Presentation on Wednesday 8 April at the Centre for Higher Education (CHED) at the University of Cape Town.

1. Critical issues in the
collection, analysis
and use of
students’ (digital)
data
By Paul Prinsloo (University of South Africa)
Presentation at the
Centre for Higher Education Development (CHED), University of Cape Town, Wednesday 8 April 2015
Image credit:
http://graffitiwatcher.deviantart.com/art/Big-
Brother-is-Watching-173890591

2. ACKNOWLEDGEMENTS
I do not own the copyright of any of the images in this
presentation and hereby acknowledge the original
copyright and licensing regime of every image and
reference used. All the images used in this presentation
have been sourced from Google and were labeled for non-
commercial reuse.
This work (excluding the images) is licensed under a
Creative Commons Attribution-NonCommercial 4.0
International License

3. Overview of the presentation
• Map the collection, analysis and use of students’ digital data
against the backdrop of discourses re surveillance/sousveillance
& Big Data/lots of data
• Problematise the collection, analysis and use of student digital
data …
• User knowledge and choice in the context of the collection,
analysis and use of data
• When our good intentions go wrong…
• Do students know?
• Points of departure
• Implications
• (In)conclusions

4. Mapping the
context…
Image credit: http://en.wikipedia.org/wiki/Mappa_mundi

5. The collection, analysis and use of students’
digital data in the context of…
• Claims that Big Data in higher education will change
everything and that student data are “the new black” and
“the new oil”
• Our “quantification fetish”, the “algorithmic turn” and
“techno-solutionism” (Morozov, 2013a, 2013b)
• The current meta-narratives of “techno-romanticism” in
education (Selwyn, 2014)
• The belief that data is “raw”, “speak for itself” and that
collecting even more data equals necessarily results in
better understanding and interventions

6. The collection, analysis and use of students’
digital data in the context of… (2)
• Ever-increasing concerns about surveillance, and new
forms of “societies of control” (Deleuze, 1992)
• The “algorithmic turn” and the “alogorithm as
institution” (Napoli, 2013)
• A possible “gnoseological turning point” where our
belief about what constitutes knowledge is changing
and where individuals are reduced to classes and
numbers (Totaro & Ninno, 2014). N=all (Lagoze, 2014)
• Claims that “Privacy is dead. Get over it” (Rambam, 2008)

7. Problematising the collection,
analysis and use of student digital
data …

8. Problematising the collection, analysis and use of
student data…
• Privacy as concept & as enforceable construct is fragile (Crawford & Schultz,
2014; Prinsloo & Slade, 2015)
• Legal & regulatory frameworks (permanently?) lag behind (Silverman, 2015)
• Consent is more than a binary of opt-in or opt-out (Miyazaki & Fernandez, 2000;
Prinsloo & Slade, 2015)
• Individuals share unprecedented amounts of information but yet, are
increasingly concerned about privacy (Murphy, 2014)
• Discrimination is a fundamental building block in the collection, analysis
& use of data (Pfeifle, 2014; Tene & Polonetsky, 2014)
• There are increasing concerns re the lack of algorithmic accountability
(Diakopoulos, 2014; Pasquale, 2014) & the fracturing of the control zone (Lagoze,
2014)
• There are also concerns about the unintended consequences of the
collection, analysis & use of data (Wigan & Clark, 2013)

9. Mapping the collection, analysis and
use of student digital data against the
discourses of
surveillance/sousveillance

10. From surveillance to sousveillance…
Image credit: http://commons.wikimedia.org/wiki/File:SurSousVeillanceByStephanieMannAge6.png

11. 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 every
before, we are watched
more than ever before and
we watch each other more
than ever before…
Privacy in flux…

12. Surveillanc
e 101
Image credit: http://en.wikipedia.org/wiki/Surveillance

13. Image source: https://www.mpiwg-berlin.mpg.de/en/news/features/feature14 Copyright
could not be established
• 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
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
neighbours 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)
The Paperholder – “le serre papiers” (1749

14. More recently…

15. “Secrets are lies”
“Sharing is caring”
“Privacy is theft”
(Eggers, 2013, p. 303)
Welcome to “The Circle”
TruYou – “one account, one identity, one
password, one payment system, per person.
(…) The devices knew where you were…
One button for the rest of your life online…
Anytime you wanted to see anything, use
anything, comment on anything or buy
anything, it was one button, one account,
everything tied together and trackable and
simple…”
(Eggers, 2013, p. 21)

