Optimum Frequency of Peer Evaluations in Capstone Design Courses
Bibliographic record
Abstract
Peer evaluations are commonly used in design courses for developmental and evaluative purposes. Peer ratings are however often higher than the instructor ratings and this can induce fears regarding their reliability. This study examined whether it may be possible to increase the agreement between peer and instructor ratings by increasing the frequency of the peer assessments. The premise was that peers may provide less lenient assessments if the impact of single evaluations on the final grade is reduced by increasing the number of evaluations. Increasing the number of peer evaluations in our senior design course from two to six per year did not increase the accuracy of the peer ratings but provided other benefits such as earlier identification of dysfunctional teams, elimination of free riding and more frequent developmental feedback. Peer and instructor ratings can be normalized however to yield similar indicators of the relative performance of teammates. The frequency and timing of peer evaluations are critical to obtain meaningful results and maximize impact on team dynamics
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".