Modelo de clasificación para la deserción estudiantil en una universidad pública del Perú

Henry Villarreal Torres; William Marín Rodriguez; Julio Ángeles Morales; Jenny Cano Mejía; Carmen Mejía Murillo

doi:10.56294/sctconf2023175

Conference abstract

Published: 2023-05-07

DOI: https://doi.org/10.56294/sctconf2023175

Classification model for student attrition in a Peru public university

Abstract

Information and communication technologies are playing a very important role in the different fields of knowledge; there is an increasing ability to identify patterns in the data of an organization; in this context, the study aimed to develop a classification model for student dropout by applying the autoML method of the H2Oai framework, taking into account socioeconomic and academic characteristics of students in order for authorities to make decisions in a timely manner. The methodology was technological, propositional level, incremental innovation, temporal scope, synchronous; the data collection was prospective, a 20-item questionnaire was applied to 237 graduate students of master’s degree programs in education. The research resulted in a supervised automatic learning model, Gradient Boosting Machine (GBM), to classify student desertion, identifying the main associated factors that influence desertion, obtaining a Gini coefficient of 92,20 %, AUC of 96,10 % and a LogLoss of 24,24 %.

Keywords:

Automl,

Machine Learning,

Student Dropout,

Higher Education,

Data Mining,

How to Cite

Villarreal Torres H, Marín Rodriguez W, Ángeles Morales J, Cano Mejía J, Mejía Murillo C. Classification model for student attrition in a Peru public university. Salud, Ciencia y Tecnología - Serie de Conferencias [Internet]. 2023 May 7 [cited 2024 Jul. 5];2:175. Available from: https://conferencias.saludcyt.ar/index.php/sctconf/article/view/175

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The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.

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IV Jornadas de Fundamentos y Aplicaciones de la Interdisciplina (JFAI2023)

Vol. 2 (2023)

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