Explanatory Factors of University Dropout Explored Through Artificial Intelligence

Explanatory Factors of University Dropout Explored Through Artificial Intelligence

This paper identifies key research on the factors that help to explain university dropout and how these factors are being explored by means of artificial intelligence (AI). The study describes the methodology employed to select 31 papers from a repository of 2,745 reported in the literature. The ana...

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Autores principales: Parra-Sánchez, Juan Sebastián, Torres Pardo, Ingrid Durley, Martínez De Merino, Carmen Ysabel
Format: Online
Sprog:spa
Udgivet: Universidad Autónoma de Baja California. Instituto de Investigación y Desarrollo Educativo 2023
Online adgang:https://redie.uabc.mx/redie/article/view/4455
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spelling redie-article-44552024-05-03T18:41:22Z Explanatory Factors of University Dropout Explored Through Artificial Intelligence Factores explicativos de la deserción universitaria abordados mediante inteligencia artificial Fatores explicativos da evasão universitária abordados por meio da inteligência artificial Parra-Sánchez, Juan Sebastián Torres Pardo, Ingrid Durley Martínez De Merino, Carmen Ysabel deserción escolar tasa de deserción escolar estudiante universitario inteligencia artificial dropping out dropout rate college students artificial intelligence evasão escolar taxa de evasão estudante universitário inteligência artificial This paper identifies key research on the factors that help to explain university dropout and how these factors are being explored by means of artificial intelligence (AI). The study describes the methodology employed to select 31 papers from a repository of 2,745 reported in the literature. The analysis centered on the main AI methods used and four categories of explanatory factors of university dropout: academic factors; factors associated with motivation and study habits; institutional factors; and economic and sociodemographic factors. The conclusion drawn from this literature review is that AI is most commonly used for decision tree-based classification, and most studies focus on predicting university dropout on the basis of explanatory factors. Este artículo identifica los principales estudios relacionados con los factores que contribuyen a explicar la deserción universitaria, y cómo estos son abordados desde el campo de la inteligencia artificial (IA). El estudio describe la metodología adoptada para seleccionar 31 documentos sobre un repositorio de 2745 reportados en la literatura. El análisis se realizó desde los principales métodos de IA adoptados, así como desde los factores explicativos de la deserción universitaria agrupados en cuatro categorías: académicos, relacionados con la motivación y hábitos de estudio, institucionales, y económicos y sociodemográficos. La revisión de la literatura permite concluir que la tarea más común desde la IA es la clasificación mediante árboles de decisión y que la mayoría de los trabajos predicen la deserción universitaria desde los factores que la explican. Este artigo identifica os principais estudos relacionados aos fatores que contribuem para explicar a evasão universitária e como eles são abordados a partir do campo da inteligência artificial (IA). O estudo descreve a metodologia adotada para selecionar 31 documentos de um repositório de 2745 relatados na literatura. A análise foi realizada a partir dos principais métodos de IA adotados, bem como dos fatores explicativos da evasão universitária agrupados em quatro categorias: acadêmica, relacionada com a motivação e hábitos de estudo, institucional, e econômica e sociodemográfica. A revisão da literatura permite concluir que a tarefa mais comum da IA é a classificação por meio de árvores de decisão e que a maioria dos trabalhos prevê a evasão universitária a partir dos fatores que a explicam. Universidad Autónoma de Baja California. Instituto de Investigación y Desarrollo Educativo 2023-06-29 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion text/html application/pdf text/xml application/epub+zip audio/mpeg https://redie.uabc.mx/redie/article/view/4455 10.24320/redie.2023.25.e18.4455 Revista Electrónica de Investigación Educativa; Vol. 25 (2023); 1-17 Revista Electrónica de Investigación Educativa; Vol. 25 (2023); 1-17 1607-4041 spa https://redie.uabc.mx/redie/article/view/4455/2424 https://redie.uabc.mx/redie/article/view/4455/2425 https://redie.uabc.mx/redie/article/view/4455/2439 https://redie.uabc.mx/redie/article/view/4455/2428 https://redie.uabc.mx/redie/article/view/4455/2430 Derechos de autor 2023 Juan Sebastián Parra-Sánchez; Ingrid Durley Torres Pardo; Carmen Ysabel Martínez De Merino https://creativecommons.org/licenses/by-nc/4.0
institution REDIE
collection OJS
language spa
format Online
author Parra-Sánchez, Juan Sebastián
Torres Pardo, Ingrid Durley
Martínez De Merino, Carmen Ysabel
spellingShingle Parra-Sánchez, Juan Sebastián
Torres Pardo, Ingrid Durley
Martínez De Merino, Carmen Ysabel
Explanatory Factors of University Dropout Explored Through Artificial Intelligence
author_facet Parra-Sánchez, Juan Sebastián
Torres Pardo, Ingrid Durley
Martínez De Merino, Carmen Ysabel
author_sort Parra-Sánchez, Juan Sebastián
title Explanatory Factors of University Dropout Explored Through Artificial Intelligence
title_short Explanatory Factors of University Dropout Explored Through Artificial Intelligence
title_full Explanatory Factors of University Dropout Explored Through Artificial Intelligence
title_fullStr Explanatory Factors of University Dropout Explored Through Artificial Intelligence
title_full_unstemmed Explanatory Factors of University Dropout Explored Through Artificial Intelligence
title_sort explanatory factors of university dropout explored through artificial intelligence
description This paper identifies key research on the factors that help to explain university dropout and how these factors are being explored by means of artificial intelligence (AI). The study describes the methodology employed to select 31 papers from a repository of 2,745 reported in the literature. The analysis centered on the main AI methods used and four categories of explanatory factors of university dropout: academic factors; factors associated with motivation and study habits; institutional factors; and economic and sociodemographic factors. The conclusion drawn from this literature review is that AI is most commonly used for decision tree-based classification, and most studies focus on predicting university dropout on the basis of explanatory factors.
publisher Universidad Autónoma de Baja California. Instituto de Investigación y Desarrollo Educativo
publishDate 2023
url https://redie.uabc.mx/redie/article/view/4455
_version_ 1798984268562563072

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