The Role of Oral Corrective Feedback Types in the Acquisition of the Grammatical Structures
Bibliographic record
Abstract
This study investigated whether the effects of different types of corrective feedback (CF) (simple clarification request, enhanced prompt and elliptical elicitation) would differ on the acquisition of different types of grammatical structures. The target grammatical structures were verb endings (morphological morphemes) in three different English tenses including the simple present third person singular “-s”, the present continuous verb formation marker “-ing”, and the simple past verb ending” -ed”. These targets were chosen because they are rather problematic for EFL learners to acquire. For this purpose, 31 L1 Persian EFL learners at intermediate level were given an opportunity to carry out some tasks and were provided with different types of CF on their erroneous utterances. Data analysis on the output accuracy following feedback on the three grammatical targets showed that the proportion of errors corrected in response to CF in the form of enhanced prompt was more than the proportion of errors corrected in response to the other two types of CF. These results suggest that the more explicit the CF, the more effective it would be in correcting language learners’ erroneous utterances regardless of the type of given grammatical structure.
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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 | 0.004 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".