The revision and transfer effects of direct and indirect comprehensive corrective feedback on ESL students’ writing
Bibliographic record
Abstract
This study investigated the short-term and delayed effects of comprehensive written corrective feedback (WCF) on L2 learners’ revision accuracy and new pieces of writing (i.e., the transfer effect of feedback). Three types of feedback were compared: direct feedback and two types of indirect feedback that differed in their degree of explicitness (i.e., underlining only and underlining+metalinguistic cues). Fifty-three intermediate level learners of English as a second language (ESL) were divided randomly into four groups: One direct, two indirect, and a control group. Students produced three pieces of writing from different picture prompts and revised them over a three-week period. Each group also produced a new piece of writing two weeks later. The study included seven sessions: Writing 1, revision of Writing 1, Writing 2, revision of Writing 2, Writing 3, revision of Writing 3, and Writing 4 (delayed writing). The results showed that all the three feedback groups significantly outperformed the control group in revision tasks. Some short-term accuracy improvements were also found on new pieces of writing for direct and underlining+metalinguistic feedback, but the effects were largely non-significant.
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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 | 0.036 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".