The Effect of Oral Corrective Feedback on Article Errors in L3 English: Prompts vs. Recasts
Bibliographic record
Abstract
This study examines the effect of prompts and recasts in providing CF for the article errors by Kurdish-Arabic bilinguals who learn English as a third language. 39 lower-intermediate Kurdish-Arabic bilingual learners of English were tested on three tests: pre-, post-, and delayed post-tests. The participants were randomly put into three groups: (1) prompt group (n =15), (2) recast group (n = 14), and (3) no feedback group (n = 10). Each group completed 28 dialogues, which included articles in a Forced Choice Elicitation Task (FCET) as a pre-test. The same test was given to the three groups as post- and delayed post-tests. Between the pre-test and the post-test, the prompt and recast groups took a treatment which involved an interactional activity that aimed the FCET, in which the former took CF in the form of prompts, and the latter took it as recasts for their article errors in L3 English.Results showed that all groups were the same in the pre-test. In addition, both the prompt and recast groups were similar in post-test but were significantly better than the group which did not receive any feedback. In delayed post-test, the prompt group significantly outperformed the other two groups. These findings suggest that prompts are more effective than recasts in providing oral feedback over the long term. The error analysis, on the other hand, revealed that among the four contexts of articles, all students had the highest error rate in the [-def, +spec] context in both pre- and post-tests. These were substitution errors rather than omission errors, which shows that the students fluctuated between definiteness and specificity settings. In delayed post-test, the prompt group significantly made fewer errors than the other two groups.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".