A comparison of item selection procedures using different ability estimation methods in computerized adaptive testing based on the generalized partial credit model



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Computerized adaptive testing (CAT) provides a highly efficient alternative to the paper-and-pencil test. By selecting items that match examinees’ ability levels, CAT not only can shorten test length and administration time but it can also increase measurement precision and reduce measurement error. In CAT, maximum information (MI) is the most widely used item selection procedure. However, the major challenge with MI is the attenuation paradox, which results because the MI algorithm may lead to the selection of items that are not well targeted at an examinee’s true ability level, resulting in more errors in subsequent ability estimates. The solution is to find an alternative item selection procedure or an appropriate ability estimation method. CAT studies have not investigated the association between these two components of a CAT system based on polytomous IRT models. The present study compared the performance of four item selection procedures (MI, MPWI, MEI, and MEPV) across four ability estimation methods (MLE, WLE, EAP-N, and EAP-PS) under the mixed-format CAT based on the generalized partial credit model (GPCM). The test-unit pool and generated responses were based on test-units calibrated from an operational national test that included both independent dichotomous items and testlets. Several test conditions were manipulated: the unconstrained CAT as well as the constrained CAT in which the CCAT was used as the content-balancing, and the progressive-restricted procedure with maximum exposure rate equal to 0.19 (PR19) served as the exposure control in this study. The performance of various CAT conditions was evaluated in terms of measurement precision, exposure control properties, and the extent of selected-test-unit overlap. Results suggested that all item selection procedures, regardless of ability estimation methods, performed equally well in all evaluation indices across two CAT conditions. The MEPV procedure, however, was favorable in terms of a slightly lower maximum exposure rate, better pool utilization, and reduced test and selected-test-unit overlap than with the other three item selection procedures when both CCAT and PR19 procedures were implemented. It is not necessary to implement the sophisticated and computing-intensive Bayesian item selection procedures across ability estimation methods under the GPCM-based CAT. In terms of the ability estimation methods, MLE, WLE, and two EAP methods, regardless of item selection procedures, did not produce practical differences in all evaluation indices across two CAT conditions. The WLE method, however, generated significantly fewer non-convergent cases than did the MLE method. It was concluded that the WLE method, instead of MLE, should be considered, because the non-convergent case is less of an issue. The EAP estimation method, on the other hand, should be used with caution unless an appropriate prior θ distribution is specified.