The Effect of Written Corrective Feedback on the Acquisition of Different Types of Linguistic Features
Bibliographic record
Abstract
The current study investigated whether there exists a differential effect of direct and indirect corrective feedback (CF) on the acquisition of rule-based features (simple present) and item-based features (prepositions). Fifty students enrolled in an EFL writing class were divided into four groups. Each group received one of the following treatments: direct CF on simple present, indirect CF on simple present, direct CF on prepositions, or indirect CF on prepositions over three sessions. In this pretest/immediate posttest/delayed posttest design, students received written CF, revised writing tasks, and completed new tasks and tests. Results showed that simple present, a type of rule-based feature, responded better to indirect CF while prepositions, a type of item-based feature, responded better to direct CF. The findings suggest that teachers should consider addressing different types of linguistic features through different types of CF.  
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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 | 0.001 | 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 | 0.000 | 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) | 0.001 | 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 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".