00515nas a2200133 4500008003900000245012900039210006900168300000900237490000600246100001800252700002700270700002400297856006000321 2018 d00aFactors Affecting the Classification Accuracy and Average Length of a Variable-Length Cognitive Diagnostic Computerized Test0 aFactors Affecting the Classification Accuracy and Average Length a1-140 v61 aHuebner, Alan1 aFinkelman, Matthew, D.1 aWeissman, Alexander uhttp://iacat.org/jcat/index.php/jcat/article/view/55/3001234nas a2200145 4500008003900000245007300039210006900112300001200181490000700193520076500200100002100965700002500986700002401011856005301035 2008 d00aA Monte Carlo Approach for Adaptive Testing With Content Constraints0 aMonte Carlo Approach for Adaptive Testing With Content Constrain a431-4460 v323 a

This article presents a new algorithm for computerized adaptive testing (CAT) when content constraints are present. The algorithm is based on shadow CAT methodology to meet content constraints but applies Monte Carlo methods and provides the following advantages over shadow CAT: (a) lower maximum item exposure rates, (b) higher utilization of the item pool, and (c) more robust ability estimates. Computer simulations with Law School Admission Test items demonstrated that the new algorithm (a) produces similar ability estimates as shadow CAT but with half the maximum item exposure rate and 100% pool utilization and (b) produces more robust estimates when a high- (or low-) ability examinee performs poorly (or well) at the beginning of the test.

1 aBelov, Dmitry, I1 aArmstrong, Ronald, D1 aWeissman, Alexander uhttp://apm.sagepub.com/content/32/6/431.abstract01310nas a2200121 4500008003900000245007300039210006900112300001000181490000700191520091400198100002401112856005201136 2007 d00aMutual Information Item Selection in Adaptive Classification Testing0 aMutual Information Item Selection in Adaptive Classification Tes a41-580 v673 a

A general approach for item selection in adaptive multiple-category classification tests is provided. The approach uses mutual information (MI), a special case of the Kullback-Leibler distance, or relative entropy. MI works efficiently with the sequential probability ratio test and alleviates the difficulties encountered with using other local- and global-information measures in the multiple-category classification setting. Results from simulation studies using three item selection methods, Fisher information (FI), posterior-weighted FI (FIP), and MI, are provided for an adaptive four-category classification test. Both across and within the four classification categories, it is shown that in general, MI item selection classifies the highest proportion of examinees correctly and yields the shortest test lengths. The next best performance is observed for FIP item selection, followed by FI.

1 aWeissman, Alexander uhttp://epm.sagepub.com/content/67/1/41.abstract01356nas a2200121 4500008003900000245010500039210006900144300001000213490000700223520092800230100002401158856005201182 2006 d00aA Feedback Control Strategy for Enhancing Item Selection Efficiency in Computerized Adaptive Testing0 aFeedback Control Strategy for Enhancing Item Selection Efficienc a84-990 v303 a

A computerized adaptive test (CAT) may be modeled as a closed-loop system, where item selection is influenced by trait level (θ) estimation and vice versa. When discrepancies exist between an examinee's estimated and true θ levels, nonoptimal item selection is a likely result. Nevertheless, examinee response behavior consistent with optimal item selection can be predicted using item response theory (IRT), without knowledge of an examinee's true θ level, yielding a specific reference point for applying an internal correcting or feedback control mechanism. Incorporating such a mechanism in a CAT is shown to be an effective strategy for increasing item selection efficiency. Results from simulation studies using maximum likelihood (ML) and modal a posteriori (MAP) trait-level estimation and Fisher information (FI) and Fisher interval information (FII) item selection are provided.

1 aWeissman, Alexander uhttp://apm.sagepub.com/content/30/2/84.abstract