Dropout prediction model: A case study of students at the Universidad Politécnica Estatal del Carchi.
DOI:
https://doi.org/10.55204/pcc.v3i2.e34Keywords:
dropout, education, university students, logistic regressionAbstract
Introduction: The student dropout rate in higher education institutions is a global problem that requires customized solutions adapted to each institution. This article is carried out at the Universidad Politécnica Estatal del Carchi (UPEC) in Ecuador.
Objective: To identify the determinants of college dropout using a logistic model to predict whether a student may leave college prematurely.
Methods: A LR logistic regression model was used as a multivariate statistical modeling technique. The sample of students corresponds to 3821 enrollment records at UPEC.
Results: The results show that the proposed logistic model is 90% accurate. In addition, an inverse relationship was identified between student dropout, the number of members in the household and the educational level of the father and mother; thus, the need to implement specific support and prevention measures for students from households with lower educational levels and higher family burdens is evident.
Conclusions: The UPEC found that the most important factors influencing student attrition are: age, number of household members, level of study and academic average. This information will allow the university authorities to propose preventive measures to improve the retention and academic success of their students.
Downloads
References
Álvarez, N. L., Callejas, Z., & Griol, D. (2020). Predicting Computer Engineering Students’ Dropout In Cuban Higher Education With Pre-Enrollment and Early Performance Data. Journal of Technology and Science Education, 10(2), 241–258. https://doi.org/10.3926/jotse.922
Bedregal-Alpaca, N., Tupacyupanqui-Jaén, D., & Cornejo-Aparicio, V. (2020). Análisis del rendimiento académico de los estudiantes de Ingeniería de Sistemas, posibilidades de deserción y propuestas para su retención Analysis of the academic performance of Systems Engineering students, desertion possibilities and proposals for retention. In Revista chilena de ingeniería (Vol. 28, Issue 4). https://doi.org/10.4067/S0718-33052020000400668
Caselli Gismondi, H. E., & Urrelo Huiman, L. V. (2021). Características para un modelo de predicción de la deserción académica universitaria. Caso Universidad Nacional de Santa. Llamkasun, 2(4), 02–22. https://doi.org/10.47797/llamkasun.v2i4.61
de Oliveira, C. F., Sobral, S. R., Ferreira, M. J., & Moreira, F. (2021). How does learning analytics contribute to prevent students’ dropout in higher education: A systematic literature review. Big Data and Cognitive Computing, 5(4). https://doi.org/10.3390/bdcc5040064
Ferreyra, M., Avitabile, C., Botero Álvarez, J., Haimovich Paz, F., & Urzúa, S. (2017). At a Crossroads Higher Education in Latin America and the Caribbean Human Development. https://doi.org/10.1596/978-1-4648-1014-5
Findiana, R., Yuniarno, E. M., & Endroyono. (2020). Classification of Graduates Student on Entrance Selection Public Higher Education through Report Card Grade Path Using Support Vector Machine Method. 2020 3rd International Conference on Information and Communications Technology, ICOIACT 2020, 7–11. https://doi.org/10.1109/ICOIACT50329.2020.9332072
Hasan, M. N. (2019). A comparison of logistic regression and linear discriminant analysis in predicting of female students attrition from school in Bangladesh. N 2019 4th International Conference on Electrical Information and Communication Technology (EICT), 1–3. https://doi.org/10.1109/EICT48899.2019.9068776
Kittinan, L., Kanda, T., & Tanasin, Y. (2018). Dropout Prediction System to Reduce Discontinue Study Rate of Information Technology Students. 5th International Conference on Business and Industrial Research (ICBIR), 110–114. https://doi.org/10.1109/ICBIR.2018.8391176
Liong, C. Y., & Foo, S. F. (2013). Comparison of linear discriminant analysis and logistic regression for data classification. AIP Conference Proceedings, 1522, 1159–1165. https://doi.org/10.1063/1.4801262
López-Aguilar, D., Álvarez-Pérez, P. R., & Ravelo-González, Y. (2022). Adaptability skills and the intention to drop out in university students. Revista de Investigacion Educativa, 40(1), 237–255. https://doi.org/10.6018/rie.463811
