Editorial: Covid19 EFL/ESL/English Teaching Environment and Digital EFL/ESL/English Learning Methods
Bibliographic record
Abstract
As a corollary to this, new trends in the field of English language teaching/learning are likely to surface, develop, and find application in the academic settings. The current global pandemic of Covid19 has caused a massive shift in teaching paradigms with previous methodologies being turned upside down due to the mass migration of education to online medium. One area that has been greatly affected is the entire ESL industry, from schools in Asia to universities in Australia, the UK and the US. No form of English teaching school has been unaffected by this global catastrophe. As a result, new methodologies especially useful for the online teaching environment must be effectively made use of. Whereas online education was formerly looked down upon in some countries, it has now become the norm. Nevertheless there are many educators who are ill-equipped to deal with this change, some students are also demotivated due to the online system of education. This special issue will focus on the Covid19 and post-Covid19 EFL/ESL/English teaching environment and digital EFL/ESL/English learning methods with the aim of creating a ready-for-reference corpus, especially for teachers with little or no formal training in online EFL/ESL/English teaching.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.009 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".