Title: Learning behaviour recognition method of English online course based on multimodal data fusion

Authors: Liangjie Li

Addresses: Department of Foreign Affairs, Henan Finance University, Zhengzhou, 450000, China

Abstract: The conventional methods for identifying English online course learning behaviours have the problems of low recognition accuracy and high time cost. Therefore, a multimodal data fusion-based method for identifying English online course learning behaviours is proposed. Firstly, the analytic hierarchy process is used for decision fusion of multimodal data of learning behaviour. Secondly, based on the fusion results of multimodal data, weight coefficients are set to minimise losses and extract learning behaviour features. Finally, based on the extracted learning behaviour characteristics, the optimal classification function is constructed to classify the learning behaviour of English online courses. Based on the transfer information of learning behaviour status, the identification of online course learning behaviour is completed. The experimental results show that the recognition accuracy of the proposed method is above 90%, and its recognition accuracy is and can shorten the recognition time of learning behaviour, with high practical application reliability.

Keywords: multimodal data fusion; English online course; learning behaviour; behaviour recognition.

DOI: 10.1504/IJBIDM.2024.140880

International Journal of Business Intelligence and Data Mining, 2024 Vol.25 No.3/4, pp.336 - 349

Received: 11 Aug 2023
Accepted: 16 Nov 2023
Published online: 03 Sep 2024 *

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