Review on Predicting Students’ Graduation Time Using Machine Learning Algorithms

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Nurafifah Mohammad Suhaimi 1,* Shuzlina Abdul Rahman 1 Sofianita Mutalib 1 Nurzeatul Hamimah Abdul Hamid 1 Ariff Md Ab Malik 1

1. Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, 40450 Shah Alam, Selangor

* Corresponding author.


Received: 19 Mar. 2019 / Revised: 13 Apr. 2019 / Accepted: 23 May 2019 / Published: 8 Jul. 2019

Index Terms

Graduate on Time, Prediction, Data Mining, Higher Education


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.

Cite This Paper

Nurafifah Mohammad Suhaimi, Shuzlina Abdul-Rahman, Sofianita Mutalib, Nurzeatul Hamimah Abdul Hamid, Ariff Md Ab Malik, "Review on Predicting Students’ Graduation Time Using Machine Learning Algorithms", International Journal of Modern Education and Computer Science(IJMECS), Vol.11, No.7, pp. 1-13, 2019.DOI: 10.5815/ijmecs.2019.07.01


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