Insights and Opportunities: Evaluating a University Teaching and Learning Grants Program
Bibliographic record
Abstract
We describe a detailed program evaluation for a University-wide teaching and learning grants program at a Canadian research-intensive university. This work was designed to determine if the program is driving the types of changes in practice it was designed to support. We administered a survey that included yes/no response and Likert-scale questions to assess supports provided, outcomes, challenges, and impact of the grants program; and a series of open-ended questions inviting participants to share qualitative narratives describing their perceptions of the program and its effects. Thematic analysis of the survey responses revealed that the grants program provides tremendous value in strengthening scholarly communities and fosters wide ranging benefits to learners and teachers through ripple effects that extend well beyond the stated goals of the program. The engagement of students as partners in scholarly work and the development of local cultures and conversations around the scholarship of teaching and learning generated exciting new opportunities. The program supported innovative teaching strategies and deep engagement in teaching and learning, and provided opportunities for presentation, publication, and support from national funding bodies. It is clear that our institutional teaching and learning grants program is key to fostering a scholarly environment on our campus that benefits the entire community.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.072 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".