ASSESSING PEER EVALUATION: A COMPARATIVE STUDY OF PEER EVALUATION METHODS USED IN AN ENGINEERING DESIGN COURSE
Bibliographic record
Abstract
Peer evaluation is one way to address group issues in undergraduate teams while at the same time providing feedback and assessment. Two common evaluation methods (point- and rubric-based peer evaluation) are examined and compared in terms of student perception within a University of British Columbia second year mechanical engineering design course. As part of normal course requirements, 118 students in 20 teams completed two full-time multi-week design projects with six regularly-spaced peer evaluations. Student feedback was gathered through two online surveys. Students expressed a slight preference for the rubric style evaluation, citing increased fairness and helpfulness in the feedback. Regardless of evaluation approach, student perceptions of peer evaluation were statistically unrelated to external factors including GPA, gender, and Myers-Briggs personality type. The findings suggest, at least in the student mind, that the use of peer evaluation as a design project assessment tool is fair and unbiased. Additional survey data show students see peer evaluation as a useful tool in undergraduate team design projects and that they feel more comfortable with the prospect of engaging in peer evaluation in the workplace in future as a result.
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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 | 0.058 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".