He Zhao This email address is being protected from spambots. You need JavaScript enabled to view it.1,2

1ChangChun GuangHua University, Changchun, Jilin province, 130000, China
2Northeast Normal University, Changchun, Jilin, 130000, China


Received: May 19, 2022
Accepted: June 23, 2022
Publication Date: August 12, 2022

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202305_26(5).0004  


With the advancement of education informatization, education management methods have gradually become information-based, and large amounts of saved data have been accumulated in teaching. How to maximize the use of these data has become a research difficulty. The whole process of data mining is introduced, including determining the object and target of mining, data preparation through data selection, data pre-processing, and data transformation. A decision tree model was generated and tested using the WEKA data mining tool based on the analysis and comparison of several standard algorithms for decision trees. The main factors affecting the quality of students were found in the extracted classification rules, which improved the data analysis and provided decision support for student training while integrating the data mining module into the comprehensive student information management model. Based on this article, an education management model based on data mining technology is proposed. This article first elaborates the challenges brought to education management in the context of big data and discusses the research status of data mining technology. Taking student management in education management as an example, an education management student information database is established, student information is summarized, and the potential value of mining information using multiple data mining algorithms is proposed. The student information test confirmed that the design was successful.

Keywords: Data mining; Education management; Decision tree; Association rules


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