Association Rule Mining for Selecting Proper Students to Take Part in Proper Discipline Competition: A Case Study of Zhejiang University of Finance and Economics

• Main Authors: Xiaoling Huang, Yangbing Xu, Shuai Zhang, Wenyu Zhang
• Format: Article
• Language: English
• Published: Kassel University Press 2018-03-01
• Series: International Journal of Emerging Technologies in Learning (iJET)
• Subjects: association rule mining; Apriori algorithm; R programming; discipline competition
• Online Access: http://online-journals.org/index.php/i-jet/article/view/8382

In recent years, the educational issues have attracted more and more researchers’ and teachers’ attention. On the other hand, the development of data mining technology, provides a new method to extract the useful information from the complex educational data. In order to increase the chance of students to be awarded in discipline competition, it is better to select the proper students to take part in the proper discipline competition. Therefore, in this study, we collect the information of 164 undergraduate students as a case study. All students majored in Software Engineering in Zhejiang University of Finance and Economics. The Apriori algorithm with group strategy is used to find the relationship between the students’ courses scores and competition awards. According to the results of association rule mining, we find that the students with higher scores of C# Development, Object-Oriented, Internet Web Design, Data Structure(C#), and Basic Programming will have a higher probability to be awarded in the competition.