Predicción de riesgo de deserción estudiantil en educación superior: Un enfoque en aprendizaje automático

Autores:: Peralta-Fragozo, Andres
Hoyos-Sánchez, Juan-Pablo

Tipo de recurso:: Article of journal

Fecha de publicación:: 2025

Institución:: Universidad Pedagógica y Tecnológica de Colombia

Repositorio:: RiUPTC: Repositorio Institucional UPTC

Idioma:

id	REPOUPTC2_9be4edf765a95761ffabe9ba014eec89
oai_identifier_str	oai:repositorio.uptc.edu.co:001/21614
network_acronym_str	REPOUPTC2
network_name_str	RiUPTC: Repositorio Institucional UPTC
repository_id_str
dc.title.es-ES.fl_str_mv	Predicción de riesgo de deserción estudiantil en educación superior: Un enfoque en aprendizaje automático
title	Predicción de riesgo de deserción estudiantil en educación superior: Un enfoque en aprendizaje automático
spellingShingle	Predicción de riesgo de deserción estudiantil en educación superior: Un enfoque en aprendizaje automático Student dropout Machine learning XGBoost Random Forest SVM Balanced Random Forest Classifier SMOTE Classification Higher education Deserción estudiantil Aprendizaje automático Random Foret Clasificación Educación superior
title_short	Predicción de riesgo de deserción estudiantil en educación superior: Un enfoque en aprendizaje automático
title_full	Predicción de riesgo de deserción estudiantil en educación superior: Un enfoque en aprendizaje automático
title_fullStr	Predicción de riesgo de deserción estudiantil en educación superior: Un enfoque en aprendizaje automático
title_full_unstemmed	Predicción de riesgo de deserción estudiantil en educación superior: Un enfoque en aprendizaje automático
title_sort	Predicción de riesgo de deserción estudiantil en educación superior: Un enfoque en aprendizaje automático
dc.creator.fl_str_mv	Peralta-Fragozo, Andres Hoyos-Sánchez, Juan-Pablo
dc.contributor.author.eng.fl_str_mv	Peralta-Fragozo, Andres Hoyos-Sánchez, Juan-Pablo
dc.subject.none.fl_str_mv	Student dropout Machine learning XGBoost Random Forest SVM Balanced Random Forest Classifier SMOTE Classification Higher education Deserción estudiantil Aprendizaje automático Random Foret Clasificación Educación superior
topic	Student dropout Machine learning XGBoost Random Forest SVM Balanced Random Forest Classifier SMOTE Classification Higher education Deserción estudiantil Aprendizaje automático Random Foret Clasificación Educación superior
publishDate	2025
dc.date.accessioned.none.fl_str_mv	2025-10-17 19:50:06 2026-04-16T17:47:50Z
dc.date.available.none.fl_str_mv	2025-10-17 19:50:06
dc.date.issued.none.fl_str_mv	2025-10-17
dc.type.spa.fl_str_mv	Artículo de revista
dc.type.coar.fl_str_mv	http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coar.none.fl_str_mv	http://purl.org/coar/resource_type/c_6501
dc.type.driver.none.fl_str_mv	info:eu-repo/semantics/article
dc.type.local.eng.fl_str_mv	Journal article
dc.type.version.none.fl_str_mv	info:eu-repo/semantics/publishedVersion
dc.type.content.none.fl_str_mv	Text
dc.type.coarversion.none.fl_str_mv	http://purl.org/coar/version/c_970fb48d4fbd8a85
format	http://purl.org/coar/resource_type/c_6501
status_str	publishedVersion
dc.identifier.issn.none.fl_str_mv	0121-1129
dc.identifier.uri.none.fl_str_mv	https://repositorio.uptc.edu.co/handle/001/21614
dc.identifier.doi.none.fl_str_mv	10.19053/uptc.01211129.v34.n73.2025.20107
dc.identifier.eissn.none.fl_str_mv	2357-5328
dc.identifier.url.none.fl_str_mv	https://doi.org/10.19053/uptc.01211129.v34.n73.2025.20107
identifier_str_mv	0121-1129 10.19053/uptc.01211129.v34.n73.2025.20107 2357-5328
