Proceedings of the 2023 3rd International Conference on Education, Information Management and Service Science (EIMSS 2023)

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Analysis of Higher Education Teaching Data Based on Data Mining Technology

Authors
Wang Zhi Qi1, *
1Criminal Investigation Police, University of China, Shenyang, 110854, Liaoning, China
*Corresponding author. Email: 709060958@qq.com
Corresponding Author
Wang Zhi Qi
Available Online 28 September 2023.
DOI
10.2991/978-94-6463-264-4_22How to use a DOI?
Keywords
Data mining; Higher education; C5.0 algorithm; Factorial analysis
Abstract

In order to effectively mine and utilize the large amount of valuable data stored in the academic management system of universities, a data mining technology based method for analyzing higher education teaching data has been proposed. This article uses data mining technology to deeply mine and analyze the grade data in the school's academic affairs system. Firstly, the data in the academic affairs system is collected and preprocessed, and then factor analysis is used to comprehensively evaluate student grades. Then, the decision tree improvement method of K-means clustering algorithm and C5.0 algorithm is used to predict the target grades. Finally, the above method is compared and analyzed with other methods. The results show that the estimation accuracy of the improved decision tree method is the highest 64.8%, and the generated decision tree has the smallest depth and the smallest number of leaf nodes. This indicates that the decision Tree model generated by it is more accurate and robust than the other two methods, and can try to avoid over fitting.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2023 3rd International Conference on Education, Information Management and Service Science (EIMSS 2023)
Series
Atlantis Highlights in Computer Sciences
Publication Date
28 September 2023
ISBN
10.2991/978-94-6463-264-4_22
ISSN
2589-4900
DOI
10.2991/978-94-6463-264-4_22How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Wang Zhi Qi
PY  - 2023
DA  - 2023/09/28
TI  - Analysis of Higher Education Teaching Data Based on Data Mining Technology
BT  - Proceedings of the 2023 3rd International Conference on Education, Information Management and Service Science (EIMSS 2023)
PB  - Atlantis Press
SP  - 198
EP  - 205
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-264-4_22
DO  - 10.2991/978-94-6463-264-4_22
ID  - Qi2023
ER  -