Multiple linear regression models applied to the prediction of student dropout in higher education
DOI:
https://doi.org/10.55204/trc.v6i2.e753Keywords:
student dropout, multiple linear regression, higher education, prediction, academic retentionAbstract
Student dropout in higher education is a multifactorial issue with significant academic, social, and economic impacts. This study analyzes the application of multiple linear regression models to predict student dropout through a systematic review based on the PRISMA methodology, selecting 40 recent scientific studies. The results show that multiple linear regression is an effective tool due to its predictive capacity and interpretability, allowing the quantification of the impact of critical variables such as academic performance and economic status. A predictive model was proposed using a dataset of 4,424 records, obtaining a coefficient of determination (R² = 0.4715) and an RMSE of 0.3284. Although the model explains 47.15% of the variability, this value is significant in the social sciences given the complexity of human behavior and the multifactorial nature of the phenomenon. These findings support the development of early warning systems, effective retention policies, and optimization of institutional resources based on influential variables such as tuition payment status and completed courses. Future research should incorporate emotional variables and post-pandemic factors. This study has determined that multiple linear regression is an accessible, reliable, and strategic tool for preventing student dropout based on solid scientific evidence.Downloads
References
Aguilar-Reyes, J. E., Mejía-Peñafiel, E. F., Morocho-Barrionuevo, T. P., Velasco Castelo, G.-M., Aguilar-Reyes, J. E., Mejía-Peñafiel, E. F., Morocho-Barrionuevo, T. P., & Velasco Castelo, G.-M. (2025). ESTUDIO DEL RENDIMIENTO ACADÉMICO MEDIANTE LA COMPARACIÓN DE MODELOS DE REGRESIÓN Y ÁRBOLES DE CLASIFICACIÓN. Telos, 27(1), 94-115. https://doi.org/10.36390/telos271.08
Andrade-Girón, D., Sandivar-Rosas, J., Marín-Rodriguez, W., Susanibar-Ramirez, E., Toro-Dextre, E., Ausejo-Sanchez, J., Villarreal-Torres, H., & Angeles-Morales, J. (2023). Predicting Student Dropout based on Machine Learning and Deep Learning: A Systematic Review. EAI Endorsed Transactions on Scalable Information Systems, 10(5). https://doi.org/10.4108/eetsis.3586
Avilés-González, J., Córdova-León, F., Duque-Espinoza, G., & Mosquera-Gutierres, J. (2025). Key Determinants of University Student Dropout: Insights from a Private Institution in Ecuador. Journal of Latinos and Education, 0(0), 1-16. https://doi.org/10.1080/15348431.2025.2566917
Barragán Moreno, S. P., & Guzmán Rincón, A. (2025). Digital divide as an explanatory variable for dropout in higher education. International Journal of Educational Technology in Higher Education, 22(1), 60. https://doi.org/10.1186/s41239-025-00550-0
Bernal Troya, A. S. (2025). Análisis predictivo de la permanencia y deserción estudiantil: Implementación del modelo de Churn en el Instituto Superior Tecnológico Wissen (2021-2024) [Tesis de Maestría en Inteligencia de Negocios y Ciencia de Datos, Universidad de las Américas]. https://dspace.udla.edu.ec/bitstream/33000/17599/1/UDLA-EC-TMINCD-2025-20.pdf
Bettahi, A., Belouadha, F.-Z., & Harroud, H. (2025). A Modular and Explainable Machine Learning Pipeline for Student Dropout Prediction in Higher Education. Algorithms, 18(10), 662. https://doi.org/10.3390/a18100662
Bouihi, B., Bousselham, A., Aoula, E., Ennibras, F., & Deraoui, A. (2024). Prediction of Higher Education Student Dropout based on Regularized Regression Models. Engineering, Technology & Applied Science Research, 14(6), 17811-17815. https://doi.org/10.48084/etasr.8644
Chacha, B. R. C., López, W. L. G., & Constante, M. B. P. (2023). Predictive Model of Student Dropout Based on Logistic Regression. ESPOCH Congresses: The Ecuadorian Journal of S.T.E.A.M., 630-656. https://doi.org/10.18502/espoch.v3i1.14477
Chui B., H., Pérez A., K., Yabar M., P., Chui B., H., Pérez A., K., & Yabar M., P. (2025). Factores que intervienen en la deserción estudiantil universitaria. Revista de Investigaciones Veterinarias del Perú, 36(3). https://doi.org/10.15381/rivep.v36i3.30906
