Influencia de la trayectoria de aprendizaje de la metodología Self Directed Based Learning en el éxito académico
Resumen Este artículo presenta los resultados de un estudio realizado con el objetivo de validar el impacto que tiene el comportamiento de un estudiante sobre su éxito académico en un programa diseñado con la metodología Self Directed Based Learning. Esta metodología docente, pensada para estudiantes de programas de posgrado en modalidad online, fue diseñada en 2013 con el propósito de generar una dinámica de trabajo que oriente al estudiante en su trayectoria de aprendizaje. El diseño de esta metodología busca aumentar el compromiso del estudiante con el aprendizaje, mejorando así tanto su progreso como su éxito académico. El objetivo de este estudio es identificar qué tipo de actividades formativas dentro de la trayectoria de aprendizaje del estudiante determinan la consecución de los resultados de aprendizaje propiciando así el proceso de asimilación. El análisis se basa en la aplicación de herramientas de Educational Data Mining a datos de aprendizaje obtenidos de una promoción de un programa de posgrado; a través de estas herramientas, se analizan las actividades formativas que están asociadas con el éxito académico del estudiante, permitiendo así determinar la relación entre ellas. Con estos resultados sería posible validar el diseño y la construcción de esta metodología docente desde la perspectiva del propósito para el que fue diseñada: lograr que la trayectoria de aprendizaje del estudiante sea exitosa.
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Gros Salvat, B., & Cano García, E. (2021). Procesos de feedback para fomentar la autorregulación con soporte tecnológico en la educación superior: Revisión sistemática. RIED. Revista Iberoamericana de Educación a Distancia, 24(2), 107–125. 10.5944/ried.24.2.28886
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Llauró, A., Fonseca, D., Amo-Filva, D., Romero, S., Aláez, M., Torres Lucas, J., & Martínez Felipe, M. (2023). Academic Analytics Applied in the Study of the Relationship Between the Initial Profile of Undergraduate Students and Early Drop-Out Rates. Defining the Variables of a Predictor Instrument. En F. J. García-Peñalvo & A. García-Holgado (Eds.), Proceedings TEEM 2022: Tenth International Conference on Technological Ecosystems for Enhancing Multiculturality (pp. 982–990). Springer Nature. 10.1007/978-981-99-0942-1_103
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Silva, C., & Fonseca, J. (2017). Educational Data Mining: A Literature Review. En Á. Rocha, M. Serrhini, & C. Felgueiras (Eds.), Europe and MENA Cooperation Advances in Information and Communication Technologies (Vol. 520, pp. 87–94). Springer International Publishing. 10.1007/978-3-319-46568-5_9
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Alkış, N., & Temizel, T. T. (2018). The impact of motivation and personality on academic performance in online and blended learning environments. Journal of Educational Technology & Society, 21(3), 35–47.
Alsina, M., Canaleta, X., Cubeles, A., & Torres, R. (2019). Self Directed Based Learning, una metodología de aprendizaje para programas no presenciales. In M. L. Sein-Echaluce Lacleta, Á. Fidalgo-Blanco, & F. J. García-Peñalvo (Eds.), Actas del V Congreso Internacional sobre Aprendizaje, Innovación y Competitividad. CINAIC 2019 (9-11 de Octubre de 2019, Madrid, España) (pp. 325–330). Servicio de Publicaciones Universidad de Zaragoza. 10.26754/CINAIC.2019.0070
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Amo, D., Prinsloo, P., Alier, M., Fonseca, D., Kompen, R. T., Canaleta, X., & Herrero-Martín, J. (2021). Local Technology to Enhance Data Privacy and Security in Educational Technology. International Journal of Interactive Multimedia and Artificial Intelligence, 7(2), 262–273. 10.9781/ijimai.2021.11.006
Amo, D., Torres, R., Canaleta, X., Herrero-Martín, J., Rodríguez-Merino, C., & Fonseca, D. (2020). Seven principles to foster privacy and security in educational tools: Local Educational Data Analytics. En F. J. García-Peñalvo (Ed.), TEEM’20 Proceedings of the Eighth International Conference on Technological Ecosystems for Enhancing Multiculturality (Salamanca, Spain, October 21st-23th, 2020) (pp. 730–737). ACM. 10.1145/3434780.3436637
Artino, A. R., & Stephens, J. M. (2009). Academic motivation and self-regulation: A comparative analysis of undergraduate and graduate students learning online. The Internet and Higher Education, 12(3-4), 146–151. 10.1016/j.iheduc.2009.02.001
Barberá-Gregori, E., & Suárez-Guerrero, C. (2021). Evaluación de la educación digital y digitalización de la evaluación. RIED. Revista Iberoamericana de Educación a Distancia, 24(2), 33–40. 10.5944/ried.24.2.30289
Bareiss, R., & Radley, M. (2010). Coaching via cognitive apprenticeship. Proceedings of the 41st ACM Technical Symposium on Computer Science Education - SIGCSE ’10 (pp. 162–166). 10.1145/1734263.1734319
Broadbent, J., & Poon, W. L. (2015). Self-regulated learning strategies & academic achievement in online higher education learning environments: A systematic review. The Internet and Higher Education, 27, 1–13. 10.1016/j.iheduc.2015.04.007
Cabero-Almenara, J., & Palacios-Rodríguez, A. (2021). La evaluación de la educación virtual: Las e-actividades. RIED. Revista Iberoamericana de Educación a Distancia, 24(2), 169–188. 10.5944/ried.24.2.28994
Canaleta, X., & Solé, X. (2012). Extracción de Conocimiento Original de los Alumnos. En VII Congreso Iberoamericano de Docencia Universitaria, Porto.
