Exploring the Relationship between Self-Regulated Learning and Reflection in Teacher Education
Bibliographic record
Abstract
Literature on teacher learning has shown links between being a self-regulated learner, reflecting effectively on one’s own practice, and being described as an “adaptive expert”. For instance, the metacognitive skills needed for effective reflection on teaching practice are seen as critically important to developing adaptive expertise in the context of the highly complex classroom environment. Similarly, self-regulated learning is often defined, at least in part, in terms of using metacognitive skill to adapt one’s approach to complex learning situations or problems. Although there is rich literature on reflective practice in teacher education, less is known about measuring teachers’ self-regulated learning or the relationship between self-regulated learning and teacher reflections. This research examines reflective practice and self-regulated learning through pre-service teachers’ written reflections. The study makes a novel adaptation of a rubric designed to evaluate teacher education candidates’ reflections to measure self-regulated learning. Findings suggest that the rubric could also be useful in understanding the self-regulated practices of teacher education candidates.
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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 | 0.014 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".