Flipped Classroom Use in Veterinary Education: A Multinational Survey of Faculty Experiences
Bibliographic record
Abstract
Active teaching approaches such as the flipped classroom are linked to better quality student learning outcomes across health care disciplines, with the potential to support students' preparedness for practice. In the flipped classroom instructional approach, students engage in significant pre-class preparation to learn foundational knowledge and skills, then undertake instructional activities in the classroom that require them to integrate, apply and extend their learning to new contexts. This study reports the results of a multinational survey of flipped classroom use in veterinary education. Participants' ( n = 165) familiarity with and extent of use of the flipped classroom technique were investigated, together with the teaching strategies used and the perceived benefits and barriers to implementation. Relationships between respondent characteristics and flipped classroom use were also explored. The results indicated that 95% of participants were familiar with the flipped classroom technique, although fewer (64%) used it in their teaching. Pre-class activities included reviewing online and printed material, and engaging in preparatory learning activities such as quizzes, case analyses, reflective assignments and group activities. A variety of active learning strategies were used in class, including discussions, presentations, quizzes, group activities, problem solving and laboratory/practical exercises. Most participants perceived that the flipped classroom technique benefited student learning, with some also identifying benefits for the faculty involved. A range of student-, faculty- and institution-related barriers to implementing the flipped classroom technique were identified. These barriers need to be considered and addressed by teachers and administrators seeking to improve students' preparedness for practice by implementing flipped classrooms in veterinary education.
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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 | 0.004 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".