The Dynamics of School Dropout in The Turkish Higher Education System

Authors

DOI:

https://doi.org/10.37241/jatss.2026.157

Keywords:

university dropout, competing risks, administrative data, disability, parental education

Abstract

Introduction: Student dropout is not only an individual failure but also leads to the ineffective use of public resources, the worsening of educational inequalities, and disruption in the labor market, thereby preventing long-term development. The aim of this study is to address the methodological gap in the area by utilising a large-scale dataset of approximately 2.9 million students covering the 2016–2024 period.

Method: The Fine-Gray Competing Risks Model, extended with time-varying coefficients, was employed, which considered graduation not simply as a censoring process, but as a competing risk with dropping out. Based on the higher education students database, the impacts of gender, age, disability status, parental education level, distance of residence, and entrance exam score type on the risk of dropout were explored.

Results or Findings: Students accepted based on quantitative test scores had the lowest dropout risk. Students whose parents were college graduates initially faced a relatively higher risk of dropping out in specific fields; this risk increased dynamically in quantitative and verbal fields, Distance represented a constant marginal risk. The probability of dropping out also increases with age; male students initially had a lower risk, but this difference increased over time.

Discussion or Conclusion: These findings may suggest that student dropout rates at Turkish universities may generally result from relatively stronger social factors. The continued education of students with disabilities in areas outside the quantitative fields and the growing advantage in school retention rates among male students presents a challenge to traditional views on the vulnerability of higher education.

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References

Aina, C. (2013). Parental background and university dropout in Italy. Higher Education, 65(4), 437–456. https://doi.org/10.1007/s10734-012-9554-z

Aina, C., Baici, E., Casalone, G., & Pastore, F. (2022). The determinants of university dropout: A review of the socio-economic literature. Socio-Economic Planning Sciences, 79, Article 101102. https://doi.org/10.1016/j.seps.2021.101102

Apumayta, R. Q., Cayllahua, J. C., Pari, A. C., Choque, V. I., Valverde, J. C. C., & Ataypoma, D. H. (2024). University dropout: A systematic review of the main determinant factors (2020-2024). F1000 Research, 13, Article 942. https://doi.org/10.12688/f1000research.154263.2

Araque, F., Roldán, C., & Salguero, A. (2009). Factors influencing university drop out rates. Computers & Education, 53(3), 563–574. https://doi.org/10.1016/j.compedu.2009.03.013

Bean, J. P. (1980). Dropouts and turnover: The synthesis and test of a causal model of student attrition. Research in Higher Education, 12(2), 155–187. https://doi.org/10.1007/bf00976194

Becker, G. S. (1962). Investment in human capital: A theoretical analysis. Journal of Political Economy, 70(5, Part 2), 9–49. https://doi.org/10.1086/258724

Behr, A., Giese, M., Teguim Kamdjou, H. D., & Theune, K. (2020). Dropping out of university: A literature review. Review of Education, 8(2), 614–652. https://doi.org/10.1002/rev3.3202

Bellare, Y., Smith, A., Cochran, K., & Lopez, S. G. (2023). Motivations and barriers for adult learner achievement: Recommendations for institutions of higher education. Adult Learning, 34(1), 30-39. https://doi.org/10.1177/10451595211059574

Belloc, F., Maruotti, A., & Petrella, L. (2011). How individual characteristics affect university students drop-out: A semiparametric mixed-effects model for an Italian case study. Journal of Applied Statistics, 38(10), 2225–2239. https://doi.org/10.1080/02664763.2010.545373

Candelo-Meneses, L. M., Castro-Velasco, M. A., Periañez, C. A. H., & Castillo-Díaz, M. A. (2026). Factors associated with university dropout intention among health sciences students: The mediating role of academic burnout and satisfaction with education. BMC Medical Education, 26(1), Article 622. https://doi.org/10.1186/s12909-026-08973-7

Curbani, F., Zanella, A., & Zibetti, A. W. (2026). A machine learning approach to predicting student dropout and supporting timely intervention. Journal of Applied Research in Higher Education, 1-18. https://doi.org/10.1108/jarhe-07-2025-0538

di Biase, G., & Giusino, D. (2026). The Psychology of working students: A Scoping review. Psychology International, 8(1), Article 11. https://doi.org/10.3390/psycholint8010011

Eurostat. (2025). Glossary: Educational attainment level. Eurostat Statistics Explained. Retrieved July 04, 2026, from https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Educational_attainment_level

Fine, J. P., & Gray, R. J. (1999). A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association, 94(446), 496–509. https://doi.org/10.1080/01621459.1999.10474144

Garza, A. N., & Fullerton, A. S. (2018). Staying close or going away: How distance to college impacts the educational attainment and academic performance of first-generation college students. Sociological Perspectives, 61(1), 164–185. https://doi.org/10.1177/0731121417711413

Geskus, R. B. (2024). Competing risks: Concepts, methods, and software. Annual review of Statistics and Its Application, 11(1) 227 - 254. https://doi.org/10.1146/annurev-statistics-040522-094556

Giraldo, A., Meggiolaro, S., Da Re, L., & Franceschini, C. (2025, October 24). Modelling university career paths: A competing risks analysis of dropout and degree completion at the University of Padua. Social Science Research Network https://doi.org/10.2139/ssrn.5652271

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

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

Jaksztat, S., Neugebauer, M., & Brandt, G. (2021). Back out or hang on? An event history analysis of withdrawal from doctoral education in Germany. Higher Education, 82(5), 937–958. https://doi.org/10.1007/s10734-021-00683-x

