Bibliographic record
Abstract
Peer feedback plays a pivotal role in stimulating students’ participation in L2 writing, which has the potential to develop students’ writing skills. The concept of metacognition has also been examined to facilitate learner writers in their learning process. As such, this study drawing upon the concept of metacognition explores the implementation of peer feedback in English as a foreign language (EFL) tertiary writing classes in Vietnam and based on the findings develops a peer feedback approach to enhance the learners’ metacognition. Data were collected from semi-structured interviews with sixteen English majors and classroom observations in two English writing classes at a university in Vietnam. Content analysis of the data revealed that peer feedback was informally implemented in two EFL writing classes under study, which might suggest that few opportunities for the students to develop their metacognition could be provided in this current feedback approach. The findings also demonstrated the learners’ expectations for changes in peer feedback practice in their writing classes. Thus, the study suggested a jigsaw peer feedback approach which met the participants’ desires and simultaneously afforded the learners a number of opportunities to improve their metacognition in EFL writing contexts, especially in Vietnam. This study helps to extend the literature in peer feedback approach in L2 writing which is underpinned by the concept of metacognition and offers both pedagogical and theoretical implications in English language teaching (ELT).
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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 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".