16. “Hidden algorithms can make (or ruin) reputations,
decide the destiny of entrepreneurs, or even
devastate an entire economy. Shrouded in secrecy
and complexity, decisions at major Silicon Valley
and Wall Street firms were long assumed to be
neutral and technical. But leaks, whistleblowers,
and legal disputes have shed new light on
automated judgment. Self-serving and reckless
behavior is surprisingly common, and easy to hide
in code protected by legal and real secrecy. Even
after billions of dollars of fines have been levied,
underfunded regulators may have only scratched
the surface of this troubling behavior.”
http://www.hup.harvard.edu/catalog.php?isbn=97806743682
79

17. Mapping the collection, analysis and
use of student digital data against the
discourses of Big Data/lots of data…

18. What is Big Data?
• Huge in volume
• High in velocity, being created in or near real time
• Diverse in variety
• Exhaustive in scope
• Fine-grained in resolution and uniquely indexical in
identification
• Relational in nature
• Flexible, holding traits of extensionality (can add new
fields easily) and scalability(can expand in size rapidly)
(Kitchen, 2013, p. 262)

19. Exploring the differences between Big
Data/lots of data… (Lagoze, 2014)
Mayer-Schönberger & Cukier (2013 –
• N=all – Big Data as presenting a “complete view” of reality
• Big permits us to lessen our desire for exactitude
• We need to shed some of our obsession for causality in
exchange for correlations – not necessarily knowing (or
caring about the why but focusing on the what
Lots of data – methodological challenges
Big Data – epistemological challenges

20. Big data as cultural, technological, and scholarly
phenomenon (Boyd & Crawford, 2012)
Big Data as interplay of
• Technological: maximising computation power and algorithmic
accuracy to gather, analyse, link, and compare large data sets
• Analysis: drawing on large data sets to identify patterns in order to
make economic, social, technical, and legal claims
• Mythology: the widespread belief that large data sets offer a higher
form of intelligence and knowledge that can generate insights that
were previously impossible, with the aura of trust, objectivity, and
accuracy
(Boyd & Crawford, 2012, p. 663)

21. 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”
Automated
Generated as “an
inherent, automatic
function of the device or
system and include
traces …”
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)

22. Different sources/variety of
quality/ integrity of data
Different role-players
with different interests
• Individuals
• Corporates
• Governments
• Higher education
• Data brokers
• Fusion centres
Different methods/types
of surveillance,
harvesting and analysis
Issues re
• Informed consent
• Reuse/contextual
integrity/context
collapse
• Ethics/privacy/
justice/care
The Trinity of Big Data
Adapted & refined from Prinsloo, P. (2014). A brave new world. Presentation at SAAIR,
16-18 October http://www.slideshare.net/prinsp/a-brave-new-world-student-
surveillance-in-higher-education

23. Image credit:
http://commons.wikimedia.org/wiki/File:Red_sandstone_Lattice_piercework,_Qutb_Minar_complex.jpg

24. Image credits: http://commons.wikimedia.org/wiki/File:DARPA_Big_Data.jpg
“Privacy and big data are simply
incompatible and the time has come
to reconfigure choices that we made
decades ago to enforce constraints”
(Lane, Stodden, Bender & Nissenbaum, 2015, p. xii)

25. Critical questions for big data – boyd & Crawford (2012)
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)

26. Critical questions for big data (2) – boyd & Crawford
(2012)
3. Bigger data are not always better data
3. Taken out of context, Big Data loses its meaning – leading to
context collapse
3. Just because it is accessible does not make it ethical – the
difference in ethical review procedures and overview
between research and ‘institutional research’
3. Limited access to Big Data creates new digital divides

27. User knowledge and
choice in the context
of the collection,
analysis and use of
data
Image credit: http://www.mailbow.net/eng/blog/opt-in-and-op-out/

28. “Providing people with notice, access, and the
ability to control their data is key to facilitating
some autonomy in a world where decisions are
increasingly made about them with the use of
personal data, automated processes, and
clandestine rationales, and where people have
minimal abilities to do anything about such
decisions”
(Solove, 2013, p. 1899; emphasis added)
Image credit: http://www.mailbow.net/eng/blog/opt-in-and-op-
out/

29. A framework for mapping the collection, use
and sharing of personal user information
(Miyazaki & Fernandez, 2000)
Never
collect
or
identity
users
Users
explicitly
opting in to
have data
collected,
used and
shared
Users
explicitly
opting out
The constant
collection, analysis
and sharing of user
data with users’
knowledge
The constant
collection,
analysis and
sharing of user
data without
users’ knowledge
Also see Prinsloo, P., & Slade, S. (2015). Student vulnerability, agency and learning analytics:
an exploration. Presentation at LAK15, Poughkkeepsie, NY, 16 March 2015
http://www.slideshare.net/prinsp/lak15-workshop-vulnerability-final

