Using Think-aloud Interviews to Examine a Clinically Oriented Performance Assessment Rubric
Bibliographic record
Abstract
Performance-based assessment is a common approach to assess the development and acquisition of practice competencies among health professions students. Judgments related to the quality of performance are typically operationalized as ratings against success criteria specified within a rubric. The extent to which the rubric is understood, interpreted, and applied by assessors is critical to support valid score interpretations and their subsequent use. Therefore, the purpose of this study was to examine evidence to support a scoring inference related to assessor ratings on a clinically oriented performance-based examination. Think-aloud data showed that rubric dimensions generally informed assessors’ ratings, but specific performance descriptors were rarely invoked. These findings support revisions to the rubric (e.g., less subjective, rating-scale language) and highlight tensions and implications of using rubrics for student evaluation and making decisions in a learning context.
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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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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 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".