Discovery and Recommendation of First-Hand Learning Resources Based on Public Opinion Cluster Analysis
ARTICLE
Haiyun Li, Xuebo Zhang, Junhui Wang, South China Normal University
iJET Volume 12, Number 12, ISSN 1863-0383 Publisher: International Journal of Emerging Technology in Learning, Kassel, Germany
Abstract
This paper explores the personalized approach of the public opinion cluster analysis for learning resources based on the server-side predetermined analysis, in order to introduce the personalized learning resource recommender into the traditional online instruction. In allusion to further validation on its implementation, the fuzzy aggregation of learning resources is mined up based on the proposed WRTC algorithm. The personalized learning resource recommender mechanism is then described. In the end, the common evaluation parameters in the personalized recommender model are applied in the evaluation on the system performance. The experiment is carried out with learner's access data online to validate whether the algorithm and the model indicators are effective for the purpose of improving the precision and coverage of learning resources.
Citation
Li, H., Zhang, X. & Wang, J. (2017). Discovery and Recommendation of First-Hand Learning Resources Based on Public Opinion Cluster Analysis. International Journal of Emerging Technologies in Learning (iJET), 12(12), 112-118. Kassel, Germany: International Journal of Emerging Technology in Learning. Retrieved August 11, 2024 from https://www.learntechlib.org/p/182035/.
Keywords
References
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