Self-regulated learning about university teaching: an exploratory study
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.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".