Title: A key feature mining method of online teaching behaviour based on k-kernel decomposition

Authors: Wei Wang

Addresses: College of Information Engineering, Henan Industry and Trade Vocational College, Zhengzhou, Henan 450053, China

Abstract: In view of the poor effect of online teaching behaviour key feature mining, an online teaching behaviour key feature mining method based on k-kernel decomposition is designed. Firstly, the adjacent data of the key features of network teaching behaviour are interpolated to determine the key features, and the singular distance function is normalised to complete the feature preprocessing. Then, the key characteristics of network teaching behaviour are transformed into weighted network, and the key characteristics are divided according to the centrality of nodes. Finally, the online behaviour feature k-kernel after classification is assigned, the feature k-kernel value index after assignment is calculated, the correlation of feature data is calculated, the probability of feature data belonging to k-clustering is determined, and the key feature mining of network teaching behaviour is completed. The results show that the mining effect of this method is good.

Keywords: K-kernel decomposition; weighted network; online teaching behaviour; key feature mining.

DOI: 10.1504/IJICT.2024.139833

International Journal of Information and Communication Technology, 2024 Vol.25 No.1, pp.88 - 100

Received: 04 Mar 2022
Accepted: 12 May 2022
Published online: 08 Jul 2024 *

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