Factors associated with performance on the Saber Pro exams using data analytics and machine learning

Autores:: Bustos-Marquez, Luis Fernando
Sepúlveda-Mora, Sergio Basilio
González-Sepúlveda, Ruth Mery

Tipo de recurso:: Article of journal

Fecha de publicación:: 2026

Institución:: Universidad Pedagógica y Tecnológica de Colombia

Repositorio:: RiUPTC: Repositorio Institucional UPTC

Idioma:: eng

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dc.title.spa.fl_str_mv	Factors associated with performance on the Saber Pro exams using data analytics and machine learning
title	Factors associated with performance on the Saber Pro exams using data analytics and machine learning
spellingShingle	Factors associated with performance on the Saber Pro exams using data analytics and machine learning academic achievement artificial intelligence socioeconomic influences standardized tests rendimiento académico inteligencia artificial influencias socioeconómicas pruebas estandarizadas
title_short	Factors associated with performance on the Saber Pro exams using data analytics and machine learning
title_full	Factors associated with performance on the Saber Pro exams using data analytics and machine learning
title_fullStr	Factors associated with performance on the Saber Pro exams using data analytics and machine learning
title_full_unstemmed	Factors associated with performance on the Saber Pro exams using data analytics and machine learning
title_sort	Factors associated with performance on the Saber Pro exams using data analytics and machine learning
dc.creator.fl_str_mv	Bustos-Marquez, Luis Fernando Sepúlveda-Mora, Sergio Basilio González-Sepúlveda, Ruth Mery
dc.contributor.author.spa.fl_str_mv	Bustos-Marquez, Luis Fernando Sepúlveda-Mora, Sergio Basilio González-Sepúlveda, Ruth Mery
dc.subject.none.fl_str_mv	academic achievement artificial intelligence socioeconomic influences standardized tests rendimiento académico inteligencia artificial influencias socioeconómicas pruebas estandarizadas
topic	academic achievement artificial intelligence socioeconomic influences standardized tests rendimiento académico inteligencia artificial influencias socioeconómicas pruebas estandarizadas
publishDate	2026
dc.date.accessioned.none.fl_str_mv	2026-08-15 00:00:00
dc.date.available.none.fl_str_mv	2026-08-15 00:00:00
dc.date.issued.none.fl_str_mv	2026-08-15
dc.type.spa.fl_str_mv	Artículo de revista
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dc.relation.references.eng.fl_str_mv	Ahmed, W., Wani, M. A., Plawiak, P., Meshoul, S., Mahmoud, A., & Hammad, M. (2025). Machine learning-based academic performance prediction with explainability for enhanced decision-making in educational institutions. Scientific Reports, 15. https://doi.org/10.1038/s41598-025-12353-4 Alnasyan, B., Basheri, M., & Alassafi, M. (2024). The power of deep learning techniques for predicting student performance in virtual learning environments: A systematic literature review. Computers and Education: Artificial Intelligence, 6. https://doi.org/10.1016/j.caeai.2024.100231 Al-Tameemi, G., Xue, J., Ali, I. H., & Ajit, S. (2024). A hybrid machine learning approach for predicting student performance using multi-class educational datasets. Procedia Computer Science, 238, 888–895. https://doi.org/10.1016/j.procs.2024.06.108 Alyahyan, E., & Düştegör, D. (2020). Predicting academic success in higher education: Literature review and best practices. Higher Education Evaluation and Development, 14 (1), 3–27. https://doi.org/10.1108/HEED-12-2019-0053 Baker, R. S. J. d., & Inventado, P. S. (2014). Educational data mining and learning analytics. In J. A. Larusson & B. White (Eds.), Learning analytics: From research to practice. 