Enhancing EFL Writing Through Online Peer Feedback: A Systematic Review of Higher Education Studies
Bibliographic record
Abstract
This systematic review critically examines the effectiveness of online peer feedback (OPF) for enhancing EFL writing skills in higher education. Analysis of 24 empirical studies reveals consistently positive impacts of OPF on writing outcomes, with effect sizes ranging from small to extremely large. Key principles for effective OPF implementation include adopting a formative approach, providing structured guidance, incorporating comprehensive training, and facilitating multiple revision opportunities. The review also highlights the benefits of asynchronous interactions and integrating diverse feedback sources. However, methodological limitations in many studies, such as small sample sizes and potential biases, necessitate a cautious interpretation of results. The findings underscore OPF's potential to transform EFL writing instruction while emphasizing the need for more rigorous, large-scale investigations. Future research should employ more stringent experimental designs, explore diverse OPF configurations, and examine the underlying mechanisms driving OPF effectiveness. This review contributes to the growing body of knowledge on technology-enhanced language learning, offering valuable insights for educators and researchers in the field of EFL writing instruction.
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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 | 0.019 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".