Exploring the Perspectives of EFL Instructors toward the Employment of L1 in EFL Reading Classes
Bibliographic record
Abstract
In recent years, numerous studies have been concerned with evaluating the effectiveness of using L1 in EFL contexts. Nevertheless, there is no consensus so far on the usefulness of the use of L1 in EFL contexts. More specifically, there is a wide disagreement between scholars and researchers regarding the use of Arabic as an L1 in EFL contexts given the significant linguistic differences between Arabic and English. In light of this argument, this study is concerned with evaluating the effectiveness and usefulness of the use of L1 in EFL classes in the Saudi universities of the instructors’ perspective. Semi- structured interviews were conducted with twelve EFL instructors in four Saudi universities. Results indicated that the majority of the participants indicated that the integration of L1 in EFL classes can be usefully used to achieve a proper classroom discipline and keep the structure of the classroom activities in a way that makes the realization of the target outcomes possible. They also stressed that L1 can be used to help EFL learners develop their English language skills. They also indicated that the use of Arabic in EFL classes helps establish a good relationship with the instructor and reduces students’ stress and anxiety. It can be finally concluded that the use of Arabic serves as a useful teaching and learning tool in EFL 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 | 0.010 | 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 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".