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

Tracking #: 539-1519

Responsible editor: 

Richard Mann

Submission Type: 

Survey Paper


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 in 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. To this end, several machine learning algorithms have been proposed in literature. This paper presents a survey of machine learning in education with focus on approaches and techniques for student-dropout prediction. Furthermore, the paper discusses the state of student dropout in developing countries and several performance metrics used by researchers to evaluate machine learning techniques in the context of education with experimental case study. Finally, the paper highlights challenges and future research directions.


Previous Version: 


  • Reviewed

Data repository URLs: 

Date of Submission: 

Monday, July 23, 2018

Date of Decision: 

Monday, September 10, 2018



Solicited Reviews:


Looks Better

Authors seem to have addressed some of the concerns. However, the paper still seems to be diverging into two topics--a review of machine learning methods and a review of educational data analysis. Doesn't work well as both, with text short-changing both topics to fit into a single paper. The machine learning algorithms being considered aren't described in detail, and the rationale for choosing them isn't clear. Should either be an education paper or revised as a machine learning paper.

Meta-Review by Editor

Your manuscript was re-reviewed by two expert referees. Their reviews indicate that they are not convinced that the weaknesses identified in the first round of review have been adequately addressed. In particular, it is still ambiguous whether the manuscript is aiming to survey the field or to present a particular case study. In the case of the former, the reviewers indicated that a more detailed review of key material is required. In the case of the latter, the reviewers indicated that the methodology for the case study needs to be described with greater precision and full justification of all methods.

Richard Mann (http://orcid.org/0000-0003-0701-1274)