy, our results suggest that statistical learning mechanisms contribute to children's and adults' acquisition of knowledge about graphotactic constraints similar to those existing in their orthography. Copyright © 2013 Elsevier Inc. All rights reserved.

Redefining "Learning" in Statistical Learning: What Does an Online Measure Reveal About the Assimilation of Visual Regularities?

PubMed

Siegelman, Noam; Bogaerts, Louisa; Kronenfeld, Ofer; Frost, Ram

2017-10-07

From a theoretical perspective, most discussions of statistical learning (SL) have focused on the possible "statistical" properties that are the object of learning. Much less attention has been given to defining what "learning" is in the context of "statistical learning." One major difficulty is that SL research has been monitoring participants' performance in laboratory settings with a strikingly narrow set of tasks, where learning is typically assessed offline, through a set of two-alternative-forced-choice questions, which follow a brief visual or auditory familiarization stream. Is that all there is to characterizing SL abilities? Here we adopt a novel perspective for investigating the processing of regularities in the visual modality. By tracking online performance in a self-paced SL paradigm, we focus on the trajectory of learning. In a set of three experiments we show that this paradigm provides a reliable and valid signature of SL performance, and it offers important insights for understanding how statistical regularities are perceived and assimilated in the visual modality. This demonstrates the promise of integrating different operational measures to our theory of SL. © 2017 Cognitive Science Society, Inc.

Toward User Interfaces and Data Visualization Criteria for Learning Design of Digital Textbooks

ERIC Educational Resources Information Center

Railean, Elena

2014-01-01

User interface and data visualisation criteria are central issues in digital textbooks design. However, when applying mathematical modelling of learning process to the analysis of the possible solutions, it could be observed that results differ. Mathematical learning views cognition in on the base on statistics and probability theory, graph…

Cognitive biases, linguistic universals, and constraint-based grammar learning.

PubMed

Culbertson, Jennifer; Smolensky, Paul; Wilson, Colin

2013-07-01

According to classical arguments, language learning is both facilitated and constrained by cognitive biases. These biases are reflected in linguistic typology-the distribution of linguistic patterns across the world's languages-and can be probed with artificial grammar experiments on child and adult learners. Beginning with a widely successful approach to typology (Optimality Theory), and adapting techniques from computational approaches to statistical learning, we develop a Bayesian model of cognitive biases and show that it accounts for the detailed pattern of results of artificial grammar experiments on noun-phrase word order (Culbertson, Smolensky, & Legendre, 2012). Our proposal has several novel properties that distinguish it from prior work in the domains of linguistic theory, computational cognitive science, and machine learning. This study illustrates how ideas from these domains can be synthesized into a model of language learning in which biases range in strength from hard (absolute) to soft (statistical), and in which language-specific and domain-general biases combine to account for data from the macro-level scale of typological distribution to the micro-level scale of learning by individuals. Copyright © 2013 Cognitive Science Society, Inc.

An Alternative Approach to Analyze Ipsative Data. Revisiting Experiential Learning Theory.

PubMed

Batista-Foguet, Joan M; Ferrer-Rosell, Berta; Serlavós, Ricard; Coenders, Germà; Boyatzis, Richard E

2015-01-01

The ritualistic use of statistical models regardless of the type of data actually available is a common practice across disciplines which we dare to call type zero error. Statistical models involve a series of assumptions whose existence is often neglected altogether, this is specially the case with ipsative data. This paper illustrates the consequences of this ritualistic practice within Kolb's Experiential Learning Theory (ELT) operationalized through its Learning Style Inventory (KLSI). We show how using a well-known methodology in other disciplines-compositional data analysis (CODA) and log ratio transformations-KLSI data can be properly analyzed. In addition, the method has theoretical implications: a third dimension of the KLSI is unveiled providing room for future research. This third dimension describes an individual's relative preference for learning by prehension rather than by transformation. Using a sample of international MBA students, we relate this dimension with another self-assessment instrument, the Philosophical Orientation Questionnaire (POQ), and with an observer-assessed instrument, the Emotional and Social Competency Inventory (ESCI-U). Both show plausible statistical relationships. An intellectual operating philosophy (IOP) is linked to a preference for prehension, whereas a pragmatic operating philosophy (POP) is linked to transformation. Self-management and social awareness competencies are linked to a learning preference for transforming knowledge, whereas relationship management and cognitive competencies are more related to approaching learning by prehension.

An Alternative Approach to Analyze Ipsative Data. Revisiting Experiential Learning Theory

PubMed Central

Batista-Foguet, Joan M.; Ferrer-Rosell, Berta; Serlavós, Ricard; Coenders, Germà; Boyatzis, Richard E.

2015-01-01

The ritualistic use of statistical models regardless of the type of data actually available is a common practice across disciplines which we dare to call type zero error. Statistical models involve a series of assumptions whose existence is often neglected altogether, this is specially the case with ipsative data. This paper illustrates the consequences of this ritualistic practice within Kolb's Experiential Learning Theory (ELT) operationalized through its Learning Style Inventory (KLSI). We show how using a well-known methodology in other disciplines—compositional data analysis (CODA) and log ratio transformations—KLSI data can be properly analyzed. In addition, the method has theoretical implications: a third dimension of the KLSI is unveiled providing room for future research. This third dimension describes an individual's relative preference for learning by prehension rather than by transformation. Using a sample of international MBA students, we relate this dimension with another self-assessment instrument, the Philosophical Orientation Questionnaire (POQ), and with an observer-assessed instrument, the Emotional and Social Competency Inventory (ESCI-U). Both show plausible statistical relationships. An intellectual operating philosophy (IOP) is linked to a preference for prehension, whereas a pragmatic operating philosophy (POP) is linked to transformation. Self-management and social awareness competencies are linked to a learning preference for transforming knowledge, whereas relationship management and cognitive competencies are more related to approaching learning by prehension. PMID:26617561

Weather to Make a Decision

ERIC Educational Resources Information Center

Hoyle, Julie E.; Mjelde, James W.; Litzenberg, Kerry K.

2006-01-01

DECIDE is a teacher-friendly, integrated approach designed to stimulate learning by allowing students to make decisions about situations they face in their lives while using scientific weather principles. This learning unit integrates weather science, decision theory, mathematics, statistics, geography, and reading in a context of decision…

Comparing associative, statistical, and inferential reasoning accounts of human contingency learning

PubMed Central

Pineño, Oskar; Miller, Ralph R.

2007-01-01

For more than two decades, researchers have contrasted the relative merits of associative and statistical theories as accounts of human contingency learning. This debate, still far from resolution, has led to further refinement of models within each family of theories. More recently, a third theoretical view has joined the debate: the inferential reasoning account. The explanations of these three accounts differ critically in many aspects, such as level of analysis and their emphasis on different steps within the information-processing sequence. Also, each account has important advantages (as well as critical flaws) and emphasizes experimental evidence that poses problems to the others. Some hybrid models of human contingency learning have attempted to reconcile certain features of these accounts, thereby benefiting from some of the unique advantages of different families of accounts. A comparison of these families of accounts will help us appreciate the challenges that research on human contingency learning will face over the coming years. PMID:17366303

Statistical Learning Theory for High Dimensional Prediction: Application to Criterion-Keyed Scale Development

PubMed Central

Chapman, Benjamin P.; Weiss, Alexander; Duberstein, Paul

2016-01-01

Statistical learning theory (SLT) is the statistical formulation of machine learning theory, a body of analytic methods common in “big data” problems. Regression-based SLT algorithms seek to maximize predictive accuracy for some outcome, given a large pool of potential predictors, without overfitting the sample. Research goals in psychology may sometimes call for high dimensional regression. One example is criterion-keyed scale construction, where a scale with maximal predictive validity must be built from a large item pool. Using this as a working example, we first introduce a core principle of SLT methods: minimization of expected prediction error (EPE). Minimizing EPE is fundamentally different than maximizing the within-sample likelihood, and hinges on building a predictive model of sufficient complexity to predict the outcome well, without undue complexity leading to overfitting. We describe how such models are built and refined via cross-validation. We then illustrate how three common SLT algorithms–Supervised Principal Components, Regularization, and Boosting—can be used to construct a criterion-keyed scale predicting all-cause mortality, using a large personality item pool within a population cohort. Each algorithm illustrates a different approach to minimizing EPE. Finally, we consider broader applications of SLT predictive algorithms, both as supportive analytic tools for conventional methods, and as primary analytic tools in discovery phase research. We conclude that despite their differences from the classic null-hypothesis testing approach—or perhaps because of them–SLT methods may hold value as a statistically rigorous approach to exploratory regression. PMID:27454257

Covering Numbers for Semicontinuous Functions

DTIC Science & Technology

2016-04-29

functions, epi-distance, Attouch-Wets topology, epi-convergence, epi-spline, approximation theory . Date: April 29, 2016 1 Introduction Covering numbers of...classes of functions play central roles in parts of information theory , statistics, and applications such as machine learning; see for example [26...probability theory because there the hypo-distance metrizes weak convergence of distribution functions on IRd, which obviously are usc [22]. Thus, as an

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

ERIC Educational Resources Information Center

Chou, Huey-Wen; Wang, Yu-Fang

1999-01-01

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

Introduction to Statistics. Learning Packages in the Policy Sciences Series, PS-26. Revised Edition.

ERIC Educational Resources Information Center

Policy Studies Associates, Croton-on-Hudson, NY.

The primary objective of this booklet is to introduce students to basic statistical skills that are useful in the analysis of public policy data. A few, selected statistical methods are presented, and theory is not emphasized. Chapter 1 provides instruction for using tables, bar graphs, bar graphs with grouped data, trend lines, pie diagrams,…

Phonetic Diversity, Statistical Learning, and Acquisition of Phonology

ERIC Educational Resources Information Center

Pierrehumbert, Janet B.

2003-01-01

In learning to perceive and produce speech, children master complex language-specific patterns. Daunting language-specific variation is found both in the segmental domain and in the domain of prosody and intonation. This article reviews the challenges posed by results in phonetic typology and sociolinguistics for the theory of language…

Phonetic diversity, statistical learning, and acquisition of phonology.

PubMed

Pierrehumbert, Janet B

2003-01-01

In learning to perceive and produce speech, children master complex language-specific patterns. Daunting language-specific variation is found both in the segmental domain and in the domain of prosody and intonation. This article reviews the challenges posed by results in phonetic typology and sociolinguistics for the theory of language acquisition. It argues that categories are initiated bottom-up from statistical modes in use of the phonetic space, and sketches how exemplar theory can be used to model the updating of categories once they are initiated. It also argues that bottom-up initiation of categories is successful thanks to the perception-production loop operating in the speech community. The behavior of this loop means that the superficial statistical properties of speech available to the infant indirectly reflect the contrastiveness and discriminability of categories in the adult grammar. The article also argues that the developing system is refined using internal feedback from type statistics over the lexicon, once the lexicon is well-developed. The application of type statistics to a system initiated with surface statistics does not cause a fundamental reorganization of the system. Instead, it exploits confluences across levels of representation which characterize human language and make bootstrapping possible.

Integrated Model for E-Learning Acceptance

NASA Astrophysics Data System (ADS)

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

2016-01-01

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

Proceedings for the Annual Symposium and Exhibition on Situational Awareness in the Tactical Air Environment, (2nd), Held at Patuxent River, Maryland, on 3-4 June 1997

DTIC Science & Technology

1997-06-01

made based on a learning mechanism. Traditional statistical regression and neural network approaches offer some utility, but suffer from practical...Columbus, OH. Kraiger, K., Ford, J. K., & Salas, E. (1993). Application of cognitive, skill- based , and affective theories of learning outcomes to new...and Feature Effects 151 Enhanced Spatial State Feedback for Night Vision Goggle Displays 159 Statistical Network Applications of Decision Aiding for

Statistical learning theory for high dimensional prediction: Application to criterion-keyed scale development.

PubMed

Chapman, Benjamin P; Weiss, Alexander; Duberstein, Paul R

2016-12-01

Statistical learning theory (SLT) is the statistical formulation of machine learning theory, a body of analytic methods common in "big data" problems. Regression-based SLT algorithms seek to maximize predictive accuracy for some outcome, given a large pool of potential predictors, without overfitting the sample. Research goals in psychology may sometimes call for high dimensional regression. One example is criterion-keyed scale construction, where a scale with maximal predictive validity must be built from a large item pool. Using this as a working example, we first introduce a core principle of SLT methods: minimization of expected prediction error (EPE). Minimizing EPE is fundamentally different than maximizing the within-sample likelihood, and hinges on building a predictive model of sufficient complexity to predict the outcome well, without undue complexity leading to overfitting. We describe how such models are built and refined via cross-validation. We then illustrate how 3 common SLT algorithms-supervised principal components, regularization, and boosting-can be used to construct a criterion-keyed scale predicting all-cause mortality, using a large personality item pool within a population cohort. Each algorithm illustrates a different approach to minimizing EPE. Finally, we consider broader applications of SLT predictive algorithms, both as supportive analytic tools for conventional methods, and as primary analytic tools in discovery phase research. We conclude that despite their differences from the classic null-hypothesis testing approach-or perhaps because of them-SLT methods may hold value as a statistically rigorous approach to exploratory regression. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Complementary learning systems within the hippocampus: a neural network modelling approach to reconciling episodic memory with statistical learning

PubMed Central

Turk-Browne, Nicholas B.; Botvinick, Matthew M.; Norman, Kenneth A.

2017-01-01

A growing literature suggests that the hippocampus is critical for the rapid extraction of regularities from the environment. Although this fits with the known role of the hippocampus in rapid learning, it seems at odds with the idea that the hippocampus specializes in memorizing individual episodes. In particular, the Complementary Learning Systems theory argues that there is a computational trade-off between learning the specifics of individual experiences and regularities that hold across those experiences. We asked whether it is possible for the hippocampus to handle both statistical learning and memorization of individual episodes. We exposed a neural network model that instantiates known properties of hippocampal projections and subfields to sequences of items with temporal regularities. We found that the monosynaptic pathway—the pathway connecting entorhinal cortex directly to region CA1—was able to support statistical learning, while the trisynaptic pathway—connecting entorhinal cortex to CA1 through dentate gyrus and CA3—learned individual episodes, with apparent representations of regularities resulting from associative reactivation through recurrence. Thus, in paradigms involving rapid learning, the computational trade-off between learning episodes and regularities may be handled by separate anatomical pathways within the hippocampus itself. This article is part of the themed issue ‘New frontiers for statistical learning in the cognitive sciences’. PMID:27872368

Complementary learning systems within the hippocampus: a neural network modelling approach to reconciling episodic memory with statistical learning.

PubMed

Schapiro, Anna C; Turk-Browne, Nicholas B; Botvinick, Matthew M; Norman, Kenneth A

2017-01-05

A growing literature suggests that the hippocampus is critical for the rapid extraction of regularities from the environment. Although this fits with the known role of the hippocampus in rapid learning, it seems at odds with the idea that the hippocampus specializes in memorizing individual episodes. In particular, the Complementary Learning Systems theory argues that there is a computational trade-off between learning the specifics of individual experiences and regularities that hold across those experiences. We asked whether it is possible for the hippocampus to handle both statistical learning and memorization of individual episodes. We exposed a neural network model that instantiates known properties of hippocampal projections and subfields to sequences of items with temporal regularities. We found that the monosynaptic pathway-the pathway connecting entorhinal cortex directly to region CA1-was able to support statistical learning, while the trisynaptic pathway-connecting entorhinal cortex to CA1 through dentate gyrus and CA3-learned individual episodes, with apparent representations of regularities resulting from associative reactivation through recurrence. Thus, in paradigms involving rapid learning, the computational trade-off between learning episodes and regularities may be handled by separate anatomical pathways within the hippocampus itself.This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'. © 2016 The Author(s).

Web-based Learning Environments Guided by Principles of Good Teaching Practice.

ERIC Educational Resources Information Center

Chizmar, John F.; Walbert, Mark S.

1999-01-01

Describes the preparation and execution of a statistics course, an undergraduate econometrics course, and a microeconomic theory course that all utilize Internet technology. Reviews seven principles of teaching practice in order to demonstrate how to enhance the quality of student learning using Web technologies. Includes reactions by Steve Hurd…

Improving the power of an efficacy study of a social and emotional learning program: application of generalizability theory to the measurement of classroom-level outcomes.

PubMed

Mashburn, Andrew J; Downer, Jason T; Rivers, Susan E; Brackett, Marc A; Martinez, Andres

2014-04-01

Social and emotional learning programs are designed to improve the quality of social interactions in schools and classrooms in order to positively affect students' social, emotional, and academic development. The statistical power of group randomized trials to detect effects of social and emotional learning programs and other preventive interventions on setting-level outcomes is influenced by the reliability of the outcome measure. In this paper, we apply generalizability theory to an observational measure of the quality of classroom interactions that is an outcome in a study of the efficacy of a social and emotional learning program called The Recognizing, Understanding, Labeling, Expressing, and Regulating emotions Approach. We estimate multiple sources of error variance in the setting-level outcome and identify observation procedures to use in the efficacy study that most efficiently reduce these sources of error. We then discuss the implications of using different observation procedures on both the statistical power and the monetary costs of conducting the efficacy study.

Statistical Learning Is Constrained to Less Abstract Patterns in Complex Sensory Input (but not the Least)

PubMed Central

Emberson, Lauren L.; Rubinstein, Dani

2016-01-01

The influence of statistical information on behavior (either through learning or adaptation) is quickly becoming foundational to many domains of cognitive psychology and cognitive neuroscience, from language comprehension to visual development. We investigate a central problem impacting these diverse fields: when encountering input with rich statistical information, are there any constraints on learning? This paper examines learning outcomes when adult learners are given statistical information across multiple levels of abstraction simultaneously: from abstract, semantic categories of everyday objects to individual viewpoints on these objects. After revealing statistical learning of abstract, semantic categories with scrambled individual exemplars (Exp. 1), participants viewed pictures where the categories as well as the individual objects predicted picture order (e.g., bird1—dog1, bird2—dog2). Our findings suggest that participants preferentially encode the relationships between the individual objects, even in the presence of statistical regularities linking semantic categories (Exps. 2 and 3). In a final experiment we investigate whether learners are biased towards learning object-level regularities or simply construct the most detailed model given the data (and therefore best able to predict the specifics of the upcoming stimulus) by investigating whether participants preferentially learn from the statistical regularities linking individual snapshots of objects or the relationship between the objects themselves (e.g., bird_picture1— dog_picture1, bird_picture2—dog_picture2). We find that participants fail to learn the relationships between individual snapshots, suggesting a bias towards object-level statistical regularities as opposed to merely constructing the most complete model of the input. This work moves beyond the previous existence proofs that statistical learning is possible at both very high and very low levels of abstraction (categories vs. individual objects) and suggests that, at least with the current categories and type of learner, there are biases to pick up on statistical regularities between individual objects even when robust statistical information is present at other levels of abstraction. These findings speak directly to emerging theories about how systems supporting statistical learning and prediction operate in our structure-rich environments. Moreover, the theoretical implications of the current work across multiple domains of study is already clear: statistical learning cannot be assumed to be unconstrained even if statistical learning has previously been established at a given level of abstraction when that information is presented in isolation. PMID:27139779

The penumbra of learning: a statistical theory of synaptic tagging and capture.

PubMed

Gershman, Samuel J

2014-01-01

Learning in humans and animals is accompanied by a penumbra: Learning one task benefits from learning an unrelated task shortly before or after. At the cellular level, the penumbra of learning appears when weak potentiation of one synapse is amplified by strong potentiation of another synapse on the same neuron during a critical time window. Weak potentiation sets a molecular tag that enables the synapse to capture plasticity-related proteins synthesized in response to strong potentiation at another synapse. This paper describes a computational model which formalizes synaptic tagging and capture in terms of statistical learning mechanisms. According to this model, synaptic strength encodes a probabilistic inference about the dynamically changing association between pre- and post-synaptic firing rates. The rate of change is itself inferred, coupling together different synapses on the same neuron. When the inputs to one synapse change rapidly, the inferred rate of change increases, amplifying learning at other synapses.

Data exploration systems for databases

NASA Technical Reports Server (NTRS)

Greene, Richard J.; Hield, Christopher

1992-01-01

Data exploration systems apply machine learning techniques, multivariate statistical methods, information theory, and database theory to databases to identify significant relationships among the data and summarize information. The result of applying data exploration systems should be a better understanding of the structure of the data and a perspective of the data enabling an analyst to form hypotheses for interpreting the data. This paper argues that data exploration systems need a minimum amount of domain knowledge to guide both the statistical strategy and the interpretation of the resulting patterns discovered by these systems.

Theory of Constraints: What Can We Learn to Support the Nursing Workforce?

PubMed

Pawlak, Roberta

2016-11-01

Demand for nurses is influenced by many factors. Labor statistics and health services literature reveal current and predicted supply gaps in meeting this demand. One strategy in response can be drawn from manufacturing industries. This column suggests the application of the Theory of Constraints in efforts to relieve bottlenecks in producing and retaining nurse labor.

Dental Students' Educational Achievement in Relation to Their Learning Styles: A Cross-Sectional Study in Iran.

PubMed

Hosseini, Seyed Masoud; Amery, Hamideh; Emadzadeh, Ali; Babazadeh, Saber

2015-02-24

In recent decades, many studies have been carried out on the importance of Kolb experiential learning theory (ELT) in teaching-learning processes and its effect on learning outcomes. However, some experts have criticized the Kolb theory and argue that there are some ambiguities on the validity of the theory as an important predictor of achievement. This study has been carried out on dental students' educational achievement in relation to their dominant learning styles based on Kolb theory in Mashhad University of Medical Sciences (Iran). In a cross sectional study, Kolb Learning Style Inventory (LSI Ver. 3.1) as well as a questionnaire containing students' demographic data, academic achievement marks including grade point average (GPA), theoretical and practical courses marks, and the comprehensive basic sciences exam (CBSE) scores were administered on a purposive sample of 162 dental students who had passed their comprehensive basic sciences exam. Educational achievement data were analyzed in relation to students' dominant learning styles, using descriptive and analytical statistics including χ2, Kruskal-Wallis and two-way ANOVA tests. The dominant learning styles of students were Assimilating (53.1%), Converging (24.1%), Diverging (14.2%) and Accommodating (8.6%). Although, the students with Assimilating and Converging learning styles had a better performance on their educational achievement, there was no significant relationship between educational achievement and dominant learning style (P≥0.05). Findings support that the dominant learning style is not exclusively an essential factor to predict educational achievement. Rather, it shows learning preferences of students that may be considered in designing learning opportunities by the teachers.

Mathematical Representation Ability by Using Project Based Learning on the Topic of Statistics

NASA Astrophysics Data System (ADS)

Widakdo, W. A.

2017-09-01

Seeing the importance of the role of mathematics in everyday life, mastery of the subject areas of mathematics is a must. Representation ability is one of the fundamental ability that used in mathematics to make connection between abstract idea with logical thinking to understanding mathematics. Researcher see the lack of mathematical representation and try to find alternative solution to dolve it by using project based learning. This research use literature study from some books and articles in journals to see the importance of mathematical representation abiliy in mathemtics learning and how project based learning able to increase this mathematical representation ability on the topic of Statistics. The indicators for mathematical representation ability in this research classifies namely visual representation (picture, diagram, graph, or table); symbolize representation (mathematical statement. Mathematical notation, numerical/algebra symbol) and verbal representation (written text). This article explain about why project based learning able to influence student’s mathematical representation by using some theories in cognitive psychology, also showing the example of project based learning that able to use in teaching statistics, one of mathematics topic that very useful to analyze data.

Learning coefficient of generalization error in Bayesian estimation and vandermonde matrix-type singularity.

PubMed

Aoyagi, Miki; Nagata, Kenji

2012-06-01

The term algebraic statistics arises from the study of probabilistic models and techniques for statistical inference using methods from algebra and geometry (Sturmfels, 2009 ). The purpose of our study is to consider the generalization error and stochastic complexity in learning theory by using the log-canonical threshold in algebraic geometry. Such thresholds correspond to the main term of the generalization error in Bayesian estimation, which is called a learning coefficient (Watanabe, 2001a , 2001b ). The learning coefficient serves to measure the learning efficiencies in hierarchical learning models. In this letter, we consider learning coefficients for Vandermonde matrix-type singularities, by using a new approach: focusing on the generators of the ideal, which defines singularities. We give tight new bound values of learning coefficients for the Vandermonde matrix-type singularities and the explicit values with certain conditions. By applying our results, we can show the learning coefficients of three-layered neural networks and normal mixture models.

Fuzzy logic of Aristotelian forms

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

Perlovsky, L.I.

1996-12-31

Model-based approaches to pattern recognition and machine vision have been proposed to overcome the exorbitant training requirements of earlier computational paradigms. However, uncertainties in data were found to lead to a combinatorial explosion of the computational complexity. This issue is related here to the roles of a priori knowledge vs. adaptive learning. What is the a-priori knowledge representation that supports learning? I introduce Modeling Field Theory (MFT), a model-based neural network whose adaptive learning is based on a priori models. These models combine deterministic, fuzzy, and statistical aspects to account for a priori knowledge, its fuzzy nature, and data uncertainties.more » In the process of learning, a priori fuzzy concepts converge to crisp or probabilistic concepts. The MFT is a convergent dynamical system of only linear computational complexity. Fuzzy logic turns out to be essential for reducing the combinatorial complexity to linear one. I will discuss the relationship of the new computational paradigm to two theories due to Aristotle: theory of Forms and logic. While theory of Forms argued that the mind cannot be based on ready-made a priori concepts, Aristotelian logic operated with just such concepts. I discuss an interpretation of MFT suggesting that its fuzzy logic, combining a-priority and adaptivity, implements Aristotelian theory of Forms (theory of mind). Thus, 2300 years after Aristotle, a logic is developed suitable for his theory of mind.« less

Neurophysiological Markers of Statistical Learning in Music and Language: Hierarchy, Entropy, and Uncertainty.

PubMed

Daikoku, Tatsuya

2018-06-19

Statistical learning (SL) is a method of learning based on the transitional probabilities embedded in sequential phenomena such as music and language. It has been considered an implicit and domain-general mechanism that is innate in the human brain and that functions independently of intention to learn and awareness of what has been learned. SL is an interdisciplinary notion that incorporates information technology, artificial intelligence, musicology, and linguistics, as well as psychology and neuroscience. A body of recent study has suggested that SL can be reflected in neurophysiological responses based on the framework of information theory. This paper reviews a range of work on SL in adults and children that suggests overlapping and independent neural correlations in music and language, and that indicates disability of SL. Furthermore, this article discusses the relationships between the order of transitional probabilities (TPs) (i.e., hierarchy of local statistics) and entropy (i.e., global statistics) regarding SL strategies in human's brains; claims importance of information-theoretical approaches to understand domain-general, higher-order, and global SL covering both real-world music and language; and proposes promising approaches for the application of therapy and pedagogy from various perspectives of psychology, neuroscience, computational studies, musicology, and linguistics.

Evidence for social learning in wild lemurs (Lemur catta).

PubMed

Kendal, Rachel L; Custance, Deborah M; Kendal, Jeremy R; Vale, Gillian; Stoinski, Tara S; Rakotomalala, Nirina Lalaina; Rasamimanana, Hantanirina

2010-08-01

Interest in social learning has been fueled by claims of culture in wild animals. These remain controversial because alternative explanations to social learning, such as asocial learning or ecological differences, remain difficult to refute. Compared with laboratory-based research, the study of social learning in natural contexts is in its infancy. Here, for the first time, we apply two new statistical methods, option-bias analysis and network-based diffusion analysis, to data from the wild, complemented by standard inferential statistics. Contrary to common thought regarding the cognitive abilities of prosimian primates, our evidence is consistent with social learning within subgroups in the ring-tailed lemur (Lemur catta), supporting the theory of directed social learning (Coussi-Korbel & Fragaszy, 1995). We also caution that, as the toolbox for capturing social learning in natural contexts grows, care is required in ensuring that the methods employed are appropriate-in particular, regarding social dynamics among study subjects. Supplemental materials for this article may be downloaded from http://lb.psychonomic-journals.org/content/supplemental.

Testing students' e-learning via Facebook through Bayesian structural equation modeling.

PubMed

Salarzadeh Jenatabadi, Hashem; Moghavvemi, Sedigheh; Wan Mohamed Radzi, Che Wan Jasimah Bt; Babashamsi, Parastoo; Arashi, Mohammad

2017-01-01

Learning is an intentional activity, with several factors affecting students' intention to use new learning technology. Researchers have investigated technology acceptance in different contexts by developing various theories/models and testing them by a number of means. Although most theories/models developed have been examined through regression or structural equation modeling, Bayesian analysis offers more accurate data analysis results. To address this gap, the unified theory of acceptance and technology use in the context of e-learning via Facebook are re-examined in this study using Bayesian analysis. The data (S1 Data) were collected from 170 students enrolled in a business statistics course at University of Malaya, Malaysia, and tested with the maximum likelihood and Bayesian approaches. The difference between the two methods' results indicates that performance expectancy and hedonic motivation are the strongest factors influencing the intention to use e-learning via Facebook. The Bayesian estimation model exhibited better data fit than the maximum likelihood estimator model. The results of the Bayesian and maximum likelihood estimator approaches are compared and the reasons for the result discrepancy are deliberated.

Testing students’ e-learning via Facebook through Bayesian structural equation modeling

PubMed Central

Moghavvemi, Sedigheh; Wan Mohamed Radzi, Che Wan Jasimah Bt; Babashamsi, Parastoo; Arashi, Mohammad

2017-01-01

Learning is an intentional activity, with several factors affecting students’ intention to use new learning technology. Researchers have investigated technology acceptance in different contexts by developing various theories/models and testing them by a number of means. Although most theories/models developed have been examined through regression or structural equation modeling, Bayesian analysis offers more accurate data analysis results. To address this gap, the unified theory of acceptance and technology use in the context of e-learning via Facebook are re-examined in this study using Bayesian analysis. The data (S1 Data) were collected from 170 students enrolled in a business statistics course at University of Malaya, Malaysia, and tested with the maximum likelihood and Bayesian approaches. The difference between the two methods’ results indicates that performance expectancy and hedonic motivation are the strongest factors influencing the intention to use e-learning via Facebook. The Bayesian estimation model exhibited better data fit than the maximum likelihood estimator model. The results of the Bayesian and maximum likelihood estimator approaches are compared and the reasons for the result discrepancy are deliberated. PMID:28886019

Learning from Success: How Original Research on Academic Resilience Informs What College Faculty Can Do to Increase the Retention of Low Socioeconomic Status Students

ERIC Educational Resources Information Center

Morales, Erik E.

2014-01-01

Utilizing resilience theory and original research conducted on fifty academically resilient low socioeconomic status students of color, this article presents specific objectives and values institutions of higher learning can adopt and emphasize to increase the retention and graduation of their most statistically at-risk students. Major findings…

Shock and Awe: Rapid-Fire Theory, Some Surprising Survey Results, and Triage Statistics in an Applied Freshman Research Seminar

ERIC Educational Resources Information Center

Kramer, Daniel Boyd; Schechter, Michael G.

2011-01-01

This article seeks to contribute to the evolving literature on the scholarship of teaching and learning. We do this by describing and then reflecting on what we have learned from a year-long freshman applied research seminar, "International Affairs Knowledge and U.S. Foreign Policy Opinions." After surveying the literature on public…

University students' learning approaches in three cultures: an investigation of Biggs's 3P model.

PubMed

Zhang, L F

2000-01-01

The relationship of various learning approaches to students' academic achievement, abilities, and other characteristics was examined in a sample of university students in Hong Kong, mainland China, and the United States. The theoretical framework for this project was J. B. Biggs's (1987) theory of student learning approaches. The participants completed the Study Process Questionnaire (based on Biggs's theory) and provided a variety of demographic information. The participants' achievement scores and self-rated scores on analytical, creative, and practical abilities were also obtained. Results indicated that scores on certain subscales of the Study Process Questionnaire statistically predicted participants' achievement beyond their self-rated abilities. In addition, certain learning approaches were significantly related to the participants' ages, gender, parents' education levels, and their travel and work experiences. Implications of these findings are discussed as they relate to teaching and learning.

Machine learning modelling for predicting soil liquefaction susceptibility

NASA Astrophysics Data System (ADS)

Samui, P.; Sitharam, T. G.

2011-01-01

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 [(N1)60] and cyclic stress ratio (CSR). Further, an attempt has been made to simplify the models, requiring only the two parameters [(N1)60 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.

Dental Students’ Educational Achievement in Relation to Their Learning Styles: A Cross-sectional Study in Iran

PubMed Central

Hosseini, Seyed Masoud; Amery, Hamideh; Emadzadeh, Ali; Babazadeh, Saber

2015-01-01

Background and Objectives: In recent decades, many studies have been carried out on the importance of Kolb experiential learning theory (ELT) in teaching-learning processes and its effect on learning outcomes. However, some experts have criticized the Kolb theory and argue that there are some ambiguities on the validity of the theory as an important predictor of achievement. This study has been carried out on dental students’ educational achievement in relation to their dominant learning styles based on Kolb theory in Mashhad University of Medical Sciences (Iran). Methods: In a cross sectional study, Kolb Learning Style Inventory (LSI Ver. 3.1) as well as a questionnaire containing students’ demographic data, academic achievement marks including grade point average (GPA), theoretical and practical courses marks, and the comprehensive basic sciences exam (CBSE) scores were administered on a purposive sample of 162 dental students who had passed their comprehensive basic sciences exam. Educational achievement data were analyzed in relation to students’ dominant learning styles, using descriptive and analytical statistics including χ2, Kruskal-Wallis and two-way ANOVA tests. Results: The dominant learning styles of students were Assimilating (53.1%), Converging (24.1%), Diverging (14.2%) and Accommodating (8.6%). Although, the students with Assimilating and Converging learning styles had a better performance on their educational achievement, there was no significant relationship between educational achievement and dominant learning style (P≥0.05). Conclusion: Findings support that the dominant learning style is not exclusively an essential factor to predict educational achievement. Rather, it shows learning preferences of students that may be considered in designing learning opportunities by the teachers. PMID:26156915

Learning what to expect (in visual perception)

PubMed Central

Seriès, Peggy; Seitz, Aaron R.

2013-01-01

Expectations are known to greatly affect our experience of the world. A growing theory in computational neuroscience is that perception can be successfully described using Bayesian inference models and that the brain is “Bayes-optimal” under some constraints. In this context, expectations are particularly interesting, because they can be viewed as prior beliefs in the statistical inference process. A number of questions remain unsolved, however, for example: How fast do priors change over time? Are there limits in the complexity of the priors that can be learned? How do an individual’s priors compare to the true scene statistics? Can we unlearn priors that are thought to correspond to natural scene statistics? Where and what are the neural substrate of priors? Focusing on the perception of visual motion, we here review recent studies from our laboratories and others addressing these issues. We discuss how these data on motion perception fit within the broader literature on perceptual Bayesian priors, perceptual expectations, and statistical and perceptual learning and review the possible neural basis of priors. PMID:24187536

Metrological traceability in education: A practical online system for measuring and managing middle school mathematics instruction

NASA Astrophysics Data System (ADS)

Torres Irribarra, D.; Freund, R.; Fisher, W.; Wilson, M.

2015-02-01

Computer-based, online assessments modelled, designed, and evaluated for adaptively administered invariant measurement are uniquely suited to defining and maintaining traceability to standardized units in education. An assessment of this kind is embedded in the Assessing Data Modeling and Statistical Reasoning (ADM) middle school mathematics curriculum. Diagnostic information about middle school students' learning of statistics and modeling is provided via computer-based formative assessments for seven constructs that comprise a learning progression for statistics and modeling from late elementary through the middle school grades. The seven constructs are: Data Display, Meta-Representational Competence, Conceptions of Statistics, Chance, Modeling Variability, Theory of Measurement, and Informal Inference. The end product is a web-delivered system built with Ruby on Rails for use by curriculum development teams working with classroom teachers in designing, developing, and delivering formative assessments. The online accessible system allows teachers to accurately diagnose students' unique comprehension and learning needs in a common language of real-time assessment, logging, analysis, feedback, and reporting.

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

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

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

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

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

DOE PAGES

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

2016-12-28

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

A computational visual saliency model based on statistics and machine learning.

PubMed

Lin, Ru-Je; Lin, Wei-Song

2014-08-01

Identifying the type of stimuli that attracts human visual attention has been an appealing topic for scientists for many years. In particular, marking the salient regions in images is useful for both psychologists and many computer vision applications. In this paper, we propose a computational approach for producing saliency maps using statistics and machine learning methods. Based on four assumptions, three properties (Feature-Prior, Position-Prior, and Feature-Distribution) can be derived and combined by a simple intersection operation to obtain a saliency map. These properties are implemented by a similarity computation, support vector regression (SVR) technique, statistical analysis of training samples, and information theory using low-level features. This technique is able to learn the preferences of human visual behavior while simultaneously considering feature uniqueness. Experimental results show that our approach performs better in predicting human visual attention regions than 12 other models in two test databases. © 2014 ARVO.

Student Perceptions of Motivational Behaviors of Instructions in a Military Setting

DTIC Science & Technology

2005-06-01

surveys of students, faculty, and alumni, (b) summaries of research, (c) theories of leading researchers, and (d) statistically identified factors. Three...habits, motivation, learning style, cogitative, socioemotional , and morale character development. Theoretical Framework of Motivation in Teacher...and not in others has been the subject of many theories McKeachie (2002). 23 McClelland, Atkinson, Clark, and Lowell (1953) stated that motivation

Cognitive components underpinning the development of model-based learning.

PubMed

Potter, Tracey C S; Bryce, Nessa V; Hartley, Catherine A

2017-06-01

Reinforcement learning theory distinguishes "model-free" learning, which fosters reflexive repetition of previously rewarded actions, from "model-based" learning, which recruits a mental model of the environment to flexibly select goal-directed actions. Whereas model-free learning is evident across development, recruitment of model-based learning appears to increase with age. However, the cognitive processes underlying the development of model-based learning remain poorly characterized. Here, we examined whether age-related differences in cognitive processes underlying the construction and flexible recruitment of mental models predict developmental increases in model-based choice. In a cohort of participants aged 9-25, we examined whether the abilities to infer sequential regularities in the environment ("statistical learning"), maintain information in an active state ("working memory") and integrate distant concepts to solve problems ("fluid reasoning") predicted age-related improvements in model-based choice. We found that age-related improvements in statistical learning performance did not mediate the relationship between age and model-based choice. Ceiling performance on our working memory assay prevented examination of its contribution to model-based learning. However, age-related improvements in fluid reasoning statistically mediated the developmental increase in the recruitment of a model-based strategy. These findings suggest that gradual development of fluid reasoning may be a critical component process underlying the emergence of model-based learning. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

An Integrative Account of Constraints on Cross-Situational Learning

PubMed Central

Yurovsky, Daniel; Frank, Michael C.

2015-01-01

Word-object co-occurrence statistics are a powerful information source for vocabulary learning, but there is considerable debate about how learners actually use them. While some theories hold that learners accumulate graded, statistical evidence about multiple referents for each word, others suggest that they track only a single candidate referent. In two large-scale experiments, we show that neither account is sufficient: Cross-situational learning involves elements of both. Further, the empirical data are captured by a computational model that formalizes how memory and attention interact with co-occurrence tracking. Together, the data and model unify opposing positions in a complex debate and underscore the value of understanding the interaction between computational and algorithmic levels of explanation. PMID:26302052

The role of reference in cross-situational word learning.

PubMed

Wang, Felix Hao; Mintz, Toben H

2018-01-01

Word learning involves massive ambiguity, since in a particular encounter with a novel word, there are an unlimited number of potential referents. One proposal for how learners surmount the problem of ambiguity is that learners use cross-situational statistics to constrain the ambiguity: When a word and its referent co-occur across multiple situations, learners will associate the word with the correct referent. Yu and Smith (2007) propose that these co-occurrence statistics are sufficient for word-to-referent mapping. Alternative accounts hold that co-occurrence statistics alone are insufficient to support learning, and that learners are further guided by knowledge that words are referential (e.g., Waxman & Gelman, 2009). However, no behavioral word learning studies we are aware of explicitly manipulate subjects' prior assumptions about the role of the words in the experiments in order to test the influence of these assumptions. In this study, we directly test whether, when faced with referential ambiguity, co-occurrence statistics are sufficient for word-to-referent mappings in adult word-learners. Across a series of cross-situational learning experiments, we varied the degree to which there was support for the notion that the words were referential. At the same time, the statistical information about the words' meanings was held constant. When we overrode support for the notion that words were referential, subjects failed to learn the word-to-referent mappings, but otherwise they succeeded. Thus, cross-situational statistics were useful only when learners had the goal of discovering mappings between words and referents. We discuss the implications of these results for theories of word learning in children's language acquisition. Copyright © 2017 Elsevier B.V. All rights reserved.

Cognitive Components Underpinning the Development of Model-Based Learning

PubMed Central

Potter, Tracey C.S.; Bryce, Nessa V.; Hartley, Catherine A.

2016-01-01

Reinforcement learning theory distinguishes “model-free” learning, which fosters reflexive repetition of previously rewarded actions, from “model-based” learning, which recruits a mental model of the environment to flexibly select goal-directed actions. Whereas model-free learning is evident across development, recruitment of model-based learning appears to increase with age. However, the cognitive processes underlying the development of model-based learning remain poorly characterized. Here, we examined whether age-related differences in cognitive processes underlying the construction and flexible recruitment of mental models predict developmental increases in model-based choice. In a cohort of participants aged 9–25, we examined whether the abilities to infer sequential regularities in the environment (“statistical learning”), maintain information in an active state (“working memory”) and integrate distant concepts to solve problems (“fluid reasoning”) predicted age-related improvements in model-based choice. We found that age-related improvements in statistical learning performance did not mediate the relationship between age and model-based choice. Ceiling performance on our working memory assay prevented examination of its contribution to model-based learning. However, age-related improvements in fluid reasoning statistically mediated the developmental increase in the recruitment of a model-based strategy. These findings suggest that gradual development of fluid reasoning may be a critical component process underlying the emergence of model-based learning. PMID:27825732

The role of partial knowledge in statistical word learning

PubMed Central

Fricker, Damian C.; Yu, Chen; Smith, Linda B.

