Ciencia de Datos Educativos y aprendizaje automático: un caso de estudio sobre la deserción estudiantil universitaria en México
Resumen Hoy en día la deserción universitaria es un fenómeno inquietante que afecta a estudiantes, instituciones educativas y el estado. Una mirada a este fenómeno desde la Ciencia de Datos Educativos y la aplicación de técnicas de aprendizaje automático permite buscar las posibilidades de permanencia de los alumnos, es por ello que el objetivo de esta investigación es predecir la deserción escolar en el primer año de estudios a nivel universitario usando dichas técnicas. Se analiza un caso de estudio práctico en el ámbito educativo con información de estudiantes de una universidad privada en México. Se evidencia en el estudio que las métricas y la visualización de la estructuración para analizar patrones permiten mostrar que las características que predicen con mejor desempeño la deserción escolar institucional en el primer año de estudios a nivel universitario son el promedio estudiantil en el primer período y el porcentaje de la beca.
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Aljohani, O. (2016). A Review of the Contemporary International Literature on Student Retention in Higher Education. International Journal of Education and Literacy Studies, 4(1), 40-52. https://doi.org/10.7575/aiac.ijels.v.4n.1p.40
Álvarez Álvarez, C., & San Fabián Maroto, J. L. (2015). La elección del estudio de caso en investigación educativa. Gazeta de Antropología, 28(1).
Aulck, L., Velagapudi, N., Blumenstock, J., & West, J. (2017). Predicting student dropout in higher education. arXiv, 1606.06364v4. https://doi.org/10.48550/arXiv.1606.06364
Balanta Viera, V., & Palacios Medina, M. (2018). Machine Learning: Redes Neuronales Artificiales. Independently Published.
Barros, R., Basgalupp, M., Carvalho, A., & Freitas, A. (2012). A Survey of Evolutionary Algorithms for Decision-Tree Induction. IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews, 42(3), 291-312. https://doi.org/10.1109/TSMCC.2011.2157494
Batista, G. E. A. P. A. & Monard, M. C. (2010). An Analysis of Four Missing Data Treatment Methods for Supervised Learning. Applied Artificial Intelligence, 17(5-6), 519-533. https://doi.org/10.1080/713827181
Bobadilla, J. (2021). Machine Learning y Deep Learning: Usando Python, Scikit y Keras. Ediciones de la U.
Burke, A. (2019). Student retention models in higher education: A literature review. College and University, 94(2), 12-21.
Bonaldo, L., & Pereira, L. N. (2016). Dropout: Demographic profile of Brazilian university students. Procedia-Social and behavioral sciences, 228, 138-143. https://doi.org/10.1016/j.sbspro.2016.07.020
Cabero‐Almenara, J., Guillén‐Gámez, F. D., Ruiz‐Palmero, J., & Palacios‐Rodríguez, A. (2022). Teachers’ digital competence to assist students with functional diversity: Identification of factors through logistic regression methods. British Journal of Educational Technology, 53(1), 41-57. https://doi.org/10.1111/bjet.13151
Caicedo Bravo, E. F., & López Sotelo, J. A. (2009). Una aproximación práctica a las redes neuronales artificiales. Programa Editorial Univalle.
Cardona, T., Cudney, E. A., Hoerl, R., & Snyder, J. (2023). Data Mining and Machine Learning Retention Models in Higher Education. Journal of College Student Retention: Research, Theory& Practice, 25(1), 51-75. https://doi.org/10.1177/1521025120964920
Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ASM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785-794). ACM Press. https://doi.org/10.1145/2939672.2939785
Donoso, S., & Schiefelbein, E. (2007). Análisis de los modelos explicativos de retención de estudiantes en la universidad: una visión desde la desigualdad social. Estudios Pedagógicos, 33(1), 7-27. https://doi.org/10.4067/S0718-07052007000100001
Espinosa-Zúñiga, J. J. (2020). Aplicación de algoritmos Random Forest y XGBoost en una base de solicitudes de tarjetas de crédito. Ingeniería, Investigación y Tecnología, 21(3). https://doi.org/10.22201/fi.25940732e.2020.21.3.022
Ferreira, S. A. & Andrade, A. (2016) Academic analytics: Anatomy of an exploratory essay. Education and Information Technology, 21, 229–243 https://doi.org/10.1007/s10639-014-9317-9
Ferreyra, M. M., Avitabile, C., Botero Álvarez, J., Haimovich Paz, F., & Urzúa, S. (2017). At a crossroads: higher education in Latin America and the Caribbean. World Bank Publications. https://doi.org/10.1596/978-1-4648-1014-5
Fike, D. S., & Fike, R. (2008). Predictors of first-year student retention in the community college. Community College Review, 36(2), 68-88 https://doi.org/10.1177/0091552108320222
Fortin, N. M. (2006). Higher-education policies and the college wage premium: Cross-state evidence from the 1990s. The American Economic Review, 96(4), 959-987. https://doi.org/10.1257/aer.96.4.959
Gitto, L., Minervini, L. F., & Monaco, L. (2016). University dropouts in Italy: Are supply side characteristics part of the problem? Economic Analysis and Policy, 49, 108-116. https://doi.org/10.1016/j.eap.2015.12.004
González Fiegehen, L. E., & Espinoza Díaz, O. (2020). Deserción en educación superior en América Latina y el Caribe. Paideia, (45), 33-46.
