The Changing Landscape of Graduate Teaching Certificate Programs in Canada
Bibliographic record
Abstract
In a 2014 paper, Kenny, Watson, and Watton analyzed 13 Canadian universities offering graduate teaching certificate programs. This research used the Kenny et al. (2014) framework to provide an update, addressing the following research questions. First, has there since been an increase in the number of graduate teaching certificate programs at Canadian universities? Second, how do the common features of these programs compare to those identified by Kenny et al. (2014)? Third, how responsive are programs to recent trends in graduate teaching development? Key features within program administration, outcomes, structure, assessment, and recognition were examined, as were some current trends in post-secondary teaching. Program-related information was collected from the institutional websites of Canadian universities and verified by program key contacts. Since 2014, there has been a considerable increase in the number of graduate teaching certificate programs, both within and across institutions (from 13 programs at 13 institutions in 2014 to 36 programs at 25 institutions in 2019). This may be impacting how programs are structured and assessed. On the one hand, there appears to be movement towards reducing barriers to access programming, yet this growth may coincide with less resource-intensive program components and assessments. The responsiveness of programming to recent trends in program administration, programming content, and recognition varied.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".