url	https://repositorio.uptc.edu.co/handle/001/21614 https://doi.org/10.19053/uptc.01211129.v34.n73.2025.20107
dc.language.iso.none.fl_str_mv
language_invalid_str_mv
dc.relation.references.none.fl_str_mv	Á. F. Tenorio-Villalón, A. M. Martín-Caraballo, “¿Influye en el rendimiento académico del estudiantado el número de matrículas realizadas en una asignatura de Matemáticas para Ingeniería?,” European Public and Social Innovation Review, vol. 10, pp. 1-20, 2025. https://doi.org/10.31637/epsir-2025-1009 P. Ngulube, M. Masumbika Ncube, “Identifying at-Risk Students with Data Analytics and Machine Learning: Insights from a Systematic Review,” Journal of Information Systems Engineering and Management, vol. 10, no. 43s, pp. 1235-1253, May 2025. https://doi.org/10.52783/jisem.v10i43s.9227 Y. Reina Marín, L. Quiñones Huatangari, O. Cruz Caro, J. L. Maicelo Guevara, J. N. Alva Tuesta, E. Sánchez Bardales, R. Chávez Santos, “Data Mining to Identify University Student Dropout Factors,” Applied Sciences, vol. 15, no. 22, e11911, Nov. 2025. https://doi.org/10.3390/app152211911 J. Niyogisubizo, L. Liao, E. Nziyumva, E. Murwanashyaka, P. C. Nshimyumukiza, “Predicting student’s dropout in university classes using two-layer ensemble machine learning approach: A novel stacked generalization,” Computers and Education: Artificial Intelligence, vol. 3, e100066, 2022. https://doi.org/10.1016/j.caeai.2022.100066 Z. Zhan, T. Shen, “Development of a prediction model for student teaching satisfaction based on 10 machine learning algorithms,” Scientific Reports, vol. 15, no. 1, e36547, Oct. 2025. https://doi.org/10.1038/s41598-025-19039-x R. Goran, L. Jovanovic, N. Bacanin, M. S. Stanković, V. Simic, M. Antonijevic, M. Zivkovic, “Identifying and Understanding Student Dropouts Using Metaheuristic Optimized Classifiers and Explainable Artificial Intelligence Techniques,” IEEE Access, vol. 12, pp. 122377-122400, 2024. https://doi.org/10.1109/ACCESS.2024.3446653 A. Zanellati, S. P. Zingaro, M. Gabbrielli, “Balancing Performance and Explainability in Academic Dropout Prediction,” IEEE Transactions on Learning Technologies, vol. 17, pp. 2086-2099, 2024. https://doi.org/10.1109/TLT.2024.3425959 C. Alonso-Fernández, A. Calvo-Morata, M. Freire, I. Martínez-Ortiz, B. Fernández-Manjón, “Evidence-based evaluation of a serious game to increase bullying awareness,” Interactive Learning Environments, vol. 31, no. 2, pp. 644-654, 2023. https://doi.org/10.1080/10494820.2020.1799031 J. Asbell-Clarke, E. Rowe, V. Almeda, T. Edwards, E. Bardar, S. Gasca, et al., “The development of students’ computational thinking practices in elementary- and middle-school classes using the learning game, Zoombinis,” Computers in Human Behavior, vol. 115, e106587, Feb. 2021. https://doi.org/10.1016/j.chb.2020.106587 J.-E. Lee, J. Y.-C. Chan, A. Botelho, E. Ottmar, “Does slow and steady win the race?: Clustering patterns of students’ behaviors in an interactive online mathematics game,” Educational Technology Research and Development, vol. 70, no. 5, pp. 1575-1599, 2022. https://doi.org/10.1007/s11423-022-10138-4 D. Jacob, R. Henriques, “Educational Data Mining to Predict Bachelors Students’ Success,” Emerging Science Journal, vol. 7, no. 2S, pp. 159-171, 2023. https://doi.org/10.28991/ESJ-2023-SIED2-013 L. Breiman, “Random Forests,” Machine Learning, vol. 45, pp. 5-32, 2001. https://doi.org/10.1023/A:1010933404324 D. Martens, M. De Backer, R. Haesen, J. Vanthienen, M. Snoeck, B. Baesens, “Classification with ant colony optimization,” IEEE Transactions on