Title: A recognition method of learning behaviour in online classroom based on feature data mining

Authors: Yingyao Wang

Addresses: School of Marxism, Changchun College of Electronic Technology, Changchun 130000, China

Abstract: In order to effectively ensure the recognition effect of online classroom learning behaviour and improve the accuracy and efficiency of online classroom learning behaviour recognition, an online classroom learning behaviour recognition method based on feature data mining is proposed. This paper analyses the concept and process of feature data mining, and extracts the characteristics of learning behaviour data in online classroom. Principal component analysis was used to pre-process the characteristics of learning behaviour data in online classroom. Using the method of feature data mining, this paper constructs the recognition model of learning behaviour in online classroom to realise the recognition of learning behaviour in online classroom. The experimental results show that the proposed method has a good effect on the recognition of learning behaviour in the online classroom, and can effectively improve the accuracy and efficiency of the recognition of learning behaviour in the online classroom.

Keywords: feature data mining; principal component analysis; online classroom; learning behaviour; behaviour recognition.

DOI: 10.1504/IJRIS.2024.137441

International Journal of Reasoning-based Intelligent Systems, 2024 Vol.16 No.1, pp.50 - 57

Received: 21 Mar 2022
Accepted: 22 Nov 2022
Published online: 19 Mar 2024 *

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