Affiliation:
1. School of Computer Science, South China Normal University, Guangzhou 510631, China
2. School of Information Technology in Education, South China Normal University, Guangzhou 510631, China
Abstract
The main reason students drop out of online courses is often that they lose interest during learning. Moreover, it is not easy for students to choose an appropriate course before actually learning it. Course recommendation is necessary to address this problem. Most existing course recommendation methods depend on the interaction result (e.g., completion rate, grades, etc.). However, the long period required to complete a course, especially large-scale online courses in higher education, can lead to serious sparsity of interaction results. In view of this, we propose a novel course recommendation method named HGE-CRec, which utilizes context formation for heterogeneous graphs to model students and courses. HGE-CRec develops meta-path embedding simulation and meta-path weight fusion to enhance the meta-path embedding set, which can expand the learning space of the prediction model and improve the representation ability of meta-path embedding, thereby avoiding tedious manual setting of the meta-path and improving the effectiveness of the resulting recommendations. Extensive experiments show that the proposed approach has advantages over a number of existing baseline methods.
Funder
National Natural Science Foundation of China
China Ministry of Education Project in the Humanities and Social Sciences
Major Project of Social Science in South China Normal University
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference33 articles.
1. Research on personalized recommendation of MOOC resources based on ontology;Li;Interact. Technol. Smart Educ.,2022
2. Adaptive course recommendation in MOOCs;Lin;Knowl. Based Syst.,2021
3. Capacity Tracing-Enhanced Course Recommendation in MOOCs;Tian;IEEE Trans. Learn. Technol.,2021
4. Heterogeneous teaching evaluation network based offline course recommendation with graph learning and tensor factorization;Zhu;Neurocomputing,2020
5. Wang, C., Peng, C., Wang, M., Yang, R., Wu, W., Rui, Q., and Xiong, N.N. (2021, January 17–20). CTHGAT: Category-aware and Time-aware Next Point-of-Interest via Heterogeneous Graph Attention Network. Proceedings of the 2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021, Melbourne, Australia.
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献