Predicting university dropout using machine learning: Applications based on multidisciplinary databases
Abstract Student dropout constitutes a challenge for higher education institutions due to, among other reasons, the diversity of students’ social, educational, and economic backgrounds, as well as the disciplines and academic degrees in which they enrol, resulting in a wide variability in educational trajectories. The study aimed to develop a predictive model of university dropout among students enrolled in various programs, utilising machine learning. An online questionnaire and academic records were used to obtain ten variables previously identified as relevant to the studied phenomenon, and classifiers were trained through supervised machine learning procedures. The sample consisted of 946 students from seven different academic disciplines at a university in Chile. The primary outcomes indicate that a model based on the RUSboosted Trees classifier presented the best performance in predicting dropout in higher education, successfully recognising 90% of dropout students. Several previous studies have predicted student dropout using machine learning, but typically on samples of students belonging to the same programmes. This study presents a predictive model that identifies future dropouts even under multidisciplinary conditions within their original programmes.
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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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Frank, E., Hall, M., & Witten, I. (2017). The WEKA workbench. In I. Witten, E. Frank, M. Hall, & C. Pal (Eds.), Data Mining: Practical Machine Learning Tools and Techniques (pp. 553–571). Elsevier, Morgan Kaufmann. 10.1016/B978-0-12-804291-5.00024-6
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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
Gedda-Muñoz, R., & Villagrán-Del Picó, N. (2020). Academic self-concept in university students: Their association with parents’ educational level and previous experience in higher education. Journal of Educational, Cultural and Psychological Studies, 2020(22), 109–124. 10.7358/ecps-2020-022-gedd
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.
Harrington, P. (2012). Machine Learning in Action. Manning Publications.
Heredia, D., Amaya, Y., & Barrientos, E. (2015). Student Dropout Predictive Model Using Data Mining Techniques. IEEE Latin America Transactions, 13(9), 3127–3134. 10.1109/tla.2015.7350068
Himmel, E. (2002). Modelo de análisis de la deserción estudiantil en la educación superior. Calidad en la Educación, 17, 91. 10.31619/caledu.n17.409
Kemper, L., Vorhoff, G., & Wigger, B. U. (2020). Predicting student dropout: A machine learning approach. European Journal of Higher Education, 10(1), 28–47. 10.1080/21568235.2020.1718520
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, e30080. 10.14201/eks.30080
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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
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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
Parra-Sánchez, J. S., Torres Pardo, I. D., & Martínez de Merino, C. Y. (2023). Factores explicativos de la deserción universitaria abordados mediante inteligencia artificial. Revista Electrónica de Investigación Educativa, 25, 1–17. 10.24320/REDIE.2023.25.E18.4455
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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
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Soto-Hernández, V. J. (2016). Estudiantes de primera generación en Chile: una aproximación cualitativa a la experiencia universitaria. Revista Complutense de Educación, 27(3). 10.5209/rev_RCED.2016.v27.n3.47562
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Tinto, V. (1975). Dropout from Higher Education: A Theoretical Synthesis of Recent Research. Review of Educational Research, 45(1), 89–125. 10.3102/00346543045001089
Tinto, V. (2022). Increasing Student Persistence: Wanting and Doing. In H. Huijser, M. Y. Kek, & F. Padró (Eds.), Student Support Services. University Development and Administration (pp. 53–70). Springer. 10.1007/978-981-16-5852-5_33
Tomasevic, N., Gvozdenovic, N., & Vranes, S. (2020). An overview and comparison of supervised data mining techniques for student exam performance prediction. Computers & Education, 143, 103676. 10.1016/j.compedu.2019.103676
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Aulck, L., & West, J. (2017). Attrition and performance of community college transfers. PloS One, 12(4), e0174683. 10.1371/journal.pone.0174683
Barramuño, M., Meza-Narváez, C., & Gálvez-García, G. (2022). Prediction of student attrition risk using machine learning. Journal of Applied Research in Higher Education, 14(3), 974–986. 10.1108/JARHE-02-2021-0073
Beer, C., & Lawson, C. (2016). The problem of student attrition in higher education: An alternative perspective. Journal of Further and Higher Education, 41(6), 773–784. 10.1080/0309877x.2016.1177171
Berens, J., Oster, S., Schneider, K., & Burghoff, J. (2018). Early Detection of Students at Risk - Predicting Student Dropouts Using Administrative Student Data and Machine Learning Methods. Schumpeter Discussion Papers, 6(1), 1–38. 10.2139/ssrn.3275433
Bourdieu, P., & Passeron, J. C. (2019). La reproducción: Elementos para una teoría del sistema educativo. Siglo XXI Editores.
Bourdieu, P., & Passeron, J. C. (2021). Los Herederos: los estudiantes y la cultura. Siglo XXI & Clave Intelectual.