Contreras, D., González, L., Láscar, S., & López, V. (2022). Negative teacher–student and student–student relationships are associated with school dropout: Evidence from a large-scale longitudinal study in Chile. International Journal of Educational Development, 91, 102576. https://doi.org/10.1016/j.ijedudev.2022.102576
Dang, T. K., & Nguyen, H. H. X. (2022). A Hybrid Approach Using Decision Tree and Multiple Linear Regression for Predicting Students’ Performance Based on Learning Progress and Behavior. SN Computer Science, 3(5), 393. https://doi.org/10.1007/s42979-022-01251-5
Daza Santacoloma, G., & Hoyos Osorio, J. K. (2023). Predictive Model to Identify College Students with High Dropout Rates. Revista Electrónica de Investigación Educativa, 25. https://doi.org/10.24320/redie.2023.25.e13.5398
Duro, B., Gomes, A., Correia, F. B., Borges, A. R., & Bernardino, J. (2026). Machine Learning and Deep Learning for Dropout Prediction in Higher Education: A Review. Computers, 15(3), 164. https://doi.org/10.3390/computers15030164
Forti, M., Bilancia, M., Cafarelli, B., del Gobbo, E., & Nigri, A. (2025). Predicting dropout from higher education with multinomial finite mixture models: Empirical evidence from Italy. Quality & Quantity. https://doi.org/10.1007/s11135-025-02135-5
Garrido Silva, C. A., Pajuelo Díaz, J., Garrido Silva, C. A., & Pajuelo Díaz, J. (2023). Deserción en estudiantes de educación superior: Un estudio de caso. Universidad, Ciencia y Tecnología, 27(119), 18-28. https://doi.org/10.47460/uct.v27i119.703
Gonzalez, B., Mendes, T. P., Pinto, R., Correia, S. V., Albuquerque, S., & Paulino, P. (2025). Predictors of higher education dropout intention in the post-pandemic era: The mediating role of academic exhaustion. PLOS ONE, 20(7), e0327643. https://doi.org/10.1371/journal.pone.0327643
Granda, L. E. Á., Yautibug, F. C., & Copa, R. A. (2024). Deserción en la Educación Superior en Ecuador, Causas y Consecuencias. Ciencia Latina Revista Científica Multidisciplinar, 8(3), 11475-11490. https://doi.org/10.37811/cl_rcm.v8i3.12472
Huynh-Cam, T.-T., Chen, L.-S., & Lu, T.-C. (2025). Early Prediction Models and Crucial Factor Extraction for First-Year Undergraduate Student Dropouts. Journal of Applied Research in Higher Education, 17(2), 624-639. https://doi.org/10.1108/JARHE-10-2023-0461
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), 3957. https://doi.org/10.1038/s41598-024-53576-1
Kocsis, Á., & Molnár, G. (2025). Factors influencing academic performance and dropout rates in higher education. Oxford Review of Education, 51(3), 414-432. https://doi.org/10.1080/03054985.2024.2316616
Lima, M. N. C. A., & Fagundes, R. A. de A. (2020). Educational DataMining: A Study of the Factors That Cause School Dropout in Higher Education Institutions in Brazil. RENOTE, 18(1). https://doi.org/10.22456/1679-1916.105950
Marca Maquera, H., Coyla Zela, M., & Paredes, R. P. (2025). Factors at the household and university level that influence student dropout at UNAM. Frontiers in Education, 10, 1598687. https://doi.org/10.3389/feduc.2025.1598687
Márquez, F., & Sánchez-Pozo, N. (2023). Modelo de predicción de deserción: Un estudio de caso de estudiantes de la Universidad Politécnica Estatal del Carchi. Prometeo Conocimiento Científico, 3, e34. https://doi.org/10.55204/pcc.v3i2.e34
Núñez-Naranjo, A. F. (2024a). Analysis of the determinant factors in university dropout: A case study of Ecuador. Frontiers in Education, 9, 1444534. https://doi.org/10.3389/feduc.2024.1444534
Núñez-Naranjo, A. F. (2024b). Analysis of the determinant factors in university dropout: A case study of Ecuador. Frontiers in Education, 9. https://doi.org/10.3389/feduc.2024.1444534
Orozco-Rodríguez, C., Viegas, C., Costa, A. R., Lima, N., & Alves, G. R. (2025). Dropout Rate Model Analysis at an Engineering School. Education Sciences, 15(3), 287. https://doi.org/10.3390/educsci15030287
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021a). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. PLoS Medicine, 18(3), e1003583. https://doi.org/10.1371/journal.pmed.1003583
