Research Article
Predicting Student Dropout based on Machine Learning and Deep Learning: A Systematic Review
@ARTICLE{10.4108/eetsis.3586, author={Daniel Andrade-Gir\^{o}n and Juana Sandivar-Rosas and William Mar\^{\i}n-Rodriguez and Edgar Susanibar-Ramirez and Eliseo Toro-Dextre and Jose Ausejo-Sanchez and Henry Villarreal-Torres and Julio Angeles-Morales}, title={Predicting Student Dropout based on Machine Learning and Deep Learning: A Systematic Review}, journal={EAI Endorsed Transactions on Scalable Information Systems}, volume={10}, number={5}, publisher={EAI}, journal_a={SIS}, year={2023}, month={7}, keywords={prediction, student attrition, machine learning, deep learning}, doi={10.4108/eetsis.3586} }- Daniel Andrade-Girón
Juana Sandivar-Rosas
William Marín-Rodriguez
Edgar Susanibar-Ramirez
Eliseo Toro-Dextre
Jose Ausejo-Sanchez
Henry Villarreal-Torres
Julio Angeles-Morales
Year: 2023
Predicting Student Dropout based on Machine Learning and Deep Learning: A Systematic Review
SIS
EAI
DOI: 10.4108/eetsis.3586
Abstract
Student dropout is one of the most complex challenges facing the education system worldwide. In order to evaluate the success of Machine Learning and Deep Learning algorithms in predicting student dropout, a systematic review was conducted. The search was carried out in several electronic bibliographic databases, including Scopus, IEEE, and Web of Science, covering up to June 2023, having 246 articles as search reports. Exclusion criteria, such as review articles, editorials, letters, and comments, were established. The final review included 23 studies in which performance metrics such as accuracy/precision, sensitivity/recall, specificity, and area under the curve (AUC) were evaluated. In addition, aspects related to study modality, training, testing strategy, cross-validation, and confounding matrix were considered. The review results revealed that the most used Machine Learning algorithm was Random Forest, present in 21.73% of the studies; this algorithm obtained an accuracy of 99% in the prediction of student dropout, higher than all the algorithms used in the total number of studies reviewed.
Copyright © 2023 Girón et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
