基于学科知识图谱的个性化学习路径推荐策略

Personalized Learning Path Recommendation Strategy Based on Subject Knowledge Graph

为解决海量学习资源所带来的“认知过载”和“学习迷航”等问题,针对学习者的个体特征和学习需求,同时考虑知识点之间的逻辑关系,文章将学科知识图谱融入学习路径推荐模型。首先构建了学科知识图谱,然后结合学习者认知特征进行知识点路径规划,最后基于知识点序列和学习者模型对关联资源进行排序过滤,得到学习资源的序列集合。实验结果表明,相对于现有经典模型,所提出的方法具有良好的推荐效用。文章的研究成果为学科教育领域个性化学习路径推荐的理论研究和技术实现提供了重要参考。

In order to solve the problems of“cognitive overload”and“learning trek”brought by mass learning resources,aiming at the learner's personalized features and learning needs and considering the logical relationship between knowledge points,this paper integrates the subject knowledge graph into the learning path recommendation model.Firstly,a subject knowledge graph is constructed,followed by knowledge point path planning based on learner cognitive characteristics.Finally,the related resources are sorted and filtered based on knowledge point sequences and learner models to obtain a sequence set of learning resources.The experimental results show that the proposed method has good recommendation effectiveness compared to existing classical models.The research results of this paper provide important references for the theoretical research and technical implementation of personalized learning path recommendation in the field of subject education.