Computerized Adaptive Testing for Cognitive Diagnosis
نویسنده
چکیده
Computerized adaptive testing (CAT) has become a popular testing mode in recent decades, because it improves testing efficiency over paper-and-pencil testing while maintaining accuracy. Usually CAT programs are built upon latent trait item response theory models. Cognitive diagnostic models, on the other hand, are constrained latent class models. It is of interest to both researchers and practitioners to build CAT based on cognitive diagnostic models, so that testing efficiency and formative feedback can be offered simultaneously. This study explored several item selection algorithms that enable adaptive testing based on cognitive diagnostic models. Performance of these item selection algorithms is evaluated by attribute recovery rate and cognitive profile recovery rate. Acknowledgment Presentation of this paper at the 2009 Conference on Computerized Adaptive Testing was supported in part with funds from GMAC. Copyright © 2009 by the Author All rights reserved. Permission is granted for non-commercial use. Citation Cheng, Y. (2009). Computerized adaptive testing for cognitive diagnosis. In D. J. Weiss (Ed.), Proceedings of the 2009 GMAC Conference on Computerized Adaptive Testing. Retrieved [date] from www.psych.umn.edu/psylabs/CATCentral/ Author Contact Ying Cheng, 118 Haggar Hall, Notre Dame, IN 46556, U.S.A. Email: [email protected]
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