Student Dataset from Tecnologico de Monterrey in Mexico to Predict Dropout in Higher Education

Joanna Alvarado-Uribe (), Paola Mejía-Almada, Ana Luisa Masetto Herrera, Roland Molontay, Isabel Hilliger, Vinayak Hegde, José Enrique Montemayor Gallegos, Renato Armando Ramírez Díaz and Hector G. Ceballos
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Joanna Alvarado-Uribe: Institute for the Future of Education, Tecnologico de Monterrey, Monterrey 64849, Mexico
Paola Mejía-Almada: Institute for the Future of Education, Tecnologico de Monterrey, Monterrey 64849, Mexico
Ana Luisa Masetto Herrera: Analytics and Business Intelligence Department, Tecnologico de Monterrey, Monterrey 64849, Mexico
Roland Molontay: Department of Stochastics, Institute of Mathematics, Budapest University of Technology and Economics, 1111 Budapest, Hungary
Isabel Hilliger: School of Engineering, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile
Vinayak Hegde: Department of Computer Science, Mysuru Campus, Amrita Vishwa Vidyapeetham, Mysore 570026, India
José Enrique Montemayor Gallegos: Analytics and Business Intelligence Department, Tecnologico de Monterrey, Monterrey 64849, Mexico
Renato Armando Ramírez Díaz: Analytics and Business Intelligence Department, Tecnologico de Monterrey, Monterrey 64849, Mexico
Hector G. Ceballos: Institute for the Future of Education, Tecnologico de Monterrey, Monterrey 64849, Mexico

Data, 2022, vol. 7, issue 9, 1-17

Abstract: High dropout rates and delayed completion in higher education are associated with considerable personal and social costs. In Latin America, 50% of students drop out, and only 50% of the remaining ones graduate on time. Therefore, there is an urgent need to identify students at risk and understand the main factors of dropping out. Together with the emergence of efficient computational methods, the rich data accumulated in educational administrative systems have opened novel approaches to promote student persistence. In order to support research related to preventing student dropout, a dataset has been gathered and curated from Tecnologico de Monterrey students, consisting of 50 variables and 143,326 records. The dataset contains non-identifiable information of 121,584 High School and Undergraduate students belonging to the seven admission cohorts from August–December 2014 to 2020, covering two educational models. The variables included in this dataset consider factors mentioned in the literature, such as sociodemographic and academic information related to the student, as well as institution-specific variables, such as student life. This dataset provides researchers with the opportunity to test different types of models for dropout prediction, so as to inform timely interventions to support at-risk students.

Keywords: dropout prediction; student attrition; machine learning; educational data mining; learning analytics; educational innovation; higher education (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2022
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