Evolutionary Computation, vol. 11, no. 5, pp. 651-665, 2007. https://doi.org/10.1109/TEVC.2006.890229 A. Jain, A. K. Dubey, S. Khan, A. Panwar, M. Alkhatib, A. M. Alshahrani, “A PSO weighted ensemble framework with SMOTE balancing for student dropout prediction in smart education systems,” Scientific Reports, vol. 15, no. 1, e17463, May 2025. https://doi.org/10.1038/s41598-025-97506-1 L. T. Trinh, “A comparative analysis of consumer credit risk models in Peer-to-Peer Lending,” Journal of Economics, Finance and Administrative Science, vol. 29, no. 58, pp. 346-365, 2024. https://doi.org/10.1108/JEFAS-04-2021-0026 P. M. M. Chicon, L. N. Paschoal, S. Sawicki, F. Roos-Frantz, R. Z. Frantz, “A Predictive Model for the Early Identification of Student Dropout Using Data Classification, Clustering, and Association Methods,” IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, vol. 20, pp. 12-21, 2025. https://doi.org/10.1109/RITA.2025.3528369 J. Pecuchova, M. Drlik, “Enhancing the Early Student Dropout Prediction Model Through Clustering Analysis of Students’ Digital Traces,” IEEE Access, vol. 12, pp. 159336-159367, 2024. https://doi.org/10.1109/ACCESS.2024.3486762 A. Luque, A. Carrasco, A. Martín, A. De Las Heras, “The impact of class imbalance in classification performance metrics based on the binary confusion matrix,” Pattern Recognition, vol. 91, pp. 216-231, 2019. https://doi.org/10.1016/j.patcog.2019.02.023 J. Dunstan, F. Villena, J. P. Hoyos, V. Riquelme, M. Royer, H. Ramírez, J. Peypouquet, “Predicting no-show appointments in a pediatric hospital in Chile using machine learning,” Health Care Managment Science, vol. 26, no. 2, pp. 313-329, Jun. 2023. https://doi.org/10.1007/s10729-022-09626-z
dc.relation.citationendpage.none.fl_str_mv	e20107
dc.relation.citationissue.eng.fl_str_mv	73
dc.relation.citationstartpage.none.fl_str_mv	e20107
dc.relation.citationvolume.eng.fl_str_mv	34
dc.relation.ispartofjournal.eng.fl_str_mv	Revista Facultad de Ingeniería
dc.relation.bitstream.none.fl_str_mv	https://revistas.uptc.edu.co/index.php/ingenieria/article/download/20107/16470 https://revistas.uptc.edu.co/index.php/ingenieria/article/download/20107/16803
dc.relation.citationedition.eng.fl_str_mv	Núm. 73 , Año 2025 : July-September 2025 (Continuous Publication)
dc.rights.none.fl_str_mv	Andres Peralta-Fragozo, Juan-Pablo Hoyos-Sánchez - 2025
dc.rights.uri.none.fl_str_mv	https://creativecommons.org/licenses/by/4.0
dc.rights.accessrights.none.fl_str_mv	info:eu-repo/semantics/openAccess
dc.rights.creativecommons.none.fl_str_mv	This work is licensed under a Creative Commons Attribution 4.0 International License.
dc.rights.coar.none.fl_str_mv	http://purl.org/coar/access_right/c_abf2
rights_invalid_str_mv	Andres Peralta-Fragozo, Juan-Pablo Hoyos-Sánchez - 2025 https://creativecommons.org/licenses/by/4.0 This work is licensed under a Creative Commons Attribution 4.0 International License. http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv	openAccess
dc.format.mimetype.none.fl_str_mv	application/pdf text/xml
dc.publisher.eng.fl_str_mv	Universidad Pedagógica y Tecnológica de Colombia
dc.source.none.fl_str_mv	https://revistas.uptc.edu.co/index.php/ingenieria/article/view/20107
institution	Universidad Pedagógica y Tecnológica de Colombia
bitstream.url.fl_str_mv	https://repositorio.uptc.edu.co/bitstreams/4a630bbf-66a3-4f1a-981c-6c2fb6d21186/download
bitstream.checksum.fl_str_mv	9ad77587b016104c9a03757765e4875d
bitstream.checksumAlgorithm.fl_str_mv	MD5
repository.name.fl_str_mv	Repositorio Institucional UPTC
