Aplicación de modelos de Machine Learning para la predicción de la deserción escolar en estudiantes de una institución colombiana de formación por competencias.
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| Title: | Aplicación de modelos de Machine Learning para la predicción de la deserción escolar en estudiantes de una institución colombiana de formación por competencias. |
|---|---|
| Alternate Title: | Application of Machine Learning Models for Predicting School Dropout in Students from a Colombian Competency-based Education Institution. |
| Authors: | Castro-Maldonado, John Jairo1 jcastrom@sena.edu.co, Londoño-Gallego, Jennifer Andrea2 jealondonog@sena.edu.co, Rodríguez-Marín, Paula Andrea3 paularodriguez7913@correo.itm.edu.co, Martínez-Vargas, Juan David4 jdmartinev@eafit.edu.co |
| Source: | Comunicar. Apr2026, Vol. 34 Issue 85, p171-202. 32p. |
| Subject Terms: | *School dropouts, *Machine learning, *Higher education, *Outcome-based education, Data mining, Prediction models, Random forest algorithms |
| Abstract (English): | Student dropout is a structural challenge in Colombian higher education, particularly in contexts with rigid curricular and pedagogical systems where the implementation of timely preventive strategies is complex. This study develops and validates a hybrid machine learning model, based on the CRISP-DM methodology, that integrates supervised algorithms (Random Forest, Ridge, XGBoost, KNN) and unsupervised approaches (K-Means, DECLA), supported by dimensionality reduction and segmentation techniques (PCA, MCA). Using sociodemographic variables, academic performance indicators, and a specifically designed monitoring instrument, the models achieved high accuracy in anticipating dropout risk and segmenting students into profiles of high, medium, and low probability of withdrawal. Tree-based algorithms, particularly Random Forest, demonstrated the best performance, identifying critical predictors such as number of complaints, grade reversals, socioeconomic status, gender, and marital status. The main contribution of this work lies in moving predictive analytics from an experimental exercise to an institutional support system in competency-based higher education, where academic rigidity often limits early interventions. By anticipating dropout through real-time empirical evidence, the model enables the design of differentiated action pathways personalized tutoring, socioeconomic support, and curricular flexibility that complement long-term educational reforms. In this way, its relevance in higher education is justified as an innovative and evidence-based resource to strengthen student retention. [ABSTRACT FROM AUTHOR] |
| Abstract (Spanish): | La deserción estudiantil constituye un desafío estructural en la educación superior colombiana, especialmente en contextos con sistemas curriculares y pedagógicos rígidos donde resulta complejo implementar estrategias preventivas oportunas. Este estudio desarrolla y valida un modelo híbrido de aprendizaje automático, fundamentado en la metodología CRISPDM, que combina algoritmos supervisados (Random Forest, Ridge, XGBoost, KNN) y no supervisados (K-Means, DECLA), apoyados en técnicas de reducción y segmentación (PCA, ACM). A partir de variables sociodemográficas, indicadores de desempeño académico y un instrumento de seguimiento diseñado ad hoc, los modelos alcanzaron una alta precisión para anticipar el riesgo de abandono y segmentar a los estudiantes en perfiles de alta, media y baja probabilidad de deserción. Los algoritmos basados en árboles, en particular Random Forest, evidenciaron el mejor desempeño, identificando predictores críticos como cantidad de quejas, reversiones de calificaciones, estrato socioeconómico, género y estado civil. La principal contribución de este trabajo radica en trasladar la analítica predictiva de un ejercicio experimental hacia un sistema de apoyo institucional en programas de educación superior por competencias, donde la rigidez académica suele limitar la intervención temprana. Al anticipar la deserción mediante evidencia empírica en tiempo real, el modelo permite diseñar rutas diferenciadas de acción: tutorías personalizadas, apoyos socioeconómicos y flexibilización curricular que complementan las reformas educativas de largo plazo. De esta manera, se justifica su relevancia en la educación superior como recurso innovador y fundamentado para fortalecer la permanencia estudiantil. [ABSTRACT FROM AUTHOR] |
| Copyright of Comunicar is the property of Oxbridge Publishing House and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 193720784 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Aplicación de modelos de Machine Learning para la predicción de la deserción escolar en estudiantes de una institución colombiana de formación por competencias. – Name: TitleAlt Label: Alternate Title Group: TiAlt Data: Application of Machine Learning Models for Predicting School Dropout in Students from a Colombian Competency-based Education Institution. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Castro-Maldonado%2C+John+Jairo%22">Castro-Maldonado, John Jairo</searchLink><relatesTo>1</relatesTo><i> jcastrom@sena.edu.co</i><br /><searchLink fieldCode="AR" term="%22Londoño-Gallego%2C+Jennifer+Andrea%22">Londoño-Gallego, Jennifer Andrea</searchLink><relatesTo>2</relatesTo><i> jealondonog@sena.edu.co</i><br /><searchLink fieldCode="AR" term="%22Rodríguez-Marín%2C+Paula+Andrea%22">Rodríguez-Marín, Paula Andrea</searchLink><relatesTo>3</relatesTo><i> paularodriguez7913@correo.itm.edu.co</i><br /><searchLink fieldCode="AR" term="%22Martínez-Vargas%2C+Juan+David%22">Martínez-Vargas, Juan David</searchLink><relatesTo>4</relatesTo><i> jdmartinev@eafit.edu.co</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Comunicar%22">Comunicar</searchLink>. Apr2026, Vol. 34 Issue 85, p171-202. 