Contenido principal del artículo

Edwin Alexander Aguilar Sánchez
Liana Carola Sánchez Cabrera
Jessica Priscila Yungaicela Jiménez
Gloria Estefany Villacres Arias

El objetivo es proponer y demostrar un marco para evaluar la robustez de la permanencia estudiantil frente a factores tempranos de riesgo académico en educación superior. La investigación es de tipo aplicada y enfoque cualitativo, con diseño de estudio de caso instrumental; la técnica es la prueba de estrés basada en análisis de escenarios, operada por un panel de expertos, y los instrumentos son un lienzo de retención de nueve componentes, un mapa de robustez codificado por colores y tres índices propios de robustez. Los resultados de una aplicación ilustrativa indican que la integración académica y el compromiso con las metas son los componentes más frágiles, mientras que la reprobación de cursos puerta y la presión financiera sostenida son los factores más severos; el índice global de robustez resultó intermedio. Se concluye que el marco aporta un diagnóstico estructural que complementa a los sistemas de alerta temprana y orienta el rediseño de los servicios de apoyo institucional.

This study develops and validates a framework to assess the robustness of student persistence relative to early academic risk factors in higher education. Adopting an applied qualitative approach and an instrumental case study design, the research utilizes scenario-based stress testing conducted by an expert panel. The assessment incorporates a nine-component retention canvas, a color-coded robustness map, and three proprietary robustness indices. Findings from an illustrative application indicate that academic integration and goal commitment are the most vulnerable components, while gateway course failure and sustained financial pressure represent the most severe risk factors. The resulting global robustness index was intermediate. Ultimately, the framework provides a structural diagnosis that complements existing early warning systems and informs the strategic redesign of institutional support services.

Descargas

Los datos de descargas todavía no están disponibles.

Detalles del artículo

Cómo citar
Aguilar Sánchez, E. A., Sánchez Cabrera, L. C., Yungaicela Jiménez, J. P., & Villacres Arias, G. E. (2026). Robustez de la permanencia estudiantil frente al riesgo académico temprano: un marco de escenarios. Revista Boliviana De Educación, 8(16), 60–72. https://doi.org/10.33996/rebe.v8i16.6
Sección
Artículos de investigación
Referencias

Akçapınar, G., Altun, A., & Aşkar, P. (2019). Using learning analytics to develop early-warning system for at-risk students. International Journal of Educational Technology in Higher Education, 16(1), 1-20. https://doi.org/10.1186/s41239-019-0172-z

Arnold, K. E., & Pistilli, M. D. (2012). Course Signals at Purdue: Using learning analytics to increase student success. In S. Buckingham Shum, D. Gašević, & R. Ferguson (Eds.), Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (LAK ’12) (pp. 267–270). ACM. https://doi.org/10.1145/2330601.2330666

Astin, A. W. (1984). Student involvement: A developmental theory for higher education. Journal of College Student Personnel, 25(4), 297-308.

Bañeres, D., Rodríguez, M. E., Guerrero-Roldán, A. E., & Karadeniz, A. (2020). An early warning system to detect at-risk students in online higher education. Applied Sciences, 10(13), 4427. https://doi.org/10.3390/app10134427

Bean, J. P. (1980). Dropouts and turnover: The synthesis and test of a causal model of student attrition. Research in Higher Education, 12(2), 155–187. https://doi.org/10.1007/BF00976194

Bean, J. P., & Metzner, B. S. (1985). A conceptual model of nontraditional undergraduate student attrition. Review of Educational Research, 55(4), 485-540. https://doi.org/10.3102/00346543055004485

Behr, A., Giese, M., Teguim Kamdjou, H. D., & Theune, K. (2020). Dropping out of university: A literature review. Review of Education, 8(2), 614-652. https://doi.org/10.1002/rev3.3202

Berens, J., Schneider, K., Görtz, S., Oster, S., & Burghoff, J. (2019). Early detection of students at risk—predicting student dropouts using administrative student data from German universities and machine learning methods. Journal of Educational Data Mining, 11(3), 1–41. https://doi.org/10.5281/zenodo.3594771

Bishop, P., Hines, A., & Collins, T. (2007). The current state of scenario development: an overview of techniques. Foresight, 9(1), 5-25. https://doi.org/10.1108/14636680710727516

Cabrera, A. F., Nora, A., & Castaneda, M. B. (1993). College persistence: Structural equations modeling test of an integrated model of student retention. The Journal of Higher Education, 34(2), 123-139. https://doi.org/10.1080/00221546.1993.11778419

Fonseca, G., & García, F. (2016). Permanencia y abandono de estudios en estudiantes universitarios: un análisis desde la teoría organizacional. Revista de la educación superior, 45(179), 25-39. https://doi.org/10.1016/j.resu.2016.06.004

Haaker, T., Bouwman, H., Janssen, W., & de Reuver, M. (2017). Business model stress testing: A practical approach to test the robustness of a business model. Futures, 19(1), 14-27. https://doi.org/10.1016/j.futures.2017.04.003

Kemper, L., Vorhoff, G., & Wigger, B. U. (2020). Predicting student dropout: A machine learning approach. European Journal of Higher Education, 10(1), 28–47. https://doi.org/10.1080/21568235.2020.1718520

Kuh, G. D., Cruce, T. M., Shoup, R., Kinzie, J., & Gonyea, R. M. (2008). Unmasking the effects of student engagement on first-year college grades and persistence. The Journal of Higher Education, 79(5), 540-563. https://doi.org/10.1080/00221546.2008.11772116

Munizaga, F. R., Cifuentes, M. B., & Beltrán, A. J. (2018). Retención y abandono estudiantil en la Educación Superior en América Latina y el Caribe: Una revisión sistemática. Education Policy Analysis Archives, 26, 61. https://doi.org/10.14507/epaa.26.3348

Pascarella, E. T., & Terenzini, P. T. (2005). How college affects students: A third decade of research (Vol. 2). Jossey-Bass. esearchgate.net/publication/281453350_How_College_Affects_Students_Vol_2_A_Third_Decade_of_Research

Rastrollo-Guerrero, J. L., Gómez-Pulido, J. A., & Durán-Domínguez, A. (2020). Analyzing and predicting students’ performance by means of machine learning: A review. Applied Sciences, 10(3), Article 1042. https://doi.org/10.3390/app10031042

Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. WIREs Data Mining Knowl Discov. 2020; 10:e1355. https://doi.org/10.1002/widm.1355

Spady, W. G. (1970). Dropouts from higher education: An interdisciplinary review and synthesis. Interchange, 1(1), 64-85. https://doi.org/10.1007/BF02214313

Suárez-Montes, N., & Díaz-Subieta, L. B. (2015). Estrés académico, deserción y estrategias de retención de estudiantes en la educación superior. Revista de salud pública, 17(2), 300-313. https://doi.org/10.15446/rsap.v17n2.52891

Tierney, W. G. (1992). An anthropological analysis of student participation in college. The Journal of Higher Education, 63(6), 603–618. https://doi.org/10.1080/00221546.1992.11778391

Tinto, V. (1975). Dropout from higher education: A theoretical synthesis of recent research. Review of Educational Research, 45(1), 89-125. https://doi.org/10.3102/00346543045001089

Tinto, V. (1993). Leaving college: Rethinking the causes and cures of student attrition (2nd ed.). University of Chicago Press. https://doi.org/10.7208/chicago/9780226922461.001.0001

Van der Heijden, K. (2005). Scenarios: the art of strategic conversation. John Wiley & Sons.