Bibliographic record
Abstract
For students to learn how to evaluate the quality of their own work, they need to be able to make sense of different sources of feedback. Peer assessment provides a useful opportunity for students to develop these feedback strategies through learning from the production and reception of peer feedback. One powerful way that students learn through peer assessment is to gain an appreciation of different peer perspectives. This experimental study uses multiple methods to investigate how perspective-taking affects peer assessment judgement and peer scoring. As past research suggests that similar experiences influence the ease of perspective-taking, similar past answering experiences of peer assessors are operationalized to examine perspective-taking in this study. Participants were randomly assigned to two experimental groups; each group answered a different question. Participants then used a rubric to rate four peer answers, two from the question they had answered, and two from the other group’s question. While peer scoring, a think-aloud interview and a reflection interview was conducted. Quantitative findings showed that there were no statistically significant differences in peer assessment scores between experimental groups. However, there were some descriptive differences between experimental groups on peer scoring patterns. Groups with similar past answering experience showed more central tendency in their peer scoring. Qualitative findings showed that perspective-taking during peer assessment was happening in several ways. Peer assessors were considering peer perspectives (other information), as well as their own perspectives (self-information). The reflection interviews revealed that similar past experience enhanced an appreciation for different perspectives. The quantitative and qualitative results were integrated in two ways. The integration of think-aloud interviews with peer assessment scores suggests that peer scoring patterns, whether lenient or severe, showed similar forms of perspective-taking, and that perspective-taking could be used to frame both positive appraisals and negative appraisals of peer assessment judgements. The integration of the reflection interviews with the correspondence analyses showed that the central tendency in peer scoring was influenced by both an appreciation of newer perspectives and a stricter appraisal of peer work after self-comparisons were made. The theoretical contributions and pedagogical considerations for peer assessment design are discussed.
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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 | 0.031 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| 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".