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Research on personalized recommendation of MOOC resources based on ontology

Research on personalized recommendation of MOOC resources based on ontology 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.Design/methodology/approachThis 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.FindingsThe 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.Originality/valueThis 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Interactive Technology and Smart Education Emerald Publishing

Research on personalized recommendation of MOOC resources based on ontology

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References (44)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
1741-5659
eISSN
1741-5659
DOI
10.1108/itse-10-2021-0190
Publisher site
See Article on Publisher Site

Abstract

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.Design/methodology/approachThis 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.FindingsThe 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.Originality/valueThis 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.

Journal

Interactive Technology and Smart EducationEmerald Publishing

Published: Oct 27, 2022

Keywords: Students; Distance learning; Modelling; E-learning; Digital learning; Web-based learning; Personalized recommendation; Ontology; MOOC resources; Learner characteristics; Semantic association

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