Abstract
This is an extension from a selected paper from JSAI2019. Computerized adaptive testing (CAT) estimates an examinee’s ability sequentially and selects test items that have the highest accuracy for estimating the ability. However, conventional CAT selects the same items for examinees who have equivalent ability. As described herein, we propose CAT that minimizes item exposure and which adaptively selects different items for examinees of equal ability, while retaining accuracy. This paper presents the method’s effectiveness using simulation data and actual test data.
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Miyazawa, Y., Ueno, M. (2020). Computerized Adaptive Testing Method Using Integer Programming to Minimize Item Exposure. In: Ohsawa, Y., et al. Advances in Artificial Intelligence. JSAI 2019. Advances in Intelligent Systems and Computing, vol 1128. Springer, Cham. https://doi.org/10.1007/978-3-030-39878-1_10
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DOI: https://doi.org/10.1007/978-3-030-39878-1_10
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