Predicting Student Dropout based on Machine Learning and Deep Learning: A Systematic Review

IF 1.1 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS EAI Endorsed Transactions on Scalable Information Systems Pub Date : 2023-07-18 DOI:10.4108/eetsis.3586
Daniel Andrade Girón, Juana Sandivar Rosas, W. Marín-Rodriguez, E. Susanibar-Ramirez, Eliseo Toro-Dextre, J. Ausejo-Sánchez, Henry Villarreal-Torres, Julio Angeles-Morales
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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.
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基于机器学习和深度学习的学生退学预测:系统综述
学生辍学是全球教育系统面临的最复杂的挑战之一。为了评估机器学习和深度学习算法在预测学生辍学方面的成功,进行了系统回顾。在Scopus、IEEE、Web of Science等多个电子书目数据库中进行检索,检索时间截止到2023年6月,检索报告246篇。建立了排除标准,如综述文章、社论、信件和评论。最终的综述包括23项研究,其中评估了准确性/精密度、灵敏度/召回率、特异性和曲线下面积(AUC)等性能指标。此外,还考虑了与研究方式、训练、测试策略、交叉验证和混杂矩阵相关的方面。综述结果显示,使用最多的机器学习算法是随机森林,占21.73%的研究;该算法在预测学生辍学方面的准确率达到99%,高于所审查研究总数中使用的所有算法。
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来源期刊
EAI Endorsed Transactions on Scalable Information Systems
EAI Endorsed Transactions on Scalable Information Systems COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
2.80
自引率
15.40%
发文量
49
审稿时长
10 weeks
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