El machine learning para abordar el abandono escolar: Una revisión de los modelos más innovadores
Resumen
El objetivo de este estudio es analizar el papel del machine learning como herramienta innovadora para identificar, predecir y abordar el abandono escolar, evaluando los modelos más efectivos y su aplicación en contextos educativos. Para ello, se llevó a cabo una revisión sistemática en las bases de datos Scopus, Web of Science (WOS) y SciELO, utilizando la metodología PRISMA y adoptando un enfoque cualitativo y descriptivo. Los criterios de inclusión comprendieron trabajos publicados entre 2020 y 2024, estudios originales, artículos en inglés o español y documentos con texto completo que estuvieran directamente relacionados con el objetivo del estudio. Se emplearon palabras clave como "machine learning", "machine learning algorithms", "learning algorithms", "machine learning models", "learning models", "school dropout", "school abandonment", "student dropout", "student attrition". De los 773 documentos identificados, se eliminaron 753 por no cumplir con los criterios establecidos, resultando en 20 artículos seleccionados para su análisis. Los resultados muestran que modelos como las Redes Neuronales Artificiales (ANN), Vecinos Más Cercanos (KNN), Regresión Lineal (LR) y Árboles de Decisión (DT) han demostrado eficacia en la clasificación y predicción. El rendimiento académico previo es un predictor clave del abandono escolar, junto con factores como dificultades financieras y la falta de apoyo social, que afectan la permanencia de los estudiantes. En conclusión, el machine learning (ML) en la educación resalta su capacidad para identificar y prevenir el abandono escolar. Las técnicas de ML permiten a las instituciones predecir con precisión los riesgos de deserción y desarrollar intervenciones personalizadas para los estudiantes.
Descargas
Citas
Alenezi, H. S., & Faisal, M. H. (2020). Utilizing crowdsourcing and machine learning in education: Literature review. Education and Information Technologies, 25(4), 2971–2986.
https://doi.org/10.1007/s10639-020-10102-w
Alhabeeb, S., Alrusayni, N., Almutiri, R., Alhumud, S., & Al-Hagery, M. A. (2024). Blockchain and machine learning in education: a literature review. IAES International Journal of Artificial Intelligence, 13(1), 581–596. https://doi.org/10.11591/ijai.v13.i1.pp581-596
Bettany-Saltikov, J., & McSherry, R. (2024). How to do a Systematic Literature Review in Nursing: A Step-by-Step Guide (3era. Ed. (ed.)). https://goo.su/wMxVzm
Delen, D., Davazdahemami, B., & Rasouli Dezfouli, E. (2023). Predicting and Mitigating Freshmen Student Attrition: A Local-Explainable Machine Learning Framework. Information Systems Frontiers, 26(2), 641–662. https://doi.org/10.1007/s10796-023-10397-3
European Commission. (2024). Reducir el abandono escolar prematuro: tratamiento y prevención del abandono escolar. https://school-education.ec.europa.eu/mk/learn/courses/reducing-early-school-leaving-treatment-and-prevention-dropouts
Fauszt, T., Erdélyi, K., Dobák, D., Bognár, L., & Kovács, E. (2023). Design of a Machine Learning Model to Predict Student Attrition. International Journal of Emerging Technologies in Learning, 18(17), 184–195. https://doi.org/10.3991/ijet.v18i17.41449
Freitas, F. A. d. S., Vasconcelos, F. F. X., Peixoto, S. A., Hassan, M. M., Ali Akber Dewan, M., de Albuquerque, V. H. C., & Rebouças Filho, P. P. (2020). IoT system for school dropout prediction using machine learning techniques based on socioeconomic data. Electronics, 9, 1–14. https://doi.org/10.3390/electronics9101613
Gómez-Pulido, J. A., Park, Y., Soto, R., & Lanza-Gutiérrez, J. M. (2023). Data Analytics and Machine Learning in Education. Applied Sciences (Switzerland), 13(3), 13–15.
https://doi.org/10.3390/app13031418
Hassan, M. A., Muse, A. H., & Nadarajah, S. (2024). applied sciences Learning : Insights from the 2022 National Education Accessibility Survey in Somaliland. Applied Sciences, 14.
