Assessing peer review pattern and the effect of face-to-face and mobile-mediated modes on students’ academic writing development
Bibliographic record
Abstract
Abstract This study examines the distribution of peer review in face-to-face and mobile-mediated peer review groups and their effects on students’ revision skills and academic writing development. Seventy-two first-year English for academic purposes (EAP) students participated in an 18-session IELTS academic writing course in a Canadian university the mobile-mediated peer review group (MMPR) used Telegram to exchange peer comments synchronously, while the face-to-face peer review group (FFPR) did peer review in the classroom. An adapted analytic scheme (Journal of English for Academic Purposes, 2, 193–227, 2003) and the IELTS academic writing assessment criteria were used to conceptualize the peer comments in terms of frequency, area, type, nature, and IELTS assessment categories. Results indicated that the total number of comments, the percentage of revision-oriented comments and actual revisions made by the MMPR group were statistically more significant than those by the FFPR group. Furthermore, the MMPR group made more local revision-oriented comments than that of FFPR. However, the revision-oriented suggestion in local areas was the most distributed type of comment made by both groups. Regarding the IELTS assessment criteria, the FFPR group made more comments on task achievement and coherence and cohesion, whereas the comments made by the MMPR group targeted more lexical resources, and grammatical range and accuracy. In addition, the results showed that both MMPR and FFPR groups developed their IELTS academic writing skills while the MMPR mode of collaboration outperformed the FFPR.
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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 | 0.015 | 0.154 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".