IEEE Access (Jan 2021)

Self-Regulated Learning in Massive Online Open Courses: A State-of-the-Art Review

  • Jhoni Ceron,
  • Silvia Baldiris,
  • Jairo Quintero,
  • Rainer Rubira Garcia,
  • Gloria Liliana Velez Saldarriaga,
  • Sabine Graf,
  • Luis De La Fuente Valentin

DOI
https://doi.org/10.1109/ACCESS.2020.3045913
Journal volume & issue
Vol. 9
pp. 511 – 528

Abstract

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Self-regulated learning (SRL) is a cyclical process through which individuals plan their objectives, execute them and self-evaluate their own behavior so as to obtain their proposed goals. SRL has been investigated by different authors such as Zimmerman, Boekaerts, Winne and Hadwin, Pintrich, Efklides and Hadwin, Järvelä and Miller and it's being applied in learning environments. This systematic review describes the current state of the art in terms of the support for SRL in Massive Online Open courses (MOOC) using technologies based on psychological models. 66 studies conducted between 2010 and 2020 were analyzed by searching three multidisciplinary databases: Scopus, Web of Science and Google Scholar. The review methodology steps were the review planning, the search, literature analysis and the results report. Results show SRL in MOOCs is an emerging study area incentivized by the high dropout rate of the participants in MOOC. Regarding models of SRL, the most representative author reported was identified as Zimmerman. The most prominent self-regulation strategies used by MOOCs participants are: Goal setting, Help Seeking, Time management, Self-evaluation and Strategic planning. The platforms with research on SRL in MOOCs that stand out are Coursera, Edx, Open Edx and Moodle. We identified tools which have been developed to support SRL in MOOC and a set of good practices useful to support SLR that can be used by MOOC designers and tutors. Finally, a series of open problems and challenges that could lead to new research on the topic of SRL in MOOCs have been identified.

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