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Modelling two types of heterogeneity in the analysis of student success. (English) Zbl 07481380

Summary: Student dropout is a worldwide problem, leading private and public universities in developed and underdeveloped countries to study the subject carefully or, as has recently been done, to analyse what drives student success. On this matter, different approaches are used to obtain useful information for decision-making. We propose a model that considers the enrolment date to the dropout or graduation date and also covariates to measure student success rates, to identify what the academic and non-academic factors are, and how they drive the student success. Our proposal assumes that there is one part of the population who is not at risk of dropping out, and that the part of the population at risk is heterogeneous, that is, we assume two types of heterogeneity. We highlight two advantages of our model: one is to identify the period of higher risk to dropout due to considering the academic survival time and the second is due to the inclusion of covariates that enable us to identify the characteristics linked to dropout. In this research, we also demonstrate the identifiability of the model and describe the estimation procedures. To exemplify the applicability of the approach, we use two real datasets.

MSC:

62Pxx Applications of statistics

Software:

R
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[1] Akaike, H., Statistical predictor identification, Ann. Inst. Stat. Math., 22, 203-217 (1970) · Zbl 0259.62076
[2] Ameri, S., Fard, M.J., Chinnam, R.B., and Reddy, C.K., Survival analysis based framework for early prediction of student dropouts, Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. ACM, 2016, pp. 903-912.
[3] Andersen, P. K.; Geskus, R. B.; de Witte, T.; Putter, H., Competing risks in epidemiology: Possibilities and pitfalls, Int. J. Epidemiol., 41, 861-870 (2012)
[4] Araque, F.; Roldán, C.; Salguero, A., Factors influencing university drop out rates, Comput. Educ., 53, 563-574 (2009)
[5] Atienza, N.; Garcia-Heras, J.; Munoz-Pichardo, J., A new condition for identifiability of finite mixture distributions, Metrika, 63, 215-221 (2006) · Zbl 1095.62016
[6] Berger, M., Welchowski, T., Schmitz-Valckenberg, S., and Schmid, M., A classification tree approach for the modeling of competing risks in discrete time, in Advances in Data Analysis and Classification, 2018. Available at doi:10.1007/s11634-018-0345-y. · Zbl 1474.62382
[7] Berkson, J.; Gage, R. P., Survival curve for cancer patients following treatment, J. Am. Stat. Assoc., 47, 501-515 (1952)
[8] Bickel, P. J.; Doksum, K. A., Mathematical Statistics: Basic Ideas and Selected Topics, Vol. I (2015), CRC Press: CRC Press, Boca Raton, FL · Zbl 1380.62002
[9] Boag, J. W., Maximum likelihood estimates of the proportion of patients cured by cancer therapy, J. R. Statist. Soc. Ser. B (Meth.), 11, 15-53 (1949) · Zbl 0034.08001
[10] Clerici, R.; Giraldo, A.; Meggiolaro, S., The determinants of academic outcomes in a competing risks approach: Evidence from Italy, Stud. High. Educ., 40, 1535-1549 (2015)
[11] Di Pietro, G.; Cutillo, A., Degree flexibility and university drop-out: The italian experience, Econ. Educ. Rev., 27, 546-555 (2008)
[12] Draper, N. R.; Smith, H., Applied Regression Analysis (2014), John Wiley & Sons: John Wiley & Sons, New York · Zbl 0158.17101
[13] Hanin, L.; Huang, L. S., Identifiability of cure models revisited, J. Multivar. Anal., 130, 261-274 (2014) · Zbl 1292.62134
[14] INEP, Censo da educação superior 2014, Tech. Rep., Instituto Nacional de Estudos e Pesquisas Educacionais Anísio Teixeira, Brasília, 2016.
[15] Juajibioy, J. C., Study of university dropout reason based on survival model, Open J. Stat., 6, 908-916 (2016)
[16] Lehmann, E. L.; Casella, G., Theory of Point Estimation (2006), Springer Science & Business Media, Springer-Verlag: Springer Science & Business Media, Springer-Verlag, Ney York
[17] Lehmann, W., “I just didn”t feel like I fit in”: The role of habitus in university dropout decisions, Canad. J. High. Educ., 37, 89-110 (2007)
[18] Li, C. S.; Taylor, J. M.; Sy, J. P., Identifiability of cure models, Stat. Probab. Lett., 54, 389-395 (2001) · Zbl 0999.62075
[19] Lima Júnior, P.; Silveira, F. L.d.; Ostermann, F., Análise de sobrevivência aplicada ao estudo do fluxo escolar nos cursos de graduação em física: Um exemplo de uma universidade brasileira, Revista Brasileira de Ensino de Física, 34, 1-10 (2012)
[20] Meggiolaro, S.; Giraldo, A.; Clerici, R., A multilevel competing risks model for analysis of university students’ careers in italy, Stud. High. Educ., 42, 1259-1274 (2017)
[21] Murtaugh, P. A.; Burns, L. D.; Schuster, J., Predicting the retention of university students, Res. High. Educ., 40, 355-371 (1999)
[22] Ortis, E.A. and Dehon, C., The roads to success: Analyzing dropout and degree completion at university, Tech. Rep., Working Papers ECARES 2011-025, ULB-Universite Libre de Bruxelles, 2011.
[23] R Core Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2013. Available at http://www.R-project.org/.
[24] Scott, M. A.; Kennedy, B. B., Pitfalls in pathways: Some perspectives on competing risks event history analysis in education research, J. Educ. Behav. Stat., 30, 413-442 (2005)
[25] Sittichai, R., Why are there dropouts among university students? Experiences in a Thai University, Int. J. Educ. Dev., 32, 283-289 (2012)
[26] Stacy, E. W., A generalization of the gamma distribution, Ann. Math. Stat., 33, 1187-1192 (1962) · Zbl 0121.36802
[27] Tontini, G.; Walter, S. A., Pode-se identificar a propensão e reduzir a evasão de alunos? Ações estratégicas e resultados táticos para instituições de ensino superior, Avaliação: Revista da Avaliação da Educação Superior, 19, 89-110 (2014)
[28] Vallejos, C. A.; Steel, M. F., Bayesian survival modelling of university outcomes, J. R. Statist. Soc. Ser. A (Stat. Soc.), 180, 613-631 (2017)
[29] Vossensteyn, J.J., Kottmann, A., Jongbloed, B.W., Kaiser, F., Cremonini, L., Stensaker, B., Hovdhaugen, E., and Wollscheid, S., Dropout and completion in higher education in Europe, Tech. Rep., Publications Office of the European Union, European Union, Luxembourg, 2015.
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