2013-01-01

A critical question about the nature of human learning is whether it is an all-or-none or a gradual, accumulative process. Associative and statistical theories of word learning rely critically on the later assumption: that the process of learning a word's meaning unfolds over time. That is, learning the correct referent for a word involves the accumulation of partial knowledge across multiple instances. Some theories also make an even stronger claim: Partial knowledge of one word–object mapping can speed up the acquisition of other word–object mappings. We present three experiments that test and verify these claims by exposing learners to two consecutive blocks of cross-situational learning, in which half of the words and objects in the second block were those that participants failed to learn in Block 1. In line with an accumulative account, Re-exposure to these mis-mapped items accelerated the acquisition of both previously experienced mappings and wholly new word–object mappings. But how does partial knowledge of some words speed the acquisition of others? We consider two hypotheses. First, partial knowledge of a word could reduce the amount of information required for it to reach threshold, and the supra-threshold mapping could subsequently aid in the acquisition of new mappings. Alternatively, partial knowledge of a word's meaning could be useful for disambiguating the meanings of other words even before the threshold of learning is reached. We construct and compare computational models embodying each of these hypotheses and show that the latter provides a better explanation of the empirical data. PMID:23702980

Linguistica matematica, statistica linguistica e linguistica applicata. Una nota storica sui lessici di frequenza e l'educazione linguistica (Mathematical Linguistics, Linguistic Statistics, and Applied Linguistics. An Historical Note on Word Frequencies and Linguistic Education)

ERIC Educational Resources Information Center

Elia, Annibale

1977-01-01

This article traces the history of several themes in applied linguistics and to show the relationships between linguistic theory and the sciences concerned with the learning and teaching of languages. Interest in word frequency statistics is discussed in particular. (Text is in Italian.) (CFM)

A rational model of function learning.

PubMed

Lucas, Christopher G; Griffiths, Thomas L; Williams, Joseph J; Kalish, Michael L

2015-10-01

Theories of how people learn relationships between continuous variables have tended to focus on two possibilities: one, that people are estimating explicit functions, or two that they are performing associative learning supported by similarity. We provide a rational analysis of function learning, drawing on work on regression in machine learning and statistics. Using the equivalence of Bayesian linear regression and Gaussian processes, which provide a probabilistic basis for similarity-based function learning, we show that learning explicit rules and using similarity can be seen as two views of one solution to this problem. We use this insight to define a rational model of human function learning that combines the strengths of both approaches and accounts for a wide variety of experimental results.

Behavioural studies of strategic thinking in games.

PubMed

Camerer, Colin F.

2003-05-01

Game theory is a mathematical language for describing strategic interactions, in which each player's choice affects the payoff of other players (where players can be genes, people, companies, nation-states, etc.). The impact of game theory in psychology has been limited by the lack of cognitive mechanisms underlying game-theoretic predictions. 'Behavioural game theory' is a recent approach linking game theory to cognitive science by adding cognitive details about 'social utility functions', theories of limits on iterated thinking, and statistical theories of how players learn and influence others. New directions include the effects of game descriptions on choice ('framing'), strategic heuristics, and mental representation. These ideas will help root game theory more deeply in cognitive science and extend the scope of both enterprises.

Statistical Mechanical Analysis of Online Learning with Weight Normalization in Single Layer Perceptron

NASA Astrophysics Data System (ADS)

Yoshida, Yuki; Karakida, Ryo; Okada, Masato; Amari, Shun-ichi

2017-04-01

Weight normalization, a newly proposed optimization method for neural networks by Salimans and Kingma (2016), decomposes the weight vector of a neural network into a radial length and a direction vector, and the decomposed parameters follow their steepest descent update. They reported that learning with the weight normalization achieves better converging speed in several tasks including image recognition and reinforcement learning than learning with the conventional parameterization. However, it remains theoretically uncovered how the weight normalization improves the converging speed. In this study, we applied a statistical mechanical technique to analyze on-line learning in single layer linear and nonlinear perceptrons with weight normalization. By deriving order parameters of the learning dynamics, we confirmed quantitatively that weight normalization realizes fast converging speed by automatically tuning the effective learning rate, regardless of the nonlinearity of the neural network. This property is realized when the initial value of the radial length is near the global minimum; therefore, our theory suggests that it is important to choose the initial value of the radial length appropriately when using weight normalization.

Investigations in Mathematics Education, Vol. 10, No. 3.

ERIC Educational Resources Information Center

Osborne, Alan R., Ed.

Eighteen research reports related to mathematics education are abstracted and analyzed in this publication. Three of the reports deal with aspects of learning theory, seven with topics in mathematics instruction (problem solving, weight, quadratic inequalities, probability and statistics, area and volume conservation, cardinality), five with…

Comparison of the effect of multiple intelligence pedagogy and traditional pedagogy on grade 5 students' achievement and attitudes towards science

NASA Astrophysics Data System (ADS)

Mussen, Kimberly S.

This quantitative research study evaluated the effectiveness of employing pedagogy based on the theory of multiple intelligences (MI). Currently, not all students are performing at the rate mandated by the government. When schools do not meet the required state standards, the school is labeled as not achieving adequate yearly progress (AYP), which may lead to the loss of funding. Any school not achieving AYP would be interested in this study. Due to low state standardized test scores in the district for science, student achievement and attitudes towards learning science were evaluated on a pretest, posttest, essay question, and one attitudinal survey. Statistical significance existed on one of the four research questions. Utilizing the Analysis of Covariance (ANCOVA) for data analysis, student attitudes towards learning science were statically significant in the MI (experimental) group. No statistical significance was found in student achievement on the posttest, delayed posttest, or the essay question test. Social change can result from this study because studying the effects of the multiple intelligence theory incorporated into classroom instruction can have significant effect on how children learn, allowing them to compete in a knowledge society.

What subject matter questions motivate the use of machine learning approaches compared to statistical models for probability prediction?

PubMed

Binder, Harald

2014-07-01

This is a discussion of the following papers: "Probability estimation with machine learning methods for dichotomous and multicategory outcome: Theory" by Jochen Kruppa, Yufeng Liu, Gérard Biau, Michael Kohler, Inke R. König, James D. Malley, and Andreas Ziegler; and "Probability estimation with machine learning methods for dichotomous and multicategory outcome: Applications" by Jochen Kruppa, Yufeng Liu, Hans-Christian Diener, Theresa Holste, Christian Weimar, Inke R. König, and Andreas Ziegler. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Learning classification trees

NASA Technical Reports Server (NTRS)

Buntine, Wray

1991-01-01

Algorithms for learning classification trees have had successes in artificial intelligence and statistics over many years. How a tree learning algorithm can be derived from Bayesian decision theory is outlined. This introduces Bayesian techniques for splitting, smoothing, and tree averaging. The splitting rule turns out to be similar to Quinlan's information gain splitting rule, while smoothing and averaging replace pruning. Comparative experiments with reimplementations of a minimum encoding approach, Quinlan's C4 and Breiman et al. Cart show the full Bayesian algorithm is consistently as good, or more accurate than these other approaches though at a computational price.

Moral foundations in an interacting neural networks society: A statistical mechanics analysis

NASA Astrophysics Data System (ADS)

Vicente, R.; Susemihl, A.; Jericó, J. P.; Caticha, N.

2014-04-01

The moral foundations theory supports that people, across cultures, tend to consider a small number of dimensions when classifying issues on a moral basis. The data also show that the statistics of weights attributed to each moral dimension is related to self-declared political affiliation, which in turn has been connected to cognitive learning styles by the recent literature in neuroscience and psychology. Inspired by these data, we propose a simple statistical mechanics model with interacting neural networks classifying vectors and learning from members of their social neighbourhood about their average opinion on a large set of issues. The purpose of learning is to reduce dissension among agents when disagreeing. We consider a family of learning algorithms parametrized by δ, that represents the importance given to corroborating (same sign) opinions. We define an order parameter that quantifies the diversity of opinions in a group with homogeneous learning style. Using Monte Carlo simulations and a mean field approximation we find the relation between the order parameter and the learning parameter δ at a temperature we associate with the importance of social influence in a given group. In concordance with data, groups that rely more strongly on corroborating evidence sustain less opinion diversity. We discuss predictions of the model and propose possible experimental tests.

Difference to Inference: teaching logical and statistical reasoning through on-line interactivity.

PubMed

Malloy, T E

2001-05-01

Difference to Inference is an on-line JAVA program that simulates theory testing and falsification through research design and data collection in a game format. The program, based on cognitive and epistemological principles, is designed to support learning of the thinking skills underlying deductive and inductive logic and statistical reasoning. Difference to Inference has database connectivity so that game scores can be counted as part of course grades.

The Convergence of Intelligences

NASA Astrophysics Data System (ADS)

Diederich, Joachim

Minsky (1985) argued an extraterrestrial intelligence may be similar to ours despite very different origins. ``Problem- solving'' offers evolutionary advantages and individuals who are part of a technical civilisation should have this capacity. On earth, the principles of problem-solving are the same for humans, some primates and machines based on Artificial Intelligence (AI) techniques. Intelligent systems use ``goals'' and ``sub-goals'' for problem-solving, with memories and representations of ``objects'' and ``sub-objects'' as well as knowledge of relations such as ``cause'' or ``difference.'' Some of these objects are generic and cannot easily be divided into parts. We must, therefore, assume that these objects and relations are universal, and a general property of intelligence. Minsky's arguments from 1985 are extended here. The last decade has seen the development of a general learning theory (``computational learning theory'' (CLT) or ``statistical learning theory'') which equally applies to humans, animals and machines. It is argued that basic learning laws will also apply to an evolved alien intelligence, and this includes limitations of what can be learned efficiently. An example from CLT is that the general learning problem for neural networks is intractable, i.e. it cannot be solved efficiently for all instances (it is ``NP-complete''). It is the objective of this paper to show that evolved intelligences will be constrained by general learning laws and will use task-decomposition for problem-solving. Since learning and problem-solving are core features of intelligence, it can be said that intelligences converge despite very different origins.

Learning cardiopulmonary resuscitation theory with face-to-face versus audiovisual instruction for secondary school students: a randomized controlled trial.

PubMed

Cerezo Espinosa, Cristina; Nieto Caballero, Sergio; Juguera Rodríguez, Laura; Castejón-Mochón, José Francisco; Segura Melgarejo, Francisca; Sánchez Martínez, Carmen María; López López, Carmen Amalia; Pardo Ríos, Manuel

2018-02-01

To compare secondary students' learning of basic life support (BLS) theory and the use of an automatic external defibrillator (AED) through face-to-face classroom instruction versus educational video instruction. A total of 2225 secondary students from 15 schools were randomly assigned to one of the following 5 instructional groups: 1) face-to-face instruction with no audiovisual support, 2) face-to-face instruction with audiovisual support, 3) audiovisual instruction without face-to-face instruction, 4) audiovisual instruction with face-to-face instruction, and 5) a control group that received no instruction. The students took a test of BLS and AED theory before instruction, immediately after instruction, and 2 months later. The median (interquartile range) scores overall were 2.33 (2.17) at baseline, 5.33 (4.66) immediately after instruction (P<.001) and 6.00 (3.33) (P<.001). All groups except the control group improved their scores. Scores immediately after instruction and 2 months later were statistically similar after all types of instruction. No significant differences between face-to-face instruction and audiovisual instruction for learning BLS and AED theory were found in secondary school students either immediately after instruction or 2 months later.

Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community

NASA Astrophysics Data System (ADS)

Ball, John E.; Anderson, Derek T.; Chan, Chee Seng

2017-10-01

In recent years, deep learning (DL), a rebranding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, and natural language processing. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV, e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should not only be aware of advancements such as DL, but also be leading researchers in this area. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools, and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as they relate to (i) inadequate data sets, (ii) human-understandable solutions for modeling physical phenomena, (iii) big data, (iv) nontraditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial, and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.

Learning styles of medical students, general surgery residents, and general surgeons: implications for surgical education.

PubMed

Engels, Paul T; de Gara, Chris

2010-06-30

Surgical education is evolving under the dual pressures of an enlarging body of knowledge required during residency and mounting work-hour restrictions. Changes in surgical residency training need to be based on available educational models and research to ensure successful training of surgeons. Experiential learning theory, developed by David Kolb, demonstrates the importance of individual learning styles in improving learning. This study helps elucidate the way in which medical students, surgical residents, and surgical faculty learn. The Kolb Learning Style Inventory, which divides individual learning styles into Accommodating, Diverging, Converging, and Assimilating categories, was administered to the second year undergraduate medical students, general surgery resident body, and general surgery faculty at the University of Alberta. A total of 241 faculty, residents, and students were surveyed with an overall response rate of 73%. The predominant learning style of the medical students was assimilating and this was statistically significant (p < 0.03) from the converging learning style found in the residents and faculty. The predominant learning styles of the residents and faculty were convergent and accommodative, with no statistically significant differences between the residents and the faculty. We conclude that medical students have a significantly different learning style from general surgical trainees and general surgeons. This has important implications in the education of general surgery residents.

Statistical Knowledge and Learning in Phonology

ERIC Educational Resources Information Center

Dunbar, Ewan Michael

2013-01-01

This dissertation deals with the theory of the phonetic component of grammar in a formal probabilistic inference framework: (1) it has been recognized since the beginning of generative phonology that some language-specific phonetic implementation is actually context-dependent, and thus it can be said that there are gradient "phonetic…

Intelligence and Creativity Are Pretty Similar After All

ERIC Educational Resources Information Center

Silvia, Paul J.

2015-01-01

This article reviews the history of thought on how intelligence and creativity, two individual differences important to teaching and learning, are connected. For decades, intelligence and creativity have been seen as essentially unrelated abilities. Recently, however, new theories, assessment methods, and statistical tools have caused a shift in…

Estimation and Compression over Large Alphabets

ERIC Educational Resources Information Center

Acharya, Jayadev

2014-01-01

Compression, estimation, and prediction are basic problems in Information theory, statistics and machine learning. These problems have been extensively studied in all these fields, though the primary focus in a large portion of the work has been on understanding and solving the problems in the asymptotic regime, "i.e." the alphabet size…

Literature Review of Cloud Based E-learning Adoption by Students: State of the Art and Direction for Future Work

NASA Astrophysics Data System (ADS)

Hassan Kayali, Mohammad; Safie, Nurhizam; Mukhtar, Muriati

2016-11-01

Cloud computing is a new paradigm shift in information technology. Most of the studies in the cloud are business related while the studies in cloud based e-learning are few. The field is still in its infancy and researchers have used several adoption theories to discover the dimensions of this field. The purpose of this paper is to review and integrate the literature to understand the current situation of the cloud based e-learning adoption. A total of 312 articles were extracted from Science direct, emerald, and IEEE. Screening processes were applied to select only the articles that are related to the cloud based e-learning. A total of 231 removed because they are related to business organization. Next, a total of 63 articles were removed because they are technical articles. A total of 18 articles were included in this paper. A frequency analysis was conducted on the paper to identify the most frequent factors, theories, statistical software, respondents, and countries of the studies. The findings showed that usefulness and ease of use are the most frequent factors. TAM is the most prevalent adoption theories in the literature. The mean of the respondents in the reviewed studies is 377 and Malaysia is the most researched countries in terms of cloud based e-learning. Studies of cloud based e-learning are few and more empirical studies are needed.

The best motivator priorities parents choose via analytical hierarchy process

NASA Astrophysics Data System (ADS)

Farah, R. N.; Latha, P.

2015-05-01

Motivation is probably the most important factor that educators can target in order to improve learning. Numerous cross-disciplinary theories have been postulated to explain motivation. While each of these theories has some truth, no single theory seems to adequately explain all human motivation. The fact is that human beings in general and pupils in particular are complex creatures with complex needs and desires. In this paper, Analytic Hierarchy Process (AHP) has been proposed as an emerging solution to move towards too large, dynamic and complex real world multi-criteria decision making problems in selecting the most suitable motivator when choosing school for their children. Data were analyzed using SPSS 17.0 ("Statistical Package for Social Science") software. Statistic testing used are descriptive and inferential statistic. Descriptive statistic used to identify respondent pupils and parents demographic factors. The statistical testing used to determine the pupils and parents highest motivator priorities and parents' best priorities using AHP to determine the criteria chosen by parents such as school principals, teachers, pupils and parents. The moderating factors are selected schools based on "Standard Kualiti Pendidikan Malaysia" (SKPM) in Ampang. Inferential statistics such as One-way ANOVA used to get the significant and data used to calculate the weightage of AHP. School principals is found to be the best motivator for parents in choosing school for their pupils followed by teachers, parents and pupils.

Case-based statistical learning applied to SPECT image classification

NASA Astrophysics Data System (ADS)

Górriz, Juan M.; Ramírez, Javier; Illán, I. A.; Martínez-Murcia, Francisco J.; Segovia, Fermín.; Salas-Gonzalez, Diego; Ortiz, A.

2017-03-01

Statistical learning and decision theory play a key role in many areas of science and engineering. Some examples include time series regression and prediction, optical character recognition, signal detection in communications or biomedical applications for diagnosis and prognosis. This paper deals with the topic of learning from biomedical image data in the classification problem. In a typical scenario we have a training set that is employed to fit a prediction model or learner and a testing set on which the learner is applied to in order to predict the outcome for new unseen patterns. Both processes are usually completely separated to avoid over-fitting and due to the fact that, in practice, the unseen new objects (testing set) have unknown outcomes. However, the outcome yields one of a discrete set of values, i.e. the binary diagnosis problem. Thus, assumptions on these outcome values could be established to obtain the most likely prediction model at the training stage, that could improve the overall classification accuracy on the testing set, or keep its performance at least at the level of the selected statistical classifier. In this sense, a novel case-based learning (c-learning) procedure is proposed which combines hypothesis testing from a discrete set of expected outcomes and a cross-validated classification stage.

Nyström type subsampling analyzed as a regularized projection

NASA Astrophysics Data System (ADS)

Kriukova, Galyna; Pereverzyev, Sergiy, Jr.; Tkachenko, Pavlo

2017-07-01

In the statistical learning theory the Nyström type subsampling methods are considered as tools for dealing with big data. In this paper we consider Nyström subsampling as a special form of the projected Lavrentiev regularization, and study it using the approaches developed in the regularization theory. As a result, we prove that the same capacity independent learning rates that are guaranteed for standard algorithms running with quadratic computational complexity can be obtained with subquadratic complexity by the Nyström subsampling approach, provided that the subsampling size is chosen properly. We propose a priori rule for choosing the subsampling size and a posteriori strategy for dealing with uncertainty in the choice of it. The theoretical results are illustrated by numerical experiments.

Gender similarities and differences.

PubMed

Hyde, Janet Shibley

2014-01-01

Whether men and women are fundamentally different or similar has been debated for more than a century. This review summarizes major theories designed to explain gender differences: evolutionary theories, cognitive social learning theory, sociocultural theory, and expectancy-value theory. The gender similarities hypothesis raises the possibility of theorizing gender similarities. Statistical methods for the analysis of gender differences and similarities are reviewed, including effect sizes, meta-analysis, taxometric analysis, and equivalence testing. Then, relying mainly on evidence from meta-analyses, gender differences are reviewed in cognitive performance (e.g., math performance), personality and social behaviors (e.g., temperament, emotions, aggression, and leadership), and psychological well-being. The evidence on gender differences in variance is summarized. The final sections explore applications of intersectionality and directions for future research.

Problem Based Learning (PBL) - An Effective Approach to Improve Learning Outcomes in Medical Teaching.

PubMed

Preeti, Bajaj; Ashish, Ahuja; Shriram, Gosavi

2013-12-01

As the "Science of Medicine" is getting advanced day-by-day, need for better pedagogies & learning techniques are imperative. Problem Based Learning (PBL) is an effective way of delivering medical education in a coherent, integrated & focused manner. It has several advantages over conventional and age-old teaching methods of routine. It is based on principles of adult learning theory, including student's motivation, encouragement to set goals, think critically about decision making in day-to-day operations. Above all these, it stimulates challenge acceptance and learning curiosity among students and creates pragmatic educational program. To measure the effectiveness of the "Problem Based Learning" as compared to conventional theory/didactic lectures based learning. The study was conducted on 72 medical students from Dayanand Medical College & Hospital, Ludhiana. Two modules of problem based sessions designed and delivered. Pre & Post-test score's scientific statistical analysis was done. Student feed-back received based on questionnaire in the five-point Likert scale format. Significant improvement in overall performance observed. Feedback revealed majority agreement that "Problem-based learning" helped them create interest (88.8 %), better understanding (86%) & promotes self-directed subject learning (91.6 %). Substantial improvement in the post-test scores clearly reveals acceptance of PBL over conventional learning. PBL ensures better practical learning, ability to create interest, subject understanding. It is a modern-day educational strategy, an effective tool to objectively improve the knowledge acquisition in Medical Teaching.

Probability, statistics, and computational science.

PubMed

Beerenwinkel, Niko; Siebourg, Juliane

2012-01-01

In this chapter, we review basic concepts from probability theory and computational statistics that are fundamental to evolutionary genomics. We provide a very basic introduction to statistical modeling and discuss general principles, including maximum likelihood and Bayesian inference. Markov chains, hidden Markov models, and Bayesian network models are introduced in more detail as they occur frequently and in many variations in genomics applications. In particular, we discuss efficient inference algorithms and methods for learning these models from partially observed data. Several simple examples are given throughout the text, some of which point to models that are discussed in more detail in subsequent chapters.

Utah Virtual Lab: JAVA interactivity for teaching science and statistics on line.

PubMed

Malloy, T E; Jensen, G C

2001-05-01

The Utah on-line Virtual Lab is a JAVA program run dynamically off a database. It is embedded in StatCenter (www.psych.utah.edu/learn/statsampler.html), an on-line collection of tools and text for teaching and learning statistics. Instructors author a statistical virtual reality that simulates theories and data in a specific research focus area by defining independent, predictor, and dependent variables and the relations among them. Students work in an on-line virtual environment to discover the principles of this simulated reality: They go to a library, read theoretical overviews and scientific puzzles, and then go to a lab, design a study, collect and analyze data, and write a report. Each student's design and data analysis decisions are computer-graded and recorded in a database; the written research report can be read by the instructor or by other students in peer groups simulating scientific conventions.

Machine learning: Trends, perspectives, and prospects.

PubMed

Jordan, M I; Mitchell, T M

2015-07-17

Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today's most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. Recent progress in machine learning has been driven both by the development of new learning algorithms and theory and by the ongoing explosion in the availability of online data and low-cost computation. The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing. Copyright © 2015, American Association for the Advancement of Science.

Representing Learning With Graphical Models

NASA Technical Reports Server (NTRS)

Buntine, Wray L.; Lum, Henry, Jr. (Technical Monitor)

1994-01-01

Probabilistic graphical models are being used widely in artificial intelligence, for instance, in diagnosis and expert systems, as a unified qualitative and quantitative framework for representing and reasoning with probabilities and independencies. Their development and use spans several fields including artificial intelligence, decision theory and statistics, and provides an important bridge between these communities. This paper shows by way of example that these models can be extended to machine learning, neural networks and knowledge discovery by representing the notion of a sample on the graphical model. Not only does this allow a flexible variety of learning problems to be represented, it also provides the means for representing the goal of learning and opens the way for the automatic development of learning algorithms from specifications.

Rage against the Machine: Evaluation Metrics in the 21st Century

ERIC Educational Resources Information Center

Yang, Charles

2017-01-01

I review the classic literature in generative grammar and Marr's three-level program for cognitive science to defend the Evaluation Metric as a psychological theory of language learning. Focusing on well-established facts of language variation, change, and use, I argue that optimal statistical principles embodied in Bayesian inference models are…

Mental Muscularity: Shaping Implicit Theories of Intelligence via Metaphor

ERIC Educational Resources Information Center

Anderson, Scott Victor

2009-01-01

Motivating students is a central challenge for many teachers, particularly in subjects students commonly perceive as "impenetrable," such as statistics. One line of motivation research by C.S. Dweck (2006) has found that when students believe their intelligence is malleable (i.e., a growth mindset) and that learning is a function of effort, they…

The Reception of American Literature in Cameroon

ERIC Educational Resources Information Center

Djockoua, Manyaka Toko

2014-01-01

In Cameroon, popular belief associates American literature with its country's economic and political greatness. Yet, if millions of Cameroonians show a growing enthusiasm for a visit to the US, just a few are interested in learning its literature. Using theories on the reading and teaching of literature, statistical data based on a questionnaire,…

A baker's dozen of new particle flows for nonlinear filters, Bayesian decisions and transport

NASA Astrophysics Data System (ADS)

Daum, Fred; Huang, Jim

2015-05-01

We describe a baker's dozen of new particle flows to compute Bayes' rule for nonlinear filters, Bayesian decisions and learning as well as transport. Several of these new flows were inspired by transport theory, but others were inspired by physics or statistics or Markov chain Monte Carlo methods.

Modelling Trial-by-Trial Changes in the Mismatch Negativity

PubMed Central

Lieder, Falk; Daunizeau, Jean; Garrido, Marta I.; Friston, Karl J.; Stephan, Klaas E.

2013-01-01

The mismatch negativity (MMN) is a differential brain response to violations of learned regularities. It has been used to demonstrate that the brain learns the statistical structure of its environment and predicts future sensory inputs. However, the algorithmic nature of these computations and the underlying neurobiological implementation remain controversial. This article introduces a mathematical framework with which competing ideas about the computational quantities indexed by MMN responses can be formalized and tested against single-trial EEG data. This framework was applied to five major theories of the MMN, comparing their ability to explain trial-by-trial changes in MMN amplitude. Three of these theories (predictive coding, model adjustment, and novelty detection) were formalized by linking the MMN to different manifestations of the same computational mechanism: approximate Bayesian inference according to the free-energy principle. We thereby propose a unifying view on three distinct theories of the MMN. The relative plausibility of each theory was assessed against empirical single-trial MMN amplitudes acquired from eight healthy volunteers in a roving oddball experiment. Models based on the free-energy principle provided more plausible explanations of trial-by-trial changes in MMN amplitude than models representing the two more traditional theories (change detection and adaptation). Our results suggest that the MMN reflects approximate Bayesian learning of sensory regularities, and that the MMN-generating process adjusts a probabilistic model of the environment according to prediction errors. PMID:23436989

Learning styles of medical students, general surgery residents, and general surgeons: implications for surgical education

PubMed Central

2010-01-01

Background Surgical education is evolving under the dual pressures of an enlarging body of knowledge required during residency and mounting work-hour restrictions. Changes in surgical residency training need to be based on available educational models and research to ensure successful training of surgeons. Experiential learning theory, developed by David Kolb, demonstrates the importance of individual learning styles in improving learning. This study helps elucidate the way in which medical students, surgical residents, and surgical faculty learn. Methods The Kolb Learning Style Inventory, which divides individual learning styles into Accommodating, Diverging, Converging, and Assimilating categories, was administered to the second year undergraduate medical students, general surgery resident body, and general surgery faculty at the University of Alberta. Results A total of 241 faculty, residents, and students were surveyed with an overall response rate of 73%. The predominant learning style of the medical students was assimilating and this was statistically significant (p < 0.03) from the converging learning style found in the residents and faculty. The predominant learning styles of the residents and faculty were convergent and accommodative, with no statistically significant differences between the residents and the faculty. Conclusions We conclude that medical students have a significantly different learning style from general surgical trainees and general surgeons. This has important implications in the education of general surgery residents. PMID:20591159

Patterns of interactions at grade 5 classroom in learning the topic of statistics viewed from cognitive load theory

NASA Astrophysics Data System (ADS)

Setianingsih, R.

2018-01-01

The nature of interactions that occurs among teacher, students, learning sources, and learning environment creates different settings to enhance learning. Any setting created by a teacher is affected by 3 (three) types of cognitive load: intrinsic cognitive load, extraneous cognitive load, and germane cognitive load. This study is qualitative in nature, aims to analyse the patterns of interaction that are constituted in mathematics instructions by taking into account the cognitive load theory. The subjects of this study are 21 fifth-grade students who learn mathematics in small groups and whole-class interactive lessons. The data were collected through classroom observations which were videotaped, while field notes were also taken. The data analysis revealed that students engaged in productive interaction and inquiry while they were learning mathematics in small groups or in whole class setting, in which there was a different type of cognitive load that dominantly affecting the learning processes at each setting. During learning mathematics in whole class setting, the most frequently found interaction patterns were to discuss and compare solution based on self-developed models, followed by expressing opinions. This is consistent with the principles of mathematics learning, which gives students wide opportunities to construct mathematical knowledge through individual learning, learning in small groups as well as learning in whole class settings. It means that by participating in interactive learning, the students are habitually engaged in productive interactions and high level of mathematical thinking.

Space Weather in the Machine Learning Era: A Multidisciplinary Approach

NASA Astrophysics Data System (ADS)

Camporeale, E.; Wing, S.; Johnson, J.; Jackman, C. M.; McGranaghan, R.

2018-01-01

The workshop entitled Space Weather: A Multidisciplinary Approach took place at the Lorentz Center, University of Leiden, Netherlands, on 25-29 September 2017. The aim of this workshop was to bring together members of the Space Weather, Mathematics, Statistics, and Computer Science communities to address the use of advanced techniques such as Machine Learning, Information Theory, and Deep Learning, to better understand the Sun-Earth system and to improve space weather forecasting. Although individual efforts have been made toward this goal, the community consensus is that establishing interdisciplinary collaborations is the most promising strategy for fully utilizing the potential of these advanced techniques in solving Space Weather-related problems.

A Multidimensional Partial Credit Model with Associated Item and Test Statistics: An Application to Mixed-Format Tests

ERIC Educational Resources Information Center

Yao, Lihua; Schwarz, Richard D.

2006-01-01

Multidimensional item response theory (IRT) models have been proposed for better understanding the dimensional structure of data or to define diagnostic profiles of student learning. A compensatory multidimensional two-parameter partial credit model (M-2PPC) for constructed-response items is presented that is a generalization of those proposed to…

On the Optimality of Answer-Copying Indices: Theory and Practice

ERIC Educational Resources Information Center

Romero, Mauricio; Riascos, Álvaro; Jara, Diego

2015-01-01

Multiple-choice exams are frequently used as an efficient and objective method to assess learning, but they are more vulnerable to answer copying than tests based on open questions. Several statistical tests (known as indices in the literature) have been proposed to detect cheating; however, to the best of our knowledge, they all lack mathematical…

A Study Comparing Fifth Grade Student Achievement in Mathematics in Departmentalized and Non-Departmentalized Settings

ERIC Educational Resources Information Center

Nelson, Karen Ann

2014-01-01

The purpose of this quantitative, causal-comparative study was to examine the application of the teaching and learning theory of social constructivism in order to determine if mathematics instruction provided in a departmentalized classroom setting at the fifth grade level resulted in a statistically significant difference in student achievement…

Entropy, a Unifying Concept: from Physics to Cognitive Psychology

NASA Astrophysics Data System (ADS)

Tsallis, Constantino; Tsallis, Alexandra C.

Together with classical, relativistic and quantum mechanics, as well as Maxwell electromagnetism, Boltzmann-Gibbs (BG) statistical mechanics constitutes one of the main theories of contemporary physics. This theory primarily concerns inanimate matter, and at its generic foundation we find nonlinear dynamical systems satisfying the ergodic hypothesis. This hypothesis is typically guaranteed for systems whose maximal Lyapunov exponent is positive. What happens when this crucial quantity is zero instead? We suggest here that, in what concerns thermostatistical properties, we typically enter what in some sense may be considered as a new world — the world of living systems — . The need emerges, at least for many systems, for generalizing the basis of BG statistical mechanics, namely the Boltzmann-Gibbs-von Neumann-Shannon en-tropic functional form, which connects the oscopic, thermodynamic quantity, with the occurrence probabilities of microscopic configurations. This unifying approach is briefly reviewed here, and its widespread applications — from physics to cognitive psychology — are overviewed. Special attention is dedicated to the learning/memorizing process in humans and computers. The present observations might be related to the gestalt theory of visual perceptions and the actor-network theory.

Statistical mechanics of unsupervised feature learning in a restricted Boltzmann machine with binary synapses

NASA Astrophysics Data System (ADS)

Huang, Haiping

2017-05-01

Revealing hidden features in unlabeled data is called unsupervised feature learning, which plays an important role in pretraining a deep neural network. Here we provide a statistical mechanics analysis of the unsupervised learning in a restricted Boltzmann machine with binary synapses. A message passing equation to infer the hidden feature is derived, and furthermore, variants of this equation are analyzed. A statistical analysis by replica theory describes the thermodynamic properties of the model. Our analysis confirms an entropy crisis preceding the non-convergence of the message passing equation, suggesting a discontinuous phase transition as a key characteristic of the restricted Boltzmann machine. Continuous phase transition is also confirmed depending on the embedded feature strength in the data. The mean-field result under the replica symmetric assumption agrees with that obtained by running message passing algorithms on single instances of finite sizes. Interestingly, in an approximate Hopfield model, the entropy crisis is absent, and a continuous phase transition is observed instead. We also develop an iterative equation to infer the hyper-parameter (temperature) hidden in the data, which in physics corresponds to iteratively imposing Nishimori condition. Our study provides insights towards understanding the thermodynamic properties of the restricted Boltzmann machine learning, and moreover important theoretical basis to build simplified deep networks.

Developmental insights into mature cognition.

PubMed

Keil, Frank C

2015-02-01

Three cases are described that illustrate new ways in which developmental research is informing the study of cognition in adults: statistical learning, neural substrates of cognition, and extended concepts. Developmental research has made clear the ubiquity of statistical learning while also revealing is limitations as a stand-alone way to acquire knowledge. With respect to neural substrates, development has uncovered links between executive processing and fronto-striatal circuits while also pointing to many aspects of high-level cognition that may not be neatly reducible to coherent neural descriptions. For extended concepts, children have made especially clear the weaknesses of intuitive theories in both children and adults while also illustrating other cognitive capacities that are used at all ages to navigate the socially distributed aspects of knowledge. Copyright © 2014 Elsevier B.V. All rights reserved.

Local Patterns to Global Architectures: Influences of Network Topology on Human Learning.

PubMed

Karuza, Elisabeth A; Thompson-Schill, Sharon L; Bassett, Danielle S

2016-08-01

A core question in cognitive science concerns how humans acquire and represent knowledge about their environments. To this end, quantitative theories of learning processes have been formalized in an attempt to explain and predict changes in brain and behavior. We connect here statistical learning approaches in cognitive science, which are rooted in the sensitivity of learners to local distributional regularities, and network science approaches to characterizing global patterns and their emergent properties. We focus on innovative work that describes how learning is influenced by the topological properties underlying sensory input. The confluence of these theoretical approaches and this recent empirical evidence motivate the importance of scaling-up quantitative approaches to learning at both the behavioral and neural levels. Copyright © 2016 Elsevier Ltd. All rights reserved.

Information-theoretic approach to interactive learning

NASA Astrophysics Data System (ADS)

Still, S.

2009-01-01

The principles of statistical mechanics and information theory play an important role in learning and have inspired both theory and the design of numerous machine learning algorithms. The new aspect in this paper is a focus on integrating feedback from the learner. A quantitative approach to interactive learning and adaptive behavior is proposed, integrating model- and decision-making into one theoretical framework. This paper follows simple principles by requiring that the observer's world model and action policy should result in maximal predictive power at minimal complexity. Classes of optimal action policies and of optimal models are derived from an objective function that reflects this trade-off between prediction and complexity. The resulting optimal models then summarize, at different levels of abstraction, the process's causal organization in the presence of the learner's actions. A fundamental consequence of the proposed principle is that the learner's optimal action policies balance exploration and control as an emerging property. Interestingly, the explorative component is present in the absence of policy randomness, i.e. in the optimal deterministic behavior. This is a direct result of requiring maximal predictive power in the presence of feedback.

Geometry of the perceptual space

NASA Astrophysics Data System (ADS)

Assadi, Amir H.; Palmer, Stephen; Eghbalnia, Hamid; Carew, John

1999-09-01

The concept of space and geometry varies across the subjects. Following Poincare, we consider the construction of the perceptual space as a continuum equipped with a notion of magnitude. The study of the relationships of objects in the perceptual space gives rise to what we may call perceptual geometry. Computational modeling of objects and investigation of their deeper perceptual geometrical properties (beyond qualitative arguments) require a mathematical representation of the perceptual space. Within the realm of such a mathematical/computational representation, visual perception can be studied as in the well-understood logic-based geometry. This, however, does not mean that one could reduce all problems of visual perception to their geometric counterparts. Rather, visual perception as reported by a human observer, has a subjective factor that could be analytically quantified only through statistical reasoning and in the course of repetitive experiments. Thus, the desire to experimentally verify the statements in perceptual geometry leads to an additional probabilistic structure imposed on the perceptual space, whose amplitudes are measured through intervention by human observers. We propose a model for the perceptual space and the case of perception of textured surfaces as a starting point for object recognition. To rigorously present these ideas and propose computational simulations for testing the theory, we present the model of the perceptual geometry of surfaces through an amplification of theory of Riemannian foliation in differential topology, augmented by statistical learning theory. When we refer to the perceptual geometry of a human observer, the theory takes into account the Bayesian formulation of the prior state of the knowledge of the observer and Hebbian learning. We use a Parallel Distributed Connectionist paradigm for computational modeling and experimental verification of our theory.

Using reusable learning objects (RLOs) in wound care education: Undergraduate student nurse's evaluation of their learning gain.

PubMed

Redmond, Catherine; Davies, Carmel; Cornally, Deirdre; Adam, Ewa; Daly, Orla; Fegan, Marianne; O'Toole, Margaret

2018-01-01

Both nationally and internationally concerns have been expressed over the adequacy of preparation of undergraduate nurses for the clinical skill of wound care. This project describes the educational evaluation of a series of Reusable Learning Objects (RLOs) as a blended learning approach to facilitate undergraduate nursing students learning of wound care for competence development. Constructivism Learning Theory and Cognitive Theory of Multimedia Learning informed the design of the RLOs, promoting active learner approaches. Clinically based case studies and visual data from two large university teaching hospitals provided the authentic learning materials required. Interactive exercises and formative feedback were incorporated into the educational resource. Evaluation of student perceived learning gains in terms of knowledge, ability and attitudes were measured using a quantitative pre and posttest Wound Care Competency Outcomes Questionnaire. The RLO CETL Questionnaire was used to identify perceived learning enablers. Statistical and deductive thematic analyses inform the findings. Students (n=192) reported that their ability to meet the competency outcomes for wound care had increased significantly after engaging with the RLOs. Students rated the RLOs highly across all categories of perceived usefulness, impact, access and integration. These findings provide evidence that the use of RLOs for both knowledge-based and performance-based learning is effective. RLOs when designed using clinically real case scenarios reflect the true complexities of wound care and offer innovative interventions in nursing curricula. Copyright © 2017 Elsevier Ltd. All rights reserved.

Neural network approaches versus statistical methods in classification of multisource remote sensing data

NASA Technical Reports Server (NTRS)

Benediktsson, Jon A.; Swain, Philip H.; Ersoy, Okan K.

1990-01-01

Neural network learning procedures and statistical classificaiton methods are applied and compared empirically in classification of multisource remote sensing and geographic data. Statistical multisource classification by means of a method based on Bayesian classification theory is also investigated and modified. The modifications permit control of the influence of the data sources involved in the classification process. Reliability measures are introduced to rank the quality of the data sources. The data sources are then weighted according to these rankings in the statistical multisource classification. Four data sources are used in experiments: Landsat MSS data and three forms of topographic data (elevation, slope, and aspect). Experimental results show that two different approaches have unique advantages and disadvantages in this classification application.

Application and Exploration of Big Data Mining in Clinical Medicine.

PubMed

Zhang, Yue; Guo, Shu-Li; Han, Li-Na; Li, Tie-Ling

2016-03-20

To review theories and technologies of big data mining and their application in clinical medicine. Literatures published in English or Chinese regarding theories and technologies of big data mining and the concrete applications of data mining technology in clinical medicine were obtained from PubMed and Chinese Hospital Knowledge Database from 1975 to 2015. Original articles regarding big data mining theory/technology and big data mining's application in the medical field were selected. This review characterized the basic theories and technologies of big data mining including fuzzy theory, rough set theory, cloud theory, Dempster-Shafer theory, artificial neural network, genetic algorithm, inductive learning theory, Bayesian network, decision tree, pattern recognition, high-performance computing, and statistical analysis. The application of big data mining in clinical medicine was analyzed in the fields of disease risk assessment, clinical decision support, prediction of disease development, guidance of rational use of drugs, medical management, and evidence-based medicine. Big data mining has the potential to play an important role in clinical medicine.

How to inhibit a distractor location? Statistical learning versus active, top-down suppression.

PubMed

Wang, Benchi; Theeuwes, Jan

2018-05-01

Recently, Wang and Theeuwes (Journal of Experimental Psychology: Human Perception and Performance, 44(1), 13-17, 2018a) demonstrated the role of lingering selection biases in an additional singleton search task in which the distractor singleton appeared much more often in one location than in all other locations. For this location, there was less capture and selection efficiency was reduced. It was argued that statistical learning induces plasticity within the spatial priority map such that particular locations that are high likely to contain a distractor are suppressed relative to all other locations. The current study replicated these findings regarding statistical learning (Experiment 1) and investigated whether similar effects can be obtained by cueing the distractor location in a top-down way on a trial-by-trial basis. The results show that top-down cueing of the distractor location with long (1,500 ms; Experiment 2) and short stimulus-onset symmetries (SOAs) (600 ms; Experiment 3) does not result in suppression: The amount of capture nor the efficiency of selection was affected by the cue. If anything, we found an attentional benefit (instead of the suppression) for the short SOA. We argue that through statistical learning, weights within the attentional priority map are changed such that one location containing a salient distractor is suppressed relative to all other locations. Our cueing experiments show that this effect cannot be accomplished by active, top-down suppression. Consequences for recent theories of distractor suppression are discussed.

Experience and Explanation: Using Videogames to Prepare Students for Formal Instruction in Statistics

NASA Astrophysics Data System (ADS)

Arena, Dylan A.; Schwartz, Daniel L.

2014-08-01

Well-designed digital games can deliver powerful experiences that are difficult to provide through traditional instruction, while traditional instruction can deliver formal explanations that are not a natural fit for gameplay. Combined, they can accomplish more than either can alone. An experiment tested this claim using the topic of statistics, where people's everyday experiences often conflict with normative statistical theories and a videogame might provide an alternate set of experiences for students to draw upon. The research used a game called Stats Invaders!, a variant of the classic videogame Space Invaders. In Stats Invaders!, the locations of descending alien invaders follow probability distributions, and players need to infer the shape of the distributions to play well. The experiment tested whether the game developed participants' intuitions about the structure of random events and thereby prepared them for future learning from a subsequent written passage on probability distributions. Community-college students who played the game and then read the passage learned more than participants who only read the passage.

The Use of Fuzzy Theory in Grading of Students in Math

ERIC Educational Resources Information Center

Bjelica, Momcilo; Rankovic, Dragica

2010-01-01

The development of computer science, statistics and other technological fields, give us more opportunities to improve the process of evaluation of degree of knowledge and achievements in a learning process of our students. More and more we are relying on the computer software to guide us in the grading process. An improved way of grading can help…

Attitudes to statistics in primary health care physicians, Qassim province.

