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  4. Supporting Decision-Making Process on Higher Education Dropout by Analyzing Academic, Socioeconomic, and Equity Factors through Machine Learning and Survival Analysis Methods in the Latin American Context
 
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Supporting Decision-Making Process on Higher Education Dropout by Analyzing Academic, Socioeconomic, and Equity Factors through Machine Learning and Survival Analysis Methods in the Latin American Context

Journal
Education Sciences
ISSN
2227-7102
Date Issued
2023-02-01
Author(s)
Daniel A. Gutierrez-Pachas
Germain Garcia-Zanabria
Ernesto Cuadros-Vargas
Guillermo Camara-Chavez
Gómez Nieto, Erick Mauricio  
Departamento de Ciencias Económicas y Empresariales  
DOI
http://dx.doi.org/10.3390/educsci13020154
Abstract
The prediction of university dropout is a complex problem, given the number and diversity of variables involved. Therefore, different strategies are applied to understand this educational phenomenon, although the most outstanding derive from the joint application of statistical approaches and computational techniques based on machine learning. Student Dropout Prediction (SDP) is a challenging problem that can be addressed following various strategies. On the one hand, machine learning approaches formulate it as a classification task whose objective is to compute the probability of belonging to a class based on a specific feature vector that will help us to predict who will drop out. Alternatively, survival analysis techniques are applied in a time-varying context to predict when abandonment will occur. This work considered analytical mechanisms for supporting the decision-making process on higher education dropout. We evaluated different computational methods from both approaches for predicting who and when the dropout occurs and sought those with the most-consistent results. Moreover, our research employed a longitudinal dataset including demographic, socioeconomic, and academic information from six academic departments of a Latin American university over thirteen years. Finally, this study carried out an in-depth analysis, discusses how such variables influence estimating the level of risk of dropping out, and questions whether it occurs at the same magnitude or not according to the academic department, gender, socioeconomic group, and other variables.
Project(s)
Ciencia de Datos en la Educación: Análisis de grandes volúmenes de datos usando métodos computacionales para detectar y prevenir problemas de violencia y deserción en entornos educativos  
Subjects

Student dropout predi...

Machine learning mode...

Survival analysis

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