Adapting cognitive diagnosis computerized adaptive testing item selection rules to traditional item response theory


Autoři: Miguel A. Sorrel aff001;  Juan R. Barrada aff002;  Jimmy de la Torre aff003;  Francisco José Abad aff001
Působiště autorů: Department of Social Psychology and Methodology, Universidad Autónoma de Madrid, Spain aff001;  Department of Psychology and Sociology, Universidad de Zaragoza, Spain aff002;  Faculty of Education, The University of Hong Kong, Hong Kong aff003
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
doi: 10.1371/journal.pone.0227196

Souhrn

Currently, there are two predominant approaches in adaptive testing. One, referred to as cognitive diagnosis computerized adaptive testing (CD-CAT), is based on cognitive diagnosis models, and the other, the traditional CAT, is based on item response theory. The present study evaluates the performance of two item selection rules (ISRs) originally developed in the CD-CAT framework, the double Kullback-Leibler information (DKL) and the generalized deterministic inputs, noisy “and” gate model discrimination index (GDI), in the context of traditional CAT. The accuracy and test security associated with these two ISRs are compared to those of the point Fisher information and weighted KL using a simulation study. The impact of the trait level estimation method is also investigated. The results show that the new ISRs, particularly DKL, could be used to improve the accuracy of CAT. Better accuracy for DKL is achieved at the expense of higher item overlap rate. Differences among the item selection rules become smaller as the test gets longer. The two CD-CAT ISRs select different types of items: items with the highest possible a parameter with DKL, and items with the lowest possible c parameter with GDI. Regarding the trait level estimator, expected a posteriori method is generally better in the first stages of the CAT, and converges with the maximum likelihood method when a medium to large number of items are involved. The use of DKL can be recommended in low-stakes settings where test security is less of a concern.

Klíčová slova:

Algorithms – Bayesian method – Monte Carlo method – Personality tests – Personality traits – Probability distribution – Psychometrics – Simulation and modeling


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