Title: Dynamic grouping method for online learning behaviour based on social network analysis

Authors: Juan Guo

Addresses: School of Construction, Changchun University of Architecture and Civil Engineering, Chang'chun, 130607, China

Abstract: In this paper, a dynamic grouping method for online learning behaviour based on social network analysis is proposed. The Louvain community detection algorithm is utilised for social network analysis. Based on the analysis results and distributed web crawler, online learning behaviour information is mined and subjected to standardised processing, including outlier removal, noise filtering, and data segment alignment, to extract relevant features. A grouping objective function based on the XGBoost algorithm is constructed for the dynamic grouping of online learning behaviour. The objective function is solved to obtain the dynamic grouping results. Experimental results demonstrate that the proposed method achieves a minimum relative error rate of 1.3% in feature extraction, a maximum accuracy of 97.9% in grouping, and an average task completion time of 0.77 s.

Keywords: social network analysis; online learning behaviour; dynamic grouping; distributed web crawler; XGBoost algorithm; objective function.

DOI: 10.1504/IJNVO.2024.136770

International Journal of Networking and Virtual Organisations, 2024 Vol.30 No.1, pp.27 - 43

Received: 25 Jun 2023
Accepted: 17 Sep 2023
Published online: 21 Feb 2024 *

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