PubMed

Jahan, Saulat; Al-Saigul, Abdullah Mohammed; Suliman, Amel Abdalrhim

2016-07-01

Aim To investigate primary health care (PHC) physicians' attitudes to statistics, their self-reported knowledge level, and their perceived training needs in statistics. In spite of realization of the importance of statistics, inadequacies in physicians' knowledge and skills have been found, underscoring the need for in-service training. Understanding physicians' attitudes to statistics is vital in planning statistics training. The study was based on theory of planned behavior. A cross-sectional survey of all PHC physicians was conducted in Qassim province, from August to October 2014. Attitudes to statistics were determined by a self-administered questionnaire. The attitudes were assessed on four subscales including general perceptions; perceptions of knowledge and training; perceptions of statistics and evidence-based medicine; and perceptions of future learning. Findings Of 416 eligible participants, 338 (81.25%) responded to the survey. On a scale of 1-10, the majority (73.6%) of the participants self-assessed their level of statistics knowledge as five or below. The attitude scores could have a minimum of 20 and a maximum of 100, with higher scores showing a positive attitude. The participants showed a positive attitude with the mean score of 71.14 (±7.73). Out of the four subscales, 'perceptions of statistics and evidence-based medicine' subscale scored the highest, followed by 'perceptions of future learning'. PHC physicians have a positive attitude to statistics. However, they realize their gaps in knowledge in statistics, and are keen to fill these gaps. Statistics training, resulting in improved statistics knowledge is expected to lead to clinical care utilizing evidence-based medicine, and thus improvement to health care services.

The Web as an educational tool for/in learning/teaching bioinformatics statistics.

PubMed

Oliver, J; Pisano, M E; Alonso, T; Roca, P

2005-12-01

Statistics provides essential tool in Bioinformatics to interpret the results of a database search or for the management of enormous amounts of information provided from genomics, proteomics and metabolomics. The goal of this project was the development of a software tool that would be as simple as possible to demonstrate the use of the Bioinformatics statistics. Computer Simulation Methods (CSMs) developed using Microsoft Excel were chosen for their broad range of applications, immediate and easy formula calculation, immediate testing and easy graphics representation, and of general use and acceptance by the scientific community. The result of these endeavours is a set of utilities which can be accessed from the following URL: http://gmein.uib.es/bioinformatica/statistics. When tested on students with previous coursework with traditional statistical teaching methods, the general opinion/overall consensus was that Web-based instruction had numerous advantages, but traditional methods with manual calculations were also needed for their theory and practice. Once having mastered the basic statistical formulas, Excel spreadsheets and graphics were shown to be very useful for trying many parameters in a rapid fashion without having to perform tedious calculations. CSMs will be of great importance for the formation of the students and professionals in the field of bioinformatics, and for upcoming applications of self-learning and continuous formation.

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

ERIC Educational Resources Information Center

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…

Toward a Meta-Theory of Learning and Performance

ERIC Educational Resources Information Center

Russ-Eft, Darlene

2004-01-01

This purpose of this paper is to identify implications of various learning theories for workplace learning and performance and HRD. It begins with a review of various theoretical positions on learning including behaviorism, Gestalt theory, cognitive theory, schema theory, connectionist theory, social learning or behavior modeling, social…

Teaching 5th grade science for aesthetic understanding

NASA Astrophysics Data System (ADS)

Girod, Mark A.

Many scientists speak with great zeal about the role of aesthetics and beauty in their science and inquiry. Few systematic efforts have been made to teach science in ways that appeal directly to aesthetics and this research is designed to do just that. Drawing from the aesthetic theory of Dewey, I describe an analytic lens called learning for aesthetic understanding that finds power in the degree to which our perceptions of the world are transformed, our interests and enthusiasm piqued, and our actions changed as we seek further experiences in the world. This learning theory is contrasted against two other current and popular theories of science learning, that of learning for conceptual understanding via conceptual change theory and learning for a language-oriented or discourse-based understanding. After a lengthy articulation of the pedagogical strategies used to teach for aesthetic understanding the research is described in which comparisons are drawn between students in two 5th grade classrooms---one taught for the goal of conceptual understanding and the other taught for the goal of aesthetic understanding. Results of this comparison show that more students in the treatment classroom had aesthetic experiences with science ideas and came to an aesthetic understanding when studying weather, erosion, and structure of matter than students in the control group. Also statistically significant effects are shown on measures of interest, affect, and efficacy for students in the treatment class. On measures of conceptual understanding it appears that treatment class students learned more and forgot less over time than control class students. The effect of the treatment does not generally depend on gender, ethnicity, or prior achievement except in students' identity beliefs about themselves as science learners. In this case, a significant interaction for treatment class females on science identity beliefs did occur. A discussion of these results as well as elaboration and extension of the pedagogical model used in teaching for aesthetic understanding is discussed.

The Centrality of Aboriginal Cultural Workshops and Experiential Learning in a Pre-Service Teacher Education Course: A Regional Victorian University Case Study

ERIC Educational Resources Information Center

Weuffen, Sara L.; Cahir, Fred; Pickford, Aunty Marjorie

2017-01-01

This paper discusses a cross-cultural pedagogical approach, couched in a theory-practice nexus, used at a Victorian regional university to guide non-Indigenous pre-service teachers' (PSTs) engagement with Aboriginal and Torres Strait Islander perspectives and cultures. We have drawn on qualitative and statistical data, and current issues in…

Short-term improvement in oral self-care of adolescents with social-cognitive theory-guided intervention.

PubMed

Hall-Scullin, Emma P

2015-12-01

Cluster randomised controlled trial. Clusters of adolescents (classrooms of 15- to 16-year-olds) in each school were allocated either into a control group or into an intervention group. The interventions consisted of peer cooperation (peer support) and peer interactive learning (observational learning) facilitated through feedback from a dentist (professional support). Three intervention sessions with preselected pairs of adolescents were delivered in the first three weeks. Gender, family socio-economic status (baseline) and different social-cognitive domain variables (baseline, six, and 12 months) were assessed using a questionnaire. Dental plaque levels were the primary outcome measure and they were measured at baseline, after the intervention measured only in the social-cognitive theory-guided group, at six and 12 months. At the six-month follow-up there was a statistically significant difference in means ± SD between the social-cognitive intervention group (27.4 ± 19.4) and the control group (35.1 ± 20.0). At the 12-month follow-up, there was no statistically significant difference in means ± SD between the social-cognitive intervention group (27.4 ± 18.5) and the control group (31.9 ± 17.8). Variations in dental plaque levels at different time periods were explained by the following predictors: family's socio-economic status, social-cognitive domain variables, group affiliation and baseline plaque levels. Social-cognitive theory-guided interventions improved oral self-care of adolescents in the short term. This improvement lasted only for five months after the intervention was discontinued.

Score As You Lift (SAYL): A Statistical Relational Learning Approach to Uplift Modeling.

PubMed

Nassif, Houssam; Kuusisto, Finn; Burnside, Elizabeth S; Page, David; Shavlik, Jude; Costa, Vítor Santos

We introduce Score As You Lift (SAYL), a novel Statistical Relational Learning (SRL) algorithm, and apply it to an important task in the diagnosis of breast cancer. SAYL combines SRL with the marketing concept of uplift modeling, uses the area under the uplift curve to direct clause construction and final theory evaluation, integrates rule learning and probability assignment, and conditions the addition of each new theory rule to existing ones. Breast cancer, the most common type of cancer among women, is categorized into two subtypes: an earlier in situ stage where cancer cells are still confined, and a subsequent invasive stage. Currently older women with in situ cancer are treated to prevent cancer progression, regardless of the fact that treatment may generate undesirable side-effects, and the woman may die of other causes. Younger women tend to have more aggressive cancers, while older women tend to have more indolent tumors. Therefore older women whose in situ tumors show significant dissimilarity with in situ cancer in younger women are less likely to progress, and can thus be considered for watchful waiting. Motivated by this important problem, this work makes two main contributions. First, we present the first multi-relational uplift modeling system, and introduce, implement and evaluate a novel method to guide search in an SRL framework. Second, we compare our algorithm to previous approaches, and demonstrate that the system can indeed obtain differential rules of interest to an expert on real data, while significantly improving the data uplift.

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

PubMed

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.

Bayesian truncation errors in chiral effective field theory: model checking and accounting for correlations

NASA Astrophysics Data System (ADS)

Melendez, Jordan; Wesolowski, Sarah; Furnstahl, Dick

2017-09-01

Chiral effective field theory (EFT) predictions are necessarily truncated at some order in the EFT expansion, which induces an error that must be quantified for robust statistical comparisons to experiment. A Bayesian model yields posterior probability distribution functions for these errors based on expectations of naturalness encoded in Bayesian priors and the observed order-by-order convergence pattern of the EFT. As a general example of a statistical approach to truncation errors, the model was applied to chiral EFT for neutron-proton scattering using various semi-local potentials of Epelbaum, Krebs, and Meißner (EKM). Here we discuss how our model can learn correlation information from the data and how to perform Bayesian model checking to validate that the EFT is working as advertised. Supported in part by NSF PHY-1614460 and DOE NUCLEI SciDAC DE-SC0008533.

Deal or No Deal: using games to improve student learning, retention and decision-making

NASA Astrophysics Data System (ADS)

Chow, Alan F.; Woodford, Kelly C.; Maes, Jeanne

2011-03-01

Student understanding and retention can be enhanced and improved by providing alternative learning activities and environments. Education theory recognizes the value of incorporating alternative activities (games, exercises and simulations) to stimulate student interest in the educational environment, enhance transfer of knowledge and improve learned retention with meaningful repetition. In this case study, we investigate using an online version of the television game show, 'Deal or No Deal', to enhance student understanding and retention by playing the game to learn expected value in an introductory statistics course, and to foster development of critical thinking skills necessary to succeed in the modern business environment. Enhancing the thinking process of problem solving using repetitive games should also improve a student's ability to follow non-mathematical problem-solving processes, which should improve the overall ability to process information and make logical decisions. Learning and retention are measured to evaluate the success of the students' performance.

Mutual interference between statistical summary perception and statistical learning.

PubMed

Zhao, Jiaying; Ngo, Nhi; McKendrick, Ryan; Turk-Browne, Nicholas B

2011-09-01

The visual system is an efficient statistician, extracting statistical summaries over sets of objects (statistical summary perception) and statistical regularities among individual objects (statistical learning). Although these two kinds of statistical processing have been studied extensively in isolation, their relationship is not yet understood. We first examined how statistical summary perception influences statistical learning by manipulating the task that participants performed over sets of objects containing statistical regularities (Experiment 1). Participants who performed a summary task showed no statistical learning of the regularities, whereas those who performed control tasks showed robust learning. We then examined how statistical learning influences statistical summary perception by manipulating whether the sets being summarized contained regularities (Experiment 2) and whether such regularities had already been learned (Experiment 3). The accuracy of summary judgments improved when regularities were removed and when learning had occurred in advance. In sum, calculating summary statistics impeded statistical learning, and extracting statistical regularities impeded statistical summary perception. This mutual interference suggests that statistical summary perception and statistical learning are fundamentally related.

Application and Exploration of Big Data Mining in Clinical Medicine

PubMed Central

Zhang, Yue; Guo, Shu-Li; Han, Li-Na; Li, Tie-Ling

2016-01-01

Objective: To review theories and technologies of big data mining and their application in clinical medicine. Data Sources: Literatures published in English or Chinese regarding theories and technologies of big data mining and the concrete applications of data mining technology in clinical medicine were obtained from PubMed and Chinese Hospital Knowledge Database from 1975 to 2015. Study Selection: Original articles regarding big data mining theory/technology and big data mining's application in the medical field were selected. Results: This review characterized the basic theories and technologies of big data mining including fuzzy theory, rough set theory, cloud theory, Dempster–Shafer theory, artificial neural network, genetic algorithm, inductive learning theory, Bayesian network, decision tree, pattern recognition, high-performance computing, and statistical analysis. The application of big data mining in clinical medicine was analyzed in the fields of disease risk assessment, clinical decision support, prediction of disease development, guidance of rational use of drugs, medical management, and evidence-based medicine. Conclusion: Big data mining has the potential to play an important role in clinical medicine. PMID:26960378

Interpreting support vector machine models for multivariate group wise analysis in neuroimaging

PubMed Central

Gaonkar, Bilwaj; Shinohara, Russell T; Davatzikos, Christos

2015-01-01

Machine learning based classification algorithms like support vector machines (SVMs) have shown great promise for turning a high dimensional neuroimaging data into clinically useful decision criteria. However, tracing imaging based patterns that contribute significantly to classifier decisions remains an open problem. This is an issue of critical importance in imaging studies seeking to determine which anatomical or physiological imaging features contribute to the classifier’s decision, thereby allowing users to critically evaluate the findings of such machine learning methods and to understand disease mechanisms. The majority of published work addresses the question of statistical inference for support vector classification using permutation tests based on SVM weight vectors. Such permutation testing ignores the SVM margin, which is critical in SVM theory. In this work we emphasize the use of a statistic that explicitly accounts for the SVM margin and show that the null distributions associated with this statistic are asymptotically normal. Further, our experiments show that this statistic is a lot less conservative as compared to weight based permutation tests and yet specific enough to tease out multivariate patterns in the data. Thus, we can better understand the multivariate patterns that the SVM uses for neuroimaging based classification. PMID:26210913

Broadening conceptions of learning in medical education: the message from teamworking.

PubMed

Bleakley, Alan

2006-02-01

There is a mismatch between the broad range of learning theories offered in the wider education literature and a relatively narrow range of theories privileged in the medical education literature. The latter are usually described under the heading of 'adult learning theory'. This paper critically addresses the limitations of the current dominant learning theories informing medical education. An argument is made that such theories, which address how an individual learns, fail to explain how learning occurs in dynamic, complex and unstable systems such as fluid clinical teams. Models of learning that take into account distributed knowing, learning through time as well as space, and the complexity of a learning environment including relationships between persons and artefacts, are more powerful in explaining and predicting how learning occurs in clinical teams. Learning theories may be privileged for ideological reasons, such as medicine's concern with autonomy. Where an increasing amount of medical education occurs in workplace contexts, sociocultural learning theories offer a best-fit exploration and explanation of such learning. We need to continue to develop testable models of learning that inform safe work practice. One type of learning theory will not inform all practice contexts and we need to think about a range of fit-for-purpose theories that are testable in practice. Exciting current developments include dynamicist models of learning drawing on complexity theory.

Bootstrapping in a language of thought: a formal model of numerical concept learning.

PubMed

Piantadosi, Steven T; Tenenbaum, Joshua B; Goodman, Noah D

2012-05-01

In acquiring number words, children exhibit a qualitative leap in which they transition from understanding a few number words, to possessing a rich system of interrelated numerical concepts. We present a computational framework for understanding this inductive leap as the consequence of statistical inference over a sufficiently powerful representational system. We provide an implemented model that is powerful enough to learn number word meanings and other related conceptual systems from naturalistic data. The model shows that bootstrapping can be made computationally and philosophically well-founded as a theory of number learning. Our approach demonstrates how learners may combine core cognitive operations to build sophisticated representations during the course of development, and how this process explains observed developmental patterns in number word learning. Copyright © 2011 Elsevier B.V. All rights reserved.

Addressing astronomy misconceptions and achieving national science standards utilizing aspects of multiple intelligences theory in the classroom and the planetarium

NASA Astrophysics Data System (ADS)

Sarrazine, Angela Renee

The purpose of this study was to incorporate multiple intelligences techniques in both a classroom and planetarium setting to create a significant increase in student learning about the moon and lunar phases. Utilizing a free-response questionnaire and a 25 item multiple choice pre-test/post-test design, this study identified middle school students' misconceptions and measured increases in student learning about the moon and lunar phases. The study spanned two semesters and contained six treatment groups which consisted of both single and multiple interventions. One group only attended the planetarium program. Two groups attended one of two classes a week prior to the planetarium program, and two groups attended one of two classes a week after the planetarium program. The most rigorous treatment group attended a class both a week before and after the planetarium program. Utilizing Rasch analysis techniques and parametric statistical tests, all six groups exhibited statistically significant gains in knowledge at the 0.05 level. There were no significant differences between students who attended only a planetarium program versus a single classroom program. Also, subjects who attended either a pre-planetarium class or a post- planetarium class did not show a statistically significant gain over the planetarium only situation. Equivalent effects on student learning were exhibited by the pre-planetarium class groups and post-planetarium class groups. Therefore, it was determined that the placement of the second intervention does not have a significant impact on student learning. However, a decrease in learning was observed with the addition of a third intervention. Further instruction and testing appeared to hinder student learning. This is perhaps an effect of subject fatigue.

The Attribution Theory of Learning and Advising Students on Academic Probation

ERIC Educational Resources Information Center

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…

Flight School in the Virtual Environment: Capabilities and Risks of Executing a Simulations-Based Flight Training Program

DTIC Science & Technology

2012-05-17

theories work together to explain learning in aviation—behavioral learning theory , cognitive learning theory , constructivism, experiential ...solve problems, and make decisions. Experiential learning theory incorporates both behavioral and cognitive theories .104 This theory harnesses the...34Evaluation of the Effectiveness of Flight School XXI," 7. 106 David A. Kolb , Experiential Learning : Experience as the Source of

A fast elitism Gaussian estimation of distribution algorithm and application for PID optimization.

PubMed

Xu, Qingyang; Zhang, Chengjin; Zhang, Li

2014-01-01

Estimation of distribution algorithm (EDA) is an intelligent optimization algorithm based on the probability statistics theory. A fast elitism Gaussian estimation of distribution algorithm (FEGEDA) is proposed in this paper. The Gaussian probability model is used to model the solution distribution. The parameters of Gaussian come from the statistical information of the best individuals by fast learning rule. A fast learning rule is used to enhance the efficiency of the algorithm, and an elitism strategy is used to maintain the convergent performance. The performances of the algorithm are examined based upon several benchmarks. In the simulations, a one-dimensional benchmark is used to visualize the optimization process and probability model learning process during the evolution, and several two-dimensional and higher dimensional benchmarks are used to testify the performance of FEGEDA. The experimental results indicate the capability of FEGEDA, especially in the higher dimensional problems, and the FEGEDA exhibits a better performance than some other algorithms and EDAs. Finally, FEGEDA is used in PID controller optimization of PMSM and compared with the classical-PID and GA.

A Fast Elitism Gaussian Estimation of Distribution Algorithm and Application for PID Optimization

PubMed Central

Xu, Qingyang; Zhang, Chengjin; Zhang, Li

2014-01-01

Estimation of distribution algorithm (EDA) is an intelligent optimization algorithm based on the probability statistics theory. A fast elitism Gaussian estimation of distribution algorithm (FEGEDA) is proposed in this paper. The Gaussian probability model is used to model the solution distribution. The parameters of Gaussian come from the statistical information of the best individuals by fast learning rule. A fast learning rule is used to enhance the efficiency of the algorithm, and an elitism strategy is used to maintain the convergent performance. The performances of the algorithm are examined based upon several benchmarks. In the simulations, a one-dimensional benchmark is used to visualize the optimization process and probability model learning process during the evolution, and several two-dimensional and higher dimensional benchmarks are used to testify the performance of FEGEDA. The experimental results indicate the capability of FEGEDA, especially in the higher dimensional problems, and the FEGEDA exhibits a better performance than some other algorithms and EDAs. Finally, FEGEDA is used in PID controller optimization of PMSM and compared with the classical-PID and GA. PMID:24892059

Applications of Support Vector Machines In Chemo And Bioinformatics

NASA Astrophysics Data System (ADS)

Jayaraman, V. K.; Sundararajan, V.

2010-10-01

Conventional linear & nonlinear tools for classification, regression & data driven modeling are being replaced on a rapid scale by newer techniques & tools based on artificial intelligence and machine learning. While the linear techniques are not applicable for inherently nonlinear problems, newer methods serve as attractive alternatives for solving real life problems. Support Vector Machine (SVM) classifiers are a set of universal feed-forward network based classification algorithms that have been formulated from statistical learning theory and structural risk minimization principle. SVM regression closely follows the classification methodology. In this work recent applications of SVM in Chemo & Bioinformatics will be described with suitable illustrative examples.

A Hebbian learning rule gives rise to mirror neurons and links them to control theoretic inverse models.

PubMed

Hanuschkin, A; Ganguli, S; Hahnloser, R H R

2013-01-01

Mirror neurons are neurons whose responses to the observation of a motor act resemble responses measured during production of that act. Computationally, mirror neurons have been viewed as evidence for the existence of internal inverse models. Such models, rooted within control theory, map-desired sensory targets onto the motor commands required to generate those targets. To jointly explore both the formation of mirrored responses and their functional contribution to inverse models, we develop a correlation-based theory of interactions between a sensory and a motor area. We show that a simple eligibility-weighted Hebbian learning rule, operating within a sensorimotor loop during motor explorations and stabilized by heterosynaptic competition, naturally gives rise to mirror neurons as well as control theoretic inverse models encoded in the synaptic weights from sensory to motor neurons. Crucially, we find that the correlational structure or stereotypy of the neural code underlying motor explorations determines the nature of the learned inverse model: random motor codes lead to causal inverses that map sensory activity patterns to their motor causes; such inverses are maximally useful, by allowing the imitation of arbitrary sensory target sequences. By contrast, stereotyped motor codes lead to less useful predictive inverses that map sensory activity to future motor actions. Our theory generalizes previous work on inverse models by showing that such models can be learned in a simple Hebbian framework without the need for error signals or backpropagation, and it makes new conceptual connections between the causal nature of inverse models, the statistical structure of motor variability, and the time-lag between sensory and motor responses of mirror neurons. Applied to bird song learning, our theory can account for puzzling aspects of the song system, including necessity of sensorimotor gating and selectivity of auditory responses to bird's own song (BOS) stimuli.

A Hebbian learning rule gives rise to mirror neurons and links them to control theoretic inverse models

PubMed Central

Hanuschkin, A.; Ganguli, S.; Hahnloser, R. H. R.

2013-01-01

Mirror neurons are neurons whose responses to the observation of a motor act resemble responses measured during production of that act. Computationally, mirror neurons have been viewed as evidence for the existence of internal inverse models. Such models, rooted within control theory, map-desired sensory targets onto the motor commands required to generate those targets. To jointly explore both the formation of mirrored responses and their functional contribution to inverse models, we develop a correlation-based theory of interactions between a sensory and a motor area. We show that a simple eligibility-weighted Hebbian learning rule, operating within a sensorimotor loop during motor explorations and stabilized by heterosynaptic competition, naturally gives rise to mirror neurons as well as control theoretic inverse models encoded in the synaptic weights from sensory to motor neurons. Crucially, we find that the correlational structure or stereotypy of the neural code underlying motor explorations determines the nature of the learned inverse model: random motor codes lead to causal inverses that map sensory activity patterns to their motor causes; such inverses are maximally useful, by allowing the imitation of arbitrary sensory target sequences. By contrast, stereotyped motor codes lead to less useful predictive inverses that map sensory activity to future motor actions. Our theory generalizes previous work on inverse models by showing that such models can be learned in a simple Hebbian framework without the need for error signals or backpropagation, and it makes new conceptual connections between the causal nature of inverse models, the statistical structure of motor variability, and the time-lag between sensory and motor responses of mirror neurons. Applied to bird song learning, our theory can account for puzzling aspects of the song system, including necessity of sensorimotor gating and selectivity of auditory responses to bird's own song (BOS) stimuli. PMID:23801941

Effect of moderate learning style–teaching mode mismatch on academic performance among 2nd year medical students in Pakistan

PubMed Central

Hamza, Muhammad; Inam-Ul-Haq; Hamid, Sidra; Nadir, Maha; Mehmood, Nadir

2018-01-01

Introduction: The vagueness surrounding “learning style–teaching mode mismatch” makes its effects uncertain. This study tried to tackle that controversy by comparing and assessing the effect of different learning styles on performance in physiology examination when teaching mode was somewhat different than learning preferences of the 2nd year medical students. Methods: A total of 102 2nd year medical students participated in this study. Honey and Mumford learning style questionnaire was used to categorize the participants into one of the four learning styles (activist, reflector, theorist, and pragmatist). Many teaching modes were used in the medical college. The first professional theory and practical physiology scores of these 102 students of University of Health Sciences were obtained online. Learning styles were compared with physiology scores and age using one-way analysis of variance and post hoc statistical analysis and between males and females by using Chi-square test. Results: Pragmatists had the lowest total physiology score (P < 0.001), while theorists had the highest total physiology scores (P < 0.001). Activists and reflectors had scores in between pragmatists and theorists, and there was no statistical difference between these two styles of learning (P = 0.9). No student scored below 60%. Conclusion: This study demonstrated that the effect of moderate teaching–learning mismatch is different for different learners. Theorists excelled as they had the highest physiology score, while pragmatists lagged in comparison. Reflectors and activists performed better than pragmatists but were worse than theorists. Despite this, none of the students scored below 60%. This shows that a moderate learning style–teaching mode mismatch is not harmful for learning. PMID:29736072

Effect of moderate learning style-teaching mode mismatch on academic performance among 2nd year medical students in Pakistan.

PubMed

Hamza, Muhammad; Inam-Ul-Haq; Hamid, Sidra; Nadir, Maha; Mehmood, Nadir

2018-01-01

The vagueness surrounding "learning style-teaching mode mismatch" makes its effects uncertain. This study tried to tackle that controversy by comparing and assessing the effect of different learning styles on performance in physiology examination when teaching mode was somewhat different than learning preferences of the 2 nd year medical students. A total of 102 2 nd year medical students participated in this study. Honey and Mumford learning style questionnaire was used to categorize the participants into one of the four learning styles (activist, reflector, theorist, and pragmatist). Many teaching modes were used in the medical college. The first professional theory and practical physiology scores of these 102 students of University of Health Sciences were obtained online. Learning styles were compared with physiology scores and age using one-way analysis of variance and post hoc statistical analysis and between males and females by using Chi-square test. Pragmatists had the lowest total physiology score ( P < 0.001), while theorists had the highest total physiology scores ( P < 0.001). Activists and reflectors had scores in between pragmatists and theorists, and there was no statistical difference between these two styles of learning ( P = 0.9). No student scored below 60%. This study demonstrated that the effect of moderate teaching-learning mismatch is different for different learners. Theorists excelled as they had the highest physiology score, while pragmatists lagged in comparison. Reflectors and activists performed better than pragmatists but were worse than theorists. Despite this, none of the students scored below 60%. This shows that a moderate learning style-teaching mode mismatch is not harmful for learning.

Active-Passive-Intuitive Learning Theory: A Unified Theory of Learning and Development

ERIC Educational Resources Information Center

Sigette, Tyson

2009-01-01

This paper addresses many theories of learning and human development which are very similar with regards as to how they suggest learning occurs. The differences in most of the theories exist in how they treat the development of the learner compared to methods of teaching. Most of the major learning theories taught to educators today are based on…

Learning Theory Foundations of Simulation-Based Mastery Learning.

PubMed

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.

Sociocultural Theory and the Mediated Learning Experience.

ERIC Educational Resources Information Center

Kozulin, Alex

2002-01-01

Discusses the two theories that have contributed most to the development of the mediational approach to learning, Vygotskian sociocultural theory and Feuerstein's theory of Mediated Learning Experience. Both theories emphasize the importance of sociocultural forces in shaping a child's development and learning, and have generated a number of…

Framework for Conducting Empirical Observations of Learning Processes.

ERIC Educational Resources Information Center

Fischer, Hans Ernst; von Aufschnaiter, Stephan

1993-01-01

Reviews four hypotheses about learning: Comenius's transmission-reception theory, information processing theory, Gestalt theory, and Piagetian theory. Uses the categories preunderstanding, conceptual change, and learning processes to classify and assess investigations on learning processes. (PR)

The Interactions of Relationships, Interest, and Self-Efficacy in Undergraduate Physics

NASA Astrophysics Data System (ADS)

Dou, Remy

This collected papers dissertation explores students' academic interactions in an active learning, introductory physics settings as they relate to the development of physics self-efficacy and interest. The motivation for this work extends from the national call to increase participation of students in the pursuit of science, technology, engineering, and mathematics (STEM) careers. Self-efficacy and interest are factors that play prominent roles in popular, evidence-based, career theories, including the Social cognitive career theory (SCCT) and the identity framework. Understanding how these constructs develop in light of the most pervasive characteristic of the active learning introductory physics classroom (i.e., peer-to-peer interactions) has implications on how students learn in a variety of introductory STEM classrooms and settings structured after constructivist and sociocultural learning theories. I collected data related to students' in-class interactions using the tools of social network analysis (SNA). Social network analysis has recently been shown to be an effective and useful way to examine the structure of student relationships that develop in and out of STEM classrooms. This set of studies furthers the implementation of SNA as a tool to examine self-efficacy and interest formation in the active learning physics classroom. Here I represent a variety of statistical applications of SNA, including bootstrapped linear regression (Chapter 2), structural equation modeling (Chapter 3), and hierarchical linear modeling for longitudinal analyses (Chapter 4). Self-efficacy data were collected using the Sources of Self-Efficacy for Science Courses - Physics survey (SOSESC-P), and interest data were collected using the physics identity survey. Data for these studies came from the Modeling Instruction sections of Introductory Physics with Calculus offered at Florida International University in the fall of 2014 and 2015. Analyses support the idea that students' perceptions of one another impact the development of their social network centrality, which in turn affects their self-efficacy building experiences and their overall self-efficacy. It was shown that unlike career theories that emphasize causal relationships between the development of self-efficacy and the subsequent growth of student interest, in this context student interest takes precedence before the development of student self-efficacy. This outcome also has various implications for career theories.

A Comparative Analysis of Three Unique Theories of Organizational Learning

ERIC Educational Resources Information Center

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…

A Conceptual Model for Effective Distance Learning in Higher Education

ERIC Educational Resources Information Center

Farajollahi, Mehran; Zare, Hosein; Hormozi, Mahmood; Sarmadi, Mohammad Reza; Zarifsanaee, Nahid

2010-01-01

The present research aims at presenting a conceptual model for effective distance learning in higher education. Findings of this research shows that an understanding of the technological capabilities and learning theories especially constructive theory and independent learning theory and communicative and interaction theory in Distance learning is…

Statistical Inference for Data Adaptive Target Parameters.

PubMed

Hubbard, Alan E; Kherad-Pajouh, Sara; van der Laan, Mark J

2016-05-01

Consider one observes n i.i.d. copies of a random variable with a probability distribution that is known to be an element of a particular statistical model. In order to define our statistical target we partition the sample in V equal size sub-samples, and use this partitioning to define V splits in an estimation sample (one of the V subsamples) and corresponding complementary parameter-generating sample. For each of the V parameter-generating samples, we apply an algorithm that maps the sample to a statistical target parameter. We define our sample-split data adaptive statistical target parameter as the average of these V-sample specific target parameters. We present an estimator (and corresponding central limit theorem) of this type of data adaptive target parameter. This general methodology for generating data adaptive target parameters is demonstrated with a number of practical examples that highlight new opportunities for statistical learning from data. This new framework provides a rigorous statistical methodology for both exploratory and confirmatory analysis within the same data. Given that more research is becoming "data-driven", the theory developed within this paper provides a new impetus for a greater involvement of statistical inference into problems that are being increasingly addressed by clever, yet ad hoc pattern finding methods. To suggest such potential, and to verify the predictions of the theory, extensive simulation studies, along with a data analysis based on adaptively determined intervention rules are shown and give insight into how to structure such an approach. The results show that the data adaptive target parameter approach provides a general framework and resulting methodology for data-driven science.

Application of Statistical Learning Theory to Plankton Image Analysis

DTIC Science & Technology

2006-06-01

linear distance interval from 1 to 40 pixels and two directions formula (horizontal & vertical, and diagonals), EF2 is EF with 7 ex- ponential distance...and four directions formula (horizontal, vertical and two diagonals). It is clear that exponential distance inter- val works better than the linear ...PSI - PS by Vincent, linear and pseudo opening and closing spectra, each has 40 elements, total feature length of 160. PS2 - PS modified from Mei- jster

Alterations in choice behavior by manipulations of world model.

PubMed

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

2010-09-14

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

Alterations in choice behavior by manipulations of world model

PubMed Central

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

2010-01-01

How to compute initially unknown reward values makes up one of the key problems in reinforcement learning theory, with two basic approaches being used. Model-free algorithms rely on the accumulation of substantial amounts of experience to compute the value of actions, whereas in model-based learning, the agent seeks to learn the generative process for outcomes from which the value of actions can be predicted. Here we show that (i) “probability matching”—a consistent example of suboptimal choice behavior seen in humans—occurs in an optimal Bayesian model-based learner using a max decision rule that is initialized with ecologically plausible, but incorrect beliefs about the generative process for outcomes and (ii) human behavior can be strongly and predictably altered by the presence of cues suggestive of various generative processes, despite statistically identical outcome generation. These results suggest human decision making is rational and model based and not consistent with model-free learning. PMID:20805507

Aids to Computer-Based Multimedia Learning.

ERIC Educational Resources Information Center

Mayer, Richard E.; Moreno, Roxana

2002-01-01

Presents a cognitive theory of multimedia learning that draws on dual coding theory, cognitive load theory, and constructivist learning theory and derives some principles of instructional design for fostering multimedia learning. These include principles of multiple representation, contiguity, coherence, modality, and redundancy. (SLD)

A Learning-Style Theory for Understanding Autistic Behaviors

PubMed Central

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. PMID:21886617

Who Needs Learning Theory Anyway?

ERIC Educational Resources Information Center

Zemke, Ron

2002-01-01

Looks at a variety of learning theories: andragogy, behaviorism, cognitivism, conditions of learning, Gestalt, and social learning. Addresses the difficulty of selecting an appropriate theory for training. (JOW)

Enhancing predictive accuracy and reproducibility in clinical evaluation research: Commentary on the special section of the Journal of Evaluation in Clinical Practice.

PubMed

Bryant, Fred B

2016-12-01

This paper introduces a special section of the current issue of the Journal of Evaluation in Clinical Practice that includes a set of 6 empirical articles showcasing a versatile, new machine-learning statistical method, known as optimal data (or discriminant) analysis (ODA), specifically designed to produce statistical models that maximize predictive accuracy. As this set of papers clearly illustrates, ODA offers numerous important advantages over traditional statistical methods-advantages that enhance the validity and reproducibility of statistical conclusions in empirical research. This issue of the journal also includes a review of a recently published book that provides a comprehensive introduction to the logic, theory, and application of ODA in empirical research. It is argued that researchers have much to gain by using ODA to analyze their data. © 2016 John Wiley & Sons, Ltd.

Effects of a National Public Service Information Campaign on Crime Prevention: Perspectives from Social Learning and Social Control Theory.

ERIC Educational Resources Information Center

Lordan, Edward J.; Kwon, Joongrok

This study examined the effects of public service advertising from two theoretical backgrounds: social learning theory and social control theory. Traditional social learning theory assumes that learning occurs by subjects performing responses and experiencing their effects, with reinforcement as the main determinant. Social control theory, as…

Perception of hospital learning environment: a survey of Hong Kong nursing students.

PubMed

Chan, Dominic S K; Ip, Wan Y

2007-10-01

The last two decades have seen widespread changes to nurse education but the clinical field remains an essential and invaluable resource in preparing students for the reality of their professional role, supporting the integration of theory and practice, and linking the 'knowing what' to do with the 'knowing how' to deliver care. The clinical learning environment represents a vital element of nurse education that needs to be measurable and warrants further investigation. This survey study examined Hong Kong nursing students' perception of the social climate of the clinical learning environment. Participants were invited to complete the two versions, the Actual and Preferred Forms, of the Clinical Learning Environment Inventory following the completion of their clinical field placement. Two hundred eighty one Actual Forms and 243 Preferred Forms returned. SPPS version 11 was employed to analyse data with descriptive and inferential statistics. It was found that there were significant differences between students' perceptions of the actual clinical learning environment and the ideal clinical learning environment they desired. The study highlights the need for a supportive clinical learning environment which is of paramount importance for students in clinical practice.

Autistic traits, but not schizotypy, predict increased weighting of sensory information in Bayesian visual integration.

PubMed

Karvelis, Povilas; Seitz, Aaron R; Lawrie, Stephen M; Seriès, Peggy

2018-05-14

Recent theories propose that schizophrenia/schizotypy and autistic spectrum disorder are related to impairments in Bayesian inference that is, how the brain integrates sensory information (likelihoods) with prior knowledge. However existing accounts fail to clarify: (i) how proposed theories differ in accounts of ASD vs. schizophrenia and (ii) whether the impairments result from weaker priors or enhanced likelihoods. Here, we directly address these issues by characterizing how 91 healthy participants, scored for autistic and schizotypal traits, implicitly learned and combined priors with sensory information. This was accomplished through a visual statistical learning paradigm designed to quantitatively assess variations in individuals' likelihoods and priors. The acquisition of the priors was found to be intact along both traits spectra. However, autistic traits were associated with more veridical perception and weaker influence of expectations. Bayesian modeling revealed that this was due, not to weaker prior expectations, but to more precise sensory representations. © 2018, Karvelis et al.

Robots Learn to Recognize Individuals from Imitative Encounters with People and Avatars

NASA Astrophysics Data System (ADS)

Boucenna, Sofiane; Cohen, David; Meltzoff, Andrew N.; Gaussier, Philippe; Chetouani, Mohamed

2016-02-01

Prior to language, human infants are prolific imitators. Developmental science grounds infant imitation in the neural coding of actions, and highlights the use of imitation for learning from and about people. Here, we used computational modeling and a robot implementation to explore the functional value of action imitation. We report 3 experiments using a mutual imitation task between robots, adults, typically developing children, and children with Autism Spectrum Disorder. We show that a particular learning architecture - specifically one combining artificial neural nets for (i) extraction of visual features, (ii) the robot’s motor internal state, (iii) posture recognition, and (iv) novelty detection - is able to learn from an interactive experience involving mutual imitation. This mutual imitation experience allowed the robot to recognize the interactive agent in a subsequent encounter. These experiments using robots as tools for modeling human cognitive development, based on developmental theory, confirm the promise of developmental robotics. Additionally, findings illustrate how person recognition may emerge through imitative experience, intercorporeal mapping, and statistical learning.

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

PubMed

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

2015-05-01

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

Teaching differential diagnosis to nurse practitioner students in a distance program.

PubMed

Colella, Christine L; Beery, Theresa A

2014-08-01

An interactive case study (ICS) is a novel way to enhance the teaching of differential diagnosis to distance learning nurse practitioner students. Distance education renders the use of many teaching strategies commonly used with face-to-face students difficult, if not impossible. To meet this new pedagogical dilemma and to provide excellence in education, the ICS was developed. Kolb's theory of experiential learning supported efforts to follow the utilization of the ICS. This study sought to determine whether learning outcomes for the distance learning students were equivalent to those of on-campus students who engaged in a live-patient encounter. Accuracy of differential diagnosis lists generated by onsite and online students was compared. Equivalency testing assessed clinical, rather than only statistical, significance in data from 291 students. The ICS responses from the distance learning and onsite students differed by 4.9%, which was within the a priori equivalence estimate of 10%. Narrative data supported the findings. Copyright 2014, SLACK Incorporated.

Robots Learn to Recognize Individuals from Imitative Encounters with People and Avatars

PubMed Central

Boucenna, Sofiane; Cohen, David; Meltzoff, Andrew N.; Gaussier, Philippe; Chetouani, Mohamed

2016-01-01

Prior to language, human infants are prolific imitators. Developmental science grounds infant imitation in the neural coding of actions, and highlights the use of imitation for learning from and about people. Here, we used computational modeling and a robot implementation to explore the functional value of action imitation. We report 3 experiments using a mutual imitation task between robots, adults, typically developing children, and children with Autism Spectrum Disorder. We show that a particular learning architecture - specifically one combining artificial neural nets for (i) extraction of visual features, (ii) the robot’s motor internal state, (iii) posture recognition, and (iv) novelty detection - is able to learn from an interactive experience involving mutual imitation. This mutual imitation experience allowed the robot to recognize the interactive agent in a subsequent encounter. These experiments using robots as tools for modeling human cognitive development, based on developmental theory, confirm the promise of developmental robotics. Additionally, findings illustrate how person recognition may emerge through imitative experience, intercorporeal mapping, and statistical learning. PMID:26844862

Advanced Learning Theories Applied to Leadership Development

DTIC Science & Technology

2006-11-01

Theory . We combined the cognitive , experiential and motivational components of advanced learning theories to develop a training application...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

Global, broad, or specific cognitive differences? Using a MIMIC model to examine differences in CHC abilities in children with learning disabilities.

PubMed

Niileksela, Christopher R; Reynolds, Matthew R

2014-01-01

This study was designed to better understand the relations between learning disabilities and different levels of latent cognitive abilities, including general intelligence (g), broad cognitive abilities, and specific abilities based on the Cattell-Horn-Carroll theory of intelligence (CHC theory). Data from the Differential Ability Scales-Second Edition (DAS-II) were used to create a multiple-indicator multiple cause model to examine the latent mean differences in cognitive abilities between children with and without learning disabilities in reading (LD reading), math (LD math), and reading and writing(LD reading and writing). Statistically significant differences were found in the g factor between the norm group and the LD groups. After controlling for differences in g, the LD reading and LD reading and writing groups showed relatively lower latent processing speed, and the LD math group showed relatively higher latent comprehension-knowledge. There were also some differences in some specific cognitive abilities, including lower scores in spatial relations and numerical facility for the LD math group, and lower scores in visual memory for the LD reading and writing group. These specific mean differences were above and beyond any differences in the latent cognitive factor means.

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

ERIC Educational Resources Information Center

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…

Climate Change Conceptual Change: Scientific Information Can Transform Attitudes.

PubMed

Ranney, Michael Andrew; Clark, Dav

2016-01-01

Of this article's seven experiments, the first five demonstrate that virtually no Americans know the basic global warming mechanism. Fortunately, Experiments 2-5 found that 2-45 min of physical-chemical climate instruction durably increased such understandings. This mechanistic learning, or merely receiving seven highly germane statistical facts (Experiment 6), also increased climate-change acceptance-across the liberal-conservative spectrum. However, Experiment 7's misleading statistics decreased such acceptance (and dramatically, knowledge-confidence). These readily available attitudinal and conceptual changes through scientific information disconfirm what we term "stasis theory"--which some researchers and many laypeople varyingly maintain. Stasis theory subsumes the claim that informing people (particularly Americans) about climate science may be largely futile or even counterproductive--a view that appears historically naïve, suffers from range restrictions (e.g., near-zero mechanistic knowledge), and/or misinterprets some polarization and (noncausal) correlational data. Our studies evidenced no polarizations. Finally, we introduce HowGlobalWarmingWorks.org--a website designed to directly enhance public "climate-change cognition." Copyright © 2016 Cognitive Science Society, Inc.

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

ERIC Educational Resources Information Center

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…

The Application of Learning Theories in Community College Classrooms. UCLA Community College Bibliography

ERIC Educational Resources Information Center

Carducci, Rozana

2006-01-01

The references in this document provide an overview of empirical and conceptual scholarship on the application of learning theories in community college classrooms. Specific theories discussed in the citations include: active learning, cooperative learning, multiple intelligences, problem-based learning, and self-regulated learning. In addition to…

Theory to Practice: How Developing a K-12 Curriculum in Emergency Preparedness, Life Safety, or Homeland Security Can Lead to Resiliency

DTIC Science & Technology

2012-09-01

learning . ( Bandura , 1977) Additionally, the main concepts as posited by Bandura about the Social Learning theory are that... social learning had taken place, a piece seemed to be missing that could be explained through another theory . Bandura developed and posited the...of the Social Learning theory . ( Bandura , 1986) 2. Social Cognitive Theory The theory revolves around the process of acquiring

Can a theory-based educational intervention change nurses' knowledge and attitudes concerning cancer pain management? A quasi-experimental design.

