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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.008 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".