An Educational Data Mining Model for Supervision of Network Learning Process

Jianhui Chen, Jing Zhao


To improve the school's teaching plan, optimize the online learning system, and help students achieve better learning outcomes, an educative data mining model for the supervision of the e-learning process was established. Statistical analysis and visualization in data mining techniques, association rule algorithms, and clustering algorithms were applied. The teaching data of a college English teaching management platform was systematically analyzed. A related conclusion was drawn on the relationship between students' English learning effects and online learning habits. The results showed that this method could effectively help teachers judge students' online learning results, understand their online learning status, and improve their online learning process. Therefore, the model can improve the effectiveness of students' online learning.


data mining; statistical analysis visualization; association rule algorithm; clustering algorithm

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Copyright (c) 2018 Jianhui Chen, Jing Zhao

International Journal of Emerging Technologies in Learning (iJET) – eISSN: 1863-0383
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