A Survey to Establish the Extent of Flipped Classroom Use Prior to Clinical Skills Laboratory Teaching and Determine Potential Benefits, Challenges, and Possibilities
Bibliographic record
Abstract
The flipped classroom is a technique that involves a reordering of classroom and at-home activities. Content provided prior to classroom interactions is used to prepare students for face-to-face classes. The flipped classroom has been shown to benefit students, including improving examination results, and there is increasing interest in using it in veterinary education. The current study aimed to investigate the potential of the flipped classroom approach to preparing students for practicals in a clinical skills laboratory. An online survey was distributed to the international veterinary clinical skills community to determine the extent to which a flipped classroom is used prior to teaching in a clinical skills laboratory and how educators viewed the benefits, challenges, and possibilities. There were 101 survey participants representing 22 countries, and all were involved in clinical skills teaching; 42 were using flipped classroom techniques prior to teaching in a clinical skills laboratory, and 55 others would consider using the technique in this context in the future. Videos were the most common resource used. The main benefits, experienced or anticipated, were positive changes in student behavior, including preparation and better use of time during practicals by both the students and instructors. The main challenges were the time needed for instructors to develop the materials, lack of student engagement with the flipped classroom, space in the curriculum, and institutional issues. In conclusion, many potential benefits could be realized with a flipped classroom approach embedded prior to clinical skills laboratory practicals.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".