Towards Multi-Objective Behavior and Knowledge Modeling in Students

Traditional knowledge modeling methods have primarily focused on student knowledge modeling using assessed learning activities, often overlooking the critical interplay between students' knowledge and behavioral preferences. However, students typically interact with multiple types of learning materials, such as questions (assessed), video lectures (non-assessed), and textbooks (non-assessed). We argue that student knowledge can affect their behavioral preferences, and the choice of learning material type can influence their knowledge. In this paper, we address this gap by proposing a novel framework that models student knowledge and behavior as a multi-task learning problem with two objectives. Our dual objectives are to predict student performance and their preferences for selecting different types of learning materials. We utilize the Pareto Multi-Task Learning (MTL) algorithm to effectively handle the complexities of this multi-objective optimization, applying it to two advanced multi-activity knowledge modeling methods, TAMKOT and GMKT, which we refer to as Pareto-TAMKOT and Pareto-GMKT, respectively. We evaluate the framework on one real-world dataset. Our experimental results demonstrate that both Pareto-TAMKOT and Pareto-GMKT improve upon their original models and outperform all baseline models. This underscores the benefits of treating the modeling of student knowledge and behavior as a multi-task learning problem and addresses this multi-objective challenge through the application of Pareto MTL.