Hernández, C., & Rodríguez, J. (2008). Preprocesamiento de datos estructurados. Revista Vínculos, 4 (2) 27-48.
Herzog, S. (2005). Measuring determinants of student return vs. dropout/stopout vs. transfer: A first-to-second year analysis of new freshmen. Research in Higher Education, 46(8), 883-928. https://doi.org/10.1007/s11162-005-6933-7
Huo, H., Cui, J., Hein, S., Padgett, Z., Ossolinski, M., Raim, R., & Zhang, J. (2023). Predicting Dropout for Nontraditional Undergraduate Students: A Machine Learning Approach. Journal of College Student Retention: Research, Theory & Practice, 24(4), 1054–1077. https://doi.org/10.1177/1521025120963821
INEE. (2019). Principales cifras nacionales. Educación básica y media superior. Inicio del ciclo escolar 2016-2017. INNE. https://bit.ly/432IX4q
INEE. (2022). Directrices para mejorar la permanencia escolar en la educación media superior. INNE. https://bit.ly/3odsvPZ
Kirk, G. (2018). Retention in a Bachelor of Education (Early childhood studies) course: students say why they stay and others leave. Higher Education Research & Development, 37(4), 773-787. https://doi.org/10.1080/07294360.2018.1455645
Luckman, M., & Harvey, A. (2019). The financial and educational outcomes of Bachelor degree non-completers. Journal of Higher Education Policy and Management, 41(1), 3-17. https://doi.org/10.1080/1360080X.2018.1553106
Márquez‐Vera, C., Cano, A., Romero, C., Noaman, A. Y. M., Mousa Fardoun, H., & Ventura, S. (2016). Early dropout prediction using data mining: a case study with high school students. Expert Systems, 33(1), 107-124. https://doi.org/10.1111/exsy.12135
Melguizo, T., Torres, F. S., & Jaime, H. (2011). The association between financial aid availability and the college dropout rates in Colombia. Higher Education, 62(2), 231-247. https://doi.org/10.1007/s10734-010-9385-8
Morales Salas, R. E., & Rodríguez Pavón, P. R. (2022). Retos y desafíos en la Educación Superior: una mirada desde la percepción de los docentes. Education in the Knowledge Society, 23, e264020. https://doi.org/10.14201/eks.26420
Nair, R., & Bhagat, A. (2019). Feature Selection Method to improve the accuracy of classification algorithm. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8(6) 124-127.
Nieuwoudt, J. E., & Pedler, M. L. (2021). Student Retention in Higher Education: Why Students Choose to Remain at University. Journal of College Student Retention: Research, Theory & Practice. https://doi.org/10.1177/1521025120985228
Peláez, I. M. (2016). Modelos de regresión: lineal simple y regresión logística. Revista Seden, 14, 195-214.
Rojas-López, A. (2017). Intervención de tres estrategias educativas para cursos de programación en educación superior Education in the Knowledge Society, 8(4), 21-34. https://doi.org/10.14201/eks20171842134
Santos, A. C., Iglesias Rodríguez, A., & Pinto-Llorente, A. M. (2020). Identification of characteristics and functionalities for the design of an academic analytics model for Higher Education. Proceeding of the TEEM'20: Eighth International Conference on Technological Ecosystems for Enhancing Multiculturality (pp. 997-1003). ACM Press. https://doi.org/10.1145/3434780.3436578
Simpson, O. (2005). The costs and benefits of student retention for students, institutions, and governments. Studies in Learning, Evaluation Innovation and Development, 2(3), 34-43.
