Attitudes and Opinions of Kuwaiti EFL Instructors about Online Distance Learning
Bibliographic record
Abstract
This study investigates the attitudes and opinions of Kuwaiti instructors of English as a Foreign Language (EFL) on the shift from face-to-face classes to online distance learning during the COVID-19 pandemic. A mixed-methods approach to data collection and analysis was utilised. A total of 70 EFL instructors participated in this study. Quantitative and qualitative data were collected using a questionnaire that contained closed- and open-ended items. Quantitative data were descriptively analysed using Microsoft Excel Software, and thematic analysis was used for the responses to the open-ended questions. Results revealed participants’ satisfaction with their online teaching experience, demonstrating positive attitudes towards online distance learning. Findings also revealed that several advantages characterised the shift to online distance learning. The unexpected transition also resulted in some drawbacks and difficulties. The study has pedagogical and practical implications for educational decision-makers and EFL instructors that must be considered. The findings contribute to the literature on EFL teachers’ attitudes and opinions on online distance learning by providing data from the Kuwaiti educational context.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".