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.Request access from your librarian to read this chapter's full text.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".