The noticeability and effectiveness of corrective feedback in relation to target type
Bibliographic record
Abstract
This quasi-experimental study investigated the noticeability and effectiveness of three corrective feedback (CF) techniques (recasts, prompts and a combination of the two) delivered in the language classroom. The participants were four groups of high-beginner college level francophone learners of English as a second language (ESL) ( n = 99) and their teachers. Each teacher was assigned to a treatment condition that fit his CF style, but the researcher taught the controls. CF was provided to the learners in response to their production problems with the simple past and questions in the past. While the noticing of CF was assessed through immediate recall protocols, learning outcomes were measured by way of picture description and spot-the-differences tasks administered through a pre-test/post-test design. The results indicated that the noticeability of CF is dependent on the grammatical target it addresses (i.e. feedback on past tense errors was noticed more) and that the CF techniques that push learners to self-correct alone or in combination with target exemplars are more effective in bringing out the corrective intent of the feedback move. In relation to the learning outcomes, the past tense accuracy levels increased more than those for questions, but the differences between the two targets were not significant across 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 | 0.008 | 0.060 |
| 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 | 0.001 |
| 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".