GraphRAG-Induced Dual Knowledge Structure Graphs for Personalized Learning Path Recommendation

Learning path recommendation seeks to provide students with a structured sequence of learning items (e.g., knowledge concepts or exercises) to optimize their learning efficiency. Despite significant efforts in this area, most existing methods primarily rely on prerequisite relations, which present two major limitations: (1) Prerequisite relations between knowledge concepts are difficult to obtain due to the cost of expert annotation, hindering the application of current learning path recommendation methods. (2) Relying on a single sequentially dependent knowledge structure based on prerequisite relations implies that a confusing knowledge concept can disrupt subsequent learning processes, which is referred to as blocked learning. To address these two challenges, we propose a novel approach, GraphRAG-Induced Dual Knowledge Structure Graphs for Personalized Learning Path Recommendation (KnowLP), which enhances learning path recommendations by incorporating both prerequisite and similarity relations between knowledge concepts. Specifically, we introduce a knowledge structure graph generation module EDU-GraphRAG that constructs knowledge structure graphs for different educational datasets, significantly improving the applicability of learning path recommendation methods. We then propose a Discrimination Learning-driven Reinforcement Learning (DLRL) module that utilizes similarity relations as fallback relations when prerequisite relations become ineffective, thereby alleviating the blocked learning. Finally, we conduct extensive experiments on three benchmark datasets, demonstrating that our method not only achieves state-of-the-art performance but also generates more effective and longer learning paths.

Authors

• Xinghe Cheng Jinan University
• Zihan Zhang Microsoft Corporation
• Jiapu Wang Beijing University of Technology
• Liangda Fang Jinan University Pazhou Lab
• Chaobo He South China Normal University
• Quanlong Guan Jinan University
• Shirui Pan Griffith University, Queensland, Australia
• Weiqi Luo Jinan University

Keywords

No keywords are indexed for this paper.