Implementing the Flipped Classroom in a Veterinary Pre-clinical Science Course: Student Engagement, Performance, and Satisfaction
Bibliographic record
Abstract
There has been a recent move toward active learning pedagogies in veterinary education, with increasing use of a blended approach that incorporates both online resources and live classroom sessions. In this study, an established veterinary pre-clinical course in introductory animal health was transitioned from a traditional didactic lecture delivery mode to a flipped classroom approach with core content delivered online. This study compared the experiences of two cohorts of students who studied the same course in the different formats in consecutive years. Online learning resources included short video segments and a variety of short problems and activities. Online materials were complemented with weekly small-group case-based learning classes facilitated by academic staff. A mixed methods evaluation strategy was applied using student grades, surveys, and focus groups to compare student academic performance, satisfaction, and engagement between the two cohorts. The flipped classroom cohort achieved significantly higher grades in the written answer section of the final examination. Student satisfaction with learning resources was also higher in this cohort. However, satisfaction with other aspects of the course was largely the same for both cohorts. This study revealed some of the challenges associated with achieving adequate student preparation for class using online resources. The outcomes of this study have implications for veterinary educators considering the design and development of new online learning resources.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".