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

Xiaoling Huang, Yangbing Xu, Shuai Zhang, Wenyu Zhang


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.


association rule mining; Apriori algorithm; R programming; discipline competition

Full Text:


Copyright (c) 2018 Xiaoling Huang, Yangbing Xu, Shuai Zhang, Wenyu Zhang

International Journal of Emerging Technologies in Learning. ISSN: 1863-0383
Creative Commons License SPARC Europe Seal
Web of Science ESCI logo Engineering Information logo INSPEC logo DBLP logo ELSEVIER Scopus logo EDiTLib logo EBSCO logo Ulrich's logo Google Scholar logo Microsoft® Academic SearchDOAJ logo