A Mixed Methods Study on the Effect of Flipping the Undergraduate Medical Classroom
Bibliographic record
Abstract
The flipped classroom model is increasingly being adopted in healthcare education, despite the fact that recent systematic reviews in the nursing and medical education literature suggest that this method of instructional design is not inherently better or worse than the traditional classroom. In this study, we used a sequential, explanatory mixed methods design to assess the impact of flipping the hepatology classroom for preclinical medical students. Compared to students in the traditional classroom, students in the flipped classroom had significantly lower mean (SD) ratings of their learning experiences (3.48 (1.10) vs. 4.50 (0.72), p < 0.001, d = 1.10), but better performance on the hepatology content of the end-of-course examination (78.0% (11.7%) vs. 74.2 (15.1%), respectively, p < 0.01, d = 0.3). Based upon our qualitative data analyses, we propose that the flipped classroom induced a change in the learning process of students by requiring increased preparation for classroom learning and promoting greater learner autonomy, which resulted in better retention of learned material, but reduced enjoyment of the learning experience. This dissonance in outcomes is captured in the words of one flipped classroom student: “…I hated it while I was learning it, but boy did I remember it…”. Based upon our dissonant outcomes and the inconsistent findings in the literature, we feel that there is still equipoise regarding the effectiveness of the flipped classroom, and further studies are needed to describe ways of making the flipped classroom a more effective (±more enjoyable) learning experience.
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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 | 0.069 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".