Acortando la brecha: aplicación de la IA y la estadística bayesiana a la formación tradicional en liderazgo educativo

Autores/as

DOI:

https://doi.org/10.31637/epsir-2024-916

Palabras clave:

Liderazgo, Inteligencia Artificial, educación superior, Análisis Bayesiano, Aprendizaje basado en proyectos, Estrategias educativas, desarrollo curricular, éxito profesional

Resumen

Introducción: En el dinámico panorama de las organizaciones modernas, el liderazgo es una competencia vital. Sin embargo, los enfoques tradicionales uniformes a menudo no abordan los desafíos diversos que presentan los entornos contemporáneos. Las transformaciones sociales, educativas y tecnológicas, incluyendo el auge de la inteligencia artificial (IA), requieren un cambio hacia estilos de liderazgo más adaptativos. Metodología: Este estudio explora el liderazgo situacional en la educación superior, centrándose en su capacidad de respuesta a la gestión de recursos impulsada por IA y su papel en el fomento del éxito profesional. Utilizando análisis bayesiano, el estudio evalúa la efectividad del aprendizaje basado en proyectos (PBL) en el desarrollo de cualidades de liderazgo. Se analizaron datos de 404 educadores de 28 instituciones, abarcando diez variables relacionadas con el liderazgo y PBL. Resultados: Los resultados ofrecen ideas sobre el desarrollo curricular y estrategias educativas para dotar a los estudiantes de habilidades esenciales para el éxito profesional. Discusión: El liderazgo situacional, desarrollado por Hersey y Blanchard, enfatiza la adaptación de los estilos de liderazgo a la madurez de los seguidores y situaciones específicas, destacando la necesidad de flexibilidad en entornos dinámicos. Conclusiones: El enfoque en la madurez de los seguidores distingue este modelo de los modelos de liderazgo estáticos, subrayando su relevancia duradera.

Descargas

Los datos de descargas todavía no están disponibles.

Biografía del autor/a

Diego José Donoso Vargas, Universidad Tecnológica Ecotec

Doctor en Ciencias Políticas, Doctor en Administración Pública. Líder del Colectivo Internacional de Empresas y Administración Pública, ECOTEC. Profesor Visitante - Investigador Postdoctoral Universidad Complutense de Madrid, Universidad de Granada, Universidad de Málaga y Universidad de DEUSTO.

Ana María Gallardo Cornejo, Universidad Tecnológica Ecotec

Ex Viceministro de Promoción de Exportaciones e Inversiones/ Director Ejecutivo/ Negocios Internacionales/ Ex Comisario de Comercio de Ecuador/Decano de Economía y Estudios Globales. Universidad de Harvard, Programa de Innovación y Emprendimiento Programa de Innovación y Emprendimiento IDE Business School. Universitat Pompeu Fabra, Master International Business, América Latina, Asia y Europa.

Citas

Altbach, P. G., & de Wit, H. (2017). The globalization challenge for European higher education: Convergence and divergence in the Bologna Process. International Higher Education, 88, 2-4. https://ejournals.bc.edu/index.php/ihe/article/view/9684

Anderson, J. R., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995). Cognitive tutors: Lessons learned. The Journal of the Learning Sciences, 4(2), 167-207. https://doi.org/10.1207/s15327809jls0402_2

Baker, R., & Inner, P. S. (2014). Educational data mining and learning analytics: Applications to constructionist research. Technology, Knowledge and Learning, 19(1-2), 205-220. https://doi.org/10.1007/s10758-014-9226-x

Baker, R., & Inventado, P. S. (2014). Educational data mining and learning analytics. Springer.

Bessen, J. E. (2019). AI and jobs: The role of demand. National Bureau of Economic Research.

Blikstein, P., & Wilensky, U. (2004). MaterialSim: An agent-based simulation toolkit for engineering education. Advances in Engineering Education, 2(1), 1-21. https://ccl.northwestern.edu/papers/nu_msim_icee.pdf

Buckingham Shum, S., & Deakin Crick, R. (2012). Learning dispositions and transferable competencies: Pedagogy, modelling and learning analytics. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 92-101). https://doi.org/10.1145/2330601.2330666

Chui, M., & Manyika, J. (2018). AI and the continuous learning imperative. McKinsey Quarterly.

Dennen, V. P., & Burner, K. J. (2008). The cognitive apprenticeship model in educational practice. In Handbook of research on educational communications and technology (pp. 425-439). Routledge.

Dignum, V. (2018). Ethics in artificial intelligence: introduction to the special issue. Ethics and Information Technology, 20(1), 1-3. https://bit.ly/3SpQYNJ

Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin's Press.

Graeff, C. L. (1983). The situational leadership theory: A critical view. Academy of Management Review, 8(2), 285-291. https://journals.aom.org/doi/abs/10.5465/AMR.1983.4284738

Greller, W., & Drachsler, H. (2012). Translating learning into numbers: A generic framework for learning analytics. Educational Technology & Society, 15(3), 42-57. https://bit.ly/3Sq3JrH

Hersey, P., & Blanchard, K. H. (1977). Management of organizational behavior: Utilizing human resources. Academy of Management Journal.

