學術(shù)空間 / 論文 / 會議論文
Context-Driven Learning Path Recommendation: From Static Records to Dynamic Contexts
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| 作 者 | Yunxuan Lin , Zhengyang Wu , Ronghua Lin , Yong Tang |
| 會議名稱 | AAAI 2026 AI4EDU |
| 發(fā)表日期 | 2025 年 11 月 |
| 摘 要 |
Learning path recommendation (LPR) is essential for alleviating information overload in large-scale online education. However, existing approaches often rely on static records of student histories and underexploit the semantic richness of learning resources, leading to recommendations that are misaligned with learners’ evolving knowledge states. In this work, we move from static records to dynamic contexts, introducing a context-driven framework that systematically acquires, organizes, compresses, and continuously updates multi-source information. Within this framework, Large Language Models (LLMs) interpret educational content and generate candidate paths, while Reinforcement Learning (RL) and Knowledge Tracing Models (KTM) iteratively refine recommendations through adaptive feedback. This context-driven perspective enhances adaptability, diversity, and explainability of LPR. We validate our approach through extensive experiments on real-world datasets, including Math, Physics, and MOOPer. Results show that the proposed approach consistently outperforms strong baselines across multiple evaluation metrics, achieving significant gains in both learning promotion and diversity, while maintaining competitive efficiency. |
| 關 鍵 字 | Large Language Model, Reinforcement Learning, Coxtext-driven, Learning Path Recommendation |
| 附 件 |