Paksi, B., Széll, K., & Fehérvári, A. (2023). Empirical Testing of a Multidimensional Model of School Dropout Risk. Social Sciences, 12(2), 50. https://doi.org/10.3390/socsci12020050
Pavón, M. A. L., & Paez, S. A. C. (2025). Modelado del índice de deserción estudiantil en Carreras de Ingeniería en una Institución de Educación Superior.: Modeling of the student dropout rate in Engineering Degrees in a Higher Education Institution. Revista Científica Multidisciplinar G-nerando, 6(2), ág. 228-242. https://doi.org/10.60100/rcmg.v6i2.733
Quimiz-Moreira, M., Delgadillo, R., Parraga-Alava, J., Maculan, N., & Mauricio, D. (2025). Factors, Prediction, Explainability, and Simulating University Dropout Through Machine Learning: A Systematic Review, 2012–2024. Computation, 13(8), 198. https://doi.org/10.3390/computation13080198
Rabelo, A. M., & Zárate, L. E. (2025). A model for predicting dropout of higher education students. Data Science and Management, 8(1), 72-85. https://doi.org/10.1016/j.dsm.2024.07.001
Rahmani, A. M., Groot, W., & Rahmani, H. (2024). Dropout in online higher education: A systematic literature review. International Journal of Educational Technology in Higher Education, 21(1), 19. https://doi.org/10.1186/s41239-024-00450-9
Realinho, V., Machado, J., Baptista, L., & Martins, M. V. (2022). Predicting Student Dropout and Academic Success. Data, 7(11), 146. https://doi.org/10.3390/data7110146
Rebelo Marcolino, M., Reis Porto, T., Thompsen Primo, T., Targino, R., Ramos, V., Marques Queiroga, E., Munoz, R., & Cechinel, C. (2025). Student dropout prediction through machine learning optimization: Insights from moodle log data. Scientific Reports, 15(1), 9840. https://doi.org/10.1038/s41598-025-93918-1
Romero, S., & Liao, X. (2025). Statistical and machine learning models for predicting university dropout and scholarship impact. PLOS ONE, 20(6), e0325047. https://doi.org/10.1371/journal.pone.0325047
Segura, M., Mello, J., & Hernández, A. (2022). Machine Learning Prediction of University Student Dropout: Does Preference Play a Key Role? Mathematics, 10(18), 3359. 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
Seo, E.-Y., Yang, J., Lee, J.-E., & So, G. (2024). Predictive modelling of student dropout risk: Practical insights from a South Korean distance university. Heliyon, 10(11). https://doi.org/10.1016/j.heliyon.2024.e30960
Song, Z., Sung, S.-H., Park, D.-M., & Park, B.-K. (2023). All-Year Dropout Prediction Modeling and Analysis for University Students. Applied Sciences, 13(2), 1143. https://doi.org/10.3390/app13021143
Tong, T., & Li, Z. (2025). Predicting learning achievement using ensemble learning with result explanation. PLOS ONE, 20(1), e0312124. https://doi.org/10.1371/journal.pone.0312124
Villar, A., & de Andrade, C. R. V. (2024). Supervised machine learning algorithms for predicting student dropout and academic success: A comparative study. Discover Artificial Intelligence, 4(1), 2. https://doi.org/10.1007/s44163-023-00079-z
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Gloria Viviana Paredes Barrigas, Washington Gonzalo Pomoboza Junez, Adrián Bladimir Andrade Carvajal, Katy Marisol Gaibor Naranjo, Maritza Geoconda Andrade Carvajal, Paulo Cesar Poma Villafuerte
This work is licensed under a Creative Commons Attribution 4.0 International License.
The authors retain the moral and patrimonial rights of their works. They only give to the magazine Tesla Revista Científica the right to the first publication of this. Since Tesla Revista Científica is an open access publication, readers can fully or partially reproduce its content as long as they properly credit the corresponding authors and the journal itself. Tesla Revista Científica undertakes not to make commercial use of the texts it receives and/or publishes.
Our journal is governed by the international policies SHERPA/RoMEO: Green journal: They allow the self-archiving of both the pre-print (draft of a paper) and the post-print (the version corrected and reviewed by peers) and even the final version ( layout as it will be published in the journal).
See also "Copyright and licences".
