Student Surveillance in Higher Ed

A brave new world:
student surveillance
in higher education
– revisited
By Paul Prinsloo
(University of South Africa)
Presentation at the Association for Institutional Research (AIR) Forum, Denver
May 26-29 – “Data and decisions for higher education”
Photograph: Paul Prinsloo

ACKNOWLEDGEMENTS
• Except for the photographs on the title and last slides, 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
• An earlier version of this presentation won the Best Paper Award at
the Southern African Association for Institutional Research (SAAIR)
Conference 16-18 October 2014, held in Pretoria, South Africa

When we hear ‘surveillance’ we may
think of…
Source credit: http://mashable.com/2013/06/09/edward-snowden/

Source: http://www.pewinternet.org/2015/03/16/Americans-Privacy-Strategies-Post-Snowden/#the-public-has-divided-
sentiments-about-the-surveillance-programs

Source: http://www.pewinternet.org/2015/03/16/Americans-Privacy-Strategies-Post-
Snowden/#the-public-has-divided-sentiments-about-the-surveillance-programs

Or we may think of…
Image credit: http://commons.wikimedia.org/wiki/File:Penetentiary_Panopticon_Plan.jpg
‘Panopticon’
Jeremy Bentham,
1873
Greek mythology –
Argus Panoptes –
A giant with 100
eyes

“Secrets are lies”
“Sharing is caring”
“Privacy is theft”
(Eggers, 2013, p. 303)
And more recently…
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)
The eerie resemblance with the way higher
education sees the institutional learning
management system (LMS) is accidental…

Every breath you take
Every move you make
Every bond you break
Every step you take
I'll be watching you
Every single day
Every word you say
Every game you play
Every night you stay
O can't you see
You belong to me…
Sting – Every breath you take

Image credit: http://en.wikipedia.org/wiki/List_of_Back_to_the_Future_characters

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)

Image credit:
http://iconicphotos.wordpress.com/2010/07/29/the-
great-ivy-league-photo-scandal/
“… 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)
The great Ivy League
photo scandal 1940-
1970

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

“… ‘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)
If we accept that
…what are the implications for the
collection, analysis and use of student
data?

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

of student data in the context of some of the
broader trends in higher education
1. Changes in funding regimes – funding follows performance rather
than preceding it
2. Increasing concerns regarding student retention and dropout
3. Ranking systems and the internationalization of higher education
4. Higher education as business – the dominant neoliberal paradigm
5. The algorithmic turn and quantification fetish in higher
education
6. The increasing digitization of learning and teaching
7. The gospel of technosolutionism in higher education
8. The hype, promise and dangers of (Big) data

Higher education is
mesmerized/seduced
by the potential of
the collection,
analysis and use of
student data
Image credit: http://en.wikipedia.org/wiki/Medusa
(Student) data as Medusa – techno-solutionism
in action

Every page you view
Every click you make
Every link you follow
Every step you (don’t) take
Every single day
Every word you say
Every game you play
Every night you stay
O can't you see
You belong to me…
Adapted from Sting – Every breath you take

As student data are increasingly
used to personalise learning and
to allocate resources, we are
creating a “brave new world” with
Delta children wearing khaki,
Epsilons wearing black (“they’re
too stupid to be able to read or
write”), and Gammas and Deltas
(Huxley, 2007, p. 22)
The dream/nightmare of personalizing learning…

of student data in the context of the change
from surveillance to sousveillance
Image credit: http://commons.wikimedia.org/wiki/File:SurSousVeillanceByStephanieMannAge6.png

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…
From surveillance to sousveillance…

We are increasingly
watched/measured
We increasingly
watch/measure each
other
We increasingly watch
ourselves
Image credit: http://en.wikipedia.org/wiki/Surveillance#/media/File:Surveillance_video_cameras,_Gdynia.jpeg

Understanding the collection,
analysis and use of student data in
the context of the discourses on
(Big) data

Different sources/variety of
quality/ integrity of data
Different role-players
with different interests
• Individuals
• Corporates
• Governments
• Higher education
• Data brokers
• Fusion centers
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

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)

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

Big data presents “a
paradigm shift in the ways
we understand and study
our world”
(Eynon, 2013, p. 237)
Image credits: http://commons.wikimedia.org/wiki/File:DARPA_Big_Data.jpg

Amidst the hype and the potential there are
also…
• “Rule by algorithm? Big data and the threat of algocracy” –
Danaher (2014)
• “Big Data’s big unintended consequences” – Wigan & Clarke (2013)
• “Six provocations for Big Data” – Boyd & Crawford (2011)
• “Critical questions for Big Data” – Boyd & Crawford (2012)
• “Judged by the Tin Man: Individual rights in the age of Big Data” –
Tene & Polonetsky (2013)
• “Algorithmic accountability” – Diakopoulos (2014)
• “The scored society: Due process for automated predictions” –
Citron & Pasquale (2014)
• “Big data, data integrity, and the fracturing of the control zone” –
Lagoze (2014)

Caught between visions of Utopias and
Dystopias…

Shi(f)t happens…
• 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…

“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”
Source: http://www.hup.harvard.edu/catalog.php?isbn=9780674368279

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)

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 & lack of contextual integrity
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

Points of departure
(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)

The collection, analysis and use of student
data: Where surveillance and sousveillance
meet…

Towards a typology of surveillance in
higher education (Knox, 2010)
• Panoptic – Automated/internalised visibility
• Rhizomatic – Multidirectional/sousveillance/
Surveillant assemblages – merging of
different spheres/multiplying/amplifying
• Predictive

Predictive surveillance
•The (digital) past + (digital) real-time => future
behavior … “The past becomes a simulated prologue”
(Knows, 2010, p. 6; emphasis added)
•Structuring/constraining choices/possibilities –
shaping material conditions (Henman, 2004; Napoli,
2013)
•Issues of identity/race/gender/class
•The end of forgetting (Mayer-Schönberger, 2009;
Rosen, 2010)

Preliminary seven dimensions of surveillance
(Knox 2010)
1. Automation
2. Visibility
3. Directionality
4. Assemblage
5. Temporality
6. Sorting
7. Structuring

1. Automation
Key questions Dimensional intensity
What is the timing of the
collection?
Intermittently/i
nfrequently
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

2. Visibility
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

3. Directionality
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

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

5. Temporality
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

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

7. Structuring
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

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

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

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

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…

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

(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)

(In)conclusions
Learning analytics are a structuring device, not
neutral, 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…

Last thing I remember, I was
Running for the door
I had to find the passage back
To the place I was before
"Relax, " said the night man,
"We are programmed to receive.
You can check-out any time you like,
But you can never leave! "
Eagles – Hotel California

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.co
m
Twitter profile: @14prinsp
Photograph: Paul Prinsloo

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.

References and additional reading (cont.)
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

References and additional reading (cont)
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.
.

References and additional reading (cont)
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, E., & 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.

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