The document provides an overview of the keynote presentation titled "Datafication of Higher Education: Considering an ethical approach to enhancing the student experience" given by Paul Prinsloo at the University of Stirling. The presentation discusses several issues related to the collection, analysis, and use of student data in higher education, including: (1) broken/incomplete data, (2) outdated categories that do not accurately reflect reality, and (3) potential biases in algorithms. It emphasizes the need to consider these issues and approach student data through a framework of data justice and ethics. The presentation also reviews several principles and examples of addressing ethics in learning analytics from 2013 to 2017.

1. Imagecredit:https://pixabay.com/en/art-sculpture-scrap-sculpture-human-1699977/
Zombie categories, broken data and biased
algorithms: What else can go wrong? Ethics in
the collection, analysis and use of student data
Paul Prinsloo
University of South Africa (Unisa)
@14prinsp
Keynote:
Datafication of
Higher Education:
Considering an
ethical approach to
enhancing the
student experience
28 August, 2018
University of Stirling)

2. Imagecredit:https://pixabay.com/en/travel-sculpture-man-stone-move-3034459/
• Re-assess/redefine/destabilise our categories of analysis
• Acknowledge the brokenness of data and epistemic arrogance
• Carefully and ethically optimise the potential of algorithmic
decision-making systems
We also have to consider what can go
right when we…

4. Under what conditions can the
collection, analysis and use of
student data be just and ethical?

5. Acknowledgement
I do not own the copyright of any of the images in this
presentation. I therefore acknowledge the original copyright
and licensing regime of every image used.
This presentation (excluding the images) is licensed
under a Creative Commons Attribution 4.0 International
License.

6. Overview of presentation
• The data revolution and how it unfolds/impacts on our
lives
• The data revolution in higher education – the collection,
analysis and use of student data
• (Broken) data
• (Zombie) categories
• (Biased) algorithms
• Data justice
• The ethical collection, analysis and use of student data
• Some guiding principles
• (In)conclusions

7. The data revolution
• More data (volume)
• Greater variety of data from a range of sources
• Real-time collection, analysis and use (velocity)
• Exhaustive (n=all)
• “Fine-grained in resolution and uniquely indexical in
identification”
• Relational
• “flexible, holding the traits of extensionality (can add
new fields easily) and scaleability (can expand in size
rapidly)”
Kitchin, R., & Lauriault, T. (2014). Towards critical data studies: Charting and unpacking data assemblages
and their work. Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2474112

8. Image credit: https://pixabay.com/en/puzzle-jigsaw-jigsaw-puzzle-1487340/
Data assemblages arise from a variety of intersecting and
often mutually constitutive technological, political, social
and economic processes and apparatuses that frame the
nature, operation and work of these assemblages

9. Image credit: https://pixabay.com/en/puzzle-cooperation-together-1020002/
Central to these assemblages are, inter alia,
(broken) data, (zombie) categories and (biased)
algorithms

10. Source credit: http://uk.businessinsider.com/how-to-see-everything-google-
knows-about-you-2016-6?r=US&IR=T
Source credit: https://medium.com/productivity-in-the-cloud/6-links-that-will-show-you-what-google-knows-about-you-
f39b8af9decc

11. Source credit: https://www.theguardian.com/news/2018/mar/17/cambridge-analytica-facebook-influence-us-
election
“We exploited Facebook to harvest millions
of people’s profiles. And built models to
exploit what we knew about them and target
their inner demons. That was the basis the
entire company was built on.”

12. Source credit: https://www.washingtonpost.com/blogs/post-partisan/wp/2018/07/17/how-your-data-is-
used-by-police-and-where-it-goes-wrong/?noredirect=on&utm_term=.61ec3d7e7206

13. “Algorithms that may conceal hidden biases are already
routinely used to make vital financial and legal decisions.
Proprietary algorithms are used to decide, for instance, who
gets a job interview, who gets granted parole, and who gets a
loan.”
Source credit: https://www.technologyreview.com/s/608248/biased-algorithms-are-everywhere-and-no-one-seems-to-care/

14. The world of (student) data
Academic Analytics
Learning Analytics
(Higher)
Education
• Individuals
• Corporates
• Governments
• Data brokers
• Fusion centers
• Directed
• Automated
• Gifted

15. Source credit: https://tekri.athabascau.ca/analytics/
“Learning institutions and corporations make little use of the
data learners ‘throw off’ in the process of accessing learning
materials, interacting with educators and peers, and creating
new content. In an age where educational institutions are under
growing pressure to reduce costs and increase efficiency,
analytics promises to be an important lens through which to
view and plan for change at course and institution levels”
(emphasis added).

