A Bayesian General Model to Account for Individual Differences in Operation-Specific Learning within a Test

Autor/inn/en	Lozano, José H.; Revuelta, Javier
Titel	A Bayesian General Model to Account for Individual Differences in Operation-Specific Learning within a Test
Quelle	In: Educational and Psychological Measurement, 83 (2023) 4, S.782-807 (26 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Lozano, José H.) ORCID (Revuelta, Javier)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/00131644221109796
Schlagwörter	Bayesian Statistics; Learning Processes; Test Items; Item Analysis; Accuracy; Learning Analytics; Evaluation Methods; Logical Thinking; Monte Carlo Methods; Markov Processes; Models; Cognitive Ability; Goodness of Fit + Suchen Sie Ihr Suchwort? Learning process; Lernprozess; Test content; Testaufgabe; Itemanalyse; Monte-Carlo-Methode; Markowscher Prozess; Analogiemodell; Denkfähigkeit
Abstract	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 incorrect responses, which allows for distinguishing different types of learning effects in the data. Model estimation and evaluation is based on a Bayesian framework. A simulation study is presented that examines the performance of the estimation and evaluation methods. The results show accuracy in parameter recovery as well as good performance in model evaluation and selection. An empirical study illustrates the applicability of the model to data from a logical ability test. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01