Professional staff perspectives on their contributions to teaching and learning at Cape Breton University
Bibliographic record
Abstract
Despite the significant role professional staff play in supporting teaching and learning in higher education, research about their perspectives is limited. This research seeks to explore staff perspectives at a small university in Eastern Canada, with a focus on staff experiences engaging with teaching and learning and considering potential future opportunities for staff engagement. Reflexive thematic analysis was used to construct themes based on data from presentation abstracts, a questionnaire, and semi-structured interviews. The document analysis, which outlines the many relevant topics relating to staff participation and/or interest at the most recent (2024) Society for Teaching and Learning in Higher Education (STLHE) and International Society for the Scholarship of Teaching and Learning (ISSOTL) conferences, gives national and international insight into current research, while the questionnaire and semi-structured interviews give voice to the staff within a specific university context. Staff perspectives on their contributions to teaching and learning align with many of the topics emerging from those conferences, including student and faculty support, accessibility, and community-building. Additionally, navigating modern issues in education including artificial intelligence and the evolving demographics of the student population are areas of importance. This research finds that some staff desire deeper connection to teaching and learning alongside faculty and students, suggesting that there may be value in institutions supporting a more cohesive approach to supporting teaching and learning that takes into consideration the many individuals who care about student success.
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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".