Advances in medical education and practice: student perceptions of the flipped classroom
Bibliographic record
Abstract
The flipped classroom (FC) approach to teaching has been increasingly employed in undergraduate medical education in recent years. In FC applications, students are first exposed to content via online resources. Subsequent face-to-face class time can then be devoted to student-centered activities that promote active learning. Although the FC has been well received by students in other contexts, the perceptions of medical students regarding this innovation are unclear. This review serves as an early exploration into medical student perceptions of benefits and limitations of the FC. Medical students have generally expressed strong appreciation for the pre-class preparation activities (especially when facilitated by concise, readily accessed online tools) as well as for interactive, engaging small group classroom activities. Some students have expressed concerns with the FC and noted that suboptimal student preparation and insufficient direction and structure during active learning sessions may limit the student-centered benefits. Although students generally perceive that FC approaches can improve their learning and knowledge, this has not been conclusively shown via performances on assessment tools, which may be related to caveats with the assessment tools used. In any case, lifelong self-directed learning skills are perceived by medical students to be enhanced by the FC. In conclusion, medical students have generally expressed strong satisfaction with early applications of the FC to undergraduate medical education, and generally prefer this method to lecture-based instruction.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".