Exploring When and Why to Use Arabic in the Saudi Arabian EFL Classroom: Viewing L1 Use as Eclectic Technique
Bibliographic record
Abstract
This study aims to investigate when and why to use Arabic as L1 in the Saudi Arabian EFL classroom. For this purpose, 45 classroom observations were performed for beginning, intermediate, and advanced levels of students. 5 classes were chosen randomly for each level and each class was observed three times. Based on the classroom observations, structured interviews were conducted with 94 students as well as 15 teachers. Analysis of the data shows that Arabic can be used as eclectic technique in certain instances regardless of what teaching method is employed. For example, teachers sometimes used it as long as they talk in English for a long time so as to avoid as grammatical mistakes as possible. This reflects the teachers’ cultural norm, namely, it is shameful to commit mistakes in front of the students. In addition, it is apparent that learners follow certain language strategies such as literal translation and substitution. Despite the use of these strategies, L2 speech produced by some learners is sometimes difficult to understand because of their bad command in English particularly at the beginning and intermediate level. Thus, the teachers or the learners resort to use Arabic forms or translation as a way to explain what wanted to be conveyed in English. Besides, Arabic is used when students are not able to express difficult L2 constructions at time they could not be disallowed to use Arabic counterparts as they are dynamic individuals. On the contrary, some advanced students insist to use specific Arabic concepts although they can translate them into English because, as they believed, such concepts miss their cultural and religious value if translated.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".