Hou, L., Liu, D., & Zhang, L. (2018). Application of big data in career guidance: Based on employment information for Chinese universities. Journal of Physics: Conference Series, 1063, 012107. https://doi.org/10.1088/1742-6596/1063/1/012107

Kapoor, K., Bigdeli, A. Z., Dwivedi, Y. K., Schroeder, A., Beltagui, A., & Baines, T. (2021). A socio-technical view of platform ecosystems: Systematic review and research agenda. Journal of Business Research, 128, 94-108. https://www.sciencedirect.com/science/article/pii/S0148296321000680

Koedinger, K. R., & Corbett, A. T. (2006). Cognitive tutors: Technology bringing learning science to the classroom. In K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 61-78). Cambridge University Press. https://doi.org/10.1017/CBO9780511816833.006

Kovanović, V., Joksimović, S., Gašević, D., Hatala, M., & Adesope, O. (2015). Analytics of communities of inquiry: Effects of learning technology use on cognitive presence in asynchronous online discussions. The Internet and Higher Education, 27, 74-89. https://doi.org/10.1016/j.iheduc.2015.05.005

Lane, H. C., Yacef, K., Mostow, J., & Pavlik, P. (2015). Teaching with big data: The future of education. In O. C. Santos Jr., J. G. Boticario, C. Romero, M. Pechenizkiy, A. Merceron, P. Mitros, & J. M. Luna (Eds.), Proceedings of the 7th International Conference on Learning Analytics and Knowledge (pp. 205-209). https://doi.org/10.1145/2723576.2723580

Lee, H. S., & Lee, J. (2021). Applying artificial intelligence in physical education and future perspectives. Sustainability, 13(1), 351. https://www.mdpi.com/2071-1050/13/1/351

Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson Education.

Lynch, R., & Dembo, M. (2004). The relationship between self-regulation and online learning in a blended learning context. International Review of Research in Open and Distributed Learning, 5(2), 1-16. https://bit.ly/3WDr0sI

Northouse, P. G. (2015). Leadership: Theory and practice. Sage publications.

Nosenko, Y. (2023). Alta solution from Knewton as a tool of support for adaptive learning in mathematics. Episteme, 45, 7-9. https://acortar.link/60o2LR

Papamitsiou, Z., & Economides, A. A. (2014). Learning analytics and educational data mining in practice: A systematic literature review of empirical evidence. Educational Technology & Society, 17(4), 49-64. https://www.j-ets.net/ETS/journals/17_4/5.pdf

Pappas, C., & Pigueiras, A. (2019). Artificial intelligence in education: A review. Informatics in Education, 18(1), 17-31. https://doi.org/10.15388/infedu.2019.02

Selwyn, N. (2017). The rise of educational data: The hollowing out of privacy? Learning, Media and Technology, 42(2), 213-225. https://doi.org/10.1080/17439884.2016.1173036

Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 30. https://bit.ly/3Wsj6Bk

Siemens, G., & Baker, R. S. D. (2012, April). Learning analytics and educational data mining: towards communication and collaboration. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 252-254). https://bit.ly/3Wl0Z03

Thakur, D., & Sharma, S. (2020). The role of artificial intelligence in organisations for recruitment process: A review. Pacific Business Review International, 13(1), 74-76. https://bit.ly/4bWXC53

Turing, A. M. (2021). Computing machinery and intelligence (1950). https://doi.org/10.1093/mind/LIX.236.433

Van den Berghe, R., Verhagen, J., Oudgenoeg-Paz, O., Van der Ven, S., & Leseman, P. (2019). Social robots for language learning: A review. Review of Educational Research, 89(2), 259-295. https://journals.sagepub.com/doi/full/10.3102/0034654318821286

VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational psychologist, 46(4), 197-221. https://www.tandfonline.com/doi/abs/10.1080/00461520.2011.611369

Veletsianos, G., & Doering, A. (2010). Long-term student experiences in a hybrid, open-ended and problem-based Adventure Learning program. Australasian Journal of Educational Technology, 26(2). https://ajet.org.au/index.php/AJET/article/download/1096/351/

Wang, Y. (2020). Analysis on the construction of ideological and political education system for college students based on mobile artificial intelligence terminal. Soft Computing, 24(11), 8365-8375. https://link.springer.com/article/10.1007/s00500-020-04932-6 (Retracted article)

Woods, P., Culshaw, S., Jarvis, J., Payne, H., Roberts, A., & Smith, K. (2021). Developing distributed leadership through arts-based and embodied methods: an evaluation of the UK action research trials of collage and gesture response. ENABLES Project, University of Hertfordshire. https://bit.ly/4d8lHHc

Woolf, B. P. (2010). A roadmap for education technology. Beverly Park Woolf.

Yukl, G. (2012). Leadership. Cases in Leadership. Thousand Oaks. Sage.

Zhu, C., Du, J., Shahzad, F., & Wattoo, M. (2022). Environment sustainability is a corporate social responsibility: measuring the nexus between sustainable supply chain management, big data analytics capabilities, and organizational performance. Sustainability, 14(6), 3379. https://www.mdpi.com/2071-1050/14/6/3379

Descargas

Publicado

2024-09-23

Cómo citar

Donoso Vargas, D. J., & Gallardo Cornejo, A. M. (2024). Acortando la brecha: aplicación de la IA y la estadística bayesiana a la formación tradicional en liderazgo educativo. European Public & Social Innovation Review, 9, 1–19. https://doi.org/10.31637/epsir-2024-916

Número

Sección

INNOVACIÓN EN LA VIRTUALIZACIÓN EN LOS PROCESOS FORMATIVOS