SA-MBKT: Surrogate Model-assisted Multi-skills Bayesian Knowledge Tracing
Chenyang Bu, Longhui Li, Haotian Zhang, Shuai Sun, Lei Li, Wenjian Luo · ACM Transactions on Evolutionary Learning and Optimization · 2025
Knowledge Tracing (KT) is a fundamental task in educational data mining that mainly focuses on tracing students’ dynamic knowledge states of skills. Bayesian Knowledge Tracing (BKT) has been widely researched and applied due to its good interpretability, using the hidden Markov model to model students’ question–answering process. Standard BKT considers only one skill in each question. To address this limitation, we proposed a Multi-skills Bayesian Knowledge Tracing (MBKT) method based on evolutionary algorithms in our previous work. MBKT employs evolutionary algorithms as the optimization method for BKT, enabling it to trace changes in students’ mastery of multiple skills simultaneously. However, MBKT has the drawback of taking too long for a single individual evaluation, and a large number of valueless individuals invoke the real evaluation process, especially when dealing with a large amount of data to be evaluated. This hinders its application in real online education scenarios. Therefore, the Surrogate Model-assisted Multi-skills Bayesian Knowledge Tracing (SA-MBKT) method is proposed to address these issues by introducing a window strategy and a surrogate model method. Extensive experiments on real-world datasets demonstrate that SA-MBKT significantly enhances temporal performance without affecting the predictive performance of the model.