Teachers’ Perceptions and Practices of Using L1 (Arabic) in EFL Classrooms at UTAS-Salalah
Bibliographic record
Abstract
Using the first language (L1) in teaching English as a foreign language (EFL) has been debatable for decades. Therefore, this study draws on prior research and investigates teachers’ perceptions and the use of Arabic (L1) in EFL classes in the Sultanate of Oman. A sample of 45 EFL teachers, chosen from the English Language Unit, Center for Preparatory Studies at the University of Technology and Applied Sciences (UTAS)-Salalah, answered a 5-point Likert scale questionnaire. It was distributed as a Google form to collect data. The dataset was analyzed statistically using SPSS (Version 26.0). The study findings showed that the EFL teachers at the English Language Unit have positive perceptions towards using L1 in EFL teaching, especially for translation purposes. Besides, a positive correlation value (r= .826**) was found between perceptions and practices of L1 in the EFL classes. In addition, the findings showed no significant differences between the groups, neither in perceptions of nor in practices of L1 in the EFL classroom (p.>.05). Based on the findings, the study provides some important recommendations and implications for EFL teachers in Omani EFL settings and can be useful in other similar contexts.
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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 | 0.003 | 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 | 0.000 |
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".