Title: An evaluation method of English online learning behaviour based on feature mining

Authors: Chao Han

Addresses: Department of Foreign Language, Kaifeng University, Kaifeng 475004, China

Abstract: In order to overcome the problems of low precision of feature retrieval and poor clustering effect of traditional online learning behaviour evaluation methods, this paper designed an English online learning behaviour evaluation method based on feature mining. Firstly, the unbiased estimation theory is used to quantitatively sample English online learning behaviour. After data preprocessing, the clustering centre is divided. The data is allocated to different clusters through clustering processing, and the feature mining results are obtained through iteration. Then the evaluation index system is constructed, and the final evaluation results are obtained on the basis of the grey interval clustering treatment of the evaluation index. According to the test results, the retrieval accuracy of behaviour features of method of this paper is closer to one, and its clustering effect on different online learning behaviour features is good, which proves that it has achieved the design expectation.

Keywords: online learning behaviour; data clustering; characteristics of the mining; evaluation indicators.

DOI: 10.1504/IJCEELL.2023.129215

International Journal of Continuing Engineering Education and Life-Long Learning, 2023 Vol.33 No.2/3, pp.326 - 336

Received: 19 Apr 2021
Accepted: 16 Jul 2021
Published online: 01 Mar 2023 *

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