Research on the Effect of Peer Feedback Training in English Writing Teaching—A Case Study of Students in Business English Major
Bibliographic record
Abstract
Based on peer feedback, this paper further explores the application of peer feedback training to English writing teaching in China. From the theoretical perspective, compared with teacher feedback, peer feedback is beneficial to motivate students to take the initiative in learning, practice the student-centered concept, and promote cooperative learning among students. Peer feedback training, namely peer feedback under teacher intervention in this paper, combines the advantages of teacher feedback and peer feedback, which can not only be accepted by learners but also achieve significant pragmatic effects. From a practical point of view, peer feedback training can be applied to teach large groups of students, thus reducing the pressure and burden of teachers and improving the quality of peer feedback. The results also show that peer feedback training mainly works during the training stage rather than the modification stage. It is crucial to pay attention to the mechanism of peer feedback training and apply it to practice to promote the quality of English writing teaching.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.033 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".