Presentation at the Ethics Roundtable, 3 September 2015, University of South Africa (Unisa)

1. The ethics of (not)
knowing our students
Paul Prinsloo
ODL Research Professor
Presentation @
the Ethics Roundtable
University of South Africa
(Unisa)
3 September
2015

2. Acknowledgement
• I don’t own the copyright of any of the images
used and hereby acknowledge their original
copyright and licensing regimes. All the images
used in this presentation have been sourced
from Google and were labeled for non-
commercial reuse
• This work (excluding the licencing regimes of
the images from Google) is licensed under a
Creative Commons Attribution-NonCommercial
4.0 International License

3. • I don’t have the answers
• I think we need to problematise ethics in the context of
knowing, not knowing and the (im)possibility of un-knowing
• There are many possible approaches to and lenses on the
ethics of (not)knowing and I approach the ethics of
(not)knowing from a social critical perspective in the broader
context of surveillance studies
• This presentation further develops ideas flowing from, inter
alia, my collaborative research with Dr Sharon Slade, Open
University, United Kingdom
Disclaimer

4. Do we know our students?
What are the challenges of planning for an
unknown student population?
What do we need to do to address the
problem?

5. A counter question: What does “knowing”
look like in the context of a mega distance
education institution?
Image credit: https://commons.wikimedia.org/wiki/File:BinaryData50.png

6. Some more counter-questions:
What responsibility comes with knowing our
students? [We cannot un-know knowing…]
To know our students does not necessarily imply
understanding …
Even if we knew and understood our students, do
we have the will and the resources to do
something about what we (think we) know?

7. Therefore – tread carefully…
Image credit: https://www.flickr.com/photos/timrich26/3308513067/

8. OVERVIEW OF THE PRESENTATION
• What we know, who knows what, and what
we do about what we (think we) know…
• Responding to what we don’t know, if only
we knew…
• The responsibility (and ethics) arising from
knowing more…
• Towards a fiduciary duty of care…

9. So what do we know about our students?
• Demographic details – provided on
application/registration
• Registration data – qualification, number of courses
• Historical data of previously registered students
• Learning data – assignments (not) submitted,
learning histories – asynchronous, synchronous
and (increasingly) digital
• Contact/correspondence with various actors in the
institution
• Increasingly personal information

10. Who knows these things of our students?
• The ‘system’ – disparate databases that do
not (necessarily) talk to one another
• Various stakeholders – student advisors,
ICT, counsellors, academics, tutors, e-tutors,
& researchers, external markers
• Other external stakeholders – employers,
law enforcement agencies, data brokers,
labor brokers, commercial stakeholders
• Social media platforms and networks

11. We also know what we don’t know…
• Is s/he a “first generation” student or not?
• Socio-economic circumstances?
• Access, sustainability of access and cost of access
to the Internet?
• Do they have access to prescribed learning
resources?
• Motivation for registering for the qualification?
• Reading/comprehension skills?
• Support networks?
• Health and parental status, etc.?

12. What we don’t know and may never know…
What happens in the nexus between students
(and their life-worlds) and institutional
(operational, academic and social) identities and
processes and how do these impact and shape
student success and retention as a complex,
dynamic, non-linear, unfolding process consisting
of mutually constitutive and often
incommensurable factors…?

13. Processes
Inter & intra-
personal
domains
Modalities:
• Attribution
• Locus of control
• Self-efficacy
Processes
Modalities:
• Attribution
• Locus of control
• Self-efficacy
Domains
Academic
Operational
Social
TRANSFORMED INSTITUTIONAL IDENTITY & ATTRIBUTES
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)
Choice,
Admission
Learning
activities
Course
success
Gradua-
tion
THE STUDENT WALK
Multiple, mutually constitutive
interactions between student,
institution & networks
F
I
T
FIT
F
I
T
FIT
Employ-
ment/
citizenship
TRANSFORMED STUDENT IDENTITY & ATTRIBUTES
F
I
T
F
I
T
F
I
T
F
I
T
F
I
T
F
I
T
F
I
T
F
I
T
Retention/Progression/Positive experience
(From Subotzky & Prinsloo, 2011)

14. Who acts (if we do) on what we (think
we) know?
• Faculty – often, due to workloads and
student: staff ratios in a generalised, one-
size-fits-all way
• E-tutors
• Administrators – for everyone (new) contact,
a different administrator, starting over,
explaining everything again
• Tutors, counsellors, regional staff

15. How do we (they) verify & update what
we (they) know
• Do students have access to what we know
and/or think we know about them?
• How do we verify our assumptions about our
students, their learning needs and
trajectories?
• How do they verify and provide context to
their (digital) profiles?

