Classification model for student dropouts using machine learning: A case study

IF 1.1 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS EAI Endorsed Transactions on Scalable Information Systems Pub Date : 2023-06-15 DOI:10.4108/eetsis.vi.3455
Henry Villarreal-Torres, Julio Angeles-Morales, W. Marín-Rodriguez, Daniel Andrade Girón, Jenny Cano-Mejía, Carmen Mejía-Murillo, Gumercindo Flores-Reyes, Manuel Palomino-Márquez
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引用次数: 1

Abstract

Information and communication technologies have been fulfilling a highly relevant role in the different fields of knowledge, addressing problems in various disciplines; there is an increased capacity to identify patterns and anomalies in an organization's data using data mining; In this context, the study aimed to develop a classification model for student dropout, applying machine learning with the autoML method of the H2O.ai framework; the dimensionality of the socioeconomic and academic characteristics has been taken into account, with the purpose that the directors make reasonable decisions to counteract the abandonment of the students in the study programs. The methodology used was of a technological type, purposeful level, incremental innovation, temporal scope, and synchronous; data collection was prospective. For this, a 20-item questionnaire was applied to 237 students enrolled in the master's degree programs in the education of the Graduate School. The research resulted in a supervised machine learning model, Gradient Reinforcement Machine (GBM), to classify student dropout, thus identifying the main associated factors that influence dropout, obtaining a Gini coefficient of 92.20%, AUC of 96.10% and a LogLoss of 24.24% representing a model with efficient performance.
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使用机器学习的学生辍学分类模型:一个案例研究
信息和通信技术在不同的知识领域发挥着高度相关的作用,解决了不同学科的问题;使用数据挖掘识别组织数据中的模式和异常的能力有所提高;在此背景下,本研究旨在利用机器学习和H2O的autoML方法,开发一个学生辍学分类模型。ai框架;考虑到社会经济和学术特征的维度,目的是让主管做出合理的决定,以抵消学生在学习计划中的放弃。采用的方法是技术型、目的性、渐进式创新、时间范围和同步的;数据收集是前瞻性的。为此,对237名研究生院教育专业硕士研究生进行了问卷调查。研究建立了一个有监督的机器学习模型——梯度强化机(Gradient Reinforcement machine, GBM),对学生辍学进行分类,从而识别出影响辍学的主要相关因素,得到的Gini系数为92.20%,AUC为96.10%,LogLoss为24.24%,表明该模型具有高效的性能。
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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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