SELF-BIAS AND GENDER-BIAS IN STUDENT PEER EVALUATION: AN EXPANDED STUDY
Why this work is in the frame
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Bibliographic record
Abstract
This paper expands on two studies first presented in 2015 in which gender-based differences in peer evaluation and the self-bias of students in engineering design courses were considered. In the current study, 50 evaluation events across 12 cohorts in four different courses, from first to third year at the University of British Columbia (UBC) are considered. Self evaluations and peer evaluations are used to measure how students rank their own contributions to the team relative to how they rank their teammates’ contributions. In addition, this study examines potential gender-bias when students evaluate peers, focusing on differences in evaluation score given to like- and different-gender individuals.
 The study confirms the presence of significant gender bias when evaluating others. Consistently across the contexts considered, both male and female students tend to evaluate female students more favourably. This study also confirms the presence of a self-bias—the tendency to rate one’s own contributions more favourably than peers’ contributions—that grows as a course progresses. In both forms of bias, statistically significant effects due to the course, team size and composition, the timing of evaluations, and various other interaction effects are observed. Overall, gender biases and self biases of 5% are shown to be common, and biases at times exceeding 10% are also observed. This has implications when considering the validity and reliability of peer and self evaluation as a summative assessment tool. This work suggests care should be taken in the use of self and peer evaluation data, as well as in team formation, since that appears to impact biases. One favourable outcome from this work seems to be the observation that peer and self evaluation biases diminish in later years, as students have more teamwork and evaluation experience.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it