CECAT: Certainty-Guaranteed Environment for Computerized Adaptive Testing

Jingwei Yu, Zhenyu Mu, Weiwen Liu, Ting Long, Weiming Zhang, Weinan Zhang, Yong Yu

Abstract: Computerized Adaptive Testing (CAT) is a fundamental issue in intelligent education, which aims to select a relatively small number of questions to assess student’s ability. However, existing approaches often suffer from a limited selection area within the question bank, as questions without ground truth answers in the practice log are considered invalid for selection. The selection area refers to the range of questions in the question bank that have answers. To expand the selection area, we proposed a framework called Certainty-Guaranteed Environment for Computerized Adaptive Testing (CECAT). First, Simulative Interaction Module (SIM) leverages questions and their answer results in practice log in training data, so as to predict the distribution of answer result for each question without answer result in practice log. Second, α-Certainty is designed to measure the statistical similarity of the predicted distribution of answer results obtained from SIM, so as to exclude the statistically dissimilar expanded answer results. Extensive experiments on three public datasets demonstrate that CECAT outperforms twelve strong baselines in terms of assessing the student’s ability. Our code is available at https://anonymous.4open.science/r/SimCAT-F31B/README.md.

External IDs:dblp:conf/ijcnn/YuMLLZZY25