16. And… who has access to what we know,
& under what conditions?
• We protect students from harm when we
approve research but how do we protect
students from harm when we act – change
pedagogy, assessment, staff allocation?
• How do we govern student databases, for
how long do we keep student data, on what
conditions do we share student data, with
whom?

17. We are stumbling through a dark room, not
knowing the meaning of the noises we hear,
reacting in kneejerk fashion, often in
uncoordinated ways, our actions based on
assumptions, hearsay, well-intended but non-
empirical, context-disjointed, fragmented
and possibly discipline-inappropriate ways…
Image credit: http://www.elmundodehector.com/wp-content/uploads/2015/04/door-dark.jpg

18. So, what are the ethical implications?
• The ethics of knowing – not only what we
know, but who knows what?
• The ethics of knowing – how do we
verify/test what we know? What are the
implications if we are wrong?
• The ethics of knowing and not acting
• The ethics of not knowing…

19. (Student) data as Medusa
Higher education is
mesmerized and
seduced by the
potential of the
collection, analysis
and use of student
data. If only we
know more…
Image credit: http://en.wikipedia.org/wiki/Medusa

20. We therefore need to critically consider the
ethical implications of …
• Knowing
• Not knowing
• Knowing more
The solution is not necessarily in knowing
more, but ensuring that once we know, we
respond in ethical, caring, discipline and
context-appropriate ways

21. The Paperholder – “le serre papiers” (1749)
The technology will allow the sovereign “…to know
every inch of the city as well as his own house, he
will know more about ordinary citizens than their
own neighbors and the people who see them
everyday (…) in their mass, copies of these
certificates will provide him with an absolute
faithful image of the city” (Chamayou, n.d)
• 1749 Jacques Francois
Gaullauté proposed “le
serre-papiers” – The
Paperholder – to King
Louis the 15th
• One of the first attempts
to articulate a new
technology of power –
one based on traces and
archives (Chamayou, nd)
• The stored documents
comprised individual
reports on each and
every citizen of Paris
Image source: https://www.mpiwg-berlin.mpg.de/en/news/features/feature14
Copyright could not be established

22. The great Ivy League photo scandal
1940-1970
“… a person’s body, measured
and analysed, could tell much
about intelligence, moral worth,
and probably future
achievement…
The data accumulated… will
eventually lead on to proposals
to ‘control and limit the
production of inferior and
useless organisms’”
(Rosenbaum, 1995)Image credit:
http://iconicphotos.wordpress.com/2010/07/29/the-
great-ivy-league-photo-scandal/

23. So how do we
understand and
critically engage
with the ethics
surrounding the
increasing
surveillance of
students in higher
education?
Image credit:
http://graffitiwatcher.deviantart.com/art/Big-
Brother-is-Watching-173890591

24. Understanding the collection, analysis and
use of student data in the contexts of
• Broader trends in higher education
• From surveillance to sousveillance
• The discourses in data and increasingly
Big Data

25. So what do we need to consider when
thinking about what we (don’t) know about
our students… (1)
1. Changes in funding regimes – funding follows
performance rather than preceding it – evidence-
based policy versus research led…
2. Increasing concerns regarding student retention
and dropout
3. Ranking systems and the internationalization
of higher education

26. So what do we need to consider when
thinking about what we (don’t) know about
our students… (2)
4. Higher education as business
5. The algorithmic turn and the quantification
fetish in higher education
6. The increasing digitization of learning and
teaching – and our beliefs about the ‘evidence’
7. The gospel of technosolutionism in higher
education
8. The hype, promise and dangers of (Big) data

27. The ethics of the collection, analysis and
use of student data in the context of the
change from surveillance to sousveilance
Image credit: http://commons.wikimedia.org/wiki/File:SurSousVeillanceByStephanieMannAge6.png

28. Jennifer Ringely – 1996-2003 – webcam
Source: http://onedio.com/haber/tum-zamanlarin-en-
etkili-ve-onemli-internet-videolari-36465
If I did not share it on
Facebook, did it really
happen?
We share more than
ever before, we are
watched more than
ever before and we
watch each other more
than ever before…