Canaleta, X., Solé, X., & Navarro, J. (2012). Herramienta de soporte a la evaluación del aprendizaje y la gestión docente. Actas XVIII JENUI 2012, Ciudad Real, 10-13 de julio 2012 (pp. 359–364).
Chesser, S., Murrah, W., & Forbes, S. A. (2020). Impact of Personality on Choice of Instructional Delivery and Students’ Performance. American Journal of Distance Education, 34(3), 211–223. 10.1080/08923647.2019.1705116
Cohen, A., & Baruth, O. (2017). Personality, learning, and satisfaction in fully online academic courses. Computers in Human Behavior, 72, 1–12. 10.1016/j.chb.2017.02.030
The Design-Based Research Collective. (2003). Design-Based Research: An Emerging Paradigm for Educational Inquiry. (2003). Educational Researcher, 32(1), 5–8. 10.3102/0013189X032001005
Eddy, P. L., Hao, Y., Markiewicz, C., & Iverson, E. (2019). Faculty Change Agents as Adult Learners: The Power of Situated Learning. Community College Journal of Research and Practice, 43(8), 539–555. 10.1080/10668926.2018.1507848
Fariba, T. B. (2013). Academic Performance of Virtual Students based on their Personality Traits, Learning Styles and Psychological Well Being: A Prediction. Procedia - Social and Behavioral Sciences, 84, 112–116. 10.1016/j.sbspro.2013.06.519
Francis, B. K., & Babu, S. S. (2019). Predicting Academic Performance of Students Using a Hybrid Data Mining Approach. Journal of Medical Systems, 43(6), 162. 10.1007/s10916-019-1295-4
Frank, E., Hall, M., Holmes, G., Kirkby, R., Pfahringer, B., Witten, I. H., & Trigg, L. (2009). Weka-A Machine Learning Workbench for Data Mining. En O. Maimon & L. Rokach (Eds.), Data Mining and Knowledge Discovery Handbook (pp. 1269–1277). Springer US. 10.1007/978-0-387-09823-4_66
García-Peñalvo, F. J., Corell, A., Abella-García, V., & Grande-de-Prado, M. (2020). Online Assessment in Higher Education in the Time of COVID-19. Education in the Knowledge Society, 21, 12. 10.14201/eks.23013
García-Peñalvo, F. J. (2023). La percepción de la Inteligencia Artificial en contextos educativos tras el lanzamiento de ChatGPT: disrupción o pánico. Education in the Knowledge Society, 24, e31279. 10.14201/eks.31279
García-Peñalvo, F. J., Llorens-Largo, F., & Vidal, J. (2024). The new reality of education in the face of advances in generative artificial intelligence. RIED: revista iberoamericana de educación a distancia, 27(1). 10.5944/ried.27.1
Giada, A., Giovanni, B., & Vincenza, C. (2014). A new indicator for higher education student performance. Higher Education, 68(5), 653–668. 10.1007/s10734-014-9737-x
Gros Salvat, B., & Cano García, E. (2021). Procesos de feedback para fomentar la autorregulación con soporte tecnológico en la educación superior: Revisión sistemática. RIED. Revista Iberoamericana de Educación a Distancia, 24(2), 107–125. 10.5944/ried.24.2.28886
Hazrati-Viari, A., Rad, A. T., & Torabi, S. S. (2012). The effect of personality traits on academic performance: The mediating role of academic motivation. Procedia - Social and Behavioral Sciences, 32, 367–371. 10.1016/j.sbspro.2012.01.055
Hung, H.-C., Liu, I.-F., Liang, C.-T., & Su, Y.-S. (2020). Applying Educational Data Mining to Explore Students’ Learning Patterns in the Flipped Learning Approach for Coding Education. Symmetry, 12(2), 213. 10.3390/sym12020213
Jacob, J., Jha, K., Kotak, P., & Puthran, S. (2015). Educational Data Mining techniques and their applications. 2015 International Conference on Green Computing and Internet of Things (ICGCIoT) (pp. 1344–1348). 10.1109/ICGCIoT.2015.7380675
