IECAT: an Individualized High-Efficiency Ability Assessment for Computerized Adaptive Testing

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

Abstract: Computerized Adaptive Testing (CAT) is a burgeoning online educational technology that assesses student ability through a relatively small number of carefully selected questions. Assessing a student’s ability from his/her short question-answer sequence is a few-shot learning problem, which is currently solved by meta-learning-based methods with a bi-level structure. However, current methods using the bi-level structure have limitations, such as inefficiency due to alternating optimization between the inner level and outer level and not fully using historical students. To address these, we propose the Individualized High-Efficiency Ability Assessment for Computerized Adaptive Testing (IECAT), which introduces (1) a Student Comparison method to fully use dissimilar students by distinguishing similar and dissimilar students, (2) an Ability Matrix to fully use practice logs of historical students to get more accurate abilities, and (3) an Individual Mapping (IM) structure to improve efficiency by optimizing in its single-level structure. Extensive experiments across three public datasets demonstrate that IECAT outperforms twelve strong baselines. Furthermore, our experiments validate that IM structure consumes less training time than bilevel structure. Our code is available at https://anonymous.4open.science/r/IECAT-8FE5/IECAT/ReadMe.txt.

External IDs:dblp:conf/ijcnn/YuMLWLZY25