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Personalized learning path recommendation for middle school students based on joint optimization of deep
Jianxia Zhao1, Jinsong Kuang2, Shuangqing Deng3
1College of Childcare Services and Health Care, Guangxi College for Preschool Education, No. 77, Minzu Avenue, Qingxiu District, Nanning, Guangxi, China.
Scientific Reports
|July 10, 2026
Summary
Path-Mentor enhances personalized education by dynamically adapting learning paths using a novel framework. This system improves knowledge tracing and optimizes learning objectives for better student outcomes.
Area of Science:
- Artificial Intelligence
- Educational Technology
- Machine Learning
Background:
- Personalized education requires intelligent systems to adapt to student knowledge.
- Existing systems lack dynamic adaptation and struggle with multi-objective learning optimization.
- Temporal dynamics of student knowledge acquisition are not adequately captured by current methods.
Purpose of the Study:
- To propose Path-Mentor, a novel framework for intelligent learning path recommendation.
- To address limitations in existing systems regarding dynamic adaptation and multi-objective optimization.
- To improve the personalization and effectiveness of educational pathways.
Main Methods:
- Developed a Temporal-Aware Graph Knowledge Tracing Network (TA-GKTN) using heterogeneous graph neural networks with temporal convolutions and gating.
- Implemented a Multi-Objective Curriculum Planner and Executor (MOCPE) using hierarchical multi-agent reinforcement learning.
- Integrated TA-GKTN and MOCPE via a Counterfactual Causal Inference-based Co-Training (CCI-CT) mechanism for end-to-end joint optimization.
Main Results:
- Path-Mentor significantly outperformed baseline models on four benchmark datasets.
- Achieved a 12.4% improvement in AUC-ROC for knowledge state prediction compared to DKT.
- Demonstrated a 23.7% reduction in learning path inefficiency and an 18.6% enhancement in long-term mastery.
Conclusions:
- The proposed joint optimization framework is effective for personalized learning path recommendation.
- Path-Mentor advances the field of intelligent tutoring systems by dynamically adapting to student needs.
- The framework offers a systematic approach to balancing conflicting learning objectives like knowledge consolidation and cognitive load.
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