Assessing Student Performance Using a Novel Rubric Based on the Dreyfus Model of Skill Acquisition
Bibliographic record
Abstract
OBJECTIVE: Pharmacy student performance on practicum was previously assessed using a Likert scale from 0 to 9, resulting in challenges with clarity and assessor subjectivity. To address these issues, an assessment rubric based on the Dreyfus model of skill acquisition was developed and implemented. This study sought to evaluate student, practice educator (PE), and faculty perceptions related to the rubric's effectiveness in assessing student performance within the direct patient care practicum setting. METHODS: An exploratory sequential mixed methods approach was used. A qualitative component using focus groups and semistructured interviews was followed by a quantitative component using a survey questionnaire. Data gathered from the qualitative component were collectively analyzed and used to inform questionnaire development intended to confirm identified themes and collect further data on stakeholder perceptions. RESULTS: A total of 7 students, 7 PEs, and 4 faculty participated in the focus groups/interviews and 70 of 645 (10.9%) students and 103 of 756 (13.6%) PEs participated in the survey questionnaire. The majority of the participants felt that the rubric clearly communicated the expectations for student performance, is relevant and consistent with pharmacy practice, and is useful in accurately assessing performance. For PEs with experience, the novel rubric was an improvement over the previous assessment processes and perceived as more thorough and clearer in describing performance expectations. The identified challenges included the rubric's visual organization, length, and redundancy of some of the assessment elements. CONCLUSION: Our findings suggest that a novel rubric based on the Dreyfus model is effective in assessing student performance on practicum and may address some of the challenges commonly observed with performance assessment.
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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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".