Challenges in Promoting Self-Regulated Learning in Technology Supported Learning Environments: An Umbrella Review of Systematic Reviews and Meta-Analyses
Bibliographic record
Abstract
Abstract Supporting learners’ self-regulated learning (SRL) processes and skills is crucial for effective learning, especially in online learning environments. In recent years, research on SRL and how it can be supported by technology has proliferated, resulting in many systematic reviews. The aims of this umbrella review are to provide orientation in a growing field, to identify challenges in the design of computer-assisted SRL (CA-SRL) supports and to derive future research needs. We identified and analysed 31 systematic reviews and meta-analyses that investigated SRL supports in computer-based, online and blended learning environments. The synthesis of the reviews highlights the critical importance of adopting comprehensive approaches in designing and implementing CA-SRL supports which integrate a variety of direct and indirect CA-SRL supports across the entire SRL cycle. The findings also call for greater precision in defining and categorising CA-SRL supports and their theoretical foundations to enhance comparability of research in this area. Finally, we conclude by providing recommendations for future research and development to effectively promote SRL for learners.
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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 | 0.160 | 0.374 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.015 |
| Bibliometrics | 0.026 | 0.019 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".