EFL Teachers’ Cognition of Teaching English Pronunciation Techniques: A Mixed-Method Approach
Bibliographic record
Abstract
In recent years, a great number of attempts have been made on teachers’ cognition with the aim of understanding the complications reinforcing the teachers’ cognitions and their classroom practices. Such studies shed light on how teachers’ cognitions expand over time and how they are reflected in their classroom practices. The aim of the present study was to investigate Iranian EFL teachers’ cognition particularly in terms of the pronunciation techniques they apply in the oral communication classrooms and their knowledge about their language learners’ characteristics. To achieve the goals of the study, the cognitions of five English teachers in the oral communication classrooms were explored. The teachers were requested to answer two semi-structured interviews to obtain the data about their cognitions regarding the pronunciation techniques. Furthermore, their students were asked to fill out a questionnaire to express their opinions about the techniques applied by their teachers during instruction of English pronunciation. The qualitative and quantitative results showed that there was an intricate relationship between language teachers’ experience with their cognitions about their language learners. Moreover, those teachers who were in higher level language courses showed to have broader cognitions about both the techniques they used in classrooms and the language learners’ characteristics as well.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".