repository.mail.fl_str_mv	bdigital@metabiblioteca.com
_version_	1881674009343426560
spelling	Peralta-Fragozo, AndresHoyos-Sánchez, Juan-Pablo2025-10-17 19:50:062026-04-16T17:47:50Z2025-10-17 19:50:062025-10-170121-1129https://repositorio.uptc.edu.co/handle/001/2161410.19053/uptc.01211129.v34.n73.2025.201072357-5328https://doi.org/10.19053/uptc.01211129.v34.n73.2025.20107application/pdftext/xmlUniversidad Pedagógica y Tecnológica de ColombiaAndres Peralta-Fragozo, Juan-Pablo Hoyos-Sánchez - 2025https://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessThis work is licensed under a Creative Commons Attribution 4.0 International License.http://purl.org/coar/access_right/c_abf2https://revistas.uptc.edu.co/index.php/ingenieria/article/view/20107Student dropoutMachine learningXGBoostRandom ForestSVMBalanced Random Forest ClassifierSMOTEClassificationHigher educationDeserción estudiantilAprendizaje automáticoRandom ForetClasificaciónEducación superiorPredicción de riesgo de deserción estudiantil en educación superior: Un enfoque en aprendizaje automáticoArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1info:eu-repo/semantics/articleJournal articleinfo:eu-repo/semantics/publishedVersionTexthttp://purl.org/coar/version/c_970fb48d4fbd8a85Á. F. Tenorio-Villalón, A. M. Martín-Caraballo, “¿Influye en el rendimiento académico del estudiantado el número de matrículas realizadas en una asignatura de Matemáticas para Ingeniería?,” European Public and Social Innovation Review, vol. 10, pp. 1-20, 2025. https://doi.org/10.31637/epsir-2025-1009P. Ngulube, M. Masumbika Ncube, “Identifying at-Risk Students with Data Analytics and Machine Learning: Insights from a Systematic Review,” Journal of Information Systems Engineering and Management, vol. 10, no. 43s, pp. 1235-1253, May 2025. https://doi.org/10.52783/jisem.v10i43s.9227Y. Reina Marín, L. Quiñones Huatangari, O. Cruz Caro, J. L. Maicelo Guevara, J. N. Alva Tuesta, E. Sánchez Bardales, R. Chávez Santos, “Data Mining to Identify University Student Dropout Factors,” Applied Sciences, vol. 15, no. 22, e11911, Nov. 2025. https://doi.org/10.3390/app152211911J. Niyogisubizo, L. Liao, E. Nziyumva, E. Murwanashyaka, P. C. Nshimyumukiza, “Predicting student’s dropout in university classes using two-layer ensemble machine learning approach: A novel stacked generalization,” Computers and Education: Artificial Intelligence, vol. 3, e100066, 2022. https://doi.org/10.1016/j.caeai.2022.100066Z. Zhan, T. Shen, “Development of a prediction model for student teaching satisfaction based on 10 machine learning algorithms,” Scientific Reports, vol. 15, no. 1, e36547, Oct. 2025. https://doi.org/10.1038/s41598-025-19039-xR. Goran, L. Jovanovic, N. Bacanin, M. S. Stanković, V. Simic, M. Antonijevic, M. Zivkovic, “Identifying and Understanding Student Dropouts Using Metaheuristic Optimized Classifiers and Explainable Artificial Intelligence Techniques,” IEEE Access, vol. 12, pp. 122377-122400, 2024. https://doi.org/10.1109/ACCESS.2024.3446653A. Zanellati, S. P. Zingaro, M. Gabbrielli, “Balancing Performance and Explainability in Academic Dropout Prediction,” IEEE Transactions on Learning Technologies, vol. 17, pp. 2086-2099, 2024. https://doi.org/10.1109/TLT.2024.3425959C. Alonso-Fernández, A. Calvo-Morata, M. Freire, I. Martínez-Ortiz, B. Fernández-Manjón, “Evidence-based evaluation of a serious game to increase bullying awareness,” Interactive Learning Environments, vol. 31, no. 2, pp. 644-654, 2023. https://doi.org/10.1080/10494820.2020.1799031J. Asbell-Clarke, E. Rowe, V. Almeda, T. Edwards, E. Bardar, S. Gasca, et al., “The development of students’ computational thinking practices in elementary- and middle-school classes using the learning game, Zoombinis,” Computers in Human Behavior, vol. 115, e106587, Feb. 2021. https://doi.org/10.1016/j.chb.2020.106587J.