16. Source credit: https://tekri.athabascau.ca/analytics/
“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.”

17. Source credit: http://timoelliott.com/blog/2013/02/gartnerbi-emea-2013-part-1-analytics-moves-to-the-core.html
Learning analytics in action

18. Source credit: https://www.theguardian.com/higher-education-network/2016/aug/03/learning-analytics-universities-data-track-students
2016

19. Page credit: http://www.npr.org/sections/ed/2017/01/11/506361845/the-higher-ed-learning-revolution-tracking-each-students-every-move
2017

20. Source credit:
https://mobile.edweek.org/c.jsp?cid=25919761&bcid=25919761&rssid=25919751&item=http%3a%2f%2fapi.edweek.org%2fv1
%2few%2f%3fuuid%3dC08929D8-6E6F-11E8-BE8B-7F0EB4743667
2018

21. 2018

22. Source credit: https://campustechnology.com/articles/2018/05/02/when-learning-analytics-violate-student-privacy.aspx
2018

23. Source credit:
https://er.educause.edu/articles/2018/5/setting-the-
table-responsible-use-of-student-data-in-higher-
education
2018

24. Kitto, K., Shum, S. B., & Gibson, A. (2018, March). Embracing imperfection in learning analytics.
In Proceedings of the 8th International Conference on Learning Analytics and Knowledge (pp. 451-460).
ACM.

25. Pink, S., Ruckenstein, M., Willim, R., & Duque, M. (2018). Broken data: Conceptualising data in an emerging
world. Big Data & Society, 5(1), 2053951717753228.
We have to recognize that
our data are broken

26. “Data is not necessarily accurate, complete or
full aggregated representations of what
individuals or societal groups have done, or
able to predict what they will do” (p. 10)
Pink, S., Ruckenstein, M., Willim, R., & Duque, M. (2018). Broken data: Conceptualising data in an emerging
world. Big Data & Society, 5(1), 2053951717753228.

27. Image credit: https://www.flickr.com/photos/itsnitram/32843850295

28. Image credit: https://pixabay.com/en/zombie-horror-creepy-night-531116/
Zombie Categories
“categories from the past that we continue to use even
though they have outlived their usefulness and even
though they mask a different reality”
(Plummer, 2011, p. 195)

29. Beck, U. (2001). Interview with Ulrich Beck. Journal of Consumer Culture, 1(2),
261-277.
Zombie categories are ‘living dead’ categories which
govern our thinking but are not really able to capture
the contemporary milieu.

30. “…what is a ‘household’ nowadays? […] it is quite
difficult to figure out how to define the contemporary
household. […].. So producing data about consumption
or voting behaviour on the basis of ‘households’ is part
of a zombie sociology. They just don’t exist anymore.”
Beck, U. (2001). Interview with Ulrich Beck. Journal of Consumer Culture, 1(2),
261-277.

31. (a) Indicators name things
“To claim ‘naming rights’, indigenous
peoples need to replace indicators
that have been constructed
according to hegemonic categories
and motivated by Global North
normative assumptions with
indicators that reflect their own local
understandings of their social world”
(Morphy, 2016, p. 104)
Morphy, F.(2016). Research as solidarity. In T. Kukutai and J. Taylor. (Eds), Indigenous data sovereignty. Toward an
agenda (pp. 99-116). Canberra, Australia: Australian National University Press. Retrieved from
https://press.anu.edu.au/publications/series/centre-aboriginal-economic-policy-research-caepr/indigenous-data-
sovereignty

