Predictive Model to Identify College Students with High Dropout Rates

Jhoan Keider Hoyos Osorio; Genaro Daza Santacoloma

doi:10.24320/redie.2023.25.e13.5398

Autores

Jhoan Keider Hoyos Osorio Universidad Tecnológica de Pereira https://orcid.org/0000-0002-7169-7963
Genaro Daza Santacoloma Universidad Tecnológica de Pereira https://orcid.org/0000-0002-1429-5925

DOI:

https://doi.org/10.24320/redie.2023.25.e13.5398

Palavras-chave:

evasão escolar, estudante universitário, previsão, análise de regressão

Resumo

Reduzir a taxa de evasão estudantil é um dos principais objetivos das instituições de ensino superior; para conseguir isso, as universidades devem identificar com precisão os alunos com maior risco de abandonar os estudos antes da conclusão do curso e concentrar seus esforços neles. Daí surge a necessidade de implementar modelos preditivos capazes de identificar os alunos que acabarão por desistir. Este artigo apresenta um sistema de alerta precoce para identificar alunos do primeiro semestre com alto risco de evasão; o sistema é baseado em um modelo de aprendizagem automático treinado a partir de dados históricos de alunos do primeiro semestre. Os resultados mostram que o sistema pode identificar os alunos “em risco” com uma sensibilidade de 61.97%, o que possibilita oferecer-lhes atendimento precoce e reduzir o índice de evasão.

Downloads

Não há dados estatísticos.

Referências

Ameri, S., Fard, M. J., Chinnam, R. B., & Reddy, C. K. (2016). Survival analysis based framework for early prediction of student dropouts. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management (pp. 903-912). https://doi.org/10.1145/2983323.2983351

Bean, J. P. (1985). Interaction effects based on class level in an explanatory model of college student dropout syndrome. American Educational Research Journal, 22(1), 35-64. https://doi.org/10.3102/00028312022001035

Berens, J., Schneider, K., Görtz, S., Oster, S., & Burghoff, J. (2018). Early detection of students at risk: Predicting student dropouts using administrative student data and machine learning methods (Working paper No. 7259). Center for Economic Studies & Ifo Institute. http://dx.doi.org/10.2139/ssrn.3275433

Bruffaerts, R., Mortier, P., Kiekens, G., Auerbach, R. P., Cuijpers, P., Demyttenaere, K., Green, G., Nock, M., & Kessler, R. C. (2018). Mental health problems in college freshmen: Prevalence and academic functioning. Journal of Affective Disorders, 225, 97-103. https://doi.org/10.1016/j.jad.2017.07.044

Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic Minority Over-Sampling Technique. The Journal of Artificial Intelligence Research, 16, 321-357. https://doi.org/10.1613/jair.953

Chen, X. W., & Jeong, J. C. (2007, December). Enhanced recursive feature elimination. In Sixth International Conference on Machine Learning and Applications (ICMLA) (pp. 429-435). IEEE. https://doi.org/10.1109/ICMLA.2007.35

Daley, F. (2010). Why college students drop out and what we do about it. College Quarterly, 13(3), 1-5. https://eric.ed.gov/?id=EJ930391

Ferreyra, M. M., Avitabile, C., Botero Álvarez, J., Haimovich Paz, F., & Urzúa, S. (2017). At a crossroads: Higher education in Latin America and the Caribbean. World Bank.

