Development of a Screencast-Based Flipped Classroom to Enrich Learning and Reduce Faculty Time Requirements in an Animal Welfare Master’s Degree
Bibliographic record
Abstract
A new distance learning Master of Science (MSc) degree in Animal Welfare Science, Ethics and Law was established in 2016 within the Centre for Animal Welfare at the University of Winchester, UK. Our program recruited students worldwide, with enrollments increasing dramatically since its inception in 2016. However, despite rapid growth, our MSc has had only one full-time equivalent faculty member. With further projected sharp increases in student numbers, significant programmatic change was required for the MSc to remain viable. After consultation with our students and program team, we decided to transition to a flipped classroom teaching model. Piloting a screencast-based flipped classroom in one course, our objectives were to provide a more enriched, engaging, and effective student learning experience and to increase student satisfaction while concurrently saving staff time in future years. We aimed to provide a series of enriched screencast videos of short (∼20-minute) durations, with contents clearly signposted. The new teaching model was well received. Within our 2021 program survey, 100% of respondents expressed a wish to see our screencast-based flipped classroom approach continued, and 71%-86% wished to see it implemented in various additional courses. This model has greatly enriched students' learning experiences, increasing student engagement and satisfaction while also freeing staff time to engage in discussion fora and additional live sessions. Learning and achievement outcomes also appear positive. We plan to steadily integrate this model across additional courses, although initial time investment will be significant. Hence, this new model will be implemented over several semesters.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".