ELT During Lockdown: A New Frontier in Online Learning in the Saudi Context
Bibliographic record
Abstract
This study explores pedagogical challenges pertaining to the new online English Language Teaching (ELT) practices that emerged due to the covid-19 pandemic outbreak and the subsequent worldwide lockdown. Based on an explanatory sequential, mixed methods, descriptive research design, quantitative and qualitative data from 265 English as a Foreign Language (EFL) teachers in the Saudi context were collected by utilising a custom designed, twenty-two items on a psychometric five-point Likert items, open-ended questions, and a questionnaire. The quantitative data were statistically analysed using SPSS whereas the qualitative textual data were analysed employing the grounded theory. The findings of the study indicate that EFL teachers regard full scale online teaching as a challenging endeavour; however, a valuable and indispensable tool in language teaching, especially, during the outbreak of covid-19 pandemic. Furthermore, most of the participants prefer to receive more professional development opportunities based on real life teaching experiences in online ELT. The study presents suggestions and recommendations for further research.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".