Student Writers’ Affective Engagement with Grammar-Centred Written Corrective Feedback: The Impact of (Mis)Aligned Practices and Perceptions
Bibliographic record
Abstract
This project firstly explored Iranian English as a foreign language (EFL) students’ perceptions about written corrective feedback (WCF)-related practices and preferences. Secondly, the student participants’ first language (L1; e.g., Farsi) learner identities were operationalized, especially focusing on the skill of writing, WCF, and grammar-centred WCF. Thirdly, the students’ affective engagement with WCF was scrutinized, particularly in light of L1 student identities. The participants in the study were 15 students in an Iranian EFL context. Analysis of interview data revealed that the skill of writing was held in low regard by the students. Also, several discrepancies emerged vis-à-vis WCF methods (e.g., direct vs. coded), error correctors (e.g., teacher feedback vs. peer feedback), the amount of correction (e.g., selective vs. comprehensive correction), and the relative importance of different components of writing (e.g., grammar vs. content vs. organization). In particular, the findings showed that the students’ L1 identities involved low regard for writing, but high regard for speaking skills, and that they attached high value to grammatical accuracy and teacher explicit feedback. Finally, the findings indicated that: (a) the students’ second language (L2) identities (e.g., WCF-related preferences) were profoundly affected by their L1 student identities, and (b) the discrepancies between the students’ L2 writing preferences (e.g., preferred amount of WCF) and the teachers’ reported practices could potentially hinder students’ affective engagement with WCF.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 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".