61–75. Springer. https://doi.org/10.1007/978-1-4614-3305-7_4 Bustos-Márquez, L. F., Sepúlveda-Mora, S. B., & González-Sepúlveda, R. M. (2024). Dataset on Saber Pro performance in electronic engineering students (2018–2022) (Data set). Mendeley Data. https://doi.org/10.17632/kdcr9rrg6r.1 Castro-Casadiego, J., Contreras-Castillo, H., & Cárdenas-Morales, J. J. (2020). Factores que influyen en los resultados de las pruebas Saber Pro en estudiantes de ingeniería electrónica. Revista Ingenio, 17 (1), 45–56. Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59 (1), 64–71. https://doi.org/10.1007/s11528-014-0822-x Gašević, D., Tsai, Y.-S., & Drachsler, H. (2022). Learning analytics in higher education: Stakeholders, strategy and scale. The Internet and Higher Education, 52. https://doi.org/10.1016/j.iheduc.2021.100833 Guevara-Reyes, R., Ortiz-Garcés, I., Andrade, R., Cox-Riquetti, F., & Villegas-Ch, W. (2025). Machine learning models for academic performance prediction: Interpretability and application in educational decision-making. Frontiers in Education, 10. https://doi.org/10.3389/feduc.2025.1632315 Hernández-Sampieri, R., Fernández-Collado, C., & Baptista-Lucio, M. del P. (2017). Metodología de la investigación. McGraw-Hill Education. Instituto Colombiano para la Evaluación de la Educación, ICFES. (2017). Guía del examen Saber Pro. https://www.icfes.gov.co Issah, I., Appiah, O., Appiahene, P., & Inusah, F. (2023). A systematic review of the literature on machine learning application of determining the attributes influencing academic performance. Decision Analytics Journal, 7. https://doi.org/10.1016/j.dajour.2023.100204 Johora, F. T., Hasan, M. N., Rajbongshi, A., Ashrafuzzaman, M., & Akter, F. (2025). An explainable AI-based approach for predicting undergraduate students academic performance. Array, 26. https://doi.org/10.1016/j.array.2025.100384 Khosravi, H., Buckingham Shum, S., Chen, G., Conati, C., Tsai, Y.-S., Kay, J., Knight, S., Martinez-Maldonado, R., Sadiq, S., & Gašević, D. (2022). Explainable artificial intelligence in education. Computers and Education: Artificial Intelligence, 3. https://doi.org/10.1016/j.caeai.2022.100074 Khor, E. T., & K, M. (2024). A systematic review of the role of learning analytics in supporting personalized learning. Education Sciences, 14 (1). https://doi.org/10.3390/educsci14010051 Kizilcec, R. F., Pérez-Sanagustín, M., & Maldonado, J. J. (2017). Self-regulated learning strategies predict learner behavior and goal attainment. Computers & Education, 104, 18–33. https://doi.org/10.1016/j.compedu.2016.10.001 Mai, J., Wei, F., He, W., Huang, H., & Zhu, H. (2024). An explainable student performance prediction method based on dual-level progressive classification belief rule base. Electronics, 13 (22). https://doi.org/10.3390/electronics13224358 Molnar, C. (2022). Interpretable machine learning. Leanpub. https://christophm.github.io/interpretable-ml-book/ Narváez-Zúñiga, M. (2022). Modelo predictivo de rendimiento académico mediante regresión lineal regularizada. Revista Colombiana de Estadística Aplicada, 12 (1), 21–35. Osma-Castellanos, A., Díaz-López, C. A., & García-Niño, M. C. (2014). Factores que inciden en el desempeño en las pruebas Saber Pro. Ingeniería Investigación y Desarrollo, 14 (2), 45–58. Oviedo-Carrascal, A., & Jiménez-Giraldo, M. (2019). Análisis del rendimiento académico mediante minería de datos. Revista Ingenierías Universidad de Medellín, 18 (34), 97–114. Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. WIREs Data Mining and Knowledge Discovery, 10 (3). https://doi.org/10.1002/widm.1355 Siemens, G., & Baker, R. S. J. d. (2012). Learning analytics and educational data mining: Towards communication and collaboration. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge. 252–254. Association for Computing Machinery. https://doi.org/10.1145/2330601.2330661 Timarán-Pereira, D., Gómez, M. A., & Núñez, L. A. (2020). Minería de datos aplicada al rendimiento académico. Revista Colombiana de Computación, 21 (2), 32–46. Yağcı, M. (2022). Educational data mining: Prediction of students’ academic performance using machine learning algorithms. Smart Learning Environments, 9. https://doi.org/10.1186/s40561-022-00192-z Zárate-Valderrama, N., García-Peñaloza, R., & Acosta-Moreno, A. (2021). Predicción del rendimiento académico mediante clasificación. Revista Electrónica de Ingeniería y Ciencias, 10 (2), 98–110.
