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Rafferty, Anna N., Ed.; Whitehill, Jacob, Ed.; Romero, Cristobal, Ed.; Cavalli-Sforza, Violetta, Ed. – International Educational Data Mining Society, 2020
The 13th iteration of the International Conference on Educational Data Mining (EDM 2020) was originally arranged to take place in Ifrane, Morocco. Due to the SARS-CoV-2 (coronavirus) epidemic, EDM 2020, as well as most other academic conferences in 2020, had to be changed to a purely online format. To facilitate efficient transmission of…
Descriptors: Educational Improvement, Teaching Methods, Information Retrieval, Data Processing
Alishahi, Afra; Stevenson, Suzanne – Cognitive Science, 2008
How children go about learning the general regularities that govern language, as well as keeping track of the exceptions to them, remains one of the challenging open questions in the cognitive science of language. Computational modeling is an important methodology in research aimed at addressing this issue. We must determine appropriate learning…
Descriptors: Semantics, Verbs, Linguistics, Cognitive Psychology
Goodman, Noah D.; Tenenbaum, Joshua B.; Feldman, Jacob; Griffiths, Thomas L. – Cognitive Science, 2008
This article proposes a new model of human concept learning that provides a rational analysis of learning feature-based concepts. This model is built upon Bayesian inference for a grammatically structured hypothesis space--a concept language of logical rules. This article compares the model predictions to human generalization judgments in several…
Descriptors: Mathematics Education, Concept Formation, Models, Prediction
Shiffrin, Richard M.; Lee, Michael D.; Kim, Woojae; Wagenmakers, Eric-Jan – Cognitive Science, 2008
This article reviews current methods for evaluating models in the cognitive sciences, including theoretically based approaches, such as Bayes factors and minimum description length measures; simulation approaches, including model mimicry evaluations; and practical approaches, such as validation and generalization measures. This article argues…
Descriptors: Bayesian Statistics, Generalization, Sciences, Models
Schulz, Laura E.; Bonawitz, Elizabeth Baraff; Griffiths, Thomas L. – Developmental Psychology, 2007
Causal learning requires integrating constraints provided by domain-specific theories with domain-general statistical learning. In order to investigate the interaction between these factors, the authors presented preschoolers with stories pitting their existing theories against statistical evidence. Each child heard 2 stories in which 2 candidate…
Descriptors: Inferences, Young Children, Bayesian Statistics, Story Telling
Peer reviewedLecoutre, Bruno; Charron, Camilo – Journal of Educational and Behavioral Statistics, 2000
Illustrates procedures for prediction analysis in 2 X 2 contingency tables through the analyses of solutions of six types of problems associated with the acquisition of fractions. Reviews and extends confidence interval procedures previously proposed for an index of predictive efficiency of implication hypotheses. Compares frequentist coverage…
Descriptors: Bayesian Statistics, Hypothesis Testing, Prediction, Probability
Griffiths, Thomas L.; Tenenbaum, Joshua B. – Cognition, 2007
People's reactions to coincidences are often cited as an illustration of the irrationality of human reasoning about chance. We argue that coincidences may be better understood in terms of rational statistical inference, based on their functional role in processes of causal discovery and theory revision. We present a formal definition of…
Descriptors: Probability, Statistical Inference, Bayesian Statistics, Theories
Peer reviewedMurphy, Gregory L.; Ross, Brian H. – Cognitive Psychology, 1994
Eleven experiments involving over 200 undergraduate students investigated how categorization of examples influences feature prediction for new examples. Results suggest that category-based prediction generally relies on a single category rather than multiple categories when there is a clear target category. (SLD)
Descriptors: Bayesian Statistics, Classification, Higher Education, Inferences
Kern, John C. – Journal of Statistics Education, 2006
Bayesian inference on multinomial probabilities is conducted based on data collected from the game Pass the Pigs[R]. Prior information on these probabilities is readily available from the instruction manual, and is easily incorporated in a Dirichlet prior. Posterior analysis of the scoring probabilities quantifies the discrepancy between empirical…
Descriptors: Bayesian Statistics, Probability, Inferences, Statistics
Diamond, James – 1964
The use of Bayesian statistics as the basis of classical analysis of data is described. Bayesian analysis is a set of procedures for changing opinions about a given phenomenon based upon rational observation of a set of data. The Bayesian arrives at a set of prior beliefs regarding some states of nature; he observes data in a study and then…
Descriptors: Bayesian Statistics, Educational Research, Newsletters, Prediction
Goenner, Cullen F.; Snaith, Sean M. – Research in Higher Education, 2004
Empirical analysis requires researchers to choose which variables to use as controls in their models. Theory should dictate this choice, yet often in social science there are several theories that may suggest the inclusion or exclusion of certain variables as controls. The result of this is that researchers may use different variables in their…
Descriptors: Models, Prediction, Graduation Rate, Universities
Linn, Shai – Journal of Statistics Education, 2004
Courses in clinical epidemiology usually include acquainting students with a single 2X2 table. All diagnostic test characteristics are explained using this table. This pedagogic approach may be misleading. A new didactic approach is hereby proposed, using two tables, each with specific analogous notations (uppercase and lowercase) and derived…
Descriptors: Epidemiology, Diagnostic Tests, Bayesian Statistics, Prediction
Rogers, Hartley, Jr. – International Journal of Mathematics Education, 1972
Basic mathematical concepts of Managerial Economics, a way of quantitatively analyzing and structuring the making of a business decision, are presented. Advantages and disadvantages of its use in business are discussed and several recent applications are given. (DT)
Descriptors: Bayesian Statistics, Business Education, Decision Making, Economics
Botvinick, Matthew M. – Cognition, 2005
Knowledge concerning domain-specific regularities in sequential structure has long been known to affect recall for serial order. However, very little work has been done toward specifying the exact role such knowledge plays. The present article proposes a theory of serial recall in structured domains, based on Bayesian decision theory and a set of…
Descriptors: Prediction, Serial Learning, Bayesian Statistics, Serial Ordering
Peer reviewedLichtenstein, Sarah; And Others – Journal of Experimental Psychology: Human Perception and Performance, 1975
Forty subjects were trained to make numerical predictions of a criterion from a cue. (Editor)
Descriptors: Bayesian Statistics, Cues, Experimental Psychology, Models

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