The Relationship between English Language Learners’ Perceptions towards Classroom Oral Error Corrections and Their Pronunciation Accuracy
Bibliographic record
Abstract
Inevitably, language learners make mistakes, and teachers correct them. It is, also, crystal clear that language learners have different attitudes towards error and error correction strategies. Needless to say, language teachers’ awareness of language learners’ perceptions towards error and error correction strategies can heighten the quality and the quantity of language teaching and learning process. This study based on the findings of a questionnaire and a test given to 82 male and female English language learners in Iran Language Institute (ILI) investigates: 1) whether ILI English language learners have positive or negative attitudes towards classroom oral error corrections; 2) whether there is a relationship between ILI English language learners’ perceptions towards classroom oral error corrections and their pronunciation accuracy; 3) if there is a relationship between ILI learners’ gender and their attitudes towards classroom oral error corrections. The findings of this study show that ILI English language learners have absolutely positive attitudes towards classroom oral error corrections, which means they want to be corrected. The findings, also, show that there is not any significant relationship between ILI English language learners’ perceptions towards classroom oral error corrections and their pronunciation accuracy. The findings, also, show that there is not any significant relationship between ILI English language learners’ perceptions towards classroom oral error corrections and their gender.
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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 | 0.008 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".