29. Three sources of data
Directed
A digital form of
surveillance
wherein the
“gaze of the
technology is
focused on a
person or place
by a human
operator”
Volunteered
“gifted by users
and include
interactions
across social
media and the
crowdsourcing of
data wherein
users generate
data” (emphasis
added)
(Kitchen, 2013, pp. 262—263)
Automated
Generated as “an
inherent,
automatic function
of the device or
system and
include traces …”

30. Image credit:
http://commons.wikimedia.org/wiki/File:Red_sandstone_Lattice_piercework,_Qutb_Minar_
complex.jpg

31. • The claim that Big Data is
equavalent to “allness” (Lagoze,
2014) – n=all – providing a
complete view of reality
• Big data “lessen our desire for
exactitude” (Mayer-Schönberger
& Cukier, 2013 in Lagoze, 2014)
• It is no longer necessary to
investigate the why things
happen… More important is to
note what is happening – data
speaks for itself…

32. Critical questions for (big) student data (1)
1. Big data changes the definition of knowledge – “Who
knows why people do what they do? The point is they do it,
and we can track and measure it with unprecedented
fidelity. With enough data, the numbers speak for
themselves” (Anderson, 2008, in boyd & Crawford, 2012, p. 666)
1. Claims to objectivity and accuracy are misleading –
“working with Big Data is still subjective, and what it
quantifies does not necessarily have a closer claim on
objective truth” (Boyd & Crawford, 2012, p. 667). Big Data
“enables the practice of apophenia: seeing patterns where
none actually exist, simply because enormous quantities of
data can offer connections that radiate in all directions”
(ibid., p. 668)

33. 3. Bigger data are not (necessarily) better data
4. Taken out of context, big and more data loses its
meaning – leading to context collapse & lack of
contextual integrity
5. Just because it is accessible does not make it
ethical – the difference in ethical review procedures
and overview between research and ‘institutional
research’
Critical questions for (big) student data (2)

34. Exploring the ethics of knowing and not
knowing through the seven dimensions
of surveillance (Knox 2010)
1. Automation
2. Visibility
3. Directionality
4. Assemblage
5. Temporality
6. Sorting
7. Structuring

35. Automation
Key questions Dimensional intensity
What is the timing of
the collection?
Intermittently/
infrequently
Continuous
Locus of control? Human Machine
Can it be turned on
and off (and by
whom?)
All the
monitoring can
be turned
on/off
None of the
monitoring can be
turned off

36. Visibility
Key questions Dimensional intensity
Is the surveillance
apparent and
transparent?
All parts
(collection,
storage,
processing and
viewing) are
visible
None of the
monitoring is visible
Ratio of self-to-
surveillant knowledge?
Subject knows
everything the
surveillant
knows
Subject does not
know anything that
the surveillant
knows

37. Directionality
Key questions Dimensional intensity
What is the relative
power of surveillant to
subject?
Subjects hold
all the power
Surveillant holds all
the power
Who has access to
monitoring/recording/
broadcasting functions?
Subjects Surveillant

38. Assemblage
Key questions Dimensional intensity
Medium of surveillance Single medium
(e.g. text)
Multimedia
Are the data stored? No Yes
Who stores the data? Subject or
collector
Third party

39. Temporality
Key questions Dimensional intensity
When does the monitoring
occur?
Confined to the
present
Combines the present
with the past
How long is the monitoring
frame?
One, isolated,
relatively short frame
(e.g. test)
Long periods, or
indefinitely
Does the system attempt to
predict future
behavior/outcomes
No – only
assessment of the
present
Present + past used to
predict the future
When are the data available? All of the data
available only after
event is completed
Available in real-time and
experienced as
instantaneous

40. Sorting
Key questions Dimensional intensity
Are subjects’ data
compared with other
data – other individuals/
groups/ abstract
configurations/ state
mandates?
None Other data are used
as basis for
comparison

41. Structuring
Key questions Dimensional intensity
Are data used to alter
the environment (i.e.
treatment, experience,
etc.)?
Not used Used to alter the
environment of all
subjects
Are data used to target
the subject for different
treatment that they
would otherwise
receive?
No data are used
as basis for
differing
treatment
Based on data,
treatment is
prescribed

42. Do students know/have the right to know…
• what data we harvest from them
• about the assumptions that guide our actions
and algorithms
• when we collect data & for what purposes
• who will have access to the data (now & later)
• how long we will keep the data & for what
purpose & in what format
• how will we verify the data &
• do they have access to confirm/enrich their
digital profiles…?
Adapted from Prinsloo, P., & Slade, S. (2015). Student privacy self-management: implications for learning
analytics. Presentation at LAK15, Poughkeepsie, NY, 16 March 2015
http://www.slideshare.net/prinsp/lak15-workshop-vulnerability-final

