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Predictive Model to Identify College Students with High Dropout Rates
Open accessAbstract
Decreasing student attrition rates is one of the main objectives of most higher education institutions. However, to achieve this goal, universities need to accurately identify and focus their efforts on students most likely to quit their studies before they graduate. This has given rise to a need to implement forecasting models to predict which students will eventually drop out. In this paper, we present an early warning system to automatically identify first-semester students at high risk of dropping out. The system is based on a machine learning model trained from historical data on first-semester students. The results show that the system can predict “at-risk” students with a sensitivity of 61.97%, which allows early intervention for those students, thereby reducing the student attrition rate.
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“HoyosOsorio (2023) describe un sistema de alerta temprana en Colombia que, mediante regresión logística y árboles de decisión, alcanzó una precisión del 88% al predecir deserciones en primer semestre universitario, permitiendo intervenciones focalizadas [12].”
From Avances, Retos Éticos y Perspectivas de la Inteligencia Artificial (IA) en la Educación de América Latina (2025) · cites this paper as [12]
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“La automatización de evaluaciones -ejemplificada en plataformas colombianas de alerta temprana [12] - emerge como respuesta regional a la sobrecarga administrativa, optimizando la retroalimentación formativa y liberando tiempo pedagógico.”
From Avances, Retos Éticos y Perspectivas de la Inteligencia Artificial (IA) en la Educación de América Latina (2025) · cites this paper as [12]
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Research works printed but not linked: 25
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Cited by
Sorted by citations
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
- Hoyos Osorio, J. K., & Daza Santacoloma, G.
- Venue
- Revista Electrónica de Investigación Educativa
- Published
- 2023-05-03
- Volume
- 25
- Pages
- 1-10
- ISSN
- 1607-4041 (ISSN-L)
- OpenAlex
- W4367856884
- Cited by
- 10
- References
- 0
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 3 · 2023: 2
- FWCI
- 2.28
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Revista Electrónica de Investigación Educativa (publishedVersion, cc-by-nc)
- Dialnet (Universidad de la Rioja) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Education › Evasion and Academic Success Factors
- Social Sciences › Business, Management and Accounting › Marketing › Customer churn and segmentation
- Keywords
- student dropout prediction, student attrition, early warning systems, at-risk students, first-semester students, university students, early intervention, predictive modeling
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- Hoyos Osorio, J. K., & Daza Santacoloma, G. (2023). Predictive Model to Identify College Students with High Dropout Rates. Revista Electrónica de Investigación Educativa. https://doi.org/10.24320/redie.2023.25.e13.5398
