Effects of Anonymity and Accountability During Online Peer Assessment
Bibliographic record
Abstract
A 2´2 experiment was conducted to determine the effects of anonymity (anonymous vs. named) and peer-accountability (more-accountable vs. less-accountable) on peer over-marking, and on the criticality and quality of peer comments during online peer assessment. Thirty-six graduate students in a Web-based education research methods course were required to critique two published research articles as a part of their course. Peer assessment was carried out on the first critique. Students were randomly assigned to one of the four groups. Peer assessors were randomly assigned three students’ critiques to assess. Peer assessors and the students being assessed were from the same group. Peer assessors assigned a numeric mark and commented on students’ critiques. The four main results were: First, significantly fewer peer assessors over-marked (i.e., assigned a higher mark relative to the instructor) in the anonymous group as compared to the named group (p < .04). Second, peer assessors in the anonymous group provided a significantly higher number of critical comments (i.e., weaknesses) as compared to the named group (p < .01). Third, peer assessors in the named groupand the more-accountable group made a significantly higher number of quality comments (i.e., cognitive statements indicating strengths and weakness along with reasoned responses and suggestions for improvement), compared to the peer assessors in the anonymous group and the less-accountable group (p < .01). Lastly, the students’ responses to the questionnaire indicated that they found the peer assessment process helpful. This study suggests that in online peer assessment, the anonymity and the degree of peer-accountability affect peer marking and comments.
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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 | 0.037 | 0.223 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".