Web-Enhanced Peer Feedback in ESL Writing Classrooms A Literature Review
Bibliographic record
Abstract
This paper aims to review literature on the impact of using web-based technology such as blogs and social networks to facilitate and promote peer feedback in ESL writing classrooms. It also investigates how giving and receiving comments from peer students can improve students’ performance in writing as well as their critical thinking skills. A combination of 47 peer reviewed studies were included in this review. All these studies were found on MUN online library and the selection criteria I used in searching was studies that are relevant to: ESL writing, the importance of peer feedback and the role that web-based technology can do to facilitate peer feedback in ESL writing classrooms. The results showed that reflective assessment of peers’ writing helps students develop their peers’ and their own writing performance. They also stressed the role of web-based technology in providing a stimulating environment for students to reflect on peers’ written work. However, some studies revealed the challenges that might affect using this technology such as students’ reluctance, fear of sharing writing online and their sensitivity to being criticized publicly.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".