Students’ Perspectives as Providers and Receivers of Peer Formative Feedback on Writing
Bibliographic record
Abstract
The current study focused on exploring the benefits and challenges arising from engagement of 40 undergraduates in peer feedback in a writing course from the feedback providers’ and receivers’ perspectives. The data was collected from students through a survey and follow-up interviews at the end of two rounds of peer feedback: Round 1 (20 students acting as feedback providers while 20 as receivers) and Round 2 (reserving the roles of students). The findings revealed several benefits of peer feedback for both providers and receivers, which are synthesized under three major themes: being beneficial for students’ learning and writing, fostering students’ positive feelings and engaging students in reviewing and revising strategies. Despite its benefits, peer feedback is of several challenging concerns for providers and receivers. For providers, they are challenged by their lacking feeling of comfort and low self-confidence as well as lacking certainty about the quality of their feedback. For receivers, they are challenged by their doubts about the reliability of peer feedback and difficulty understanding some feedback as well as the poor quality of records of oral feedback. The study, therefore, provides important pedagogical implications for effective peer feedback practices in writing classrooms.
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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 | 0.028 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".