Improving Student Modeling in Game-Based Learning with Multi-task Learning for Stealth Assessment and Goal Recognition.

Student modeling has been widely investigated to enable adaptive support in game-based learning environments. Two key aspects of student modelling in game-based learning are stealth assessment and goal recognition. Stealth assessment infers students' knowledge and skills without disrupting gameplay, while goal recognition predicts their in-game objectives based on their interactions. Prior work has largely treated these tasks separately, yet students' learning processes, outcomes, and their in-game goals often influence each other. This paper presents a multi-task student modeling framework that enhances stealth assessment and goal recognition by jointly predicting students' learning outcomes and in-game goals. The framework integrates game trace logs and students' written reflections as input features, enabling shared learning across related tasks. We investigate stealth assessment at two levels: post-test score prediction for generalizability and concept-level predictions for more fine-grained insights where domain expertise is available. Empirical evaluations suggest that multi-task learning significantly improves predictive performance for concept-level stealth assessment and goal recognition compared to single-task baselines. These findings highlight the potential of multi-task learning to enhance student modeling that aligns with students' learning trajectories and goals in game-based learning environments.