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

• URL: http://arxiv.org/abs/2506.22303v2
• Date: Wed, 06 Aug 2025 09:12:14 GMT
• Title: GraphRAG-Induced Dual Knowledge Structure Graphs for Personalized Learning Path Recommendation
• Authors: Xinghe Cheng, Zihan Zhang, Jiapu Wang, Liangda Fang, Chaobo He, Quanlong Guan, Shirui Pan, Weiqi Luo,
• Abstract summary: We introduce a knowledge concept structure graph generation module EDU-GraphRAG.<n>We then propose a Discrimination Learning-driven Reinforcement Learning (DLRL) module, which mitigates the issue of blocked learning paths.<n>We conduct extensive experiments on three benchmark datasets, demonstrating that our method achieves state-of-the-art performance.
• Score: 56.37740554448673
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning path recommendation seeks to provide learners with a structured sequence of learning items (\eg, knowledge concepts or exercises) to optimize their learning efficiency. Despite significant efforts in this area, most existing methods primarily rely on prerequisite relationships, which present two major limitations: 1) Requiring prerequisite relationships between knowledge concepts, which 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 relationships implies that difficulties at any stage can cause learning blockages, which in turn disrupt subsequent learning processes. To address these 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 relationships between knowledge concepts. Specifically, we introduce a knowledge concept structure graph generation module EDU-GraphRAG that adaptively constructs knowledge concept structure graphs for different educational datasets, significantly improving the generalizability of learning path recommendation methods. We then propose a Discrimination Learning-driven Reinforcement Learning (DLRL) module, which mitigates the issue of blocked learning paths, further enhancing the efficacy of learning path recommendations. Finally, we conduct extensive experiments on three benchmark datasets, demonstrating that our method not only achieves state-of-the-art performance but also provides interpretable reasoning for the recommended learning paths.

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