Peer Support and Pedagogical Conversations: Keys to Building Faculty Capacity in a Digital Age
Bibliographic record
Abstract
With the COVID-19 pandemic, post-secondary institutions pivoted to providing hybrid or fully online courses and recognized the need to mitigate the challenges faced by faculty in navigating this shift. This study was conducted at one Western Canadian university and followed a design-based research approach that included three phases and utilized mixed methods (interviews and surveys). The purpose of this research was to build faculty capacity for online teaching and learning. Overall, findings indicated that while the need for capacity building and improving collective practice was heightened during the pandemic, it remains a persistent need because faculty are continually faced with adjusting to ongoing complexities related to teaching and learning. One of the areas identified to build faculty capacity in this study was ongoing professional development emphasizing peer support and collegial conversations to aid faculty in adjusting teaching practices to various modalities including online learning. This study is significant for post-secondary institutions and researchers interested in building faculty capacity and improving collective teaching practices.
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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 | 0.019 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.022 | 0.021 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.003 | 0.004 |
| 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".