Attrition of Oral Communicative Ability among Saudi EFL Graduates A Study in Qassim University
Bibliographic record
Abstract
After graduation, many Saudi EFL graduates find themselves losing their oral communicative competence, which could be attributed to the period of their English non-use. To explore such a pressing issue, the following study investigates the effect of English non-use on a surveyed sample of Saudi EFL graduates, and how such disuse correlates with the attrition of their English speaking and understanding abilities. The study also sheds light on the most used language maintenance strategies among the subjects. Situated in Qassim University, the study surveyed 101 female and male Saudi EFL graduates majoring in English fields, using a structured questionnaire design. The quantitative results suggest that the longer the period of language disuse, the more likely the attrition of oral communicative abilities of Saudi EFL graduates will occur. They also suggest that EFL Saudi graduates are more likely to lose their understanding abilities of English than they are to lose their speaking abilities. Finally, it was found that Saudi EFL graduates engage in a variety of language maintenance techniques, such as watching movies and conversing in English with both native and non-native speakers. The study urges employers and decision-makers to help Saudi EFL graduates in making the best use of their abilities as soon as they graduate, as they are more likely to be vulnerable to attrition as the years pass.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".