基于特征相似性和杰卡德中值理论的学习路径推荐方法

Learning Path Recommendation Method Based on Feature Similarity and Jaccard Median

新高考改革使得越来越多的高校从专业招生向大类招生迈进。相关研究指出,学生在专业分流选择时存在盲目性。如何解决由专业选择不平衡问题所导致的“冷门专业过冷,热门专业过热”的局面,成为大类培养模式面临的核心难题。文中提出了一种结合特征相似性和杰卡德中值理论的学习路径推荐方法CFSJM,旨在为学生在选择专业时提供方向导航和学习路径推荐。该方法利用图嵌入模型Node2vec学习学生与知识点之间的交互,以获取学生节点的特征表示。通过训练线性回归模型预测学生的专业方向,并根据特征相似性生成学习路径候选集,进而引入杰卡德中值理论生成学习路径。实验结果表明,CFSJM方法在大类招生模式下的线下教学数据中的准确率优于现有方法,为充分发挥大类招生在培养创新型人才和提升高校办学质量方面的优势提供了新的思路。

The advancement of the new college entrance examination has prompted more and more colleges to convert their enrollment mode from professional enrollment to enrollment in general categories.However,relevant studies indicate that there is a lack of rationality in students’choices when it comes to major shunts.How to break the situation of“cold majors and hot majors”caused by the imbalance of major selection has become the core problem faced by large types of training models.A learning path recommendation method based on feature similarity and Jaccard median(CFSJM)is proposed in this paper,aiming to provide direction navigation and learning path recommendations for students when choosing their majors.The method utilizes Node2vec to learn the interactions between students and knowledge points to obtain a feature representation of student nodes.A linear regression model is trained to predict the students’major direction,and a learning path candidate set is generated based on feature similarity,which in turn introduces the Jaccard median theory to generate learning paths.Experimental results show that the accuracy of CFSJM in the offline teaching data is better than that of the existing methods,which provides a new idea to give full play to the advantages of enrollment in general categories in cultivating innovative talents and improving the quality of university education.