Collecting, analysing & using student data: Breaking or serving cycles of inequality & injustice?

Collecting, analysing
& using student data:
Breaking or serving
cycles of inequality
& injustice?
By Paul Prinsloo
(University of South Africa)
Presentation at the
14 – 15 May 2015 Launch Conference of the Siyaphumelela Programme
GARDEN COURT OR TAMBO INTERNATIONAL AIRPORT HOTEL
Image credit:
http://libguides.humboldt.edu/content.php?pid=63
0957&sid=5219761

ACKNOWLEDGEMENTS
This presentation forms part of a collaborative research
project with Prof Laura Czerniewicz (University of Cape
Town)
I do not own the copyright of any of the images in this
presentation. I 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

Overview of the presentation
• Education, social justice, equality and other myths
• Always remember, never forget…
• Mapping a conceptual framework for understanding the role of the
collection, analysis and use of student data to break cycles of inequality
and injustice
• Diversity, inequality and injustice 101
• The collection, analysis and use of students’ digital data
• Some pointers for consideration
• Problematising student success and retention
• What are the implications for the collection, analysis and use of student
(digital) data?
• (In)conclusions

Audrey Watters (2014) – Social justice - http://hackeducation.com/2014/12/18/top-ed-tech-trends-2014-justice/

Education, social justice, equality and other myths
• “Education is the civil rights issue of our time” (Watters, 2014) versus “things
that the ‘education gospel cannot fix’” (McMillan Cottom, 2014)
• Evidence suggests that inequalities and injustice are on the increase and
that, somehow, education does not (on its own) address inter-generational
and structural injustice/inequality
• Knowledge, education and science cannot (and will not) “end the conflicts in
history. It is an instrument that humans use to achieve their goals, whether
winning wars or curing the sick, alleviating poverty or committing genocide”
(Gray,2004, p. 70)
• Data, data collection and analysis are neither neutral or an unqualified good
(Henman, 2003; Gitelman, 2013; Morozov, 2013)
• Techno-solutionism - To save everything, click here (Morozov, 2013)
Our collection, analyses & use of student data: Will it break or serve cycles of
inequality & injustice?

Image credit: http://dallten.deviantart.com/art/Always-remember-never-forget-281408542

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)

http://iconicphotos.wordpress.com/201
0/07/29/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

Image credit:
http://www.preventgenocide.org/edu/pastgenocides/rwanda/in
dangamuntu.htm
When data goes wrong…
Image credit:
http://www.genocidearchiverwanda.org.rw/index.php/Category:
Identity_Documents

Diversity,
inequality and
injustice
The collection,
analysis and use
of (student)
data
Collecting,
analysing and
using student
data to break
cycles of
inequality and
injustice
Broad overview of the presentation

Diversity, nequality and injustice 101
• Diversity ≠ necessarily inequality
• Inequality should be understood as a plurality, as inequalities
• “How, through what processes, are inequalities actually
produced, increased, or reduced?” (Therborn, 2006, p. xiii)
• In knowledge economies/knowledge societies, knowledge and
access to knowledge are crucial resources and can be used to
challenge or maintain inequalities
• How is the collection and analysis of data used to break or
perpetuate cycles of inequality and injustice?

Vital inequality –
based on a moral
conception of
fundamental
human equality
Life expectancy, health expectancy (expected
years of life without serious illness), etc.
Existential
inequality
Access to opportunities
The impact of patriarchy, slavery, caste, class,
racism, & sexism on social mobility/opportunities
for a dignified life
Resource inequality Differentiated access to resources and their ability
to act. Important to note that networks not only
include but also exclude
(Therborn, 2006)

Rethinking the mechanisms of inequality
• “…what is called ‘achievement’ is in fact largely dependent
on systemic game construction and reward structuration”
(Therborn, 2006, p. 11)
• The ideological blind spot of achievement – “it is blind to
everything but the achieving actor, telling us nothing about
her relations to others, or about the contexts of
opportunities and rewards (Therborn, 2006, p. 12)
• “…‘equality of opportunity’ is no more than a fleeting
moment in the overall process of inequality” (Therborn,
2006, p. 11)
Image credit: http://commons.wikimedia.org/wiki/File:Gears.JPG

