Saudi EFL Learners’ Attitudes with Regard to Using Online and Virtual Solutions for Learning English Accents during the Covid-19 Pandemic
Bibliographic record
Abstract
This study investigated Saudi EFL learners’ attitudes with regard to using virtual and online solutions for learning English accents. In addition, it investigated the participants’ ability to identify British and American accents after attending virtual lectures about English accents. The participants were 42 female and 39 male learners studying at an English Department and from different academic year levels. The quantitative research method was used by using pre- and post-questionnaires, and post-tests. All the data collection tools were presented in electronic form. The findings of the pre-questionnaire showed both male and female learners prefer the American accent. With regard to the post-questionnaire, both males and females had positive attitudes with regard to using online solutions for learning English accents. The findings related to the post-test showed that both male and female EFL learners were able to distinguish between English accents (both British and American). However, gender was not found to be a statistically significant variable when it came to the participants’ attitudes towards using online solutions for learning English accents. The results of this study led to some recommendations and suggestions aimed at EFL researchers.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".