Online Corrective Feedback and Self-Regulated Writing: Exploring Student Perceptions and Challenges in Higher Education
Bibliographic record
Abstract
This study investigates the influence of teachers' online written corrective feedback (WCF) on the self-regulated writing abilities of university students, with a particular focus on varying levels of English proficiency. Conducted during the COVID-19 pandemic, the qualitative study involved ten second-year students from a private university in Bangladesh enrolled in a mandatory online writing course. Participants received personalized WCF through platforms such as Google Docs. Data derived from semi-structured interviews revealed that online WCF substantially enhanced students' self-regulation in writing, with the impact most pronounced among those with moderate and lower levels of English proficiency. The findings underscore the role of tailored feedback in improving students' writing skills while fostering essential self-regulatory practices such as goal setting, self-monitoring, and independent learning. These results highlight the transformative potential of online WCF in addressing students’ individualized needs and improving their academic writing performance. Implications for curriculum designers and policymakers emphasize the integration of effective online feedback strategies to support learners across diverse proficiency levels. However, the study acknowledges limitations, including its small, context-specific sample, which may limit generalizability to broader educational settings. Future research may examine the longitudinal impact of online WCF across varied contexts and language proficiency levels.
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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 | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".