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Research on personalized recommendation of MOOC resources based on ontology

Yuanmin Li (School of Information Technology in Education, South China Normal University, Guangzhou, China)
Dexin Chen (Normal College, Hubei University, Wuhan, China)
Zehui Zhan (School of Information Technology in Education, South China Normal University, Guangzhou, China)

Interactive Technology and Smart Education

ISSN: 1741-5659

Article publication date: 12 April 2022

Issue publication date: 27 October 2022




The purpose of this study is to analyze from multiple perspectives, so as to form an effective massive open online course (MOOC)personalized recommendation method to help learners efficiently obtain MOOC resources.


This study introduced ontology construction technology and a new semantic association algorithm to form a new MOOC resource personalized recommendation idea. On the one hand, by constructing a learner model and a MOOC resource ontology model, based on the learner’s characteristics, the learner’s MOOC resource learning preference is predicted, and a recommendation list is formed. On the other hand, the semantic association algorithm is used to calculate the correlation between the MOOC resources to be recommended and the learners’ rated resources and predict the learner’s learning preferences to form a recommendation list. Finally, the two recommendation lists were comprehensively analyzed to form the final MOOC resource personalized recommendation list.


The semantic association algorithm based on hierarchical correlation analysis and attribute correlation analysis introduced in this study can effectively analyze the semantic similarity between MOOC resources. The hybrid recommendation method that introduces ontology construction technology and performs semantic association analysis can effectively realize the personalized recommendation of MOOC resources.


This study has formed an effective method for personalized recommendation of MOOC resources, solved the problems existing in the personalized recommendation that is, the recommendation relies on the learner’s rating of the resource, the recommendation is specialized, and the knowledge structure of the recommended resource is static, and provides a new idea for connecting MOOC learners and resources.



This study was financially supported by the Major basic research and applied research projects of Guangdong Education Department (#2017WZDXM004).

Conflicts of interest: The authors have no financial or proprietary interests in any material discussed in this article.

Ethics approval: All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Consent: Informed consent was obtained from all individual participants included in the study.


Li, Y., Chen, D. and Zhan, Z. (2022), "Research on personalized recommendation of MOOC resources based on ontology", Interactive Technology and Smart Education, Vol. 19 No. 4, pp. 422-440.



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