Survival Analysis of Students’ Dropout in a Nigerian University System
DOI:
https://doi.org/10.62054/ijdm/0102.12Keywords:
Survival Analysis, Attrition, Dropouts, Event, CensoredAbstract
Formal education remains the vehicle for socio-economic development and social mobilization in any society. Academic institutions are increasingly interested in monitoring the performance of their students. This interest has given rise to the need for research, collation, analysis, and interpretation of data to formulate evidence-based academic policies that aim to improve student performance and reduce dropout rates. Data obtained from the university comprises of Gender, Age at Entry, Marital Status, Sponsorship, Mode of Entry, Program of Study and Cumulative Grade Point Average (CGPA). This study investigated how these factors influence students’ dropout at Modibbo Adama University, Yola Adamawa State Nigeria. Survival analysis was carried out using Log-logistic, Lognormal, Weibull and Cox Proportional Hazard models. The lognormal survival model was judged as the best model due to its lowest AIC value of 1199.773. The Gender, CGPA and students’ program of study have significant effect on student dropout. Also, it was observed that 41.42% of male students dropped out when compared to 38.94% female students dropped out before their graduation year. Of all the programs of study considered, Physics has the highest number of dropped out before graduation. It accounted for 68.75%. In terms of academic performance, majority (85.05%) students who had CGPA of third class at the beginning of their studies dropped out before expected years of graduation. The study recommends that government and university management provide financial support to assist the male students who are on self-sponsorship, as financial pressure may contribute to their dropout rates
References
Abrahamowicz, M., Mackenzie. T. and Esdaile, J. M. (1996). Time-dependent Hazard Ratio: Modeling and Hypothesis Testing with Application in Lupus Nephritis. Journal of American Statistical Association, Vol. 91. No. 433 pp. 1432–1439.
Akaike H (1974). A new look at the statistical model identification. IEEE Trans Automatic Control.
Akinrefon, A. A. and Adejumo, A. O. (2012). A Study of Undergraduate Students Performance in Nigeria. Bulgarian Journal of Science and Education Policy (BJSEP), Volume 6.
Ali, S., Zubair, H. and Fahad, M. (2013). Factors contributing to the students’ academic performance: A case study of Islamia University sub-campus, «American Journal of Educational Research», 1 (8), 283-289.
Balogun, O. S. and Elepo, T. A. (2016): Statistical Analysis on Students’ Performance. Covenant Journal of Informatics and Communication Technology (CJICT) Vol. 4, No. 1
Cox, D.R. (1972). Regression Models and Life-Tables. Journal of the Royal Statistical Society, Vol. 34. No. 2 pp.187-220.
Johnson, V. E. (2006). Grade Inflation: A crisis in College education Springer Science and Business media.
Lakkaraju, H., Aguiar, E., Shan, C., Miller, D., Bhanpuri, N., Ghani, R. and Addison, K. L. (2015). A machine learning framework to identify students at risk of adverse academic outcomes. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 1909–1918. ACM, 2015.
Lin, J., Imbrie, P. and Reid, K. J. (2009). Student retention modelling: An evaluation of different methods and their impact on prediction results. Research in Engineering Education Sysmposium, pages 1–6.
Sodipo, E. O., Sodipo, A. A. and Adepoju, K. A. (2015). Statistical analysis of students’ academic performance in Nigeria universities: a case study of the university of Ibadan, Nigeria. International Journal of Recent Advances in Multidisciplinary Research Vol. 02 Issue 02 pp 0187-0192
Thaithanan, J., Thinnukool, O., Chaichana, M. and Wanishsakpong, W. (2021). Investigating Undergraduate Student Dropout Rates using Survival Analysis in the College of Arts, Media and Technology, Chiang Mai Univesity. Journal of Educational Psychology, 7(10).
Thomas, L. (2002). Student retention in higher education: the role of institutional habitus. Journal of Education Policy, 17(4):423–442.
Tinto, V. (2006). Research and practice of student retention: what next? Journal of College Student Retention: Research, Theory and Practice, 8(1):1–19, 2006.
Vinzamuri, B. and Reddy, C. K. (2013). Cox regression with correlation based regularization for electronic health records. Proceedings - IEEE International Conference on Data Mining, ICDM, pages 757–766.
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