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Edelsbrunner, Peter A.; Flaig, Maja; Schneider, Michael – Journal of Research on Educational Effectiveness, 2023
Latent transition analysis is an informative statistical tool for depicting heterogeneity in learning as latent profiles. We present a Monte Carlo simulation study to guide researchers in selecting fit indices for identifying the correct number of profiles. We simulated data representing profiles of learners within a typical pre- post- follow…
Descriptors: Learning Processes, Profiles, Monte Carlo Methods, Bayesian Statistics
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Lozano, José H.; Revuelta, Javier – Educational and Psychological Measurement, 2023
The present paper introduces a general multidimensional model to measure individual differences in learning within a single administration of a test. Learning is assumed to result from practicing the operations involved in solving the items. The model accounts for the possibility that the ability to learn may manifest differently for correct and…
Descriptors: Bayesian Statistics, Learning Processes, Test Items, Item Analysis
Feng, Junchen – ProQuest LLC, 2017
The future of education is human expertise and artificial intelligence working in conjunction, a revolution that will change the education as we know it. The Intelligent Tutoring System is a key component of this future. A quantitative measurement of efficacies of practice to heterogeneous learners is the cornerstone of building an effective…
Descriptors: Intelligent Tutoring Systems, Learning Processes, Bayesian Statistics, Models
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Gershman, Samuel J.; Blei, David M.; Niv, Yael – Psychological Review, 2010
A. Redish et al. (2007) proposed a reinforcement learning model of context-dependent learning and extinction in conditioning experiments, using the idea of "state classification" to categorize new observations into states. In the current article, the authors propose an interpretation of this idea in terms of normative statistical inference. They…
Descriptors: Conditioning, Statistical Inference, Inferences, Bayesian Statistics
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Griffiths, Thomas L.; Kalish, Michael L. – Cognitive Science, 2007
Languages are transmitted from person to person and generation to generation via a process of iterated learning: people learn a language from other people who once learned that language themselves. We analyze the consequences of iterated learning for learning algorithms based on the principles of Bayesian inference, assuming that learners compute…
Descriptors: Probability, Diachronic Linguistics, Statistical Inference, Language Universals
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Hartz, Sarah; Roussos, Louis – ETS Research Report Series, 2008
This paper presents the development of the fusion model skills diagnosis system (fusion model system), which can help integrate standardized testing into the learning process with both skills-level examinee parameters for modeling examinee skill mastery and skills-level item parameters, giving information about the diagnostic power of the test.…
Descriptors: Skill Development, Educational Diagnosis, Theory Practice Relationship, Standardized Tests