Knowledge Graph Enhanced Intelligent Tutoring System Based on Exercise Representativeness and Informativeness

This paper proposes KG-EIR, a knowledge graph-based framework for exercise recommendation in online tutoring systems that selects exercises based on informativeness and representativeness, and uses a neural cognitive diagnosis model (NACD) to predict student performance and estimate knowledge states. The system recommends appropriate exercises by analyzing exercise features, skill dependencies, and student learning patterns through graph neural networks.

In the realm of online tutoring intelligent systems, e-learners are exposed to a substantial volume of learning content. The extraction and organization of exercises and skills hold significant importance in establishing clear learning objectives and providing appropriate exercise recommendations. Presently, knowledge graph-based recommendation algorithms have garnered considerable attention among researchers. However, these algorithms solely consider knowledge graphs with single relationships a

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