Massive Open Online Courses as a Tool for Global Animal Welfare Education

Jill Mackay, Fritha Langford, Natalie Waran

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

"Animal Behavior 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 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.

Original languageEnglish
Number of pages15
JournalJournal of Veterinary Medical Education
Volume43
Issue number1
DOIs
Publication statusPublished - 11 Jan 2016

Keywords / Materials (for Non-textual outputs)

  • VMED

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