02064nas a2200157 4500008003900000245009100039210006900130300001200199490000700211520157600218100001501794700001901809700001401828700001901842856004501861 2020 d00aStratified Item Selection Methods in Cognitive Diagnosis Computerized Adaptive Testing0 aStratified Item Selection Methods in Cognitive Diagnosis Compute a346-3610 v443 aCognitive diagnostic computerized adaptive testing (CD-CAT) aims to obtain more useful diagnostic information by taking advantages of computerized adaptive testing (CAT). Cognitive diagnosis models (CDMs) have been developed to classify examinees into the correct proficiency classes so as to get more efficient remediation, whereas CAT tailors optimal items to the examinee’s mastery profile. The item selection method is the key factor of the CD-CAT procedure. In recent years, a large number of parametric/nonparametric item selection methods have been proposed. In this article, the authors proposed a series of stratified item selection methods in CD-CAT, which are combined with posterior-weighted Kullback–Leibler (PWKL), nonparametric item selection (NPS), and weighted nonparametric item selection (WNPS) methods, and named S-PWKL, S-NPS, and S-WNPS, respectively. Two different types of stratification indices were used: original versus novel. The performances of the proposed item selection methods were evaluated via simulation studies and compared with the PWKL, NPS, and WNPS methods without stratification. Manipulated conditions included calibration sample size, item quality, number of attributes, number of strata, and data generation models. Results indicated that the S-WNPS and S-NPS methods performed similarly, and both outperformed the S-PWKL method. And item selection methods with novel stratification indices performed slightly better than the ones with original stratification indices, and those without stratification performed the worst.1 aYang, Jing1 aChang, Hua-Hua1 aTao, Jian1 aShi, Ningzhong uhttps://doi.org/10.1177/014662161989378301840nas a2200145 4500008004100000245010900041210006900150260005500219520128100274100001501555700001401570700001901584700002001603856007101623 2017 eng d00aA Simulation Study to Compare Classification Method in Cognitive Diagnosis Computerized Adaptive Testing0 aSimulation Study to Compare Classification Method in Cognitive D aNiigata, JapanbNiigata Seiryo Universityc08/20173 a
Cognitive Diagnostic Computerized Adaptive Testing (CD-CAT) combines the strengths of both CAT and cognitive diagnosis. Cognitive diagnosis models that can be viewed as restricted latent class models have been developed to classify the examinees into the correct profile of skills that have been mastered and those that have not so as to get more efficient remediation. Chiu & Douglas (2013) introduces a nonparametric procedure that only requires specification of Q-matrix to classify by proximity to ideal response pattern. In this article, we compare nonparametric procedure with common profile estimation method like maximum a posterior (MAP) in CD-CAT. Simulation studies consider a variety of Q-matrix structure, the number of attributes, ways to generate attribute profiles, and item quality. Results indicate that nonparametric procedure consistently gets the higher pattern and attribute recovery rate in nearly all conditions.
References
Chiu, C.-Y., & Douglas, J. (2013). A nonparametric approach to cognitive diagnosis by proximity to ideal response patterns. Journal of Classification, 30, 225-250. doi: 10.1007/s00357-013-9132-9
1 aYang, Jing1 aTao, Jian1 aChang, Hua-Hua1 aShi, Ning-Zhong uhttps://drive.google.com/open?id=1jCL3fPZLgzIdwvEk20D-FliZ15OTUtpr