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A Rate Function Approach to Computerized Adaptive Testing for Cognitive Diagnosis

Published online by Cambridge University Press:  01 January 2025

Jingchen Liu ,
Zhiliang Ying  and
Stephanie Zhang
Jingchen Liu*
Affiliation:
Columbia University
Zhiliang Ying
Affiliation:
Columbia University
Stephanie Zhang
Affiliation:
Columbia University
*
Requests for reprints should be sent to Jingchen Liu, Columbia University, 1255 Amsterdam Avenue, New York, NY 10027, USA. E-mail: jcliu@stat.columbia.edu
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Abstract

Computerized adaptive testing (CAT) is a sequential experiment design scheme that tailors the selection of experiments to each subject. Such a scheme measures subjects’ attributes (unknown parameters) more accurately than the regular prefixed design. In this paper, we consider CAT for diagnostic classification models, for which attribute estimation corresponds to a classification problem. After a review of existing methods, we propose an alternative criterion based on the asymptotic decay rate of the misclassification probabilities. The new criterion is then developed into new CAT algorithms, which are shown to achieve the asymptotically optimal misclassification rate. Simulation studies are conducted to compare the new approach with existing methods, demonstrating its effectiveness, even for moderate length tests.

Keywords

computerized adaptive testingcognitive diagnosislarge deviationclassification

Information

Type
Original Paper
Information
Psychometrika , Volume 80 , Issue 2 , June 2015 , pp. 468 - 490
Copyright
Copyright © 2013 The Psychometric Society

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