Student dropout in times of COVID-19
A case study Universidad Técnica Estatal de Quevedo
Keywords:
student dropout, ranking models, logistic regression, decision treesAbstract
This article presents a study on student dropout in COVID19 time. As a case study, the socioeconomic and academic performance data of students of the State Technical University of Quevedo, Ecuador in the periods from 2019 to 2022 where educational activities were developed in virtual modality are analyzed. With the requested information, the objective variable (Permanence) was constructed and a process of exploration and analysis of the information was carried out. From this process, 58 variables were chosen and used for the construction of two classification models using Decision Trees and Logistic Regression. Of the algorithms studied, Decision Trees was the best model for identifying students who dropped out, with a value higher than 95% correct classification.
Downloads
References
Bonaccorso, G. (2017). Machine Learninh Algorithms, A reference guide to popular algothms for data science and machine learning. Packt Publiching.
Byron Oviedo, D. M. (2016, Junio 15). A hierarchical clustering method: Applications to educational data. doi:https://doi.org/10.3233/IDA-160839
Julian, C. J. (1995). An Ad Hoc Analysis Strategy With Missing Data. The Journal of Experimental Education, 63(4), 333–342. (Vol. 63). doi:https://doi.org/10.1080/00220973.1995.9943468
Kulis, B. (2013, Julio 31). Metric Learning: A Survey. Foundations and Trends® in Machine Learning, 5(4). doi:http://dx.doi.org/10.1561/2200000019
Landgrebe, S. R. (1991, Junio Mayo). A survey of decision tree classifier methodology. IEEE Transactions on Systems, Man, and Cybernetics, 21(3). doi:https://doi.org/10.1109/21.97458
López, C. N. (2021). Diseño de un modelo matemático para estimar la deserción estudiantil mediante técnicas de análisis multivariado en una institución de educación superior tecnológica. Universidad de Ambato. doi:https://repositorio.uta.edu.ec/jspui/handle/123456789/32219
Martelo, R., Acevedo, D., & Martelo, P. (2018, Octubre 27). Análisis Multivariado aplicado a determinar factores clave de la deserción universitaria. Espacios, 39(10). Retrieved from https://www.revistaespacios.com/a18v39n10/a18v39n10p13.pdf
Miguel Ángel Medina Romero , Darwin Eliecer Solano Bent, Edwar Benjamín Bahoque Flórez, Rubén Jaime Huancapaza Cora , Pablo Ignacio Manrique Oroza , Edgar Salas Luzuriaga. A key to the quality of the educational institution: improvement plans. Journal of Positive Psychology & Wellbeing. 2021, Vol. 5, No. 4, 2390 – 2401.
Munizaga, F., Cifuentes, M. B., & Beltrán, A. (2018, Julio 5). Retención y Abandono Estudiantil en la Educación Superior Universitaria en América Latina y el Caribe: Una Revisión Sistemática. Revista Académicas Universidad Tecnológica de Panamá, 26.
Rizzo, M. L. (2019). Statistical Computing with R. New York, Estados Unidos: Tayloe & Francis Group. doi:https://doi.org/10.1201/9780429192760
Saldaña Villa, M., & Barriga, O. A. (2010, Diciembre). Adaptación del modelo de deserción universitaria de Tinto de la Universidad Católica de la Santísima Concepción, Chile. Revista Ciencias Sociales, 16(4), Universidad de Zulia Venezuela.
Tattar, P. N. (2017, Agosto 31). Statistical Application Development with R and Python - Second Edition: Develop applications using data processing, statistical models, and CART. USA: PACKT.
Torres, E. M. (2021). Análisis de la deserción estudiantil en la escuele profesional de contabilidad de la Universidad Nacional de San Agustín de Arequipa, usando técnicas multivariantes. Universidad Nacional de San Agustín de Arequipa Escuala de Posgrado. Retrieved from http://hdl.handle.net/20.500.12773/13772
Ungar, A. I. (2007, Agosto 4). Active learning for logistic regression: an evaluation. doi:https://doi.org/10.1007/s10994-007-5019-5
Nyandra, M., & Suryasa, W. (2019). Lifestyle for stress buffer and reverse cell aging. International Journal of Health Sciences, 3(1), 17–23. https://doi.org/10.29332/ijhs.v3n1.276
Published
How to Cite
Issue
Section
Copyright (c) 2022 International journal of health sciences
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Articles published in the International Journal of Health Sciences (IJHS) are available under Creative Commons Attribution Non-Commercial No Derivatives Licence (CC BY-NC-ND 4.0). Authors retain copyright in their work and grant IJHS right of first publication under CC BY-NC-ND 4.0. Users have the right to read, download, copy, distribute, print, search, or link to the full texts of articles in this journal, and to use them for any other lawful purpose.
Articles published in IJHS can be copied, communicated and shared in their published form for non-commercial purposes provided full attribution is given to the author and the journal. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
This copyright notice applies to articles published in IJHS volumes 4 onwards. Please read about the copyright notices for previous volumes under Journal History.
