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

    Research output: Contribution to journalReview articlepeer-review

    4 Scopus citations


    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.

    Original languageEnglish
    Article number9298841
    Pages (from-to)511-528
    Number of pages18
    JournalIEEE Access
    StatePublished - 2021

    Bibliographical note

    Funding Information:
    This work was supported in part by the GIDATI Research Group, Pontifical Bolivarian University, Medellín, Colombia, in part by the VIRTUALAB Research Group, Technological Institute of Putumayo, Mocoa, Colombia in part by the International University of la Rioja for funding the PL-NETO Project under Project B0036 during the academic course 2020/2021, in part by the Spanish Ministry of Science, Innnovation and Universities for funding PLENTAS Project under Grant PID2019-111430RB-I00, in part by the Doctoral Fellowship from the Ministry of Science, Technology and Innovation of Colombia, Colciencias, Call 754 of 2016, and in part by the Natural Sciences and Engineering Research Council of Canada (NSERC) under Grant RGPIN-2020-05837.

    Publisher Copyright:
    © 2013 IEEE.


    • Educational technology
    • mOOC
    • sRL strategies
    • self-regulated learning
    • systematic review


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