Análisis de características que influyen en la deserción estudiantil en el contexto de una universidad latinoamericana

Luis Fernando Castro Rojas; Esperanza Espitia Peña; Edwin Romero Cuero

doi:10.24050/reia.v20i40.1628

Analysis of characteristics influencing student dropout in the context of a latin american university

10.24050/reia.v20i40.1628

Published 2023-12-19

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Analysis of characteristics influencing student dropout in the context of a latin american university (L. F. Castro Rojas, E. Espitia Peña, & E. Romero Cuero, Trans.). (2023). Revista EIA, 20(40), 4002 pp. 1-28. https://doi.org/10.24050/reia.v20i40.1628

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Abstract

The present work aims to deepen the study of the student dropout, which is a serious problem that worries the governments, university institutions and students worldwide. To achieve the above, this study uses data mining to analyze student dropout in a Latin American university by discovering the most influential relevant characteristics and by identifying patterns to facilitate the understanding of such problem. The methodology used considers an adaptation of the steps proposed by KDD (knowledge discovery in databases) and the study design was observational, descriptive and cross-sectional, using convenience sampling. The sample is made up of 10705 students, which are distributed in 7 faculties and 33 undergraduate academic programs. A model based on decisióntree was used to verify the predictive relationships between the status of dropping out student and the influential characteristics. As a result, this work identified techniques and methods commonly used in these studies and developed a method to identify patterns of relationships among the most influential characteristics in the student dropout. We found that the main influencing characteristics in this study refer to socioeconomic level, gender, employment status and academic performance. One aspect to be highlighted is the coincidence of the findings of this study with the results of other similar studies worldwide, in which academic performance was identified as a fundamental factor that affects university dropout. As a conclusion we can state that university student dropout is caused by a set of characteristics and their interrelations rather than a single characteristic.

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Authors

Luis Fernando Castro Rojas

Universidad del Quindío - Colombia

https://orcid.org/0000-0002-7244-7069 (unauthenticated)
Esperanza Espitia Peña

Universidad del Quindío - Colombia

https://orcid.org/0000-0002-8750-5540
Edwin Romero Cuero

Universidad del Quindío - Colombia

References

Ayala, E., López, R. & Menéndez, V. (2021). Modelos predictivos de riesgo académico en carreras de computación con minería de datos educativos. Revista de Educación a Distancia (RED), 21(66), 1-36. https://doi.org/10.6018/red.463561

Bakhshinategh, B., Zaiane, O. R., Elatia, S., & Ipperciel, D. (2018). Educational data mining applications and tasks: a survey of the last 10 years. Education and Information Technologies, 23(1), 537–553. https://doi.org/10.1007/s10639-017-9616-z

Castro, L. F., Espitia, E. & Cardona, S. (2019). Analysis of Student Desertion in a Systems and Computing Engineering Undergraduate Program. Revista Colombiana de Computación, 20(1), 72-82. https://doi.org/10.29375/25392115.3608

Castro, L. F., Espitia, E. & Mantilla, A. (2018). Applying CRISP-DM in a KDD Process for the Analysis of Student Attrition.

Communications in Computer and Information Science, 885, 386-401. https://doi.org/10.1007/978-3-319-98998-3_30

Castrillón-Gómez, O. D., Sarache W., & Ruiz-Herrera, S. (2020). Predicción de las principales variables que conllevan al abandono estudiantil por medio de técnicas de minería de datos. Formación Universitaria, 13(6), 217-228. http://dx.doi.org/10.4067/S0718-50062020000600217

Clerici, R., & Da Re, L. (2019). Evaluación de la eficacia de un programa de tutoría formativa. Revista de Investigación Educativa, 37(1), 39-56. http://dx.doi.org/10.6018/rie.37.1.322331

Constante, A., Florenciano, E., Navarro, E. & Fernández, M. (2021). Factores asociados al abandono universitario. Educación XX1, 24(1), 17-44. http://doi.org/10.5944/educXX1.26889

Cuji, B., Gavilanes, W., & Sánchez, R. (2017). Modelo predictivo de deserción estudiantil basado en arboles de decisión. Espacios, 38(55), 19-25. https://www.revistaespacios.com/a17v38n55/a17v38n55p17.pdf

Ghazal, M. & Hammad, A. (2022) Application of knowledge discovery in database (KDD) techniques in cost overrun of construction projects. International Journal of Construction Management, 22(9), 1632-1646. https://doi.org/10.1080/15623599.2020.1738205.

Gupta, B., Rawat, A., Jain, A., Arora, A., & Dhami, N. (2017). Analysis of Various Decision Tree Algorithms for Classification in

Data Mining. International Journal of Computer Applications, 163(8), 15-19. https://doi.org/10.5120/ijca2017913660

Hatos, A., Coloja, R. & Sava, A. (2020). Assessing Situational Awareness of Universities Concerning Student Dropout: A Web-Based Content Analysis of Romanian Universities’ Agenda. Journal of Research in Higher Education, 4 (2), 18-34. https://doi.org/10.24193/JRHE.2020.2.2

Hernández, R., Fernández, C., & Baptista, M. (2014). Metodología de la Investigación. McGraw Hill Educación.