PubMed

Gustafsson, Markus; Borglin, Gunilla

2013-08-19

Registered Nurses (RNs) play an important role in caring for patients suffering from cancer pain. A lack of knowledge regarding pain management and the RNs' own perception of cancer pain could act as barriers to effective pain management. Educational interventions that target RNs' knowledge and attitudes have proved promising. However, an intervention consisting of evidence-based practice is a multifaceted process and demands behavioural and cognitive changes to sustain the effects of the intervention. Therefore, our study aimed to investigate if a theory-based educational intervention could change RNs' knowledge and attitudes to cancer pain and pain management, both four and 12 weeks after the start of the intervention. A quasi-experimental design with non-equivalent control groups was used. The primary outcome was measured using a modified version of the instrument Nurses' Knowledge and Attitudes Survey Regarding Pain (NKAS) at baseline, four weeks and 12 weeks after the start of the intervention to evaluate its persistence. The intervention's educational curriculum was based on the principles of Ajzen's Theory of Planned Behaviour and consisted of interactive learning activities conducted in workshops founded on evidence-based knowledge. The RN's own experiences from cancer pain management were used in the learning process. The theory-based educational intervention aimed at changing RNs knowledge and attitudes regarding cancer pain management measured by primary outcome NKAS resulted in a statistical significant (p<0.05) improvement of total mean score from baseline to four weeks at the intervention ward. The findings of this study, suggest that a theory-based educational intervention focused at RNs can be effective in changing RN's knowledge and attitudes regarding cancer pain management. However, the high number of dropouts between baseline and four weeks needs to be taken into account when evaluating our findings. Finally, this kind of theory-based educational intervention with interactive learning activities has been sparsely researched and needs to be evaluated further in larger projects. Clinical Trials. Gov: NCT01313234.

A psychometric evaluation of the digital logic concept inventory

NASA Astrophysics Data System (ADS)

Herman, Geoffrey L.; Zilles, Craig; Loui, Michael C.

2014-10-01

Concept inventories hold tremendous promise for promoting the rigorous evaluation of teaching methods that might remedy common student misconceptions and promote deep learning. The measurements from concept inventories can be trusted only if the concept inventories are evaluated both by expert feedback and statistical scrutiny (psychometric evaluation). Classical Test Theory and Item Response Theory provide two psychometric frameworks for evaluating the quality of assessment tools. We discuss how these theories can be applied to assessment tools generally and then apply them to the Digital Logic Concept Inventory (DLCI). We demonstrate that the DLCI is sufficiently reliable for research purposes when used in its entirety and as a post-course assessment of students' conceptual understanding of digital logic. The DLCI can also discriminate between students across a wide range of ability levels, providing the most information about weaker students' ability levels.

A bottom-up strategy for establishment of EER in three Nordic countries - the role of networks

NASA Astrophysics Data System (ADS)

Edström, Kristina; Kolmos, Anette; Malmi, Lauri; Bernhard, Jonte; Andersson, Pernille

2018-03-01

This paper investigates the emergence of an engineering education research (EER) community in three Nordic countries: Denmark, Finland and Sweden. First, an overview of the current state of Nordic EER authorship is produced through statistics on international publication. Then, the history of EER and its precursor activities is described in three national narratives. These national storylines are tied together in a description of recent networking activities, aiming to strengthen the EER communities on the Nordic level. Taking these three perspectives together, and drawing on concepts from community of practice theory, network theory and learning network theory, we discuss factors behind the differences in the countries, and draw some conclusions about implications for networking activities in a heterogeneous community. Further, we discuss the role of networks for affording a joint identity.

Learning Theory and the Study of Instruction

DTIC Science & Technology

1989-02-01

learning theories (e.g., Fitts 1962, Vygotsky 1978), cultural beliefs about learning, and commonsense observations of teaching and tutoring. But...LEARNING THEORY AND THE STUDY OF INSTRUCTION IRobert Glaser Miriam Bassok LEARNING RESEARCH AND DEVELOPMENT CENTER DTIC 7~jahELECTE (%Q)2 3 FEB 1989...University of Pittsburgh _o role=*--,-mW .,N89 2 23 025 a.wtt- &@I =01% N A I LEARNING THEORY AND THE STUDY OF INSTRUCTION Robert Glaser Miriam Bassok

Second Language Experience Facilitates Statistical Learning of Novel Linguistic Materials.

PubMed

Potter, Christine E; Wang, Tianlin; Saffran, Jenny R

2017-04-01

Recent research has begun to explore individual differences in statistical learning, and how those differences may be related to other cognitive abilities, particularly their effects on language learning. In this research, we explored a different type of relationship between language learning and statistical learning: the possibility that learning a new language may also influence statistical learning by changing the regularities to which learners are sensitive. We tested two groups of participants, Mandarin Learners and Naïve Controls, at two time points, 6 months apart. At each time point, participants performed two different statistical learning tasks: an artificial tonal language statistical learning task and a visual statistical learning task. Only the Mandarin-learning group showed significant improvement on the linguistic task, whereas both groups improved equally on the visual task. These results support the view that there are multiple influences on statistical learning. Domain-relevant experiences may affect the regularities that learners can discover when presented with novel stimuli. Copyright © 2016 Cognitive Science Society, Inc.

Second language experience facilitates statistical learning of novel linguistic materials

PubMed Central

Potter, Christine E.; Wang, Tianlin; Saffran, Jenny R.

2016-01-01

Recent research has begun to explore individual differences in statistical learning, and how those differences may be related to other cognitive abilities, particularly their effects on language learning. In the present research, we explored a different type of relationship between language learning and statistical learning: the possibility that learning a new language may also influence statistical learning by changing the regularities to which learners are sensitive. We tested two groups of participants, Mandarin Learners and Naïve Controls, at two time points, six months apart. At each time point, participants performed two different statistical learning tasks: an artificial tonal language statistical learning task and a visual statistical learning task. Only the Mandarin-learning group showed significant improvement on the linguistic task, while both groups improved equally on the visual task. These results support the view that there are multiple influences on statistical learning. Domain-relevant experiences may affect the regularities that learners can discover when presented with novel stimuli. PMID:27988939

Unification of field theory and maximum entropy methods for learning probability densities

NASA Astrophysics Data System (ADS)

Kinney, Justin B.

2015-09-01

The need to estimate smooth probability distributions (a.k.a. probability densities) from finite sampled data is ubiquitous in science. Many approaches to this problem have been described, but none is yet regarded as providing a definitive solution. Maximum entropy estimation and Bayesian field theory are two such approaches. Both have origins in statistical physics, but the relationship between them has remained unclear. Here I unify these two methods by showing that every maximum entropy density estimate can be recovered in the infinite smoothness limit of an appropriate Bayesian field theory. I also show that Bayesian field theory estimation can be performed without imposing any boundary conditions on candidate densities, and that the infinite smoothness limit of these theories recovers the most common types of maximum entropy estimates. Bayesian field theory thus provides a natural test of the maximum entropy null hypothesis and, furthermore, returns an alternative (lower entropy) density estimate when the maximum entropy hypothesis is falsified. The computations necessary for this approach can be performed rapidly for one-dimensional data, and software for doing this is provided.

Unification of field theory and maximum entropy methods for learning probability densities.

PubMed

Kinney, Justin B

2015-09-01

The need to estimate smooth probability distributions (a.k.a. probability densities) from finite sampled data is ubiquitous in science. Many approaches to this problem have been described, but none is yet regarded as providing a definitive solution. Maximum entropy estimation and Bayesian field theory are two such approaches. Both have origins in statistical physics, but the relationship between them has remained unclear. Here I unify these two methods by showing that every maximum entropy density estimate can be recovered in the infinite smoothness limit of an appropriate Bayesian field theory. I also show that Bayesian field theory estimation can be performed without imposing any boundary conditions on candidate densities, and that the infinite smoothness limit of these theories recovers the most common types of maximum entropy estimates. Bayesian field theory thus provides a natural test of the maximum entropy null hypothesis and, furthermore, returns an alternative (lower entropy) density estimate when the maximum entropy hypothesis is falsified. The computations necessary for this approach can be performed rapidly for one-dimensional data, and software for doing this is provided.

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

PubMed

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 take advantage of this unique teaching format as it gains increasing importance in medical education. © 2014 John Wiley & Sons Ltd.

Vocational students' learning preferences: the interpretability of ipsative data.

PubMed

Smith, P J

2000-02-01

A number of researchers have argued that ipsative data are not suitable for statistical procedures designed for normative data. Others have argued that the interpretability of such analyses of ipsative data are little affected where the number of variables and the sample size are sufficiently large. The research reported here represents a factor analysis of the scores on the Canfield Learning Styles Inventory for 1,252 students in vocational education. The results of the factor analysis of these ipsative data were examined in a context of existing theory and research on vocational students and lend support to the argument that the factor analysis of ipsative data can provide sensibly interpretable results.

Central Limit Theorem: New SOCR Applet and Demonstration Activity

PubMed Central

Dinov, Ivo D.; Christou, Nicolas; Sanchez, Juana

2011-01-01

Modern approaches for information technology based blended education utilize a variety of novel instructional, computational and network resources. Such attempts employ technology to deliver integrated, dynamically linked, interactive content and multifaceted learning environments, which may facilitate student comprehension and information retention. In this manuscript, we describe one such innovative effort of using technological tools for improving student motivation and learning of the theory, practice and usability of the Central Limit Theorem (CLT) in probability and statistics courses. Our approach is based on harnessing the computational libraries developed by the Statistics Online Computational Resource (SOCR) to design a new interactive Java applet and a corresponding demonstration activity that illustrate the meaning and the power of the CLT. The CLT applet and activity have clear common goals; to provide graphical representation of the CLT, to improve student intuition, and to empirically validate and establish the limits of the CLT. The SOCR CLT activity consists of four experiments that demonstrate the assumptions, meaning and implications of the CLT and ties these to specific hands-on simulations. We include a number of examples illustrating the theory and applications of the CLT. Both the SOCR CLT applet and activity are freely available online to the community to test, validate and extend (Applet: http://www.socr.ucla.edu/htmls/SOCR_Experiments.html and Activity: http://wiki.stat.ucla.edu/socr/index.php/SOCR_EduMaterials_Activities_GeneralCentralLimitTheorem). PMID:21833159

Central Limit Theorem: New SOCR Applet and Demonstration Activity.

PubMed

Dinov, Ivo D; Christou, Nicolas; Sanchez, Juana

2008-07-01

Modern approaches for information technology based blended education utilize a variety of novel instructional, computational and network resources. Such attempts employ technology to deliver integrated, dynamically linked, interactive content and multifaceted learning environments, which may facilitate student comprehension and information retention. In this manuscript, we describe one such innovative effort of using technological tools for improving student motivation and learning of the theory, practice and usability of the Central Limit Theorem (CLT) in probability and statistics courses. Our approach is based on harnessing the computational libraries developed by the Statistics Online Computational Resource (SOCR) to design a new interactive Java applet and a corresponding demonstration activity that illustrate the meaning and the power of the CLT. The CLT applet and activity have clear common goals; to provide graphical representation of the CLT, to improve student intuition, and to empirically validate and establish the limits of the CLT. The SOCR CLT activity consists of four experiments that demonstrate the assumptions, meaning and implications of the CLT and ties these to specific hands-on simulations. We include a number of examples illustrating the theory and applications of the CLT. Both the SOCR CLT applet and activity are freely available online to the community to test, validate and extend (Applet: http://www.socr.ucla.edu/htmls/SOCR_Experiments.html and Activity: http://wiki.stat.ucla.edu/socr/index.php/SOCR_EduMaterials_Activities_GeneralCentralLimitTheorem).

Log In to Experiential Learning Theory: Supporting Web-Based Faculty Development

PubMed Central

Brien, Sarah; Parry, Marcus

2017-01-01

Background For an increasingly busy and geographically dispersed faculty, the Faculty of Medicine at the University of Southampton, United Kingdom, developed a range of Web-based faculty development modules, based on Kolb’s experiential learning cycle, to complement the faculty’s face-to-face workshops. Objective The objective of this study was to assess users’ views and perceptions of the effectiveness of Web-based faculty development modules based on Kolb’s experiential learning cycle. We explored (1) users’ satisfaction with the modules, (2) whether Kolb’s design framework supported users’ learning, and (3) whether the design principle impacts their work as educators. Methods We gathered data from users over a 3-year period using evaluation surveys built into each of the seven modules. Quantitative data were analyzed using descriptive statistics, and responses to open-ended questions were analyzed using content analysis. Results Out of the 409 module users, 283 completed the survey (69.1% response rate). Over 80% of the users reported being satisfied or very satisfied with seven individual aspects of the modules. The findings suggest a strong synergy between the design features that users rated most highly and the key stages of Kolb’s learning cycle. The use of simulations and videos to give the users an initial experience as well as the opportunity to “Have a go” and receive feedback in a safe environment were both considered particularly useful. In addition to providing an opportunity for reflection, many participants considered that the modules would enhance their roles as educators through: increasing their knowledge on various education topics and the required standards for medical training, and improving their skills in teaching and assessing students through practice and feedback and ultimately increasing their confidence. Conclusions Kolb’s theory-based design principle used for Web-based faculty development can support faculty to improve their skills and has impact on their role as educators. Grounding Web-based training in learning theory offers an effective and flexible approach for faculty development. PMID:28954718

The semantic representation of prejudice and stereotypes.

PubMed

Bhatia, Sudeep

2017-07-01

We use a theory of semantic representation to study prejudice and stereotyping. Particularly, we consider large datasets of newspaper articles published in the United States, and apply latent semantic analysis (LSA), a prominent model of human semantic memory, to these datasets to learn representations for common male and female, White, African American, and Latino names. LSA performs a singular value decomposition on word distribution statistics in order to recover word vector representations, and we find that our recovered representations display the types of biases observed in human participants using tasks such as the implicit association test. Importantly, these biases are strongest for vector representations with moderate dimensionality, and weaken or disappear for representations with very high or very low dimensionality. Moderate dimensional LSA models are also the best at learning race, ethnicity, and gender-based categories, suggesting that social category knowledge, acquired through dimensionality reduction on word distribution statistics, can facilitate prejudiced and stereotyped associations. Copyright © 2017 Elsevier B.V. All rights reserved.

Neurocognitive mechanisms of statistical-sequential learning: what do event-related potentials tell us?

PubMed Central

Daltrozzo, Jerome; Conway, Christopher M.

2014-01-01

Statistical-sequential learning (SL) is the ability to process patterns of environmental stimuli, such as spoken language, music, or one’s motor actions, that unfold in time. The underlying neurocognitive mechanisms of SL and the associated cognitive representations are still not well understood as reflected by the heterogeneity of the reviewed cognitive models. The purpose of this review is: (1) to provide a general overview of the primary models and theories of SL, (2) to describe the empirical research – with a focus on the event-related potential (ERP) literature – in support of these models while also highlighting the current limitations of this research, and (3) to present a set of new lines of ERP research to overcome these limitations. The review is articulated around three descriptive dimensions in relation to SL: the level of abstractness of the representations learned through SL, the effect of the level of attention and consciousness on SL, and the developmental trajectory of SL across the life-span. We conclude with a new tentative model that takes into account these three dimensions and also point to several promising new lines of SL research. PMID:24994975

Educating for health service reform: clinical learning, governance and capability - a case study protocol.

PubMed

Gardner, Anne; Gardner, Glenn; Coyer, Fiona; Gosby, Helen

2016-01-01

The nurse practitioner is a growing clinical role in Australia and internationally, with an expanded scope of practice including prescribing, referring and diagnosing. However, key gaps exist in nurse practitioner education regarding governance of specialty clinical learning and teaching. Specifically, there is no internationally accepted framework against which to measure the quality of clinical learning and teaching for advanced specialty practice. A case study design will be used to investigate educational governance and capability theory in nurse practitioner education. Nurse practitioner students, their clinical mentors and university academic staff, from an Australian university that offers an accredited nurse practitioner Master's degree, will be invited to participate in the study. Semi-structured interviews will be conducted with students and their respective clinical mentors and university academic staff to investigate learning objectives related to educational governance and attributes of capability learning. Limited demographic data on age, gender, specialty, education level and nature of the clinical healthcare learning site will also be collected. Episodes of nurse practitioner student specialty clinical learning will be observed and documentation from the students' healthcare learning sites will be collected. Descriptive statistics will be used to report age groups, areas of specialty and types of facilities where clinical learning and teaching is observed. Qualitative data from interviews, observations and student documents will be coded, aggregated and explored to inform a framework of educational governance, to confirm the existing capability framework and describe any additional characteristics of capability and capability learning. This research has widespread significance and will contribute to ongoing development of the Australian health workforce. Stakeholders from industry and academic bodies will be involved in shaping the framework that guides the quality and governance of clinical learning and teaching in specialty nurse practitioner practice. Through developing standards for advanced clinical learning and teaching, and furthering understanding of capability theory for advanced healthcare practitioners, this research will contribute to evidence-based models of advanced specialty postgraduate education.

Teaching Learning Theories Via the Web.

ERIC Educational Resources Information Center

Schnackenberg, Heidi L.

This paper describes a World Wide Web site on learning theories, developed as a class assignment for a course on learning and instructional theories at Concordia University (Quebec). Groups of two to four students developed pages on selected theories of learning that were then linked to a main page developed by the instructor and a doctoral…

Incorporating Learning Theory into Existing Systems Engineering Models

DTIC Science & Technology

2013-09-01

3. Social  Cognition 22 Table 1. Classification of learning theories Behaviorism Cognitivism Constructivism Connectivism...Introdution to design of large scale systems. New York: Mcgraw-Hill. Grusec. J. (1992). Social learning theory and development psychology: The... LEARNING THEORY INTO EXISTING SYSTEMS ENGINEERING MODELS by Valentine Leo September 2013 Thesis Advisor: Gary O. Langford Co-Advisor

The computational nature of memory modification.

PubMed

Gershman, Samuel J; Monfils, Marie-H; Norman, Kenneth A; Niv, Yael

2017-03-15

Retrieving a memory can modify its influence on subsequent behavior. We develop a computational theory of memory modification, according to which modification of a memory trace occurs through classical associative learning, but which memory trace is eligible for modification depends on a structure learning mechanism that discovers the units of association by segmenting the stream of experience into statistically distinct clusters (latent causes). New memories are formed when the structure learning mechanism infers that a new latent cause underlies current sensory observations. By the same token, old memories are modified when old and new sensory observations are inferred to have been generated by the same latent cause. We derive this framework from probabilistic principles, and present a computational implementation. Simulations demonstrate that our model can reproduce the major experimental findings from studies of memory modification in the Pavlovian conditioning literature.

Using Computational Modeling to Assess the Impact of Clinical Decision Support on Cancer Screening within Community Health Centers

PubMed Central

Carney, Timothy Jay; Morgan, Geoffrey P.; Jones, Josette; McDaniel, Anna M.; Weaver, Michael; Weiner, Bryan; Haggstrom, David A.

2014-01-01

Our conceptual model demonstrates our goal to investigate the impact of clinical decision support (CDS) utilization on cancer screening improvement strategies in the community health care (CHC) setting. We employed a dual modeling technique using both statistical and computational modeling to evaluate impact. Our statistical model used the Spearman’s Rho test to evaluate the strength of relationship between our proximal outcome measures (CDS utilization) against our distal outcome measure (provider self-reported cancer screening improvement). Our computational model relied on network evolution theory and made use of a tool called Construct-TM to model the use of CDS measured by the rate of organizational learning. We employed the use of previously collected survey data from community health centers Cancer Health Disparities Collaborative (HDCC). Our intent is to demonstrate the added valued gained by using a computational modeling tool in conjunction with a statistical analysis when evaluating the impact a health information technology, in the form of CDS, on health care quality process outcomes such as facility-level screening improvement. Significant simulated disparities in organizational learning over time were observed between community health centers beginning the simulation with high and low clinical decision support capability. PMID:24953241

Adult Learning Theories: Implications for Online Instruction

ERIC Educational Resources Information Center

Arghode, Vishal; Brieger, Earl W.; McLean, Gary N.

2017-01-01

Purpose: This paper analyzes critically four selected learning theories and their role in online instruction for adults. Design/methodology/approach: A literature review was conducted to analyze the theories. Findings: The theory comparison revealed that no single theory encompasses the entirety of online instruction for adult learning; each…

["Jump in at the deep end" : simulator-based learning in acute care].

PubMed

Breuer, G; Schweizer, K; Schüttler, J; Weiß, M; Vladut, A

2014-01-01

With high-fidelity simulators in a modern blended learning setting, students are able to acquire knowledge and practical skills in acute medicine in realistic scenarios. However, it has not yet been clarified if the sequence of linking between knowledge and simulator-based training of practical skills plays an important role for increasing knowledge, for the self-concept and learning emotions of trainees. In a pilot study the influence of the type of knowledge acquisition under two independent conditions was investigated in which the order of presenting the learning material (firstly theory and then simulation vs. simulation elements before the theory) was reversed. In addition the influence of individual attributes of personality on the construction of situated knowledge was correlated with these conditions in two groups. To investigate the outcome of simulator-based learning 20 students were randomly allocated to one of the two conditions and undertook two scenarios (anaphylactic shock and myocardial infarction), whereby the theoretical lessons were given either before or after the scenarios. Using standardized questionnaires and problem-centered semi-standardized interviews, the following variables of the participants were assessed: personality traits, current positive and negative feelings, professional self-concept, general self-efficacy and coping strategies for stress. Theoretical knowledge and practical skills were assessed using a knowledge test and standardized assessment questionnaires which also focused on performance and patient safety. All together the results showed a slight advantage for the condition of theory before simulation which was not determined by the acquisition of knowledge but by a better performance of trainees as assessed by the trainers. Regarding knowledge acquisition, no statistically significant differences could be shown. Significant differences (p < 0.05) were found for negative feelings (very intense negative emotional state) and for the professional self-concept (perception of own professional skills) in favor of the theory then simulation condition. More extrovert participants showed poorer results which could not be attributed to one of the conditions. However, the participants always assessed the allocated learning condition as the best premise for effective learning outcome. Reaction to stress has been described as "jumping in at the deep end" as well as the lasting effect on learning from errors. In the context of simulation-based teaching, the learning outcome not only depends on knowledge, practical skills and motivational variables but also on the presence of negative feelings, ability self-concepts and various personality traits. There was a trend which showed that simulation in the field of anesthesiology and emergency medicine should be set up with the theoretical basis first in order to avoid negative feelings.

Transformative Learning as an "Inter-Practice" Phenomenon

ERIC Educational Resources Information Center

Hodge, Steven

2014-01-01

Transformative learning theory and practice-based theory both offer compelling but distinct accounts of adult learning. The vicissitudes of individual meaning-making is the focus of transformative learning theory whereas practice-based accounts view participation in social practices as the key to understanding learning. Despite their differing…

Online incidental statistical learning of audiovisual word sequences in adults: a registered report.

PubMed

Kuppuraj, Sengottuvel; Duta, Mihaela; Thompson, Paul; Bishop, Dorothy

2018-02-01

Statistical learning has been proposed as a key mechanism in language learning. Our main goal was to examine whether adults are capable of simultaneously extracting statistical dependencies in a task where stimuli include a range of structures amenable to statistical learning within a single paradigm. We devised an online statistical learning task using real word auditory-picture sequences that vary in two dimensions: (i) predictability and (ii) adjacency of dependent elements. This task was followed by an offline recall task to probe learning of each sequence type. We registered three hypotheses with specific predictions. First, adults would extract regular patterns from continuous stream (effect of grammaticality). Second, within grammatical conditions, they would show differential speeding up for each condition as a factor of statistical complexity of the condition and exposure. Third, our novel approach to measure online statistical learning would be reliable in showing individual differences in statistical learning ability. Further, we explored the relation between statistical learning and a measure of verbal short-term memory (STM). Forty-two participants were tested and retested after an interval of at least 3 days on our novel statistical learning task. We analysed the reaction time data using a novel regression discontinuity approach. Consistent with prediction, participants showed a grammaticality effect, agreeing with the predicted order of difficulty for learning different statistical structures. Furthermore, a learning index from the task showed acceptable test-retest reliability ( r  = 0.67). However, STM did not correlate with statistical learning. We discuss the findings noting the benefits of online measures in tracking the learning process.

Online incidental statistical learning of audiovisual word sequences in adults: a registered report

PubMed Central

Duta, Mihaela; Thompson, Paul

2018-01-01

Statistical learning has been proposed as a key mechanism in language learning. Our main goal was to examine whether adults are capable of simultaneously extracting statistical dependencies in a task where stimuli include a range of structures amenable to statistical learning within a single paradigm. We devised an online statistical learning task using real word auditory–picture sequences that vary in two dimensions: (i) predictability and (ii) adjacency of dependent elements. This task was followed by an offline recall task to probe learning of each sequence type. We registered three hypotheses with specific predictions. First, adults would extract regular patterns from continuous stream (effect of grammaticality). Second, within grammatical conditions, they would show differential speeding up for each condition as a factor of statistical complexity of the condition and exposure. Third, our novel approach to measure online statistical learning would be reliable in showing individual differences in statistical learning ability. Further, we explored the relation between statistical learning and a measure of verbal short-term memory (STM). Forty-two participants were tested and retested after an interval of at least 3 days on our novel statistical learning task. We analysed the reaction time data using a novel regression discontinuity approach. Consistent with prediction, participants showed a grammaticality effect, agreeing with the predicted order of difficulty for learning different statistical structures. Furthermore, a learning index from the task showed acceptable test–retest reliability (r = 0.67). However, STM did not correlate with statistical learning. We discuss the findings noting the benefits of online measures in tracking the learning process. PMID:29515876

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

PubMed

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.

Dynamical Systems Theory: Application to Pedagogy

NASA Astrophysics Data System (ADS)

Abraham, Jane L.

Theories of learning affect how cognition is viewed, and this subsequently leads to the style of pedagogical practice that is used in education. Traditionally, educators have relied on a variety of theories on which to base pedagogy. Behavioral learning theories influenced the teaching/learning process for over 50 years. In the 1960s, the information processing approach brought the mind back into the learning process. The current emphasis on constructivism integrates the views of Piaget, Vygotsky, and cognitive psychology. Additionally, recent scientific advances have allowed researchers to shift attention to biological processes in cognition. The problem is that these theories do not provide an integrated approach to understanding principles responsible for differences among students in cognitive development and learning ability. Dynamical systems theory offers a unifying theoretical framework to explain the wider context in which learning takes place and the processes involved in individual learning. This paper describes how principles of Dynamic Systems Theory can be applied to cognitive processes of students, the classroom community, motivation to learn, and the teaching/learning dynamic giving educational psychologists a framework for research and pedagogy.

Dataset on Investigating the role of onsite learning in the optimisation of craft gang's productivity in the construction industry.

PubMed

Ugulu, Rex Asibuodu; Allen, Stephen

2017-12-01

The data presented in this article is an original data on "Investigating the role of onsite learning in the optimisation of craft gang's productivity in the construction industry". This article describes the constraints influencing craft gang's productivity and the influence of onsite learning on the blockwork craft gang's productivity. It also presented the method of data collection, using a semi-structured interview and an observation method to collect data from construction organisations. We provided statistics on the top most important constraints affecting the craft gang's productivity using 3-D Bar charts. In addition, we computed the correlation coefficients and the regression model on the influence of onsite learning on craft gang's productivity using the man-hour as the dependent variable. The relationship between blockwork inputs and cycle numbers was determined at 5% significance level. Finally, we presented data information on the application of the learning curve theory using the unit straight-line model equations and computed the learning rate of the observed craft gang's blockwork repetitive work.

Examining Hypermedia Learning: The Role of Cognitive Load and Self-Regulated Learning

ERIC Educational Resources Information Center

Moos, Daniel

2013-01-01

Distinct theoretical perspectives, Cognitive Load Theory and Self-Regulated Learning (SRL) theory, have been used to examine individual differences the challenges faced with hypermedia learning. However, research has tended to use these theories independently, resulting in less robust explanations of hypermedia learning. This study examined the…

From Constructivism to Dialogism in the Classroom. Theory and Learning Environments

ERIC Educational Resources Information Center

de Mello, Roseli Rodrigues

2012-01-01

This paper discusses the move from learning theories from the industrial society to learning theories from and for dialogic societies. While in the past intrapsychological elements, such as mental schemata of prior knowledge, were the key to explain learning, today's theories point to interaction and dialogue as the main means for achieving deep…

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

ERIC Educational Resources Information Center

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…

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

PubMed

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

2017-01-01

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

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

PubMed Central

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

2017-01-01

There has been a national movement to transition college science courses from passive lectures to active learning environments. Active learning has been shown to be a more effective way for students to learn, yet there is concern that some students are resistant to active learning approaches. Although there is much discussion about student resistance to active learning, few studies have explored this topic. Furthermore, a limited number of studies have applied theoretical frameworks to student engagement in active learning. We propose using a theoretical lens of expectancy value theory to understand student resistance to active learning. In this study, we examined student perceptions of active learning after participating in 40 hours of active learning. We used the principal components of expectancy value theory to probe student experience in active learning: student perceived self-efficacy in active learning, value of active learning, and potential cost of participating in active learning. We found that students showed positive changes in the components of expectancy value theory and reported high levels of engagement in active learning, which provide proof of concept that expectancy value theory can be used to boost student perceptions of active learning and their engagement in active learning classrooms. From these findings, we have built a theoretical framework of expectancy value theory applied to active learning. PMID:28861130

An investigation into introductory astronomy students' difficulties with cosmology, and the development, validation, and efficacy of a new suite of cosmology lecture-tutorials

NASA Astrophysics Data System (ADS)

Wallace, Colin S.

This study reports the results of the first systematic investigation into Astro 101 students' conceptual and reasoning difficulties with cosmology. We developed four surveys with which we measured students' conceptual knowledge of the Big Bang, the expansion and evolution of the universe, and the evidence for dark matter. Our classical test theory and item response theory analyses of over 2300 students' pre- and post-instruction responses, combined with daily classroom observations, videotapes of students working in class, and one-on-one semi-structured think-aloud interviews with nineteen Astro 101 students, revealed several common learning difficulties. In order to help students overcome these difficulties, we used our results to inform the development of a new suite of cosmology lecture-tutorials. In our initial testing of the new lecture-tutorials at the University of Colorado at Boulder and the University of Arizona, we found many cases in which students who used the lecture-tutorials achieved higher learning gains (as measured by our surveys) at statistically significant levels than students who did not. Subsequent use of the lecture-tutorials at a variety of colleges and universities across the United States produced a wide range of learning gains, suggesting that instructors' pedagogical practices and implementations of the lecture-tutorials significantly affect whether or not students achieve high learning gains.

Uniting Cheminformatics and Chemical Theory To Predict the Intrinsic Aqueous Solubility of Crystalline Druglike Molecules

PubMed Central

2014-01-01

We present four models of solution free-energy prediction for druglike molecules utilizing cheminformatics descriptors and theoretically calculated thermodynamic values. We make predictions of solution free energy using physics-based theory alone and using machine learning/quantitative structure–property relationship (QSPR) models. We also develop machine learning models where the theoretical energies and cheminformatics descriptors are used as combined input. These models are used to predict solvation free energy. While direct theoretical calculation does not give accurate results in this approach, machine learning is able to give predictions with a root mean squared error (RMSE) of ∼1.1 log S units in a 10-fold cross-validation for our Drug-Like-Solubility-100 (DLS-100) dataset of 100 druglike molecules. We find that a model built using energy terms from our theoretical methodology as descriptors is marginally less predictive than one built on Chemistry Development Kit (CDK) descriptors. Combining both sets of descriptors allows a further but very modest improvement in the predictions. However, in some cases, this is a statistically significant enhancement. These results suggest that there is little complementarity between the chemical information provided by these two sets of descriptors, despite their different sources and methods of calculation. Our machine learning models are also able to predict the well-known Solubility Challenge dataset with an RMSE value of 0.9–1.0 log S units. PMID:24564264

A Selective Overview of Variable Selection in High Dimensional Feature Space

PubMed Central

Fan, Jianqing

2010-01-01

High dimensional statistical problems arise from diverse fields of scientific research and technological development. Variable selection plays a pivotal role in contemporary statistical learning and scientific discoveries. The traditional idea of best subset selection methods, which can be regarded as a specific form of penalized likelihood, is computationally too expensive for many modern statistical applications. Other forms of penalized likelihood methods have been successfully developed over the last decade to cope with high dimensionality. They have been widely applied for simultaneously selecting important variables and estimating their effects in high dimensional statistical inference. In this article, we present a brief account of the recent developments of theory, methods, and implementations for high dimensional variable selection. What limits of the dimensionality such methods can handle, what the role of penalty functions is, and what the statistical properties are rapidly drive the advances of the field. The properties of non-concave penalized likelihood and its roles in high dimensional statistical modeling are emphasized. We also review some recent advances in ultra-high dimensional variable selection, with emphasis on independence screening and two-scale methods. PMID:21572976

Walking through the statistical black boxes of plant breeding.

PubMed

Xavier, Alencar; Muir, William M; Craig, Bruce; Rainey, Katy Martin

2016-10-01

The main statistical procedures in plant breeding are based on Gaussian process and can be computed through mixed linear models. Intelligent decision making relies on our ability to extract useful information from data to help us achieve our goals more efficiently. Many plant breeders and geneticists perform statistical analyses without understanding the underlying assumptions of the methods or their strengths and pitfalls. In other words, they treat these statistical methods (software and programs) like black boxes. Black boxes represent complex pieces of machinery with contents that are not fully understood by the user. The user sees the inputs and outputs without knowing how the outputs are generated. By providing a general background on statistical methodologies, this review aims (1) to introduce basic concepts of machine learning and its applications to plant breeding; (2) to link classical selection theory to current statistical approaches; (3) to show how to solve mixed models and extend their application to pedigree-based and genomic-based prediction; and (4) to clarify how the algorithms of genome-wide association studies work, including their assumptions and limitations.

Dynamics of EEG functional connectivity during statistical learning.

PubMed

Tóth, Brigitta; Janacsek, Karolina; Takács, Ádám; Kóbor, Andrea; Zavecz, Zsófia; Nemeth, Dezso

2017-10-01

Statistical learning is a fundamental mechanism of the brain, which extracts and represents regularities of our environment. Statistical learning is crucial in predictive processing, and in the acquisition of perceptual, motor, cognitive, and social skills. Although previous studies have revealed competitive neurocognitive processes underlying statistical learning, the neural communication of the related brain regions (functional connectivity, FC) has not yet been investigated. The present study aimed to fill this gap by investigating FC networks that promote statistical learning in humans. Young adults (N=28) performed a statistical learning task while 128-channels EEG was acquired. The task involved probabilistic sequences, which enabled to measure incidental/implicit learning of conditional probabilities. Phase synchronization in seven frequency bands was used to quantify FC between cortical regions during the first, second, and third periods of the learning task, respectively. Here we show that statistical learning is negatively correlated with FC of the anterior brain regions in slow (theta) and fast (beta) oscillations. These negative correlations increased as the learning progressed. Our findings provide evidence that dynamic antagonist brain networks serve a hallmark of statistical learning. Copyright © 2017 Elsevier Inc. All rights reserved.

Genetic Epidemiology and Public Health: The Evolution From Theory to Technology.

PubMed

Fallin, M Daniele; Duggal, Priya; Beaty, Terri H

2016-03-01

Genetic epidemiology represents a hybrid of epidemiologic designs and statistical models that explicitly consider both genetic and environmental risk factors for disease. It is a relatively new field in public health; the term was first coined only 35 years ago. In this short time, the field has been through a major evolution, changing from a field driven by theory, without the technology for genetic measurement or computational capacity to apply much of the designs and methods developed, to a field driven by rapidly expanding technology in genomic measurement and computational analyses while epidemiologic theory struggles to keep up. In this commentary, we describe 4 different eras of genetic epidemiology, spanning this evolution from theory to technology, what we have learned, what we have added to the broader field of public health, and what remains to be done. © The Author 2016. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Topics in Computational Learning Theory and Graph Algorithms.

ERIC Educational Resources Information Center

Board, Raymond Acton

This thesis addresses problems from two areas of theoretical computer science. The first area is that of computational learning theory, which is the study of the phenomenon of concept learning using formal mathematical models. The goal of computational learning theory is to investigate learning in a rigorous manner through the use of techniques…

Out on the Floor: Experiential Learning and the Implications for the Preparation of Docents

ERIC Educational Resources Information Center

Grenier, Robin S.; Sheckley, Barry

2008-01-01

Drawing on the foundational theories of experiential learning, this article explores recent developments in theory and research on experiential learning and addresses how this work can enhance the professional development of museum docents. We introduce theories of adult learning and professional development that emphasize experiential learning as…

Perceptual statistical learning over one week in child speech production.

PubMed

Richtsmeier, Peter T; Goffman, Lisa

2017-07-01

What cognitive mechanisms account for the trajectory of speech sound development, in particular, gradually increasing accuracy during childhood? An intriguing potential contributor is statistical learning, a type of learning that has been studied frequently in infant perception but less often in child speech production. To assess the relevance of statistical learning to developing speech accuracy, we carried out a statistical learning experiment with four- and five-year-olds in which statistical learning was examined over one week. Children were familiarized with and tested on word-medial consonant sequences in novel words. There was only modest evidence for statistical learning, primarily in the first few productions of the first session. This initial learning effect nevertheless aligns with previous statistical learning research. Furthermore, the overall learning effect was similar to an estimate of weekly accuracy growth based on normative studies. The results implicate other important factors in speech sound development, particularly learning via production. Copyright © 2017 Elsevier Inc. All rights reserved.

Some Implications of Learning Theories on a Theory of Reading and Reading Instruction.

ERIC Educational Resources Information Center

Parsons, James B.

While stimulus-response theories of learning maintain the reality and importance of the stimulus outside the perception of the person, a cognitive-field learning theory insists that, in order to make meaning, a person must perceive and react with the stimulus. Holding to this or any learning model has implications for the following: a definition…

Chinese lexical networks: The structure, function and formation

NASA Astrophysics Data System (ADS)

Li, Jianyu; Zhou, Jie; Luo, Xiaoyue; Yang, Zhanxin

2012-11-01

In this paper Chinese phrases are modeled using complex networks theory. We analyze statistical properties of the networks and find that phrase networks display some important features: not only small world and the power-law distribution, but also hierarchical structure and disassortative mixing. These statistical traits display the global organization of Chinese phrases. The origin and formation of such traits are analyzed from a macroscopic Chinese culture and philosophy perspective. It is interesting to find that Chinese culture and philosophy may shape the formation and structure of Chinese phrases. To uncover the structural design principles of networks, network motif patterns are studied. It is shown that they serve as basic building blocks to form the whole phrase networks, especially triad 38 (feed forward loop) plays a more important role in forming most of the phrases and other motifs. The distinct structure may not only keep the networks stable and robust, but also be helpful for information processing. The results of the paper can give some insight into Chinese language learning and language acquisition. It strengthens the idea that learning the phrases helps to understand Chinese culture. On the other side, understanding Chinese culture and philosophy does help to learn Chinese phrases. The hub nodes in the networks show the close relationship with Chinese culture and philosophy. Learning or teaching the hub characters, hub-linking phrases and phrases which are meaning related based on motif feature should be very useful and important for Chinese learning and acquisition.

Distributed Bayesian Computation and Self-Organized Learning in Sheets of Spiking Neurons with Local Lateral Inhibition

PubMed Central

Bill, Johannes; Buesing, Lars; Habenschuss, Stefan; Nessler, Bernhard; Maass, Wolfgang; Legenstein, Robert

2015-01-01

During the last decade, Bayesian probability theory has emerged as a framework in cognitive science and neuroscience for describing perception, reasoning and learning of mammals. However, our understanding of how probabilistic computations could be organized in the brain, and how the observed connectivity structure of cortical microcircuits supports these calculations, is rudimentary at best. In this study, we investigate statistical inference and self-organized learning in a spatially extended spiking network model, that accommodates both local competitive and large-scale associative aspects of neural information processing, under a unified Bayesian account. Specifically, we show how the spiking dynamics of a recurrent network with lateral excitation and local inhibition in response to distributed spiking input, can be understood as sampling from a variational posterior distribution of a well-defined implicit probabilistic model. This interpretation further permits a rigorous analytical treatment of experience-dependent plasticity on the network level. Using machine learning theory, we derive update rules for neuron and synapse parameters which equate with Hebbian synaptic and homeostatic intrinsic plasticity rules in a neural implementation. In computer simulations, we demonstrate that the interplay of these plasticity rules leads to the emergence of probabilistic local experts that form distributed assemblies of similarly tuned cells communicating through lateral excitatory connections. The resulting sparse distributed spike code of a well-adapted network carries compressed information on salient input features combined with prior experience on correlations among them. Our theory predicts that the emergence of such efficient representations benefits from network architectures in which the range of local inhibition matches the spatial extent of pyramidal cells that share common afferent input. PMID:26284370

Organizational Learning Theory in Schools

ERIC Educational Resources Information Center

Fauske, Janice R.; Raybould, Rebecca

2005-01-01

Purpose: The paper's purposes are to establish organizational learning theory as evolving from the theoretical and empirical study of organizations and to build grounded theory explaining organizational learning in schools. Design/methodology/approach: Implementation of instructional technology as a process of organizational learning was explored…

Situativity theory: a perspective on how participants and the environment can interact: AMEE Guide no. 52.

PubMed

Durning, Steven J; Artino, Anthony R

2011-01-01

Situativity theory refers to theoretical frameworks which argue that knowledge, thinking, and learning are situated (or located) in experience. The importance of context to these theories is paramount, including the unique contribution of the environment to knowledge, thinking, and learning; indeed, they argue that knowledge, thinking, and learning cannot be separated from (they are dependent upon) context. Situativity theory includes situated cognition, situated learning, ecological psychology, and distributed cognition. In this Guide, we first outline key tenets of situativity theory and then compare situativity theory to information processing theory; we suspect that the reader may be quite familiar with the latter, which has prevailed in medical education research. Contrasting situativity theory with information processing theory also serves to highlight some unique potential contributions of situativity theory to work in medical education. Further, we discuss each of these situativity theories and then relate the theories to the clinical context. Examples and illustrations for each of the theories are used throughout. We will conclude with some potential considerations for future exploration. Some implications of situativity theory include: a new way of approaching knowledge and how experience and the environment impact knowledge, thinking, and learning; recognizing that the situativity framework can be a useful tool to "diagnose" the teaching or clinical event; the notion that increasing individual responsibility and participation in a community (i.e., increasing "belonging") is essential to learning; understanding that the teaching and clinical environment can be complex (i.e., non-linear and multi-level); recognizing that explicit attention to how participants in a group interact with each other (not only with the teacher) and how the associated learning artifacts, such as computers, can meaningfully impact learning.

