Analysis and prediction of online learning behavior based on data mining technology

Chengqiong Ye; Yang Chen

doi:10.1117/12.3031167

19 July 2024 Analysis and prediction of online learning behavior based on data mining technology

Chengqiong Ye, Yang Chen

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 1318150 (2024) https://doi.org/10.1117/12.3031167
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

With the development of big data and education informatization, the learning platform is generating a large amount of data. By mining and analyzing these behavioral data, we can have a better understanding of students' online learning situation, which is helpful to carry out real-time intervention and targeted guidance for students in the learning process, so as to achieve the goal of personalized training. Therefore, this paper uses the learning behavior data generated by students' online learning platform for cluster analysis, and then uses correlation analysis, regression analysis, CHAID algorithm, Logistic regression modeling algorithm, etc., to predict different students' online learning behavior models, which has certain practical significance and application value.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chengqiong Ye and Yang Chen "Analysis and prediction of online learning behavior based on data mining technology", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 1318150 (19 July 2024); https://doi.org/10.1117/12.3031167

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