How Iranian Instructors Teach L2 Pragmatics in Their Classroom Practices? A Mixed-Methods Approach
Bibliographic record
Abstract
This study examined the teaching approaches and techniques that Iranian instructors utilize for teaching L2 pragmatics in their classroom practices. 238 Iranian instructors participated in this study. The data for this study were accumulated through questionnaire and semi-structured interviews. In terms of the instructional approaches, both the quantitative and qualitative results showed that instructors make use of inductive and implicit approaches more than other two approaches, deductive and explicit, to teach L2 pragmatics .With regard to the pragmatic consciousness-raising techniques, the results revealed that instructors mostly make use of conversation topics and also situations to raise learners’ awareness of the speech act under study. In addition to this technique, instructors make use of field experience to give input to learners. Regarding the pragmatic communicative practice techniques, the quantitative and qualitative results showed that instructors mostly make use of role-play and pair- work techniques to engage learners to practice speech acts. Moreover, the results of the questionnaire and interview with regard to pragmatic corrective feedback techniques showed that instructors almost give feedback implicitly by reformulating learners’ mistakes and repeating their errors On the contrary, instructors give less metalinguistic information and explain the inappropriate expressions to learners. In terms of culture teaching techniques, the results illustrated that instructors share their knowledge of what they hear or read about other cultures with their learners. The results of this study have some implications for language instructors.
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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 | 0.014 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".