Metalinguistic Awareness and Language Dominance: How Do Bilingual Saudi Graduate EFL Learners Use These in Learning?
Bibliographic record
Abstract
The present study addresses the research gap in studies on the role of metalinguistic awareness and language dominance amongst EFL learners in the Saudi context. It empirically contributes to the research context on metalinguistic awareness and its association to the phenomenon of language dominance in the language development of EFL learners. The study was conducted in the English Department of Hail University, Saudi Arabia. Using systematic random sampling set at 95% level of confidence with proper observance of the assumptions in inferential statistics, results revealed that most of the respondents were Bilingual English Dominant (BED). Moreover, there is a highly favorable level of metalinguistic awareness on learning L2, knowledge and regulation categories were registered among the respondents. In like manner, the respondents reported a medium level of language dominance on use and exposure which is a sign of their departure from the full dominance of their L1 into mastering English language. Further, test of correlation showed a high direct and positive interaction between metalinguistic awareness and language dominance (r= .923) indicating that the higher the level of metalinguistic awareness the more the students manifest a favorable adherence to their dominant language. Findings positively contribute on how curriculum and language experts may adopt English language teaching strategies to prepare Saudi EFL learners become proficient speakers to participate in the global market and set them firmly on their career path.
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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 | 0.000 |
| 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) | 0.002 | 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".