Sustainable Development of College and University Education by use of Data Mining Methods

• Main Authors: Liwen Wang, Soo-Jin Chung
• Format: Article
• Language: English
• Published: Kassel University Press 2021-03-01
• Series: International Journal of Emerging Technologies in Learning (iJET)
• Subjects: students-oriented; data mining; association rules; apriori algorithm; sustainable development
• Online Access: https://online-journals.org/index.php/i-jet/article/view/20303

To improve the education efficiency of the students, the student-centered education plan is explored. First, the Apriori algorithm of association rules is used to mine the potential related patterns in the score data of college students and establish a reasonable teaching method. Second, aided by the decision tree model, the factors affecting students' academic performance are studied, and the potential relationship between different courses is studied. Finally, the Apriori algorithm of association rules combined with decision tree model is used to generate the early warning mechanism of students' achievement, and the course performance of college students is empirically analyzed. The results show that: C language has two sides of dependence on many subjects; higher mathematics → linear algebra → mathematical statistics → computer composition principle → computer network. The teaching scheme of C language → C + + → Java more conforms to the learning mechanism of college students. Through empirical analysis, the early warning mechanism of association rule Apriori algorithm and decision tree model can effectively analyze student's course and give student's achievement. It is found that the method proposed can provide theoretical basis for students, teachers, and university administrators to carry out education reform and education management decision-making, improve students' performance and education quality, and realize the "student-oriented" education concept, so it can be applied to the actual education management.