-E. Lee, J. Y.-C. Chan, A. Botelho, E. Ottmar, “Does slow and steady win the race?: Clustering patterns of students’ behaviors in an interactive online mathematics game,” Educational Technology Research and Development, vol. 70, no. 5, pp. 1575-1599, 2022. https://doi.org/10.1007/s11423-022-10138-4D. Jacob, R. Henriques, “Educational Data Mining to Predict Bachelors Students’ Success,” Emerging Science Journal, vol. 7, no. 2S, pp. 159-171, 2023. https://doi.org/10.28991/ESJ-2023-SIED2-013L. Breiman, “Random Forests,” Machine Learning, vol. 45, pp. 5-32, 2001. https://doi.org/10.1023/A:1010933404324D. Martens, M. De Backer, R. Haesen, J. Vanthienen, M. Snoeck, B. Baesens, “Classification with ant colony optimization,” IEEE Transactions on Evolutionary Computation, vol. 11, no. 5, pp. 651-665, 2007. https://doi.org/10.1109/TEVC.2006.890229A. Jain, A. K. Dubey, S. Khan, A. Panwar, M. Alkhatib, A. M. Alshahrani, “A PSO weighted ensemble framework with SMOTE balancing for student dropout prediction in smart education systems,” Scientific Reports, vol. 15, no. 1, e17463, May 2025. https://doi.org/10.1038/s41598-025-97506-1L. T. Trinh, “A comparative analysis of consumer credit risk models in Peer-to-Peer Lending,” Journal of Economics, Finance and Administrative Science, vol. 29, no. 58, pp. 346-365, 2024. https://doi.org/10.1108/JEFAS-04-2021-0026P. M. M. Chicon, L. N. Paschoal, S. Sawicki, F. Roos-Frantz, R. Z. Frantz, “A Predictive Model for the Early Identification of Student Dropout Using Data Classification, Clustering, and Association Methods,” IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, vol. 20, pp. 12-21, 2025. https://doi.org/10.1109/RITA.2025.3528369J. Pecuchova, M. Drlik, “Enhancing the Early Student Dropout Prediction Model Through Clustering Analysis of Students’ Digital Traces,” IEEE Access, vol. 12, pp. 159336-159367, 2024. https://doi.org/10.1109/ACCESS.2024.3486762A. Luque, A. Carrasco, A. Martín, A. De Las Heras, “The impact of class imbalance in classification performance metrics based on the binary confusion matrix,” Pattern Recognition, vol. 91, pp. 216-231, 2019. https://doi.org/10.1016/j.patcog.2019.02.023J. Dunstan, F. Villena, J. P. Hoyos, V. Riquelme, M. Royer, H. Ramírez, J. Peypouquet, “Predicting no-show appointments in a pediatric hospital in Chile using machine learning,” Health Care Managment Science, vol. 26, no. 2, pp. 313-329, Jun. 2023. https://doi.org/10.1007/s10729-022-09626-ze2010773e2010734Revista Facultad de Ingenieríahttps://revistas.uptc.edu.co/index.php/ingenieria/article/download/20107/16470https://revistas.uptc.edu.co/index.php/ingenieria/article/download/20107/16803Núm. 73 , Año 2025 : July-September 2025 (Continuous Publication)PublicationOREORE.xmltext/xml2641https://repositorio.uptc.edu.co/bitstreams/4a630bbf-66a3-4f1a-981c-6c2fb6d21186/download9ad77587b016104c9a03757765e4875dMD51falseAnonymousREAD001/21614oai:repositorio.uptc.edu.co:001/216142026-10-29 14:05:26.925https://creativecommons.org/licenses/by/4.0metadata.onlyhttps://repositorio.uptc.edu.coRepositorio Institucional UPTCbdigital@metabiblioteca.com

Predicción de riesgo de deserción estudiantil en educación superior: Un enfoque en aprendizaje automático

Publicaciones similares