Self-regulated learning about university teaching: an exploratory study
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
While research on self-regulated learning has been proliferating over the past decade, also within higher education settings, only very few studies apply the notion of self-regulated learning to teaching. We offer this exploratory study as a contribution to our understanding of the role of self-regulated learning in university instructors’ growth as teachers. Thirty-one academic science staff participated in semi-structured interviews designed to explore whether they engage in self-regulatory processes when learning about teaching. Interview questions were based on two theories: Zimmerman's self-regulated learning cycle and Kreber and Cranton's scholarship of teaching model. Cluster analyses revealed different patterns of responses for various subgroups of staff. For the two main groups, Chi-square analyses identified the specific variables on which differences between groups were observed. Participation in certain educational development activities as well as discipline affiliation was shown to be associated with self-regulated learning processes. We make concrete suggestions for how future research on self-regulated learning about teaching can build on these findings and conclude the article with some recommendations for the practice of educational development.
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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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