Stoops, N. (2004). Educational attainment in the United States: 2003. Current population. https://bit.ly/3M8pN6e
Tinto, V. (1982). Limits of Theory and Practice in Student Attrition. The Journal of Higher Education, 53(6), 687-700. https://doi.org/10.2307/1981525
Von Hippel, P. T., & Hofflinger, A. (2021). The data revolution comes to higher education: identifying students at risk of dropout in Chile. Journal of Higher Education Policy and Management, 43(1), 2-23. https://doi.org/10.1080/1360080X.2020.1739800
Álvarez Álvarez, C., & San Fabián Maroto, J. L. (2015). La elección del estudio de caso en investigación educativa. Gazeta de Antropología, 28(1).
Aulck, L., Velagapudi, N., Blumenstock, J., & West, J. (2017). Predicting student dropout in higher education. arXiv, 1606.06364v4. https://doi.org/10.48550/arXiv.1606.06364
Balanta Viera, V., & Palacios Medina, M. (2018). Machine Learning: Redes Neuronales Artificiales. Independently Published.
Barros, R., Basgalupp, M., Carvalho, A., & Freitas, A. (2012). A Survey of Evolutionary Algorithms for Decision-Tree Induction. IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews, 42(3), 291-312. https://doi.org/10.1109/TSMCC.2011.2157494
Batista, G. E. A. P. A. & Monard, M. C. (2010). An Analysis of Four Missing Data Treatment Methods for Supervised Learning. Applied Artificial Intelligence, 17(5-6), 519-533. https://doi.org/10.1080/713827181
Bobadilla, J. (2021). Machine Learning y Deep Learning: Usando Python, Scikit y Keras. Ediciones de la U.
Burke, A. (2019). Student retention models in higher education: A literature review. College and University, 94(2), 12-21.
Bonaldo, L., & Pereira, L. N. (2016). Dropout: Demographic profile of Brazilian university students. Procedia-Social and behavioral sciences, 228, 138-143. https://doi.org/10.1016/j.sbspro.2016.07.020
Cabero‐Almenara, J., Guillén‐Gámez, F. D., Ruiz‐Palmero, J., & Palacios‐Rodríguez, A. (2022). Teachers’ digital competence to assist students with functional diversity: Identification of factors through logistic regression methods. British Journal of Educational Technology, 53(1), 41-57. https://doi.org/10.1111/bjet.13151
Caicedo Bravo, E. F., & López Sotelo, J. A. (2009). Una aproximación práctica a las redes neuronales artificiales. Programa Editorial Univalle.
Cardona, T., Cudney, E. A., Hoerl, R., & Snyder, J. (2023). Data Mining and Machine Learning Retention Models in Higher Education. Journal of College Student Retention: Research, Theory& Practice, 25(1), 51-75. https://doi.org/10.1177/1521025120964920
Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ASM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785-794). ACM Press. https://doi.org/10.1145/2939672.2939785
Donoso, S., & Schiefelbein, E. (2007). Análisis de los modelos explicativos de retención de estudiantes en la universidad: una visión desde la desigualdad social. Estudios Pedagógicos, 33(1), 7-27. https://doi.org/10.4067/S0718-07052007000100001
Espinosa-Zúñiga, J. J. (2020). Aplicación de algoritmos Random Forest y XGBoost en una base de solicitudes de tarjetas de crédito. Ingeniería, Investigación y Tecnología, 21(3). https://doi.org/10.22201/fi.25940732e.2020.21.3.022
Ferreira, S. A. & Andrade, A. (2016) Academic analytics: Anatomy of an exploratory essay. Education and Information Technology, 21, 229–243 https://doi.org/10.1007/s10639-014-9317-9
Ferreyra, M. M., Avitabile, C., Botero Álvarez, J., Haimovich Paz, F., & Urzúa, S. (2017). At a crossroads: higher education in Latin America and the Caribbean. World Bank Publications. https://doi.org/10.1596/978-1-4648-1014-5
Fike, D. S., & Fike, R. (2008). Predictors of first-year student retention in the community college. Community College Review, 36(2), 68-88 https://doi.org/10.1177/0091552108320222
Fortin, N. M. (2006). Higher-education policies and the college wage premium: Cross-state evidence from the 1990s. The American Economic Review, 96(4), 959-987. https://doi.org/10.1257/aer.96.4.959
Gitto, L., Minervini, L. F., & Monaco, L. (2016). University dropouts in Italy: Are supply side characteristics part of the problem? Economic Analysis and Policy, 49, 108-116. https://doi.org/10.1016/j.eap.2015.12.004
González Fiegehen, L. E., & Espinoza Díaz, O. (2020). Deserción en educación superior en América Latina y el Caribe. Paideia, (45), 33-46.
