Student Management and Career Guidance in Schools Through Data Analysis

Authors

  • Hongshuo Chen Student Affairs Office, North China University of Science and Technology, Tangshan, Hebei 063210, China
  • Xueli Zhang Jitang College, North China University of Science and Technology, Tangshan, Hebei 063210, China

Abstract

This paper analyzed the learning status of sophomore students at the North China University of Science and Technology using the random forest (RF) algorithm, and the employment direction of graduates from the same university using the association rule algorithm. The results indicated that the random forest (RF) algorithm performed best when the number of decision trees was set to 70. Among the factors influencing learning status, the factor with the highest relative importance was “final exam score”, followed by “midterm exam score” and “online learning time”. The association rule algorithm was effective in mining the rules that impact the employment direction of graduates.

Keywords: random forest, association rule, learning status, career guidance

Cite As

H. Chen, X. Zhang, " Student Management and Career Guidance in Schools Through Data Analysis",
Engineering Intelligent Systems, vol. 33 no. 5, pp. 591-595, 2025.

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Published

2025-09-01

Issue

Vol. 33 No. 5 (2025): International Journal of Engineering Intelligent Systems

Section

Article

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