“COVID-19 Challenged Me to Re-Create My Teaching Entirely”: Adaptation Challenges of Four Novice EFL Teachers of Moving from ‘Face-to-Face’ To ‘Face-to-Screen’ Teaching
Bibliographic record
Abstract
Language teaching is noted to be a stressful profession at the best of times, but in 2020 it became even more difficult for all teachers because of the spread of COVID-19 pandemic worldwide. Teachers were required to switch suddenly to deliver their lessons on online platforms, with many having little or no prior training. This has certainly been the case for language teachers, language students and language schools because most language courses, initially designed for face-to-face instruction, were suddenly ‘forced’ to move to online platforms. This sudden move meant that language schools, language teachers and their students needed to adapt fast to a new virtual world that for many was an unknown teaching world. For language teachers the main challenge was how to adapt their courses and lessons to make them suitable for this new online delivery mode. This paper reports on the reflections of the adaptation challenges of four English as a foreign language (EFL) teachers at a prominent English language institution in Costa Rica, Central America, as they suddenly had to shift to online lesson delivery due to the COVID-19 pandemic.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.029 | 0.014 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".