30. The constraints of privacy self-management …
• It is almost impossible to comprehend the scope of data
collected, analysed and used, the combination with other
sources of information, the future uses for historical information
and the possibilities of re-identification of de-personalized data
• These various sources of information and combinations of
sources start to resemble “electronic collages” and an “elaborate
lattice of information networking” (Solove, 2004, p. 3)
• The fragility of consent… what may be innocuous data in one
context, may be damning in another
Adapted from Prinsloo, P., & Slade, S. (2015). Student privacy self-management: implications
for learning analytics. Presentation at LAK15, Poughkkeepsie, NY, 16 March 2015
http://www.slideshare.net/prinsp/lak15-workshop-vulnerability-final

31. When our good intentions go wrong…

32. Using student data and student vulnerability: between
the devil and the deep blue sea?
Students (some
more vulnerable
than others)
Generation,
harvesting and
analysis of data
Our assumptions,
selection of data
and algorithms
may be ill-defined
Turning ‘pathogenic’ – “a
response intended to
ameliorate vulnerability
has the paradoxical effect
of exacerbating existing
vulnerabilities or
generating new ones”
(Mackenzie et al, 2014, p.
9)
Adapted from Prinsloo, P., & Slade, S. (2015). Student vulnerability, agency and learning
analytics: an exploration. Presentation at LAK15, Poughkkeepsie, NY, 16 March 2015
http://www.slideshare.net/prinsp/lak15-workshop-vulnerability-final

33. So, do students know…?

34. Do students know/have the right to know…
• what data we harvest from them
• about the assumptions that guide our 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, Poughkkeepsie, NY, 16 March 2015
http://www.slideshare.net/prinsp/lak15-workshop-vulnerability-final

35. 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, Poughkkeepsie, NY, 16 March 2015
http://www.slideshare.net/prinsp/lak15-workshop-vulnerability-final

36. Points of departure (1)
(Big) data is…
…not an unqualified good (Boyd and Crawford, 2011)
and “raw data is an oxymoron” (Gitelman, 2013) – see
Kitchen, 2014
Technology and specifically the use of data have been
and will always be ideological (Henman, 2004; Selwyn,
2014) and embedded in relations of power (Apple,
2004; Bauman, 2012)

37. “… ‘educational technology’ needs to be
understood as a knot of social, political,
economic and cultural agendas that are riddled
with complications, contradictions and conflicts”
(Selwyn, 2014, p. 6)
Points of departure (2):
If we accept that
…what are the implications for the
collection, analysis and use of
student data?

38. Points of departure (3):The (current?)
limitations of our surveillance
• Students’ digital lives are but a minute part of a bigger
whole – but our collection and analysis pretend as if this
minute part represents the whole
• We create smoke and claim we see a fire – so what
does the number of clicks mean?
• We seldom wonder what if our algorithms are wrong,
and what are the long-term implications for students?

39. What are the implications for the collection,
analysis and use of student (digital) data?
(Prinsloo & Slade, 2015)
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
• 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)

40. What are the implications …? (2)
2. The contextual integrity of privacy and data – ensure the contextual
integrity and lifespan of personal data. Context matters…
2. 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)

41. 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)

42. Ethical use of Student Data for Learning
Analytics Policy
An example of the institutionalisation of
thinking about the ethical implications of using
student data
Available at: http://www.open.ac.uk/students/charter/essential-documents/ethical-use-
student-data-learning-analytics-policy

43. (In)conclusions
“The way forward involves
(1) developing a coherent approach to consent, one that
accounts for the social science discoveries about how
people make decisions about personal data;
(2) recognising that people can engage in privacy self
management only selectively;
(3) adjusting privacy law’s timing to focus on downstream
uses; and
(4) developing more substantive privacy rules.
These are enormous challenges, but they must be tackled”
(Solove, 2013)

44. (In)conclusions
“Technology is neither good or bad; nor is it neutral…
technology’s interaction with social ecology is such that
technical developments frequently have environmental,
social, and human consequences that go far beyond the
immediate purposes of the technical devices and practices
themselves”
Melvin Kranzberg (1986, p. 545 in boyd & Crawford, 2012, p. 1)

45. 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