Online Educational Resources of International Chinese Education on MOOC Platform Based on Personalized Learning

Authors

  • Yi Sun School of International Culture and Education, Nanjing Normal University, Nanjing, Jiangsu 210097, China

Abstract

In this paper, the international Chinese education courses on the Massive Open Online Courses (MOOC) platform are examined, and two recommendation algorithms are analyzed: the user-based collaborative filtering (UserCF) algorithm and the item-based collaborative filtering (ItemCF) algorithm. The UserCF algorithm was improved by the K-means algorithm to obtain a Kmeans-UserCF algorithm. The similarity of users was calculated according to their Chinese level and course preferences. Then, resources were recommended by the K-means-UserCF algorithm. The experimental results showed that compared with UserCF and ItemCF algorithms, the K-means-UserCF algorithm had a higher recall rate (56.64%), accuracy (46.79%), and coverage rate (52.76%) when the number of nearest neighbors was 20. The experimental results verify the reliability of the K-means-UserCF algorithm in recommending online educational resources on the MOOC platform, which is conducive to realizing personalized learning.

Keywords: personalized learning, massive open online courses, international Chinese education, online education resources

Cite As

Y. Sun, "Online Educational Resources of International Chinese Education on MOOC Platform Based on
Personalized Learning", Engineering Intelligent Systems, vol. 31 no. 5, pp. 389-394, 2023.



Downloads

Published

2023-09-01

Issue

Vol. 31 No. 5 (2023): International Journal of Engineering Intelligent Systems

Section

Article

License

The submission of a paper implies that, if accepted for publication, it will not be published elsewhere in the same form, in any language, without the prior consent of the publisher. Before publication, authors are requested to assign copyright to CRL Publishing Ltd. This allows CRL to sanction photocopying, and to authorize the reprinting of issues or volumes according to demand. Authors' traditional rights will not be jeopardized by assigning Copyright in this way, as they retain the right to reuse the material following publication, and to veto third-party publication.