Braxton, J. M. (2019). Leaving College: Rethinking the Causes and Cures of Student Attrition by Vincent Tinto. Journal of College Student Development, 60(1), 129–134. 10.1353/csd.2019.0012
Cancino-Marchant, A., Gedda-Muñoz, R., Herrera-Valenzuela, T., Magnani Branco, B., Guzmán-Muñoz, E., López-Fuenzalida, A., & Valdés-Badilla, P. (2022). Autoestima y autoconcepto académico en escolares durante la pandemia por la COVID-19. Revista Cubana de Medicina Militar, 51(3), e02202012.
Chawla, N. V, Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research, 16, 321–357. 10.1613/jair.953
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
CNED. (2022). Base de datos índices CNED 2005-2022. Consejo Nacional de Educación, Ministerio de Educación-Chile.
Cuji Chacha, B. R., Gavilanes López, W. L., Vicente Guerrero, V. X., & Villacis Villacis, W. G. (2020). Student Dropout Model Based on Logistic Regression. In Communications in Computer and Information Science (pp. 321–333). 10.1007/978-3-030-42520-3_26
Donoso, S., & Schiefelbein, E. (2007). Análisis de los modelos explicativos de retención de estudiantes en la universidad: una visión desde la desigualdad social. Estudios Pedagógicos (Valdivia), 33(1). 10.4067/s0718-07052007000100001
Dutt, A. (2015). Clustering Algorithms Applied in Educational Data Mining. International Journal of Information and Electronics Engineering, 5(2), 112–116. 10.7763/ijiee.2015.v5.513
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
Fahd, K., Venkatraman, S., Miah, S. J., & Ahmed, K. (2021). Application of machine learning in higher education to assess student academic performance, at-risk, and attrition: A meta-analysis of literature. Education and Information Technologies, 2021, 1–33. 10.1007/S10639-021-10741-7
Frank, E., Hall, M., & Witten, I. (2017). The WEKA workbench. In I. Witten, E. Frank, M. Hall, & C. Pal (Eds.), Data Mining: Practical Machine Learning Tools and Techniques (pp. 553–571). Elsevier, Morgan Kaufmann. 10.1016/B978-0-12-804291-5.00024-6
Garbanzo Vargas, G. M. (2012). Factores asociados al rendimiento académico en estudiantes universitarios, una reflexión desde la calidad de la educación superior pública. Revista Educación, 31(1), 43. 10.15517/revedu.v31i1.1252
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
Gedda-Muñoz, R., & Villagrán-Del Picó, N. (2020). Academic self-concept in university students: Their association with parents’ educational level and previous experience in higher education. Journal of Educational, Cultural and Psychological Studies, 2020(22), 109–124. 10.7358/ecps-2020-022-gedd
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.
Harrington, P. (2012). Machine Learning in Action. Manning Publications.
Heredia, D., Amaya, Y., & Barrientos, E. (2015). Student Dropout Predictive Model Using Data Mining Techniques. IEEE Latin America Transactions, 13(9), 3127–3134. 10.1109/tla.2015.7350068
Himmel, E. (2002). Modelo de análisis de la deserción estudiantil en la educación superior. Calidad en la Educación, 17, 91. 10.31619/caledu.n17.409
Kemper, L., Vorhoff, G., & Wigger, B. U. (2020). Predicting student dropout: A machine learning approach. European Journal of Higher Education, 10(1), 28–47. 10.1080/21568235.2020.1718520
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, e30080. 10.14201/eks.30080
Larroucau, T. (2016). Estudio de los factores determinantes de la deserción en el sistema universitario chileno. Revista Estudios de Políticas Públicas, 1. 10.5354/0719-6296.2015.38351
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
OECD. (2020). Education at a Glance 2020: OECD Indicators. 10.1787/69096873-en
OECD. (2023). Education at a Glance 2023. OECD. 10.1787/e13bef63-en
OFD-CIAE. (2021). Informe N°9 Observatorio Formación Docente: Deserción en carreras de Pedagogía en Chile (9).
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
Parra-Sánchez, J. S., Torres Pardo, I. D., & Martínez de Merino, C. Y. (2023). Factores explicativos de la deserción universitaria abordados mediante inteligencia artificial. Revista Electrónica de Investigación Educativa, 25, 1–17. 10.24320/REDIE.2023.25.E18.4455
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
Siri, A. (2015). Predicting Students’ Dropout at University Using Artificial Neural Networks. Italian Journal of Sociology of Education, 7(2), 225–247.
Soto-Hernández, V. J. (2016). Estudiantes de primera generación en Chile: una aproximación cualitativa a la experiencia universitaria. Revista Complutense de Educación, 27(3). 10.5209/rev_RCED.2016.v27.n3.47562
Teixeira, P., Kim, S., Landoni, P., & Gilani, Z. (2017). Rethinking the public-private mix in Higher Education. Global trends and national policy challenges. Brill. 10.1007/978-94-6300-911-9
Tinto, V. (1975). Dropout from Higher Education: A Theoretical Synthesis of Recent Research. Review of Educational Research, 45(1), 89–125. 10.3102/00346543045001089
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