Title: Deep mining method of online learning behaviour data based on big data analysis

Authors: Weijuan Li

Addresses: Dean's Office, Yellow River Conservancy Technical Institute, Kaifeng 475004, China

Abstract: Aiming at the problems of low mining accuracy and long mining time in learning behaviour data mining, a deep mining method of online learning behaviour data based on big data analysis is proposed. The initial signals of learners' subject, object, learning environment, learning means, time and result data are set, and the data components of online learning behaviour are obtained through EMD. EEMD is used to extract the key features of online learning behaviour data, and different contribution rates in learning sequence are calculated by linear weighting method. With the help of the first-order polynomial decision function in big data technology, the inner product is calculated, and the online learning behaviour data deep mining model based on big data technology is constructed to complete the data deep mining. The experimental results show that the accuracy of the proposed method is about 95%, and the mining time is short.

Keywords: big data analysis; online learning behaviour data; depth mining; EEMD; linear weighting; depth mining model.

DOI: 10.1504/IJCEELL.2023.132417

International Journal of Continuing Engineering Education and Life-Long Learning, 2023 Vol.33 No.4/5, pp.364 - 375

Received: 12 May 2021
Accepted: 09 Aug 2021

Published online: 19 Jul 2023 *

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