Revisión sistemática de las características de gestión del tiempo en la realización de actividades educativas en los sistemas de gestión del aprendizaje.

Autores/as

DOI:

https://doi.org/10.17398/1695-288X.19.1.63

Palabras clave:

Gestión del tiempo, Analíticas de aprendizaje, Minería de datos educativos, Revisión sistemática de la literatura, Sistemas de Gestión del Aprendizaje

Resumen

El uso de sistemas de gestión del aprendizaje es cada vez más frecuente como modalidad de enseñanza-aprendizaje. Esto se debe al hecho de que permite una mayor flexibilidad de tiempo y espacio en relación con el aprendizaje presencial. Por lo tanto, este trabajo tiene como objetivo presentar cómo las áreas de minería de datos educativos y análisis de aprendizaje están contribuyendo a extraer conocimiento de la autorregulación de la gestión del tiempo en entornos de e-learning Para esto, consideramos el concepto de gestión del tiempo de Pintrich (2000) y llevamos a cabo una revisión sistemática de la literatura. Fue posible evidenciar que la mayoría de los trabajos analizados no tienen como objetivo investigar sobre la gestión del tiempo, a pesar de que informan sobre resultados. También se observa que los datos que representan la gestión del tiempo, utilizados en la investigación, son datos agregados, es decir, el fenómeno no se estudia con el tiempo. Con estos resultados, tenemos una visión general de cómo el campo de Learning Analytics y Educational Data Mining están contribuyendo a extraer conocimiento sobre la autorregulación de la gestión del tiempo en entornos en línea.

Descargas

Los datos de descarga aún no están disponibles.

Referencias

Baker, R., Isotani, S., & Carvalho, A. (2011). Mineração de Dados Educacionais: Oportunidades para o Brasil. Revista Brasileira de Informática Na Educação, 19(2). https://doi.org/10.5753/rbie.2011.19.02.03

Bittencourt, I. I., & Isotani, S. (2018). Informática na Educação baseada em Evidências: Um Manifesto. Revista Brasileira de Informática Na Educação, 26(3), 108. https://doi.org/10.5753/rbie.2018.26.03.108

Boroujeni, M. S., Sharma, K., Kidziński, Ł., Lucignano, L., & Dillenbourg, P. (2016). How to Quantify Student’s Regularity? In Adaptive and Adaptable Learning (pp. 277–291). Springer International Publishing. https://doi.org/10.1007/978-3-319-45153-4_21

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. https://doi.org/10.1016/j.iheduc.2015.04.007

Cerezo, R., Esteban, M., Sánchez-Santillán, M., & Núñez, J. C. (2017). Procrastinating Behavior in Computer-Based Learning Environments to Predict Performance: A Case Study in Moodle. Frontiers in Psychology, 8. https://doi.org/10.3389/fpsyg.2017.01403

Claessens, B. J. C., van Eerde, W., Rutte, C. G., & Roe, R. A. (2007). A review of the time management literature. Personnel Review, 36(2), 255–276. https://doi.org/10.1108/00483480710726136

Dunlosky, J., & Ariel, R. (2011). The influence of agenda-based and habitual processes on item selection during study. Journal of Experimental Psychology: Learning, Memory, and Cognition, 37(4), 899–912. https://doi.org/10.1037/a0023064

Feldmann, B. (2014). Two Decades of e-learning in Distance Teaching – From Web 1.0 to Web 2.0 at the University of Hagen. In Communications in Computer and Information Science (pp. 163–172). Springer International Publishing. https://doi.org/10.1007/978-3-319-10671-7_16

Hadwin, A., Järvelä, S., & Miller, M. (2017, August 31). Self-Regulation, Co-Regulation, and Shared Regulation in Collaborative Learning Environments. Retrieved from https://www.routledgehandbooks.com/doi/10.4324/9781315697048.ch6

Jo, I.-H., Kim, D., & Yoon, M. (2014). Analyzing the log patterns of adult learners in LMS using learning analytics. In Proceedings of the Fourth International Conference on Learning Analytics And Knowledge - LAK ’14. ACM Press. https://doi.org/10.1145/2567574.2567616

Jo, I.-H., Yu, T., Lee, H., & Kim, Y. (2014). Relations between Student Online Learning Behavior and Academic Achievement in Higher Education: A Learning Analytics Approach. In Emerging Issues in Smart Learning (pp. 275–287). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-44188-6_38

Jo, Il-Hyun & Kim, Dongho & Yoon, Meehyun. (2015). Constructing proxy variables to measure adult learners' time management strategies in LMS. Educational Technology and Society. 18. 214-225.

Kim, D., Yoon, M., Jo, I.-H., & Branch, R. M. (2018). Learning analytics to support self-regulated learning in asynchronous online courses: A case study at a women’s university in South Korea. Computers & Education, 127, 233–251. https://doi.org/10.1016/j.compedu.2018.08.023

Kitchenham, B., & Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. Tech. Rep. EBSE 2007-001, Keele University and Durham University Joint Report.

