Percepción de los conocimientos de la analítica del aprendizaje en la educación superior

Maria de Jesús Araiza-Vázquez; Federico Guadalupe Figueroa-Garza; José Felipe Ramirez-Ramirez

doi:10.29105/vtga9.5-506

Autores/as

Maria de Jesús Araiza-Vázquez Universidad Autónoma de Nuevo León https://orcid.org/0000-0002-2622-805X
Federico Guadalupe Figueroa-Garza Universidad Autónoma de Nuevo León
José Felipe Ramirez-Ramirez Universidad Autónoma de Nuevo León

DOI:

https://doi.org/10.29105/vtga9.5-506

Palabras clave:

Analítica del Aprendizaje, Educación Superior, Conocimiento de analítica de datos, Gestión del Cambio

Resumen

Se pueden obtener múltiples beneficios del análisis de aprendizaje (AA) en las Instituciones de Educación Superior (IES) y las partes interesadas, mediante el uso de una variedad de estrategias de análisis de datos para generar recomendaciones y conocimientos sumativos, predictivos y en tiempo real. Sin embargo, es necesario analizar si los entornos educativos y el personal académico y administrativo están capacitados para llevar a cabo estos procesos. En este trabajo se utilizó una matriz de beneficios de la AA para investigar las capacidades actuales de la AA en las IES, se exploró la fuente de datos para generar un marco valido de AA y comprender como se perciben los conocimientos relacionados con la AA. Concluimos que se necesita más investigación empírica sobre la solidez y los beneficios esperados de los marcos de análisis de aprendizaje para la enseñanza y el aprendizaje para confirmar la promesa de esta nueva tecnología prometedora.

Descargas

Los datos de descargas todavía no están disponibles.

Citas

Azevedo, R., Taub, M., y Mudrick, N.V. (2018). Understanding and reasoning about real-time cognitive, affective, and metacognitive processes to foster self-regulation with advanced learning technologies. In D.H. Schunk y J.A. Greene (Eds.), Handbook of self-regulation of learning and performance (pp. 254-270). Routledge/Taylor & Francis Group. https://doi.org/10.4324/9781315697048-17 DOI: https://doi.org/10.4324/9781315697048-17

Blumenstein, M. (2020). Synergies of Learning Analytics and Learning Design: A Systematic Review of Student Outcomes. Journal of Learning Analytics, 7(3), 13-32. https://dx.doi.org/10.18608/jla.2020.73.3 DOI: https://doi.org/10.18608/jla.2020.73.3

Cervantes, J., Garcia-Lamont, F., Rodríguez-Mazahua, L., & Lopez, A. (2020). A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing, 408, 189-215. https://doi.org/10.1016/j.neucom.2019.10.118 DOI: https://doi.org/10.1016/j.neucom.2019.10.118

Cogliano, M., Bernacki, M. L., Hilpert, J. C., & Strong, C. L. (2022). A self-regulated learning analytics prediction-and-intervention design: Detecting and supporting struggling biology students. Journal of Educational Psychology. https://doi.org/10.1037/edu0000745 DOI: https://doi.org/10.1037/edu0000745

Fan, Y., van der Graaf, J., Lim L., Raković, M., Singh, S., Kilgour, J., ... & Gašević, D. (2022). Towards investigating the validity of measurement of self-regulated learning based on trace data. Metacognition and Learning, 17(3), 949-987. https://doi.org/10.1007/s11409-022-09291-1 DOI: https://doi.org/10.1007/s11409-022-09291-1

Farrell, T., Alani, H., & Mikroyannidis, A. (2022). Mediating learning with learning analytics technology: guidelines for practice. Teaching in Higher Education, 1-21. https://doi.org/10.1080/13562517.2022.2067745 DOI: https://doi.org/10.1080/13562517.2022.2067745

Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: learning analytics are about learning. TechTrends, 59(1), 64–71. https://doi.org/10.1007/s11528-014-0822-x DOI: https://doi.org/10.1007/s11528-014-0822-x

Gašević, D., Dawson, S., Rogers, T., & Gašević, D. (2016). Learning analytics should not promote one size fits all: the effects of instructional conditions in predicting academic success. Internet and Higher Education, 28, 68–84. https://doi.org/10.1016/j.iheduc.2015.10.002 DOI: https://doi.org/10.1016/j.iheduc.2015.10.002

