Embedding the Flipped Classroom Approach to Support Student Learning in Animal Handling and Clinical Skills: Practical Classes Throughout a Veterinary Curriculum
Bibliographic record
Abstract
A comprehensive bank of flipped classrooms was developed to help students prepare for animal handling and clinical skills practical classes. Flipped classroom is a type of blended learning. In the context of clinical skills, it is designed to provide students with online learning resources prior to attendance at practical classes. The initiative was catalyzed by the pandemic, and the resources continue to be embedded throughout the curriculum. A team approach was used for the development of the bank, and the design embraced relevant pedagogical frameworks and active learning techniques. Feedback was gathered from a small group of students who completed an online form after each practical class throughout the academic year and wrote a reflective piece at the end of the project. Instructors who delivered practical classes participated in focus group discussions. Students particularly liked flipped classrooms that were well designed with a range of content and interactivity. The main benefits for students of the flipped approach were being more prepared and confident, being less anxious, and making better use of in-class time to focus on learning skills. One of the main challenges encountered by instructors was managing a group when some students had not done the prework. A few other issues were mentioned, including when the standardized design template was not followed and the workload involved in continuing to enhance and expand the resources. Teamwork and training were crucial to the successful production of the large bank of flipped classrooms. Sharing our experience with the wider education community, within and beyond our institution, continues to be one of team's aims.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".