Abstract
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.
Highlights
Reducing student dropout rates is one of the challenges facing in the education sector globally
In Tanzania, for example, student dropout is higher in lower secondary education compared to higher level where girls are much less likely to finish secondary education comparing to boys; 30% of girls dropout before reaching form 4 as compared to 15% percent for boys (President’s Office et al, 2016)
The surveyed papers focused on several works which have been done on machine learning in education such as student dropout prediction, student academic performance prediction, student final result prediction etc
Summary
Reducing student dropout rates is one of the challenges facing in the education sector globally. Enabling students to complete their education means investing in future progress and better standards of life with multiplier effects To effectively address this problem, it is crucial to ensure that all students finish their school on time through early intervention on students who might be at risk of dropping classes. A UNESCO (2011) report points out, that about one thirty million children in the developing world denied their right to education through dropping out (Latif et al, 2015) In responding to this problem of dropping out and other challenges facing secondary schools, Tanzania as one among developing countries introduced an Education Training Policy (ETP) and Education Sector Development Plan (ESDP) (TAMISEMI, 2004). The intervention points included issues related to algorithms for predicting dropouts
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