Hernández, C., & Rodríguez, J. (2008). Preprocesamiento de datos estructurados. Revista Vínculos, 4 (2) 27-48.
Herzog, S. (2005). Measuring determinants of student return vs. dropout/stopout vs. transfer: A first-to-second year analysis of new freshmen. Research in Higher Education, 46(8), 883-928. https://doi.org/10.1007/s11162-005-6933-7
Huo, H., Cui, J., Hein, S., Padgett, Z., Ossolinski, M., Raim, R., & Zhang, J. (2023). Predicting Dropout for Nontraditional Undergraduate Students: A Machine Learning Approach. Journal of College Student Retention: Research, Theory & Practice, 24(4), 1054–1077. https://doi.org/10.1177/1521025120963821
INEE. (2019). Principales cifras nacionales. Educación básica y media superior. Inicio del ciclo escolar 2016-2017. INNE. https://bit.ly/432IX4q
INEE. (2022). Directrices para mejorar la permanencia escolar en la educación media superior. INNE. https://bit.ly/3odsvPZ
Kirk, G. (2018). Retention in a Bachelor of Education (Early childhood studies) course: students say why they stay and others leave. Higher Education Research & Development, 37(4), 773-787. https://doi.org/10.1080/07294360.2018.1455645
Luckman, M., & Harvey, A. (2019). The financial and educational outcomes of Bachelor degree non-completers. Journal of Higher Education Policy and Management, 41(1), 3-17. https://doi.org/10.1080/1360080X.2018.1553106
Márquez‐Vera, C., Cano, A., Romero, C., Noaman, A. Y. M., Mousa Fardoun, H., & Ventura, S. (2016). Early dropout prediction using data mining: a case study with high school students. Expert Systems, 33(1), 107-124. https://doi.org/10.1111/exsy.12135
Melguizo, T., Torres, F. S., & Jaime, H. (2011). The association between financial aid availability and the college dropout rates in Colombia. Higher Education, 62(2), 231-247. https://doi.org/10.1007/s10734-010-9385-8
Morales Salas, R. E., & Rodríguez Pavón, P. R. (2022). Retos y desafíos en la Educación Superior: una mirada desde la percepción de los docentes. Education in the Knowledge Society, 23, e264020. https://doi.org/10.14201/eks.26420
Nair, R., & Bhagat, A. (2019). Feature Selection Method to improve the accuracy of classification algorithm. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8(6) 124-127.
Nieuwoudt, J. E., & Pedler, M. L. (2021). Student Retention in Higher Education: Why Students Choose to Remain at University. Journal of College Student Retention: Research, Theory & Practice. https://doi.org/10.1177/1521025120985228
Peláez, I. M. (2016). Modelos de regresión: lineal simple y regresión logística. Revista Seden, 14, 195-214.
Rojas-López, A. (2017). Intervención de tres estrategias educativas para cursos de programación en educación superior Education in the Knowledge Society, 8(4), 21-34. https://doi.org/10.14201/eks20171842134
Santos, A. C., Iglesias Rodríguez, A., & Pinto-Llorente, A. M. (2020). Identification of characteristics and functionalities for the design of an academic analytics model for Higher Education. Proceeding of the TEEM'20: Eighth International Conference on Technological Ecosystems for Enhancing Multiculturality (pp. 997-1003). ACM Press. https://doi.org/10.1145/3434780.3436578
Simpson, O. (2005). The costs and benefits of student retention for students, institutions, and governments. Studies in Learning, Evaluation Innovation and Development, 2(3), 34-43.
Stoops, N. (2004). Educational attainment in the United States: 2003. Current population. https://bit.ly/3M8pN6e
Tinto, V. (1982). Limits of Theory and Practice in Student Attrition. The Journal of Higher Education, 53(6), 687-700. https://doi.org/10.2307/1981525
Von Hippel, P. T., & Hofflinger, A. (2021). The data revolution comes to higher education: identifying students at risk of dropout in Chile. Journal of Higher Education Policy and Management, 43(1), 2-23. https://doi.org/10.1080/1360080X.2020.1739800
Kuz, A., & Morales, R. (2023). Ciencia de Datos Educativos y aprendizaje automático: un caso de estudio sobre la deserción estudiantil universitaria en México. Education in the Knowledge Society (EKS), 24, e30080. https://doi.org/10.14201/eks.30080
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