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Student Dataset from Tecnologico de Monterrey in Mexico to Predict Dropout in Higher Education
Open accessAbstract
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.
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“SCM systems, often data-heavy and logistics-driven, benefit from dashboard simplification and scenario-based prototyping to aid decision-making (Alvarado-Uribe et al., 2022).”
From A Systematic Literature Review of User-Centric Design in Digital Business Systems Enhancing Accessibility, Adoption, and Organizational Impact (2023) · cites this paper as Alvarado-Uribe et al., 2022
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“For instance, a website may meet WCAG 2.1 standards but still be frustrating to navigate for users with cognitive impairments if content is overly complex or navigation is unintuitive (Alvarado-Uribe et al., 2022).”
From A Systematic Literature Review of User-Centric Design in Digital Business Systems Enhancing Accessibility, Adoption, and Organizational Impact (2023) · cites this paper as Alvarado-Uribe et al., 2022
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Fahd, K., Venkatraman, S., Miah, S. J., & Ahmed, K. (2022) · Education and Information Technologies · doi:10.1007/s10639-021-10741-7 · Cited by 110
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Cited by
Master of Science in Management Information Systems, L. U., Barua, T., Rahman, M. A., & MBA in Management Information System, I. A. U. (2023) · American Journal of Scholarly Research and Innovation · doi:10.63125/36w7fn47 · Cited by 24
Metadata
- Authors
- Alvarado-Uribe, J., Mejía-Almada, P., Masetto Herrera, A. L., Molontay, R., Hilliger, I., Hegde, V., Montemayor Gallegos, J. E., Ramírez Díaz, R. A., & Ceballos, H. G.
- Affiliations
- Tecnológico de Monterrey (MX)
- Analytics and Business Intelligence Department, Tecnologico de Monterrey, Monterrey 64849, Mexico
- Budapest University of Technology and Economics (HU)
- Pontificia Universidad Católica de Chile (CL)
- Amrita Vishwa Vidyapeetham (IN)
- Institute for the Future of Education, Tecnologico de Monterrey, Monterrey 64849, Mexico
- Corresponding author
- Alvarado-Uribe, Joanna
- ORCID
- Venue
- Data
- Publisher
- MDPI AG
- Published
- 2022-08-25
- Volume
- 7
- Issue
- 9
- Pages
- 119
- ISSN
- 2306-5729 (ISSN-L)
- OpenAlex
- W4293103712
- Cited by
- 55
- References
- 1
- Citations by year
- 2026: 10 · 2025: 22 · 2024: 12 · 2023: 11
- FWCI
- 7.72
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Data (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- the Institute for the Future of Education
- the Tecnologico de Monterrey
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Education › Evasion and Academic Success Factors
- Social Sciences › Education › Higher Education Research Studies
- Keywords
- student dropout prediction, higher education dropout, student persistence, at-risk students, dropout risk factors, sociodemographic data, academic performance, student life, university students, Tecnológico de Monterrey
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- CoChat. (2026). Citation check: Student Dataset from Tecnologico de Monterrey in Mexico to Predict Dropout in Higher Education. https://app.cochat.ai/papers/10.3390/data7090119
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- Alvarado-Uribe, J., Mejía-Almada, P., Masetto Herrera, A. L., Molontay, R., Hilliger, I., Hegde, V., Montemayor Gallegos, J. E., Ramírez Díaz, R. A., & Ceballos, H. G. (2022). Student Dataset from Tecnologico de Monterrey in Mexico to Predict Dropout in Higher Education. Data. https://doi.org/10.3390/data7090119
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