Knowles, M.S., Holton III, E.F., & Swanson, R.A. (2011). The Adult Learner (7th ed.). Routledge. 10.4324/9780080964249
Križanić, S. (2020). Educational data mining using cluster analysis and decision tree technique: A case study. International Journal of Engineering Business Management, 12. 10.1177/1847979020908675
Llauró, A., Fonseca, D., Amo-Filva, D., Romero, S., Aláez, M., Torres Lucas, J., & Martínez Felipe, M. (2023). Academic Analytics Applied in the Study of the Relationship Between the Initial Profile of Undergraduate Students and Early Drop-Out Rates. Defining the Variables of a Predictor Instrument. En F. J. García-Peñalvo & A. García-Holgado (Eds.), Proceedings TEEM 2022: Tenth International Conference on Technological Ecosystems for Enhancing Multiculturality (pp. 982–990). Springer Nature. 10.1007/978-981-99-0942-1_103
Llauró, A., Fonseca, D., Villegas, E., Aláez, M., & Romero, S. (2021). Educational data mining application for improving the academic tutorial sessions, and the reduction of early dropout in undergraduate students. En M. Alier & D. Fonseca (Eds.), Proceedings TEEM’21. Ninth International Conference on Technological Ecosystems for Enhancing Multiculturality (Barcelona, Spain, October 27th – 29th, 2021) (pp. 212–218). ACM. 10.1145/3486011.3486449
Lyons, P., & Bandura, R. P. (2020). Stimulating employee learning: The confluence of case-based and self-regulated learning. Industrial and Commercial Training, 52(3), 171–183. 10.1108/ICT-12-2019-0109
Merriam, S. B. (2001). Andragogy and Self-Directed Learning: Pillars of Adult Learning Theory. New Directions for Adult and Continuing Education, 2001(89), 3–14. 10.1002/ace.3
Navarro, Á. M., & Moreno-Ger, P. (2018). Comparison of Clustering Algorithms for Learning Analytics with Educational Datasets. International Journal of Interactive Multimedia and Artificial Intelligence, 5(2), 9–16. 10.9781/ijimai.2018.02.003
Romero, C., & Ventura, S. (2013). Data mining in education: Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1), 12–27. 10.1002/widm.1075
Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. WIREs Data Mining and Knowledge Discovery, 10(3), e1355. 10.1002/widm.1355
Sandoval, W. A., & Bell, P. (2004). Design-Based Research Methods for Studying Learning in Context: Introduction. Educational Psychologist, 39(4), 199–201. 10.1207/s15326985ep3904_1
Scott, E. E., Wenderoth, M. P., & Doherty, J. H. (2020). Design-Based Research: A Methodology to Extend and Enrich Biology Education Research. CBE—Life Sciences Education, 19(3), es11. 10.1187/cbe.19-11-0245
Silén, C., & Uhlin, L. (2008). Self-directed learning – a learning issue for students and faculty! Teaching in Higher Education, 13(4), 461–475. 10.1080/13562510802169756
Silva, C., & Fonseca, J. (2017). Educational Data Mining: A Literature Review. En Á. Rocha, M. Serrhini, & C. Felgueiras (Eds.), Europe and MENA Cooperation Advances in Information and Communication Technologies (Vol. 520, pp. 87–94). Springer International Publishing. 10.1007/978-3-319-46568-5_9
Alsina-Claret, M., Canaleta-Llampallas, X., Torres-Kompen, R., De-Torres-Gómez, E., & Fonseca-Escudero, D. (2023). Influencia de la trayectoria de aprendizaje de la metodología Self Directed Based Learning en el éxito académico. Education in the Knowledge Society (EKS), 24, e31494. https://doi.org/10.14201/eks.31494
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