Abstract
The research aims to identify applying data mining to identify the main factors that influence the dropout of university students in public universities in Latin America. A documentary analysis was carried out to contextualize the problem of student desertion, and relevant antecedents on the subject were presented. The study's main findings identified that socioeconomic problems, institutional conditions, and social and cultural environment situations are the main factors influencing student dropout in public universities in Latin America. Finally, it is possible to affirm that data mining is helpful for different engineering applications that contribute to the attention of social problems.
References
[2] M. Alzahrani and A. Alharthi, "Predicting student dropout in higher education using decision tree and logistic regression. " Journal of Computational Science, vol. 42, p. 101148., 2020.
[3] Y. Zou, Q. Liu, Y. Liu, and Y. Peng, "A predictive model for student dropout risk in higher education: A comparative study of feature selection and classification algorithms. " Journal of Educational Computing Research, vol. 59, no. 2, pp. 238-262.
[4] M. Fernández-Diego, I. García-García and J. García-Sánchez, "The Use of Data Mining Techniques to Analyze Student Dropout in Higher Education.," Sustainability, vol. 13, no. 10, p. 5689, 2021.
[5] M. Montoya-Valdez, M. Gutiérrez-Martínez and O. Medina-Ramírez, "Análisis de factores de deserción estudiantil en educación superior mediante técnicas de minería de datos.," Revista Electrónica Educare, vol. 25, no. 1, pp. 1-20. , 2021.
[6] L. Quiñonez and Y. Carrasco, "Rendimiento académico empleando minería de datos," Espacios, vol. 41, no. 44, pp. 277-285, 2020.
[7] M. Kaur and L. Goyal, "Student Dropout Prediction in Higher Education using Data Mining Techniques: A Review. International," Journal of Advanced Research in Computer Science and Software Engineering, vol. 11, no. 1, pp. 389-395, 2021.
[8] Y. Hirakawa, M. Mizuno, and Y. Matsuda, "Predicting university student dropout using an ensemble learning approach. " Journal of Educational Computing Research, vol. 57, no. 7, pp. 1585-1604, 2019.
[9] K. Bastian, E. Puentes-Rosas and D. Herrera-Araujo, "¿Qué factores influyen en la deserción universitaria? Una revisión sistemática.," Revista Electrónica de Investigación y Evaluación Educativa, vol. 25, no. 2, pp. 1-21, 2019.
[10] M. Kaur and L. M. Goyal, "Student Dropout Prediction in Higher Education using Data Mining Techniques: A Review.," International Journal of Advanced Research in Computer Science and Software Engineering, vol. 11, no. 1, pp. 389-395., 2021.
[11] L. González and O. Espinoza, "Deserción en Educacion Superior en América Latina y el Caribe," 2015. [Online]. Available:
https://www.researchgate.net/publication/275275484_Desercion_en_educacion_superior_en_America_Latina_y_el_Caribe_2008-16. [Accessed 2023].
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