Journal of Computer Science

A Data Mining Model for Students’ Choice of College Major Based on Rough Set Theory

Luai Al-Shalabi

DOI : 10.3844/jcssp.2019.1150.1160

Journal of Computer Science

Volume 15, Issue 8

Pages 1150-1160

Abstract

Literature is focusing on identifying factors that influence students’ initial choice of major and few have studied students’ involvements after registration in a selected major and this study is one of the few. This study aims to determine the important factors that influence high school students’ choice of major based on data mining techniques. A questionnaire was designed to collect data from students in different universities in Kuwait and in different faculties such as science, literature, medicine and engineering. Rough set theory for feature selection was used to highlight and explain the significant factors related to students’ skills and preferences awareness as well as their experience reflection that are responsible for the development of their satisfaction with the choice of their university majors. The findings of the study revealed that the calculated reducts have a significant influence on the students’ choice of the university and collage major. This research contributes to literature by identifying the relationship between the conditional factors of the reduct (also known as the independent variables) and the classification attribute (also known as the dependent variable). The results of the study give valuable information to the high school students so they know the best majors which suite their skills, preference and experiences. This research also help students not to continually change their major because of the wrong choice of major they made which accordingly lead them to dissatisfaction of their major.

Copyright

© 2019 Luai Al-Shalabi. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.