A Survey of Machine Learning Approaches and Techniques for Student Dropout Prediction

• Published in: Data Science Journal
• Main Authors: Neema Mduma, Khamisi Kalegele, Dina Machuve
• Format: Article
• Language: English
• Published: Ubiquity Press 2019-04-01
• Subjects: Machine Learning (ML); Imbalanced learning classification; Secondary education
• Online Access: https://datascience.codata.org/articles/896

School dropout is absenteeism from school for no good reason for a continuous number of days. Addressing this challenge requires a thorough understanding of the underlying issues and effective planning for interventions. Over the years machine learning has gained much attention on addressing the problem of students dropout. This is because machine learning techniques can effectively facilitate determination of at-risk students and timely planning for interventions. In order to collect, organize, and synthesize existing knowledge in the field of machine learning on addressing student dropout; literature in academic journals, books and case studies have been surveyed. The survey reveal that, several machine learning algorithms have been proposed in literature. However, most of those algorithms have been developed and tested in developed countries. Hence, developing countries are facing lack of research on the use of machine learning on addressing this problem. Furthermore, many studies focus on addressing student dropout using student level datasets. However, developing countries need to include school level datasets due to the issue of limited resources. Therefore, this paper presents an overview of machine learning in education with the focus on techniques for student dropout prediction. Furthermore, the paper highlights open challenges for future research directions.