The Retrospective Pre–Post: A Practical Method to Evaluate Learning from an Educational Program
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
OBJECTIVES: Program evaluation remains a critical but underutilized step in medical education. This study compared traditional and retrospective pre-post self-assessment methods to objective learning measures to assess which correlated better to actual learning. METHODS: Forty-seven medical students participated in a 4-hour pediatric resuscitation course. They completed pre and post self-assessments on pediatric resuscitation and two distracter topics. Postcourse, students also retrospectively rated their understanding as it was precourse (the "retrospective pre" instrument). Changes in traditional and retrospective pre- to postcourse self-assessment measures were compared to an objectives-based multiple-choice exam. RESULTS: The traditional pre to post self-assessment means showed an increase from 1.9 of 5 to 3.7 of 5 (p < 0.001); the retrospective pre to post scores also increased from 1.9 of 5 to 3.7 of 5 (p < 0.001). Although the group means were the same, individual participants demonstrated a response shift by either increasing or decreasing their traditional pre to retrospective pre scores. Scores on the 22-item objective multiple choice test also increased, from a median score of 13.0 to 18.0 (p < 0.001). There was no correlation between the change in self-assessments and objective measures as demonstrated by a Spearman correlation of -0.02 and -0.13 for the traditional and retrospective pre-post methods, respectively. Students reported fewer changes on the two distracters using the retrospective pre-post versus the traditional method (11 vs. 29). CONCLUSIONS: Students were able to accurately identify, but not quantify, learning using either traditional or retrospective pre-post "self-assessment" measures. Retrospective pre-post self-assessment was more accurate in excluding perceived change in understanding of subject matter that was not taught.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it