Title: Deep mining of mobile learning data based on multi-scale clustering analysis

Authors: Yuxin Ji

Addresses: School of Sports Health Technology, Jilin Sport University, Changchun 130022, China

Abstract: In order to overcome the problems of low accuracy and long processing times of traditional mobile learning data mining methods, this paper proposes a new mobile learning data deep mining method based on multi-scale clustering analysis. The original dataset was constructed according to the database of the mobile learning platform, and the data in the original dataset was merged and the missing value was filled. On this basis, the data processing results are divided into multiple scales, and the appropriate benchmark scale is selected for scale clustering, so as to carry out the depth mining of mobile learning data. Experimental results show that the accuracy of mobile learning data mining of the proposed method is always above 95%, the average mining time is only 0.59 s, and the stability of the mining process is good, so it can be applied and promoted in practice.

Keywords: multi-scale clustering analysis; mobile learning; data depth mining; missing value filling.

DOI: 10.1504/IJCEELL.2023.132414

International Journal of Continuing Engineering Education and Life-Long Learning, 2023 Vol.33 No.4/5, pp.456 - 467

Received: 21 May 2021
Accepted: 09 Aug 2021

Published online: 19 Jul 2023 *

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