Resonating Voices: Unpacking EFL Teachers’ Beliefs Regarding Pronunciation Instruction in Chinese Tertiary Context
Bibliographic record
Abstract
This study investigates the beliefs of three English as a Foreign Language (EFL) teachers regarding pronunciation instruction at a university in Northwest China, focusing on an area that remains under-explored in Chinese contexts. Data was collected using semi-structured interviews and narratives. The findings demonstrated that, despite recognizing the significance of pronunciation in language learning, the teachers’ approach to pronunciation instruction was often unsystematic and reactive, primarily addressing segmental errors through corrective feedback. In addition, the teachers’ trajectories of pronunciation beliefs were shaped by several factors, including their own educational backgrounds, the constraints of the curriculum, the perceived needs of their students. Furthermore, insufficient professional development opportunities led to the undervaluation of pronunciation in their teaching practices. These findings underscore the necessity for tailored teacher education programs that provide a range of effective strategies for pronunciation teaching. By offering systematic and comprehensive training, such programs could help close the gap between teachers’ beliefs and practices, thereby enhancing the overall quality of pronunciation instruction in EFL classrooms.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".