Effects of Computer-Mediated Communication (CMC) Peer Review in an EFL Writing Course
Bibliographic record
Abstract
This study aims to investigate the effects of the two-step blended computer mediated communication (CMC) peer review process (Word commenting followed by Zoom discussion) in an English writing course for 29 Chinese EFL learners, and their perceptions of this mode. Compared with previous studies, the findings of this study are encouraging: 1) the proportions of revision-oriented comments students gave reached at a high level of above 85% of the total comments; 2) students gave more local comments, but the ratio of revision-oriented comments in global areas to revision-oriented comments in local areas was more balanced (approximately 40% vs 60%); 3) the adoption rates of revision-oriented comments in text revision were also at a high level (63%-73%). What’s more, most students held positive attitudes toward this mode, perceiving it useful in their text revision and development of writing ability. 65% of them expressed their willingness to attend this mode of review activities in the future. This study reveals that the two-step CMC peer review process with Word commenting followed by discussion via online video conferencing system can be used as a useful tool in EFL writing class. This study contributes to the current research on CMC peer review since most previous studies investigated effects of peer review using text-based CMC tools and little research has been done on speech-based tools.
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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 | 0.005 | 0.039 |
| 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 | 0.001 |
| 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".