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<p>The rapid advancement of personalized education has created an urgent need for intelligent learning path recommendation systems that can dynamically adapt to students’ evolving knowledge states. However, existing learning path recommendation systems predominantly rely on static knowledge graphs or simple heuristic rules, failing to capture the temporal dynamics of student knowledge acquisition and the complex multi-objective nature of learning optimization. This paper proposes Path-Mentor, a novel framework that integrates a Temporal-Aware Graph Knowledge Tracing Network (TA-GKTN) with a Multi-Objective Curriculum Planner and Executor (MOCPE) through a Counterfactual Causal Inference-based Co-Training (CCI-CT) mechanism. The TA-GKTN module models students’ dynamic knowledge states as graph-structured representations by incorporating temporal convolution and gating mechanisms into heterogeneous graph neural networks. The MOCPE module employs a hierarchical multi-agent reinforcement learning framework to decouple high-level knowledge point sequencing from low-level learning activity execution, enabling systematic balancing of conflicting objectives including knowledge consolidation, novelty exploration, and cognitive load management. The CCI-CT method establishes an end-to-end joint optimization pipeline by generating counterfactual training data and enabling bidirectional knowledge transfer between the knowledge tracing and path recommendation modules. Comprehensive experiments conducted on four benchmark datasets (ASSISTments 2012-2013, Junyi Academy, EdNet, and Eedi) demonstrate that Path-Mentor significantly outperforms baseline models across six evaluation metrics, achieving a 12.4% improvement in AUC-ROC compare with DKT model for knowledge state prediction, 23.7% reduction in learning path efficiency, and 18.6% enhancement in long-term mastery improvement. These results validate the effectiveness of the proposed joint optimization framework in advancing personalized learning path recommendation for middle school students.</p>

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Personalized learning path recommendation for middle school students based on joint optimization of deep reinforcement learning and knowledge tracing

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月10日

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  • Jianxia Zhao,
  • Jinsong Kuang,
  • Shuangqing Deng,
  • Rongrong Kuang,
  • Zhiqiang Zhang

摘要

The rapid advancement of personalized education has created an urgent need for intelligent learning path recommendation systems that can dynamically adapt to students’ evolving knowledge states. However, existing learning path recommendation systems predominantly rely on static knowledge graphs or simple heuristic rules, failing to capture the temporal dynamics of student knowledge acquisition and the complex multi-objective nature of learning optimization. This paper proposes Path-Mentor, a novel framework that integrates a Temporal-Aware Graph Knowledge Tracing Network (TA-GKTN) with a Multi-Objective Curriculum Planner and Executor (MOCPE) through a Counterfactual Causal Inference-based Co-Training (CCI-CT) mechanism. The TA-GKTN module models students’ dynamic knowledge states as graph-structured representations by incorporating temporal convolution and gating mechanisms into heterogeneous graph neural networks. The MOCPE module employs a hierarchical multi-agent reinforcement learning framework to decouple high-level knowledge point sequencing from low-level learning activity execution, enabling systematic balancing of conflicting objectives including knowledge consolidation, novelty exploration, and cognitive load management. The CCI-CT method establishes an end-to-end joint optimization pipeline by generating counterfactual training data and enabling bidirectional knowledge transfer between the knowledge tracing and path recommendation modules. Comprehensive experiments conducted on four benchmark datasets (ASSISTments 2012-2013, Junyi Academy, EdNet, and Eedi) demonstrate that Path-Mentor significantly outperforms baseline models across six evaluation metrics, achieving a 12.4% improvement in AUC-ROC compare with DKT model for knowledge state prediction, 23.7% reduction in learning path efficiency, and 18.6% enhancement in long-term mastery improvement. These results validate the effectiveness of the proposed joint optimization framework in advancing personalized learning path recommendation for middle school students.