Lee, Y. (2018). Effect of uninterrupted time-on-task on students’ success in Massive Open Online Courses (MOOCs). Computers in Human Behavior, 86, 174–180. https://doi.org/10.1016/j.chb.2018.04.043

Littlejohn, A., Hood, N., Milligan, C., & Mustain, P. (2016). Learning in MOOCs: Motivations and self-regulated learning in MOOCs. The Internet and Higher Education, 29, 40–48. https://doi.org/10.1016/j.iheduc.2015.12.003

Mega, C., Ronconi, L., & De Beni, R. (2014). What makes a good student? How emotions, self-regulated learning, and motivation contribute to academic achievement. Journal of Educational Psychology, 106(1), 121–131. https://doi.org/10.1037/a0033546

Nadinloyi, K. B., Hajloo, N., Garamaleki, N. S., & Sadeghi, H. (2013). The Study Efficacy of Time Management Training on Increase Academic Time Management of Students. Procedia - Social and Behavioral Sciences, 84, 134–138. https://doi.org/10.1016/j.sbspro.2013.06.523

Panadero, E. (2017). A Review of Self-regulated Learning: Six Models and Four Directions for Research. Frontiers in Psychology, 8. https://doi.org/10.3389/fpsyg.2017.00422

Pintrich, P. R. (2000). The Role of Goal Orientation in Self-Regulated Learning. In Handbook of Self-Regulation (pp. 451–502). Elsevier. https://doi.org/10.1016/b978-012109890-2/50043-3

Pintrich, P. R. (2004). A Conceptual Framework for Assessing Motivation and Self-Regulated Learning in College Students. Educational Psychology Review, 16(4), 385–407. https://doi.org/10.1007/s10648-004-0006-x

Rasid, N., Nohuddin, P. N. E., Alias, H., Hamzah, I., & Nordin, A. I. (2017). Using Data Mining Strategy in Qualitative Research. In Advances in Visual Informatics (pp. 100–111). Springer International Publishing. https://doi.org/10.1007/978-3-319-70010-6_10

Siemens, G., & Baker, R. S. J. d. (2012). Learning analytics and educational data mining. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge - LAK ’12. ACM Press. https://doi.org/10.1145/2330601.2330661

Tabuenca, B., Kalz, M., Drachsler, H., & Specht, M. (2015). Time will tell: The role of mobile learning analytics in self-regulated learning. Computers & Education, 89, 53–74. https://doi.org/10.1016/j.compedu.2015.08.004

Wolters, C. A., Won, S., & Hussain, M. (2017). Examining the relations of time management and procrastination within a model of self-regulated learning. Metacognition and Learning, 12(3), 381–399. https://doi.org/10.1007/s11409-017-9174-1

Won You, Ji. (2015). Examining the Effect of Academic Procrastination on Achievement Using LMS Data in e-Learning. Educational Technology and Society. 18. 64-74.

Yen, C.-J., Tu, C.-H., Sujo-Montes, L. E., Armfield, S. W. J., & Chan, J.-Y. (2013). Learner Self-Regulation and Web 2.0 Tools Management in Personal Learning Environment. International Journal of Web-Based Learning and Teaching Technologies, 8(1), 46–65. https://doi.org/10.4018/jwltt.2013010104

You, J. W. (2016). Identifying significant indicators using LMS data to predict course achievement in online learning. The Internet and Higher Education, 29, 23–30. https://doi.org/10.1016/j.iheduc.2015.11.003

Zacharis, N. Z. (2015). A multivariate approach to predicting student outcomes in web-enabled blended learning courses. The Internet and Higher Education, 27, 44–53. https://doi.org/10.1016/j.iheduc.2015.05.002

Zimmerman, B. J. (1989). A social cognitive view of self-regulated academic learning. Journal of Educational Psychology, 81(3), 329–339. https://doi.org/10.1037/0022-0663.81.3.329

Zimmerman, B. J. (2000). Attaining Self-Regulation. In Handbook of Self-Regulation (pp. 13–39). Elsevier. https://doi.org/10.1016/b978-012109890-2/50031-7

Zimmerman, B. J. (2002). Becoming a Self-Regulated Learner: An Overview. Theory Into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2

Publicado

2020-07-03

Número

Sección

Artículos / Articles

Cómo citar

Revisión sistemática de las características de gestión del tiempo en la realización de actividades educativas en los sistemas de gestión del aprendizaje. (2020). Revista Latinoamericana De Tecnología Educativa - RELATEC, 19(1), 63-75. https://doi.org/10.17398/1695-288X.19.1.63

Artículos similares

11-20 de 509

También puede Iniciar una búsqueda de similitud avanzada para este artículo.

Artículos más leídos del mismo autor/a