43. Do they know?
Do they have the right to know?
Can they opt out and what are
the implications if they do/don’t?
Adapted from Prinsloo, P., & Slade, S. (2015). Student privacy self-management:
implications for learning analytics. Presentation at LAK15, Poughkeepsie, NY, 16
March 2015
http://www.slideshare.net/prinsp/lak15-workshop-vulnerability-final

44. What are the implications for the collection,
analysis and use of student (digital) data?
1. The duty of reciprocal care
• Make TOCs as accessible and understandable (the
latter may mean longer…)
• Make it clear what data is collected, when, for what
purpose, for how long it will be kept and who will have
access and under what circumstances
• Students as stakeholders – current, correct information
• Provide users access to information and data held
about them, to verify and/or question the conclusions
drawn, and where necessary, provide context
• Provide access to a neutral ombudsperson
(Prinsloo & Slade, 2015)

45. What are the implications …? (2)
2. The contextual integrity of privacy and data – ensure the
contextual integrity and lifespan of personal data. Context
matters…
3. Student agency and privacy self-management
• The fiduciary duty of higher education implies a social
contract of goodwill and ‘do no harm’
• The asymmetrical power relationship between institution and
students necessitates transparency, accountability, access
and input/collaboration
• Empower students – digital citizenship/care
• The costs and benefits of sharing data with the institution
should be clear
• Higher education should not accept a non-response as equal
to opting in…
(Prinsloo & Slade, 2015)

46. What are the implications …? (3)
4. Future direction and reflection
• Rethink consent and employ nudges – move away from
thinking just in terms of a binary of opting in or out – but
provide a range of choices in specific contexts or needs
• Develop partial privacy self-management – based on
context/need/value
• Adjust privacy’s timing and focus - the downstream use of
data, the importance of contextual integrity, the lifespan of
data
• Moving toward substance over neutrality – blocking
troublesome and immoral practices, but also soft, negotiated
spaces of reciprocal care
(Prinsloo & Slade, 2015)

47. (In)conclusions
The gathering, analysis and use of student data
act as a structuring device. It is not neutral. It is
informed by current beliefs about what counts as
knowledge and learning, colored by assumptions
about gender/race/class/capital/literacy and in
service of and perpetuating existing or new power
relations.
Welcome to a brave new world…

48. THANK YOU
Paul Prinsloo (Prof)
Research Professor in Open Distance Learning (ODL)
College of Economic and Management Sciences,
Office number 3-15, Club 1, Hazelwood, P O Box 392
Unisa, 0003, Republic of South Africa
T: +27 (0) 12 433 4719 (office)
T: +27 (0) 82 3954 113 (mobile)
prinsp@unisa.ac.za
Skype: paul.prinsloo59
Personal blog: http://opendistanceteachingandlearning.wordpress.com
Twitter profile: @14prinsp

49. REFERENCES AND ADDITIONAL READING
Apple, M.W. (Ed.). (2010). Global crises, social justice, and education. New York, NY: Routledge.
Bauman, Z. (2012). On education. conversations with Riccardo Mazzeo. Cambridge, UK: Polity.
Booth, M. (2012, July 18). Learning analytics: the new black. EDUCAUSEreview, [online]. Retrieved
from http://www.educause.edu/ero/article/learning-analytics-new-black
Boyd, D., & Crawford, K. (2013). Six provocations for Big Data. Retrieved from
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1926431
Chamayou, G. (n.d.). Every move will be recorded. [Web log post]. Retrieved from https://www.mpiwg-
berlin.mpg.de/en/news/features/feature14
Cirton, D.K., & Pasquale, F. (2014). The scored society: Due process for automated predictions.
http://ssrn.com/abstract=2376209
Danaher, J. (2014, January 6). Rule by algorithm? Big Data and the threat of algocracy.[Web log post].
Retrieved from http://philosophicaldisquisitions.blogspot.com/2014/01/rule-by-algorithm-big-data-
and-threat.html
Deleuze. G. (1992). Postscript on the societies of control. October, 59 pp. 3-7.
Diakopoulos, N. (2014). Algorithmic accountability. Digital Journalism. DOI:
10.1080/21670811.2014.976411
Gitelman, L. (Ed.). (2013). “Raw data” is an oxymoron. London, UK: MIT Press.
Henman, P. (2004). Targeted!: Population segmentation, electronic surveillance and governing the
unemployed in Australia. International Sociology, 19, 173-191
Gray, J. (2004). Heresies. Against progress and other illusions. London, UK: Granta Books.

