Abstract
The increasing demand for personalized learning experience has driven the need for more effective and accurate computerized adaptive testing (CAT) in education. In this study, we present a novel CAT algorithm grounded in the Competence-based Knowledge Space Theory. The algorithm employs maximum likelihood estimation (MLE) for parameter estimation, utilizing a uniform prior distribution in the absence of prior information. It employs a “half split rule” for question selection, ensuring the efficient and accurate estimation of student abilities, and incorporates Laplace smoothing to mitigate overfitting. An information entropy-based termination rule is proposed to strike a balance between efficiency and accuracy in the adaptive testing process. The proposed algorithm contributes to the development of more effective and personalized intelligent tutoring system (ITS) by accurately assessment student competence state and minimizing testing time.
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Rong, Q., Kong, W., Xiao, Y., Gao, X. (2023). An Adaptive Testing Approach for Competence Using Competence-Based Knowledge Space Theory. In: Anutariya, C., Liu, D., Kinshuk, Tlili, A., Yang, J., Chang, M. (eds) Smart Learning for A Sustainable Society. ICSLE 2023. Lecture Notes in Educational Technology. Springer, Singapore. https://doi.org/10.1007/978-981-99-5961-7_18
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DOI: https://doi.org/10.1007/978-981-99-5961-7_18
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