Title: Mining method of students' learning behaviour characteristics in online classroom of colleges and universities based on dense clustering method

Authors: Jing Wen

Addresses: College of Marxism (Law), Chizhou University, Chizhou 247000, China

Abstract: Aiming at the problem of poor mining performance of traditional learning behaviour characteristics mining methods, this paper proposes a new learning behaviour characteristics mining method based on dense clustering method. Firstly, students' learning behaviour data from the online classroom software platform is collected. Secondly, the information synthesis parameter processing algorithm combined with ant colony algorithm is used to calculate the sensitivity of characteristics and modify the students' learning behaviour characteristics data. Finally, the dense density clustering method is used to mine the learning behaviour characteristics, and the characteristics mining results are obtained. The simulation results show that this research effectively improves the accuracy and efficiency of characteristics mining, and the accuracy of characteristics matching is always maintained at more than 90%.

Keywords: dense clustering method; learning behaviour; characteristics mining; data revision.

DOI: 10.1504/IJCEELL.2024.135268

International Journal of Continuing Engineering Education and Life-Long Learning, 2024 Vol.34 No.1, pp.77 - 87

Received: 05 Aug 2021
Accepted: 09 Feb 2022
Published online: 03 Dec 2023 *

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