Factores de deserción estudiantil y estrategias de retención en carreras universitarias de Ingeniería y Áreas Técnicas

Xavier Rubio-Tobar

doi:10.56124/finibus.v8i15.014

Authors

Xavier Rubio-Tobar Universidad de Investigación e Innovación de México (UIIX). Escuela de Doctorado. https://orcid.org/0009-0004-6660-6491

DOI:

https://doi.org/10.56124/finibus.v8i15.014

Keywords:

Student attrition, academic retention, higher education, engineering, machine learning

Abstract

Student dropout in higher education, particularly in engineering and technical fields, entails well-documented academic, social, and economic implications. This study presents a Systematic Literature Review (SLR) based on 57 articles published between 2000 and 2024 from the Scopus database. The review identified factors associated with student attrition and documented strategies aimed at improving retention. Findings were organized into thematic clusters, highlighting the use of machine learning technologies to predict academic risk, the influence of socioeconomic and psychosocial factors on dropout rates, and the effectiveness of institutional policies in fostering retention. The literature indicates that variables such as prior academic performance, self-efficacy, and access to financial resources are key predictors of attrition. Educational analytics tools, such as deep learning algorithms, have demonstrated high accuracy in early identification of at-risk students; however, their implementation faces constraints in rural areas or settings with limited technological infrastructure. This analysis underscores the importance of integrated strategies that combine technological, pedagogical, and psychosocial approaches. While advanced technologies offer notable advantages, their effectiveness and sustainability depend on their adaptability to diverse contexts and the integration of human factors into predictive models.

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References

Acevedo, F. (2023). The deficient factual basis of the main explanatory models of dropout in higher education. Journal of Education and Learning, 17(2), 241–248. https://doi.org/10.11591/edulearn.v17i2.20756

Asenjo, E., Santos, L., Puican, L., Ventura, F., Huanca, L., Valladolid, A., & Hinojosa, C. (2024). Fostering equity in rural education: a literature review on student dropout and retention strategies. Revista de Gestao Social e Ambiental, 18(1). https://doi.org/10.24857/RGSA.V18N1-083

Barragán Moreno, S. P., & González Támara, L. (2017). Approach to student dropouts from the perspective of social and academic integration. Revista de La Educacion Superior, 46(183), 63–86. https://doi.org/10.1016/j.resu.2017.05.004

Branson, N., & Whitelaw, E. (2024). South African student retention during 2020: Evidence from system-wide higher education institutional data. South African Journal of Economics, 92(1), 9–30. https://doi.org/10.1111/saje.12361

Cădariu, I.-E., & Rad, D. (2023). Predictors of Romanian Psychology Students’ Intention to Successfully Complete Their Courses—A Process-Based Psychology Theory Approach. Behavioral Sciences, 13(7). https://doi.org/10.3390/bs13070549

Castillo, P., Morales, T., & Miranda, C. (2019). Evaluation of a psycho-social support program about concepts of persistence and university retention. Revista Brasileira de Educacao, 24, 1–24. https://doi.org/10.1590/S1413-24782019240058

Cocoradă, E., Curtu, A. L., Năstasă, L. E., & Vorovencii, I. (2021). Dropout intention, motivation and socio-demographics of forestry students in Romania. Forests, 12(5). https://doi.org/10.3390/f12050618

Flores, V., Heras, S., & Julián, V. (2022). A New Methodological Framework for Project Design to Analyse and Prevent Students from Dropping Out of Higher Education. Electronics (Switzerland), 11(18). https://doi.org/10.3390/electronics11182902

Gallegos, J. A., Campos, N. A., Canales, K. A., & González, E. N. (2018). Determining factors of university dropout. A case of the faculty of economy and administration sciences of the Catholic University of the Most Holy Conception (Chile). Formacion Universitaria, 11(3), 11–18. https://doi.org/10.4067/S0718-50062018000300011

García Aretio, L. (2019). The problem with distance education dropout rate. Answers from the Mediated Educational Dialogue. RIED-Revista Iberoamericana de Educacion a Distancia, 22(1), 245–270. https://doi.org/10.5944/ried.22.1.22433

González-Campos, J. A., Carvajal-Muquillaza, C. M., & Aspeé-Chacón, J. E. (2020). Modelación de la deserción universitaria mediante cadenas de Markov. Uniciencia, 34(1), 129–146. https://www.redalyc.org/articulo.oa?id=475962995008

Goran, R., Jovanovic, L., Bacanin, N., Stankovic, M. S., Simic, V., Antonijevic, M., & Zivkovic, M. (2024). Identifying and Understanding Student Dropouts Using Metaheuristic Optimized Classifiers and Explainable Artificial Intelligence Techniques. IEEE Access, 12, 122377–122400. https://doi.org/10.1109/ACCESS.2024.3446653

Guanin-Fajardo, J. H., Guaña-Moya, J., & Casillas, J. (2024). Predicting Academic Success of College Students Using Machine Learning Techniques. Data, 9(4). https://doi.org/10.3390/data9040060

Hinojosa, M., Derpich, I., Alfaro, M., Ruete, D., Caroca, A., & Gatica, G. (2022). Procedimento de agrupamento de alunos de acordo com o risco de evasão para melhorar a gestão estudantil no ensino superior. Texto Livre, 15, e37275. https://doi.org/10.35699/1983-3652.2022.37275

Huerta-Manzanilla, E. L., Ohland, M. W., & Peniche-Vera, R. D. R. (2021). Co-enrollment density predicts engineering students’ persistence and graduation: College networks and logistic regression analysis. Studies in Educational Evaluation, 70. https://doi.org/10.1016/j.stueduc.2021.101025

Kitchenham, B., & Charters, S. (2007). Guidelines for performing Systematic Literature Reviews in Software Engineering. 2.

