Learning to read the (digital) room during the COVID-19 pandemic
Bibliographic record
Abstract
With the advent of the COVID-19 pandemic across the globe, teachers have made sudden adjustments and dealt with continually changing circumstances. In our own region, in Western Canada, pandemic restrictions, lockdowns, and changing protocols meant that teachers experienced significant shifts to their instruction and classroom practices. This has included remote and online instruction, significant changes in school procedures, and changes in the provision of professional development and teacher supports. Our chapter draws from a qualitative study of teachers who were primarily located in Western Canada who participated in surveys and interviews examining their experiences of teaching from March 2020 to Fall 2022, during a period of time significantly impacted by COVID-19. We share teacher perspectives, challenges, unexpected learnings, and insights in connection to classroom experiences during the pandemic. Three themes that we address in further depth are (1) areas of challenge and inequity that the pandemic revealed; (2) impacts on digital practices for teachers in our study, and (3) implications for educational change.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".