From emotion to action: investigating the role of affective rhetorical moves in peer feedback implementation in university classrooms
Bibliographic record
Abstract
Peer feedback is often used to support peer learning, but implementing feedback has been challenging for students. However, the complexity of affective features within one feedback and their impact on peer feedback implementation remain underexplored. From the perspective of the rhetorical structure theory (RST), this study aims to investigate the affective rhetorical moves of peer feedback and its role in feedback implementation. A total of 69 fourth-year undergraduates from Singapore participated in computer-supported peer feedback activities. The sequence mining technique was used to examine the affective rhetorical moves of implemented versus unimplemented peer feedback. Neutral state was found more in implemented peer feedback while unimplemented feedback contained continuously positive emotions. Semi-structured interview further reveals how students understood the different affective rhetorical moves and made their implementation decisions. This study highlights the importance of strategic construction of feedback with various affective rhetorical moves, providing insights for instruction and designs of peer feedback activities.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".