01810nas a2200133 4500008003900000245010200039210006900141300001200210490000700222520136300229100001901592700002001611856004501631 2019 d00aMultidimensional Computerized Adaptive Testing Using Non-Compensatory Item Response Theory Models0 aMultidimensional Computerized Adaptive Testing Using NonCompensa a464-4800 v433 aCurrent use of multidimensional computerized adaptive testing (MCAT) has been developed in conjunction with compensatory multidimensional item response theory (MIRT) models rather than with non-compensatory ones. In recognition of the usefulness of MCAT and the complications associated with non-compensatory data, this study aimed to develop MCAT algorithms using non-compensatory MIRT models and to evaluate their performance. For the purpose of the study, three item selection methods were adapted and compared, namely, the Fisher information method, the mutual information method, and the Kullback–Leibler information method. The results of a series of simulations showed that the Fisher information and mutual information methods performed similarly, and both outperformed the Kullback–Leibler information method. In addition, it was found that the more stringent the termination criterion and the higher the correlation between the latent traits, the higher the resulting measurement precision and test reliability. Test reliability was very similar across the dimensions, regardless of the correlation between the latent traits and termination criterion. On average, the difficulties of the administered items were found to be at a lower level than the examinees’ abilities, which shed light on item bank construction for non-compensatory items.1 aHsu, Chia-Ling1 aWang, Wen-Chung uhttps://doi.org/10.1177/014662161880028002152nas a2200157 4500008004100000245008800041210006900129260005400198520153900252653002901791653001101820100001901831700002001850700001801870856010601888 2017 eng d00aUsing Bayesian Decision Theory in Cognitive Diagnosis Computerized Adaptive Testing0 aUsing Bayesian Decision Theory in Cognitive Diagnosis Computeriz aNiigata JapanbNiigata Seiryo Universityc08/20173 a

Cognitive diagnosis computerized adaptive testing (CD-CAT) purports to provide each individual a profile about the strengths and weaknesses of attributes or skills with computerized adaptive testing. In the CD-CAT literature, researchers dedicated to evolving item selection algorithms to improve measurement efficiency, and most algorithms were developed based on information theory. By the discontinuous nature of the latent variables in CD-CAT, this study introduced an alternative for item selection, called the minimum expected cost (MEC) method, which was derived based on Bayesian decision theory. Using simulations, the MEC method was evaluated against the posterior weighted Kullback-Leibler (PWKL) information, the modified PWKL (MPWKL), and the mutual information (MI) methods by manipulating item bank quality, item selection algorithm, and termination rule. Results indicated that, regardless of item quality and termination criterion, the MEC, MPWKL, and MI methods performed very similarly and they all outperformed the PWKL method in classification accuracy and test efficiency, especially in short tests; the MEC method had more efficient item bank usage than the MPWKL and MI methods. Moreover, the MEC method could consider the costs of incorrect decisions and improve classification accuracy and test efficiency when a particular profile was of concern. All the results suggest the practicability of the MEC method in CD-CAT.

Session Video

10aBayesian Decision Theory10aCD-CAT1 aHsu, Chia-Ling1 aWang, Wen-Chung1 aChen, ShuYing uhttp://www.iacat.org/using-bayesian-decision-theory-cognitive-diagnosis-computerized-adaptive-testing