Teachers’ Communities of Practice in Response to the COVID-19 Pandemic: Will Innovation in Teaching Practices Persist and Prosper?
Bibliographic record
Abstract
This study explores how teachers' communities of practice facilitated the transition to emergency remote teaching after school closures in South Korea during the coronavirus disease 2019 (COVID-19) pandemic. We also investigate whether and how teachers’ online education experiences in times of crisis continue to influence face-to-face teaching practices. We first conducted a literature review on teachers building communities of practice to cope with the pandemic and their execution of emergency remote teaching with collective professionalism. Five experienced teachers from elementary and middle schools participated in a semi-structured interview to share their experiences since the outbreak of COVID-19 until Spring 2022. We found that in response to significant pandemic-related challenges, teachers rapidly adapted to the digital educational environment, with assistance from their peers, through online and offline learning communities. Moreover, teachers’ online education experiences have contributed to innovative teaching practices with technological integration. We present the implications for teacher education research and practice in the post-COVID-19 era.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".