基于特征相似性和杰卡德中值理论的学习路径推荐方法

doi:10.11896/jsjkx.240400111

Computer Science ›› 2024, Vol. 51 ›› Issue (10): 153-161.doi: 10.11896/jsjkx.240400111

• Technology and Application of Intelligent Education • Previous Articles Next Articles

Learning Path Recommendation Method Based on Feature Similarity and Jaccard Median

YANG Pengfei^1,2, WANG Shuqi¹, HUANG Jiayang^1,2, ZHANG Wenjuan³, WANG Quan^1,2, ZHONG Haodi^1,2

1 School of Computer Science and Technology,Xidian University,Xi'an 710126,China
2 The Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province,Xi'an 710126,China
3 Mental Health Education Center,Xidian University,Xi'an 710126,China

Received:2024-04-15 Revised:2024-06-27 Online:2024-10-15 Published:2024-10-11
About author:YANG Pengfei,born in 1985,associate professor,is a senior member of CCF(No.20736S).His main research intere-sts include embedded system architecture,human-computer interactions and heterogeneous parallel computing.
HUANG Jiayang,born in 1995, postdoctoral researcher,is a member of CCF(No.I3835M).Her main research interests include human-computer interactions,wearable sensing and machine learning.
Supported by:
Shaanxi Province Key Industry Innovation Chain Project(2021ZDLGY07-01) and Youth Fund for Humanities and Social Sciences Research, Ministry of Education(19YJC190028).

Abstract

Abstract: 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.

Key words: College enrollment in general categories, College major shunt, Learning path, Jaccard, Node2vec

CLC Number:

TP391

YANG Pengfei, WANG Shuqi, HUANG Jiayang, ZHANG Wenjuan, WANG Quan, ZHONG Haodi. Learning Path Recommendation Method Based on Feature Similarity and Jaccard Median[J].Computer Science, 2024, 51(10): 153-161.

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