期刊名称：International Journal of Modern Education and Computer Science
印刷版ISSN：2075-0161
电子版ISSN：2075-017X
出版年度：2019
卷号：11
期号：7
页码：1-13
DOI：10.5815/ijmecs.2019.07.01
出版社：MECS Publisher
摘要：Nowadays, the application of data mining is widely prevalent in the education system. The ability of data mining to obtain meaningful information from meaningless data makes it very useful to predict students’ achievement, university’s performance, and many more. According to the Department of Statistics Malaysia, the numbers of student who do not manage to graduate on time rise dramatically every year. This challenging scenario worries many parties, especially university management teams. They have to timely devise strategies in order to enhance the students’ academic achievement and discover the main factors contributing to the timely graduation of undergraduate students. This paper discussed the factors utilized by other researchers from previous studies to predict students’ graduation time and to study the impact of different types of factors with different prediction methods. Taken together, findings of this research confirmed the usefulness of Neural Network and Support Vector Machine as the most competitive classifiers compared with Naïve Bayes and Decision Tree. Furthermore, our findings also indicate that the academic assessment was a prominent factor when predicting students’ graduation time.
关键词：Graduate on Time;Prediction;Data Mining;Higher Education
