The Differential Effects of Focused and Comprehensive Corrective Feedback on Accuracy of Revision and New Writing
Bibliographic record
Abstract
This study compared the differential effects of focused and comprehensive written corrective feedback (WCF) on the accuracy of revision and new pieces of writing. It also examined whether the effects depended on feedback sub-types. Data were collected from 87 Grade 6 students in an intensive English program over seven sessions. Students were first divided into three groups: two treatment groups and one control group. The treatment groups were then divided into three subgroups based on three feedback sub-types (i.e., direct, indirect underline, and indirect metalinguistic cues). Students produced three pieces of writing, revised them, and produced new writings. Findings revealed that both focused and comprehensive feedback types improved learners’ accuracy of revision and subsequent writings, and focused feedback was more effective than comprehensive feedback. The effect of both feedback types, however, varied across writing sessions. Additionally, an interaction of comprehensive feedback and feedback subtypes was found on the accuracy of revision.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".