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<p>Personalized learning path recommendation is a key issue in intelligent education systems, aiming to generate learning sequences that conform to teaching principles based on learners’ knowledge status and learning objectives. However, English learning has a significant hierarchical structure and prerequisite dependence, making it difficult for traditional recommendation methods to simultaneously characterize knowledge structure constraints and learner dynamic evolution. Therefore, this paper proposes a personalized English learning path recommendation model based on Knowledge Graphs (KG) and learner profiles. First, an English learning knowledge graph covering knowledge points, skills, resources, assessments, and CEFR levels is constructed to explicitly model multi-type educational semantic relationships. Second, a dynamic learner profile composed of knowledge mastery vectors and skill ability vectors is designed to characterize learning states at multiple granularities. Furthermore, a knowledge tracing method integrating a knowledge tracing model and knowledge graph neighborhood propagation is proposed to improve the stability and generalization ability of state prediction. Based on this, candidate learning paths are generated and ranked under the pre-requirement constraints of the knowledge graph, achieving a balance between teaching rationality and personalized needs. The proposed model is evaluated using standard performance metrics, including Precision (0.381), Recall (0.357), and NDCG (0.401). Experimental results show that the proposed method significantly outperforms the comparative models in terms of recommendation accuracy, learning gain, and path completion rate, while also possessing good interpretability and adaptability.</p>

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A personalized english learning path recommendation model based on knowledge graph and learner profiling

  • 期刊论文

  • 发表日期：

    2026年7月17日

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  • Yueqi Pan,
  • Jian Huang

摘要

Personalized learning path recommendation is a key issue in intelligent education systems, aiming to generate learning sequences that conform to teaching principles based on learners’ knowledge status and learning objectives. However, English learning has a significant hierarchical structure and prerequisite dependence, making it difficult for traditional recommendation methods to simultaneously characterize knowledge structure constraints and learner dynamic evolution. Therefore, this paper proposes a personalized English learning path recommendation model based on Knowledge Graphs (KG) and learner profiles. First, an English learning knowledge graph covering knowledge points, skills, resources, assessments, and CEFR levels is constructed to explicitly model multi-type educational semantic relationships. Second, a dynamic learner profile composed of knowledge mastery vectors and skill ability vectors is designed to characterize learning states at multiple granularities. Furthermore, a knowledge tracing method integrating a knowledge tracing model and knowledge graph neighborhood propagation is proposed to improve the stability and generalization ability of state prediction. Based on this, candidate learning paths are generated and ranked under the pre-requirement constraints of the knowledge graph, achieving a balance between teaching rationality and personalized needs. The proposed model is evaluated using standard performance metrics, including Precision (0.381), Recall (0.357), and NDCG (0.401). Experimental results show that the proposed method significantly outperforms the comparative models in terms of recommendation accuracy, learning gain, and path completion rate, while also possessing good interpretability and adaptability.