Lázaro, N., Callejas, Z., & Griol, D. (2020). Factors that affect student desertion in careers in Computer Engineering profile. Revista Fuentes, 22(1), 105–126. https://doi.org/10.12795/revistafuentes.2020.v22.i1.09

Marcq, K., Donayre, E. J. C., & Braeken, J. (2024). The role of item format in the PISA 2018 mathematics literacy assessment: A cross-country study. Studies in Educational Evaluation, 83, 101401. https://doi.org/https://doi.org/10.1016/j.stueduc.2024.101401

Matz, S. C., Bukow, C. S., Peters, H., Deacons, C., & Stachl, C. (2023). Using machine learning to predict student retention from socio-demographic characteristics and app-based engagement metrics. Scientific Reports, 13(1). https://doi.org/10.1038/s41598-023-32484-w

Miranda, M. A., & Guzmán, J. (2017). Análisis de la deserción de estudiantes universitarios usando técnicas de minería de datos. Formacion Universitaria, 10(3), 61–68. https://doi.org/10.4067/S0718-50062017000300007

Olaya, D., Vásquez, J., Maldonado, S., Miranda, J., & Verbeke, W. (2020). Uplift Modeling for preventing student dropout in higher education. Decision Support Systems, 134. https://doi.org/10.1016/j.dss.2020.113320

Oqaidi, K., Aouhassi, S., & Mansouri, K. (2022). Towards a Students’ Dropout Prediction Model in Higher Education Institutions Using Machine Learning Algorithms. International Journal of Emerging Technologies in Learning, 17(18), 103–117. https://doi.org/10.3991/ijet.v17i18.25567

Patacsil, F. F. (2020). Survival analysis approach for early prediction of student dropout using enrollment student data and ensemble models. Universal Journal of Educational Research, 8(9), 4036–4047. https://doi.org/10.13189/ujer.2020.080929

Pertegal-Felices, M. L., Valdivieso-Salazar, D. A., Espín-León, A., & Jimeno-Morenilla, A. (2022). Resilience and Academic Dropout in Ecuadorian University Students during COVID-19. Sustainability (Switzerland), 14(13). https://doi.org/10.3390/su14138066

Petticrew, M., & Roberts, H. (2006). Why Do We Need Systematic Reviews? In Systematic Reviews in the Social Sciences (pp. 1–26). Wiley online Library. https://doi.org/https://doi.org/10.1002/9780470754887.ch1

Sacală, M.-D., Pătărlăgeanu, S. R., Popescu, M.-F., & Constantin, M. (2021). Econometric research of the mix of factors influencing first-year students’ dropout decision at the faculty of agri-food and environmental economics. Economic Computation and Economic Cybernetics Studies and Research, 55(3), 203–220. https://doi.org/10.24818/18423264/55.3.21.13

Sayed, M. (2024). Student Progression and Dropout Rates Using Convolutional Neural Network: A Case Study of the Arab Open University. Journal of Advanced Computational Intelligence and Intelligent Informatics, 28(3), 668–678. https://doi.org/10.20965/jaciii.2024.p0668

Segovia, N., Said Hung, E., & Aguilera, F. J. (2024). Abandono en la educación superior virtual colombiana: factores de riesgo para grupos vulnerables. Revista Colombiana de Educación, 90, 261–284. https://doi.org/10.17227/rce.num90-15581

Shmeleva, E., & Froumin, I. (2020). Factors of Attrition among Computer Science and Engineering Undergraduates in Russia. Voprosy Obrazovaniya / Educational Studies Moscow, 3, 56–74. https://doi.org/10.17323/1814-9545-2020-3-110-136

Silva, P. T. de F. e, & Sampaio, L. M. B. (2022). Student retention policies in higher education: reflections from a literature review for the Brazilian context. Revista de Administração Pública, 56(5), 603–631. https://doi.org/10.1590/0034-761220220034x

Uliyan, D., Aljaloud, A. S., Alkhalil, A., Amer, H. S. A., Mohamed, M. A. E. A., & Alogali, A. F. M. (2021). Deep Learning Model to Predict Students Retention Using BLSTM and CRF. IEEE Access, 9, 135550–135558. https://doi.org/10.1109/ACCESS.2021.3117117

Urbina-Nájera, A. B., Camino-Hampshire, J. C., & Cruz Barbosa, R. (2020). University dropout: Prevention patterns through the application of educational data mining. RELIEVE - Revista Electronica de Investigacion y Evaluacion Educativa, 26(1), 1–19. https://doi.org/10.7203/relieve.26.1.16061

Urteaga, I., Siri, L., & Garófalo, G. (2020). Early dropout prediction via machine learning in professional online courses. RIED-Revista Iberoamericana de Educacion a Distancia, 23(2), 147–167. https://doi.org/10.5944/ried.23.2.26356

Vidal, J., Gilar-Corbi, R., Pozo-Rico, T., Castejón, J.-L., & Sánchez-Almeida, T. (2022). Predictors of University Attrition: Looking for an Equitable and Sustainable Higher Education. Sustainability (Switzerland), 14(17). https://doi.org/10.3390/su141710994