32. (b) Indicators compare
and rank
“Encapsulated indigenous
minorities within settler states
constantly find themselves being
compared, as a ‘population’, with
the ‘mainstream population’—and
found wanting. […] They have
‘gaps’ that need to be ‘closed’, and
improvement is defined in terms
of the indicators that measure the
gaps” (Morphy, 2016, p. 105)
Morphy, F.(2016). Research as solidarity. In T. Kukutai and J. Taylor. (Eds), Indigenous data sovereignty. Toward an
agenda (pp. 99-116). Canberra, Australia: Australian National University Press. Retrieved from
https://press.anu.edu.au/publications/series/centre-aboriginal-economic-policy-research-caepr/indigenous-data-
sovereignty

33. (c) Indicators simplify
complex phenomena
“…categorisation is used as a tool
of simplification with respect to
complex phenomena such as the
‘family’ and the ‘household’”
(Morphy, 2016; p. 106).
Morphy, F.(2016). Research as solidarity. In T. Kukutai and J. Taylor. (Eds), Indigenous data sovereignty. Toward an
agenda (pp. 99-116). Canberra, Australia: Australian National University Press. Retrieved from
https://press.anu.edu.au/publications/series/centre-aboriginal-economic-policy-research-caepr/indigenous-data-
sovereignty

34. To what extent do
our categories of
analysis dismember
and dislocate our
students, alienate
them from what
they know and how
they see the world
and their learning
journeys?

35. Gullion (2018) does not
propose that we stop
using categories, but we
need to recognise the
ontologies and
epistemologies that
informed our categories of
analysis and the effects of
their normalisation
Image credit: Amazon

36. Source credit: https://www.technologyreview.com/s/610026/algorithms-are-making-american-inequality-worse/

37. Image credits: Amazon

38. Image credit: https://www.flickr.com/photos/donpezzano/4903384635
What happens when
data assemblages
depend on broken
data, zombie
categories and biased
algorithms?

39. Viberg, O., Hatakka, M., Bälter, O., & Mavroudi, A. (2018). The Current Landscape of Learning Analytics in
Higher Education. Computers in Human Behavior.
2018

40. Viberg, O., Hatakka, M., Bälter, O., & Mavroudi, A. (2018). The Current Landscape of Learning Analytics in Higher
Education. Computers in Human Behavior.
Four propositions of learning analytics
(Ferguson & Clow, 2017)
1)improve learning outcomes
2)support learning and teaching?
3) deployed widely; and
4) used ethically?
To what extent does learning analytics
To what extent is learning analytics

41. Viberg, O., Hatakka, M., Bälter, O., & Mavroudi, A. (2018). The Current Landscape of Learning Analytics in Higher
Education. Computers in Human Behavior.
“It is worrying that more than 80% of the papers do
not mention ethics at all. Moreover, there are only
few studies that approach ethical issues (e.g., data
privacy and security, informed consent) in a
systematic way. However, we should not jump to the
conclusion that most studies are done in an unethical
way, but we call for more explicit reflection on ethics
to rise in the coming years. The increase of the
studies that reflect on the ethical issues for the year
2017 (36%) might indicate that there is already a
positive move in this direction” (emphasis added).

42. Source credit: https://www.technologyreview.com/s/526401/laws-and-ethics-cant-keep-pace-with-technology/

43. Heeks, R., & Renken, J. (2018). Data justice for development: What would it mean?. Information
Development, 34(1), 90-102.

44. Data justice
1. Instrumental data justice – fair use – will the outcome of the use
of data be fair?
2. Procedural data justice – the handling of data – the sampling, the
‘cleaning’, the analysis, the storage and governance of data.
Individuals feel that a process is fair if they are in control of the
process. Issues surrounding consent – opting in/out. Individual
control/input ito consistency, correctness and correctability
3. Distributive data justice – who has access to what data – issues
surrounding privacy
4. Agentic justice – “the personal choice to exercise rights-as-
capabilities and convert them into rights-as-achieved functionings”
5. Structural justice – based on the above, what frameworks/
structures/laws will support, enable, enact
Heeks, R., & Renken, J. (2018). Data justice for development: What would it mean?. Information
Development, 34(1), 90-102.

