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Lozano, José H.; Revuelta, Javier – Applied Measurement in Education, 2021
The present study proposes a Bayesian approach for estimating and testing the operation-specific learning model, a variant of the linear logistic test model that allows for the measurement of the learning that occurs during a test as a result of the repeated use of the operations involved in the items. The advantages of using a Bayesian framework…
Descriptors: Bayesian Statistics, Computation, Learning, Testing
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Trippas, Dries; Handley, Simon J.; Verde, Michael F.; Morsanyi, Kinga – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2016
A key assumption of dual process theory is that reasoning is an explicit, effortful, deliberative process. The present study offers evidence for an implicit, possibly intuitive component of reasoning. Participants were shown sentences embedded in logically valid or invalid arguments. Participants were not asked to reason but instead rated the…
Descriptors: Evidence, Logical Thinking, Validity, Sentences
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Markovits, Henry; Brisson, Janie; de Chantal, Pier-Luc – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2015
One of the major debates concerning the nature of inferential reasoning is between counterexample-based theories such as mental model theory and probabilistic theories. This study looks at conclusion updating after the addition of statistical information to examine the hypothesis that deductive reasoning cannot be explained by probabilistic…
Descriptors: Logical Thinking, Theories, Bayesian Statistics, Probability
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van Rijn, Peter W.; Rijmen, Frank – ETS Research Report Series, 2012
Hooker and colleagues addressed a paradoxical situation that can arise in the application of multidimensional item response theory (MIRT) models to educational test data. We demonstrate that this MIRT paradox is an instance of the explaining-away phenomenon in Bayesian networks, and we attempt to enhance the understanding of MIRT models by placing…
Descriptors: Item Response Theory, Educational Testing, Bayesian Statistics, Statistical Analysis
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Kemp, Charles; Perfors, Amy; Tenenbaum, Joshua B. – Developmental Science, 2007
Inductive learning is impossible without overhypotheses, or constraints on the hypotheses considered by the learner. Some of these overhypotheses must be innate, but we suggest that hierarchical Bayesian models can help to explain how the rest are acquired. To illustrate this claim, we develop models that acquire two kinds of…
Descriptors: Bayesian Statistics, Logical Thinking, Models, Statistical Analysis
Barnes, Tiffany, Ed.; Desmarais, Michel, Ed.; Romero, Cristobal, Ed.; Ventura, Sebastian, Ed. – International Working Group on Educational Data Mining, 2009
The Second International Conference on Educational Data Mining (EDM2009) was held at the University of Cordoba, Spain, on July 1-3, 2009. EDM brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large data sets to answer educational research questions. The increase in instrumented…
Descriptors: Data Analysis, Educational Research, Conferences (Gatherings), Foreign Countries