Instructional Communities of Practice during COVID-19: Social Networks and Their Implications for Resilience<sup>†</sup>
Bibliographic record
Abstract
In response to the COVID-19 pandemic, most spring 2020 university courses were abruptly transitioned mid-semester to remote learning. The current study was an exploratory investigation into the interactions among individuals within a single biology department during this transition. Our goal was to describe the patterns of interactions among members of this community, including with whom they gave advice on instruction, shared materials, co-constructed materials, and shared emotions, during the rapid online transition. We explored how instructional teams (i.e., the instructor of record and graduate teaching assistants, or GTAs, assigned to a single course) organized themselves, and what interactions exist outside of these instructional teams. Using social network analysis, we found that the flow of resources and support among instructional staff within this department suggest a collaborative and resilient community of practice. Most interactions took place between instructional staff teaching in the same course. While faculty members tended to have more connections than GTAs, GTAs remained highly interactive in this community. We consider how the observed networks might reflect a mobilization of social resources that are important for individual and departmental resilience in a time of crisis. Actively promoting supportive networks and network structures may be important as higher education continues to cope and adapt to the changing landscape brought on by COVID-19.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".