Image credit: http://commons.wikimedia.org/wiki/File:Gears.JPG
• Distantiation: what is regarded as capital, by whom, what are
the criteria of success, what are the rewards & penalities
• Hierarchisation: the structuration of privilege &power,
membership
• Exclusion: barring the advance or access to resources – who is
worthy and who is not
• Exploitation: groups of superiors and inferiors, abuse
These four mechanisms are cumulative
Four mechanisms of inequality
(Therborn, 2006)

The reality of cycles of inequality and injustice
in South African higher education
• Despite substantial government funding incentives,
numerous policy initiatives and well-intentioned
institutional efforts, retention and success rates are
notoriously poor
• Higher education institutions are as ill-prepared for
underprepared students as vice versa…
• The legacy of colonialism and apartheid, inter-generational
cycles of injustice, poverty and inequality
• The revolving door and equal opportunities
(Subotzky & Prinsloo, 2011)

Image credit: http://i.imgur.com/r99mO5b.jpg
Moving from equality to justice

How will the collection,
analyses, and use of student
data assist us to move from
equality to justice?
Image credit: http://commons.wikimedia.org/wiki/File:DARPA_Big_Data.jpg

The collection, analysis and use of students’
digital data in the context of…
• Claims that Big Data in higher education will change everything &
that student data are “the new black” (Booth, 2012) & “the new
oil” (Watters, 2013)
• 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)
• Claims that “Privacy is dead. Get over it” (Rambam, 2008)

(Big) data is…
…not an unqualified good (Boyd and Crawford, 2011) and “raw
data is an oxymoron” (Gitelman, 2013)(Also see Kitchen, 2013)
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, 2010; Bauman, 2012)
Points of departure (1)

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

• 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 (n≠ the whole)
• We create smoke and claim we see a fire – so what does the
number of clicks mean? 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” (Boyd & Crawford, 2012,p. 668)
• We collect and analyse what we think matters – how sure are
we that it does?
• We seldom wonder what if our algorithms are wrong, and what
are the long-term implications for students?
(See Slade & Prinsloo, 2013; Prinsloo & Slade, 2015)

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

Problematising student success
and retention
Image credit: http://commons.wikimedia.org/wiki/File:Revolving_door-base.jpg

If…
student success is the result of mostly non-linear,
multidimensional, interdependent interactions at
different phases in the nexus between student,
institution and broader societal factors
(Prinsloo, 2009)
… what data should we collect, when, why
and what then?

What are the implications for the collection,
analysis and use of student (digital) data?
1. Students and institutions are situated agents
• In the context of he asymmetrical power relationship
between institution and students the social contract and
duty of fiduciary care matters
• How much choice should students be provided regarding
alternative curricula, assessments and attendance of
compulsory support?
• The need for algorithmic accountability, transparency,
diagnosis, prognosis and outcomes
• Educational triage as moral practice requires having the best
interests of students as rationale
(Prinsloo & Slade, 2014)

What are the implications …? (2)
2. The need for non-maleficence – do no harm. Beneficence and non-
maleficence are sides of the same coin.
3. The notion and practice of distributive justice – difficult and
complex. We cannot just assess or consider school leaving marks, or
examination marks. Context and the historical and inter-
generational legacies of inequality and injustice matter. We cannot
and should not negate the impact of he “causal power of social
structures”
(Prinsloo & Slade, 2014)

(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)

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

References
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
Deleuze. G. (1992). Postscript on the societies of control. October, 59 pp. 3-7.
Gitelman, L. (Ed.). (2013). “Raw data” is an oxymoron. London, UK: MIT Press.
Gray, J. (2004). Heresies. Against progress and other illusions. London, UK: Granta Books.
Kitchen, R. (2013). Big data and human geography: opportunities, challenges and risks. Dialogues in
Human Geography, 3, 262-267. SOI: 10.1177/2043820613513388
Kranzberg, M. (1986) Technology and history: Kranzberg's laws’. Technology and Culture, 27(3), 544—
560.
McMillan-Cottom, T. (2014). Reparations: What the education gospel cannot Fix
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

References (cont.)
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
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
Selwyn, N. (2014). Distrusting educational technology. Critical questions for changing times. New York, NY:
Routlegde.
Slade, S., & Prinsloo, P. (2013). Learning analytics: ethical issues and dilemmas. American Behavioural Scientist,
57(1) pp. 1509–1528.
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.
Therborn, G. (ed.).(2006). Inequalities of the world. New theoretical frameworks, multiple empirical
approaches. London, UK: Verso Books.

References (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
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

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