Effects of Motion in the Far Peripheral Visual Field on Cognitive Test Performance and Cognitive Load.

PubMed

Bevilacqua, Andy; Paas, Fred; Krigbaum, Genomary

2016-04-01

Cognitive load theory posits that limited attention is in actuality a limitation in working memory resources. The load theory of selective attention and cognitive control sees the interplay between attention and awareness as separate modifying functions that act on working memory. Reconciling the theoretical differences in these two theories has important implications for learning. Thirty-nine adult participants performed a cognitively demanding test, with and without movement in the far peripheral field. Although the results for movement effects on cognitive load in this experiment were not statistically significant, men spent less time on the cognitive test in the peripheral movement condition than in the conditions without peripheral movement. No such difference was found for women. The implications of these results and recommendations for future research that extends the present study are presented. © The Author(s) 2016.

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

ERIC Educational Resources Information Center

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…

Sociocultural Theories of Learning and Motivation: Looking Back, Looking Forward. Research on Sociocultural Influences on Motivation and Learning

ERIC Educational Resources Information Center

McInerney, Dennis M., Ed.; Walker, Richard A., Ed.; Liem, Gregory Arief D., Ed.

2011-01-01

It is now nearly thirty years since sociocultural theories of learning created great excitement and debate amongst those concerned with learning in diverse contexts. Since that time significant advances have been made in sociocultural theory and research. Various sociocultural approaches to the understanding of learning (for example, sociocultural…

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

PubMed

Sadideen, Hazim; Kneebone, Roger

2012-09-01

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

Learning the Language of Statistics: Challenges and Teaching Approaches

ERIC Educational Resources Information Center

Dunn, Peter K.; Carey, Michael D.; Richardson, Alice M.; McDonald, Christine

2016-01-01

Learning statistics requires learning the language of statistics. Statistics draws upon words from general English, mathematical English, discipline-specific English and words used primarily in statistics. This leads to many linguistic challenges in teaching statistics and the way in which the language is used in statistics creates an extra layer…

Towards a theory of individual differences in statistical learning

PubMed Central

Bogaerts, Louisa; Christiansen, Morten H.; Frost, Ram

2017-01-01

In recent years, statistical learning (SL) research has seen a growing interest in tracking individual performance in SL tasks, mainly as a predictor of linguistic abilities. We review studies from this line of research and outline three presuppositions underlying the experimental approach they employ: (i) that SL is a unified theoretical construct; (ii) that current SL tasks are interchangeable, and equally valid for assessing SL ability; and (iii) that performance in the standard forced-choice test in the task is a good proxy of SL ability. We argue that these three critical presuppositions are subject to a number of theoretical and empirical issues. First, SL shows patterns of modality- and informational-specificity, suggesting that SL cannot be treated as a unified construct. Second, different SL tasks may tap into separate sub-components of SL that are not necessarily interchangeable. Third, the commonly used forced-choice tests in most SL tasks are subject to inherent limitations and confounds. As a first step, we offer a methodological approach that explicitly spells out a potential set of different SL dimensions, allowing for better transparency in choosing a specific SL task as a predictor of a given linguistic outcome. We then offer possible methodological solutions for better tracking and measuring SL ability. Taken together, these discussions provide a novel theoretical and methodological approach for assessing individual differences in SL, with clear testable predictions. This article is part of the themed issue ‘New frontiers for statistical learning in the cognitive sciences’. PMID:27872377

Repositioning Ideology Critique in a Critical Theory of Adult Learning.

ERIC Educational Resources Information Center

Brookfield, Stephen

2001-01-01

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

Learning Theories and Assessment Methodologies--An Engineering Educational Perspective

ERIC Educational Resources Information Center

Hassan, O. A. B.

2011-01-01

This paper attempts to critically review theories of learning from the perspective of engineering education in order to align relevant assessment methods with each respective learning theory, considering theoretical aspects and practical observations and reflections. The role of formative assessment, taxonomies, peer learning and educational…

Assessment of a Professional Development Program on Adult Learning Theory

ERIC Educational Resources Information Center

Malik, Melinda

2016-01-01

Librarians at colleges and universities invested in graduate education must understand and incorporate adult learning theories in their reference and instruction interactions with graduate students to more effectively support the students' learning. After participating in a professional development program about adult learning theory, librarians…

A Classification of Remote Sensing Image Based on Improved Compound Kernels of Svm

NASA Astrophysics Data System (ADS)

Zhao, Jianing; Gao, Wanlin; Liu, Zili; Mou, Guifen; Lu, Lin; Yu, Lina

The accuracy of RS classification based on SVM which is developed from statistical learning theory is high under small number of train samples, which results in satisfaction of classification on RS using SVM methods. The traditional RS classification method combines visual interpretation with computer classification. The accuracy of the RS classification, however, is improved a lot based on SVM method, because it saves much labor and time which is used to interpret images and collect training samples. Kernel functions play an important part in the SVM algorithm. It uses improved compound kernel function and therefore has a higher accuracy of classification on RS images. Moreover, compound kernel improves the generalization and learning ability of the kernel.

Optimism as a Prior Belief about the Probability of Future Reward

PubMed Central

Kalra, Aditi; Seriès, Peggy

2014-01-01

Optimists hold positive a priori beliefs about the future. In Bayesian statistical theory, a priori beliefs can be overcome by experience. However, optimistic beliefs can at times appear surprisingly resistant to evidence, suggesting that optimism might also influence how new information is selected and learned. Here, we use a novel Pavlovian conditioning task, embedded in a normative framework, to directly assess how trait optimism, as classically measured using self-report questionnaires, influences choices between visual targets, by learning about their association with reward progresses. We find that trait optimism relates to an a priori belief about the likelihood of rewards, but not losses, in our task. Critically, this positive belief behaves like a probabilistic prior, i.e. its influence reduces with increasing experience. Contrary to findings in the literature related to unrealistic optimism and self-beliefs, it does not appear to influence the iterative learning process directly. PMID:24853098

The computational nature of memory modification

PubMed Central

Gershman, Samuel J; Monfils, Marie-H; Norman, Kenneth A; Niv, Yael

2017-01-01

Retrieving a memory can modify its influence on subsequent behavior. We develop a computational theory of memory modification, according to which modification of a memory trace occurs through classical associative learning, but which memory trace is eligible for modification depends on a structure learning mechanism that discovers the units of association by segmenting the stream of experience into statistically distinct clusters (latent causes). New memories are formed when the structure learning mechanism infers that a new latent cause underlies current sensory observations. By the same token, old memories are modified when old and new sensory observations are inferred to have been generated by the same latent cause. We derive this framework from probabilistic principles, and present a computational implementation. Simulations demonstrate that our model can reproduce the major experimental findings from studies of memory modification in the Pavlovian conditioning literature. DOI: http://dx.doi.org/10.7554/eLife.23763.001 PMID:28294944

Computational and experimental single cell biology techniques for the definition of cell type heterogeneity, interplay and intracellular dynamics.

PubMed

de Vargas Roditi, Laura; Claassen, Manfred

2015-08-01

Novel technological developments enable single cell population profiling with respect to their spatial and molecular setup. These include single cell sequencing, flow cytometry and multiparametric imaging approaches and open unprecedented possibilities to learn about the heterogeneity, dynamics and interplay of the different cell types which constitute tissues and multicellular organisms. Statistical and dynamic systems theory approaches have been applied to quantitatively describe a variety of cellular processes, such as transcription and cell signaling. Machine learning approaches have been developed to define cell types, their mutual relationships, and differentiation hierarchies shaping heterogeneous cell populations, yielding insights into topics such as, for example, immune cell differentiation and tumor cell type composition. This combination of experimental and computational advances has opened perspectives towards learning predictive multi-scale models of heterogeneous cell populations. Copyright © 2014 Elsevier Ltd. All rights reserved.

Optimizing Computer Assisted Instruction By Applying Principles of Learning Theory.

ERIC Educational Resources Information Center

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…

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

ERIC Educational Resources Information Center

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)

Origin of the spike-timing-dependent plasticity rule

NASA Astrophysics Data System (ADS)

Cho, Myoung Won; Choi, M. Y.

2016-08-01

A biological synapse changes its efficacy depending on the difference between pre- and post-synaptic spike timings. Formulating spike-timing-dependent interactions in terms of the path integral, we establish a neural-network model, which makes it possible to predict relevant quantities rigorously by means of standard methods in statistical mechanics and field theory. In particular, the biological synaptic plasticity rule is shown to emerge as the optimal form for minimizing the free energy. It is further revealed that maximization of the entropy of neural activities gives rise to the competitive behavior of biological learning. This demonstrates that statistical mechanics helps to understand rigorously key characteristic behaviors of a neural network, thus providing the possibility of physics serving as a useful and relevant framework for probing life.

The Scientific Status of Learning Styles Theories

ERIC Educational Resources Information Center

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…

Design of Learning Model of Logic and Algorithms Based on APOS Theory

ERIC Educational Resources Information Center

Hartati, Sulis Janu

2014-01-01

This research questions were "how do the characteristics of learning model of logic & algorithm according to APOS theory" and "whether or not these learning model can improve students learning outcomes". This research was conducted by exploration, and quantitative approach. Exploration used in constructing theory about the…

Extending Transfer of Learning Theory to Transformative Learning Theory: A Model for Promoting Teacher Leadership

ERIC Educational Resources Information Center

Harris, Sandra; Lowery-Moore, Hollis; Farrow, Vicky

2008-01-01

This article describes collaborative efforts to frame university teacher preparation program activities within transfer of learning and transformative learning theories to promote teacher leadership. Specifically, we describe (a) a community sponsored, public school, campus-based experience during an introductory teacher preparation course; (b) a…

The Learning Theory Jungle

ERIC Educational Resources Information Center

Minter, Robert L.

2011-01-01

This article addresses the myriad of pedagogical and andragogical issues facing university educators in the student learning process. It briefly explores the proliferation of learning theories in an attempt to develop awareness among faculty who teach at the university/college levels that not all theories of learning apply to the adult learner. In…

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

ERIC Educational Resources Information Center

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…

An Assessment of the Army Officer Education System From an Adult Learning Perspective

DTIC Science & Technology

2005-05-26

learning 2 Brockett, Ralph, and Roger Hiemstra. "Bridging the Theory -Practice Gap in Self-Directed... Learning ." In Self-Directed Learning : From Theory to Practice, edited by S. Brookfield. New Directions for Continuing Education No. 25. (San...offers conclusions and recommendations about the Army Officer Education System based on analysis from adult learning theory . Statement of the

Web-Based Virtual Patients in Nursing Education: Development and Validation of Theory-Anchored Design and Activity Models

PubMed Central

2014-01-01

Background Research has shown that nursing students find it difficult to translate and apply their theoretical knowledge in a clinical context. Virtual patients (VPs) have been proposed as a learning activity that can support nursing students in their learning of scientific knowledge and help them integrate theory and practice. Although VPs are increasingly used in health care education, they still lack a systematic consistency that would allow their reuse outside of their original context. There is therefore a need to develop a model for the development and implementation of VPs in nursing education. Objective The aim of this study was to develop and evaluate a virtual patient model optimized to the learning and assessment needs in nursing education. Methods The process of modeling started by reviewing theoretical frameworks reported in the literature and used by practitioners when designing learning and assessment activities. The Outcome-Present State Test (OPT) model was chosen as the theoretical framework. The model was then, in an iterative manner, developed and optimized to the affordances of virtual patients. Content validation was performed with faculty both in terms of the relevance of the chosen theories but also its applicability in nursing education. The virtual patient nursing model was then instantiated in two VPs. The students’ perceived usefulness of the VPs was investigated using a questionnaire. The result was analyzed using descriptive statistics. Results A virtual patient Nursing Design Model (vpNDM) composed of three layers was developed. Layer 1 contains the patient story and ways of interacting with the data, Layer 2 includes aspects of the iterative process of clinical reasoning, and finally Layer 3 includes measurable outcomes. A virtual patient Nursing Activity Model (vpNAM) was also developed as a guide when creating VP-centric learning activities. The students perceived the global linear VPs as a relevant learning activity for the integration of theory and practice. Conclusions Virtual patients that are adapted to the nursing paradigm can support nursing students’ development of clinical reasoning skills. The proposed virtual patient nursing design and activity models will allow the systematic development of different types of virtual patients from a common model and thereby create opportunities for sharing pedagogical designs across technical solutions. PMID:24727709

Self-Regulated Learning Strategies in Relation with Statistics Anxiety

ERIC Educational Resources Information Center

Kesici, Sahin; Baloglu, Mustafa; Deniz, M. Engin

2011-01-01

Dealing with students' attitudinal problems related to statistics is an important aspect of statistics instruction. Employing the appropriate learning strategies may have a relationship with anxiety during the process of statistics learning. Thus, the present study investigated multivariate relationships between self-regulated learning strategies…

What do K-12 students feel when dealing with technology and engineering issues? Gardner's multiple intelligence theory implications in technology lessons for motivating engineering vocations at Spanish Secondary School

NASA Astrophysics Data System (ADS)

Sánchez-Martín, Jesús; Álvarez-Gragera, García J.; Dávila-Acedo, M. Antonia; Mellado, Vicente

2017-11-01

The interest on engineering and scientific studies can be raised up even from the early years of academic instructional process. This vocation may be linked to emotions and aptitudes towards technological education. Particularly, students get in touch with these technological issues (namely STEM) during the Compulsory Secondary Education in Spain (12-16 years old).This work presents a preliminary evaluation of how relevant is Gardner's multiple intelligence theory (MIT) in the teaching-learning process within the Technology Lessons. In this sense, MIT was considered as an explanation variable of the emotional response within the different educational parts (so-called syllabus units, SU) in the Technology spanish curriculum. Different intelligence style (IS) will orient the student to a vision of the engineering and technology. This work tries to identify which relationships can be established between IS and specific technology and engineering learning. This research involved up to 135 students were subsequently tested about their predominant (IS) and on the emotions that arouse in them when working with each SU. The results were statistically significant and only those with a Logic-arithmetic or Environmental IS were not affected by the SU.Best teaching and learning practicesare required for encouraging further engineering studies.

A Path Less Chosen: An Assessment of the School of Advanced Military Studies

DTIC Science & Technology

2014-05-22

the theory learned in course one.40 This course used theory , history, doctrine (both US and Soviet), and practical exercises to study the basic...relationships between learning domains, levels of learning and learning objectives, and the experiential learning model.96 In short, there is a major emphasis...discussion. There are multiple theories of education related to the use of discussion in learning . The most frequently cited or referred to amongst

Implicit Statistical Learning and Language Skills in Bilingual Children

ERIC Educational Resources Information Center

Yim, Dongsun; Rudoy, John

2013-01-01

Purpose: Implicit statistical learning in 2 nonlinguistic domains (visual and auditory) was used to investigate (a) whether linguistic experience influences the underlying learning mechanism and (b) whether there are modality constraints in predicting implicit statistical learning with age and language skills. Method: Implicit statistical learning…

Relationship between perceptual learning in speech and statistical learning in younger and older adults

PubMed Central

Neger, Thordis M.; Rietveld, Toni; Janse, Esther

2014-01-01

Within a few sentences, listeners learn to understand severely degraded speech such as noise-vocoded speech. However, individuals vary in the amount of such perceptual learning and it is unclear what underlies these differences. The present study investigates whether perceptual learning in speech relates to statistical learning, as sensitivity to probabilistic information may aid identification of relevant cues in novel speech input. If statistical learning and perceptual learning (partly) draw on the same general mechanisms, then statistical learning in a non-auditory modality using non-linguistic sequences should predict adaptation to degraded speech. In the present study, 73 older adults (aged over 60 years) and 60 younger adults (aged between 18 and 30 years) performed a visual artificial grammar learning task and were presented with 60 meaningful noise-vocoded sentences in an auditory recall task. Within age groups, sentence recognition performance over exposure was analyzed as a function of statistical learning performance, and other variables that may predict learning (i.e., hearing, vocabulary, attention switching control, working memory, and processing speed). Younger and older adults showed similar amounts of perceptual learning, but only younger adults showed significant statistical learning. In older adults, improvement in understanding noise-vocoded speech was constrained by age. In younger adults, amount of adaptation was associated with lexical knowledge and with statistical learning ability. Thus, individual differences in general cognitive abilities explain listeners' variability in adapting to noise-vocoded speech. Results suggest that perceptual and statistical learning share mechanisms of implicit regularity detection, but that the ability to detect statistical regularities is impaired in older adults if visual sequences are presented quickly. PMID:25225475

Relationship between perceptual learning in speech and statistical learning in younger and older adults.

PubMed

Neger, Thordis M; Rietveld, Toni; Janse, Esther

2014-01-01

Within a few sentences, listeners learn to understand severely degraded speech such as noise-vocoded speech. However, individuals vary in the amount of such perceptual learning and it is unclear what underlies these differences. The present study investigates whether perceptual learning in speech relates to statistical learning, as sensitivity to probabilistic information may aid identification of relevant cues in novel speech input. If statistical learning and perceptual learning (partly) draw on the same general mechanisms, then statistical learning in a non-auditory modality using non-linguistic sequences should predict adaptation to degraded speech. In the present study, 73 older adults (aged over 60 years) and 60 younger adults (aged between 18 and 30 years) performed a visual artificial grammar learning task and were presented with 60 meaningful noise-vocoded sentences in an auditory recall task. Within age groups, sentence recognition performance over exposure was analyzed as a function of statistical learning performance, and other variables that may predict learning (i.e., hearing, vocabulary, attention switching control, working memory, and processing speed). Younger and older adults showed similar amounts of perceptual learning, but only younger adults showed significant statistical learning. In older adults, improvement in understanding noise-vocoded speech was constrained by age. In younger adults, amount of adaptation was associated with lexical knowledge and with statistical learning ability. Thus, individual differences in general cognitive abilities explain listeners' variability in adapting to noise-vocoded speech. Results suggest that perceptual and statistical learning share mechanisms of implicit regularity detection, but that the ability to detect statistical regularities is impaired in older adults if visual sequences are presented quickly.

Comparison of learning preferences of Turkish children who had been applied cochlear implantation in Turkey and Germany according to theory of multiple intelligence.

PubMed

Sahli, Sanem; Laszig, Roland; Aschendorff, Antje; Kroeger, Stefanie; Wesarg, Thomas; Belgin, Erol

2011-12-01

The aim of the study is to determinate the using dominant multiple intelligence types and compare the learning preferences of Turkish cochlear implanted children aged four to ten in Turkey and Germany according to Theory of multiple intelligence. The study has been conducted on a total of 80 children and four groups in Freiburg/Germany and Ankara/Turkey. The applications have been done in University of Freiburg, Cochlear Implant Center in Germany, and University of Hacettepe, ENT Department, Audiology and Speech Pathology Section in Turkey. In this study, the data have been collected by means of General Information Form and Cochlear Implant Information Form applied to parents. To determine the dominant multiple intelligence types of children, the TIMI (Teele Inventory of Multiple Intelligences) which was developed by Sue Teele have been used. The study results exposed that there was not a statistically significant difference on dominant intelligence areas and averages of scores of multiple intelligence types in control groups (p>0.05). Although, the dominant intelligence areas were different (except for first dominant intelligence) in cochlear implanted children in Turkey and Germany, there was not a statistically significant difference on averages of scores of dominant multiple intelligence types. Every hearing impaired child who started training, should be evaluated in terms of multiple intelligence areas and identified strengths and weaknesses. Multiple intelligence activities should be used in their educational programs. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

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

PubMed

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.

Spiraling into Transformative Learning

ERIC Educational Resources Information Center

Cranton, Patricia

2010-01-01

This article explores how technical and vocational learning may spiral into transformative learning. Transformative learning theory is reviewed and the learning tasks of critical theory are used to integrate various approaches to transformative learning. With this as a foundation, the article explores how transformative learning can be fostered in…

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

PubMed Central

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

2015-01-01

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

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

PubMed

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

2015-11-01

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

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

ERIC Educational Resources Information Center

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…

A Critical Comparison of Transformation and Deep Approach Theories of Learning

ERIC Educational Resources Information Center

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…

The Process of Developing Theories-in-Action with OELEs: A Qualitative Study.

ERIC Educational Resources Information Center

Land, Susan M.; Hannafin, Michael J.

Open-ended learning environments (OELEs) like microworlds have been touted as one approach for blending learning theory and emerging technology to support the building of student-centered understanding. The learning process involves developing a theory-in-action--an intuitive theory that is generated and changed by learners as they reflect upon…

Simulation Methodology in Nursing Education and Adult Learning Theory

ERIC Educational Resources Information Center

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.…

A Beautiful Metaphor: Transformative Learning Theory

ERIC Educational Resources Information Center

Howie, Peter; Bagnall, Richard

2013-01-01

This article presents a critique of both transformative learning theory and critical comments on it to date. It argues that transformative learning theory remains substantively the same as its initial exposition, in spite of a raft of problematic contentions voiced against it. The theory is argued here to be conceptually problematic, except at the…

From Organized High-Throughput Data to Phenomenological Theory using Machine Learning: The Example of Dielectric Breakdown

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

Kim, Chiho; Pilania, Ghanshyam; Ramprasad, Ramamurthy

Understanding the behavior (and failure) of dielectric insulators experiencing extreme electric fields is critical to the operation of present and emerging electrical and electronic devices. Despite its importance, the development of a predictive theory of dielectric breakdown has remained a challenge, owing to the complex multiscale nature of this process. We focus on the intrinsic dielectric breakdown field of insulators—the theoretical limit of breakdown determined purely by the chemistry of the material, i.e., the elements the material is composed of, the atomic-level structure, and the bonding. Starting from a benchmark dataset (generated from laborious first principles computations) of the intrinsicmore » dielectric breakdown field of a variety of model insulators, simple predictive phenomenological models of dielectric breakdown are distilled using advanced statistical or machine learning schemes, revealing key correlations and analytical relationships between the breakdown field and easily accessible material properties. Lastly, the models are shown to be general, and can hence guide the screening and systematic identification of high electric field tolerant materials.« less

From Organized High-Throughput Data to Phenomenological Theory using Machine Learning: The Example of Dielectric Breakdown

DOE PAGES

Kim, Chiho; Pilania, Ghanshyam; Ramprasad, Ramamurthy

2016-02-02

Understanding the behavior (and failure) of dielectric insulators experiencing extreme electric fields is critical to the operation of present and emerging electrical and electronic devices. Despite its importance, the development of a predictive theory of dielectric breakdown has remained a challenge, owing to the complex multiscale nature of this process. We focus on the intrinsic dielectric breakdown field of insulators—the theoretical limit of breakdown determined purely by the chemistry of the material, i.e., the elements the material is composed of, the atomic-level structure, and the bonding. Starting from a benchmark dataset (generated from laborious first principles computations) of the intrinsicmore » dielectric breakdown field of a variety of model insulators, simple predictive phenomenological models of dielectric breakdown are distilled using advanced statistical or machine learning schemes, revealing key correlations and analytical relationships between the breakdown field and easily accessible material properties. Lastly, the models are shown to be general, and can hence guide the screening and systematic identification of high electric field tolerant materials.« less

Learning and Development of Second and Foreign Language Pragmatics as a Higher-Order Language Skill: A Brief Overview of Relevant Theories. Research Report. ETS RR-16-35

ERIC Educational Resources Information Center

Timpe-Laughlin, Veronika

2016-01-01

The development of effective second and foreign (L2) language learning materials needs to be grounded in two types of theories: (a) a theory of language and language use and (b) a theory of language learning. Both are equally important, insofar as an effective learning environment requires an understanding of the knowledge, skills, and abilities…

Correcting for population structure and kinship using the linear mixed model: theory and extensions.

PubMed

Hoffman, Gabriel E

2013-01-01

Population structure and kinship are widespread confounding factors in genome-wide association studies (GWAS). It has been standard practice to include principal components of the genotypes in a regression model in order to account for population structure. More recently, the linear mixed model (LMM) has emerged as a powerful method for simultaneously accounting for population structure and kinship. The statistical theory underlying the differences in empirical performance between modeling principal components as fixed versus random effects has not been thoroughly examined. We undertake an analysis to formalize the relationship between these widely used methods and elucidate the statistical properties of each. Moreover, we introduce a new statistic, effective degrees of freedom, that serves as a metric of model complexity and a novel low rank linear mixed model (LRLMM) to learn the dimensionality of the correction for population structure and kinship, and we assess its performance through simulations. A comparison of the results of LRLMM and a standard LMM analysis applied to GWAS data from the Multi-Ethnic Study of Atherosclerosis (MESA) illustrates how our theoretical results translate into empirical properties of the mixed model. Finally, the analysis demonstrates the ability of the LRLMM to substantially boost the strength of an association for HDL cholesterol in Europeans.

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

ERIC Educational Resources Information Center

Afify, Mohammed Kamal

2018-01-01

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

Teaching Research Methods and Statistics in eLearning Environments: Pedagogy, Practical Examples, and Possible Futures

PubMed Central

Rock, Adam J.; Coventry, William L.; Morgan, Methuen I.; Loi, Natasha M.

2016-01-01

Generally, academic psychologists are mindful of the fact that, for many students, the study of research methods and statistics is anxiety provoking (Gal et al., 1997). Given the ubiquitous and distributed nature of eLearning systems (Nof et al., 2015), teachers of research methods and statistics need to cultivate an understanding of how to effectively use eLearning tools to inspire psychology students to learn. Consequently, the aim of the present paper is to discuss critically how using eLearning systems might engage psychology students in research methods and statistics. First, we critically appraise definitions of eLearning. Second, we examine numerous important pedagogical principles associated with effectively teaching research methods and statistics using eLearning systems. Subsequently, we provide practical examples of our own eLearning-based class activities designed to engage psychology students to learn statistical concepts such as Factor Analysis and Discriminant Function Analysis. Finally, we discuss general trends in eLearning and possible futures that are pertinent to teachers of research methods and statistics in psychology. PMID:27014147

Teaching Research Methods and Statistics in eLearning Environments: Pedagogy, Practical Examples, and Possible Futures.

PubMed

Rock, Adam J; Coventry, William L; Morgan, Methuen I; Loi, Natasha M

2016-01-01

Generally, academic psychologists are mindful of the fact that, for many students, the study of research methods and statistics is anxiety provoking (Gal et al., 1997). Given the ubiquitous and distributed nature of eLearning systems (Nof et al., 2015), teachers of research methods and statistics need to cultivate an understanding of how to effectively use eLearning tools to inspire psychology students to learn. Consequently, the aim of the present paper is to discuss critically how using eLearning systems might engage psychology students in research methods and statistics. First, we critically appraise definitions of eLearning. Second, we examine numerous important pedagogical principles associated with effectively teaching research methods and statistics using eLearning systems. Subsequently, we provide practical examples of our own eLearning-based class activities designed to engage psychology students to learn statistical concepts such as Factor Analysis and Discriminant Function Analysis. Finally, we discuss general trends in eLearning and possible futures that are pertinent to teachers of research methods and statistics in psychology.

Infant Statistical-Learning Ability Is Related to Real-Time Language Processing

ERIC Educational Resources Information Center

Lany, Jill; Shoaib, Amber; Thompson, Abbie; Estes, Katharine Graf

2018-01-01

Infants are adept at learning statistical regularities in artificial language materials, suggesting that the ability to learn statistical structure may support language development. Indeed, infants who perform better on statistical learning tasks tend to be more advanced in parental reports of infants' language skills. Work with adults suggests…

Statistical Learning Is Related to Early Literacy-Related Skills

ERIC Educational Resources Information Center

Spencer, Mercedes; Kaschak, Michael P.; Jones, John L.; Lonigan, Christopher J.

2015-01-01

It has been demonstrated that statistical learning, or the ability to use statistical information to learn the structure of one's environment, plays a role in young children's acquisition of linguistic knowledge. Although most research on statistical learning has focused on language acquisition processes, such as the segmentation of words from…

The Conceptual Mechanism for Viable Organizational Learning Based on Complex System Theory and the Viable System Model

ERIC Educational Resources Information Center

Sung, Dia; You, Yeongmahn; Song, Ji Hoon

2008-01-01

The purpose of this research is to explore the possibility of viable learning organizations based on identifying viable organizational learning mechanisms. Two theoretical foundations, complex system theory and viable system theory, have been integrated to provide the rationale for building the sustainable organizational learning mechanism. The…

Adult Learning Assumptions

ERIC Educational Resources Information Center

Baskas, Richard S.

2011-01-01

The purpose of this study is to examine Knowles' theory of andragogy and his six assumptions of how adults learn while providing evidence to support two of his assumptions based on the theory of andragogy. As no single theory explains how adults learn, it can best be assumed that adults learn through the accumulation of formal and informal…

Case-Based Modeling for Learning: Socially Constructed Skill Development

ERIC Educational Resources Information Center

Lyons, Paul; Bandura, Randall P.

2018-01-01

Purpose: Grounded on components of experiential learning theory (ELT) and self-regulation of learning (SRL) theory, augmented by elements of action theory and script development, the purpose of this paper is to demonstrate the case-based modeling (CBM) instructional approach that stimulates learning in groups or teams. CBM is related to individual…

Metacognitive Load--Useful, or Extraneous Concept? Metacognitive and Self-Regulatory Demands in Computer-Based Learning

ERIC Educational Resources Information Center

Schwonke, Rolf

2015-01-01

Instructional design theories such as the "cognitive load theory" (CLT) or the "cognitive theory of multimedia learning" (CTML) explain learning difficulties in (computer-based) learning usually as a result of design deficiencies that hinder effective schema construction. However, learners often struggle even in well-designed…

Sociocultural Learning Theory in Practice: Implications for Athletic Training Educators

PubMed Central

Peer, Kimberly S.; McClendon, Ronald C.

2002-01-01

Objective: To discuss cognitive and sociocultural learning theory literature related to athletic training instructional and evaluation strategies while providing support for the application of these practices in the didactic and clinical components of athletic training education programs. Data Sources: We searched Educational Resources Information Center (ERIC) and Education Abstracts from 1975–2001 using the key words social cognitive, sociocultural learning theory, constructivism, and athletic training education. Current literature in the fields of educational psychology and athletic training education provides the foundation for applying theory to practice with specific emphasis on the theoretic framework and application of sociocultural learning theory strategies in athletic training education. Data Synthesis: Athletic training educators must have a strong fundamental knowledge of learning theory and a commitment to incorporate theory into educational practice. We integrate literature from both fields to generate practical strategies for using sociocultural learning theory in athletic training education. Conclusions/Recommendations: Social cognitive and sociocultural learning theory advocates a constructive, self-regulated, and goal-oriented environment with the student at the center of the educational process. Although a shift exists in athletic training education toward more active instructional strategies with the implementation of competency-based education, many educational environments are still dominated by traditional didactic instructional methods promoting student passivity. As athletic training education programs strive to increase accountability, educators in the field must critically analyze teaching and evaluation methods and integrate new material to ensure that learning is maximized. PMID:12937534

The history of imitation in learning theory: the language acquisition process.

PubMed Central

Kymissis, E; Poulson, C L

1990-01-01

The concept of imitation has undergone different analyses in the hands of different learning theorists throughout the history of psychology. From Thorndike's connectionism to Pavlov's classical conditioning, Hull's monistic theory, Mowrer's two-factor theory, and Skinner's operant theory, there have been several divergent accounts of the conditions that produce imitation and the conditions under which imitation itself may facilitate language acquisition. In tracing the roots of the concept of imitation in the history of learning theory, the authors conclude that generalized imitation, as defined and analyzed by operant learning theorists, is a sufficiently robust formulation of learned imitation to facilitate a behavior-analytic account of first-language acquisition. PMID:2230633

The extraction and integration framework: a two-process account of statistical learning.

PubMed

Thiessen, Erik D; Kronstein, Alexandra T; Hufnagle, Daniel G

2013-07-01

The term statistical learning in infancy research originally referred to sensitivity to transitional probabilities. Subsequent research has demonstrated that statistical learning contributes to infant development in a wide array of domains. The range of statistical learning phenomena necessitates a broader view of the processes underlying statistical learning. Learners are sensitive to a much wider range of statistical information than the conditional relations indexed by transitional probabilities, including distributional and cue-based statistics. We propose a novel framework that unifies learning about all of these kinds of statistical structure. From our perspective, learning about conditional relations outputs discrete representations (such as words). Integration across these discrete representations yields sensitivity to cues and distributional information. To achieve sensitivity to all of these kinds of statistical structure, our framework combines processes that extract segments of the input with processes that compare across these extracted items. In this framework, the items extracted from the input serve as exemplars in long-term memory. The similarity structure of those exemplars in long-term memory leads to the discovery of cues and categorical structure, which guides subsequent extraction. The extraction and integration framework provides a way to explain sensitivity to both conditional statistical structure (such as transitional probabilities) and distributional statistical structure (such as item frequency and variability), and also a framework for thinking about how these different aspects of statistical learning influence each other. 2013 APA, all rights reserved

Treatment of adolescent sexual offenders: theory-based practice.

PubMed

Sermabeikian, P; Martinez, D

1994-11-01

The treatment of adolescent sexual offenders (ASO) has its theoretical underpinnings in social learning theory. Although social learning theory has been frequently cited in literature, a comprehensive application of this theory, as applied to practice, has not been mapped out. The social learning and social cognitive theories of Bandura appear to be particularly relevant to the group treatment of this population. The application of these theories to practice, as demonstrated in a program model, is discussed as a means of demonstrating how theory-driven practice methods can be developed.

A Joyful Classroom Learning System with Robot Learning Companion for Children to Learn Mathematics Multiplication

ERIC Educational Resources Information Center

Wei, Chun-Wang; Hung, I-Chun; Lee, Ling; Chen, Nian-Shing

2011-01-01

This research demonstrates the design of a Joyful Classroom Learning System (JCLS) with flexible, mobile and joyful features. The theoretical foundations of this research include the experiential learning theory, constructivist learning theory and joyful learning. The developed JCLS consists of the robot learning companion (RLC), sensing input…

Embedding Evidence-based Practice Education into a Post-graduate Physiotherapy Program: Eight Years of pre-Post Course Evaluations.

PubMed

Perraton, L; Machotka, Z; Grimmer, K; Gibbs, C; Mahar, C; Kennedy, K

2017-04-01

Little has been published about the effectiveness of training postgraduate physiotherapy coursework students in research methods and evidence-based practice (EBP) theory. Graduate qualities in most universities include lifelong learning. Inclusion of EBP in post-graduate coursework students' training is one way for students to develop the knowledge and skills needed to implement current best evidence in their clinical practice after graduation, thereby facilitating lifelong learning. This paper reports on change in confidence and anxiety in knowledge of statistical terminology and concepts related to research design and EBP in eight consecutive years of post-graduate physiotherapy students at one Australian university. Pre-survey/post-survey instruments were administered to students in an intensive 3-week post-graduate course, which taught health research methods, biostatistics and EBP. This course was embedded into a post-graduate physiotherapy programme from 2007 to 2014. The organization and delivery of the course was based on best pedagogical evidence for effectively teaching adult physiotherapists. The course was first delivered each year in the programme, and no other course was delivered concurrently. There were significant improvements in confidence, significantly decreased anxiety and improvements in knowledge of statistical terminology and concepts related to research design and EBP, at course completion. Age, gender and country of origin were not confounders on learning outcomes, although there was a (non-significant) trend that years of practice negatively impacted on learning outcomes (p = 0.09). There was a greater improvement in confidence in statistical terminology than in concepts related to research design and EBP. An intensive teaching programme in health research methods and biostatistics and EBP, based on best practice adult physiotherapy learning principles, is effective immediately post-course, in decreasing anxiety and increasing confidence in the terminology used in research methods and EBP. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Bose-Einstein condensates form in heuristics learned by ciliates deciding to signal 'social' commitments.

PubMed

Clark, Kevin B

2010-03-01

Fringe quantum biology theories often adopt the concept of Bose-Einstein condensation when explaining how consciousness, emotion, perception, learning, and reasoning emerge from operations of intact animal nervous systems and other computational media. However, controversial empirical evidence and mathematical formalism concerning decoherence rates of bioprocesses keep these frameworks from satisfactorily accounting for the physical nature of cognitive-like events. This study, inspired by the discovery that preferential attachment rules computed by complex technological networks obey Bose-Einstein statistics, is the first rigorous attempt to examine whether analogues of Bose-Einstein condensation precipitate learned decision making in live biological systems as bioenergetics optimization predicts. By exploiting the ciliate Spirostomum ambiguum's capacity to learn and store behavioral strategies advertising mating availability into heuristics of topologically invariant computational networks, three distinct phases of strategy use were found to map onto statistical distributions described by Bose-Einstein, Fermi-Dirac, and classical Maxwell-Boltzmann behavior. Ciliates that sensitized or habituated signaling patterns to emit brief periods of either deceptive 'harder-to-get' or altruistic 'easier-to-get' serial escape reactions began testing condensed on initially perceived fittest 'courting' solutions. When these ciliates switched from their first strategy choices, Bose-Einstein condensation of strategy use abruptly dissipated into a Maxwell-Boltzmann computational phase no longer dominated by a single fittest strategy. Recursive trial-and-error strategy searches annealed strategy use back into a condensed phase consistent with performance optimization. 'Social' decisions performed by ciliates showing no nonassociative learning were largely governed by Fermi-Dirac statistics, resulting in degenerate distributions of strategy choices. These findings corroborate previous work demonstrating ciliates with improving expertise search grouped 'courting' assurances at quantum efficiencies and verify efficient processing by primitive 'social' intelligences involves network forms of Bose-Einstein condensation coupled to preceding thermodynamic-sensitive computational phases. 2009 Elsevier Ireland Ltd. All rights reserved.

Three Key Concepts of the Theory of Objectification: Knowledge, Knowing, and Learning

ERIC Educational Resources Information Center

Radford, Luis

2013-01-01

In this article I sketch three key concepts of a cultural-historical theory of mathematics teaching and learning--the theory of objectification. The concepts are: knowledge, knowing and learning. The philosophical underpinning of the theory revolves around the work of Georg W. F. Hegel and its further development in the philosophical works of K.…

Using Learning and Motivation Theories to Coherently Link Formative Assessment, Grading Practices, and Large-Scale Assessment

ERIC Educational Resources Information Center

Shepard, L. A.; Penuel, W. R.; Pellegrino, J. W.

2018-01-01

To support equitable and ambitious teaching practices, classroom assessment design must be grounded in a research-based theory of learning. Compared to other theories, sociocultural theory offers a more powerful, integrative account of how motivational aspects of learning--such as self-regulation, self-efficacy, sense of belonging, and…

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

ERIC Educational Resources Information Center

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…

Experiential learning: transforming theory into practice.

PubMed

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.

A Role for Chunk Formation in Statistical Learning of Second Language Syntax

ERIC Educational Resources Information Center

Hamrick, Phillip

2014-01-01

Humans are remarkably sensitive to the statistical structure of language. However, different mechanisms have been proposed to account for such statistical sensitivities. The present study compared adult learning of syntax and the ability of two models of statistical learning to simulate human performance: Simple Recurrent Networks, which learn by…

Bias-Free Chemically Diverse Test Sets from Machine Learning.

PubMed

Swann, Ellen T; Fernandez, Michael; Coote, Michelle L; Barnard, Amanda S

2017-08-14

Current benchmarking methods in quantum chemistry rely on databases that are built using a chemist's intuition. It is not fully understood how diverse or representative these databases truly are. Multivariate statistical techniques like archetypal analysis and K-means clustering have previously been used to summarize large sets of nanoparticles however molecules are more diverse and not as easily characterized by descriptors. In this work, we compare three sets of descriptors based on the one-, two-, and three-dimensional structure of a molecule. Using data from the NIST Computational Chemistry Comparison and Benchmark Database and machine learning techniques, we demonstrate the functional relationship between these structural descriptors and the electronic energy of molecules. Archetypes and prototypes found with topological or Coulomb matrix descriptors can be used to identify smaller, statistically significant test sets that better capture the diversity of chemical space. We apply this same method to find a diverse subset of organic molecules to demonstrate how the methods can easily be reapplied to individual research projects. Finally, we use our bias-free test sets to assess the performance of density functional theory and quantum Monte Carlo methods.

Statistical Learning is Related to Early Literacy-Related Skills

PubMed Central

Spencer, Mercedes; Kaschak, Michael P.; Jones, John L.; Lonigan, Christopher J.

2015-01-01

It has been demonstrated that statistical learning, or the ability to use statistical information to learn the structure of one’s environment, plays a role in young children’s acquisition of linguistic knowledge. Although most research on statistical learning has focused on language acquisition processes, such as the segmentation of words from fluent speech and the learning of syntactic structure, some recent studies have explored the extent to which individual differences in statistical learning are related to literacy-relevant knowledge and skills. The present study extends on this literature by investigating the relations between two measures of statistical learning and multiple measures of skills that are critical to the development of literacy—oral language, vocabulary knowledge, and phonological processing—within a single model. Our sample included a total of 553 typically developing children from prekindergarten through second grade. Structural equation modeling revealed that statistical learning accounted for a unique portion of the variance in these literacy-related skills. Practical implications for instruction and assessment are discussed. PMID:26478658

Can a theory-based educational intervention change nurses’ knowledge and attitudes concerning cancer pain management? A quasi-experimental design

PubMed Central

2013-01-01

Background Registered Nurses (RNs) play an important role in caring for patients suffering from cancer pain. A lack of knowledge regarding pain management and the RNs’ own perception of cancer pain could act as barriers to effective pain management. Educational interventions that target RNs’ knowledge and attitudes have proved promising. However, an intervention consisting of evidence-based practice is a multifaceted process and demands behavioural and cognitive changes to sustain the effects of the intervention. Therefore, our study aimed to investigate if a theory-based educational intervention could change RNs’ knowledge and attitudes to cancer pain and pain management, both four and 12 weeks after the start of the intervention. Methods A quasi-experimental design with non-equivalent control groups was used. The primary outcome was measured using a modified version of the instrument Nurses’ Knowledge and Attitudes Survey Regarding Pain (NKAS) at baseline, four weeks and 12 weeks after the start of the intervention to evaluate its persistence. The intervention’s educational curriculum was based on the principles of Ajzen’s Theory of Planned Behaviour and consisted of interactive learning activities conducted in workshops founded on evidence-based knowledge. The RN’s own experiences from cancer pain management were used in the learning process. Results The theory-based educational intervention aimed at changing RNs knowledge and attitudes regarding cancer pain management measured by primary outcome NKAS resulted in a statistical significant (p<0.05) improvement of total mean score from baseline to four weeks at the intervention ward. Conclusions The findings of this study, suggest that a theory-based educational intervention focused at RNs can be effective in changing RN’s knowledge and attitudes regarding cancer pain management. However, the high number of dropouts between baseline and four weeks needs to be taken into account when evaluating our findings. Finally, this kind of theory-based educational intervention with interactive learning activities has been sparsely researched and needs to be evaluated further in larger projects. Trial registration Clinical Trials. Gov: NCT01313234 PMID:23958335

The Effect of Interactivity and Instructional Exposure on Learning Effectiveness and Knowledge Retention: A Comparative Study of Two U.S. Air Force Computer-Based Training (CBT) Courses for Network User Licensing

DTIC Science & Technology

2003-03-01

sociocultural theory of learning was pioneered by Lev Vygotsky in the early twentieth century Soviet Union. Although his works were not published...Overview ....................................................................................................................... 14 Learning Theories ...and Teaching Strategies .................................................................. 14 Learning Theories and CBT

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

ERIC Educational Resources Information Center

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…

Theory and Practice: How Filming "Learning in the Real World" Helps Students Make the Connection

ERIC Educational Resources Information Center

Commander, Nannette Evans; Ward, Teresa E.; Zabrucky, Karen M.

