A Descriptive Overview of Pronunciation Instruction in Iranian High Schools
Bibliographic record
Abstract
The emergence of English as an international language has undoubtedly influenced the way the language is taught all around the world. Among all the skills and components of English, pronunciation has perhaps been the one most highly affected by this trend. In a country, such as Iran, where learners come from a variety of dialectical backgrounds, English is taught using the same national syllabus and textbooks in all parts of the country. Hence, an investigation into the most prevalent approaches to pronunciation instruction can shed light on the techniques employed by teachers to overcome the difficulties brought about by linguistic diversity. The present study seeks to fulfill this aim by developing and administering a questionnaire among 130 teachers in the Iranian public education system, asking them about the most common approaches and techniques they use for teaching pronunciation in their classrooms. An exploratory factor analysis of the responses revealed that four major sets of techniques were commonly employed by the teachers surveyed in this study. Comparisons drawn between the participants revealed some important differences based on the teachers’ age, gender, years of experience and educational background. These differences are discussed in light of the multilingual context of education in Iran.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".