Characterizing SoTL Across Canada
Bibliographic record
Abstract
SoTL Canada recently conducted a survey to gain insight into the Scholarship of Teaching and Learning (SoTL) activities of faculty and staff at institutions of higher education across Canada. Questions were guided by several principles, including: (a) identifying who is doing SoTL (such as personal, institutional, geographical and professional characteristics); (b) characterizing how SoTL is being conducted, supported, and disseminated (such as collaborations with peers and students, the number of active projects), and (c) differentiating the levels at which SoTL activities are occurring and funded. While we likely did not fully capture the work being done, our inquiry nonetheless provides important data related to the current nature and scope of SoTL in Canada. We noted that the people doing SoTL show a wide diversity of characteristics including appointments and disciplinary affiliations although a majority of respondents were female, and that collaborations with both colleagues and students were commonplace. SoTL continues to be conducted primarily at the classroom level, and approximately 65% of respondents have received funding, mostly from the institutional level. We also found an increased amount of activity compared to the last Canadian survey conducted in 2012, in particular by staff from Centres for Teaching and Learning. Survey participants reported discussing their SoTL findings with their colleagues more often than with their students. A number of areas of future research are identified.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.018 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".