Beyond Average Information: How Q‐Methodology Enhances Course Evaluations in Anatomy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Course evaluations can be used for curriculum improvement and have the potential to better the student learning experience. However, because most are based on Likert scales and open-ended feedback, understanding diversity in student opinion and uncovering optimal options for course change and improvement are often difficult. Alternatively, Q-methodology can be used to investigate patterns of thought within a group and may offer greater potential for course reform. This manuscript offers a tutorial-based explanation of the three components of Q-methodology studies (1) survey instrument development, (2) data collection, and (3) analysis and interpretation, then demonstrates, via case study, the use of Q-methodology to evaluate a fourth-year undergraduate pathoanatomy course. The goal of this article is to enable the reader to broadly apply Q-methodology in other courses to gain insight and feedback beyond that offered by traditional Likert scale methods. As demonstrated through the pathoanatomy case study, Q-methodology highlights groups (denoted by factors) of like-minded students that share opinions, preferences, and values. Overall, Q-methodology analyses support course instructors in identifying areas of course strength and improvement in an evidence-based way. This alternative to traditional Likert scales represents a promising solution to ongoing course evaluation limitations.
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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 | 0.015 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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 it