9. Using Communities of Practice to Foster Faculty Development in Higher Education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Communities of practice are becoming more widespread within higher education, yet little research has explored how these social learning networks can enhance faculty development. The focus of this paper is to describe the first-year experience of a community of practice initiative at McMaster University that was designed to engage groups of faculty, staff, and students to share ideas and foster learning. Four communities were initiated: Teaching with Technology, Teaching Professors, Pedagogy, and First Year Instructors, all of which provided a forum of safety and support, encouraging new ideas and risk taking that in turn contributed to individual and collective learning. Though in its early days, we consider communities of practice an innovative way to regenerate current learning and surface teaching practices that can build dynamic academic communities to foster faculty and staff development. Communities of practice have enabled us to reach beyond formal structures (e.g., classrooms) to create connections amongst people from different disciplinary boundaries that generate learning and foster development.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it