45. “Ethics are the mirror in which we
evaluate ourselves and hold ourselves
accountable” (emphasis added).
Holding actors and humans accountable
still works “better than every single
other system ever tried” (Brin, 2016)
Ethics and accountability

46. Ethics in learning analytics: Selected examples
2013-2017

47. Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral
Scientist, 57(10), 1510-1529.
2013

48. Source credit: https://www.open.ac.uk/students/charter/sites/www.open.ac.uk.students.charter/files/files/ecms/web-content/ethical-
use-of-student-data-policy.pdf
2014

49. Source credit: https://www.open.ac.uk/students/charter/sites/www.open.ac.uk.students.charter/files/files/ecms/web-content/ethical-
use-of-student-data-policy.pdf
Principle 1: Learning analytics is an ethical practice that should align with core
organisational principles, such as open entry to undergraduate level study.
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.
Principle 3: Students should not be 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.

50. Source credit: https://www.open.ac.uk/students/charter/sites/www.open.ac.uk.students.charter/files/files/ecms/web-content/ethical-
use-of-student-data-policy.pdf
Principle 5: The University is transparent regarding data collection, and will
provide students with the opportunity to update their own data and consent
agreements at regular intervals.
Principle 6: Students should be engaged as active agents in the implementation of
learning analytics (e.g. informed consent, personalised learning paths,
interventions).
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.

51. Willis, J. E., Slade, S., & Prinsloo, P. (2016). Ethical oversight of student data in learning analytics: A typology
derived from a cross-continental, cross-institutional perspective. Educational Technology Research and
Development, 64, 881-901. DOI: 10.1007/s11423-016-9463-4 http://link.springer.com/article/10.1007/s11423-
016-9463-4
Who will provide oversight over the ethical
issues in learning analytics?
An interpretative multiple-case study: Indiana University, Open University (UK) and
the University of South Africa (Unisa)
2016

52. (1)
Humans
perform the
task
(2)
Task is
shared with
algorithms
(3)
Algorithms
perform task:
human
supervision
(4)
Algorithms
perform task:
no human
input
Seeing Yes or No? Yes or No? Yes or No? Yes or No?
Processing Yes or No? Yes or No? Yes or No? Yes or No?
Acting Yes or No? Yes or No? Yes or No? Yes or No?
Learning Yes or No? Yes or No? Yes or No? Yes or No?
Danaher, J. (2015). How might algorithms rule our lives? Mapping the logical space of algocracy. [Web log
post]. Retrieved from http://philosophicaldisquisitions.blogspot.com/2015/06/how-might-algorithms-rule-our-
lives.html
Human-algorithm interaction in the collection, analysis and
use of student data: What are the (ethical) issues?

53. Source credit: https://www.siyaphumelela.org.za/documents/5a61c7b737ff5.pdf

54. Imagecredit:https://pixabay.com/en/binary-code-man-display-dummy-face-1327512/
Guiding principles for an ethics of care:
Principle 1: The moral, relational duty of learning
analytics
Principle 2: Defining student success in the nexus of
student, institution and macro-societal agencies and
context
Principle 3: Understanding data as framed and framing
Principle 4: Student data sovereignty
Principle 5: Accountability
Principle 6: Transparency
Principle 7: Co-responsibility

55. “If you have come to help us, you can go
home. If you have come to accompany us,
please come. We can talk”
Glesne, C. (2016). Research as solidarity. In T. Kukutai and J. Taylor. (Eds), Indigenous data sovereignty. Toward an agenda (pp. 169-178). Canberra,
Australia: Australian National University Press. Retrieved from https://press.anu.edu.au/publications/series/centre-aboriginal-economic-policy-
research-caepr/indigenous-data-sovereignty
Principle 1: The moral, relational duty of learning analytics
Image credit: https://pixabay.com/en/sculpture-bronze-2196139/

56. Principle 2: Student success as
entangled
Image credit: https://pixabay.com/en/rope-knot-string-strength-cordage-3052477/

57. THE STUDENT AS AGENT
IDENTITY, ATTRIBUTES, HABITUS
Success
THE INSTITUTION AS AGENT
IDENTITY, ATTRIBUTES, HABITUS
SHAPING CONDITIONS: (predictable as well as uncertain)
SHAPING CONDITIONS: (predictable as well as uncertain)
THE STUDENT WALK
Multiple, mutually constitutive interactions between
student, institution & networks
FIT
FIT
F
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T
F
I
T
F
I
T
F
I
T
F
I
T
F
I
T
F
I
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F
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Subotzky, G., & Prinsloo, P. (2011). Turning the tide: a socio-critical model and framework for improving student success in open distance
learning at the University of South Africa. Distance Education, 32(2): 177-19.

