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 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 | 0.003 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".