Making medical student course evaluations meaningful: Evaluating and responding to student satisfaction ratings
Bibliographic record
Abstract
Background The literature emphasizes the value of student evaluations of curriculum in medical education, but there is little information available on how this information is used or how schools monitor the impact of any changes emerging from the evaluations. Objectives To describe the intensive course review protocol for undergraduate medical courses at Memorial University implemented by the Program Evaluation Sub-Committee (PESC) in 2005 and to examine its impact on improving course ratings from 2006 to 2011. Methods PESC is the evaluation oversight committee for the undergraduate medical program at Memorial University. The minimum acceptable standard for the overall course rating is a mean score greater than or equal to 3.5/5.0.Those courses not meeting established standards are expected to undergo an intensive review which requires the course chair to present an action plan in person to PESC detailing steps taken to resolve identified problems. Courses requiring an intensive review are flagged for reassessment to track the impact of any implemented changes. Changes in course ratings and the percentage of courses either above or below the 3.5 benchmark were calculated from 2006-2011.Results In the 2006/2007 academic year, 8 courses (61%) did not meet the minimum benchmark of 3.5/5.0. The ratings of all 8 courses increased in the 2008/2009 academic year and by 2010/2011, only 1 course out of the 8 was still below the minimum bench mark. The average course ratings of all 23 courses examined were significantly higher from 2008-2011 compared to the 2006/2007 academic year (P
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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 | 0.047 | 0.164 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".