I Want but I Can’t: The Dilemma of EFL Learners in Practising English in the Saudi Context
Bibliographic record
Abstract
The current literature suggests that learners in foreign English language learning contexts may suffer from the lack of opportunities to practise English. Language practice is regarded as fundamental for mastery of English. This study aimed to investigate Saudi EFL learners’ English language use outside the domain of the classroom. A mixed-method approach was adopted for data collection by employing an online survey with an open-ended section and conducting semi-structured interviews. The study participants were male and female students undertaking BA English programs at Saudi universities across the main five regions in the country. The study adopted a convenience sampling technique, and a total of 627 students responded to the survey (M = 291; F = 336), and eight students were interviewed (M = 5; F = 3). The data collection process went through two phases to obtain deeper insights into the investigated phenomenon: first, administering the online survey and conducting the analysis to extract the main themes of inquiry; and second, using these themes as a guide for conducting the interviews. The study’s main findings include learners finding themselves in a dilemma where they desire more practice of English outside the domain of the classroom and, at the same time, lack proper opportunities for practice. Implications of the study are also discussed.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".