Item Selection Methods in Multidimensional Computerized Adaptive Testing With Polytomously Scored Items

Abstract

Multidimensional computerized adaptive testing (MCAT) has been developed over the past decades, and most of them can only deal with dichotomously scored items. However, polytomously scored items have been broadly used in a variety of tests for their advantages of providing more information and testing complicated abilities and skills. The purpose of this study is to discuss the item selection algorithms used in MCAT with polytomously scored items (PMCAT). Several promising item selection algorithms used in MCAT are extended to PMCAT, and two new item selection methods are proposed to improve the existing selection strategies. Two simulation studies are conducted to demonstrate the feasibility of the extended and proposed methods. The simulation results show that most of the extended item selection methods for PMCAT are feasible and the new proposed item selection methods perform well. Combined with the security of the pool, when two dimensions are considered (Study 1), the proposed modified continuous entropy method (MCEM) is the ideal of all in that it gains the lowest item exposure rate and has a relatively high accuracy. As for high dimensions (Study 2), results show that mutual information (MUI) and MCEM keep relatively high estimation accuracy, and the item exposure rates decrease as the correlation increases.

LASSO-Based Pattern Recognition for Replenished Items With Graded Responses in Multidimensional Computerized Adaptive Testing

“…Item selection is one of the key components of CAT, and frequently used item selection approaches are constructed based on Fisher Information or Kullback-Leibler Information (e.g., Wang et al, 2013;Tu et al, 2018). For MCAT, the item selection strategy based on Fisher information is frequently used.…”

“…The research scenario of LPRM can be expanded, such as establishing a pattern recognition framework for the MCAT with polytomous items. Note that for the multidimensional graded-response data, the MGRM is an essential compensatory MIRT model for the paper-and-pencil test or the MCAT (e.g., Jiang et al, 2016;Depaoli et al, 2018;Tu et al, 2018;Wang et al, 2018a, Wang et al, 2019Nouri et al, 2021), so it is necessary to extend the item-trait pattern recognition idea for the items with dichotomous responses to those with graded responses. Moreover, the LPRM was developed directly based on the M2PLM.…”

Construcción de un banco de ítems de facetas de neuroticismo para el desarrollo de un test adaptativo

“…Makransky et al, 2013;Paap et al, 2017). No obstante, todavía se discuten aspectos vinculados a la determinación del algoritmo adaptativo como el método de selección de ítems (Smits, Paap, & Böhnke, 2018;Tu, Han, Cai & Gao, 2018) y criterios de interrupción (Wang, Chang, & Boughton, 2013;Yao, 2013).…”