Massive Open Online Courses as a Tool for Global Animal Welfare Education
Bibliographic record
Abstract
Animal Behaviour and Welfare was a Massive Open Online Course (MOOC) hosted on Coursera as a free introductory animal welfare course. Through interrogating Coursera data and pre-/post-course student experience surveys, we investigated student retention, student experience, changes in attitudes, and changes in knowledge. The course ran for 5 weeks, and 33,501 students signed up, of which 16.4% (n=5,501) received a Certificate of Achievement, indicating they had completed all assessments within the course. This retention rate is above the industry standard of 10%; however, the value of retention rate as a metric to judge MOOC success is questionable. Instead, we focus on demographics, with Coursera data estimating that 41% of learners came from Europe, 35% from North America, 11% from Asia, 6% from Oceania, 5% from South America, and 2% from Africa. Most learners had completed an undergraduate degree. Despite this wide range of backgrounds, 57.2% of post-course respondents (n=2,399) strongly agreed that the information presented was at the right level and 64.9% strongly agreed that the course was interesting. After completion, more students (χ(2)[4]=132.40, p<.001) understood that animal welfare was based on the results of scientific study, and significantly fewer students (χ(2)[4]=361.32, p<.001) felt health was the most important part of animal welfare. Overall, learners agreed the course was enjoyable and informative, and 97.9% felt the course was a valuable use of their time. We conclude that MOOCs are an appropriate vehicle for providing animal welfare learning to a wide audience, but require a significant level of investment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".