32p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22School+dropouts%22">School dropouts</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Higher+education%22">Higher education</searchLink><br />*<searchLink fieldCode="DE" term="%22Outcome-based+education%22">Outcome-based education</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink> – Name: Abstract Label: Abstract (English) Group: Ab Data: Student dropout is a structural challenge in Colombian higher education, particularly in contexts with rigid curricular and pedagogical systems where the implementation of timely preventive strategies is complex. This study develops and validates a hybrid machine learning model, based on the CRISP-DM methodology, that integrates supervised algorithms (Random Forest, Ridge, XGBoost, KNN) and unsupervised approaches (K-Means, DECLA), supported by dimensionality reduction and segmentation techniques (PCA, MCA). Using sociodemographic variables, academic performance indicators, and a specifically designed monitoring instrument, the models achieved high accuracy in anticipating dropout risk and segmenting students into profiles of high, medium, and low probability of withdrawal. Tree-based algorithms, particularly Random Forest, demonstrated the best performance, identifying critical predictors such as number of complaints, grade reversals, socioeconomic status, gender, and marital status. The main contribution of this work lies in moving predictive analytics from an experimental exercise to an institutional support system in competency-based higher education, where academic rigidity often limits early interventions. By anticipating dropout through real-time empirical evidence, the model enables the design of differentiated action pathways personalized tutoring, socioeconomic support, and curricular flexibility that complement long-term educational reforms. In this way, its relevance in higher education is justified as an innovative and evidence-based resource to strengthen student retention. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Abstract (Spanish) Group: Ab Data: La deserción estudiantil constituye un desafío estructural en la educación superior colombiana, especialmente en contextos con sistemas curriculares y pedagógicos rígidos donde resulta complejo implementar estrategias preventivas oportunas. Este estudio desarrolla y valida un modelo híbrido de aprendizaje automático, fundamentado en la metodología CRISPDM, que combina algoritmos supervisados (Random Forest, Ridge, XGBoost, KNN) y no supervisados (K-Means, DECLA), apoyados en técnicas de reducción y segmentación (PCA, ACM). A partir de variables sociodemográficas, indicadores de desempeño académico y un instrumento de seguimiento diseñado ad hoc, los modelos alcanzaron una alta precisión para anticipar el riesgo de abandono y segmentar a los estudiantes en perfiles de alta, media y baja probabilidad de deserción. Los algoritmos basados en árboles, en particular Random Forest, evidenciaron el mejor desempeño, identificando predictores críticos como cantidad de quejas, reversiones de calificaciones, estrato socioeconómico, género y estado civil. La principal contribución de este trabajo radica en trasladar la analítica predictiva de un ejercicio experimental hacia un sistema de apoyo institucional en programas de educación superior por competencias, donde la rigidez académica suele limitar la intervención temprana. Al anticipar la deserción mediante evidencia empírica en tiempo real, el modelo permite diseñar rutas diferenciadas de acción: tutorías personalizadas, apoyos socioeconómicos y flexibilización curricular que complementan las reformas educativas de largo plazo. De esta manera, se justifica su relevancia en la educación superior como recurso innovador y fundamentado para fortalecer la permanencia estudiantil. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Comunicar is the property of Oxbridge Publishing House and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.5281/zenodo.19690782 Languages: – Code: spa Text: Spanish PhysicalDescription: Pagination: PageCount: 32 StartPage: 171 Subjects: – SubjectFull: School dropouts Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Higher education Type: general – SubjectFull: Outcome-based education Type: general – SubjectFull: Data mining Type: general – SubjectFull: Prediction models Type: general – SubjectFull: Random forest algorithms Type: general Titles: – TitleFull: Aplicación de modelos de Machine Learning para la predicción de la deserción escolar en estudiantes de una institución colombiana de formación por competencias. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Castro-Maldonado, John Jairo – PersonEntity: Name: NameFull: Londoño-Gallego, Jennifer Andrea – PersonEntity: Name: NameFull: Rodríguez-Marín, Paula Andrea – PersonEntity: Name: NameFull: Martínez-Vargas, Juan David IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 11343478 Numbering: – Type: volume Value: 34 – Type: issue Value: 85 Titles: – TitleFull: Comunicar Type: main |
| ResultId | 1 |
