Preferences of ELT Learners in the Correction of Oral Vocabulary and Pronunciation Errors
Bibliographic record
Abstract
Vocabulary is an essential component of language teaching and learning process, and correct pronunciation of lexical items is an ultimate goal for language instructors in ELT programs. Apart from how lexical items should be taught, the way teachers correct oral vocabulary errors as well as those of pronunciation in line with the preferences of learners is a crucial issue on which a consensus should be reached. This present study aimed to explore the preferences of ELT learners in a Turkish university on the correction of oral vocabulary and pronunciation errors by their instructors. The data were gathered from 213 ELT students through a five-point Likert scale, the items of which were derived from the answers given by the students to the open-ended questions regarding the research question. The findings in our study reveal that instructors teaching various field courses including the skill courses in freshman level in ELT departments need to be sensitive towards the preferences of learners in the correction of oral vocabulary and pronunciation errors, and they should explore how the learners would prefer their errors to be corrected as this can enable them to treat such errors more effectively and facilitate the learning process.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".