A Framework for Creating, Facilitating, and Sustaining an Online Community of Practice for Instructors in Higher Education
Bibliographic record
Abstract
Abstract Faculty learning communities have historically occurred in-person, on-cam-pus, within specific departments and disciplines. The global pandemic sparked a revolution in the way that higher education engaged with online learning, transforming our shared understanding of the potential of online spaces. The transition to emergency remote teaching also highlighted the importance of community and relationships for educators and students. Whereas we once engaged in conversations with colleagues in the hallways or walking between buildings, we were suddenly removed from the four walls that had instilled a sense of community. In this chapter, we present an innovative framework for an online faculty learning community for educators of large enrollment courses. The online community of practice (CoP) has resulted in interdisciplinary conversations, collaborations, and critiques that have advanced Scholarship of Teaching and Learning (SoTL) across campus. As a result of participating in the CoP, members have expanded their mentorship and leadership by contributing to teaching development workshops, conference presentations, and knowledge mobilization on topics related to student engagement, communication, and assessment in large enrollment courses. While the primary goal of the CoP was to create connections between faculty members, we found that this approach to a faculty learning community increased access to and visibility of SoTL practices. We conclude by reflecting upon where we began and where we are going, specifically the transformative potential of an online community of practice to influence SoTL in higher education.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".