sis 23(5):

Research Article

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

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  • @ARTICLE{10.4108/,
        author={Henry Villarreal-Torres and Julio \^{A}ngeles-Morales and William Mar\^{\i}n-Rodriguez and Daniel Andrade-Gir\^{o}n and Jenny Cano-Mej\^{\i}a and Carmen Mej\^{\i}a-Murillo and Gumercindo Flores-Reyes and Manuel Palomino-M\^{a}rquez},
        title={Classification model for student dropouts using machine learning: A case study},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        keywords={autoML, machine learning, Student dropout, higher education,, data mining},
  • Henry Villarreal-Torres
    Julio Ángeles-Morales
    William Marín-Rodriguez
    Daniel Andrade-Girón
    Jenny Cano-Mejía
    Carmen Mejía-Murillo
    Gumercindo Flores-Reyes
    Manuel Palomino-Márquez
    Year: 2023
    Classification model for student dropouts using machine learning: A case study
    DOI: 10.4108/
Henry Villarreal-Torres1, Julio Ángeles-Morales1, William Marín-Rodriguez2,*, Daniel Andrade-Girón2, Jenny Cano-Mejía1, Carmen Mejía-Murillo1, Gumercindo Flores-Reyes1, Manuel Palomino-Márquez1
  • 1: Universidad San Pedro
  • 2: Universidad Nacional José Faustino Sánchez Carrión
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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 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.