Using Measurement Invariance to Explore the Source of Variation in Basic Medical Science Students’ Evaluation of Teaching Effectiveness
Bibliographic record
Abstract
Introduction: Many research studies have shown that students' evaluations of teaching(SET) are affected by different variables without testing the requirement of fair comparisons. These studies have not tested the measurement equivalency of SET surveys according to these variables. Measurement equivalency of SET refers to whether a SET survey is interpreted similarly across different groups of individuals (Variable Levels). Without evidence of measurement invariance across different variables under investigation, the SET ratings should not be compared across these variables and this is the goal of this study. Methods: Measurement Invariance analysis of SET survey was investigated using 1649 responses to SET of four different medical core courses offered by the College of Science and College of Medicine and from different levels. Results: The results showed the existence of teaching practices in the SET survey that are not equivalently loaded on its factor across the levels of targeted variables, and the college offered medical courses were a source of variation in basic medical science students’ evaluation of teaching effectiveness. On the other hand, teaching practices in the SET survey are equivalently loaded on its factor across course levels. Discussion: The study results showed that the SET of medical courses is comparable to the courses only taught by the College of Medicine. These results provide evidence that medical courses are different from other courses offered by other colleges. This means that comparing SET of the College of Medicine with other colleges and colleges of medicine needs to compare SET results at the college level only.
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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 | 0.053 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| 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 | 0.000 |
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 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".