2012-01-01

This article describes an assignment, titled "Learning in the Real World," designed for graduate students in a learning theory course. Students work in small groups to create high quality audio-visual films that present "real learning" through interviews and/or observations of learners. Students select topics relevant to theories we are discussing…

Alternative Organisational Learning Therapy: An Empirical Case Study Using Behaviour and U Theory

ERIC Educational Resources Information Center

Ho, Li-An; Kuo, Tsung-Hsien

2009-01-01

This paper draws on the concept and process of deeper learning, namely the U theory (Senge, Scharmer, Jaworski, & Flowers, 2004a). As a driver to get a deeper exploration of organisational change process, the theory of U goes beyond the interpersonal aspects of learning, instead focusing on a deeper personal generative learning that emphasizes…

The development of ensemble theory. A new glimpse at the history of statistical mechanics

NASA Astrophysics Data System (ADS)

Inaba, Hajime

2015-12-01

This paper investigates the history of statistical mechanics from the viewpoint of the development of the ensemble theory from 1871 to 1902. In 1871, Ludwig Boltzmann introduced a prototype model of an ensemble that represents a polyatomic gas. In 1879, James Clerk Maxwell defined an ensemble as copies of systems of the same energy. Inspired by H.W. Watson, he called his approach "statistical". Boltzmann and Maxwell regarded the ensemble theory as a much more general approach than the kinetic theory. In the 1880s, influenced by Hermann von Helmholtz, Boltzmann made use of ensembles to establish thermodynamic relations. In Elementary Principles in Statistical Mechanics of 1902, Josiah Willard Gibbs tried to get his ensemble theory to mirror thermodynamics, including thermodynamic operations in its scope. Thermodynamics played the role of a "blind guide". His theory of ensembles can be characterized as more mathematically oriented than Einstein's theory proposed in the same year. Mechanical, empirical, and statistical approaches to foundations of statistical mechanics are presented. Although it was formulated in classical terms, the ensemble theory provided an infrastructure still valuable in quantum statistics because of its generality.

Improvement of Radiological Teaching - Effects of Focusing of Learning Targets and Increased Consideration of Learning Theory Knowledge.

PubMed

Wirth, Stefan; William, York-Alexander; Paolini, Marco; Wirth, Kathrin; Maxien, Daniel; Reiser, Maximilian; Fischer, Martin R

2018-02-01

 Based on evaluation and examination results of students, a necessity for improvement of so far purely instructor-based radiological teaching at the local institution was determined. Aim of our study was to use one out of eight seminars to exemplify adaptation of the teaching concept according to learning theory knowledge, to determine the resulting effects and to interpret them.  The institutional review board approved the prospective study of the seminar conversion, which was performed after the end of the winter semester 2015/2016. Didactically, this included a course split into online preparation, attendance phase and online follow-up with integration of interactive scaffolding, practice-oriented clinical teaching according to Stanford, Peyton skills transfer and extensive feedback into the attendance phase. At the beginning and at the end of each course, each student filled in identical, standardized questionnaires (n = 256 before and after conversion) using a 5-point Likert scale (1: very good; to 5: deficient) and additionally answered two randomly chosen written examination questions from a content-adapted questionnaire pool of the last five years. For statistical evaluation, the Mann-Whitney U-Test was used for evaluation data and Fisher's Exact test for exam questions.  Before/after conversion, the subjective total evaluation score of students was 3.22 (mean value) ± 1.51 (standard deviation) / 1.66 ± 0.78 (p < 0.001) and the objective proportion of correctly answered examination questions in the respective cohort at the beginning of the seminar 37.7/53.9 % and at the end of the seminar 55.1/84.6 % (p < 0.001).  The conversion of the test seminar resulted in both a better evaluation of the teaching unit by the students (evaluation) and a considerably higher rate of correctly answered examination questions from past state examinations (learning success). This supports transferring the concept to comparable teaching units.   · Radiological teaching allows integration of current learning theory concepts with reasonable effort.. · In a test seminar this improved the evaluation results of the teaching unit by the students.. · In addition, this also led to a higher rate of correctly answered examination questions from past state examinations.. · This supports further steps towards excellent radiological teaching.. · Wirth S, William Y, Paolini M et al. Improvement of Radiological Teaching - Effects of Focusing of Learning Targets and Increased Consideration of Learning Theory Knowledge. Fortschr Röntgenstr 2018; 190: 161 - 174. © Georg Thieme Verlag KG Stuttgart · New York.

Situated Learning in Computer Science Education

ERIC Educational Resources Information Center

Ben-Ari, Mordechai

2004-01-01

Sociocultural theories of learning such as Wenger and Lave's situated learning have been suggested as alternatives to cognitive theories of learning like constructivism. This article examines situated learning within the context of computer science (CS) education. Situated learning accurately describes some CS communities like open-source software…

Examination of the teaching styles of nursing professional development specialists, part I: best practices in adult learning theory, curriculum development, and knowledge transfer.

PubMed

Curran, Mary K

2014-05-01

The American Nurses Association advocates for nursing professional development (NPD) specialists to have an earned graduate degree, as well as educational and clinical expertise. However, many NPD specialists have limited exposure to adult learning theory. Limited exposure to adult learning theory may affect NPD educational practices, learning outcomes, organizational knowledge transfer, and subsequently, the professional development of the nurses they serve and quality of nursing care. An examination of current teaching practices may reveal opportunities for NPD specialists to enhance educational methods to promote learning, learning transfer, and organizational knowledge and excellence. This article, the first in a two-part series, examines best practices of adult learning theories, nursing professional development, curriculum design, and knowledge transfer. Part II details the results of a correlational study that examined the effects of four variables on the use of adult learning theory to guide curriculum development for NPD specialists in hospitals. Copyright 2014, SLACK Incorporated.

Finding accurate frontiers: A knowledge-intensive approach to relational learning

NASA Technical Reports Server (NTRS)

Pazzani, Michael; Brunk, Clifford

1994-01-01

An approach to analytic learning is described that searches for accurate entailments of a Horn Clause domain theory. A hill-climbing search, guided by an information based evaluation function, is performed by applying a set of operators that derive frontiers from domain theories. The analytic learning system is one component of a multi-strategy relational learning system. We compare the accuracy of concepts learned with this analytic strategy to concepts learned with an analytic strategy that operationalizes the domain theory.

Learning control system design based on 2-D theory - An application to parallel link manipulator

NASA Technical Reports Server (NTRS)

Geng, Z.; Carroll, R. L.; Lee, J. D.; Haynes, L. H.

1990-01-01

An approach to iterative learning control system design based on two-dimensional system theory is presented. A two-dimensional model for the iterative learning control system which reveals the connections between learning control systems and two-dimensional system theory is established. A learning control algorithm is proposed, and the convergence of learning using this algorithm is guaranteed by two-dimensional stability. The learning algorithm is applied successfully to the trajectory tracking control problem for a parallel link robot manipulator. The excellent performance of this learning algorithm is demonstrated by the computer simulation results.

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

PubMed

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.

A Study of the Effectiveness of the Contextual Lab Activity in the Teaching and Learning Statistics at the UTHM (Universiti Tun Hussein Onn Malaysia)

ERIC Educational Resources Information Center

Kamaruddin, Nafisah Kamariah Md; Jaafar, Norzilaila bt; Amin, Zulkarnain Md

2012-01-01

Inaccurate concept in statistics contributes to the assumption by the students that statistics do not relate to the real world and are not relevant to the engineering field. There are universities which introduced learning statistics using statistics lab activities. However, the learning is more on the learning how to use software and not to…

Statistical Machine Learning for Structured and High Dimensional Data

DTIC Science & Technology

2014-09-17

AFRL-OSR-VA-TR-2014-0234 STATISTICAL MACHINE LEARNING FOR STRUCTURED AND HIGH DIMENSIONAL DATA Larry Wasserman CARNEGIE MELLON UNIVERSITY Final...Re . 8-98) v Prescribed by ANSI Std. Z39.18 14-06-2014 Final Dec 2009 - Aug 2014 Statistical Machine Learning for Structured and High Dimensional...area of resource-constrained statistical estimation. machine learning , high-dimensional statistics U U U UU John Lafferty 773-702-3813 > Research under

Second Language Experience Facilitates Statistical Learning of Novel Linguistic Materials

ERIC Educational Resources Information Center

Potter, Christine E.; Wang, Tianlin; Saffran, Jenny R.

2017-01-01

Recent research has begun to explore individual differences in statistical learning, and how those differences may be related to other cognitive abilities, particularly their effects on language learning. In this research, we explored a different type of relationship between language learning and statistical learning: the possibility that learning…

Log In to Experiential Learning Theory: Supporting Web-Based Faculty Development.

PubMed

Omer, Selma; Choi, Sunhea; Brien, Sarah; Parry, Marcus

2017-09-27

For an increasingly busy and geographically dispersed faculty, the Faculty of Medicine at the University of Southampton, United Kingdom, developed a range of Web-based faculty development modules, based on Kolb's experiential learning cycle, to complement the faculty's face-to-face workshops. The objective of this study was to assess users' views and perceptions of the effectiveness of Web-based faculty development modules based on Kolb's experiential learning cycle. We explored (1) users' satisfaction with the modules, (2) whether Kolb's design framework supported users' learning, and (3) whether the design principle impacts their work as educators. We gathered data from users over a 3-year period using evaluation surveys built into each of the seven modules. Quantitative data were analyzed using descriptive statistics, and responses to open-ended questions were analyzed using content analysis. Out of the 409 module users, 283 completed the survey (69.1% response rate). Over 80% of the users reported being satisfied or very satisfied with seven individual aspects of the modules. The findings suggest a strong synergy between the design features that users rated most highly and the key stages of Kolb's learning cycle. The use of simulations and videos to give the users an initial experience as well as the opportunity to "Have a go" and receive feedback in a safe environment were both considered particularly useful. In addition to providing an opportunity for reflection, many participants considered that the modules would enhance their roles as educators through: increasing their knowledge on various education topics and the required standards for medical training, and improving their skills in teaching and assessing students through practice and feedback and ultimately increasing their confidence. Kolb's theory-based design principle used for Web-based faculty development can support faculty to improve their skills and has impact on their role as educators. Grounding Web-based training in learning theory offers an effective and flexible approach for faculty development. ©Selma Omer, Sunhea Choi, Sarah Brien, Marcus Parry. Originally published in JMIR Medical Education (http://mededu.jmir.org), 27.09.2017.

Theories of Learning and Their Impact on OPAC Instruction.

ERIC Educational Resources Information Center

Frick, Elizabeth

1989-01-01

Describes four major types of learning theories (behavioral, cognitive, cybernetic, and andragogical); examines pertinent literature for each; and traces links between the literature of learning theory and that of the design of online public access catalog instructional systems. (32 references) (Author/CLB)

The Effect of Learning Type and Avatar Similarity on Learning Outcomes in Educational Video Games

ERIC Educational Resources Information Center

Lewis, Melissa L.

2009-01-01

Two theories guide two very different ideas about learning. Social cognitive theory (Bandura, 1977, 1989) places the greater emphasis on observational learning, or learning by watching a model produce a behavior before doing it oneself. Other researchers purport that experiential learning, or learning by doing, results in stronger learning (Kolb,…

Applying learning theories and instructional design models for effective instruction.

PubMed

Khalil, Mohammed K; Elkhider, Ihsan A

2016-06-01

Faculty members in higher education are involved in many instructional design activities without formal training in learning theories and the science of instruction. Learning theories provide the foundation for the selection of instructional strategies and allow for reliable prediction of their effectiveness. To achieve effective learning outcomes, the science of instruction and instructional design models are used to guide the development of instructional design strategies that elicit appropriate cognitive processes. Here, the major learning theories are discussed and selected examples of instructional design models are explained. The main objective of this article is to present the science of learning and instruction as theoretical evidence for the design and delivery of instructional materials. In addition, this article provides a practical framework for implementing those theories in the classroom and laboratory. Copyright © 2016 The American Physiological Society.

Taking Stock.

ERIC Educational Resources Information Center

Merriam, Sharan B.

1993-01-01

A complete theory of adult learning must take into consideration the learner, learning process, and context. Andragogy, self-directed learning, consciousness, critical theory, feminism, transformational learning, and situated cognition contribute to understanding of this complex phenomenon. (SK)

Developing a Taxonomy of Characteristics and Features of Learning Systems and Internet Gaming Environments

DTIC Science & Technology

2006-09-01

Learning methodologies have been developed over a number of years and it has evolved as technologies advance and new learning theories emerge. We...can be used to justify learning systems. Many theories are developed . We introduce significant learning theories in this section. 2.1 Behaviorism...not fitting well with traditional classroom environment. 3 2.3 Cognitivism Piaget believed that humans desire a state of cognitive balance or

Birds, primates, and spoken language origins: behavioral phenotypes and neurobiological substrates

PubMed Central

Petkov, Christopher I.; Jarvis, Erich D.

2012-01-01

Vocal learners such as humans and songbirds can learn to produce elaborate patterns of structurally organized vocalizations, whereas many other vertebrates such as non-human primates and most other bird groups either cannot or do so to a very limited degree. To explain the similarities among humans and vocal-learning birds and the differences with other species, various theories have been proposed. One set of theories are motor theories, which underscore the role of the motor system as an evolutionary substrate for vocal production learning. For instance, the motor theory of speech and song perception proposes enhanced auditory perceptual learning of speech in humans and song in birds, which suggests a considerable level of neurobiological specialization. Another, a motor theory of vocal learning origin, proposes that the brain pathways that control the learning and production of song and speech were derived from adjacent motor brain pathways. Another set of theories are cognitive theories, which address the interface between cognition and the auditory-vocal domains to support language learning in humans. Here we critically review the behavioral and neurobiological evidence for parallels and differences between the so-called vocal learners and vocal non-learners in the context of motor and cognitive theories. In doing so, we note that behaviorally vocal-production learning abilities are more distributed than categorical, as are the auditory-learning abilities of animals. We propose testable hypotheses on the extent of the specializations and cross-species correspondences suggested by motor and cognitive theories. We believe that determining how spoken language evolved is likely to become clearer with concerted efforts in testing comparative data from many non-human animal species. PMID:22912615

The Development of a Comprehensive and Coherent Theory of Learning

ERIC Educational Resources Information Center

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…

Experiential Learning Theory as One of the Foundations of Adult Learning Practice Worldwide

ERIC Educational Resources Information Center

Dernova, Maiya

2015-01-01

The paper presents the analysis of existing theory, assumptions, and models of adult experiential learning. The experiential learning is a learning based on a learning cycle guided by the dual dialectics of action-reflection and experience-abstraction. It defines learning as a process of knowledge creation through experience transformation, so…

Design and implementation of a flipped classroom learning environment in the biomedical engineering context.

PubMed

Corrias, Alberto; Cho Hong, James Goh

2015-01-01

The design and implementation of a learning environment that leverages on the use of various technologies is presented. The context is an undergraduate core engineering course within the biomedical engineering curriculum. The topic of the course is data analysis in biomedical engineering problems. One of the key ideas of this study is to confine the most mathematical and statistical aspects of data analysis in prerecorded video lectures. Students are asked to watch the video lectures before coming to class. Since the classroom session does not need to cover the mathematical theory, the time is spent on a selected real world scenario in the field of biomedical engineering that exposes students to an actual application of the theory. The weekly cycle is concluded with a hands-on tutorial session in the computer rooms. A potential problem would arise in such learning environment if the students do not follow the recommendation of watching the video lecture before coming to class. In an attempt to limit these occurrences, two key instruments were put in place: a set of online self-assessment questions that students are asked to take before the classroom session and a simple rewards system during the classroom session. Thanks to modern learning analytics tools, we were able to show that, on average, 57.9% of students followed the recommendation of watching the video lecture before class. The efficacy of the learning environment was assessed through various means. A survey was conducted among the students and the gathered data support the view that the learning environment was well received by the students. Attempts were made to quantify the impacts on learning of the proposed measures by taking into account the results of selected questions of the final examination of the course. Although the presence of confounding factors demands caution in the interpretation, these data seem to indicate a possible positive effect of the use of video lectures in this technologically enhanced learning environment.

Full Spectrum Training and Development: Soldier Skills and Attributes

DTIC Science & Technology

2010-07-01

aspects of social interdependence theory , which suggests that socioemotional as well as cognitive benefits can accrue from such training (O’Donnell...sociocognitive learning theories . In the ARC, P2P training can guide cadre and student interaction while creating an active learning environment...learning theories (Costanza et al., 2009). Behavioral theory prescribes gradually approximating, or shaping, the desired response until it meets

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

ERIC Educational Resources Information Center

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…

Statistical mechanics of complex neural systems and high dimensional data

NASA Astrophysics Data System (ADS)

Advani, Madhu; Lahiri, Subhaneil; Ganguli, Surya

2013-03-01

Recent experimental advances in neuroscience have opened new vistas into the immense complexity of neuronal networks. This proliferation of data challenges us on two parallel fronts. First, how can we form adequate theoretical frameworks for understanding how dynamical network processes cooperate across widely disparate spatiotemporal scales to solve important computational problems? Second, how can we extract meaningful models of neuronal systems from high dimensional datasets? To aid in these challenges, we give a pedagogical review of a collection of ideas and theoretical methods arising at the intersection of statistical physics, computer science and neurobiology. We introduce the interrelated replica and cavity methods, which originated in statistical physics as powerful ways to quantitatively analyze large highly heterogeneous systems of many interacting degrees of freedom. We also introduce the closely related notion of message passing in graphical models, which originated in computer science as a distributed algorithm capable of solving large inference and optimization problems involving many coupled variables. We then show how both the statistical physics and computer science perspectives can be applied in a wide diversity of contexts to problems arising in theoretical neuroscience and data analysis. Along the way we discuss spin glasses, learning theory, illusions of structure in noise, random matrices, dimensionality reduction and compressed sensing, all within the unified formalism of the replica method. Moreover, we review recent conceptual connections between message passing in graphical models, and neural computation and learning. Overall, these ideas illustrate how statistical physics and computer science might provide a lens through which we can uncover emergent computational functions buried deep within the dynamical complexities of neuronal networks.

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

ERIC Educational Resources Information Center

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)

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

ERIC Educational Resources Information Center

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…

The CABES (Clare Adult Basic Education Service) Framework as a Tool for Teaching and Learning

ERIC Educational Resources Information Center

Greene, Moira

2015-01-01

This article describes a Framework that can be used to help bridge the gap between theory and practice in adult learning. The Framework promotes practice informed by three strands important to adult literacy work: social theories of literacy, social-constructivist learning theory and principles of adult learning. The Framework shows how five key…

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

ERIC Educational Resources Information Center

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…

Motivating Learners in Open and Distance Learning: Do We Need a New Theory of Learner Support?

ERIC Educational Resources Information Center

Simpson, Ormond

2008-01-01

This paper calls for a new theory of learner support in distance learning based on recent findings in the fields of learning and motivational psychology. It surveys some current learning motivation theories and proposes that models drawn from the relatively new field of Positive Psychology, such as the "Strengths Approach", together with…

What is Informal Learning and What are its Antecedents? An Integrative and Meta-Analytic Review

DTIC Science & Technology

2014-07-01

formal training. Unfortunately, theory and research surrounding informal learning remains fragmented. Given that there has been little systematic...future-oriented. Applying this framework, the construct domain of informal learning in organizations is articulated. Second, an interactionist theory ...theoretical framework and outline an agenda for future theory development, research, and application of informal learning principles in organizations

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

ERIC Educational Resources Information Center

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…

Analyzing Learning in Professional Learning Communities: A Conceptual Framework

ERIC Educational Resources Information Center

Van Lare, Michelle D.; Brazer, S. David

2013-01-01

The purpose of this article is to build a conceptual framework that informs current understanding of how professional learning communities (PLCs) function in conjunction with organizational learning. The combination of sociocultural learning theories and organizational learning theories presents a more complete picture of PLC processes that has…

Commentary on "Distributed Revisiting: An Analytic for Retention of Coherent Science Learning"

ERIC Educational Resources Information Center

Hewitt, Jim

2015-01-01

The article, "Distributed Revisiting: An Analytic for Retention of Coherent Science Learning" is an interesting study that operates at the intersection of learning theory and learning analytics. The authors observe that the relationship between learning theory and research in the learning analytics field is constrained by several…

The Psychology of Mathematics Learning: Past and Present.

ERIC Educational Resources Information Center

Education and Urban Society, 1985

1985-01-01

Reviews trends in applying psychology to mathematics learning. Discusses the influence of behaviorism and other functionalist theories, Gestalt theory, Piagetian theory, and the "new functionalism" evident in computer-oriented theories of information processing. (GC)

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

PubMed

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

2015-01-01

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

Learning theories application in nursing education

PubMed Central

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 located with the meaning and meaningful. It applies the minds and feelings of the person. From this group, the main focus will be on the works of Rogers and Novels. Finally, it could be concluded that the usage of any of these theoriesin its place would be desired and useful. PMID:25767813

Learning theories application in nursing education.

PubMed

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 located with the meaning and meaningful. It applies the minds and feelings of the person. From this group, the main focus will be on the works of Rogers and Novels. Finally, it could be concluded that the usage of any of these theoriesin its place would be desired and useful.

Is Statistical Learning Constrained by Lower Level Perceptual Organization?

PubMed Central

Emberson, Lauren L.; Liu, Ran; Zevin, Jason D.

2013-01-01

In order for statistical information to aid in complex developmental processes such as language acquisition, learning from higher-order statistics (e.g. across successive syllables in a speech stream to support segmentation) must be possible while perceptual abilities (e.g. speech categorization) are still developing. The current study examines how perceptual organization interacts with statistical learning. Adult participants were presented with multiple exemplars from novel, complex sound categories designed to reflect some of the spectral complexity and variability of speech. These categories were organized into sequential pairs and presented such that higher-order statistics, defined based on sound categories, could support stream segmentation. Perceptual similarity judgments and multi-dimensional scaling revealed that participants only perceived three perceptual clusters of sounds and thus did not distinguish the four experimenter-defined categories, creating a tension between lower level perceptual organization and higher-order statistical information. We examined whether the resulting pattern of learning is more consistent with statistical learning being “bottom-up,” constrained by the lower levels of organization, or “top-down,” such that higher-order statistical information of the stimulus stream takes priority over the perceptual organization, and perhaps influences perceptual organization. We consistently find evidence that learning is constrained by perceptual organization. Moreover, participants generalize their learning to novel sounds that occupy a similar perceptual space, suggesting that statistical learning occurs based on regions of or clusters in perceptual space. Overall, these results reveal a constraint on learning of sound sequences, such that statistical information is determined based on lower level organization. These findings have important implications for the role of statistical learning in language acquisition. PMID:23618755

Noninvasive fetal QRS detection using an echo state network and dynamic programming.

PubMed

Lukoševičius, Mantas; Marozas, Vaidotas

2014-08-01

We address a classical fetal QRS detection problem from abdominal ECG recordings with a data-driven statistical machine learning approach. Our goal is to have a powerful, yet conceptually clean, solution. There are two novel key components at the heart of our approach: an echo state recurrent neural network that is trained to indicate fetal QRS complexes, and several increasingly sophisticated versions of statistics-based dynamic programming algorithms, which are derived from and rooted in probability theory. We also employ a standard technique for preprocessing and removing maternal ECG complexes from the signals, but do not take this as the main focus of this work. The proposed approach is quite generic and can be extended to other types of signals and annotations. Open-source code is provided.

Myths and legends in learning classification rules

NASA Technical Reports Server (NTRS)

Buntine, Wray

1990-01-01

A discussion is presented of machine learning theory on empirically learning classification rules. Six myths are proposed in the machine learning community that address issues of bias, learning as search, computational learning theory, Occam's razor, universal learning algorithms, and interactive learning. Some of the problems raised are also addressed from a Bayesian perspective. Questions are suggested that machine learning researchers should be addressing both theoretically and experimentally.

The theoretical base of e-learning and its role in surgical education.

PubMed

Evgeniou, Evgenios; Loizou, Peter

2012-01-01

The advances in Internet and computer technology offer many solutions that can enhance surgical education and increase the effectiveness of surgical teaching. E-learning plays an important role in surgical education today, with many e-learning projects already available on the Internet. E-learning is based on a mixture of educational theories that derive from behaviorist, cognitivist, and constructivist educational theoretical frameworks. CAN EDUCATIONAL THEORY IMPROVE E-LEARNING?: Conventional educational theory can be applied to improve the quality and effectiveness of e-learning. The theory of "threshold concepts" and educational theories on reflection, motivation, and communities of practice can be applied when designing e-learning material. E-LEARNING IN SURGICAL EDUCATION: E-learning has many advantages but also has weaknesses. Studies have shown that e-learning is an effective teaching method that offers high levels of learner satisfaction. Instead of trying to compare e-learning with traditional methods of teaching, it is better to integrate in e-learning elements of traditional teaching that have been proven to be effective. E-learning can play an important role in surgical education as a blended approach, combined with more traditional methods of teaching, which offer better face-to-interaction with patients and colleagues in different circumstances and hands on practice of practical skills. National provision of e-learning can make evaluation easier. The correct utilization of Internet and computer resources combined with the application of valid conventional educational theory to design e-learning relevant to the various levels of surgical training can be effective in the training of future surgeons. Copyright © 2012 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.

Distinct contributions of attention and working memory to visual statistical learning and ensemble processing.

PubMed

Hall, Michelle G; Mattingley, Jason B; Dux, Paul E

2015-08-01

The brain exploits redundancies in the environment to efficiently represent the complexity of the visual world. One example of this is ensemble processing, which provides a statistical summary of elements within a set (e.g., mean size). Another is statistical learning, which involves the encoding of stable spatial or temporal relationships between objects. It has been suggested that ensemble processing over arrays of oriented lines disrupts statistical learning of structure within the arrays (Zhao, Ngo, McKendrick, & Turk-Browne, 2011). Here we asked whether ensemble processing and statistical learning are mutually incompatible, or whether this disruption might occur because ensemble processing encourages participants to process the stimulus arrays in a way that impedes statistical learning. In Experiment 1, we replicated Zhao and colleagues' finding that ensemble processing disrupts statistical learning. In Experiments 2 and 3, we found that statistical learning was unimpaired by ensemble processing when task demands necessitated (a) focal attention to individual items within the stimulus arrays and (b) the retention of individual items in working memory. Together, these results are consistent with an account suggesting that ensemble processing and statistical learning can operate over the same stimuli given appropriate stimulus processing demands during exposure to regularities. (c) 2015 APA, all rights reserved).

Toward an instructionally oriented theory of example-based learning.

PubMed

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 worked examples, observational learning, and analogical reasoning. This theory has descriptive and prescriptive elements. The descriptive subtheory deals with (a) the relevance and effectiveness of examples, (b) phases of skill acquisition, and (c) learning processes. The prescriptive subtheory proposes instructional principles that make full exploitation of the potential of example-based learning possible. Copyright © 2013 Cognitive Science Society, Inc.

What's statistical about learning? Insights from modelling statistical learning as a set of memory processes

PubMed Central

2017-01-01

Statistical learning has been studied in a variety of different tasks, including word segmentation, object identification, category learning, artificial grammar learning and serial reaction time tasks (e.g. Saffran et al. 1996 Science 274, 1926–1928; Orban et al. 2008 Proceedings of the National Academy of Sciences 105, 2745–2750; Thiessen & Yee 2010 Child Development 81, 1287–1303; Saffran 2002 Journal of Memory and Language 47, 172–196; Misyak & Christiansen 2012 Language Learning 62, 302–331). The difference among these tasks raises questions about whether they all depend on the same kinds of underlying processes and computations, or whether they are tapping into different underlying mechanisms. Prior theoretical approaches to statistical learning have often tried to explain or model learning in a single task. However, in many cases these approaches appear inadequate to explain performance in multiple tasks. For example, explaining word segmentation via the computation of sequential statistics (such as transitional probability) provides little insight into the nature of sensitivity to regularities among simultaneously presented features. In this article, we will present a formal computational approach that we believe is a good candidate to provide a unifying framework to explore and explain learning in a wide variety of statistical learning tasks. This framework suggests that statistical learning arises from a set of processes that are inherent in memory systems, including activation, interference, integration of information and forgetting (e.g. Perruchet & Vinter 1998 Journal of Memory and Language 39, 246–263; Thiessen et al. 2013 Psychological Bulletin 139, 792–814). From this perspective, statistical learning does not involve explicit computation of statistics, but rather the extraction of elements of the input into memory traces, and subsequent integration across those memory traces that emphasize consistent information (Thiessen and Pavlik 2013 Cognitive Science 37, 310–343). This article is part of the themed issue ‘New frontiers for statistical learning in the cognitive sciences'. PMID:27872374

What's statistical about learning? Insights from modelling statistical learning as a set of memory processes.

PubMed

Thiessen, Erik D

2017-01-05

Statistical learning has been studied in a variety of different tasks, including word segmentation, object identification, category learning, artificial grammar learning and serial reaction time tasks (e.g. Saffran et al. 1996 Science 274: , 1926-1928; Orban et al. 2008 Proceedings of the National Academy of Sciences 105: , 2745-2750; Thiessen & Yee 2010 Child Development 81: , 1287-1303; Saffran 2002 Journal of Memory and Language 47: , 172-196; Misyak & Christiansen 2012 Language Learning 62: , 302-331). The difference among these tasks raises questions about whether they all depend on the same kinds of underlying processes and computations, or whether they are tapping into different underlying mechanisms. Prior theoretical approaches to statistical learning have often tried to explain or model learning in a single task. However, in many cases these approaches appear inadequate to explain performance in multiple tasks. For example, explaining word segmentation via the computation of sequential statistics (such as transitional probability) provides little insight into the nature of sensitivity to regularities among simultaneously presented features. In this article, we will present a formal computational approach that we believe is a good candidate to provide a unifying framework to explore and explain learning in a wide variety of statistical learning tasks. This framework suggests that statistical learning arises from a set of processes that are inherent in memory systems, including activation, interference, integration of information and forgetting (e.g. Perruchet & Vinter 1998 Journal of Memory and Language 39: , 246-263; Thiessen et al. 2013 Psychological Bulletin 139: , 792-814). From this perspective, statistical learning does not involve explicit computation of statistics, but rather the extraction of elements of the input into memory traces, and subsequent integration across those memory traces that emphasize consistent information (Thiessen and Pavlik 2013 Cognitive Science 37: , 310-343).This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'. © 2016 The Author(s).

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

PubMed

McCullough Chavis, Annie

2011-01-01

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

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

PubMed

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

2003-07-01

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

Three Theories of Psychological Development; Implications for Children's Dentistry.

ERIC Educational Resources Information Center

George, James M.; McIver, F. Thomas

1983-01-01

A slide-tape series developed for introduction of developmental and learning theories in freshman dental curriculum is described. Theories of social-emotional development, cognitive development, and theories of conditioning and observational learning are included. (MSE)

Learning by doing - Piagets Konstruktivistische Lerntheorie und Ihre Konsequenzen FÜR Die PÄDAGOGISCHE Praxis

NASA Astrophysics Data System (ADS)

Vollmers, Burkhard

1997-01-01

Piaget's theory of genetic recognition has a number of pedagogical implications. With the swing from structuralism to constructivism, Piaget created one of the first constructivist learning theories around the middle of this century. After this has been briefly presented, its relationship to present-day teaching and learning research, pedagogical practice and other forms of constructivism is examined critically. Although Piaget's theory does not embrace all forms of human learning, it does contain some significant pointers for pedagogical practice. An appropriate practical application of Piaget's learning theory would be to teach by encouraging spontaneous activity and the interests of the pupils.

Musicians' edge: A comparison of auditory processing, cognitive abilities and statistical learning.

PubMed

Mandikal Vasuki, Pragati Rao; Sharma, Mridula; Demuth, Katherine; Arciuli, Joanne

2016-12-01

It has been hypothesized that musical expertise is associated with enhanced auditory processing and cognitive abilities. Recent research has examined the relationship between musicians' advantage and implicit statistical learning skills. In the present study, we assessed a variety of auditory processing skills, cognitive processing skills, and statistical learning (auditory and visual forms) in age-matched musicians (N = 17) and non-musicians (N = 18). Musicians had significantly better performance than non-musicians on frequency discrimination, and backward digit span. A key finding was that musicians had better auditory, but not visual, statistical learning than non-musicians. Performance on the statistical learning tasks was not correlated with performance on auditory and cognitive measures. Musicians' superior performance on auditory (but not visual) statistical learning suggests that musical expertise is associated with an enhanced ability to detect statistical regularities in auditory stimuli. Copyright © 2016 Elsevier B.V. All rights reserved.

The Leader's Role in Strategic Knowledge Creation and Mobilization

ERIC Educational Resources Information Center

Reid, Steven

2013-01-01

The purpose of this paper is to explore how leaders influence knowledge creation and mobilization processes. As a basis for the theoretical framework, the researcher selected theories that informed the investigation of this influence: leadership theory, knowledge theory, learning theory, organizational learning theory, and organizational knowledge…

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

ERIC Educational Resources Information Center

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…

The impact of a Classroom Performance System on learning gains in a biology course for science majors

NASA Astrophysics Data System (ADS)

Marin, Nilo Eric

This study was conducted to determine if the use of the technology known as Classroom Performance System (CPS), specifically referred to as "Clickers", improves the learning gains of students enrolled in a biology course for science majors. CPS is one of a group of developing technologies adapted for providing feedback in the classroom using a learner-centered approach. It supports and facilitates discussion among students and between them and teachers, and provides for participation by passive students. Advocates, influenced by constructivist theories, claim increased academic achievement. In science teaching, the results have been mixed, but there is some evidence of improvements in conceptual understanding. The study employed a pretest-posttest, non-equivalent groups experimental design. The sample consisted of 226 participants in six sections of a college biology course at a large community college in South Florida with two instructors trained in the use of clickers. Each instructor randomly selected their sections into CPS (treatment) and non-CPS (control) groups. All participants filled out a survey that included demographic data at the beginning of the semester. The treatment group used clicker questions throughout, with discussions as necessary, whereas the control groups answered the same questions as quizzes, similarly engaging in discussion where necessary. The learning gains were assessed on a pre/post-test basis. The average learning gains, defined as the actual gain divided by the possible gain, were slightly better in the treatment group than in the control group, but the difference was statistically non-significant. An Analysis of Covariance (ANCOVA) statistic with pretest scores as the covariate was conducted to test for significant differences between the treatment and control groups on the posttest. A second ANCOVA was used to determine the significance of differences between the treatment and control groups on the posttest scores, after controlling for sex, GPA, academic status, experience with clickers, and instructional style. The results indicated a small increase in learning gains but these were not statistically significant. The data did not support an increase in learning based on the use of the CPS technology. This study adds to the body of research that questions whether CPS technology merits classroom adaptation.

Designing Serious Computer Games for People With Moderate and Advanced Dementia: Interdisciplinary Theory-Driven Pilot Study.

PubMed

Tziraki, Chariklia; Berenbaum, Rakel; Gross, Daniel; Abikhzer, Judith; Ben-David, Boaz M

2017-07-31

The field of serious games for people with dementia (PwD) is mostly driven by game-design principals typically applied to games created by and for younger individuals. Little has been done developing serious games to help PwD maintain cognition and to support functionality. We aimed to create a theory-based serious game for PwD, with input from a multi-disciplinary team familiar with aging, dementia, and gaming theory, as well as direct input from end users (the iterative process). Targeting enhanced self-efficacy in daily activities, the goal was to generate a game that is acceptable, accessible and engaging for PwD. The theory-driven game development was based on the following learning theories: learning in context, errorless learning, building on capacities, and acknowledging biological changes-all with the aim to boost self-efficacy. The iterative participatory process was used for game screen development with input of 34 PwD and 14 healthy community dwelling older adults, aged over 65 years. Development of game screens was informed by the bio-psychological aging related disabilities (ie, motor, visual, and perception) as well as remaining neuropsychological capacities (ie, implicit memory) of PwD. At the conclusion of the iterative development process, a prototype game with 39 screens was used for a pilot study with 24 PwD and 14 healthy community dwelling older adults. The game was played twice weekly for 10 weeks. Quantitative analysis showed that the average speed of successful screen completion was significantly longer for PwD compared with healthy older adults. Both PwD and controls showed an equivalent linear increase in the speed for task completion with practice by the third session (P<.02). Most important, the rate of improved processing speed with practice was not statistically different between PwD and controls. This may imply that some form of learning occurred for PwD at a nonsignificantly different rate than for controls. Qualitative results indicate that PwD found the game engaging and fun. Healthy older adults found the game too easy. Increase in self-reported self-efficacy was documented with PwD only. Our study demonstrated that PwD's speed improved with practice at the same rate as healthy older adults. This implies that when tasks are designed to match PwD's abilities, learning ensues. In addition, this pilot study of a serious game, designed for PwD, was accessible, acceptable, and enjoyable for end users. Games designed based on learning theories and input of end users and a multi-disciplinary team familiar with dementia and aging may have the potential of maintaining capacity and improving functionality of PwD. A larger longer study is needed to confirm our findings and evaluate the use of these games in assessing cognitive status and functionality. ©Chariklia Tziraki, Rakel Berenbaum, Daniel Gross, Judith Abikhzer, Boaz M Ben-David. Originally published in JMIR Serious Games (http://games.jmir.org), 31.07.2017.

Designing Serious Computer Games for People With Moderate and Advanced Dementia: Interdisciplinary Theory-Driven Pilot Study

PubMed Central

Gross, Daniel; Abikhzer, Judith

2017-01-01

Background The field of serious games for people with dementia (PwD) is mostly driven by game-design principals typically applied to games created by and for younger individuals. Little has been done developing serious games to help PwD maintain cognition and to support functionality. Objectives We aimed to create a theory-based serious game for PwD, with input from a multi-disciplinary team familiar with aging, dementia, and gaming theory, as well as direct input from end users (the iterative process). Targeting enhanced self-efficacy in daily activities, the goal was to generate a game that is acceptable, accessible and engaging for PwD. Methods The theory-driven game development was based on the following learning theories: learning in context, errorless learning, building on capacities, and acknowledging biological changes—all with the aim to boost self-efficacy. The iterative participatory process was used for game screen development with input of 34 PwD and 14 healthy community dwelling older adults, aged over 65 years. Development of game screens was informed by the bio-psychological aging related disabilities (ie, motor, visual, and perception) as well as remaining neuropsychological capacities (ie, implicit memory) of PwD. At the conclusion of the iterative development process, a prototype game with 39 screens was used for a pilot study with 24 PwD and 14 healthy community dwelling older adults. The game was played twice weekly for 10 weeks. Results Quantitative analysis showed that the average speed of successful screen completion was significantly longer for PwD compared with healthy older adults. Both PwD and controls showed an equivalent linear increase in the speed for task completion with practice by the third session (P<.02). Most important, the rate of improved processing speed with practice was not statistically different between PwD and controls. This may imply that some form of learning occurred for PwD at a nonsignificantly different rate than for controls. Qualitative results indicate that PwD found the game engaging and fun. Healthy older adults found the game too easy. Increase in self-reported self-efficacy was documented with PwD only. Conclusions Our study demonstrated that PwD’s speed improved with practice at the same rate as healthy older adults. This implies that when tasks are designed to match PwD’s abilities, learning ensues. In addition, this pilot study of a serious game, designed for PwD, was accessible, acceptable, and enjoyable for end users. Games designed based on learning theories and input of end users and a multi-disciplinary team familiar with dementia and aging may have the potential of maintaining capacity and improving functionality of PwD. A larger longer study is needed to confirm our findings and evaluate the use of these games in assessing cognitive status and functionality. PMID:28760730

Mobile Affordances and Learning Theories in Supporting and Enhancing Learning

ERIC Educational Resources Information Center

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…

ChemApproach: Validation of a Questionnaire to Assess the Learning Approaches of Chemistry Students

ERIC Educational Resources Information Center

Lastusaari, Mika; Laakkonen, Eero; Murtonen, Mari

2016-01-01

The theory of learning approaches has proven to be one of the most powerful theories explaining university students' learning. However, learning approaches are sensitive to the situation and the content of learning. Chemistry has its own specific features that should be considered when exploring chemistry students' learning habits, specifically…

The Nature of Student Teachers' Regulation of Learning in Teacher Education

ERIC Educational Resources Information Center

Endedijk, Maaike D.; Vermunt, Jan D.; Verloop, Nico; Brekelmans, Mieke

2012-01-01

Background: Self-regulated learning (SRL) has mainly been conceptualized to involve student learning within academic settings. In teacher education, where learning from theory and practice is combined, student teachers also need to regulate their learning. Hence, there is an urgent need to extend SRL theories to the domain of teacher learning and…

Reflection of Learning Theories in Iranian ELT Textbooks

ERIC Educational Resources Information Center

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…

Personality and Second Language Learning. Language in Education: Theory and Practice, No. 12.

ERIC Educational Resources Information Center

Hodge, Virginia D.

This annotated reading list addresses the problem of the paucity of literature dealing specifically with the relationship between personality and language learning. There is no general theoretical model that encompasses personality theory, self-concept, ego development, learning theory, motivation, and body image as they relate to…

Adult Learning Theory: A Primer. Information Series.

ERIC Educational Resources Information Center

Baumgartner, Lisa M.; Lee, Ming-Yeh; Birden, Susan; Flowers, Doris

The purpose of this monograph is to serve as a primer for practitioners on the foundational theories of adult learning. It begins with an explanation two lenses through which learning theory is viewed: behaviorism and constructivism. The next section defines andragogy and delineates Knowles's five assumptions about adult learners. This is followed…

Applying Distributed Learning Theory in Online Business Communication Courses.

ERIC Educational Resources Information Center

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)

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

ERIC Educational Resources Information Center

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

2010-01-01

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

Applying Learning Theories and Instructional Design Models for Effective Instruction

ERIC Educational Resources Information Center

Khalil, Mohammed K.; Elkhider, Ihsan A.