Fodor, I. K. (2002). A survey of dimension reduction techniques (Technical Report No. UCRL-ID-148494). Lawrence Livermore National Lab. https://www.osti.gov/biblio/15002155

García, S., Luengo, J., & Herrera, F. (2015). Feature selection. In Data preprocessing in data mining (pp. 163-193). Springer International Publishing. https://doi.org/10.1007/978-3-319-10247-4_7

González, F. I., & Arismendi, K. J. (2018). Deserción estudiantil en la educación superior técnico-profesional: explorando los factores que inciden en alumnos de primer año [Student dropout in technical and vocational higher education: Exploring factors that influence freshmen]. Revista de la Educación Superior, 47(188), 109-137. https://doi.org/10.36857/resu.2018.188.510

Guo, X., Yin, Y., Dong, C., Yang, G., & Zhou, G. (2008, October). On the class imbalance problem. In 2008 Fourth international conference on natural computation (Vol. 4, pp. 192-201). IEEE. https://doi.org/10.1109/ICNC.2008.871

Hariri, S., Kind, M. C., & Brunner, R. J. (2019). Extended isolation forest. IEEE Transactions on Knowledge and Data Engineering, 33(4), 1479-1489. https://doi.org/10.1109/TKDE.2019.2947676

He, H., & Ma, Y. (2013). Imbalanced learning: Foundations, algorithms, and applications. John Wiley & Sons.

Himmel, E. (2002). Modelo de análisis de la deserción estudiantil en la educación superior [Higher education student dropout analysis model]. Calidad en la Educación, (17), 91-108. http://dx.doi.org/10.31619/caledu.n17.409

Hosmer Jr, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression (Vol. 398). John Wiley & Sons.

Lakkaraju, H., Aguiar, E., Shan, C., Miller, D., Bhanpuri, N., Ghani, R., & Addison, K. L. (2015, August). 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 (pp. 1909-1918), Sydney NSW Australia. https://doi.org/10.1145/2783258.2788620

Lin, J. J., Imbrie, P. K., & Reid, K. J. (2009. July). Student retention modelling: An evaluation of different methods and their impact on prediction results. Proceedings of the Research in Engineering Education Symposium (REES), Palm Cove, Australia. https://www.proceedings.com/content/023/023353webtoc.pdf

Observatorio de Educación Superior. (2017, July 1). Deserción en la educación superior [Dropout in higher education]. ODES Boletín (5). https://www.sapiencia.gov.co/wp-content/uploads/2017/11/5_JULIO_BOLETIN_ODES_DESERCION_EN_LA_EDUCACION_SUPERIOR.pdf

Patrick, M. E., Schulenberg, J. E., & O’Malley, P. M. (2016). High school substance use as a predictor of college attendance, completion, and dropout: A national multicohort longitudinal study. Youth & society, 48(3), 425-447. https://doi.org/10.1177/0044118X13508961

Pérez, A. M., Escobar, C. R., Toledo, M. R., Gutierrez, L. B., & Reyes, G. M. (2018). Prediction model of first-year student desertion at Universidad Bernardo O’ Higgins (UBO). Educação e Pesquisa, 44. https://doi.org/10.1590/S1678-4634201844172094

Raschka, S. (2018). Model evaluation, model selection, and algorithm selection in machine learning. arXiv Cornell University. http://arxiv.org/abs/1811.12808

Sandoval-Palis, I., Naranjo, D., Vidal, J., & Gilar-Corbi, R. (2020). Early dropout prediction model: A case study of university leveling course students. Sustainability, 12(22), 2-17. https://doi.org/10.3390/su12229314

Sistema para la Prevención de la Deserción en las Instituciones de Educación Superior-SPADIES. (2016). Reporte sobre deserción y graduación en educación superior año 2016 [Report on dropout and graduation in higher education, year 2016]. https://bit.ly/3K0RQmc

Thomas, L. (2002). Student retention in higher education: the role of institutional habitus. Journal of Education Policy, 17(4), 423-442. https://doi.org/10.1080/02680930210140257

Tinto, V. (1982). Defining dropout: A matter of perspective. New Directions for Institutional Research, (36), 3-15. https://doi.org/10.1002/ir.37019823603

Urzúa, S. (2017). The economic impact of higher education. In M. M. Ferreyra, C. Avitabile, J. Botero, F. Haimovich, & S. Urzúa (Eds.), At a crossroads: Higher education in Latin America and the Caribbean (pp. 115-148). World Bank. https://doi.org/10.1596/978-1-4648-1014-5_ch3