Bibliographic record
Abstract
This quasi-experimental study investigated the potential benefits of two corrective feedback techniques (recasts and prompts) for learners of different proficiency levels. Sixty-four students in three intact grade 6 intensive English as a second language classes in the Montreal area were assigned to the two experimental conditions—one received corrective feedback in the form of recasts and the other in the form of prompts—and a control group. The instructional intervention, which was spread over a period of 4 weeks, targeted third-person possessive determiners his and her, a difficult aspect of English grammar for these Francophone learners of English. Participants' knowledge of the target structure was tested immediately before the experimental intervention, once immediately after it ended, and again 4 weeks later through written and oral tasks. All three groups benefited from the instructional intervention, with both experimental groups benefiting the most. Results also indicated that, overall, prompts were more effective than recasts and that the effectiveness of recasts depended on the learners' proficiency. In particular, high-proficiency learners benefited equally from both prompts and recasts, whereas low-proficiency learners benefited significantly more from prompts than recasts.This study is based on the first author's Ph.D. research (Ammar, 2003). We gratefully acknowledge the cooperation of the participating teachers and students. We thank Patsy Lightbown, Roy Lyster, Pavel Trofimovich, and the anonymous SSLA reviewers for their valuable input and feedback on earlier versions of this paper.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".