Title: Data mining and learning behaviour analysis of French online education data-driven teaching based on generative adversarial network improvement Apriori algorithm

Authors: Liqun Zhang

Addresses: College of Asia-Europe Languages and Cultures, Xi'an Fanyi University, Xi'an, Shaanxi, China

Abstract: With the rise of online education, French learning platforms are gaining popularity. Improving learning efficiency is a key challenge. This study uses the Apriori algorithm for data mining, enhances it with adversarial networks, and constructs a data-driven teaching system for French online education. The improved Apriori algorithm shows average accuracy, recall and F1-values of 90.1%, 0.92 and 0.93, respectively, making it ideal for mining French online education data. This system provides real-time, personalised feedback, helping optimise learning behaviour and significantly boosting learning outcomes. Analysis of behaviours like login times, browsing time and forum posts shows a positive correlation with learning success, allowing for targeted learning plans to enhance efficiency.

Keywords: data mining; visualisation; French; online education; data-driven; Apriori algorithm.

DOI: 10.1504/IJWMC.2025.144202

International Journal of Wireless and Mobile Computing, 2025 Vol.28 No.2, pp.205 - 215

Received: 08 Dec 2023
Accepted: 04 Jun 2024
Published online: 31 Jan 2025 *

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