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

TL;DR

This paper introduces a novel method for personalized learning path recommendation that combines prerequisite and similarity relationships between knowledge concepts, using dual knowledge structure graphs to improve recommendation accuracy and interpretability.

Contribution

The paper proposes GraphRAG-Induced Dual Knowledge Structure Graphs, including an adaptive graph generation module and a reinforcement learning approach, to enhance learning path recommendations without relying solely on prerequisite relationships.

Findings

Achieves state-of-the-art performance on benchmark datasets.

Effectively mitigates learning path blockages with the DLRL module.

Provides interpretable reasoning for recommendations.

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.