Patterns to Identify Dropout University Students with Educational Data Mining

Patterns to Identify Dropout University Students with Educational Data Mining

This paper applies educational data mining algorithms to present an analysis of the most relevant characteristics of potential dropout students. The study used a dataset of 10,635 instances, acquired between 2014 and 2019 from 53 bachelor’s degree programs at a private university in the state of Pue...

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Autores principales: Urbina-Nájera, Argelia Berenice, Téllez-Velázquez, Arturo, Cruz Barbosa, Raúl
Formáid: Online
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Foilsithe: Universidad Autónoma de Baja California. Instituto de Investigación y Desarrollo Educativo 2021
Rochtain Ar Líne:https://redie.uabc.mx/redie/article/view/3918
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spelling redie-article-39182024-05-08T19:08:37Z Patterns to Identify Dropout University Students with Educational Data Mining Patrones que identifican a estudiantes universitarios desertores aplicando minería de datos educativa Urbina-Nájera, Argelia Berenice Téllez-Velázquez, Arturo Cruz Barbosa, Raúl deserción escolar características de la deserción toma de decisiones dropping out dropouts characteristics decision making This paper applies educational data mining algorithms to present an analysis of the most relevant characteristics of potential dropout students. The study used a dataset of 10,635 instances, acquired between 2014 and 2019 from 53 bachelor’s degree programs at a private university in the state of Puebla (Mexico). The results show that the model obtained from the decision trees performs better than other algorithms and allows for easy interpretation through decision rules. Furthermore, the model performs better than other related models in the literature that have been applied to the same problem. The methods used to select characteristics yielded the most important attributes to identify potential dropouts, such as the period, last semester completed, credits completed, attendance, courses failed, and program. These attributes and decision rules can be used to create mechanisms that help prevent dropout. En este trabajo se presenta un análisis de las características más relevantes de un potencial desertor universitario, mediante la aplicación de algoritmos de minería de datos educativa. Se utilizó un conjunto de datos de 10 635 instancias, adquiridas en el período 2014-2019, de 53 programas de licenciatura de una institución privada del estado de Puebla (México). Los resultados muestran que el modelo obtenido por los árboles de decisión ofrece mayor desempeño que otros algoritmos, así como una fácil interpretación de éste mediante reglas de decisión. Además, el rendimiento del modelo es mejor que otros modelos relacionados en la literatura aplicados al mismo problema. Los métodos de selección de características permitieron encontrar los atributos más importantes que identifican a un potencial desertor, tales como: el período, el último semestre cursado, créditos cursados, asistencia, materias reprobadas y programa. Utilizando los atributos y reglas de decisión encontradas se podrían crear mecanismos que favorezcan la prevención de la deserción. Universidad Autónoma de Baja California. Instituto de Investigación y Desarrollo Educativo 2021-12-20 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion text/html application/pdf text/xml https://redie.uabc.mx/redie/article/view/3918 10.24320/redie.2021.23.e29.3918 Revista Electrónica de Investigación Educativa; Vol. 23 (2021); 1-15 Revista Electrónica de Investigación Educativa; Vol. 23 (2021); 1-15 1607-4041 spa https://redie.uabc.mx/redie/article/view/3918/2195 https://redie.uabc.mx/redie/article/view/3918/2163 https://redie.uabc.mx/redie/article/view/3918/2170 Derechos de autor 2021 Argelia Berenice Urbina-Nájera, Arturo Téllez-Velázquez, Raúl Cruz Barbosa https://creativecommons.org/licenses/by-nc/4.0
institution REDIE
collection OJS
language spa
format Online
author Urbina-Nájera, Argelia Berenice
Téllez-Velázquez, Arturo
Cruz Barbosa, Raúl
spellingShingle Urbina-Nájera, Argelia Berenice
Téllez-Velázquez, Arturo
Cruz Barbosa, Raúl
Patterns to Identify Dropout University Students with Educational Data Mining
author_facet Urbina-Nájera, Argelia Berenice
Téllez-Velázquez, Arturo
Cruz Barbosa, Raúl
author_sort Urbina-Nájera, Argelia Berenice
title Patterns to Identify Dropout University Students with Educational Data Mining
title_short Patterns to Identify Dropout University Students with Educational Data Mining
title_full Patterns to Identify Dropout University Students with Educational Data Mining
title_fullStr Patterns to Identify Dropout University Students with Educational Data Mining
title_full_unstemmed Patterns to Identify Dropout University Students with Educational Data Mining
title_sort patterns to identify dropout university students with educational data mining
description This paper applies educational data mining algorithms to present an analysis of the most relevant characteristics of potential dropout students. The study used a dataset of 10,635 instances, acquired between 2014 and 2019 from 53 bachelor’s degree programs at a private university in the state of Puebla (Mexico). The results show that the model obtained from the decision trees performs better than other algorithms and allows for easy interpretation through decision rules. Furthermore, the model performs better than other related models in the literature that have been applied to the same problem. The methods used to select characteristics yielded the most important attributes to identify potential dropouts, such as the period, last semester completed, credits completed, attendance, courses failed, and program. These attributes and decision rules can be used to create mechanisms that help prevent dropout.
publisher Universidad Autónoma de Baja California. Instituto de Investigación y Desarrollo Educativo
publishDate 2021
url https://redie.uabc.mx/redie/article/view/3918
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