1. Learning Analytics in HE Ethics
Niall Sclater @sclater
Dublin eLearning Summer School
Dublin Institute of Technology
22nd June 2016

2. “learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs”
SoLAR – Society for Learning Analytics Research

3. Applications of learning analytics

4. Early alert and student success

5. Recommender systems

6. Adaptive learning

7. Curriculum design
A key piece of learning content is not being accessed by most students.
Some students are not participating well in collaborative work.
A particular minority group is underperforming in an aspect of the curriculum.
Students across several discussion groups are making only minimal contributions to their forums

8. Student-facing tools

9. From your perspective, what’s the biggest ethical problem with learning analytics?

10. $100m plus pumped in by the Gates Foundation and others

11. “It is clear now that there are things we should have done differently
“It is clear now that there are things we should have done differently. For example, we should have considered other non-experimental ways to do this research. The research would also have benefited from more extensive review by a wider and more senior group of people. Last, in releasing the study, we failed to communicate clearly why and how we did it.”
Mike Schroepfer, Chief Technology Officer, Facebook

12. Education is different

13. The obligation of knowing
If the analytics tell us that a student is likely to drop out do we have a responsibility to do something about it?

14. The problem of flawed or inadequate data
Teacher setting up test account
Enmeshed identities
IP address may not relate to an individual at all

15. Predictions are not always valid or correct
Confusion between causation and correlation

16. Loss of autonomy
Could student autonomy in decision making be undermined by predictive analytics?
Are we infantilising students by spoon-feeding them with automated suggestions?
Are we creating echo chambers to reinforce our ideas?

17. Potential demotivation of students
Self-fulfilling prophecy? As algorithms get more sophisticated will the effect intensify.

18. Negative impacts of continual monitoring
Will a student whose ebook usage is being monitored feel

19. Manipulation of the analytics by students
Student at the door of the library
But gaming the system could be spotted by more intelligent systems

20. Prejudicial categorisation and treatment of students
Race
Mental health
Increase social differentials among students
But could help to identify issues of prejudice and differential treatment

21. Reduction of the individual to a metric
Beware of simplistic metrics which reduce an individual to a traffic light.
Much of the data is outside the institution and difficult or impossible to capture
Systems don’t know the reasons for a student’s failure to engage.

22. Triage

23. Should students be asked for their consent to collect data about them for use in learning analytics?

24. The logistics of collecting consent
“Allow us to collect data about your learning activities or quit your studies now” not a fair choice

25. Allowing students to opt out
Consent is arguably meaningless if there is no realistic option to opt out.
Software can’t function without collecting data
Impact on other students
Impact on individual
Analysing patients’ records can have benefits for whole society

26. Should interventions always be mediated by a human?

27. Data stewardship

28. European Data Protection Directive
Data must be:
processed fairly and lawfully
obtained only for specific lawful purposes
adequate, relevant and not excessive for those purposes
kept accurate and up to date
kept for no longer than is necessary for those purposes
processed in accordance with the data subject's rights
kept safe from unauthorised or unlawful processing, accidental loss, destruction or damage to the data
not transferred outside the European Economic Area unless that country has equivalent levels of protection for processing personal data

29. EU General Data Protection Regulation proposed “to strengthen privacy rights and boost Europe’s digital economy”
Right to be forgotten
Easier access to your own data
Putting you in control
Data protection first, not an afterthought

30. Anonymisation

31. Unknown future uses of the data

32. External access to student data

33. Giving students access to their data

34. Available from Effective learning analytics blog: analytics
Available from Effective learning analytics blog: analytics.jiscinvolve.org

35. 86 issues in 9 groups Group Name Question Main type Importance
Responsibility
2 Consent
Adverse impact of opting out on individual
If a student is allowed to opt out of data collection and analysis could this have a negative impact on their academic progress?
Ethical 1
Analytics Committee
7 Action
Conflict with study goals
What should a student do if the suggestions are in conflict with their study goals? 3
Student
8 Adverse impact
Oversimplification
How can institutions avoid overly simplistic metrics and decision making which ignore personal circumstances?
Educational researcher
86 issues in 9 groups

36. Group
Name
Question
Main type
Importance
Responsibility
2 Consent
Adverse impact of opting out on individual
If a student is allowed to opt out of data collection and analysis could this have a negative impact on their academic progress?
Ethical 1
Analytics Committee
7 Action
Conflict with study goals
What should a student do if the suggestions are in conflict with their study goals? 3
Student
8 Adverse impact
Oversimplification
How can institutions avoid overly simplistic metrics and decision making which ignore personal circumstances?
Educational researcher

37. jisc.ac.uk/guides/code-of-practice-for-learning-analytics

38. jisc.ac.uk/guides/code-of-practice-for-learning-analytics

39. jisc.ac.uk/guides/code-of-practice-for-learning-analytics

40. @sclater
sclater.com/blog