What Factors Affect Studentsʼ Overall Ratings of a Course?
Bibliographic record
Abstract
PURPOSE: Medical students are typically asked to complete course evaluations, but little is known about how students decide to rate courses. This study sought to examine the student feedback process by exploring the dimensionality of a course evaluation tool and examining the relationship between resulting factors and the overall rating of a course. METHOD: During the 2007-2008 academic year, all first- and second-year students were asked to provide feedback on various aspects of curricular content, delivery, and assessment for seven courses taught in the first two years of a clinical presentation curriculum. The authors examined the structure of the evaluation instrument using principal component factor analysis and used multiple linear regression to study the relationship between factors and overall course ratings. RESULTS: Four stable and reliable factors were identified (assessment of students, small-group learning, basic science teaching, and teaching diagnostic approaches) that accounted for about 50% of the total variance in overall course ratings. Student assessment displayed the strongest association with overall course ratings, and for second-year students it was the only variable associated with overall course ratings. CONCLUSIONS: Of the four factors, student assessment was by far the strongest predictor of overall course ratings, and this association strengthened over time. These results are consistent with the "peak-end rule" and "negativity dominance" for rating emotional experiences.
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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 | 0.004 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".