Prediciendo el abandono universitario utilizando técnicas de aprendizaje automático: Aplicaciones sobre bases de datos multidisciplinarias
Resumen La deserción estudiantil constituye un desafío para las instituciones de Educación Superior debido, entre otras razones, a la diversidad de los estudiantes en cuanto a su origen social, educativo y económico, así como a las disciplinas y grados académicos en los que se matriculan, produciendo una amplia variabilidad en las trayectorias educativas. El objetivo de este estudio fue desarrollar un modelo predictivo para el abandono universitario en estudiantes pertenecientes a diferentes titulaciones, basado en machine learning. Se utilizaron un cuestionario online y los expedientes académicos del estudiantado para obtener diez variables previamente identificadas como relevantes para el fenómeno estudiado y se entrenaron clasificadores mediante procedimientos de aprendizaje automático supervisado. La muestra incluyó 946 estudiantes pertenecientes a siete áreas de conocimiento en una universidad de Chile. Los principales resultados indican que un modelo basado en el clasificador RUSboosted Trees presentó el mejor desempeño para la predicción de la deserción en Educación Superior, reconociendo exitosamente al 90% de las y los estudiantes desertores. Varios estudios previos han enfrentado la tarea de proporcionar herramientas para la predicción de la deserción estudiantil utilizando aprendizaje automático, pero generalmente sobre muestras de estudiantes pertenecientes a las mismas titulaciones o áreas del conocimiento. Este estudio proporciona un modelo predictivo que identifica a los futuros estudiantes desertores, incluso en condiciones de multidisciplinariedad dentro de sus programas de origen.
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Choudhary, N., Shinde, Y., Kannan, R., & Venkatraman, V. (2014). Impact of attribute selection on the accuracy of Multilayer Perceptron. International Journal of IT & Knowledge Management, 7(2), 32–36.
Christou, V., Tsoulos, I., Loupas, V., Tzallas, A. T., Gogos, C., Karvelis, P. S., Antoniadis, N., Glavas, E., & Giannakeas, N. (2023). Performance and early drop prediction for higher education students using machine learning. Expert Systems with Applications, 225, 120079. 10.1016/j.eswa.2023.120079
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Eegdeman, I., Cornelisz, I., Meeter, M., & van Klaveren, C. (2022). Identifying false positives when targeting students at risk of dropping out. Education Economics, 31(3) 313–325. 10.1080/09645292.2022.2067131
Eegdeman, I., Cornelisz, I., van Klaveren, C., & Meeter, M. (2022). Computer or teacher: Who predicts dropout best? Frontiers in Education, 7, 1–10. 10.3389/FEDUC.2022.976922/BIBTEX
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Gedda-Muñoz, R., & Carrasco-Bahamonde, J. A. (2023). Inclusión y acceso en universidades chilenas: El caso de la Política de Gratuidad. Revista Educación Superior y Sociedad (ESS), 35(1), 365–395. 10.54674/ESS.V35I1.630
Gedda-Muñoz, R., Fuentez Campos, Á., Valenzuela Sakuda, A., Retamal Torres, I., Cruz Fuentes, M., Badicu, G., Herrera-Valenzuela, T., & Valdés-Badilla, P. (2023). Factors Associated with Anxiety, Depression, and Stress Levels in High School Students. European Journal of Investigation in Health, Psychology and Education, 13(9), 1776–1786. 10.3390/ejihpe13090129
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Gnanambal, S., Thangaraj, M., Meenatchi V, & Gayathri, V. (2018). Classification Algorithms with Attribute Selection: an evaluation study using WEKA. International Journal of Advanced Networking and Applications, 9(6), 3640–3644.
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Liang, J., Yang, J., Wu, Y., Li, C., & Zheng, L. (2016). Big Data Application in Education: Dropout Prediction in Edx MOOCs. 2016 IEEE Second International Conference on Multimedia Big Data (BigMM), 440–443. 10.1109/BigMM.2016.70
Limsathitwong, K., Tiwatthanont, K., & Yatsungnoen, T. (2018). Dropout prediction system to reduce discontinue study rate of information technology students. 2018 5th International Conference on Business and Industrial Research (ICBIR), 110–114. 10.1109/ICBIR.2018.8391176
Márquez-Vera, C., Cano, A., Romero, C., Noaman, A. Y. M., Fardoun, H. M., & Ventura, S. (2016). Early dropout prediction using data mining: a case study with high school students. Expert Systems, 33(1), 107–124. 10.1111/exsy.12135
Mduma, N. (2023). Data Balancing Techniques for Predicting Student Dropout Using Machine Learning. Data, 8(3), 49. 10.3390/data8030049
Miranda, M. A., & Guzmán, J. (2017). Análisis de la Deserción de Estudiantes Universitarios usando Técnicas de Minería de Datos. Formación Universitaria, 10(3), 61–68. 10.4067/S0718-50062017000300007
Nasteski, V. (2017). An overview of the supervised machine learning methods. HORIZONS.B, 4, 51–62. 10.20544/HORIZONS.B.04.1.17.P05
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Ortiz-Lozano, J. M., Rua-Vieites, A., Bilbao-Calabuig, P., & Casadesús-Fa, M. (2018). University student retention: Best time and data to identify undergraduate students at risk of dropout. Innovations in Education and Teaching International, 57(1), 74–85. 10.1080/14703297.2018.1502090
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(4), 485. 10.3390/e23040485
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Pitman, T., Koshy, P., & Phillimore, J. (2014). Does accelerating access to higher education lower its quality? The Australian experience. Higher Education Research & Development, 34(3), 609–623. 10.1080/07294360.2014.973385
Reay, D. (2004). ‘It’s all becoming a habitus’: beyond the habitual use of habitus in educational research. British Journal of Sociology of Education, 25(4), 431–444. 10.1080/0142569042000236934
Reay, D. (2022). 'The more things change the more they stay the same’: The continuing relevance of Bourdieu and Passeron’s Reproduction in Education, Society and Culture. Revista Española de Sociología, 31(3), a116. 10.22325/FES/RES.2022.116
Rokach, L., & Maimon, O. (2014). Data mining with decision trees: Theory and applications. World Scientific.
Sani, N. S., Fikri, A., Ali, Z., Zakree, M., & Nadiyah, K. (2020). Drop-Out Prediction in Higher Education Among B40 Students. International Journal of Advanced Computer Science and Applications, 11(11). 10.14569/IJACSA.2020.0111169
Santelices, M., Horn, C., Catalán, X., & Venegas, A. (2021). Aggregated results of access programs implemented by universities in Chile: Students’ persistence using a matched sample. Higher Education Policy, 35, 498–521. 10.1057/s41307-021-00223-3
SIES. (2019). Deserción de primer año y Reingreso a la Educación Superior en Chile. https://d66z.short.gy/K7cjsq
SIES. (2023). Informe Retención SIES 2023. Servicio de Información de Educación Superior, Ministerio de Educación. https://d66z.short.gy/7uEvS3
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