Huda, M., Maseleno, A., Shahrill, M., Jasmi, K. A., Mustari, I., & Basiron, B. (2017). Exploring adaptive teaching competencies in big data era. International Journal of Emerging Technologies in Learning, 12(3). DOI: https://doi.org/10.3991/ijet.v12i03.6434

Ifenthaler, D., & Drachsler, H. (2020). Learning analytics. Handbuch Bildungstechnologie. Konzeption und Einsatz digitaler Lernumgebungen, 515-525. ISBN: 978-3-662-54368-9 DOI: https://doi.org/10.1007/978-3-662-54368-9_42

Kevan, J. M., & Ryan, P. R. (2016). Experience API: flexible, decentralized and activity-centric data collection. Technology, Knowledge and Learning, 21(1), 143–149. https://doi.org/10.1007/s10758-015-9260-x DOI: https://doi.org/10.1007/s10758-015-9260-x

Mangaroska, K., & Giannakos, M. (2018). Learning analytics for learning design: A systematic literature review of analytics-driven design to enhance learning. IEEE Transactions on Learning Technologies, 12(4), 516-534. https://dx.doi.org/10.1109/TLT.2018.2868673 DOI: https://doi.org/10.1109/TLT.2018.2868673

Nieto, Y., Gacía-Díaz, V., Montenegro, C., González, C. C., & Crespo, R. G. (2019). Usage of machine learning for strategic decision making at higher educational institutions. IEEE Access, 7, 75007-75017. https://doi.org/10.1109/ACCESS.2019.2919343 DOI: https://doi.org/10.1109/ACCESS.2019.2919343

Schumacher, C., & Ifenthaler, D. (2018). Features students really expect from learning analytics. Computers in human behavior, 78, 397-407. https://doi.org/10.1016/j.chb.2017.06.030 DOI: https://doi.org/10.1016/j.chb.2017.06.030

Swan, K., Matthews, D., Bogle, L., Boles, E., & Day, S. (2012). Linking online course design and implementation to learning outcomes: A design experiment. The Internet and Higher Education, 15(2), 81–88. http://dx.doi.org/10.1016/j.iheduc.2011.07.002 DOI: https://doi.org/10.1016/j.iheduc.2011.07.002

Tai, J., Ajjawi, R., Boud, D., Dawson, P., & Panadero, E. (2018). Developing evaluative judgement: enabling students to make decisions about the quality of work. Higher education, 76, 467-481. https://doi.org/10.1007/s10734-017-0220-3 DOI: https://doi.org/10.1007/s10734-017-0220-3

Tsai, Y. S., Whitelock-Wainwright, A., & Gašević, D. (2020, March). The privacy paradox and its implications for learning analytics. In Proceedings of the tenth international conference on learning analytics & knowledge (pp. 230-239). https://doi.org/10.1145/3375462.3375536 DOI: https://doi.org/10.1145/3375462.3375536

Tsai, Y.-S., Moreno-Marcos, P. M., Jivet, I., Scheffel, M., Tammets, K., Kollom, K., & Gašević, D. (2018). The SHEILA Framework: Informing Institutional Strategies and Policy Processes of Learning Analytics. Journal of Learning Analytics, 5(3), 5–20. https://doi.org/10.18608/jla.2018.53.2 DOI: https://doi.org/10.18608/jla.2018.53.2

Viberg, O., Khalil, M., & Baars, M. (2020, March). Self-regulated learning and learning analytics in online learning environments: A review of empirical research. In Proceedings of the tenth international conference on learning analytics & knowledge (pp. 524-533). https://doi.org/10.1145/3375462.3375483 DOI: https://doi.org/10.1145/3375462.3375483

Waldman, A. E. (2018). Privacy as trust: Information privacy for an information age. Cambridge University Press. DOI: https://doi.org/10.1017/9781316888667

Whitelock-Wainwright, A., Tsai, Y. S., Drachsler, H., Scheffel, M., & Gašević, D. (2021). An exploratory latent class analysis of student expectations towards learning analytics services. The Internet and Higher Education, 51, 100818. https://doi.org/10.1016/j.iheduc.2021.100818 DOI: https://doi.org/10.1016/j.iheduc.2021.100818