2016-01-01

Faculty members in higher education are involved in many instructional design activities without formal training in learning theories and the science of instruction. Learning theories provide the foundation for the selection of instructional strategies and allow for reliable prediction of their effectiveness. To achieve effective learning…

Putting Transformative Learning Theory into Practice

ERIC Educational Resources Information Center

Christie, Michael; Carey, Michael; Robertson, Ann; Grainger, Peter

2015-01-01

This paper elaborates on a number of key criticisms of Mezirow's transformative learning theory as well as providing arguments that validate it. Our paper exemplifies how Mezirow's theory can help adult educators and prospective school teachers understand that social structures and belief systems can influence student learning, that learners make…

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

ERIC Educational Resources Information Center

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)

Centering Marxist-Feminist Theory in Adult Learning

ERIC Educational Resources Information Center

Carpenter, Sara

2012-01-01

Using feminist extensions of Marxist theory, this article argues that a Marxist-feminist theory of adult learning offers a significant contribution to feminist pedagogical debates concerning the nature of experience and learning. From this theoretical perspective, the individual and the social are understood to exist in a mutually determining…

A Study of Flow Theory in the Foreign Language Classroom.

ERIC Educational Resources Information Center

Egbert, Joy

2003-01-01

Focuses on the relationship between flow experiences and language learning. Flow theory suggests that flow experiences can lead to optimal learning. Findings suggest flow does exist in the foreign language classroom and that flow theory offers an interesting and useful framework for conceptualizing and evaluating language learning activities.…

Cognitive theories and the design of e-learning environments.

PubMed

Gillani, Bijan; O'Guinn, Christina

2004-01-01

Cognitive development refers to a mental process by which knowledge is acquired, stored, and retrieved to solve problems. Therefore, cognitive developmental theories attempt to explain cognitive activities that contribute to students' intellectual development and their capacity to learn and solve problems. Cognitive developmental research has had a great impact on the constructivism movement in education and educational technology. In order to appreciate how cognitive developmental theories have contributed to the design, process and development of constructive e-learning environments, we shall first present Piaget's cognitive theory and derive an inquiry training model from it that will support a constructivism approach to teaching and learning. Second, we will discuss an example developed by NASA that used the Web as an appropriate instructional delivery medium to apply Piaget's cognitive theory to create e-learning environments.

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

PubMed Central

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

2013-01-01

The purpose of the present review was to provide a theoretical understanding of the learning advantages underlying a self-controlled practice context through the tenets of the self-determination theory (SDT). Three micro-theories within the macro-theory of SDT (Basic psychological needs theory, Cognitive Evaluation Theory, and Organismic Integration Theory) are used as a framework for examining the current self-controlled motor learning literature. A review of 26 peer-reviewed, empirical studies from the motor learning and medical training literature revealed an important limitation of the self-controlled research in motor learning: that the effects of motivation have been assumed rather than quantified. The SDT offers a basis from which to include measurements of motivation into explanations of changes in behavior. This review suggests that a self-controlled practice context can facilitate such factors as feelings of autonomy and competence of the learner, thereby supporting the psychological needs of the learner, leading to long term changes to behavior. Possible tools for the measurement of motivation and regulation in future studies are discussed. The SDT not only allows for a theoretical reinterpretation of the extant motor learning research supporting self-control as a learning variable, but also can help to better understand and measure the changes occurring between the practice environment and the observed behavioral outcomes. PMID:23430980

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

PubMed

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

2012-01-01

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

Learning a theory of causality.

PubMed

Goodman, Noah D; Ullman, Tomer D; Tenenbaum, Joshua B

2011-01-01

The very early appearance of abstract knowledge is often taken as evidence for innateness. We explore the relative learning speeds of abstract and specific knowledge within a Bayesian framework and the role for innate structure. We focus on knowledge about causality, seen as a domain-general intuitive theory, and ask whether this knowledge can be learned from co-occurrence of events. We begin by phrasing the causal Bayes nets theory of causality and a range of alternatives in a logical language for relational theories. This allows us to explore simultaneous inductive learning of an abstract theory of causality and a causal model for each of several causal systems. We find that the correct theory of causality can be learned relatively quickly, often becoming available before specific causal theories have been learned--an effect we term the blessing of abstraction. We then explore the effect of providing a variety of auxiliary evidence and find that a collection of simple perceptual input analyzers can help to bootstrap abstract knowledge. Together, these results suggest that the most efficient route to causal knowledge may be to build in not an abstract notion of causality but a powerful inductive learning mechanism and a variety of perceptual supports. While these results are purely computational, they have implications for cognitive development, which we explore in the conclusion.

Socio-material perspectives on interprofessional team and collaborative learning.

PubMed

McMurtry, Angus; Rohse, Shanta; Kilgour, Kelly N

2016-02-01

Interprofessional teamwork and collaboration have become important parts of health care practice and education. Most of the literature on interprofessional learning, however, assumes that learning is something acquired by individuals and readily transferred to other contexts. This assumption severely limits the ways in which interprofessional educators and researchers can conceptualise and support learning related to collaborative interprofessional health care. Socio-material theories provide an alternative to individualistic, acquisition-oriented notions by reconceiving learning in terms of collective dynamics, participation in social communities and active engagement with material contexts. Socio-material literature and theories were reviewed to identify concepts relevant to interprofessional learning. After briefly summarising the origins and key principles of socio-material approaches, the authors draw upon specific socio-material theories--including complexity theory, cultural-historical activity theory and actor-network theory--in order to reconceive how learning happens in interprofessional contexts. This reframing of interprofessional learning focuses less on individuals and more on collective dynamics and the actual social and material relations involved in practice. The paper proposes five ways in which learning may be enacted in interprofessional teamwork and collaboration from a socio-material perspective: (i) diverse contributions; (ii) social interactions and relationships; (iii) synthesis of professional ideas; (iv) integration of material elements, and (v) connections to large-scale organisations. For each of these categories, the paper provides practical illustrations to assist educators and researchers who wish to identify and assess this learning. Although more exploratory than comprehensive, this paper articulates many key aspects of socio-material learning theories and offers practical guidance for those who wish to employ and assess them in interprofessional contexts. © 2016 John Wiley & Sons Ltd.

Explorations in Statistics: Hypothesis Tests and P Values

ERIC Educational Resources Information Center

Curran-Everett, Douglas

2009-01-01

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This second installment of "Explorations in Statistics" delves into test statistics and P values, two concepts fundamental to the test of a scientific null hypothesis. The essence of a test statistic is that it compares what…

Improving Education in Medical Statistics: Implementing a Blended Learning Model in the Existing Curriculum

PubMed Central

Milic, Natasa M.; Trajkovic, Goran Z.; Bukumiric, Zoran M.; Cirkovic, Andja; Nikolic, Ivan M.; Milin, Jelena S.; Milic, Nikola V.; Savic, Marko D.; Corac, Aleksandar M.; Marinkovic, Jelena M.; Stanisavljevic, Dejana M.

2016-01-01

Background Although recent studies report on the benefits of blended learning in improving medical student education, there is still no empirical evidence on the relative effectiveness of blended over traditional learning approaches in medical statistics. We implemented blended along with on-site (i.e. face-to-face) learning to further assess the potential value of web-based learning in medical statistics. Methods This was a prospective study conducted with third year medical undergraduate students attending the Faculty of Medicine, University of Belgrade, who passed (440 of 545) the final exam of the obligatory introductory statistics course during 2013–14. Student statistics achievements were stratified based on the two methods of education delivery: blended learning and on-site learning. Blended learning included a combination of face-to-face and distance learning methodologies integrated into a single course. Results Mean exam scores for the blended learning student group were higher than for the on-site student group for both final statistics score (89.36±6.60 vs. 86.06±8.48; p = 0.001) and knowledge test score (7.88±1.30 vs. 7.51±1.36; p = 0.023) with a medium effect size. There were no differences in sex or study duration between the groups. Current grade point average (GPA) was higher in the blended group. In a multivariable regression model, current GPA and knowledge test scores were associated with the final statistics score after adjusting for study duration and learning modality (p<0.001). Conclusion This study provides empirical evidence to support educator decisions to implement different learning environments for teaching medical statistics to undergraduate medical students. Blended and on-site training formats led to similar knowledge acquisition; however, students with higher GPA preferred the technology assisted learning format. Implementation of blended learning approaches can be considered an attractive, cost-effective, and efficient alternative to traditional classroom training in medical statistics. PMID:26859832

Improving Education in Medical Statistics: Implementing a Blended Learning Model in the Existing Curriculum.

PubMed

Milic, Natasa M; Trajkovic, Goran Z; Bukumiric, Zoran M; Cirkovic, Andja; Nikolic, Ivan M; Milin, Jelena S; Milic, Nikola V; Savic, Marko D; Corac, Aleksandar M; Marinkovic, Jelena M; Stanisavljevic, Dejana M

2016-01-01

Although recent studies report on the benefits of blended learning in improving medical student education, there is still no empirical evidence on the relative effectiveness of blended over traditional learning approaches in medical statistics. We implemented blended along with on-site (i.e. face-to-face) learning to further assess the potential value of web-based learning in medical statistics. This was a prospective study conducted with third year medical undergraduate students attending the Faculty of Medicine, University of Belgrade, who passed (440 of 545) the final exam of the obligatory introductory statistics course during 2013-14. Student statistics achievements were stratified based on the two methods of education delivery: blended learning and on-site learning. Blended learning included a combination of face-to-face and distance learning methodologies integrated into a single course. Mean exam scores for the blended learning student group were higher than for the on-site student group for both final statistics score (89.36±6.60 vs. 86.06±8.48; p = 0.001) and knowledge test score (7.88±1.30 vs. 7.51±1.36; p = 0.023) with a medium effect size. There were no differences in sex or study duration between the groups. Current grade point average (GPA) was higher in the blended group. In a multivariable regression model, current GPA and knowledge test scores were associated with the final statistics score after adjusting for study duration and learning modality (p<0.001). This study provides empirical evidence to support educator decisions to implement different learning environments for teaching medical statistics to undergraduate medical students. Blended and on-site training formats led to similar knowledge acquisition; however, students with higher GPA preferred the technology assisted learning format. Implementation of blended learning approaches can be considered an attractive, cost-effective, and efficient alternative to traditional classroom training in medical statistics.

Know your RO from your AE? Learning styles in practice.

PubMed

Woods, Helen Buckley

2012-06-01

In this article, Kolb's cycle of learning is put forward as a useful theory to consult when planning information literacy or other teaching sessions. The learning cycle is contextualised and Kolb's and other theories are briefly explored. The author then considers how learning style theories can be utilised when planning teaching and learning activities. The use of planning tools is advocated and ideas for sessions are suggested. HS. © 2012 The authors. Health Information and Libraries Journal © 2012 Health Libraries Group.

From Tootsie Rolls to Composites: Assessing a Spectrum of Active Learning Activities in Engineering Mechanics

DTIC Science & Technology

2009-05-01

Appendix 9.1. Learning Styles & Pedagogical Theory Overview Educational theory plays a foundational role for the methodology and the development...of ALPs. We selected two methods to categorize student’s learning styles: (1) MBTI, (2) VARK, and five models of the learning process: (1) Kolb , (2... learning process which gives our work a more balanced foundation than may be possible if one bases their approach on one or two theories only, 2) our work

Myths and legends in learning classification rules

NASA Technical Reports Server (NTRS)

Buntine, Wray

1990-01-01

This paper is a discussion of machine learning theory on empirically learning classification rules. The paper proposes six myths in the machine learning community that address issues of bias, learning as search, computational learning theory, Occam's razor, 'universal' learning algorithms, and interactive learnings. Some of the problems raised are also addressed from a Bayesian perspective. The paper concludes by suggesting questions that machine learning researchers should be addressing both theoretically and experimentally.

How Do Students Learn Theology?

ERIC Educational Resources Information Center

Saines, Don

2009-01-01

This paper explores the way students learn theology through a small qualitative research project. It is undertaken in conversation with current higher education learning theory. This learning theory suggests that it is important to discover how a student conceptualizes learning and how they perceive the teaching environment. Students interviewed…

Co-Operative Learning and Development Networks.

ERIC Educational Resources Information Center

Hodgson, V.; McConnell, D.

1995-01-01

Discusses the theory, nature, and benefits of cooperative learning. Considers the Cooperative Learning and Development Network (CLDN) trial in the JITOL (Just in Time Open Learning) project and examines the relationship between theories about cooperative learning and the reality of a group of professionals participating in a virtual cooperative…

Snapshots of Informed Learning: LIS and Beyond

ERIC Educational Resources Information Center

Hughes, Hilary; Bruce, Christine

2013-01-01

Responding to the need for innovative LIS curriculum and pedagogy, grounded in both information and learning theory, this paper introduces the theory and practice of "informed learning" [3]. After explaining how informed learning originated within the LIS discipline we outline the principles and characteristics of informed learning. Then…

Cultural Learning Redux

ERIC Educational Resources Information Center

Tomasello, Michael

2016-01-01

M. Tomasello, A. Kruger, and H. Ratner (1993) proposed a theory of cultural learning comprising imitative learning, instructed learning, and collaborative learning. Empirical and theoretical advances in the past 20 years suggest modifications to the theory; for example, children do not just imitate but overimitate in order to identify and…

Design 2000: Theory-Based Design Models of the Future.

ERIC Educational Resources Information Center

Richey, Rita C.

The influence of theory on instructional-design models of the future is explored on the basis of the theoretical developments of today. Anticipated model changes are expected to result from disparate theoretical thinking in areas such as chaos theory, constructivism, situated learning, cognitive-learning theory, and general systems theory.…

Neural Correlates of Morphology Acquisition through a Statistical Learning Paradigm.

PubMed

Sandoval, Michelle; Patterson, Dianne; Dai, Huanping; Vance, Christopher J; Plante, Elena

2017-01-01

The neural basis of statistical learning as it occurs over time was explored with stimuli drawn from a natural language (Russian nouns). The input reflected the "rules" for marking categories of gendered nouns, without making participants explicitly aware of the nature of what they were to learn. Participants were scanned while listening to a series of gender-marked nouns during four sequential scans, and were tested for their learning immediately after each scan. Although participants were not told the nature of the learning task, they exhibited learning after their initial exposure to the stimuli. Independent component analysis of the brain data revealed five task-related sub-networks. Unlike prior statistical learning studies of word segmentation, this morphological learning task robustly activated the inferior frontal gyrus during the learning period. This region was represented in multiple independent components, suggesting it functions as a network hub for this type of learning. Moreover, the results suggest that subnetworks activated by statistical learning are driven by the nature of the input, rather than reflecting a general statistical learning system.

Neural Correlates of Morphology Acquisition through a Statistical Learning Paradigm

PubMed Central

Sandoval, Michelle; Patterson, Dianne; Dai, Huanping; Vance, Christopher J.; Plante, Elena

2017-01-01

The neural basis of statistical learning as it occurs over time was explored with stimuli drawn from a natural language (Russian nouns). The input reflected the “rules” for marking categories of gendered nouns, without making participants explicitly aware of the nature of what they were to learn. Participants were scanned while listening to a series of gender-marked nouns during four sequential scans, and were tested for their learning immediately after each scan. Although participants were not told the nature of the learning task, they exhibited learning after their initial exposure to the stimuli. Independent component analysis of the brain data revealed five task-related sub-networks. Unlike prior statistical learning studies of word segmentation, this morphological learning task robustly activated the inferior frontal gyrus during the learning period. This region was represented in multiple independent components, suggesting it functions as a network hub for this type of learning. Moreover, the results suggest that subnetworks activated by statistical learning are driven by the nature of the input, rather than reflecting a general statistical learning system. PMID:28798703

The Effects of Embedded Generative Learning Strategies and Collaboration on Knowledge Acquisition in a Cognitive Flexibility-Based Computer Learning Environment

DTIC Science & Technology

1998-08-07

cognitive flexibility theory and generative learning theory which focus primarily on the individual student’s cognitive development , collaborative... develop "Handling Transfusion Hazards," a computer program based upon cognitive flexibility theory principles. The Program: Handling Transfusion Hazards...computer program was developed according to cognitive flexibility theory principles. A generative version was then developed by embedding

Online neural monitoring of statistical learning

PubMed Central

Batterink, Laura J.; Paller, Ken A.

2017-01-01

The extraction of patterns in the environment plays a critical role in many types of human learning, from motor skills to language acquisition. This process is known as statistical learning. Here we propose that statistical learning has two dissociable components: (1) perceptual binding of individual stimulus units into integrated composites and (2) storing those integrated representations for later use. Statistical learning is typically assessed using post-learning tasks, such that the two components are conflated. Our goal was to characterize the online perceptual component of statistical learning. Participants were exposed to a structured stream of repeating trisyllabic nonsense words and a random syllable stream. Online learning was indexed by an EEG-based measure that quantified neural entrainment at the frequency of the repeating words relative to that of individual syllables. Statistical learning was subsequently assessed using conventional measures in an explicit rating task and a reaction-time task. In the structured stream, neural entrainment to trisyllabic words was higher than in the random stream, increased as a function of exposure to track the progression of learning, and predicted performance on the RT task. These results demonstrate that monitoring this critical component of learning via rhythmic EEG entrainment reveals a gradual acquisition of knowledge whereby novel stimulus sequences are transformed into familiar composites. This online perceptual transformation is a critical component of learning. PMID:28324696

Theories of Learning for the Workplace: Building Blocks for Training and Professional Development Programs. Routledge Psychology in Education

ERIC Educational Resources Information Center

Dochy, Filip; Gijbels, David; Segers, Mien; Van den Bossche, Piet

2011-01-01

Workplace and professional learning, lifelong learning, adult learning, learning in different contexts have become of more and more interest and now dominate all aspects of 21st century life. Learning is no longer about "storing and recall" but "development and flow". "Theories of Learning in the Workplace" offers fascinating overviews into some…

From Learning Cultures to Educational Cultures: Values and Judgements in Educational Research and Educational Improvement

ERIC Educational Resources Information Center

Biesta, Gert

2011-01-01

This article outlines a new approach to the study of learning and the improvement of education. The approach consists of two elements: a theory of learning cultures and a cultural theory of learning. Learning cultures are different from learning contexts or learning environments in that they are to be understood as the social practices through…

The specificity of parenting effects: Differential relations of parent praise and criticism to children's theories of intelligence and learning goals.

PubMed

Gunderson, Elizabeth A; Donnellan, M Brent; Robins, Richard W; Trzesniewski, Kali H

2018-04-24

Individuals who believe that intelligence can be improved with effort (an incremental theory of intelligence) and who approach challenges with the goal of improving their understanding (a learning goal) tend to have higher academic achievement. Furthermore, parent praise is associated with children's incremental theories and learning goals. However, the influences of parental criticism, as well as different forms of praise and criticism (e.g., process vs. person), have received less attention. We examine these associations by analyzing two existing datasets (Study 1: N = 317 first to eighth graders; Study 2: N = 282 fifth and eighth graders). In both studies, older children held more incremental theories of intelligence, but lower learning goals, than younger children. Unexpectedly, the relation between theories of intelligence and learning goals was nonsignificant and did not vary with children's grade level. In both studies, overall perceived parent praise positively related to children's learning goals, whereas perceived parent criticism negatively related to incremental theories of intelligence. In Study 2, perceived parent process praise was the only significant (positive) predictor of children's learning goals, whereas perceived parent person criticism was the only significant (negative) predictor of incremental theories of intelligence. Finally, Study 2 provided some support for our hypothesis that age-related differences in perceived parent praise and criticism can explain age-related differences in children's learning goals. Results suggest that incremental theories of intelligence and learning goals might not be strongly related during childhood and that perceived parent praise and criticism have important, but distinct, relations with each motivational construct. Copyright © 2018 Elsevier Inc. All rights reserved.

Universal self-similar dynamics of relativistic and nonrelativistic field theories near nonthermal fixed points

NASA Astrophysics Data System (ADS)

Piñeiro Orioli, Asier; Boguslavski, Kirill; Berges, Jürgen

2015-07-01

We investigate universal behavior of isolated many-body systems far from equilibrium, which is relevant for a wide range of applications from ultracold quantum gases to high-energy particle physics. The universality is based on the existence of nonthermal fixed points, which represent nonequilibrium attractor solutions with self-similar scaling behavior. The corresponding dynamic universality classes turn out to be remarkably large, encompassing both relativistic as well as nonrelativistic quantum and classical systems. For the examples of nonrelativistic (Gross-Pitaevskii) and relativistic scalar field theory with quartic self-interactions, we demonstrate that infrared scaling exponents as well as scaling functions agree. We perform two independent nonperturbative calculations, first by using classical-statistical lattice simulation techniques and second by applying a vertex-resummed kinetic theory. The latter extends kinetic descriptions to the nonperturbative regime of overoccupied modes. Our results open new perspectives to learn from experiments with cold atoms aspects about the dynamics during the early stages of our universe.

Statistics of Smoothed Cosmic Fields in Perturbation Theory. I. Formulation and Useful Formulae in Second-Order Perturbation Theory

NASA Astrophysics Data System (ADS)

Matsubara, Takahiko

2003-02-01

We formulate a general method for perturbative evaluations of statistics of smoothed cosmic fields and provide useful formulae for application of the perturbation theory to various statistics. This formalism is an extensive generalization of the method used by Matsubara, who derived a weakly nonlinear formula of the genus statistic in a three-dimensional density field. After describing the general method, we apply the formalism to a series of statistics, including genus statistics, level-crossing statistics, Minkowski functionals, and a density extrema statistic, regardless of the dimensions in which each statistic is defined. The relation between the Minkowski functionals and other geometrical statistics is clarified. These statistics can be applied to several cosmic fields, including three-dimensional density field, three-dimensional velocity field, two-dimensional projected density field, and so forth. The results are detailed for second-order theory of the formalism. The effect of the bias is discussed. The statistics of smoothed cosmic fields as functions of rescaled threshold by volume fraction are discussed in the framework of second-order perturbation theory. In CDM-like models, their functional deviations from linear predictions plotted against the rescaled threshold are generally much smaller than that plotted against the direct threshold. There is still a slight meatball shift against rescaled threshold, which is characterized by asymmetry in depths of troughs in the genus curve. A theory-motivated asymmetry factor in the genus curve is proposed.

Wants and Needs: SAMS’ Relationship With the Army

DTIC Science & Technology

2008-05-19

Experiential Learning Model (ELM) is a theory most closely attributed to David Kolb and his book, Experiential Learning : Experience...experience alone.57 This last principle is an important aspect of the Experiential Learning Model, which is the prevailing theory of adult education in use...depth to learn the theory behind current methods and techniques, and thus achieve mastery of the art o war at the tactical and operational level.” ,

The Subject-Object Approach as a Direction of the Learning Theory in the Context of Modern Linguistic Education Paradigms

ERIC Educational Resources Information Center

Salkhanova, Zhanat H.; Lee, Valentine S.; Tumanova, Ainakul B.; Zhusanbaeva, Aida T.

2016-01-01

The research object is the activity-based learning theory. The purpose of the study is to prove the assumption that the subject-object approach as a direction of the learning theory is the most effective one in the context of development of modern paradigms of linguistic education. The authors believe that the main content of the learning activity…

Perceptions of teaching and learning automata theory in a college-level computer science course

NASA Astrophysics Data System (ADS)

Weidmann, Phoebe Kay

This dissertation identifies and describes student and instructor perceptions that contribute to effective teaching and learning of Automata Theory in a competitive college-level Computer Science program. Effective teaching is the ability to create an appropriate learning environment in order to provide effective learning. We define effective learning as the ability of a student to meet instructor set learning objectives, demonstrating this by passing the course, while reporting a good learning experience. We conducted our investigation through a detailed qualitative case study of two sections (118 students) of Automata Theory (CS 341) at The University of Texas at Austin taught by Dr. Lily Quilt. Because Automata Theory has a fixed curriculum in the sense that many curricula and textbooks agree on what Automata Theory contains, differences being depth and amount of material to cover in a single course, a case study would allow for generalizable findings. Automata Theory is especially problematic in a Computer Science curriculum since students are not experienced in abstract thinking before taking this course, fail to understand the relevance of the theory, and prefer classes with more concrete activities such as programming. This creates a special challenge for any instructor of Automata Theory as motivation becomes critical for student learning. Through the use of student surveys, instructor interviews, classroom observation, material and course grade analysis we sought to understand what students perceived, what instructors expected of students, and how those perceptions played out in the classroom in terms of structure and instruction. Our goal was to create suggestions that would lead to a better designed course and thus a higher student success rate in Automata Theory. We created a unique theoretical basis, pedagogical positivism, on which to study college-level courses. Pedagogical positivism states that through examining instructor and student perceptions of teaching and learning, improvements to a course are possible. These improvements can eventually develop a "best practice" instructional environment. This view is not possible under a strictly constructivist learning theory as there is no way to teach a group of individuals in a "best" way. Using this theoretical basis, we examined the gathered data from CS 341. (Abstract shortened by UMI.)

The logical primitives of thought: Empirical foundations for compositional cognitive models.

PubMed

Piantadosi, Steven T; Tenenbaum, Joshua B; Goodman, Noah D

2016-07-01

The notion of a compositional language of thought (LOT) has been central in computational accounts of cognition from earliest attempts (Boole, 1854; Fodor, 1975) to the present day (Feldman, 2000; Penn, Holyoak, & Povinelli, 2008; Fodor, 2008; Kemp, 2012; Goodman, Tenenbaum, & Gerstenberg, 2015). Recent modeling work shows how statistical inferences over compositionally structured hypothesis spaces might explain learning and development across a variety of domains. However, the primitive components of such representations are typically assumed a priori by modelers and theoreticians rather than determined empirically. We show how different sets of LOT primitives, embedded in a psychologically realistic approximate Bayesian inference framework, systematically predict distinct learning curves in rule-based concept learning experiments. We use this feature of LOT models to design a set of large-scale concept learning experiments that can determine the most likely primitives for psychological concepts involving Boolean connectives and quantification. Subjects' inferences are most consistent with a rich (nonminimal) set of Boolean operations, including first-order, but not second-order, quantification. Our results more generally show how specific LOT theories can be distinguished empirically. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Pattern Activity Clustering and Evaluation (PACE)

NASA Astrophysics Data System (ADS)

Blasch, Erik; Banas, Christopher; Paul, Michael; Bussjager, Becky; Seetharaman, Guna

2012-06-01

With the vast amount of network information available on activities of people (i.e. motions, transportation routes, and site visits) there is a need to explore the salient properties of data that detect and discriminate the behavior of individuals. Recent machine learning approaches include methods of data mining, statistical analysis, clustering, and estimation that support activity-based intelligence. We seek to explore contemporary methods in activity analysis using machine learning techniques that discover and characterize behaviors that enable grouping, anomaly detection, and adversarial intent prediction. To evaluate these methods, we describe the mathematics and potential information theory metrics to characterize behavior. A scenario is presented to demonstrate the concept and metrics that could be useful for layered sensing behavior pattern learning and analysis. We leverage work on group tracking, learning and clustering approaches; as well as utilize information theoretical metrics for classification, behavioral and event pattern recognition, and activity and entity analysis. The performance evaluation of activity analysis supports high-level information fusion of user alerts, data queries and sensor management for data extraction, relations discovery, and situation analysis of existing data.

Statistical learning and language acquisition

PubMed Central

Romberg, Alexa R.; Saffran, Jenny R.

2011-01-01

Human learners, including infants, are highly sensitive to structure in their environment. Statistical learning refers to the process of extracting this structure. A major question in language acquisition in the past few decades has been the extent to which infants use statistical learning mechanisms to acquire their native language. There have been many demonstrations showing infants’ ability to extract structures in linguistic input, such as the transitional probability between adjacent elements. This paper reviews current research on how statistical learning contributes to language acquisition. Current research is extending the initial findings of infants’ sensitivity to basic statistical information in many different directions, including investigating how infants represent regularities, learn about different levels of language, and integrate information across situations. These current directions emphasize studying statistical language learning in context: within language, within the infant learner, and within the environment as a whole. PMID:21666883

Student Learning Theory Goes (Back) to (High) School

ERIC Educational Resources Information Center

Ginns, Paul; Martin, Andrew J.; Papworth, Brad

2014-01-01

Biggs' 3P (Presage-Process-Product) model, a key framework in Student Learning Theory, provides a powerful means of understanding relations between students' perceptions of the teaching and learning environment, learning strategies, and learning outcomes. While influential in higher education, fewer tests of the model in secondary education…

Toward an Instructionally Oriented Theory of Example-Based Learning

ERIC Educational Resources Information Center

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…

Conceptualizing the Essence of Presence in E-Learning through Digital Dasein

ERIC Educational Resources Information Center

Haj-Bolouri, Amir; Flensburg, Per

2017-01-01

Previous research on e-learning elucidates the notion of presence and learning. Scholars have conceptualized different concepts and theories based on the idea of distance education and learning. However, the "experience" of learning has been overshadowed with emphasizes on pedagogical models for social presence, theories on how to…

Learning Across Senses: Cross-Modal Effects in Multisensory Statistical Learning

PubMed Central

Mitchel, Aaron D.; Weiss, Daniel J.

2014-01-01

It is currently unknown whether statistical learning is supported by modality-general or modality-specific mechanisms. One issue within this debate concerns the independence of learning in one modality from learning in other modalities. In the present study, the authors examined the extent to which statistical learning across modalities is independent by simultaneously presenting learners with auditory and visual streams. After establishing baseline rates of learning for each stream independently, they systematically varied the amount of audiovisual correspondence across 3 experiments. They found that learners were able to segment both streams successfully only when the boundaries of the audio and visual triplets were in alignment. This pattern of results suggests that learners are able to extract multiple statistical regularities across modalities provided that there is some degree of cross-modal coherence. They discuss the implications of their results in light of recent claims that multisensory statistical learning is guided by modality-independent mechanisms. PMID:21574745

Infant Statistical Learning

PubMed Central

Saffran, Jenny R.; Kirkham, Natasha Z.

2017-01-01

Perception involves making sense of a dynamic, multimodal environment. In the absence of mechanisms capable of exploiting the statistical patterns in the natural world, infants would face an insurmountable computational problem. Infant statistical learning mechanisms facilitate the detection of structure. These abilities allow the infant to compute across elements in their environmental input, extracting patterns for further processing and subsequent learning. In this selective review, we summarize findings that show that statistical learning is both a broad and flexible mechanism (supporting learning from different modalities across many different content areas) and input specific (shifting computations depending on the type of input and goal of learning). We suggest that statistical learning not only provides a framework for studying language development and object knowledge in constrained laboratory settings, but also allows researchers to tackle real-world problems, such as multilingualism, the role of ever-changing learning environments, and differential developmental trajectories. PMID:28793812

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

NASA Astrophysics Data System (ADS)

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.

Learning Statistics at the Farmers Market? A Comparison of Academic Service Learning and Case Studies in an Introductory Statistics Course

ERIC Educational Resources Information Center

Hiedemann, Bridget; Jones, Stacey M.

2010-01-01

We compare the effectiveness of academic service learning to that of case studies in an undergraduate introductory business statistics course. Students in six sections of the course were assigned either an academic service learning project (ASL) or business case studies (CS). We examine two learning outcomes: students' performance on the final…

Electrophysiological evidence of heterogeneity in visual statistical learning in young children with ASD.

PubMed

Jeste, Shafali S; Kirkham, Natasha; Senturk, Damla; Hasenstab, Kyle; Sugar, Catherine; Kupelian, Chloe; Baker, Elizabeth; Sanders, Andrew J; Shimizu, Christina; Norona, Amanda; Paparella, Tanya; Freeman, Stephanny F N; Johnson, Scott P

2015-01-01

Statistical learning is characterized by detection of regularities in one's environment without an awareness or intention to learn, and it may play a critical role in language and social behavior. Accordingly, in this study we investigated the electrophysiological correlates of visual statistical learning in young children with autism spectrum disorder (ASD) using an event-related potential shape learning paradigm, and we examined the relation between visual statistical learning and cognitive function. Compared to typically developing (TD) controls, the ASD group as a whole showed reduced evidence of learning as defined by N1 (early visual discrimination) and P300 (attention to novelty) components. Upon further analysis, in the ASD group there was a positive correlation between N1 amplitude difference and non-verbal IQ, and a positive correlation between P300 amplitude difference and adaptive social function. Children with ASD and a high non-verbal IQ and high adaptive social function demonstrated a distinctive pattern of learning. This is the first study to identify electrophysiological markers of visual statistical learning in children with ASD. Through this work we have demonstrated heterogeneity in statistical learning in ASD that maps onto non-verbal cognition and adaptive social function. © 2014 John Wiley & Sons Ltd.

Changing viewer perspectives reveals constraints to implicit visual statistical learning.

PubMed

Jiang, Yuhong V; Swallow, Khena M

2014-10-07

Statistical learning-learning environmental regularities to guide behavior-likely plays an important role in natural human behavior. One potential use is in search for valuable items. Because visual statistical learning can be acquired quickly and without intention or awareness, it could optimize search and thereby conserve energy. For this to be true, however, visual statistical learning needs to be viewpoint invariant, facilitating search even when people walk around. To test whether implicit visual statistical learning of spatial information is viewpoint independent, we asked participants to perform a visual search task from variable locations around a monitor placed flat on a stand. Unbeknownst to participants, the target was more often in some locations than others. In contrast to previous research on stationary observers, visual statistical learning failed to produce a search advantage for targets in high-probable regions that were stable within the environment but variable relative to the viewer. This failure was observed even when conditions for spatial updating were optimized. However, learning was successful when the rich locations were referenced relative to the viewer. We conclude that changing viewer perspective disrupts implicit learning of the target's location probability. This form of learning shows limited integration with spatial updating or spatiotopic representations. © 2014 ARVO.

Functional Differences between Statistical Learning with and without Explicit Training

ERIC Educational Resources Information Center

Batterink, Laura J.; Reber, Paul J.; Paller, Ken A.

2015-01-01

Humans are capable of rapidly extracting regularities from environmental input, a process known as statistical learning. This type of learning typically occurs automatically, through passive exposure to environmental input. The presumed function of statistical learning is to optimize processing, allowing the brain to more accurately predict and…

Statistically Modeling Individual Students' Learning over Successive Collaborative Practice Opportunities

ERIC Educational Resources Information Center

Olsen, Jennifer; Aleven, Vincent; Rummel, Nikol

2017-01-01

Within educational data mining, many statistical models capture the learning of students working individually. However, not much work has been done to extend these statistical models of individual learning to a collaborative setting, despite the effectiveness of collaborative learning activities. We extend a widely used model (the additive factors…

Statistical Learning as a Key to Cracking Chinese Orthographic Codes

ERIC Educational Resources Information Center

He, Xinjie; Tong, Xiuli

2017-01-01

This study examines statistical learning as a mechanism for Chinese orthographic learning among children in Grades 3-5. Using an artificial orthography, children were repeatedly exposed to positional, phonetic, and semantic regularities of radicals. Children showed statistical learning of all three regularities. Regularities' levels of consistency…

Extraction of business relationships in supply networks using statistical learning theory.

PubMed

Zuo, Yi; Kajikawa, Yuya; Mori, Junichiro

2016-06-01

Supply chain management represents one of the most important scientific streams of operations research. The supply of energy, materials, products, and services involves millions of transactions conducted among national and local business enterprises. To deliver efficient and effective support for supply chain design and management, structural analyses and predictive models of customer-supplier relationships are expected to clarify current enterprise business conditions and to help enterprises identify innovative business partners for future success. This article presents the outcomes of a recent structural investigation concerning a supply network in the central area of Japan. We investigated the effectiveness of statistical learning theory to express the individual differences of a supply chain of enterprises within a certain business community using social network analysis. In the experiments, we employ support vector machine to train a customer-supplier relationship model on one of the main communities extracted from a supply network in the central area of Japan. The prediction results reveal an F-value of approximately 70% when the model is built by using network-based features, and an F-value of approximately 77% when the model is built by using attribute-based features. When we build the model based on both, F-values are improved to approximately 82%. The results of this research can help to dispel the implicit design space concerning customer-supplier relationships, which can be explored and refined from detailed topological information provided by network structures rather than from traditional and attribute-related enterprise profiles. We also investigate and discuss differences in the predictive accuracy of the model for different sizes of enterprises and types of business communities.

Explorations in Statistics: the Bootstrap

ERIC Educational Resources Information Center

Curran-Everett, Douglas

2009-01-01

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This fourth installment of Explorations in Statistics explores the bootstrap. The bootstrap gives us an empirical approach to estimate the theoretical variability among possible values of a sample statistic such as the…

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

ERIC Educational Resources Information Center

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…

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

ERIC Educational Resources Information Center

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…

Relating Theory and Practice in Laboratory Work: A Variation Theoretical Study

ERIC Educational Resources Information Center

Eckerdal, Anna

2015-01-01

Computer programming education has practice-oriented as well as theory-oriented learning goals. Here, lab work plays an important role in students' learning. It is however widely reported that many students face great difficulties in learning theory as well as practice. This paper investigates the important but problematic relation between the…

Factors Affecting Training Transfer: Participants' Motivation to Transfer Training, Literature Review

ERIC Educational Resources Information Center

Alawneh, Muhammad K.

2008-01-01

This article investigates factors that motivate participants in learning and training activities to transfer skills, knowledge and attitude from the learning setting to the workplace. Based on training transfer theories hypothesized by Holton (1996), one of the major theories that affect an organization's learning is motivation to transfer theory.…

Teaching Sociological Theory through Active Learning: The Irrigation Exercise

ERIC Educational Resources Information Center

Holtzman, Mellisa

2005-01-01

For students, theory is often one of the most daunting aspects of sociology--it seems abstract, removed from the concrete events of their everyday lives, and therefore intimidating. In an attempt to break down student resistance to theory, instructors are increasingly turning to active learning approaches. Active learning exercises, then, appear…

Lifting as We Climb: Developing Constellations of Learning within an Informal Online Radio Format

ERIC Educational Resources Information Center

Mistry, Margaret Egan

2012-01-01

This mixed-methods study combines the sociocultural theories of Vygotsky's research on thought and language, Mezirow's Transformational Learning Theory, situated learning theory of Rogoff, Lave, and Wenger, to explore individual and group process and resulting products within an online university radio station system. The study…

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

ERIC Educational Resources Information Center

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…

Transformative Learning Challenges in a Context of Trauma and Fear: An Educator's Story

ERIC Educational Resources Information Center

John, Vaughn M.

2016-01-01

After more than three decades of development, transformative learning theory is currently a major theory of adult learning. It has also attracted substantial critique, leading to further development, application and differentiation. Recent contributions to this vast scholarship show a quest for a more unified theory. This article examines…

Game Engagement Theory and Adult Learning

ERIC Educational Resources Information Center

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…

Social Learning, Reinforcement and Crime: Evidence from Three European Cities

ERIC Educational Resources Information Center

Tittle, Charles R.; Antonaccio, Olena; Botchkovar, Ekaterina

2012-01-01

This study reports a cross-cultural test of Social Learning Theory using direct measures of social learning constructs and focusing on the causal structure implied by the theory. Overall, the results strongly confirm the main thrust of the theory. Prior criminal reinforcement and current crime-favorable definitions are highly related in all three…

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.

NASA Astrophysics Data System (ADS)

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.

Applying Sociocultural Theory to Teaching Statistics for Doctoral Social Work Students

ERIC Educational Resources Information Center

Mogro-Wilson, Cristina; Reeves, Michael G.; Charter, Mollie Lazar

2015-01-01

This article describes the development of two doctoral-level multivariate statistics courses utilizing sociocultural theory, an integrative pedagogical framework. In the first course, the implementation of sociocultural theory helps to support the students through a rigorous introduction to statistics. The second course involves students…

Increasing the Use of Student-Centered Pedagogies from Moderate to High Improves Student Learning and Attitudes about Biology

PubMed Central

Connell, Georgianne L.; Donovan, Deborah A.; Chambers, Timothy G.

2016-01-01

Student-centered strategies are being incorporated into undergraduate classrooms in response to a call for reform. We tested whether teaching in an extensively student-centered manner (many active-learning pedagogies, consistent formative assessment, cooperative groups; the Extensive section) was more effective than teaching in a moderately student-centered manner (fewer active-learning pedagogies, less formative assessment, without groups; the Moderate section) in a large-enrollment course. One instructor taught both sections of Biology 101 during the same quarter, covering the same material. Students in the Extensive section had significantly higher mean scores on course exams. They also scored significantly higher on a content postassessment when accounting for preassessment score and student demographics. Item response theory analysis supported these results. Students in the Extensive section had greater changes in postinstruction abilities compared with students in the Moderate section. Finally, students in the Extensive section exhibited a statistically greater expert shift in their views about biology and learning biology. We suggest our results are explained by the greater number of active-learning pedagogies experienced by students in cooperative groups, the consistent use of formative assessment, and the frequent use of explicit metacognition in the Extensive section. PMID:26865643

[Prosthodontic research design from the standpoint of statistical analysis: learning and knowing the research design].

PubMed

Tanoue, Naomi

2007-10-01

For any kind of research, "Research Design" is the most important. The design is used to structure the research, to show how all of the major parts of the research project. It is necessary for all the researchers to begin the research after planning research design for what is the main theme, what is the background and reference, what kind of data is needed, and what kind of analysis is needed. It seems to be a roundabout route, but, in fact, it will be a shortcut. The research methods must be appropriate to the objectives of the study. Regarding the hypothesis-testing research that is the traditional style of the research, the research design based on statistics is undoubtedly necessary considering that the research basically proves "a hypothesis" with data and statistics theory. On the subject of the clinical trial, which is the clinical version of the hypothesis-testing research, the statistical method must be mentioned in a clinical trial planning. This report describes the basis of the research design for a prosthodontics study.

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

PubMed

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

2018-04-01

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

An Incremental Type-2 Meta-Cognitive Extreme Learning Machine.

PubMed

Pratama, Mahardhika; Zhang, Guangquan; Er, Meng Joo; Anavatti, Sreenatha

2017-02-01

Existing extreme learning algorithm have not taken into account four issues: 1) complexity; 2) uncertainty; 3) concept drift; and 4) high dimensionality. A novel incremental type-2 meta-cognitive extreme learning machine (ELM) called evolving type-2 ELM (eT2ELM) is proposed to cope with the four issues in this paper. The eT2ELM presents three main pillars of human meta-cognition: 1) what-to-learn; 2) how-to-learn; and 3) when-to-learn. The what-to-learn component selects important training samples for model updates by virtue of the online certainty-based active learning method, which renders eT2ELM as a semi-supervised classifier. The how-to-learn element develops a synergy between extreme learning theory and the evolving concept, whereby the hidden nodes can be generated and pruned automatically from data streams with no tuning of hidden nodes. The when-to-learn constituent makes use of the standard sample reserved strategy. A generalized interval type-2 fuzzy neural network is also put forward as a cognitive component, in which a hidden node is built upon the interval type-2 multivariate Gaussian function while exploiting a subset of Chebyshev series in the output node. The efficacy of the proposed eT2ELM is numerically validated in 12 data streams containing various concept drifts. The numerical results are confirmed by thorough statistical tests, where the eT2ELM demonstrates the most encouraging numerical results in delivering reliable prediction, while sustaining low complexity.

The Futile Search for a Theory of Learning Disabilities.

ERIC Educational Resources Information Center

Blachman, Benita A.

1988-01-01

In response to a previous article, the paper suggests it is unrealistic to expect one theory or even multiple theories within one paradigm to explain learning disabilities. The emphasis on reaching a consensus regarding theory or paradigm is seen as unproductive. (DB)

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

ERIC Educational Resources Information Center

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)

A Model for Designing Instructional Narratives for Adult Learners: Connecting the Dots

ERIC Educational Resources Information Center

Smith, Debra M.

