The Effect of Explicit Pronunciation Instruction on Undergraduate EFL Learners' Vowel Perception
Bibliographic record
Abstract
Since English pronunciation errors are often caused by the transfer of the Persian language sound system, the present study investigated the effect of explicit pronunciation instruction on undergraduate English as a Foreign Language (EFL) learners’ vowel perception enhancement. The nonequivalent group, pretest-posttest design was employed to study two classes of English literature and English teaching students at Kosar University of Bojnord (KUB) as the experimental group (EG) and control group (CG) respectively. A 40-item minimal pair test was developed based on the 3rd edition of the book Ship or Sheep written by Baker (2006). The reliability of the test was estimated 0.75 through KR-21 formula. After the pretest administration, both groups were exposed to the same activities; however, only the EG received the treatment regarding explicit pronunciation instruction. At the end of an eight-week training program, the pretest was used as the posttest. The results of the independent samples t-test from the posttest revealed that the EG had a better performance than the CG suggesting that EFL learners’ vowel perception can improve if they are explicitly made aware of their pronunciation errors.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".