Postdoctoral scholars’ perceptions of a university teaching certificate program
Bibliographic record
Abstract
Purpose This study aims to explore postdoctoral scholars’ experiences and perceptions of a teaching certificate program and identify how they use the knowledge and skills developed through the certificate program to improve their teaching practices. Design/methodology/approach In this case study, the authors explored postdoctoral scholars’ experiences and perceptions of a teaching certificate using a multiple methods and data sources including documents, course evaluations, interviews and surveys. Findings The teaching certificate program helped postdocs learn the language and theory of teaching and learning in post-secondary education; practice specific strategies and develop confidence in how to teach; network with colleagues about teaching and learning; develop a reflective teaching practice; and contribute to the scholarship of teaching and learning. Practical implications The findings from this study will inform efforts to develop new or refine existing approaches to promote teaching and learning professional development opportunities for postdoctoral scholars. Originality/value This paper fulfills an identified need to study teaching and learning development for postdoctoral scholars.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".