Learners’ Interpersonal Beliefs and Generated Feedback in an Online Role-Playing Peer- Feedback Activity: An Exploratory Study
Bibliographic record
Abstract
Peer feedback affords interaction and critical thinking opportunities for learners in online courses. However, various factors prevent learners from taking advantage of these promising benefits. This study explored learners’ perceptions of the interpersonal factors in a role-playing peer-feedback activity, and examined the types of peer feedback that learners generated when playing a role. Participants were 16 graduate students engaged in an online role-playing peer-feedback activity. The results from survey responses revealed learners’ positive interpersonal beliefs, including psychological safety and trust, toward the role-playing peer-feedback activity. In addition, more than sixty percent of the participants reported being more comfortable critiquing peers’ work when playing a role. The content analysis of the peer-feedback entries indicated that learners were able to generate highly constructive feedback entries. In addition to adding supportive comments, those feedback entries identified problems, asked questions, and provided suggestions. The results show that role-play strategy has great potential to enhance learners’ interpersonal beliefs in peer-feedback activity and their feedback quality.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".