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spelling	Bustos-Marquez, Luis FernandoSepúlveda-Mora, Sergio BasilioGonzález-Sepúlveda, Ruth Mery2026-08-15 00:00:002026-08-15 00:00:002026-08-152027-830610.19053/uptc.20278306.v16.n2.2026.219572389-9417https://doi.org/10.19053/uptc.20278306.v16.n2.2026.21957application/pdfengUniversidad Pedagógica y Tecnológica de ColombiaRevista de Investigación, Desarrollo e Innovación - 2026https://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessEsta obra está bajo una licencia internacional Creative Commons Atribución 4.0.http://purl.org/coar/access_right/c_abf2https://revistas.uptc.edu.co/index.php/investigacion_duitama/article/view/21957academic achievementartificial intelligencesocioeconomic influencesstandardized testsrendimiento académicointeligencia artificialinfluencias socioeconómicaspruebas estandarizadasFactors associated with performance on the Saber Pro exams using data analytics and machine learningArtículo 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_970fb48d4fbd8a85Ahmed, W., Wani, M. A., Plawiak, P., Meshoul, S., Mahmoud, A., & Hammad, M. (2025). Machine learning-based academic performance prediction with explainability for enhanced decision-making in educational institutions. Scientific Reports, 15. https://doi.org/10.1038/s41598-025-12353-4Alnasyan, B., Basheri, M., & Alassafi, M. (2024). The power of deep learning techniques for predicting student performance in virtual learning environments: A systematic literature review. Computers and Education: Artificial Intelligence, 6. https://doi.org/10.1016/j.caeai.2024.100231Al-Tameemi, G., Xue, J., Ali, I. H., & Ajit, S. (2024). A hybrid machine learning approach for predicting student performance using multi-class educational datasets. Procedia Computer Science, 238, 888–895. https://doi.org/10.1016/j.procs.2024.06.108Alyahyan, E., & Düştegör, D. (2020). Predicting academic success in higher education: Literature review and best practices. Higher Education Evaluation and Development, 14 (1), 3–27. https://doi.org/10.1108/HEED-12-2019-0053Baker, R. S. J. d., & Inventado, P. S. (2014). Educational data mining and learning analytics. In J. A. Larusson & B. White (Eds.), Learning analytics: From research to practice. 61–75. Springer. https://doi.org/10.1007/978-1-4614-3305-7_4Bustos-Márquez, L. F., Sepúlveda-Mora, S. B., & González-Sepúlveda, R. M. (2024). Dataset on Saber Pro performance in electronic engineering students (2018–2022) (Data set). Mendeley Data. https://doi.org/10.17632/kdcr9rrg6r.1Castro-Casadiego, J., Contreras-Castillo, H., & Cárdenas-Morales, J. J. (2020). Factores que influyen en los resultados de las pruebas Saber Pro en estudiantes de ingeniería electrónica. Revista Ingenio, 17 (1), 45–56.Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59 (1), 64–71. https://doi.org/10.1007/s11528-014-0822-xGašević, D., Tsai, Y.-S., & Drachsler, H. (2022). Learning analytics in higher education: Stakeholders, strategy and scale. The Internet and Higher Education, 52. https://doi.org/10.1016/j.iheduc.2021.100833Guevara-Reyes, R., Ortiz-Garcés, I., Andrade, R., Cox-Riquetti, F., & Villegas-Ch, W. (2025). Machine learning models for academic performance prediction: Interpretability and application in educational decision-making. Frontiers in Education, 10. https://doi.org/10.3389/feduc.2025.1632315Hernández-Sampieri, R., Fernández-Collado, C., & Baptista-Lucio, M. del P. (2017). Metodología de la investigación. McGraw-Hill