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Application of the performance of machine learning techniques as support in the prediction of school dropout
Open accessAbstract
This article presents a study, intending to design a model with 90% reliability, which helps in the prediction of school dropouts in higher and secondary education institutions, implementing machine learning techniques. The collection of information was carried out with open data from the 2015 Intercensal Survey and the 2010 and 2020 Population and Housing censuses carried out by the National Institute of Statistics and Geography, which contain information about the inhabitants and homes. in the 32 federal entities of Mexico. The data were homologated and twenty variables were selected, based on the correlation. After cleaning the data, there was a sample of 1,080,782 records in total. Supervised learning was used to create the model, automating data processing with training and testing, applying the following techniques, Artificial Neural Networks, Support Vector Machines, Linear Ridge and Lasso Regression, Bayesian Optimization, Random Forest, the first two with a reliability greater than 99% and the last with 91%.
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“Jiménez-Gutiérrez et al. (2024) presentan un modelo de machine learning con más del 90% de fiabilidad en la predicción de abandono escolar en nivel medio, integrando variables académicas y socioeconómicas para orientar estrategias preventivas [14].”
From Avances, Retos Éticos y Perspectivas de la Inteligencia Artificial (IA) en la Educación de América Latina (2025) · cites this paper as [14]
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Research works printed but not linked: 14
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- Martínez, J. & Mateus, S. Propuesta de un Modelo Predictivo utilizando Aprendizaje Profundo para el análisis de deserción estudiantil en Universidades Colombianas Virtuales. Revista Innovación Digital y Desarrollo Sostenible-IDS 1(1), 51–57 (2020). · DOI
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Cited by
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Cabrera-Fuentes, A., Martínez Pérez, D. Z., & Rementeria Fuentes, J. J. (2025) · Ciencia Latina Revista Científica Multidisciplinar · doi:10.37811/cl_rcm.v9i4.18603 · Cited by 4
Metadata
- Authors
- Jiménez-Gutiérrez, A. L., Mota-Hernández, C. I., Mezura-Montes, E., & Alvarado-Corona, R.
- Affiliations
- Universidad de Guadalajara (MX)
- Universidad del Valle de México (MX)
- Corresponding author
- Jiménez-Gutiérrez, Auria Lucia
- Venue
- Scientific Reports
- Published
- 2024-02-17
- Volume
- 14
- Issue
- 1
- Pages
- 3957-3957
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC10874415
- OpenAlex
- W4391913445
- Cited by
- 14
- References
- 0
- Citations by year
- 2026: 5 · 2025: 5 · 2024: 4
- FWCI
- 5.27
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Universidad de Guadalajara
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Education › Educational Outcomes and Influences
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Keywords
- student dropout prediction, student retention, higher education, secondary education, census data, supervised learning, artificial neural networks, support vector machine, random forest, ridge regression, LASSO, Bayesian optimization, Mexico
- MeSH terms
- Machine Learning, Bayes Theorem, Humans, Student Dropouts, Reproducibility of Results, Neural Networks, Computer, Support Vector Machine
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- The paper
- Jiménez-Gutiérrez, A. L., Mota-Hernández, C. I., Mezura-Montes, E., & Alvarado-Corona, R. (2024). Application of the performance of machine learning techniques as support in the prediction of school dropout. Scientific Reports. https://doi.org/10.1038/s41598-024-53576-1
