A Bayesian generalized explanatory item response model to account for learning during the test
EntityUAM. Departamento de Psicología Social y Metodología
10.1007/s11336-021-09786-xPsychometrika 86.4 (2021): 994-1015
Funded byThe project was supported by the Grants PSI2015-66366-P (Ministerio de Economía y Competitividad) and PGC2018-093838-B-I00 (Ministerio de Ciencia, Innovación y Universidades). The computations were run with the support of the Scientific Computing Centre at Universidad Autónoma de Madrid (CCC-UAM)
ProjectGobierno de España. PSI2015-66366-P; Gobierno de España. PGC2018-093838-B-I00
SubjectsBayesian estimation; componential models; item response theory; learning models; Psicología
Rights© 2021 by the authors
Esta obra está bajo una Licencia Creative Commons Atribución 4.0 Internacional.
The present paper introduces a new explanatory item response model to account for the learning that takes place during a psychometric test due to the repeated use of the operations involved in the items. The proposed model is an extension of the operation-specific learning model (Fischer and Formann in Appl Psychol Meas 6:397–416, 1982; Scheiblechner in Z für Exp Angew Psychol 19:476–506, 1972; Spada in Spada and Kempf (eds.) Structural models of thinking and learning, Huber, Bern, Germany, pp 227–262, 1977). The paper discusses special cases of the model, which, together with the general formulation, differ in the type of response in which the model states that learning occurs: (1) correct and incorrect responses equally (non-contingent learning); (2) correct responses only (contingent learning); and (3) correct and incorrect responses to a different extent (differential contingent learning). A Bayesian framework is adopted for model estimation and evaluation. A simulation study is conducted to examine the performance of the estimation and evaluation methods in recovering the true parameters and selecting the true model. Finally, an empirical study is presented to illustrate the applicability of the model to detect learning effects using real data
Google Scholar:Lozano, José H. - Revuelta, Javier
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