Optimization of Course Sequence in Private Universities Based on Random Forest and Association Rules

Authors

  • Junhong Liu Guangzhou Vocational University of Science and Technology, Guangzhou 510550, China
  • Yan Tu Guangzhou Vocational University of Science and Technology, Guangzhou 510550, China
  • Wensong Yu Guangzhou Vocational University of Science and Technology, Guangzhou 510550, China
  • Ruiping Hou Guangzhou Vocational University of Science and Technology, Guangzhou 510550, China

Abstract

The learning process of college students is a gradual one. The study of prerequisite courses helps students build a knowledge foundation, enabling them to more easily achieve flow experience in subsequent courses, which has a significant impact on their course satisfaction. This study took 372 upgraded students majoring in Tourism Management at GuangzhouVocational and Technical University of Science and Technology as samples. The study investigated their satisfaction with 15 professional courses and overall course satisfaction, conducted decision tree model and random forest model analyses to identify the important professional courses affecting overall course satisfaction, and then used the association rules model to mine the prerequisite courses of these important professional courses. The results show that Tourism Psychology, Ecotourism, Tourism English,
Tourism Hospitality, and Tourism Consumer Behavior are important professional courses affecting overall course satisfaction, and there are valuable associations between important professional courses and other professional courses. The conclusion indicates that by scientifically formulating the teaching sequence of professional courses in private universities, this can significantly improve overall course satisfaction.

Keywords: flow theory, random forest, association rules, course sequence

Cite As

J. Liu, Y. Tu, W. Yu, "Optimization of Course Sequence in Private Universities Based on Random Forest
and Association Rules", Engineering Intelligent Systems, vol. 34 no. 1, pp. 119-127, 2026.




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Published

2026-01-01

Issue

Vol. 34 No. 1 (2026): International Journal of Engineering Intelligent Systems

Section

Article

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