A Study on Middle School English Teachers’ Corrective Feedback in Different Instructions
Bibliographic record
Abstract
Teachers’ corrective feedback has been the focus for some time in SLA. The study, based on the framework of teaching focus, corrective feedback and learner uptake by these researchers, explores how teachers’ corrective feedback is related to focus on instruction. The research method is a corpus-based approach, which relies on computer and corpus tool—Antconc 3.2.0w and Repetition Tool. The findings show that (a) MF Instru. invites the most CFSs, followed by FM (b)When teachers correct students’ errors, they pay much more attention to form-focused errors (FF errors) than to meaning-focused errors (MF errors); grammatical errors attract the most attention whichever the instruction it is; in MF Instru. and FM (c) In general, the majority of feedback type after FF errors (phonological, grammatical and lexical errors) is recast, whereas the majority of feedback type after MF errors is Negotia.C; as it is related to instruction types, in FF Instru., teachers prefer to use Negotia.C to follow phonological and lexical errors, and recast to follow grammatical errors; in MF Instru., teachers prefer to use recast to follow FF errors (phonological, grammatical and lexical errors); in FM (d) Negotia.C invites the most learner repair, followed by Expli.C and recast respectively; As it is related to instruction types, Negotia.C brings about the highest repair rate, and recast leads students to produce the lowest rate of repair in FF Instru., MF Instru. and F& M Instru. as well.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".