Presentation at the Milpark Business School Research Colloquium, Johannesburg, South Africa, 25 June 2016

1. Presentation at the
Milpark Business School (MBS)
Research Colloquium 25 June 2016
Image credit: Image compiled and adapted from an image retrieved from-
https://pixabay.com/en/binary-code-man-display-dummy-face-1327498/
Using student data to
inform support, pedagogy
& curricula:
ethical issues & dilemmas
By Paul Prinsloo
(University of South Africa, Unisa)

2. Acknowledgements
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-NonCommercial 4.0
International License

3. Overview of the presentation
• The broader context of research on students in higher
education
• Student data as Medusa – mapping the field, the
tools, the actors, the dimensions and the implications
• Looking away: Pointers for consideration
• (In)conclusions

4. Higher education should…
• Do more with less
• Expect funding to follow performance
rather than precede it
• Realise it costs too much, spends carelessly, teaches poorly, plans
myopically, and when questioned, acts defensively
(Hartley, 1995, p. 412, 861)
We also cannot & shouldn’t underestimate the impact of the dominant
models of neoliberalism and its not-so-humble servant – managerialism
– on higher education (Diefenbach, 2007)
The broader higher education context
Image credit:
http://commons.wikimedia.org/wiki/File:Mcdonalds_logo.png

5. • Changes in funding and audit
regimes –evidence-based policy
versus research-led…
• Increasing concerns regarding
student retention and dropout
• International ranking systems,
increased competition in higher
education
The broader higher education context (2)
(See: Murphie, A. (2014). Auditland. PORTAL
Journal of Multidisciplinary International Studies, 11(2), 1-41.)

6. The broader higher education context (3)
• How do national,
institutional, disciplinary
contexts support or
frustrate efforts to
remove barriers?
• What are the issues of
costs and scalability in
erasing inter-
generational inequality?
• What data do we need
in order to move
towards more just,
caring and
compassionate access,
teaching and learning?Image sources: https://twitter.com/urbandata/status/695261718344290304
https://za.pinterest.com/barbaralley/fair-is-not-equal/

7. • Looking for sustainable business models
#FeesMustFall
• The algorithmic turn and quantification fetish in
higher education
• The increasing digitisation of learning and
teaching, and access to students’ digital shadows
• The gospel of technosolutionism in higher
education
• The lure of Big(ger) data
The broader higher education context (4)

8. (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

9. There is an increasing need for data/evidence
We have access to increasing amounts and granularity
of student data
We have increased capacity & technologies
for analysis and visualisation
The impact of impotent, static, &
obsolete legislation, policies and
guidelines
And a lack of oversight and
enforcement
Image credit: Retrieved from https://www.flickr.com/photos/timrich26/3308513067

10. We need to ensure the
sustainability of higher
education in the light of
• funding constraints
• increased competition
• the socioeconomic
downturn
• student needs
• increased need for
efficiency/effectiveness
• audit & quality
assurance regimes
• #FeesMustFall
The fiduciary duty of higher
education to
• care
• create supportive,
appropriate and effective
teaching and learning
environments
• ethical collection,
analysis and use of
student data
• transparency
A balancing act
See: 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

11. So…, who has access to and use
student data to inform student
support, pedagogy & curricula; and
under what conditions and who
provides oversight

12. See: Willis, J. E., Slade, S., & Prinsloo, P. (in review). Ethical oversight of student data in learning analytics: A typology
derived from a cross-continental, cross-institutional perspective. Submitted to special issue of Educational Technology
Research and Development (Exploring the Relationship of Ethics in Design and Learning Analytics: Implications for the Field
of Instructional Design and Technology), guest edited by M. Tracey and D. Ifenthaler.
When the collection, analysis and use
of student data have an internal focus
• Departmental/institutional reports
& planning
• Scholarship of teaching and
learning
• Provide appropriate and effective
student support
• Allocation of staff/resources
When the collection, analysis and use
of student data have an external focus
• Reporting to a range of
stakeholders, e.g. government,
industry, etc., and for a range of
purposes, e.g., funding
• Conference presentations
• Journal articles
• Monographs & edited volumes
• Popular press
• Marketing

13. Institutional Research
• Often located in a
designated department
• Staffed by data
scientists, analysts
• Inform strategy and
policy
• Use student data
already ‘gifted’ during
application/
registration process and
from Learning
Management System
(LMS)
• Specific data collection
• Often blanket ethical
clearance
Research (capital ‘R’)
• Mostly faculty, but
increasingly support
and professional staff
• Varying skills and
understanding
• Chasing outputs, h-
index, citations
• Results mostly not used
to inform teaching and
learning
• Use primary and
secondary student data
• Oversight provided by
Institutional Review
Boards (IRBs)
Emerging forms of research
• Mostly faculty, but increasingly
support and professional staff
• Varying skills and understanding
• Not produced for formal
outputs eg publication, but to
inform pedagogy, assessment,
personalisation, departmental
reports
• Often use student data already
‘gifted’ during application/
registration process and from
Learning Management System
(LMS) or personal synchronous
or asynchronous communication
• No ethical review/oversight
Academic & learning analytics

