Promoting Peer Feedback in Developing Students’ English Writing Ability in L2 Writing Class
Bibliographic record
Abstract
The study aimed at investigating the effects of peer feedback on students’ English writing ability in L2 writing class. A mixed-methods research, an embedded experimental design was employed, making use of a writing pretest and post-test and self-written reflection in the experiment. Data were quantitatively analyzed through a dependent simple t-test, and content was thematically analyzed for qualitative data. The participants were 21 undergraduate students majoring in English in the three southernmost border provinces of Thailand. The findings revealed that from the mean scores of the pretest and post-test, the students had made significant progress in their writing ability. Additionally, the effect size was calculated at 1.97, which means that its magnitude was “large”. Moreover, students reflected that peer feedback was a worthwhile experience for social interaction, and provided them with perceiving the writing process, developing affective strategies, supporting critical thinking skills, and developing socially and intellectually by means of working collaboratively. In addition, it helped them practice to become more autonomous learners. As a result, peer feedback should be implemented in L2 writing classes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".