The analysis of student Intelligent Systems achievement using data mining in practical teaching informatization

Authors

  • Ying Zhong Office of Educational Administration, Guilin University, Guilin, Guangxi 541006, China
  • Juncheng Mo Guilin Medical University, Guilin, Guangxi 541000, China

Abstract

This paper offers a brief introduction to the decision tree model in data mining technology. A clustering algorithm was employed to discretize the continuous data samples to facilitate processing. Then, the one-semester English course scores of students studying at Guilin Medical University were used for the case study. The improved decision tree model was compared with the ID3 decision tree model and the unmodified decision tree model. The results showed that the improved decision tree model was faster to build and, moreover, had higher classification accuracy than the other two models. The final exam score had the most impact on the overall score, followed by the scores for in-class tests, completion of daily homework, and number of lateness to class.

Keywords: educational informatization, data mining, decision tree, K-means

Cite As

Y. Zhong, J. Mo, "The analysis of student Intelligent Systems achievement using data mining in practical
teaching informatization", Engineering Intelligent Systems, vol. 33 no. 3, pp. 345-350, 2025.

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Published

2025-05-01

Issue

Vol. 33 No. 3 (2025): International Journal of Engineering Intelligent Systems

Section

Article

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