Muzumdar, P., Basyal, G. P., & Vyas, P. (2020). Antecedents of Student Retention: A Predictive Modelling Approach. International Journal of Contemporary Research and Review, 11(11). https://doi.org/10.15520/ijcrr.v11i11.860
Niyogisubizo, J., Liao, L., Nziyumva, E., Murwanashyaka, E., & Nshimyumukiza, P. C. (2022). Predicting student’s dropout in university classes using two-layer ensemble machine learning approach: A novel stacked generalization. Computers and Education: Artificial Intelligence, 3. https://doi.org/10.1016/j.caeai.2022.100066
Nuankaew, P., Nuankaew, W., Phanniphong, K., Fooprateepsiri, R., & Bussaman, S. (2020). Analysis dropout situation of business computer students at University of Phayao. In Advances in Intelligent Systems and Computing: Vol. 1134 AISC. https://doi.org/10.1007/978-3-030-40274-7_42
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). Educacao e Pesquisa, 44. https://doi.org/10.1590/S1678-4634201844172094
Pierella, M. P., Peralta, N. S., & Pozzo, M. I. (2020). The first year of college. Working conditions, admission procedures and academic dropout from the teachers’ perspective. Revista Iberoamericana de Educacion Superior, 11(31), 68–84. https://doi.org/10.22201/iisue.20072872e.2020.31.706
Pohar, M., Blas, M., & Turk, S. (2004). Comparison of logistic regression and linear discriminant analysis. Advances in Methodology and Statistics, 1(1), 143–161. https://doi.org/10.51936/ayrt6204
Poveda Velasco, I. M. (2019). Los factores que influyen sobre la deserción universitaria. Estudio en la UMRPSFXCh-Bolivia, análisis con ecuaciones estructurales. Revista Investigación y Negocios, 12(20), 63–80. http://www.scielo.org.bo/scielo.php?script=sci_arttext&pid=S2521-27372019000200007&nrm=iso
Quiñones Huatangari, L., Jara, D. M., Alvarado, N., Milla, M. E., & Gamarra, O. A. (2020). Modelo para la estimación de la deserción estudiantil Awajún y Wampis empleando minería de datos. Argentine Journal of Science and Technology, 34(1), 45–60. https://doi.org/10.36995/j.recyt.2020.34.006
Ramírez, T., Díaz, R. B., & Salcedo, A. (2017). ¿Abandono o deserción estudiantil? Una necesaria discusión conceptual. Investigación y Postgrado, 32(1), 63–74. https://dialnet.unirioja.es/servlet/articulo?codigo=6430685
Sanchez-Pozo, N. N., Mejia-Ordonez, J. S., Chamorro, D. C., Mayorca-Torres, D., & Peluffo-Ordonez, D. H. (2021). Predicting High School Students’ Academic Performance: A Comparative Study of Supervised Machine Learning Techniques. Future of Educational Innovation Workshop Series - Machine Learning-Driven Digital Technologies for Educational Innovation Workshop 2021. https://doi.org/10.1109/IEEECONF53024.2021.9733756
Suárez, A. A. G., Núñez, R. P., & Suárez, C. A. H. (2021). Deserción estudiantil en contextos vulnerables: comparativo entre dos ciudades fronterizas colombianas. Revista Boletín Redipe, 10(4), 178–189. https://doi.org/10.36260/rbr.v10i4.1261
Tomás, J. M., & Gutiérrez, M. (2019). Contributions of the self-determination theory in predicting university students’ academic satisfaction. In Revista de Investigacion Educativa (Vol. 37, Issue 2, pp. 471–485). Asociacion Interuniversitaria de Investigacion en Pedagogia. https://doi.org/10.6018/rie.37.2.328191
Zárate-Valderrama, J., Bedregal-Alpaca, N., & Cornejo-Aparicio, V. (2021). Classification models to recognize patterns of desertion in university students. In Revista chilena de ingeniería (Vol. 29). https://doi.org/10.4067/S0718-33052021000100168.
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Francisco Javier Montalvo Márquez, Nadia Nathaly Sánchez Pozo
This work is licensed under a Creative Commons Attribution 4.0 International License.
The authors retain the moral and patrimonial rights of their works. Since Prometeo Journal is an open access publication, readers can fully or partially reproduce its content as long as they properly credit the corresponding authors and the journal itself.
Our journal is governed by the international policies SHERPA/RoMEO: Green journal: They allow the self-archiving of both the pre-print (draft of a paper) and the post-print (the version corrected and reviewed by peers) and even the final version ( layout as it will be published in the journal).
See also "Copyright and licences".
