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

被引：1

作者：

Zhang, Liqun ^{[1
]}

机构：

[1] College of Asia-Europe Languages and Cultures, Xi’an Fanyi University, Shaanxi, Xi’an

来源：

International Journal of Wireless and Mobile Computing | 2025年 / 28卷 / 02期

关键词：

Apriori algorithm; data mining; data-driven; French; online education; visualisation;

D O I：

10.1504/IJWMC.2025.144202

中图分类号：

学科分类号：

摘要：

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. Copyright © 2025 Inderscience Enterprises Ltd.

引用

页码：205 / 215

页数：10

共 21 条

[1]

Araka E., Oboko R., Maina E., Gitonga R., Using educational data mining techniques to identify profiles in self-regulated learning: an empirical evaluation, The International Review of Research in Open and Distributed Learning, 23, 1, pp. 131-162, (2022)

[2]

Arslan O., Xing W., Inan F.A., Du H.X., Understanding topic duration in Twitter learning communities using data mining, Journal of Computer Assisted Learning, 38, 2, pp. 513-525, (2022)

[3]

Asselman A., Khaldi M., Aammou S., Enhancing the prediction of student performance based on the machine learning XGBoost algorithm, Interactive Learning Environments, 31, 6, pp. 3360-3379, (2023)

[4]

Baek C., Doleck T., Educational data mining versus learning analytics: a review of publications from 2015 to 2019, Interactive Learning Environments, 31, 6, pp. 3828-3850, (2023)

[5]

Batool S., Rashid J., Nisar M.W., Kim J., Kwon H.Y., Hussain A., Educational data mining to predict students’ academic performance: a survey study, Education and Information Technologies, 28, 1, pp. 905-971, (2023)

[6]

Fuchs K., Challenges with gamification in higher education: a narrative review with implications for educators and policymakers, International Journal of Changes in Education, 1, 1, pp. 51-56, (2024)

[7]

Guleria P., Sood M., Explainable AI and machine learning: performance evaluation and explainability of classifiers on educational data mining inspired career counseling, Education and Information Technologies, 28, 1, pp. 1081-1116, (2023)

[8]

Hamza M.A., Hassine S.B.H, Abunadi I., Wesabi F.A., Feature selection with optimal stacked sparse autoencoder for data mining, Computers, Materials and Continua, 72, 2, pp. 2581-2596, (2022)

[9]

Hao Q., Choi W.J., Meng J., A data mining-based analysis of cognitive intervention for college students sports health using Apriori algorithm, Soft Computing, 27, 21, pp. 16353-16371, (2023)

[10]

Karabatak S., Alanoglu M., The Relationship between teacher candidates’ technology addictions and their social connectedness: a data-mining approach, Malaysian Online Journal of Educational Technology, 10, 4, pp. 265-275, (2022)

← 1 2 3 →