2013-01-01

The purpose of this study was to develop a research-based model for designing and deploying instructional narratives based on principles derived from narrative theory, development theory, communication theory, learning theory and instructional design theory to enable adult learning and retention and the effective transfer of that retained learning…

A theory-based approach to teaching young children about health: A recipe for understanding

PubMed Central

Nguyen, Simone P.; McCullough, Mary Beth; Noble, Ashley

2011-01-01

The theory-theory account of conceptual development posits that children’s concepts are integrated into theories. Concept learning studies have documented the central role that theories play in children’s learning of experimenter-defined categories, but have yet to extensively examine complex, real-world concepts such as health. The present study examined whether providing young children with coherent and causally-related information in a theory-based lesson would facilitate their learning about the concept of health. This study used a pre-test/lesson/post-test design, plus a five month follow-up. Children were randomly assigned to one of three conditions: theory (i.e., 20 children received a theory-based lesson); nontheory (i.e., 20 children received a nontheory-based lesson); and control (i.e., 20 children received no lesson). Overall, the results showed that children in the theory condition had a more accurate conception of health than children in the nontheory and control conditions, suggesting the importance of theories in children’s learning of complex, real-world concepts. PMID:21894237

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

PubMed

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.

Impact of Teacher Value Orientations on Student Learning in Physical Education

PubMed Central

Chen, Ang; Zhang, Tan; Wells, Stephanie L.; Schweighardt, Ray; Ennis, Catherine D.

2017-01-01

Based on the value orientation theory, the purpose of this study was to determine the impact of value orientation incongruence between physical education teachers and an externally designed curriculum on student learning in a concept-based fitness-centered physical education curriculum. Physical education teachers (n = 15) with different value orientations taught an externally designed, standards-based fitness/healthful living curriculum to their middle school students (n = 3,827) in 155 sixth, seventh, and eighth grade intact classes. A pre-post assessment design was used to determine whether student fitness/healthful living knowledge gains differed in terms of teachers’ value orientations. An ANOVA on class means of residual-adjusted knowledge gain scores revealed no statistically significant differences based on value orientations. The evidence suggests that teacher value orientation impact may be mediated by curriculum impact. This finding supports the observation that a well-designed physical education curriculum may minimize the impact of teachers’ diverse value orientations on the curriculum implementation and student learning. PMID:29200587

Impact of Teacher Value Orientations on Student Learning in Physical Education.

PubMed

Chen, Ang; Zhang, Tan; Wells, Stephanie L; Schweighardt, Ray; Ennis, Catherine D

2017-04-01

Based on the value orientation theory, the purpose of this study was to determine the impact of value orientation incongruence between physical education teachers and an externally designed curriculum on student learning in a concept-based fitness-centered physical education curriculum. Physical education teachers ( n = 15) with different value orientations taught an externally designed, standards-based fitness/healthful living curriculum to their middle school students ( n = 3,827) in 155 sixth, seventh, and eighth grade intact classes. A pre-post assessment design was used to determine whether student fitness/healthful living knowledge gains differed in terms of teachers' value orientations. An ANOVA on class means of residual-adjusted knowledge gain scores revealed no statistically significant differences based on value orientations. The evidence suggests that teacher value orientation impact may be mediated by curriculum impact. This finding supports the observation that a well-designed physical education curriculum may minimize the impact of teachers' diverse value orientations on the curriculum implementation and student learning.

Professional development design considerations in climate change education: teacher enactment and student learning

NASA Astrophysics Data System (ADS)

Drewes, Andrea; Henderson, Joseph; Mouza, Chrystalla

2018-01-01

Climate change is one of the most pressing challenges facing society, and climate change educational models are emerging in response. This study investigates the implementation and enactment of a climate change professional development (PD) model for science educators and its impact on student learning. Using an intrinsic case study methodology, we focused analytic attention on how one teacher made particular pedagogical and content decisions, and the implications for student's conceptual learning. Using anthropological theories of conceptual travel, we traced salient ideas through instructional delivery and into student reasoning. Analysis showed that students gained an increased understanding of the enhanced greenhouse effect and the implications of human activity on this enhanced effect at statistically significant levels and with moderate effect sizes. However, students demonstrated a limited, though non-significant gain on the likely effects of climate change. Student reasoning on the tangible actions to deal with these problems also remained underdeveloped, reflecting omissions in both PD and teacher enactment. We discuss implications for the emerging field of climate change education.

Large-Scale Linear Optimization through Machine Learning: From Theory to Practical System Design and Implementation

DTIC Science & Technology

2016-08-10

AFRL-AFOSR-JP-TR-2016-0073 Large-scale Linear Optimization through Machine Learning: From Theory to Practical System Design and Implementation ...2016 4.  TITLE AND SUBTITLE Large-scale Linear Optimization through Machine Learning: From Theory to Practical System Design and Implementation 5a...performances on various machine learning tasks and it naturally lends itself to fast parallel implementations . Despite this, very little work has been

What You Learn is What You See: Using Eye Movements to Study Infant Cross-Situational Word Learning

PubMed Central

Smith, Linda

2016-01-01

Recent studies show that both adults and young children possess powerful statistical learning capabilities to solve the word-to-world mapping problem. However, the underlying mechanisms that make statistical learning possible and powerful are not yet known. With the goal of providing new insights into this issue, the research reported in this paper used an eye tracker to record the moment-by-moment eye movement data of 14-month-old babies in statistical learning tasks. Various measures are applied to such fine-grained temporal data, such as looking duration and shift rate (the number of shifts in gaze from one visual object to the other) trial by trial, showing different eye movement patterns between strong and weak statistical learners. Moreover, an information-theoretic measure is developed and applied to gaze data to quantify the degree of learning uncertainty trial by trial. Next, a simple associative statistical learning model is applied to eye movement data and these simulation results are compared with empirical results from young children, showing strong correlations between these two. This suggests that an associative learning mechanism with selective attention can provide a cognitively plausible model of cross-situational statistical learning. The work represents the first steps to use eye movement data to infer underlying real-time processes in statistical word learning. PMID:22213894

A new consequence of Simpson's paradox: stable cooperation in one-shot prisoner's dilemma from populations of individualistic learners.

PubMed

Chater, Nick; Vlaev, Ivo; Grinberg, Maurice

2008-08-01

Theories of choice in economics typically assume that interacting agents act individualistically and maximize their own utility. Specifically, game theory proposes that rational players should defect in one-shot prisoners' dilemmas (PD). Defection also appears to be the inevitable outcome for agents who learn by reinforcement of past choices, because whatever the other player does, defection leads to greater reinforcement on each trial. In a computer simulation and 4 experiments, the authors show that, apparently paradoxically, when players' choices are correlated by an exogenous factor (here, the cooperativeness of the specific PD chosen), people obtain greater average reinforcement for cooperating, which can sustain cooperation. This effect arises from a well-known statistical paradox, Simpson's paradox. The authors speculate that this effect may be relevant to aspects of real-world human cooperative behavior.

Statistical Learning Is Not Affected by a Prior Bout of Physical Exercise.

PubMed

Stevens, David J; Arciuli, Joanne; Anderson, David I

2016-05-01

This study examined the effect of a prior bout of exercise on implicit cognition. Specifically, we examined whether a prior bout of moderate intensity exercise affected performance on a statistical learning task in healthy adults. A total of 42 participants were allocated to one of three conditions-a control group, a group that exercised for 15 min prior to the statistical learning task, and a group that exercised for 30 min prior to the statistical learning task. The participants in the exercise groups cycled at 60% of their respective V˙O2 max. Each group demonstrated significant statistical learning, with similar levels of learning among the three groups. Contrary to previous research that has shown that a prior bout of exercise can affect performance on explicit cognitive tasks, the results of the current study suggest that the physiological stress induced by moderate-intensity exercise does not affect implicit cognition as measured by statistical learning. Copyright © 2015 Cognitive Science Society, Inc.

The human side of science education: Using McGregor's theory Y as a framework for improving student motivation*.

PubMed

Markwell, John

2004-09-01

Student motivation is correlated with learning. Douglas McGregor's Theory X and Theory Y as a basis for understanding and improving motivation in the business world can be directly applied to the science classroom. Teachers with a Theory Y perspective (students naturally want to learn) provide increased motivation for students and promote more active learning than Theory X-style teachers who do not view students as active learners. Many teachers are not aware of their Theory X/Theory Y orientation and how this bias may be impacting their interaction with students. This article explores the benefits of moving from a Theory X to a more Theory Y style of teaching. Copyright © 2004 International Union of Biochemistry and Molecular Biology, Inc.

The Development of Bayesian Theory and Its Applications in Business and Bioinformatics

NASA Astrophysics Data System (ADS)

Zhang, Yifei

2018-03-01

Bayesian Theory originated from an Essay of a British mathematician named Thomas Bayes in 1763, and after its development in 20th century, Bayesian Statistics has been taking a significant part in statistical study of all fields. Due to the recent breakthrough of high-dimensional integral, Bayesian Statistics has been improved and perfected, and now it can be used to solve problems that Classical Statistics failed to solve. This paper summarizes Bayesian Statistics’ history, concepts and applications, which are illustrated in five parts: the history of Bayesian Statistics, the weakness of Classical Statistics, Bayesian Theory and its development and applications. The first two parts make a comparison between Bayesian Statistics and Classical Statistics in a macroscopic aspect. And the last three parts focus on Bayesian Theory in specific -- from introducing some particular Bayesian Statistics’ concepts to listing their development and finally their applications.

Electrophysiological Evidence of Heterogeneity in Visual Statistical Learning in Young Children with ASD

ERIC Educational Resources Information Center

Jeste, Shafali S.; Kirkham, Natasha; Senturk, Damla; Hasenstab, Kyle; Sugar, Catherine; Kupelian, Chloe; Baker, Elizabeth; Sanders, Andrew J.; Shimizu, Christina; Norona, Amanda; Paparella, Tanya; Freeman, Stephanny F. N.; Johnson, Scott P.

2015-01-01

Statistical learning is characterized by detection of regularities in one's environment without an awareness or intention to learn, and it may play a critical role in language and social behavior. Accordingly, in this study we investigated the electrophysiological correlates of visual statistical learning in young children with autism…

The Necessity of the Hippocampus for Statistical Learning

PubMed Central

Covington, Natalie V.; Brown-Schmidt, Sarah; Duff, Melissa C.

2018-01-01

Converging evidence points to a role for the hippocampus in statistical learning, but open questions about its necessity remain. Evidence for necessity comes from Schapiro and colleagues who report that a single patient with damage to hippocampus and broader medial temporal lobe cortex was unable to discriminate new from old sequences in several statistical learning tasks. The aim of the current study was to replicate these methods in a larger group of patients who have either damage localized to hippocampus or a broader medial temporal lobe damage, to ascertain the necessity of the hippocampus in statistical learning. Patients with hippocampal damage consistently showed less learning overall compared with healthy comparison participants, consistent with an emerging consensus for hippocampal contributions to statistical learning. Interestingly, lesion size did not reliably predict performance. However, patients with hippocampal damage were not uniformly at chance and demonstrated above-chance performance in some task variants. These results suggest that hippocampus is necessary for statistical learning levels achieved by most healthy comparison participants but significant hippocampal pathology alone does not abolish such learning. PMID:29308986

Determinants of Mobile Learning Acceptance: An Empirical Investigation in Higher Education

ERIC Educational Resources Information Center

Akour, Hassan

2010-01-01

Scope and method of study: The purpose of this study was to investigate the determinants of mobile learning acceptance in higher education. Mobile learning is a rapidly growing method of learning that utilizes mobile devices to deliver content. Acceptance of mobile learning theory was derived from technology acceptance theories. The study…

Adult Learning for Social Change in Museums: An Exploration of Sociocultural Learning Approaches to Community Engagement

ERIC Educational Resources Information Center

Kim, Junghwan; You, Jieun; Yeon Park, Soo

2016-01-01

This integrative literature review critically examined how scholars were articulating the work of museums to make a space for "adult learning for social change through community engagement". We applied sociocultural adult learning theories (situated learning and cultural-historical activity theory), to 25 theoretical and empirical…

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

PubMed

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

2015-10-01

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

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

PubMed

Howell, Dana

2009-01-01

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

What Did They Learn in School Today? A Method for Exploring Aspects of Learning in Physical Education

ERIC Educational Resources Information Center

Quennerstedt, Mikael; Annerstedt, Claes; Barker, Dean; Karlefors, Inger; Larsson, Håkan; Redelius, Karin; Öhman, Marie

2014-01-01

This paper outlines a method for exploring learning in educational practice. The suggested method combines an explicit learning theory with robust methodological steps in order to explore aspects of learning in school physical education. The design of the study is based on sociocultural learning theory, and the approach adds to previous research…

Theorizing Learning Process: An Experiential, Constructivist Approach to Young People's Learning about Global Poverty and Development

ERIC Educational Resources Information Center

Brown, Kate

2015-01-01

Learning processes in global education have not been significantly theorized, with the notable exception of the application of transformative learning theory. No theory of learning is complete, and to understand the complexity of learning, multiple theoretical lenses must be applied. This article looks at Jarvis's (2006) model of lifelong learning…

Learning Actions, Objects and Types of Interaction: A Methodological Analysis of Expansive Learning among Pre-Service Teachers

ERIC Educational Resources Information Center

Rantavuori, Juhana; Engeström, Yrjö; Lipponen, Lasse

2016-01-01

The paper analyzes a collaborative learning process among Finnish pre-service teachers planning their own learning in a self-regulated way. The study builds on cultural-historical activity theory and the theory of expansive learning, integrating for the first time an analysis of learning actions and an analysis of types of interaction. We examine…

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

ERIC Educational Resources Information Center

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…

Towards application of rule learning to the meta-analysis of clinical data: an example of the metabolic syndrome.

PubMed

Wojtusiak, Janusz; Michalski, Ryszard S; Simanivanh, Thipkesone; Baranova, Ancha V

2009-12-01

Systematic reviews and meta-analysis of published clinical datasets are important part of medical research. By combining results of multiple studies, meta-analysis is able to increase confidence in its conclusions, validate particular study results, and sometimes lead to new findings. Extensive theory has been built on how to aggregate results from multiple studies and arrive to the statistically valid conclusions. Surprisingly, very little has been done to adopt advanced machine learning methods to support meta-analysis. In this paper we describe a novel machine learning methodology that is capable of inducing accurate and easy to understand attributional rules from aggregated data. Thus, the methodology can be used to support traditional meta-analysis in systematic reviews. Most machine learning applications give primary attention to predictive accuracy of the learned knowledge, and lesser attention to its understandability. Here we employed attributional rules, the special form of rules that are relatively easy to interpret for medical experts who are not necessarily trained in statistics and meta-analysis. The methodology has been implemented and initially tested on a set of publicly available clinical data describing patients with metabolic syndrome (MS). The objective of this application was to determine rules describing combinations of clinical parameters used for metabolic syndrome diagnosis, and to develop rules for predicting whether particular patients are likely to develop secondary complications of MS. The aggregated clinical data was retrieved from 20 separate hospital cohorts that included 12 groups of patients with present liver disease symptoms and 8 control groups of healthy subjects. The total of 152 attributes were used, most of which were measured, however, in different studies. Twenty most common attributes were selected for the rule learning process. By applying the developed rule learning methodology we arrived at several different possible rulesets that can be used to predict three considered complications of MS, namely nonalcoholic fatty liver disease (NAFLD), simple steatosis (SS), and nonalcoholic steatohepatitis (NASH).

Using rock art as an alternative science pedagogy

NASA Astrophysics Data System (ADS)

Allen, Casey D.

College-level and seventh-grade science students were studied to understand the power of a field index, the Rock Art Stability Index (RASI), for student learning about complex biophysical environmental processes. In order to determine if the studied population was representative, 584 college and seventh-grade students undertook a concept mapping exercise after they had learned basic weathering science via in-class lecture. Of this large group, a subset of 322 college students and 13 seventh-grade students also learned RASI through a field experience involving the analysis of rock weathering associated with petroglyphs. After learning weathering through RASI, students completed another concept map. This was a college population where roughly 46% had never taken a "lab science" course and nearly 22% were from minority (non-white) populations. Analysis of student learning through the lens of actor-network theory revealed that when landscape is viewed as process (i.e. many practices), science education embodies both an alternative science philosophy and an alternative materialistic worldview. When RASI components were analyzed after only lecture, student understanding of weathering displayed little connection between weathering form and weathering process. After using RASI in the field however, nearly all students made illustrative concept maps rich in connections between weathering form and weathering process for all subcomponents of RASI. When taken as an aggregate, and measured by an average concept map score, learning increased by almost 14%, Among college minority students, the average score increase approached 23%. Among female students, the average score increase was 16%. For seventh-grade students, scores increased by nearly 36%. After testing for normalcy with Kolmogorov-Smirnov, t-tests reveal that all of these increases were highly statistically significant at p<0.001. The growth in learning weathering science by minority students, as compared to non-minority students, was also statistically significant at p<0.01. These findings reveal the power of field work through RASI to strengthen cognitive linkages between complex biophysical processes and the corresponding rock weathering forms.

Improving Critical Thinking Using a Web-Based Tutorial Environment.

PubMed

Wiesner, Stephen M; Walker, J D; Creeger, Craig R

2017-01-01

With a broad range of subject matter, students often struggle recognizing relationships between content in different subject areas. A scenario-based learning environment (SaBLE) has been developed to enhancing clinical reasoning and critical thinking among undergraduate students in a medical laboratory science program and help them integrate their new knowledge. SaBLE incorporates aspects of both cognitive theory and instructional design, including reduction of extraneous cognitive load, goal-based learning, feedback timing, and game theory. SaBLE is a website application that runs in most browsers and devices, and is used to develop randomly selected scenarios that challenge user thinking in almost any scenario-based instruction. User progress is recorded to allow comprehensive data analysis of changes in user performance. Participation is incentivized using a point system and digital badges or awards. SaBLE was deployed in one course with a total exposure for the treatment group of approximately 9 weeks. When assessing performance of SaBLE participants, and controlling for grade point average as a possible confounding variable, there was a statistically significant correlation between the number of SaBLE levels completed and performance on selected critical-thinking exam questions addressing unrelated content.

Failures to replicate blocking are surprising and informative-Reply to Soto (2018).

PubMed

Maes, Elisa; Krypotos, Angelos-Miltiadis; Boddez, Yannick; Alfei Palloni, Joaquín Matías; D'Hooge, Rudi; De Houwer, Jan; Beckers, Tom

2018-04-01

The blocking effect has inspired numerous associative learning theories and is widely cited in the literature. We recently reported a series of 15 experiments that failed to obtain a blocking effect in rodents. On the basis of those consistent failures, we claimed that there is a lack of insight into the boundary conditions for blocking. In his commentary, Soto (2018) argued that contemporary associative learning theory does provide a specific boundary condition for the occurrence of blocking, namely the use of same- versus different-modality stimuli. Given that in 10 of our 15 experiments same-modality stimuli were used, he claims that our failure to observe a blocking effect is unsurprising. We disagree with that claim, because of theoretical, empirical, and statistical problems with his analysis. We also address 2 other possible reasons for a lack of blocking that are referred to in Soto's (2018) analysis, related to generalization and salience, and dissect the potential importance of both. Although Soto's (2018) analyses raise a number of interesting points, we see more merit in an empirically guided analysis and call for empirical testing of boundary conditions on blocking. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

How Knowledge of Ancient Egyptian Women Can Influence Today's Gender Role: Does History Matter in Gender Psychology?

PubMed

Khalil, Radwa; Moustafa, Ahmed A; Moftah, Marie Z; Karim, Ahmed A

2016-01-01

A gender role is a set of societal norms dictating what types of behaviors are considered desirable or appropriate for a person based on their sex. However, socially constructed gender roles can lead to equal rights between genders but also to severe disadvantages and discrimination with a remarkable variety between different countries. Based on social indicators and gender statistics, "women in the Arab region are on average more disadvantaged economically, politically, and socially than women in other regions." According to Banduras' social learning theory, we argue that profound knowledge of the historical contributions of Ancient Egyptian female pioneers in science, arts, and even in ruling Egypt as Pharaohs can improve today's gender role in Egypt and Middle Eastern countries. Therefore, this article provides an elaborate review of the gender role of women in Ancient Egypt, outlining their prominence, influence, and admiration in ancient societies, and discusses the possible psychological impact of this knowledge on today's gender role. We suggest that future empirical research should investigate how enhancing the knowledge of women from Ancient Egypt can improve today's gender role in Egypt and the Middle East. Bandura's social learning theory is outlined as a possible framework for future research.

Online neural monitoring of statistical learning.

PubMed

Batterink, Laura J; Paller, Ken A

2017-05-01

The extraction of patterns in the environment plays a critical role in many types of human learning, from motor skills to language acquisition. This process is known as statistical learning. Here we propose that statistical learning has two dissociable components: (1) perceptual binding of individual stimulus units into integrated composites and (2) storing those integrated representations for later use. Statistical learning is typically assessed using post-learning tasks, such that the two components are conflated. Our goal was to characterize the online perceptual component of statistical learning. Participants were exposed to a structured stream of repeating trisyllabic nonsense words and a random syllable stream. Online learning was indexed by an EEG-based measure that quantified neural entrainment at the frequency of the repeating words relative to that of individual syllables. Statistical learning was subsequently assessed using conventional measures in an explicit rating task and a reaction-time task. In the structured stream, neural entrainment to trisyllabic words was higher than in the random stream, increased as a function of exposure to track the progression of learning, and predicted performance on the reaction time (RT) task. These results demonstrate that monitoring this critical component of learning via rhythmic EEG entrainment reveals a gradual acquisition of knowledge whereby novel stimulus sequences are transformed into familiar composites. This online perceptual transformation is a critical component of learning. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

ERIC Educational Resources Information Center

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…

Learning Is an Ontological Process: Jarvis and Theories of Christian Religious Education in Dialogue

ERIC Educational Resources Information Center

Le Cornu, Alison

2017-01-01

Both Jarvis and theories of Christian Religious Education (CRE) emphasise that learning develops the whole person, yet they differ in their understandings of how and why this is the case. Jarvis's experiential learning theory begins "from below" with experience, whereas many approaches of CRE begin with the end result: individuals…

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

ERIC Educational Resources Information Center

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…

Expansive Learning: Benefits and Limitations of Subject-Scientific Learning Theory

ERIC Educational Resources Information Center

Grotluschen, Anke

2005-01-01

One critical learning theory that has survived is once again being acclaimed. Subject-scientific theory requires learners to be taken seriously. Their reasons and resistance need to be brought into the open. This requirement was too radical for schools since it does not allow a fixed syllabus. It has borne fruit, however, in continuing education.…

A Theoretical Analysis of Learning with Graphics--Implications for Computer Graphics Design.

ERIC Educational Resources Information Center

ChanLin, Lih-Juan

This paper reviews the literature pertinent to learning with graphics. The dual coding theory provides explanation about how graphics are stored and precessed in semantic memory. The level of processing theory suggests how graphics can be employed in learning to encourage deeper processing. In addition to dual coding theory and level of processing…

Exploring the Conceptual Compatibility of Transformative Learning Theory in Accounts of Christian Spiritual Renewal at Wheaton College in 1995

ERIC Educational Resources Information Center

McLaughlin, Richard J.

2014-01-01

This research explored the conceptual compatibility of Transformative Learning Theory in accounts of Christian spiritual renewal at Wheaton College in 1995. The literature review examined two domains: Transformative Learning Theory (TLT) and renewal of spiritual life in American students. TLT was applied as quadrants of experience, critical…

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

ERIC Educational Resources Information Center

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…

Learning, Action and Solutions in Action Learning: Investigation of Facilitation Practice Using the Concept of Living Theories

ERIC Educational Resources Information Center

Sanyal, Chandana

2018-01-01

This paper explores the practice of action learning (AL) facilitation in supporting AL set members to address their 'messy' problems through a self-reflexive approach using the concept of 'living theory' [Whitehead, J., and J. McNiff. 2006. "Action Research Living Theory." London: Sage]. The facilitation practice is investigated through…

A Model of E-Learning Uptake and Continued Use in Higher Education Institutions

ERIC Educational Resources Information Center

Pinpathomrat, Nakarin; Gilbert, Lester; Wills, Gary B.

2013-01-01

This research investigates the factors that affect a students' take-up and continued use of E-learning. A mathematical model was constructed by applying three grounded theories; Unified Theory of Acceptance and Use of Technology, Keller's ARCS model, and Expectancy Disconfirm Theory. The learning preference factor was included in the model.…

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

ERIC Educational Resources Information Center

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…

Constructing a Grounded Theory of E-Learning Assessment

ERIC Educational Resources Information Center

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…

Why formal learning theory matters for cognitive science.

PubMed

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.

A neuroconstructivist model of past tense development and processing.

PubMed

Westermann, Gert; Ruh, Nicolas

2012-07-01

We present a neural network model of learning and processing the English past tense that is based on the notion that experience-dependent cortical development is a core aspect of cognitive development. During learning the model adds and removes units and connections to develop a task-specific final architecture. The model provides an integrated account of characteristic errors during learning the past tense, adult generalization to pseudoverbs, and dissociations between verbs observed after brain damage in aphasic patients. We put forward a theory of verb inflection in which a functional processing architecture develops through interactions between experience-dependent brain development and the structure of the environment, in this case, the statistical properties of verbs in the language. The outcome of this process is a structured processing system giving rise to graded dissociations between verbs that are easy and verbs that are hard to learn and process. In contrast to dual-mechanism accounts of inflection, we argue that describing dissociations as a dichotomy between regular and irregular verbs is a post hoc abstraction and is not linked to underlying processing mechanisms. We extend current single-mechanism accounts of inflection by highlighting the role of structural adaptation in development and in the formation of the adult processing system. In contrast to some single-mechanism accounts, we argue that the link between irregular inflection and verb semantics is not causal and that existing data can be explained on the basis of phonological representations alone. This work highlights the benefit of taking brain development seriously in theories of cognitive development. Copyright 2012 APA, all rights reserved.

Can You Read Me Now? Disciplinary Literacy Reading Strategies in the 7th Grade Science Classroom

NASA Astrophysics Data System (ADS)

McQuaid, Kelly Kathleen

Adolescent readers require a broad range of reading skills to deal with the challenges of reading complex text. Some researchers argue for a discipline-specific focus to address the low reading proficiency rates among secondary students. Disciplinary literacy attends to the different ways disciplines, such as science, generate and communicate knowledge. The purpose of this quasi-experimental study was to examine if and to what degree disciplinary literacy reading strategies impact student learning outcomes in reading comprehension and science content knowledge for 132 7th grade science students in five Southern Arizona charter schools and whether reading ability moderates that impact. The theoretical foundation for this study rested on expert-novice theory and Halliday's theory of critical moments of language development. It is not known if and to what degree disciplinary literacy reading strategies impact student learning outcomes in reading comprehension and science content knowledge for 7th grade science students and whether or not reading ability has a moderating effect on those student learning outcomes. The results for MANCOVA did not produce statistically significant results nor did the moderation analysis for the influence of reading ability on reading comprehension in the disciplinary literacy group. However, the moderation analysis for the influence of reading ability on science content knowledge resulted in conditional significant results for low (p < .01) and average readers (p <. 05). Low to average readers in the disciplinary literacy group appeared to benefit the most from reading comprehension instruction focused on learning science content in the science classroom.

Effect of quantum learning model in improving creativity and memory

NASA Astrophysics Data System (ADS)

Sujatmika, S.; Hasanah, D.; Hakim, L. L.

2018-04-01

Quantum learning is a combination of many interactions that exist during learning. This model can be applied by current interesting topic, contextual, repetitive, and give opportunities to students to demonstrate their abilities. The basis of the quantum learning model are left brain theory, right brain theory, triune, visual, auditorial, kinesthetic, game, symbol, holistic, and experiential learning theory. Creativity plays an important role to be success in the working world. Creativity shows alternatives way to problem-solving or creates something. Good memory plays a role in the success of learning. Through quantum learning, students will use all of their abilities, interested in learning and create their own ways of memorizing concepts of the material being studied. From this idea, researchers assume that quantum learning models can improve creativity and memory of the students.

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

PubMed

Klobučar, Nataša Rijavec

2016-08-01

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

Online Collaborative Learning: Theory and Practice

ERIC Educational Resources Information Center

Roberts, Tim, Ed.

2004-01-01

"Online Collaborative Learning: Theory and Practice" provides a resource for researchers and practitioners in the area of online collaborative learning (also known as CSCL, computer-supported collaborative learning), particularly those working within a tertiary education environment. It includes articles of relevance to those interested in both…

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

ERIC Educational Resources Information Center

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…

Peter Jarvis and the Understanding of Adult Learning

ERIC Educational Resources Information Center

Illeris, Knud

2017-01-01

By comparing Peter Jarvis' understanding of learning with two other approaches--which Jarvis himself has referred to as "the most comprehensive": Etienne Wenger's "social theory of learning" and my own psychologically oriented theory of "the three dimensions of learning"--it becomes evident that Jarvis' understanding…

The Highly Adaptive Lasso Estimator

PubMed Central

Benkeser, David; van der Laan, Mark

2017-01-01

Estimation of a regression functions is a common goal of statistical learning. We propose a novel nonparametric regression estimator that, in contrast to many existing methods, does not rely on local smoothness assumptions nor is it constructed using local smoothing techniques. Instead, our estimator respects global smoothness constraints by virtue of falling in a class of right-hand continuous functions with left-hand limits that have variation norm bounded by a constant. Using empirical process theory, we establish a fast minimal rate of convergence of our proposed estimator and illustrate how such an estimator can be constructed using standard software. In simulations, we show that the finite-sample performance of our estimator is competitive with other popular machine learning techniques across a variety of data generating mechanisms. We also illustrate competitive performance in real data examples using several publicly available data sets. PMID:29094111

Adult learning theories: implications for learning and teaching in medical education: AMEE Guide No. 83.

PubMed

Taylor, David C M; Hamdy, Hossam

2013-11-01

There are many theories that explain how adults learn and each has its own merits. This Guide explains and explores the more commonly used ones and how they can be used to enhance student and faculty learning. The Guide presents a model that combines many of the theories into a flow diagram which can be followed by anyone planning learning. The schema can be used at curriculum planning level, or at the level of individual learning. At each stage of the model, the Guide identifies the responsibilities of both learner and educator. The role of the institution is to ensure that the time and resources are available to allow effective learning to happen. The Guide is designed for those new to education, in the hope that it can unravel the difficulties in understanding and applying the common learning theories, whilst also creating opportunities for debate as to the best way they should be used.

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

PubMed

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.

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

ERIC Educational Resources Information Center

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…

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

ERIC Educational Resources Information Center

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…

Activity Theory and Situated Learning Theory: Contrasting Views of Educational Practice

ERIC Educational Resources Information Center

Arnseth, Hans Christian

2008-01-01

The purpose of this article is to offer a critical discussion of the practice turn in contemporary educational research. In order to make the discussion specific, I use two influential theories, namely activity theory and situated learning theory. They both turn to the notion of practice in order to overcome the limitations of mentalist and…

The Relationship of Stated Learning Preferences, Personality Type, and Career Background to Academic and Leadership Performance at the United States Air Command and Staff College

DTIC Science & Technology

1999-12-11

Kolb envisioned experiential 26 Table 2 Subscales on the NASSP Learning Styles Profile Cognitive Styles Perceptual Responses Analytic Skill...Research Type Theory and Learning Preferences Jung and the Theory of Psychological Types Isabel Briggs Myers’ Contribution to Jung’s Work The Myers...Implications Recommendations for Further Study Summary of Specific Conclusions Discussion Grounded Curriculum Learning Preferences Type Theory Student

Applying Information Processing Theory to Supervision: An Initial Exploration

ERIC Educational Resources Information Center

Tangen, Jodi L.; Borders, L. DiAnne

2017-01-01

Although clinical supervision is an educational endeavor (Borders & Brown, [Borders, L. D., 2005]), many scholars neglect theories of learning in working with supervisees. The authors describe 1 learning theory--information processing theory (Atkinson & Shiffrin, 1968, 1971; Schunk, 2016)--and the ways its associated interventions may…

In a year, memory will benefit from learning, tomorrow it won't: distance and construal level effects on the basis of metamemory judgments.

PubMed

Halamish, Vered; Nussinson, Ravit; Ben-Ari, Liat

2013-09-01

Metamemory judgments may rely on 2 bases of information: subjective experience and abstract theories about memory. On the basis of construal level theory, we predicted that psychological distance and construal level (i.e., concrete vs. abstract thinking) would have a qualitative impact on the relative reliance on these 2 bases: When considering learning from proximity or under a low-construal mindset, learners would rely more heavily on their experience, whereas when considering learning from a distance or under a high-construal mindset, they would rely more heavily on their abstract theories. Consistent with this prediction, results of 2 experiments revealed that temporal distance (Experiment 1) and construal level (Experiment 2) affected the stability bias--the failure to predict the benefits of learning. When considering learning from proximity or using a low-construal mindset, participants relied less heavily on their theory regarding the benefits of learning and were therefore insensitive to future learning. However, when considering learning from temporal distance or using a high-construal mindset, participants relied more heavily on their theory and were therefore better able to predict the benefits of future learning, thus overcoming the stability bias. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Learning: from association to cognition.

PubMed

Shanks, David R

2010-01-01

Since the very earliest experimental investigations of learning, tension has existed between association-based and cognitive theories. Associationism accounts for the phenomena of both conditioning and "higher" forms of learning via concepts such as excitation, inhibition, and reinforcement, whereas cognitive theories assume that learning depends on hypothesis testing, cognitive models, and propositional reasoning. Cognitive theories have received considerable impetus in regard to both human and animal learning from recent research suggesting that the key illustration of cue selection in learning, blocking, often arises from inferential reasoning. At the same time, a dichotomous view that separates noncognitive, unconscious (implicit) learning from cognitive, conscious (explicit) learning has gained favor. This review selectively describes key findings from this research, evaluates evidence for and against associative and cognitive explanatory constructs, and critically examines both the dichotomous view of learning as well as the claim that learning can occur unconsciously.

Spatiotemporal information during unsupervised learning enhances viewpoint invariant object recognition

PubMed Central

Tian, Moqian; Grill-Spector, Kalanit

2015-01-01

Recognizing objects is difficult because it requires both linking views of an object that can be different and distinguishing objects with similar appearance. Interestingly, people can learn to recognize objects across views in an unsupervised way, without feedback, just from the natural viewing statistics. However, there is intense debate regarding what information during unsupervised learning is used to link among object views. Specifically, researchers argue whether temporal proximity, motion, or spatiotemporal continuity among object views during unsupervised learning is beneficial. Here, we untangled the role of each of these factors in unsupervised learning of novel three-dimensional (3-D) objects. We found that after unsupervised training with 24 object views spanning a 180° view space, participants showed significant improvement in their ability to recognize 3-D objects across rotation. Surprisingly, there was no advantage to unsupervised learning with spatiotemporal continuity or motion information than training with temporal proximity. However, we discovered that when participants were trained with just a third of the views spanning the same view space, unsupervised learning via spatiotemporal continuity yielded significantly better recognition performance on novel views than learning via temporal proximity. These results suggest that while it is possible to obtain view-invariant recognition just from observing many views of an object presented in temporal proximity, spatiotemporal information enhances performance by producing representations with broader view tuning than learning via temporal association. Our findings have important implications for theories of object recognition and for the development of computational algorithms that learn from examples. PMID:26024454

Derivative Free Optimization of Complex Systems with the Use of Statistical Machine Learning Models

DTIC Science & Technology

2015-09-12

AFRL-AFOSR-VA-TR-2015-0278 DERIVATIVE FREE OPTIMIZATION OF COMPLEX SYSTEMS WITH THE USE OF STATISTICAL MACHINE LEARNING MODELS Katya Scheinberg...COMPLEX SYSTEMS WITH THE USE OF STATISTICAL MACHINE LEARNING MODELS 5a.  CONTRACT NUMBER 5b.  GRANT NUMBER FA9550-11-1-0239 5c.  PROGRAM ELEMENT...developed, which has been the focus of our research. 15. SUBJECT TERMS optimization, Derivative-Free Optimization, Statistical Machine Learning 16. SECURITY

Modelling unsupervised online-learning of artificial grammars: linking implicit and statistical learning.

PubMed

Rohrmeier, Martin A; Cross, Ian

2014-07-01

Humans rapidly learn complex structures in various domains. Findings of above-chance performance of some untrained control groups in artificial grammar learning studies raise questions about the extent to which learning can occur in an untrained, unsupervised testing situation with both correct and incorrect structures. The plausibility of unsupervised online-learning effects was modelled with n-gram, chunking and simple recurrent network models. A novel evaluation framework was applied, which alternates forced binary grammaticality judgments and subsequent learning of the same stimulus. Our results indicate a strong online learning effect for n-gram and chunking models and a weaker effect for simple recurrent network models. Such findings suggest that online learning is a plausible effect of statistical chunk learning that is possible when ungrammatical sequences contain a large proportion of grammatical chunks. Such common effects of continuous statistical learning may underlie statistical and implicit learning paradigms and raise implications for study design and testing methodologies. Copyright © 2014 Elsevier Inc. All rights reserved.

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

PubMed

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, employers and the receivers of nursing care. Copyright © 2018 Elsevier Ltd. All rights reserved.

Investigating Students' Acceptance of a Statistics Learning Platform Using Technology Acceptance Model

ERIC Educational Resources Information Center

Song, Yanjie; Kong, Siu-Cheung

2017-01-01

The study aims at investigating university students' acceptance of a statistics learning platform to support the learning of statistics in a blended learning context. Three kinds of digital resources, which are simulations, online videos, and online quizzes, were provided on the platform. Premised on the technology acceptance model, we adopted a…

The Impact of Language Experience on Language and Reading: A Statistical Learning Approach

ERIC Educational Resources Information Center

Seidenberg, Mark S.; MacDonald, Maryellen C.

2018-01-01

This article reviews the important role of statistical learning for language and reading development. Although statistical learning--the unconscious encoding of patterns in language input--has become widely known as a force in infants' early interpretation of speech, the role of this kind of learning for language and reading comprehension in…

Reducing statistics anxiety and enhancing statistics learning achievement: effectiveness of a one-minute strategy.

PubMed

Chiou, Chei-Chang; Wang, Yu-Min; Lee, Li-Tze

2014-08-01

Statistical knowledge is widely used in academia; however, statistics teachers struggle with the issue of how to reduce students' statistics anxiety and enhance students' statistics learning. This study assesses the effectiveness of a "one-minute paper strategy" in reducing students' statistics-related anxiety and in improving students' statistics-related achievement. Participants were 77 undergraduates from two classes enrolled in applied statistics courses. An experiment was implemented according to a pretest/posttest comparison group design. The quasi-experimental design showed that the one-minute paper strategy significantly reduced students' statistics anxiety and improved students' statistics learning achievement. The strategy was a better instructional tool than the textbook exercise for reducing students' statistics anxiety and improving students' statistics achievement.

Commentary: Academic Enablers and School Learning.

ERIC Educational Resources Information Center

Keith, Timothy Z.

2002-01-01

This commentary presents academic enablers within the broader, overlapping context of school learning theory, including the theories of Carroll, Harnishfeger and Wiley, Walberg, and others. Multivariate models are needed to understand the influences of academic enabler and school learning variables on learning, as well as the influences of these…

Connectivism and Information Literacy: Moving from Learning Theory to Pedagogical Practice

ERIC Educational Resources Information Center

Transue, Beth M.

2013-01-01

Connectivism is an emerging learning theory positing that knowledge comprises networked relationships and that learning comprises the ability to successfully navigate through these networks. Successful pedagogical strategies involve the instructor helping students to identify, navigate, and evaluate information from their learning networks. Many…

Modules as Learning Tools in Linear Algebra

ERIC Educational Resources Information Center

Cooley, Laurel; Vidakovic, Draga; Martin, William O.; Dexter, Scott; Suzuki, Jeff; Loch, Sergio

2014-01-01

This paper reports on the experience of STEM and mathematics faculty at four different institutions working collaboratively to integrate learning theory with curriculum development in a core undergraduate linear algebra context. The faculty formed a Professional Learning Community (PLC) with a focus on learning theories in mathematics and…

Culture in the mind's mirror: how anthropology and neuroscience can inform a model of the neural substrate for cultural imitative learning.

PubMed

Losin, Elizabeth A Reynolds; Dapretto, Mirella; Iacoboni, Marco

2009-01-01

Cultural neuroscience, the study of how cultural experience shapes the brain, is an emerging subdiscipline in the neurosciences. Yet, a foundational question to the study of culture and the brain remains neglected by neuroscientific inquiry: "How does cultural information get into the brain in the first place?" Fortunately, the tools needed to explore the neural architecture of cultural learning - anthropological theories and cognitive neuroscience methodologies - already exist; they are merely separated by disciplinary boundaries. Here we review anthropological theories of cultural learning derived from fieldwork and modeling; since cultural learning theory suggests that sophisticated imitation abilities are at the core of human cultural learning, we focus our review on cultural imitative learning. Accordingly we proceed to discuss the neural underpinnings of imitation and other mechanisms important for cultural learning: learning biases, mental state attribution, and reinforcement learning. Using cultural neuroscience theory and cognitive neuroscience research as our guides, we then propose a preliminary model of the neural architecture of cultural learning. Finally, we discuss future studies needed to test this model and fully explore and explain the neural underpinnings of cultural imitative learning.

Self-Determination, Engagement, and Identity in Learning German: Some Directions in the Psychology of Language Learning Motivation

ERIC Educational Resources Information Center

Noels, Kimberly A.; Chaffee, Kathryn; Lou, Nigel Mantou; Dincer, Ali

2016-01-01

Drawing from Self-Determination Theory and diverse theories of language learning motivation, we present a framework that (1) represents a range of orientations that students may take towards learning German, and (2) explains how these orientations are connected to language learning engagement and diverse linguistic and non-linguistic outcomes. We…

What Makes Learners Learn? Motivational Learning Theory in Home Study.

ERIC Educational Resources Information Center

Feingold, S. Norman

1979-01-01

Motivation is vital to all learning with the possible exception of incidental learning. Home study may adapt motivational learning theory to its goals by considering basic concepts with related examples. First, the more advance reasons an individual has to believe in the value of home study, the more likely will be the success of educational…

Beyond the Learning Process and toward the Knowledge Creation Process: Linking Learning and Knowledge in the Supportive Learning Culture

ERIC Educational Resources Information Center

Yoon, Seung Won; Song, Ji Hoon; Lim, Doo Hun

2009-01-01

This integrative literature review synthesizes the concepts and process of organizational knowledge creation with theories of individual learning. The knowledge conversion concept (Nonaka & Takeuchi, 1995; Nonaka, Toyama, & Byosiere, 2001) is used as the basis of the organizational knowledge creation process, while major learning theories relevant…

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

ERIC Educational Resources Information Center

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…