Computerized Adaptive Testing Method Based on Reinforcement Learning for Series Diagnosis

Computerized adaptive testing is designed to select appropriate questions for students based on their historical performance, thereby measuring their actual ability quickly and effectively. However, in intelligent education scenarios, the existing selection strategy of traditional computerized adaptive testing is still faced with some problems such as target complexity and knowledge sparseness. To solve these problems, a computerized adaptive testing method based on reinforcement learning for series diagnosis is proposed in this paper to accurately assess students’ knowledge proficiency for intelligent scenarios. A student simulator and a student portrait model based on series diagnosis model are adopted. To address the complexity of computerized adaptive testing goals in real-world scenarios, five evaluation indicators are designed, including accuracy of weak points, coupling of prediction performance, adaptive testing duration, testing anomaly rate and testing difficulty structure. Furthermore, a selection strategy for reinforcement learning based computerized adaptive testing is proposed. The dual-channel self-attention learning module and the contradiction learning module are utilized to ameliorate knowledge sparseness problem. Experiments on real datasets show that the proposed selection strategy not only efficiently measures students’ actual abilities, but also optimizes their answering experience. The selected questions exhibit a certain level of interpretability, and the method provides a feasible solution for computerized adaptive testing in intelligent education scenarios. © 2024 Science Press. All rights reserved.