TY - JOUR T1 - Stratified Item Selection Methods in Cognitive Diagnosis Computerized Adaptive Testing JF - Applied Psychological Measurement Y1 - 2020 A1 - Jing Yang A1 - Hua-Hua Chang A1 - Jian Tao A1 - Ningzhong Shi AB - Cognitive 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. VL - 44 UR - https://doi.org/10.1177/0146621619893783 ER - TY - CONF T1 - New Challenges (With Solutions) and Innovative Applications of CAT T2 - IACAT 2017 Conference Y1 - 2017 A1 - Chun Wang A1 - David J. Weiss A1 - Xue Zhang A1 - Jian Tao A1 - Yinhong He A1 - Ping Chen A1 - Shiyu Wang A1 - Susu Zhang A1 - Haiyan Lin A1 - Xiaohong Gao A1 - Hua-Hua Chang A1 - Zhuoran Shang KW - CAT KW - challenges KW - innovative applications AB -

Over the past several decades, computerized adaptive testing (CAT) has profoundly changed the administration of large-scale aptitude tests, state-wide achievement tests, professional licensure exams, and health outcome measures. While many challenges of CAT have been successfully addressed due to the continual efforts of researchers in the field, there are still many remaining, longstanding challenges that have yet to be resolved. This symposium will begin with three presentations, each of which provides a sound solution to one of the unresolved challenges. They are (1) item calibration when responses are “missing not at random” from CAT administration; (2) online calibration of new items when person traits have non-ignorable measurement error; (3) establishing consistency and asymptotic normality of latent trait estimation when allowing item response revision in CAT. In addition, this symposium also features innovative applications of CAT. In particular, there is emerging interest in using cognitive diagnostic CAT to monitor and detect learning progress (4th presentation). Last but not least, the 5th presentation illustrates the power of multidimensional polytomous CAT that permits rapid identification of hospitalized patients’ rehabilitative care needs in health outcomes measurement. We believe this symposium covers a wide range of interesting and important topics in CAT.

Session Video

JF - IACAT 2017 Conference PB - Niigata Seiryo University CY - Niigata, Japan UR - https://drive.google.com/open?id=1Wvgxw7in_QCq_F7kzID6zCZuVXWcFDPa ER - TY - CONF T1 - A Simulation Study to Compare Classification Method in Cognitive Diagnosis Computerized Adaptive Testing T2 - IACAT 2017 Conference Y1 - 2017 A1 - Jing Yang A1 - Jian Tao A1 - Hua-Hua Chang A1 - Ning-Zhong Shi AB -

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

Session Video

JF - IACAT 2017 Conference PB - Niigata Seiryo University CY - Niigata, Japan UR - https://drive.google.com/open?id=1jCL3fPZLgzIdwvEk20D-FliZ15OTUtpr ER -