@article {2733, title = {Stratified Item Selection Methods in Cognitive Diagnosis Computerized Adaptive Testing}, journal = {Applied Psychological Measurement}, volume = {44}, number = {5}, year = {2020}, pages = {346-361}, abstract = {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{\textquoteright}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{\textendash}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.}, doi = {10.1177/0146621619893783}, url = {https://doi.org/10.1177/0146621619893783}, author = {Jing Yang and Hua-Hua Chang and Jian Tao and Ningzhong Shi} } @article {2742, title = {Item Selection Criteria With Practical Constraints in Cognitive Diagnostic Computerized Adaptive Testing}, journal = {Educational and Psychological Measurement}, volume = {79}, number = {2}, year = {2019}, pages = {335-357}, abstract = {For item selection in cognitive diagnostic computerized adaptive testing (CD-CAT), ideally, a single item selection index should be created to simultaneously regulate precision, exposure status, and attribute balancing. For this purpose, in this study, we first proposed an attribute-balanced item selection criterion, namely, the standardized weighted deviation global discrimination index (SWDGDI), and subsequently formulated the constrained progressive index (CP\_SWDGDI) by casting the SWDGDI in a progressive algorithm. A simulation study revealed that the SWDGDI method was effective in balancing attribute coverage and the CP\_SWDGDI method was able to simultaneously balance attribute coverage and item pool usage while maintaining acceptable estimation precision. This research also demonstrates the advantage of a relatively low number of attributes in CD-CAT applications.}, doi = {10.1177/0013164418790634}, url = {https://doi.org/10.1177/0013164418790634}, author = {Chuan-Ju Lin and Hua-Hua Chang} } @article {2626, title = {Item Selection Criteria With Practical Constraints in Cognitive Diagnostic Computerized Adaptive Testing}, journal = {Educational and Psychological Measurement}, year = {2018}, pages = {0013164418790634}, abstract = {For item selection in cognitive diagnostic computerized adaptive testing (CD-CAT), ideally, a single item selection index should be created to simultaneously regulate precision, exposure status, and attribute balancing. For this purpose, in this study, we first proposed an attribute-balanced item selection criterion, namely, the standardized weighted deviation global discrimination index (SWDGDI), and subsequently formulated the constrained progressive index (CP\_SWDGDI) by casting the SWDGDI in a progressive algorithm. A simulation study revealed that the SWDGDI method was effective in balancing attribute coverage and the CP\_SWDGDI method was able to simultaneously balance attribute coverage and item pool usage while maintaining acceptable estimation precision. This research also demonstrates the advantage of a relatively low number of attributes in CD-CAT applications.}, doi = {10.1177/0013164418790634}, url = {https://doi.org/10.1177/0013164418790634}, author = {Chuan-Ju Lin and Hua-Hua Chang} } @conference {2650, title = {Item Parameter Drifting and Online Calibration}, booktitle = {IACAT 2017 Conference}, year = {2017}, month = {08/2017}, publisher = {Niigata Seiryo University}, organization = {Niigata Seiryo University}, address = {Niigata, Japan}, abstract = {

Item calibration is a part of the most important topics in item response theory (IRT). Since many largescale testing programs have switched from paper and pencil (P\&P) testing mode to computerized adaptive testing (CAT) mode, developing methods for efficiently calibrating new items have become vital. Among many proposed item calibration processes in CAT, online calibration is the most cost-effective. This presentation introduces an online (re)calibration design to detect item parameter drift for computerized adaptive testing (CAT) in both unidimensional and multidimensional environments. Specifically, for online calibration optimal design in unidimensional computerized adaptive testing model, a two-stage design is proposed by implementing a proportional density index algorithm. For a multidimensional computerized adaptive testing model, a four-quadrant online calibration pretest item selection design with proportional density index algorithm is proposed. Comparisons were made between different online calibration item selection strategies. Results showed that under unidimensional computerized adaptive testing, the proposed modified two-stage item selection criterion with the proportional density algorithm outperformed the other existing methods in terms of item parameter calibration and item parameter drift detection, and under multidimensional computerized adaptive testing, the online (re)calibration technique with the proposed four-quadrant item selection design with proportional density index outperformed other methods.

Session Video

}, keywords = {online calibration, Parameter Drift}, author = {Hua-Hua Chang and Rui Guo} } @conference {2648, title = {New Challenges (With Solutions) and Innovative Applications of CAT}, booktitle = {IACAT 2017 Conference}, year = {2017}, month = {08/2017}, publisher = {Niigata Seiryo University}, organization = {Niigata Seiryo University}, address = {Niigata, Japan}, abstract = {

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

}, keywords = {CAT, challenges, innovative applications}, url = {https://drive.google.com/open?id=1Wvgxw7in_QCq_F7kzID6zCZuVXWcFDPa}, author = {Chun Wang and David J. Weiss and Xue Zhang and Jian Tao and Yinhong He and Ping Chen and Shiyu Wang and Susu Zhang and Haiyan Lin and Xiaohong Gao and Hua-Hua Chang and Zhuoran Shang} } @conference {2639, title = {A Simulation Study to Compare Classification Method in Cognitive Diagnosis Computerized Adaptive Testing}, booktitle = {IACAT 2017 Conference}, year = {2017}, month = {08/2017}, publisher = {Niigata Seiryo University}, organization = {Niigata Seiryo University}, address = {Niigata, Japan}, abstract = {

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

}, url = {https://drive.google.com/open?id=1jCL3fPZLgzIdwvEk20D-FliZ15OTUtpr}, author = {Jing Yang and Jian Tao and Hua-Hua Chang and Ning-Zhong Shi} }