How a socially shared approach may rescue the teaching of learning regulation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Self-regulated learning (SRL) is a fundamental skill for school and life. Much is known about how to effectively teach and support it in a classroom, though teachers often retreat to more structured, external learning regulation. Experts have identified the important role of pedagogical knowledge and personal self-regulated learning in helping teachers persevere with SRL teaching attempts. Teacher training programs target these specifically with pre-, post-, and concurrent learning experiences, and the act of carrying out regular SRL-oriented conversations with students itself fosters these insights and wisdom. In this article, the authors explore the way a structured, socially shared protocol for learning regulation support (SSLR) can increase teacher adherence to – and learning from – SRL-supportive teaching practices. They present qualitative interview data gathered from 12 users of an SSLR intervention to characterize the in-service learning and growth that the use of this approach may enable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it