https://doi.org/10.3390/app14177593
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, 14, 1–8. https://doi.org/10.1038/s41598-024-53576-1
Kabathova, J., & Drlik, M. (2021). Towards predicting student’s dropout in university courses using different machine learning techniques. Applied Sciences, 11, 1–19. https://doi.org/10.3390/app11073130
Krüger, J. G. C., Britto, A. de S., & Barddal, J. P. (2023). An explainable machine learning approach for student dropout prediction. Expert Systems with Applications, 233, 1–9. https://doi.org/10.1016/j.eswa.2023.120933
Kuz, A., & Morales, R. (2023). Ciencia de Datos Educativos y aprendizaje automático: un caso de estudio sobre la deserción estudiantil universitaria en México. Education in the Knowledge Society, 24, 1–14. https://doi.org/10.14201/eks.30080
Matz, S. C., Bukow, C. S., Peters, H., Deacons, C., Dinu, A., & Stachl, C. (2023). Using machine learning to predict student retention from socio‑demographic characteristics and app‑based engagement metrics. Scientific Reports, 13(1), 1–16. https://doi.org/10.1038/s41598-023-32484-w
Mduma, N. (2023a). Data Balancing Techniques for Predicting Student Dropout Using Machine Learning. Data, 8(3). https://doi.org/10.3390/data8030049
Mduma, N. (2023b). Data Balancing Techniques for Predicting Student Dropout Using Machine Learning. Data, 8(49), 1–14. https://doi.org/10.3390/data8030049
Mnyawami, Y. N., Maziku, H. H., & Mushi, J. C. (2022). Enhanced Model for Predicting Student Dropouts in Developing Countries Using Automated Machine Learning Approach: A Case of Tanzanian’s Secondary Schools. Applied Artificial Intelligence, 36(1). https://doi.org/10.1080/08839514.2022.2071406
Nimbalkar, A. A., & Berad, A. T. (2021). The increasing importance of AI applications in E-Commerce. Vidyabharati International Interdisciplinary Research Journal, 13(1), 67–77. https://www.viirj.org/vol13issue1/56.pdf
Niyogisubizo, J., Liao, L., Nziyumva, E., Murwanashyaka, E., & Nshimyumukiza, P. C. (2022). Predicting student’s dropout in university classes using two-layer ensemble machine learning approach: A novel stacked generalization. Computers and Education: Artificial Intelligence, 3, 1–12. https://doi.org/10.1016/j.caeai.2022.100066
Okagbue, E. F., Ezeachikulo, U. P., Akintunde, T. Y., Tsakuwa, M. B., Ilokanulo, S. N., Obiasoanya, K. M., Ilodibe, C. E., & Ouattara, C. A. T. (2023). A comprehensive overview of artificial intelligence and machine learning in education pedagogy: 21 Years (2000–2021) of research indexed in the scopus database. Social Sciences and Humanities Open, 8(1), 100655. https://doi.org/10.1016/j.ssaho.2023.100655
Okoye, K., Nganji, J. T., Escamilla, J., & Hosseini, S. (2024a). Machine learning model (RG-DMML) and ensemble algorithm for prediction of students’ retention and graduation in education. Computers and Education: Artificial Intelligence, 6, 100205. https://doi.org/10.1016/j.caeai.2024.100205
Okoye, K., Nganji, J. T., Escamilla, J., & Hosseini, S. (2024b). Machine learning model (RG-DMML) and ensemble algorithm for prediction of students’ retention and graduation in education. Computers and Education: Artificial Intelligence, 6, 1–13. https://doi.org/10.1016/j.caeai.2024.100205
Opazo, D., Moreno, S., Álvarez-Miranda, E., & Pereira, J. (2021). Analysis of first-year university student dropout through machine learning models: A comparison between universities. Mathematics, 9, 1–27. https://doi.org/10.3390/math9202599
Palacios, C. A., Reyes-Suárez, J. A., Bearzotti, L. A., Leiva, V., & Marchant, C. (2021). Knowledge discovery for higher education student retention based on data mining: Machine learning algorithms and case study in chile. Entropy, 23, 1–23. https://doi.org/10.3390/e23040485
