Article 2024

Leverage Learning Behaviour Data for Students' Learning Performance Prediction and Influence Factor Analysis

NQ
Ni, Qin
 ZY
Zhu, Yanjin
 ZL
Zhang, Lingying
 LX
Lu, Xiaochen
 ZL
Zhang, Lei
IEEE Transactions on Artificial Intelligence
Journal · Vol. 5 · Issue 5 · pp. 2422-2433
Abstract

Online education has become increasingly significant for university students and faculty, especially in the context of the modern remote education landscape. However, the inherent space-time separation in online education can create communication delays between teachers and students, making it challenging to monitor students' behaviors effectively. In an effort to understand the connection between students' online engagement and their learning performance, data annotation has been implemented to address the issue of accurately representing students' learning behaviors. Taking the online teaching data of Shanghai Normal University platform as the research object, the primary online education problem is explored through data mining, which includes correlation analysis, Gini importance ranking, and principal component analysis (PCA). Then, constructing the learning performance prediction model using random forest (RF) based on PCA by comparing various machine learning algorithms. As a consequence, the most influential online learning behaviors are course duration time, document learning time, test average score, and video completion rate. The overall classification accuracy of the learning performance prediction model is 87.45%, and the highest prediction accuracy for a single category is 96.52%. © 2020 IEEE.

Keywords

Author Keywords

Data mining Classification prediction correlation analysis learning behavior representation random forest (RF)

Index Keywords

E-learning Teaching Education computing Students Machine learning Data mining Behavioral research Learning algorithms Behavioral science correlation analysis Learning behavior Predictive models Principal component analysis Job analysis Task analysis Correlation methods Forecasting Behavior representation Classification prediction Learning behavior representation Learning performance Random forests
Author Affiliations
Key Laboratory of Multilingual Education with AI, Shanghai International Studies University, Shanghai, Shanghai, China
College of Information Science and Technology, Donghua University, Shanghai, Shanghai, China, Engineering Research Center of Digitized Textile & Apparel Technology, Donghua University, Shanghai, Shanghai, China
Shanghai Normal University, Shanghai, Shanghai, China
Funding & Acknowledgements
Natural Science Foundation of Shanghai Municipality
Grant: 21ZR1446900
This work was supported in part by the National Natural Science Foundation ofChina underGrant 62371118, Grant 6210020445, and Grant 61901104, in part by the Natural Science Foundation of Shanghai under Grant 21ZR1446900, in part by Shanghai Baoshan Future Learning Research and Development Centre under Grant 21511100102, and in part by the Science and Technology Research Project of Shanghai Songjiang District under Grant 20SJKJGG4C.
Natural Science Foundation of Shanghai Municipality
This work was supported in part by the National Natural Science Foundation ofChina underGrant 62371118, Grant 6210020445, and Grant 61901104, in part by the Natural Science Foundation of Shanghai under Grant 21ZR1446900, in part by Shanghai Baoshan Future Learning Research and Development Centre under Grant 21511100102, and in part by the Science and Technology Research Project of Shanghai Songjiang District under Grant 20SJKJGG4C.
National Natural Science Foundation of China, NSFC
Grant: 61901104, 62371118, 6210020445
This work was supported in part by the National Natural Science Foundation ofChina underGrant 62371118, Grant 6210020445, and Grant 61901104, in part by the Natural Science Foundation of Shanghai under Grant 21ZR1446900, in part by Shanghai Baoshan Future Learning Research and Development Centre under Grant 21511100102, and in part by the Science and Technology Research Project of Shanghai Songjiang District under Grant 20SJKJGG4C.
National Natural Science Foundation of China, NSFC
This work was supported in part by the National Natural Science Foundation ofChina underGrant 62371118, Grant 6210020445, and Grant 61901104, in part by the Natural Science Foundation of Shanghai under Grant 21ZR1446900, in part by Shanghai Baoshan Future Learning Research and Development Centre under Grant 21511100102, and in part by the Science and Technology Research Project of Shanghai Songjiang District under Grant 20SJKJGG4C.
Grant: 20SJKJGG4C
This work was supported in part by the National Natural Science Foundation ofChina underGrant 62371118, Grant 6210020445, and Grant 61901104, in part by the Natural Science Foundation of Shanghai under Grant 21ZR1446900, in part by Shanghai Baoshan Future Learning Research and Development Centre under Grant 21511100102, and in part by the Science and Technology Research Project of Shanghai Songjiang District under Grant 20SJKJGG4C.
Grant: 21511100102
This work was supported in part by the National Natural Science Foundation ofChina underGrant 62371118, Grant 6210020445, and Grant 61901104, in part by the Natural Science Foundation of Shanghai under Grant 21ZR1446900, in part by Shanghai Baoshan Future Learning Research and Development Centre under Grant 21511100102, and in part by the Science and Technology Research Project of Shanghai Songjiang District under Grant 20SJKJGG4C.
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