Multi-Task Modeling of Student Knowledge and Behavior

Knowledge Tracing (KT) and Behavior Modeling (BM) are essential mining and discovery problems in education. KT models student knowledge based on prior performance with learning materials, while BM focuses on patterns such as student preferences, engagement, and procrastination. Traditional research in these areas focuses on each task individually, thereby overlooking their interconnections. However, recent research on multi-activity knowledge tracing suggests that student preferences for learning materials are key to understanding student learning. In this paper, we propose a novel multi-task model, the Multi-Task Student Knowledge and Behavior Model (KTBM), which combines KT and BM to improve both performance and interoperability. KTBM includes a multi-activity KT component and a preference behavior component while enabling robust information transfer between them. We conceptualize this approach as a multi-task learning problem with two objectives: predicting students' performance and their choices concerning learning material types. To address this dual-objective challenge, we employ a Pareto multi-task learning optimization algorithm. Our experiments on three real-world datasets show that KTBM significantly enhances both KT and BM performance, demonstrating improvement across various settings and providing interpretable results.