Self-Regulated Learning in Massive Online Open Courses: A State-of-the-Art Review
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".