Title: An online learning behaviour recognition method based on tag set correlation learning

Authors: Ruijing Ma

Addresses: Students' Affairs Division, Zhengzhou University of Light Industry, Zhengzhou, 450001, China

Abstract: Aiming at the problems of poor fitting degree of loss function and low confidence of behaviour recognition in online learning behaviour recognition, an online learning behaviour recognition method based on tag set correlation learning is proposed. Firstly, learners' online learning behaviour is analysed, and their online learning behaviour data is extracted through convolutional layer models. Then, Gaussian mixture model is used to extract feature data, and EM algorithm is used to preprocess feature data. Finally, the label set correlation learning method is used to obtain the label rating results of each behaviour data, and normalisation processing is performed to identify and judge its correlation with the behaviour sample, completing the final recognition. The results show that the loss function value of the proposed method approaches 0, has a high fitting degree, and the confidence is 98%, and the recognition effect is better.

Keywords: online learning; learning behaviour recognition; Gaussian mixture model; EM algorithm; label set correlation learning.

DOI: 10.1504/IJBM.2024.138232

International Journal of Biometrics, 2024 Vol.16 No.3/4, pp.350 - 363

Received: 14 Jun 2023
Accepted: 29 Aug 2023
Published online: 30 Apr 2024 *

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