Teachers' Voices: Pandemic Lessons for the Future of Education
Bibliographic record
Abstract
In late 2019 and early 2020, governments around the world closed educational institutions due to the COVID-19 pandemic. A similar response occurred in Canada and resulted in a sudden pivot by teachers from classroom-based instruction to remote teaching. During and shortly after this time, we undertook a survey study of over 2000 Canadian teachers, as well as follow-up interviews with a representative sub-set of those who took part in the initial round of the survey, to gain perspectives on teaching during the pandemic crisis. We summarize the foundations of the entire study and focus on the analysis and discussion of interview data to provide enhanced understanding of initial survey results. This study presents five lessons from the voices of teachers in the initial stages of COVID-19 in Canadian K-12 schools. Each lesson addresses a reality of teaching that was magnified by the pandemic and is highlighted for future consideration of educators in times of uncertainty and 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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.032 | 0.024 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 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".