Discovering the anatomy of school dropouts with data science: case study, Technical Professional Education in Mexico estudio de caso, Educación Técnica Profesional en México
DOI:
https://doi.org/10.55040/ydfc9j29Keywords:
school dropout, data science, decision trees, classificationAbstract
School dropouts are a major educational problem that influences the economic development of a country, social well-being, and individual growth. This article uses data science techniques to identify the determinants of dropout in career technical education in Mexico. To do this, we consider students' academic achievements and information related to socioeconomic and psychological aspects. The results indicate the importance of exploring factors beyond academic performance to understand the causes of school dropout in CONALEP.
References
Alvarado-Uribe, J., Mejía-Almada, P., Masetto Herrera, A. L., Molontay, R., Hilliger, I., Hegde, V., Montemayor Gallegos, J. E., Ramírez Díaz, R. A., & Ceballos, H. G. (2022). Student dataset from Tecnológico de Monterrey in Mexico to predict dropout in higher education. Data, 7(9), 119. https://doi.org/10.3390/data7090119.
Bernal-Reyes, L. (2020). El CONALEP. desarrollo de una estrategia de formación técnica para el trabajo. Revista Mexicana de Investigación Educativa, 25(84), 121–152.
Bošnjaković, N., & Đurđević Babić, I. (2023). Systematic review on educational data mining in educational gamification. Technology Knowledge and Learning. https://doi.org/10.1007/s10758-023-09686-2
De Witte, K., Cabus, S., Thyssen, G., Groot, W., & van den Brink, H. M. (2013). A critical review of the literature on school dropout. Educational Research Review, 10, 13–28. https://doi.org/10.1016/j.edurev.2013.05.002
Fernandez-Haddad, M., & Lara Gonzalez, R. C. (2023). Key factors that influence school dropouts amongst adolescents in marginalized urban areas of Mexico. Journal of Nonprofit & Public Sector Marketing, 35(5), 494–520. https://doi.org/10.1080/10495142.2021.1982111
Instituto Mexicano para la Competitividad IMCO (2022). Bachillerato, el escalón más frágil de la educación, consultado el 30 de julio de 2024 https://imco.org.mx/bachillerato-el-escalon-fragil-de-la-educacion/
Lottering, R., Hans, R., & Lall, M. (2020). A model for the identification of students at risk of dropout at a university of technology. 2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD). DOI: 10.1109/icABCD49160.2020.9183874
Martinez-Plumed, F., Contreras-Ochando, L., Ferri, C., Hernandez-Orallo, J., Kull, M., Lachiche, N., Ramirez-Quintana, M. J., & Flach, P. (2021). CRISP-DM twenty years later: From data mining processes to data science trajectories. IEEE transactions on knowledge and data engineering, 33(8), 3048–3061. https://doi.org/10.1109/tkde.2019.2962680
Nahar, K., Shova, B. I., Ria, T., Rashid, H. B., & Islam, A. H. M. S. (2021). Mining educational data to predict students performance: A comparative study of data mining techniques. Education and Information Technologies, 26(5), 6051–6067. https://doi.org/10.1007/s10639-021-10575-3
Nai, R., Meo, R., Morina, G., & Pasteris, P. (2023). Public tenders, complaints, machine learning and recommender systems: a case study in public administration. Computer Law and Security Report, 51(105887), 105887. https://doi.org/10.1016/j.clsr.2023.105887
Nájera, A., & Ortega, L. (2022). Predictive model for taking decision to prevent university dropout. International Journal of Interactive Multimedia and Artificial Intelligence, 7(4), 205–213. https://doi.org/10.9781/ijimai.2022.01.006
Pérez-Gutiérrez, B. R. (2020). Comparación de técnicas de minería de datos para identificar indicios de deserción estudiantil, a partir del desempeño académico. Revista UIS ingenierías, 19(1), 193–204. https://doi.org/10.18273/revuin.v19n1-2020018
Quinn, J. (2013). Drop-out and Completion in Higher Education in Europe Among Students from Under-represented Groups. An independent report authored for the European Commission.
Ribeiro, R. C., & Canedo, E. D. (2020). Using data mining techniques to perform school dropout prediction: A case study. In Advances in Intelligent Systems and Computing (pp. 211–217). Springer International Publishing. https://doi.org/10.1007/978-3-030-43020-7_28
Rodríguez, P., Villanueva, A., Dombrovskaia, L., & Valenzuela, J. P. (2023). A methodology to design, develop, and evaluate machine learning models for predicting dropout in school systems: the case of Chile. Education and Information Technologies, 28(8), 1–47. https://doi.org/10.1007/s10639-022-11515-5
Saini, T., & Chhabra, A. (2024). Performance analysis of different machine learning classifiers for prediction of lung cancer. Artificial Intelligence of Things (págs). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-48774-3_18
Salinas-Chipana, J., Obregon-Palomino, L., Iparraguirre-Villanueva, O., & Cabanillas-Carbonell, M. (2024). Machine learning models for predicting student dropout a review. In Proceedings of Eighth International Congress on Information and Communication Technology (pp. 1003–1014). Springer. https://doi.org/10.1007/978-981-99-3043-2_83
Santoso, L. W., & Yulia. (2019). The analysis of student performance using data mining. In Advances in Intelligent Systems and Computing (pp. 559–573). Springer Singapore. https://repository.petra.ac.id/18101/1/Publikasi1_03023_4745.pdf
Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN Computer Science, 2(3), 160. https://doi.org/10.1007/s42979-021-00592-x
Urbina-Nájera, A. B., Camino-Hampshire, J. C., & Cruz Barbosa, R. (2020). Deserción escolar universitaria: Patrones para prevenirla aplicando minería de datos educativa. RELIEVE - Revista Electrónica de Investigación y Evaluación Educativa, 26(1). https://doi.org/10.7203/relieve.26.1.16061
Yagcı, M. (2022). Educational data mining: prediction of students’ academic performance using machine learning algorithms. Smart Learning Environments, 9(1). https://doi.org/10.1186/s40561-022-00192-z
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Rosa Maria Valdovinos Rosas
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
https://creativecommons.org/licenses/by-nc-nd/4.0