Kumar, M., Singh, A. J., & Handa, D. (2017). Literature Survey on Student’s Performance Prediction in Education using Data Mining Techniques. International Journal of Education and Management Engineering, 6, 40-49. https://doi.org/10.5815/ijeme.2017.06.05

Ministerio de Educación Nacional. (2021). Estadísticas de deserción y permanencia en educación superior, históricos indicadores 2010-2018. https://www.mineducacion.gov.co/sistemasdeinformacion/1735/articles-357549_recurso_7.pdf

Munizaga, F., Cifuentes, M., & Beltrán, A. (2018). Retención y abandono estudiantil en la Educación Superior Universitaria en América Latina y el Caribe: Una revisión sistemática. Archivos Analíticos de Políticas Educativas, 26(61), 1-36. http://dx.doi.org/10.14507/epaa.26.3348

Oficina Europea de Estadística. (2020). Early leavers from education and training. [Mensaje en un blog]. Blog Eurostat. https://ec.europa.eu/eurostat/statistics-explained/index.php/Early_leavers_from_education_and_training#Overview

Oñate, A. A. (2016). Análisis de la Deserción y Permanencia Académica en la Educación Superior Aplicando Minería de datos. [Tesis de Maestría, Universidad Nacional de Colombia]. Repositorio Universidad Nacional. https://repositorio.unal.edu.co/handle/unal/57387

Pando, A. & Zarate, W. (2020). Aplicación de un modelo de minería de datos para identificación de patrones que influyen en la deserción académica en el instituto superior Leonardo Davinci. [Trabajo de grado, Universidad Privada Antenor Orrego]. Repositorio de Tesis UPAO. https://hdl.handle.net/20.500.12759/7033

Proyecto ALFA-GUIA. (2013). Marco Conceptual sobre el Abandono. https://documentop.com/marco-conceptual-abandono-proyecto-alfa-guia_59fbf0b21723dda8a11794fa.html.

Quiñones, L., Jara, D., Alvarado, N., Milla, M. & Gamarra, O. (2020). Modelo para la estimación de la deserción estudiantil Awajún y Wampis empleando minería de datos. RECyT, 34, 45–50. https://doi.org/10.36995/j.recyt.2020.34.006

Ramírez, P., & Grandón, E. (2018). Predicción de la deserción académica en una universidad pública chilena a través de la clasificación basada en árboles de decisión con parámetros optimizados. Formación Universitaria, 11(3), 3–10. http://dx.doi.org/10.4067/S0718-50062018000300003

Ramírez, V. (2021). Deserción estudiantil y el costo económico en universidades chilenas. [Tesis de Maestría, Universidad del Bio-Bio]. Repositorio digital Universidad del Bio-Bio. http://repobib.ubiobio.cl/jspui/handle/123456789/3609

Sharma, H., & Kumar, S. (2016). A Survey on Decision Tree Algorithms of Classification in Data Mining. International Journal of Science and Research, 5 (4), 2094-2097. https://doi.org/10.21275/v5i4.NOV162954

Urbina-Nájera, A. B., Camino-Hampshire, J. C., & Cruz-Barbosa, R. (2020). Deserción escolar universitaria: Patrones para prevenirla aplicando minería de datos educativa. RELIEVE, 26(1), 1-21. http://doi.org/10.7203/relieve.26.1.16061

Vásquez, J. (2016). Modelo predictivo para estimar la deserción de estudiantes en una Institución de Educación Superior. [Tesis de Maestría, Universidad de Chile]. Repositorio Académico de la Universidad de Chile. http://repositorio.uchile.cl/handle/2250/144169

Vicente, V. X. (2020). Aplicación de la técnica de minería de datos para la predicción de la deserción estudiantil universitaria. [Trabajo de grado, Universidad Técnica de Ambato]. Repositorio Universidad Técnica de Ambato. https://repositorio.uta.edu.ec/jspui/bitstream/123456789/30892/1/Victor%20Xavier%20Vicente%20Guerrero..pdf

Vila, D. (2019). Detección de patrones de deserción estudiantil utilizando técnicas predictivas de clasificación y regresión de minería de datos. [Trabajo de grado, Universidad Técnica del Norte]. Repositorio Digital Universidad Técnica del Norte.

http://repositorio.utn.edu.ec/handle/123456789/9095

Villalobos, L. R. (2017). Enfoques y diseños de investigación social: cuantitativos, cualitativos y mixtos. EUNED.

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Analysis of characteristics influencing student dropout in the context of a latin american university