Optimizing Pronunciation Instruction for Vietnamese Learners of English: Applying Optimality Theory and Phonetic Principles in Language Teaching
Bibliographic record
Abstract
This study explores the perceived benefits of using phonetic principles in teaching English pronunciation from both teacher and student perspectives. The research examines how incorporating phonetic instruction and Optimality Theory can improve pronunciation accuracy and phonological awareness. Using a mixed-methods approach, the study collected quantitative data through surveys from 57 participants (6 teachers and 51 students) and qualitative insights from focus group interviews with teachers. The results showed that teachers rated the benefits of phonetic instruction higher (mean score of 4.24) compared to students (mean score of 3.31). Teachers highlighted how phonetic principles offer a systematic way to identify and correct pronunciation errors, making teaching more efficient. Students, while recognizing the advantages, faced challenges in grasping some technical aspects, such as using phonetic symbols. However, they acknowledged that their pronunciation and listening skills improved with practice. The study concludes that while phonetic instruction is valuable, more practical and accessible approaches are needed to better support students' learning. These findings provide useful insights for educators looking to enhance pronunciation teaching and suggest areas for further exploration.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".