50. REFERENCES AND ADDITIONAL READING
(2)
Kitchen, R. (2013). Big data and human geography: opportunities, challenges and risks. Dialogues in
Human Geography, 3, 262-267. SOI: 10.1177/2043820613513388
Knox, D. (2010). Spies in the ouse of learning: a typology of surveillance in online learning environments.
Paper presented at Edge, Memorial University of Newfoundland, Canada, 12-15 October.
Kranzberg, M. (1986) Technology and history: Kranzberg's laws’. Technology and Culture, 27(3), 544—
560.
Lagoze, C. (2014). Big Data, data integrity, and the fracturing of the control zone. Big Data & Society
(July-December), 1-11.
Mayer-Schönberger, V. (2009). Delete. The virtue of forgetting in the digital age. Princeton, NJ: Princeton
University Press.
Mayer-Schönberger, V., Cukier, K. (2013). Big data. London, UK: Hachette.
Morozov, E. (2013a, October 23). The real privacy problem. MIT Technology Review. Retrieved from
http://www.technologyreview.com/featuredstory/520426/the-real-privacy-problem/
Morozov, E. (2013b). To save everything, click here. London, UK: Penguin Books.
Morozov, E. (2013). To save everything, click here. London, UK: Penguin Books.
Napoli, P. (2013). The algorithm as institution: Toward a theoretical framework for automated media
production and consumption. In Media in Transition Conference (pp. 1–36). DOI:
10.2139/ssrn.2260923
Pasquale, F. (2015). The black box society. Harvard Publishing, US.
Prinsloo, P. (2009). Modelling throughput at Unisa: The key to the successful implementation of ODL.
Retrieved from http://uir.unisa.ac.za/handle/10500/6035

51. REFERENCES AND ADDITIONAL READING
(3)
Prinsloo, P., & Slade, S. (2014). Educational triage in open distance learning: Walking a moral
tightrope. The International Review of Research in Open and Distributed Learning, 15(4), 306-
331. Retrieved from http://www.irrodl.org/index.php/irrodl/article/view/1881/3060
Prinsloo, P., & Slade, S. (2015, March). Student privacy self-management: implications for learning
analytics. In Proceedings of the Fifth International Conference on Learning Analytics And
Knowledge (pp. 83-92). ACM. Retrieved from http://dl.acm.org/citation.cfm?id=2723585
Rambam, S. (2008). Privacy is dead. Get over it. Retrieved from
https://www.youtube.com/watch?v=Vsxxsrn2Tfs&index=1&list=PL8C71542205AA51E5
Rosen, J. (2010, July 21). The web means the end of forgetting. New York Times [Online].
Rosenbaum, R. (1995, January 15). The great Ivy League nude posture photo scandal. The New York
Times. Retrieved from http://www.nytimes.com/1995/01/15/magazine/the-great-ivy-league-nude-
posture-photo-scandal.html
Serlwyn, N. (2014). Distrusting educational technology. Critical questions for changing times. New
York, NY: Routledge
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), 177-193.
Tene, O. & Polonetsky, J. (2013). Judged by the Tin Man: Individual rights in the age of Big Data. J. on
Telecomm. & High Tech. L., 11, 351.
.

52. REFERENCES AND ADDITIONAL READING
(4)
Totaro, P., & Ninno, D. (2014). The concept of algorithm as an interpretive key of modern rationality.
Theory Culture Society 31, pp. 29—49. DOI: 10.1177/0263276413510051
Therborn, G. (ed.).(2006). Inequalities of the world. New theoretical frameworks, multiple empirical
approaches. London, UK: Verso Books
Wagner, D., & Ice, P. (2012, July 18). Data changes everything: delivering on the promise of learning
analytics in higher education. EDUCAUSEreview, [online]. Retrieved from
http://www.educause.edu/ero/article/data-changes-everything-delivering-promise-learning-
analytics-higher-education
Watters, A. (2013, October 13). Student data is the new oil: MOOCs, metaphor, and money. [Web log
post]. Retrieved from http://www.hackeducation.com/2013/10/17/student-data-is-the-new-oil/
Watters, A. (2014). Social justice. [Web log post]. Retrieved from
http://hackeducation.com/2014/12/18/top-ed-tech-trends-2014-justice
Wigan, M.R., & Clarke, R. (2013). Big data’s big unintended consequences. Computer,(June), 46-53.