01943nas a2200145   4500008003900000245007700039210006900116300001200185490000700197520148100204100002101685700002301706700002301729856004501752       2019                          d00aComputerized Adaptive Testing for Cognitively Based Multiple-Choice Data0 aComputerized Adaptive Testing for Cognitively Based MultipleChoi  a388-4010 v433 aCognitive diagnosis models (CDMs) are latent class models that hold great promise for providing diagnostic information about student knowledge profiles. The increasing use of computers in classrooms enhances the advantages of CDMs for more efficient diagnostic testing by using adaptive algorithms, referred to as cognitive diagnosis computerized adaptive testing (CD-CAT). When multiple-choice items are involved, CD-CAT can be further improved by using polytomous scoring (i.e., considering the specific options students choose), instead of dichotomous scoring (i.e., marking answers as either right or wrong). In this study, the authors propose and evaluate the performance of the Jensen–Shannon divergence (JSD) index as an item selection method for the multiple-choice deterministic inputs, noisy “and” gate (MC-DINA) model. Attribute classification accuracy and item usage are evaluated under different conditions of item quality and test termination rule. The proposed approach is compared with the random selection method and an approximate approach based on dichotomized responses. The results show that under the MC-DINA model, JSD improves the attribute classification accuracy significantly by considering the information from distractors, even with a very short test length. This result has important implications in practical classroom settings as it can allow for dramatically reduced testing times, thus resulting in more targeted learning opportunities.1 aYigit, Hulya, D.1 aSorrel, Miguel, A.1 ade la Torre, Jimmy  uhttps://doi.org/10.1177/0146621618798665