The Use of Persian in the EFL Classroom–The Case of English Teaching and Learning at Pre-university Level in Iran
Bibliographic record
Abstract
Inspired by the rise of Communicative Language Teaching, some scholars have vehemently rejected any use of L1 in L2 learning classes (e.g., Atkinson, 1987) while others have advocated the use of L1 as an efficient tool to facilitate communication (e.g., Nation, 2003). However, caution has been raised against the excessive use of L1 (Nation, 2001). This study was conducted to observe classroom dynamics in terms of the quantity of use of L1 in two randomly-selected pre-university English classes in Ahvaz, Iran. The objective was to seek both students and teachers’ perceptions and attitudes towards the use of L1 in L2 classes. The classes were observed and video-taped for 6 sessions and the teachers and four high-achieving/low-achieving students were interviewed. The findings showed that an excessive use of Persian could have a de-motivating effect on students. Hence, the interviewed students voiced dissatisfaction with the untimely use and domination of L1 in L2 classes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".