Title: Application of online MOOC education management technology in learning behaviour mining and dropout prediction

Authors: Yongwo Yuan

Addresses: The Faculty of Normal, Dazhou Vocational and Technical College, Dazhou 635001, China

Abstract: In order to address the issue of low completion rates in MOOC online education, this study uses the K-means clustering algorithm (K-means) model to mine behavioural data of MOOC online learners and improves the K-means model using heuristic methods. In addition, an improved long short-term memory (LSTM) network is used to construct a dropout prediction model. The improved K-means model has improved data mining performance by 4635% compared to the K-means model. Selecting six typical courses in MOOC education for dropout prediction, the improved LSTM model achieved a prediction accuracy of 95.65%, which is superior to SVM and LR models. From this, it can be seen that the proposed method has good application effects, and the research content has important reference value for improving MOOC online education.

Keywords: K-means; MOOC online education; principal component analysis; learning behaviour; heuristics.

DOI: 10.1504/IJES.2024.143762

International Journal of Embedded Systems, 2024 Vol.17 No.1/2, pp.138 - 149

Received: 27 Oct 2023
Accepted: 03 Jul 2024
Published online: 06 Jan 2025 *

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