A Study of Online Educational Resource Recommendation for College Chinese Courses Based on Personalized Learning

Authors

  • Rongjing Zhang Shaanxi Police Vocational College, Xi’an, Shaanxi 710021, China
  • Gang Wang Shaanxi Police Vocational College, Xi’an, Shaanxi 710021, China

Abstract

This paper analyzed the application of recommendation technology to online educational resources for college Chinese courses based on personalized learning. Traditional collaborative filtering algorithms, i.e., user-based collaborative filtering (CF-U) and item-based collaborative filtering (CF-I) algorithms, were analyzed and a recommendation algorithm was designed by combining the above two algorithms for recommending online educational resources. An analysis was conducted on data from the open online course (MOOC) platform of Chinese colleges. The results show that the highest precision and coverage rate of the combined algorithm was 80.2% and 59.4%, respectively, which were superior to the traditional CF algorithms, and it also had a high training efficiency. When training 100% of the data, the training time of the combined algorithm was 209 s. The experiment results demonstrate the combined algorithm is effective in resource recommendation and can be promoted and applied in practice.

Keywords: personalized learning, educational resources, online education, recommendation technology.

Cite As

R. Zhang, G. Wang, "A Study of Online Educational Resource Recommendation for College Chinese
Courses Based on Personalized Learning", vol. 30 no. 5, pp. 335-340, 2022.

 

 

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Published

2022-09-01

Issue

Vol. 30 No. 5 (2022): International Journal of Engineering Intelligent Systems

Section

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