A review on machine learning techniques used for students’ performance prediction

N.H., Abdul Rahman and Sahimel Azwal, Sulaiman and Nor Azuana, Ramli (2022) A review on machine learning techniques used for students’ performance prediction. In: The 6th National Conference for Postgraduate Research (NCON-PGR 2022) , 15 November 2022 , Virtual Conference, Universiti Malaysia Pahang, Malaysia. p. 119..

A review on machine learning techniques used for students’ performance prediction.pdf

Download (556kB) | Preview


Research on predictive models has been widely used in higher educational institutions, especially in predicting students’ performance. Results that were obtained through predictive models can help lecturers in ensuring students’ achievement so that students’ failure rates can be reduced. Higher failure rates have a negative impact not only on students but also on institutions and shareholders. In this paper, thirty journals and case studies have been reviewed where the most important part highlighted is machine learning techniques that have been used in developing predictive models to predict students’ performance from the previous six years. Although the main objective of this paper is to provide an overview of machine learning techniques in predicting students’ performance, it is also important for researchers to identify the target variable used in those techniques as these two objectives are related to each other. In conclusion, a student’s final grade is the most widely used as a target variable, and the Decision Tree method is the most frequently used machine learning technique by the authors in the previous studies.

Item Type: Conference or Workshop Item (Lecture)
Uncontrolled Keywords: Higher education; Machine learning; Predictive models; Students’ performance.
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics
Faculty/Division: Institute of Postgraduate Studies
Center for Mathematical Science
Depositing User: Mr Muhamad Firdaus Janih@Jaini
Date Deposited: 10 Feb 2023 03:49
Last Modified: 10 Feb 2023 03:49
URI: http://umpir.ump.edu.my/id/eprint/36957
Download Statistic: View Download Statistics

Actions (login required)

View Item View Item