Representation Learning of Auxiliary Concepts for Improved Student Modeling and Exercise Recommendation
This work addresses the challenge of inaccurate student modeling in intelligent tutoring systems, offering an incremental improvement over existing methods by learning latent concepts to augment human annotations.
The paper tackles the problem of incomplete or error-prone human-annotated knowledge concepts in knowledge tracing models by proposing a deep learning model that learns sparse binary representations of exercises as auxiliary concepts. The result shows that incorporating these auxiliary concepts improves predictive performance in student modeling and enhances exercise recommendation policies, leading to measurable gains in student learning outcomes in simulations.
Personalized recommendation is a key feature of intelligent tutoring systems, typically relying on accurate models of student knowledge. Knowledge Tracing (KT) models enable this by estimating a student's mastery based on their historical interactions. Many KT models rely on human-annotated knowledge concepts (KCs), which tag each exercise with one or more skills or concepts believed to be necessary for solving it. However, these KCs can be incomplete, error-prone, or overly general. In this paper, we propose a deep learning model that learns sparse binary representations of exercises, where each bit indicates the presence or absence of a latent concept. We refer to these representations as auxiliary KCs. These representations capture conceptual structure beyond human-defined annotations and are compatible with both classical models (e.g., BKT) and modern deep learning KT architectures. We demonstrate that incorporating auxiliary KCs improves both student modeling and adaptive exercise recommendation. For student modeling, we show that augmenting classical models like BKT with auxiliary KCs leads to improved predictive performance. For recommendation, we show that using auxiliary KCs enhances both reinforcement learning-based policies and a simple planning-based method (expectimax), resulting in measurable gains in student learning outcomes within a simulated student environment.