58. Principle 3: Understanding data as
broken and framed but also as acting
and framing
Image credit: https://pixabay.com/en/eyeglasses-broken-glasses-sight-366446/

59. Student data are not something separate from
students’ identities, their histories, their beings.
Data are an integral, albeit informational part of
students being.
In the light of the view that data are not
something students own but rather who they
are; what are we assuming when we say we
‘collect’ their data?
E.g. Floridi, L. (2005). The ontological interpretation of informational privacy. Ethics
and Information Technology, 7(4), 185-200.
Principle 4: Student data sovereignty

60. Ctrl Alt Del
• How much control do students have to determine
what data institutions harvest; to challenge the meaning
of their data and our categories of analysis?
• Can students (re)define/alter interpretations of data
and definitions/categories? Can they offer us counter-
narratives to our understanding of their learning and
their life-worlds?
• Can students opt out of personalised data collection,
analysis and use and have their data deleted?

61. Willis, J. E., Slade, S., & Prinsloo, P. (2016). Ethical oversight of student data in learning analytics: A typology derived from a cross-
continental, cross-institutional perspective. Educational Technology Research and Development, 64, 881-901. DOI: 10.1007/s11423-
016-9463-4 http://link.springer.com/article/10.1007/s11423-016-9463-4
Principle 5: Accountability
An interpretative multiple-case study: Indiana University, Open
University (UK) and the University of South Africa (Unisa)

62. Principle 6: Transparency
If they don’t know that we collect their data, the scope and
purpose of the collection, how we will use their data and
how it will impact on their learning journeys, how is this
ethical?
Image credit: https://pixabay.com/en/sculpture-bronze-the-listening-2209152/

63. Principle 7: Co-responsibility
Our students’ journeys are intimately weaved into our
(institutional) stories. What happens in their lives,
impact ours. And vice versa.
Image credit: https://pixabay.com/en/weave-hand-labor-samoa-exotic-55/

64. Student data are an invitation to start a
conversation

65. You call me a misfit, a risk, a dropout and stop-out
Your research indicates that ‘students like me’ may not make it
You ask me questions regarding my financial status, where I live,
how many dependents I have, and I know that once I tell you,
I will become a number on a spreadsheet
I will be color-coded
I will become part of a structural equation model that re-affirms that
People like me
Don’t belong here
Somehow I don’t fit in you spreadsheet
But I want you to know that I am so much more
I am so much more than how you define me
I am so much more than my home address
(the one I lied about to get access to funding or to get a place in residence)
I am also a brother, a sister, a mother, a dependent, a carer
I don’t fit in your spreadsheets
I am not a dropout, I am a refugee, a migrant
I am in exile
Talk to me

66. THANK YOU
Paul Prinsloo (Prof)
Research Professor in Open Distance Learning
(ODL), College of Economic and Management
Sciences, Samuel Pauw Building, Office 5-21,
P.O. Box 392, Unisa, 0003, Republic of South
Africa
T: +27 (0) 12 433 4719 (office)
prinsp@unisa.ac.za
Skype: paul.prinsloo59
Personal blog:
http://opendistanceteachingandlearning.wordpress.com
Twitter profile: @14prinsp

67. Imagecredit:https://pixabay.com/en/sculpture-bronze-child-boy-1392529/
Thank you
Paul Prinsloo (Prof)
Research Professor in Open Distance
Learning (ODL), University of South
Africa, Unisa,
T: +27 (0) 12 433 4719 (office)
prinsp@unisa.ac.za
Skype: paul.prinsloo59
Personal blog:
http://opendistanceteachingandlearning.wordpress.com
Twitter profile: @14prinsp