Incorporating Reflective Practice as a Means of Improving Student Self-Regulated Learning in a Digital Learning Environment
Bibliographic record
Abstract
Available research has shown that digital learning environments, in which students take active responsibility for controlling aspects of technology-infused learning, are often underutilized as many students lack the appropriate cognitive and metacognitive strategies - or self-regulated learning (SRL) skills. Providing SRL support in digital learning positively affects student learning, with metacognition appearing to play the central role in SRL development. In addition, there seems to be agreement that reflection is a process by which one acts metacognitively, with use of reflective prompts being a common support to provoke metacognition in the literature. While the general research into reflection is mixed, more recent research on the use of reflective prompts as a support points to a positive influence on academic performance in digital learning. This project details the research of reflection as a specific strategy to develop student SRL skill, culminating in a practical, research-backed book of the theory, strategies, and guidelines to help educators incorporate reflective training into digital learning environments to develop SRL skill in students.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".