Emergent Professional Learning Communities in Higher Education: Integrating Faculty Development, Educational Innovation, and Organizational Change at a Canadian College
Bibliographic record
Abstract
Centres for teaching and learning at postsecondary educational institutions in Canada seek to serve the professional development needs of faculty members throughout the college or university. Recognizing the limits of conventional frameworks for faculty development, such as one-time workshops, pedagogical conferences, and lunchtime discussion sessions, this interpretive inquiry explores learning communities as an additional framework for serving faculty development and cross-institutional professional development needs. The study asks: what does it mean for faculty, educational developers, support staff, and administrators to participate in a learning community at a college in Canada? Data collected through individual inquiry conversations (semi-structured interviews) and research memos were used to develop narrative descriptions representing the participants’ respective experiences of a learning community in a large, urban college context in Canada. These narrative descriptions offer portraits of the meaning that learning community members made of their own experience, revealing that the learning communities served not only as sites for professional development, but also formed microcultures within the institution, which, over time, influenced educational (academic) and organizational (administrative) change, both in policy and in practice.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.040 | 0.029 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 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".