Peer Feedback in Thai EFL Writing: Students’ Perceptions, Accuracy, and Revisions
Bibliographic record
Abstract
While previous research has underscored the implications of peer feedback in general EFL contexts, there has been limited exploration of its specific implications within the context of Thailand. This study investigated the effectiveness of peer feedback in enhancing the writing skills and compositions of Thai students. It aimed to explore students’ perceptions of its efficacy, identify the specific writing elements addressed and integrated by feedback givers and receivers, and assess the accuracy of the feedback and revisions. The participants included 35 English major students from a government university in Thailand enrolled in the English Reading and Writing course. The research instruments comprised a questionnaire, students’ descriptive compositions, and interviews. The findings revealed unanimous agreement among students regarding the positive impact of peer feedback on their writing skills and quality, despite relatively lower levels of perceived confidence in both providing and receiving peer feedback. Coherence emerged as the primary focus of feedback, followed by other writing elements, collectively achieving a remarkable accuracy rate. Notably, despite coherence being the focus, students exhibited higher levels of integration for grammar, mechanics, and vocabulary in their subsequent drafts compared to coherence and unity. Discussions were included to provide insights into Thai students’ perceptions, feedback provision and integration, and pedagogical implications for addressing challenges of peer feedback within the Thai EFL writing context, thereby improving students’ writing proficiency and compositions.
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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 | 0.013 | 0.100 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".