Education.Instituto Colombiano para la Evaluación de la Educación, ICFES. (2017). Guía del examen Saber Pro. https://www.icfes.gov.coIssah, I., Appiah, O., Appiahene, P., & Inusah, F. (2023). A systematic review of the literature on machine learning application of determining the attributes influencing academic performance. Decision Analytics Journal, 7. https://doi.org/10.1016/j.dajour.2023.100204Johora, F. T., Hasan, M. N., Rajbongshi, A., Ashrafuzzaman, M., & Akter, F. (2025). An explainable AI-based approach for predicting undergraduate students academic performance. Array, 26. https://doi.org/10.1016/j.array.2025.100384Khosravi, H., Buckingham Shum, S., Chen, G., Conati, C., Tsai, Y.-S., Kay, J., Knight, S., Martinez-Maldonado, R., Sadiq, S., & Gašević, D. (2022). Explainable artificial intelligence in education. Computers and Education: Artificial Intelligence, 3. https://doi.org/10.1016/j.caeai.2022.100074Khor, E. T., & K, M. (2024). A systematic review of the role of learning analytics in supporting personalized learning. Education Sciences, 14 (1). https://doi.org/10.3390/educsci14010051Kizilcec, R. F., Pérez-Sanagustín, M., & Maldonado, J. J. (2017). Self-regulated learning strategies predict learner behavior and goal attainment. Computers & Education, 104, 18–33. https://doi.org/10.1016/j.compedu.2016.10.001Mai, J., Wei, F., He, W., Huang, H., & Zhu, H. (2024). An explainable student performance prediction method based on dual-level progressive classification belief rule base. Electronics, 13 (22). https://doi.org/10.3390/electronics13224358Molnar, C. (2022). Interpretable machine learning. Leanpub. https://christophm.github.io/interpretable-ml-book/Narváez-Zúñiga, M. (2022). Modelo predictivo de rendimiento académico mediante regresión lineal regularizada. Revista Colombiana de Estadística Aplicada, 12 (1), 21–35.Osma-Castellanos, A., Díaz-López, C. A., & García-Niño, M. C. (2014). Factores que inciden en el desempeño en las pruebas Saber Pro. Ingeniería Investigación y Desarrollo, 14 (2), 45–58.Oviedo-Carrascal, A., & Jiménez-Giraldo, M. (2019). Análisis del rendimiento académico mediante minería de datos. Revista Ingenierías Universidad de Medellín, 18 (34), 97–114.Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. WIREs Data Mining and Knowledge Discovery, 10 (3). https://doi.org/10.1002/widm.1355Siemens, G., & Baker, R. S. J. d. (2012). Learning analytics and educational data mining: Towards communication and collaboration. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge. 252–254. Association for Computing Machinery. https://doi.org/10.1145/2330601.2330661Timarán-Pereira, D., Gómez, M. A., & Núñez, L. A. (2020). Minería de datos aplicada al rendimiento académico. Revista Colombiana de Computación, 21 (2), 32–46.Yağcı, M. (2022). Educational data mining: Prediction of students’ academic performance using machine learning algorithms. Smart Learning Environments, 9. https://doi.org/10.1186/s40561-022-00192-zZárate-Valderrama, N., García-Peñaloza, R., & Acosta-Moreno, A. (2021). Predicción del rendimiento académico mediante clasificación. Revista Electrónica de Ingeniería y Ciencias, 10 (2), 98–110.404238316Revista de Investigación, Desarrollo e Innovaciónhttps://revistas.uptc.edu.co/index.php/investigacion_duitama/article/download/21957/17532Núm. 2 , Año 2026 : Julio-DiciembrePublicationOREORE.xmltext/xml2778https://repositorio.uptc.edu.co/bitstreams/dabecf7b-e03b-4101-b21f-5bd40648ce26/downloada107a4a13c0c9c0a23b5caaaec5f5a4cMD51falseAnonymousREAD001/23113oai:repositorio.uptc.edu.co:001/231132026-10-15 05:30:14.223https://creativecommons.org/licenses/by/4.0metadata.onlyhttps://repositorio.uptc.edu.coRepositorio Institucional UPTCbdigital@metabiblioteca.com

Factors associated with performance on the Saber Pro exams using data analytics and machine learning

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