Shaping written corrective feedback perspectives and practices: comparing novice and experienced instructors of English for academic purposes in Bangladesh
Bibliographic record
Abstract
This qualitative study examined the key influences on the beliefs and approaches concerning written corrective feedback (WCF) of teachers of English for academic purposes (EAP) in Bangladesh. It fills a significant research gap concerning how Bangladeshi EAP teachers—particularly across experience levels—perceive and practice WCF, especially in relation to their understanding of writing instruction within a diverse and resource-limited higher education context. Through six interviews with experienced and novice teachers, the study explored how teacher experience affects perspectives and practices related to WCF. The findings suggest that teaching experience significantly affects beliefs about WCF because experienced teachers benefit from professional development and extensive professional networks. Furthermore, teacher education programmes and professional development opportunities do not benefit all teachers equally; novices have fewer chances, which affects their development differently from their more experienced peers. Experienced teachers—initially exposed to direct grammar-focused WCF as students—have evolved to prefer providing indirect feedback that targets broader aspects, such as content and organisation. Instead, novice teachers focus on providing direct feedback on sentence-level issues, such as grammar and spelling. Finally, it emphasises that context-aware training, institutional support, and resources are key to improving WCF and academic writing in Bangladeshi EAP programmes, with broader relevance for equitable language teaching in similar low-resource settings.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".