MMB8054 : Theoretical Aspects of Animal Welfare
- Offered for Year: 2025/26
- Module Leader(s): Dr Colline Poirier
- Lecturer: Dr Jessica Martin, Dr Fritha Langford, Dr Vivek Nityananda, Dr Catherine Douglas
- Deputy Module Leader: Professor Melissa Bateson
- Owning School: Biomedical, Nutritional and Sports Scien
- Teaching Location: Newcastle City Campus
Semesters
Your programme is made up of credits, the total differs on programme to programme.
| Semester 1 Credit Value: | 20 |
| ECTS Credits: | 10.0 |
| European Credit Transfer System | |
Aims
This module aims to provide students with an in-depth understanding of the theoretical aspects of animal welfare. It will discuss the different concepts underpinning animal welfare science and will illustrate how different scientific disciplines such as animal behaviour, cognition, physiology and neuroscience can be used to assess the welfare of laboratory, farm, companion and wild animals. The students will gain training in understanding the strength and limitations of traditional and cutting-edge welfare indicators. They will develop skills in critical appraisal of the welfare literature, hypothesis testing and experimental design, data analysis in the R environment, ethical application and academic writing. Although this module focuses on the welfare of non-human animals, it will also be of interest to those students interested in human behaviour, particularly those who study non-verbal human behaviour.
Outline Of Syllabus
This module consists of a series of lectures that cover the main concepts underlying animal welfare science, ethics and legislation relevant to animal welfare, as well as the main welfare indicators used by scientists. It will include practical sessions in writing of ethical applications and data analysis in the R environment.
• This module will cover a range of topics which may include:
• Behavioural welfare indicators
• Cognitive welfare indicators
• Physiological welfare indicators
• Neural welfare indicators
• Sentience and welfare of Invertebrates
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Guided Independent Study | Assessment preparation and completion | 1 | 30:00 | 30:00 | Assessment 2: R coding questions and R coding exercise |
| Guided Independent Study | Assessment preparation and completion | 1 | 30:00 | 30:00 | Assessment 1: ChatGPT Essay critical appraisal (written exercise) |
| Scheduled Learning And Teaching Activities | Practical | 2 | 2:00 | 4:00 | Present in person (PIP): Practical exercise 2 (data analysis in R) |
| Scheduled Learning And Teaching Activities | Practical | 1 | 1:00 | 1:00 | Present in person (PIP): Practical exercise 1 (Introduction to R) |
| Scheduled Learning And Teaching Activities | Practical | 1 | 2:00 | 2:00 | Present in person (PIP): Practical exercise 3 (Ethical application) |
| Scheduled Learning And Teaching Activities | Small group teaching | 12 | 2:00 | 24:00 | Present in person (PIP): 2 hr seminars |
| Guided Independent Study | Reflective learning activity | 1 | 57:00 | 57:00 | Additional Reading and Reflective Learning |
| Guided Independent Study | Student-led group activity | 8 | 1:00 | 8:00 | Preparation for practical session 2 (peer learning): data analysis in R |
| Guided Independent Study | Independent study | 1 | 44:00 | 44:00 | Preparing notes from lectures, seminars and reading |
| Total | 200:00 |
Teaching Rationale And Relationship
The module intersperses lectures to develop the scientific knowledge of students and their critical assessment skills, and practical sessions, structured around between-peers practical exercises, aiming to develop their research skills. The lectures will also be used to reinforce skill development via interactive components such as discussions, between-peers practical exercises, polls and quizzes, since they are recognised to promote deeper learning.
A strong emphasis will be placed on developing students’ critical thinking and assessment skills (of academic literature and AI-generated texts), a translational skill primordial for future generations of students.
Assessment Methods
The format of resits will be determined by the Board of Examiners
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Written exercise | 1 | M | 50 | Critical appraisal of AI-generated essay – between 1000 and 1300 words – Take home assignment |
| Prob solv exercises | 1 | M | 50 | R questions and R exercise - Take home assignment |
Formative Assessments
Formative Assessment is an assessment which develops your skills in being assessed, allows for you to receive feedback, and prepares you for being assessed. However, it does not count to your final mark.
| Description | Semester | When Set | Comment |
|---|---|---|---|
| Practical/lab report | 1 | M | Data analysis practical - students will receive feedback during the session. |
| Practical/lab report | 1 | M | Ethical application practical - students will receive feedback during the session. |
Assessment Rationale And Relationship
The formative assessments include testing and improving students' generic research skills to equip them to a satisfying standard for their research project in semester 2. Ability of students will be assessed informally through discussion at both an individual and group level. This discussion will include feedback on their ability to analyse data in the R environment, write an ethical application, and how these skills could be improved.
The first summative assessment will focus on evaluating the student's ability to think critically about concepts and approaches underpinning animal welfare science. It will be done using a scaffolding approach, first helping students to critically assess an AI-generated essay about a welfare topic during a lecture, then using the same exercise (on a different welfare topic) during a summative assessment. The other summative assessment will complement the first one by testing the students analytical skills via R coding questions and exercise.
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- MMB8054's Timetable