Adapting cognitive diagnosis computerized adaptive testing item selection rules to traditional item response theory
Entity
UAM. Departamento de Psicología Social y MetodologíaPublisher
Public Library of ScienceDate
2020-01-10Citation
10.1371/journal. pone.0227196
PLoS ONE 15.1 (2020): e0227196
ISSN
1932-6203DOI
10.1371/journal. pone.0227196Funded by
This research was supported by Grant PSI2017-85022-P (Ministerio de Ciencia, Innovación y Universidades, Spain) and the UAMIIC Chair «Psychometric Models and Applications"Project
Gobierno de España. PSI2017-85022-PEditor's Version
https://doi.org/10.1371/journal. pone.0227196Subjects
Adaptive testing; Diagnosis computerized; Item; Traditional; Selection rules; PsicologíaRights
© 2020 Sorrel et al.Abstract
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
Files in this item
Google Scholar:Sorrel Luján, Miguel Ángel
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Barrada, Juan R.
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Torre, Jimmy de la
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Abad Santos, Francisco
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