Incorporating Peer Response with Teacher Feedback in Teaching Writing to EFL Learners: A Literature Review
Bibliographic record
Abstract
Peer response has gradually gained popularity in teaching English writing to English as a Foreign Language (EFL) learners in recently years. A number of researches have proved its advantages while Chinese English teachers are still doubting its validity and possibility. This paper aims to prove that incorporating peer response and teacher feedback is advantageous to EFL learners’ English writing. It would start with the explanation of some key terms and then first examine the benefits of peer response to verify the necessity of introducing it to an English writing class for EFL learners. Then drawbacks of teacher feedback would be argued with the certification that it is corresponding to some advantages of peer response. Finally, the contribution of using peer response and teacher feedback together would be discussed to further attest the argument mentioned above. At the end of the paper, a conclusion will be made to generally summarize what has been discussed and how this relates to being a language professional. Hopefully, it could be informative and constructive for Chinese English teachers to take a closer step into the theoretical base of this teaching strategy which has been established by former researchers and seek for the possibility of its implementation in the real-setting classroom in EFL context.
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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 | 0.015 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".