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A Robust Computerized Adaptive Testing Approach in Educational Question Retrieval by Yan Zhuang; Qi Liu; Zhenya Huang; Zhi Li; Binbin Jin; Haoyang Bi; Enhong Chen; Shijin Wang is a scholarly article available to read on EtoBox.

What is A Robust Computerized Adaptive Testing Approach in Educational Question Retrieval about?

Computerized Adaptive Testing (CAT) is a promising testing mode in personalized online education (e.g., GRE), which aims at measuring student's proficiency accurately and reducing test length. The "adaptive" is reflected in its selection algorithm that can retrieve best-suited questions for student based on his/her estimated proficiency at each test step. Although there are many sophisticated selection algorithms for improving CAT's effectiveness, they are restricted and perturbed by the accuracy of current proficiency estimate, thus lacking robustness. To this end, we investigate a general * Corresponding Author.

Author

Yan Zhuang; Qi Liu; Zhenya Huang; Zhi Li; Binbin Jin; Haoyang Bi; Enhong Chen; Shijin Wang

Publisher

ACM

Published

2022

Language

EN