Porras, M., Lara, J. A., Romero, C., & Ventura, S. (2023). A Case-Study Comparison of Machine Learning Approaches for Predicting Student ’ s Dropout from Multiple Online Educational Entities. Algorithms, 16(554), 1–21. https://doi.org/10.3390/a16120554
Razaulla, S. M., Pasha, M., & Farooq, M. U. (2022). Integration of Machine Learning in Education: Challenges, Issues and Trends BT - Machine Learning and Internet of Things for Societal Issues (C. Satyanarayana, X.-Z. Gao, C.-Y. Ting, & N. B. Muppalaneni (eds.); pp. 23–34). Springer Nature Singapore. https://doi.org/10.1007/978-981-16-5090-1_2
Sahana, S., Singh, D., & Nath, I. (2023). Importance of AI and ML Towards Smart Sensor Network Utility Enhancement. Encyclopedia of Data Science and Machine Learning, 240–262. https://doi.org/10.4018/978-1-7998-9220-5.ch015
Segura, M., Mello, J., & Hernández, A. (2022). Machine Learning Prediction of University Student Dropout: Does Preference Play a Key Role? Mathematics, 10(18), 1–20. https://doi.org/10.3390/math10183359
Selim, K. S., & Rezk, S. S. (2023). On predicting school dropouts in Egypt: A machine learning approach. Education and Information Technologies, 28(7), 9235–9266. https://doi.org/10.1007/s10639-022-11571-x
Shah, D., Patel, D., Adesara, J., Hingu, P., & Shah, M. (2021). Exploiting the Capabilities of Blockchain and Machine Learning in Education. Augmented Human Research, 6(1), 1. https://doi.org/10.1007/s41133-020-00039-7
Sinha, A., Menon, G. R., & John, D. (2022). Beginer’s guide for systematic reviews. https://main.icmr.nic.in/sites/default/files/upload_documents/BEGINNERS_GUIDE_FINAL_BOOK.pdf
Tito, A. E. A., Condori, B. O. H., & Vera, Y. P. (2023). Comparative analysis of Machine Learning Techniques for the prediction of cases of university dropout. RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao, 51(09), 84–98. https://doi.org/10.17013/risti.51.84-98
Tiwari, R. (2023). The integration of AI and machine learning in education and its potential to personalize and improve student learning experiences. Interantional Journal of Scientific Research in Engineering and Management, 7(2). https://doi.org/10.55041/ijsrem17645
Vaarma, M., & Li, H. (2024). Predicting student dropouts with machine learning: An empirical study in Finnish higher education. Technology in Society, 76, 1–10. https://doi.org/10.1016/j.techsoc.2024.102474
Villar, A., & Velini, C. R. (2024). Supervised machine learning algorithms for predicting student dropout and academic success: a comparative study. Discover Artificial Intelligence, 4(2). https://doi.org/10.1007/s44163-023-00079-z
Villarreal-Torres, H., Ángeles-Morales, J., Cano-Mejía, J., Mejía-Murillo, C., Flores-Reyes, G., Palomino-Márquez, M., Marín-Rodriguez, W., & Andrade-Girón, D. (2023). Classification model for student dropouts using machine learning: A case study. EAI Endorsed Transactions on Scalable Information Systems, 10(5), 1–12. https://doi.org/10.4108/eetsis.vi.3455
Villegas-Ch, W., Govea, J., & Revelo-Tapia, S. (2023). Improving Student Retention in Institutions of Higher Education through Machine Learning: A Sustainable Approach. Sustainability (Switzerland), 15(19), 1–20. https://doi.org/10.3390/su151914512
Wu, J. (2020). Machine Learning in Education. 2020 International Conference on Modern Education and Information Management (ICMEIM), 56–63. https://doi.org/10.1109/ICMEIM51375.2020.00020
Zhou, Y., & Song, Z. (2020). Effectiveness analysis of machine learning in education big data. Journal of Physics: Conference Series, 1651(1). https://doi.org/10.1088/1742-6596/1651/1/012105
Derechos de autor 2025 Jules Mao Flores Satalaya
Esta obra está bajo licencia internacional Creative Commons Reconocimiento 4.0.
.png)
.png)
1.png)
