Use of research-based instructional strategies: How to avoid faculty quitting
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
We have examined the teaching practices of faculty members who adopted research-based instructional strategies (RBIS) as part of the Carl Wieman Science Education Initiative (CWSEI) at the University of British Columbia (UBC). Of the 70 that adopted such strategies with the support of the CWSEI program, only one subsequently stopped using these strategies. This is a tiny fraction of the 33% stopping rate for physics faculty in general [Phys. Rev. ST Phys. Educ. Res. 8, 020104 (2012)]. Nearly all of these UBC faculty members who had an opportunity to subsequently use RBIS in other courses (without CWSEI support) did so. We offer speculations for the difference in quitting rates. The direct support of the faculty member by a trained science education specialist in the discipline during the initial implementation of the new strategies is a particularly notable factor.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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