Self and Peer Assessment in Tutorials
Bibliographic record
Abstract
PURPOSE: While self assessment continues to be touted as being of paramount importance for continuing professional competence, problem-based learning curricula, and adult learning theory, techniques for ensuring valid judgments have proven elusive. This study tested the applicability of an innovative relative-ranking procedure to problem-based learning tutorials. METHOD: A total of 36 students in the McMaster University Faculty of Health Sciences' MD program were provided relative-ranking forms listing seven domains of competence along with their definitions. The student, two of the student's peers, and the student's tutor were asked to complete the ranking exercise after their second, fourth, and sixth tutorials. RESULTS: Combining each level of the time and rater variables generated 66 correlation coefficients, none of which was significantly different from zero. Re-performing the analysis on only the extreme domains did not improve this result. CONCLUSION: The relative-ranking instrument developed did not prove to be a reliable measure of tutorial performance. Ratings were inconsistent from one week to the next as well as across raters within a week.
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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 | 0.023 | 0.171 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".