1. Sharon Slade and Avinash Boroowa
Developing policy for the ethical use of learning analytics at the Open University
Sharon Slade and Avinash Boroowa

2. Changing the model of student support
Open University UK supports 200,000 students each year
All modules delivered by blended distance learning
Student support driven by geography and focused at module level
Progression rates needed improvement
GEOGRAPHY
SUBJECT
MODULE
QUALIFICATION

3. Uses of student data Long term categorisation of students
Move from geographic focus to subject focus
All students tracked against key study milestones
Recent introduction of piloted predictive models and student facing tools

5. Not so fast….
HEIs are making increasing use of student data to support retention and student learning
Data privacy and protection policies in place
Gaps around policy for use of learning analytics a need to address issues around:
moral purpose
purpose and boundaries
informed consent
collection, analyses, access to and storage of data
students as active agents
labelling and stereotyping

6. Developing new policy
PVC sponsored review Drawing upon existing practice and literature
No comparable policy within HE sector

7. Drafting basic principles
8 principles created covering a broad range of practical and ethical considerations
Sharon Slade and Paul Prinsloo, "Learning Analytics: Ethical Issues and Dilemmas," in American Behavioral Scientist, Vol. 57, 2013, p doi: /

8. Stakeholder consultation
Extensive stakeholder consultation, including:
student support staff,
faculties,
tutors,
PVCs,
strategy office,
analysts,
formal committees (lots) and most importantly….
students

9. New OU policy for the ethical use of learning analytics

10. Principle 1: Learning analytics is a moral practice, which should align with core organisational principles.
Principle 2: The OU has a responsibility to all stakeholders to use and extract meaning from student data for the benefit of students where feasible.

11. Principle 3: Students are not wholly defined by their visible data or our interpretation of that data.
Principle 4: The purpose and the boundaries regarding the use of learning analytics should be well defined and visible.

12. Principle 5: The OU should aim to be transparent regarding data collection, and provide students with the opportunity to update their own data at regular intervals.
Principle 6: Students should be engaged as active agents in the implementation of learning analytics (e.g., personalised learning paths, interventions, etc).

13. Principle 7: Modelling and interventions based on analysis of data should be sound and free from bias.
Principle 8: Adoption of learning analytics within the OU requires broad acceptance of the values and benefits (organisational culture) and the development of appropriate skills across the organisation.

14. transparency

17. getting the balance right