Raising Phonological Awareness to Rectify the Misarticulation of Plosive Consonants Among Adult EFL Learners
Bibliographic record
Abstract
English as a Foreign Language (EFL) speakers often face difficulties producing certain English phonemes correctly when they live in a non-English-speaking country. The sounds /p/, /t/, and /k/ are among the most common articulation problems Saudi EFL learners have to encounter. In adult learners, the speech habits formed during the early years become "fossilized" and affect the acquisition of correct pronunciation. Therefore, recent research investigates the effectiveness of targeted phonological awareness training on improving these hard-to-articulate phonemes among adult Saudi EFL learners at the tertiary level. To address this issue, this study conducted a four-week experimental teaching session that involved improving phonological awareness with explicit teaching of the Voice Onset Time (VOT), and the precise place and manner of articulation of the sounds in question to twenty pre-medical Saudi EFL learners. This was enhanced through repetitive practice of the targeted phonemes, use of minimal pairs, and extended, reading aloud of passages. The results showed that participants were able to identify their pronunciation errors and articulate the target sounds in isolated words and in continuous speech. There was also an overall improved oral fluency of the test participants in their English production, possibly indicating that the phonological awareness approach could have wider benefits beyond the simple articulation of phonemes.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".