Investigating the Constrained-Weighted Item Selection Methods for CD-CAT

Cognitive diagnostic computerized adaptive testing (CD-CAT) not only provides useful cognitive diagnostic information measured in psychological or educational assessments, but also obtains great efficiency brought by computerized adaptive testing. At present, there are only a limited numbers of previous studies examining how to optimally construct cognitive diagnostic tests. The cognitive diagnostic discrimination index (CDI) and attribute-level discrimination index (ADI) have been proposed for item selection in cognitive diagnostic tests. Zheng and Chang (Appl Psychol Measure 40:608–624, 2016) proposed the modified version of these two indices, an extension of the Kullback-Leibler (KL) and posterior-weighted KL (PWKL) methods, and suggested that they could be integrated with the constraint management procedure for item selection in CD-CAT. However, the constraint management procedure hasn’t been investigated in CD-CAT yet. Therefore, the aim of this study is two fold (a) to integrate the indices with the constraint management procedure for item selection, and (b) to investigate the efficiency of these item selection methods in CA-CAT. It was found that the constraint-weighted indices performed much better than those without constraint-weighted procedure in terms of constraint management and exposure control while maintaining similar measurement precision.