Prerequisite Structure Discovery in Intelligent Tutoring Systems
This work addresses the challenge of personalizing learning in intelligent tutoring systems, but it appears incremental as it builds on existing knowledge tracing and structure methods.
The paper tackled the problem of improving educational content recommendations in intelligent tutoring systems by proposing a knowledge tracing model that incorporates a learnable knowledge structure, enabling its discovery from learner trajectories, and evaluated it with simulated students.
This paper addresses the importance of Knowledge Structure (KS) and Knowledge Tracing (KT) in improving the recommendation of educational content in intelligent tutoring systems. The KS represents the relations between different Knowledge Components (KCs), while KT predicts a learner's success based on her past history. The contribution of this research includes proposing a KT model that incorporates the KS as a learnable parameter, enabling the discovery of the underlying KS from learner trajectories. The quality of the uncovered KS is assessed by using it to recommend content and evaluating the recommendation algorithm with simulated students.