Title: Study on abnormal behaviour recognition of MOOC online English learning based on multi-dimensional data mining

Authors: Fengxiang Zhang; Feifei Wang

Addresses: College of Foreign Languages, Hebei University of Economics and Business, Shijiazhuang 050061, China ' College of Foreign Languages, Hebei University of Economics and Business, Shijiazhuang 050061, China

Abstract: In order to overcome the problems of low recognition accuracy and long recognition time of traditional English learning abnormal behaviour recognition methods, this paper proposes MOOC online English learning abnormal behaviour recognition method based on multi-dimensional data mining. Firstly, set up the multi-dimensional association item set of MOOC online English learning behaviour, mine the learning behaviour data for correction. Secondly, students' MOOC online English learning behaviour characteristics are extracted from students' target contour and blinking behaviour characteristics. Then, taking this as the training sample subset, the individual member classifier is constructed by the mixed perturbation method to classify the learning behaviour. Finally, the abnormal behaviour identification of MOOC online English learning is completed. The experimental results show that the proposed method has high accuracy and short recognition time.

Keywords: multi-dimensional data mining; MOOC online English learning; abnormal behaviour; mixed perturbation method; individual member classifier.

DOI: 10.1504/IJCEELL.2024.135225

International Journal of Continuing Engineering Education and Life-Long Learning, 2024 Vol.34 No.1, pp.111 - 122

Received: 29 Oct 2021
Accepted: 14 Feb 2022
Published online: 03 Dec 2023 *

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