Johnes, G., & McNabb, R. (2004). Never give up on the good times: Student attrition in the UK. Oxford Bulletin of Economics and Statistics, 66(1), 23–47. https://doi.org/10.1111/j.1468-0084.2004.00068.x

Kalbfleisch, J. D., & Prentice, R. L. (2002). The statistical analysis of failure time data. John Wiley & Sons. https://doi.org/10.1002/9781118032985

Kasworm, C. E. (2010). Adult learners in a research university: Negotiating undergraduate student identity. Adult Education Quarterly, 60(2), 143-160. https://doi.org/10.1177/0741713609336110

Kehm, B. M., Larsen, M. R., & Sommersel, H. B. (2019). Student dropout from universities in Europe: A review of empirical literature. Hungarian Educational Research Journal, 9(2), 147-164. https://doi.org/10.1556/063.9.2019.1.18

Lizarte Simon, E. J., & Gijon Puerta, J. (2022). Prediction of early dropout in higher education using the SCPQ. Cogent Psychology, 9(1), Article 2123588. https://doi.org/10.1080/23311908.2022.2123588

Mouganie, P. (2020). Conscription and the returns to education: Evidence from a regression discontinuity. The Scandinavian Journal of Economics, 122(3), 1112-1139. https://doi.org/10.1111/sjoe.12361

Nurmalitasari, Awang Long, Z., & Faizuddin Mohd Noor, M. (2023). Factors influencing dropout students in higher education. Education Research International, 2023(1), Article 7704142. https://doi.org/10.1155/2023/7704142

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(20), 2599. https://doi.org/10.3390/math9202599

Oppedisano, V. (2011). The (adverse) effects of expanding higher education: Evidence from Italy. Economics of Education Review, 30(5), 997–1008. https://doi.org/10.1016/j.econedurev.2011.04.010

Pedditzi, M. L. (2024). School satisfaction and self-efficacy in adolescents and intention to drop out of school. International Journal of Environmental Research and Public Health, 21(1), 111. https://doi.org/10.3390/ijerph21010111

Portela, A. C. T., da Silva, A. D., & Silva, G. O. (2021, July 11-16). Dropout and completion in the bachelor in statistics in Brazil: Use of survival analyses in a competing risk scenario. In Proceedings 63rd ISI World Statistics Congress (pp.711-714 ). International Statistical Institute. Retrieved January 17, 2026, from https://isi-web.org/sites/default/files/import/pdf/194-day4-cps024-dropout-and-completion-in-the.pdf

Pusztai, G., Hrabéczy, A., & Csók, C. (2025). Parental involvement as an antidote to student dropout in higher education: Students’ perceptions of dropout risk. Open Education Studies, 7(1), Article 20250089. https://doi.org/10.1515/edu-2025-0089

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), Article 198. https://doi.org/10.3390/computation13080198

Russmann, M., Netz, N., & Lörz, M. (2024). Dropout intent of students with disabilities. Higher Education, 88(1), 183–208. https://doi.org/10.1007/s10734-023-01111-y

Schoenfeld, D. (1982). Partial residuals for the proportional hazards regression model. Biometrika, 69(1), 239-241. https://doi.org/10.1093/biomet/69.1.239

Sallai, G. M., Bahnson, M., Shanachilubwa, K., & Berdanier, C. G. (2023). Persistence at what cost? How graduate engineering students consider the costs of persistence within attrition considerations. Journal of Engineering Education, 112(3), 613-633. https://doi.org/10.1002/jee.20528

Sestelo, M., Meira‐Machado, L., Villanueva, N. M., & Roca‐Pardiñas, J. (2024). A method for determining groups in cumulative incidence curves in competing risk data. Biometrical Journal, 66(4), Article 2300084. https://doi.org/10.1002/bimj.202300084

Tinto, V. (1975). Dropout from higher education: A theoretical synthesis of recent research. Review of Educational Research, 45(1), 89–125. https://doi.org/10.3102/00346543045001089

Tinto, V. (1988). Stages of student departure: Reflections on the longitudinal character of student leaving. The Journal of Higher Education, 59(4), 438-455. https://doi.org/10.1080/00221546.1988.11780199

Turkish Statistical Institute. (2026). Higher education statistics [Data set]. TurkStat.

Valencia-Arias, A., Valera Aredo, J. C., Valencia, J., Cardona-Acevedo, S., Patiño-Vanegas, J. C., & Uribe Bedoya, H. (2025). Key determinants of university dropout: Academic, economic, and psychosocial factors in student retention. Frontiers in Education, 10, Article 1701644. https://doi.org/10.3389/feduc.2025.1701644

Venkatesan, R. G., Karmegam, D., & Mappillairaju, B. (2024). Exploring statistical approaches for predicting student dropout in education: A systematic review and meta-analysis. Journal of Computational Social Science, 7(1), 171-196. https://doi.org/10.1007/s42001-023-00231-w

Vignoles, A., & Powdthavee, N. (2009). The socio-economic gap in university dropout. The BE Journal of Economic Analysis & Policy, 9(1), 19-39. https://doi.org/10.2202/1935-1682.2051

Zeragaber, T. Y., Teame, G. T., & Tsighe, Z. (2024). Assessing the effect of home-to-school distance on student dropout rate in Adi-Keyih sub-zone, Eritrea. International Journal of Educational Research Open, 7, Article 100340. https://doi.org/10.1016/j.ijedro.2024.100340

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Published

25-09-2026

How to Cite

Şentürk, H. (2026). The Dynamics of School Dropout in The Turkish Higher Education System. Journal of Applied And Theoretical Social Sciences, 8(3), 395–420. https://doi.org/10.37241/jatss.2026.157