A Bayesian General Model to Account for Individual Differences in Operation-Specific Learning Within a Test. (August 2023)

• Record Type: Journal Article
• Authors: Lozano, José H.; Revuelta, Javier
• Is Part Of: Educational and psychological measurement. Volume 83:Number 4(2023)
• Page Start: 782
• Page End: 807
• Publication Date: 2023-08
• Subjects: ability to learn -- learning models -- linear logistic test model -- Markov chain Monte Carlo; Educational tests and measurements -- Periodicals; Psychological tests -- Periodicals; 151.205
• DOI: 10.1177/00131644221109796
• Languages: English
• ISSNs: 0013-1644

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.