In-service Teachers’ Intelligibility and Pronunciation Adjustment Strategies in English Language Classrooms
Bibliographic record
Abstract
A realistic goal of pronunciation teaching in the second language context is to acquire comfortably intelligible rather than native-like pronunciation. To establish a set of teaching and learning priorities necessary for English teachers and students whose first language is Chinese, the purposes of this study are three fold: (1) Identify the pronunciation aspects that are crucial for intelligible pronunciation in actual second language (L2) Hong Kong (HK) and foreign language mainland (ML) China classrooms from in-service teachers’ points of view; (2) Investigate how teachers help their students successfully understand English classroom input through teachers’ self-reflection on which aspects of their own pronunciation they modify and adapt to make classroom discourse intelligible to students; and (3) explore the most frequently taught pronunciation aspects and the most frequently used pronunciation teaching strategies used by teachers to teach pronunciation in English classrooms. Forty-seven questionnaires were collected and analysed from in-service teachers in primary schools. Four teachers were invited to attend follow-up interviews. In order to further investigate the application of adaptation strategies and pronunciation teaching strategies in real classroom settings, eight classroom videos were collected. The data were triangulated allowing for cross checking. The findings will not only help frontline teachers become self-aware of their own pronunciation, rectify students’ recurrent difficulties in using phonological features, and improve mutual intelligibility in English language classrooms but also help explore the ways to integrate phonology courses and pronunciation teaching in second/foreign language teaching and teacher education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".