14. Type of
analytics
Level or object of
analysis
Who benefits?
Learning
analytics
Course level: social networks,
conceptual development,
discourse analysis, “intelligent
curriculum”
Learners, faculty
Departmental: predictive
modelling, patterns of
success/failure
Learners, faculty
Academic
analytics
Institutional: learner profiles,
performance of academics,
knowledge flow
Administrators, funders,
marketing
Regional (state/provincial):
comparisons between systems
Funders, administrators
National and International National governments,
education authorities
Siemens, G., & Long, P. (2011). Penetrating the Fog: Analytics in Learning and Education. EDUCAUSE review, 46(5), 30-40. Retrieved from
http://er.educause.edu/articles/2011/9/penetrating-the-fog-analytics-in-learning-and-education

15. “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.”
1st International Conference on Learning Analytics and Knowledge, Banff, Alberta, February 27–March 1,
2011. In Siemens and Long (2011)

16. (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?
Human-algorithm interaction in the collection, analysis and
use of student data
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

17. We know where you
are. We know where
you’ve been. We can
more or less know
what you're thinking
about
(@FrankPasquale, 2016)
Image credit: https://en.wikipedia.org/wiki/Surveillance

18. Imagine what we could learn if we put a tracker on
everyone and everything (Jurdak, 2016)
Image credit: https://www.flickr.com/photos/jeepersmedia/13966485507

19. Page credit: http://insider.foxnews.com/2016/01/31/oklahoma-college-forcing-students-wear-fitbits

20. Page credit: https://dzone.com/articles/are-university-campuses-turning-into-big-brother

21. Page credit: http://www.theguardian.com/higher-education-network/2015/nov/27/our-obsession-with-metrics-turns-academics-into-data-
drones

22. Page credits: http://www.ft.com/cms/s/2/634624c6-312b-11e5-91ac-a5e17d9b4cff.html#slide0

23. ‘how much is enough data
to solve my problem?’
(Adryan, 2015)
Image credit: https://www.flickr.com/photos/uncle-
leo/1341913549
How much (more) student data do we need?

24. … has become saturated with data – ranging from automatically
collected, analysed and used, purposefully collected, analysed
and used and volunteered on social media and in exchange of
(perceived) benefits despite concerns about privacy, the
uncertainty of how the data will be used (and combined with
other sources of data) downstream and in the context where our
trust in the collectors of data is often misplaced, irrational or
wishful thinking (See Kitchen, 2013, pp. 262-263)
How do we think of the
collection, analysis and use of
student data in a world that…
Image credit: https://commons.wikimedia.org/wiki/File:Big_Hand_-_geograph.org.uk_-_644552.jpg

25. • Knowing
• Not knowing
• Knowing what we don’t know
• Knowing what we may never know
• Knowing more
The solution is not only (or necessarily?) in knowing more, but
ensuring that once we know, we respond in ethical, caring,
discipline and context-appropriate ways
We need to critically consider the ethical
implications of …

26. Pointers for a way forward
• Students’ digital lives are but a minute part of a bigger whole – so we
should not pretend as if our data represent the whole
• The data we collect are never ‘raw’, ‘uncontaminated’, or just ‘scraped’…
Our samples, choices, timing and tools change and impact on data. “Data
are in fact framed technically, economically, ethically, temporally, spatially
and philosophically. Data do not exist independently of the ideas,
instruments, practices, contexts and knowledges used to generate, process
and analyse them” (Kitchen, 2014, p. 2)
• Data have contexts. To re-use data outside of the original context and
purpose for which it was collected impacts on the contextual integrity.
• Knowing ‘what’ is happening, does not necessarily tell us the ‘why’…
• Education is an open, recursive system (Biesta 2007, 2010) where multiple
variables not only intersect but often also constitute one another. Let us
therefore tread carefully between correlation and causation…

27. Caught between correlation and causation
Image credit: http://www.tylervigen.com/spurious-correlations

28. Caught between correlation and causation
(cont.)
Image credit: http://www.tylervigen.com/spurious-correlations

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

30. • boyd and Crawford (2011) point to the fact that just
because we have access to increasing amounts and
granularity of personal data, does not mean that we have
to and need to collect, analyse and use this data
• While research participant involvement in Research (with a
capital ‘R’) is governed by institutional review boards and
policies, the (automatic) collection, analysis and use of
individuals’ digital data in emerging forms of research
(small ‘r’) often fall and take place outside of these policies
and review boards (Willis, Slade & Prinsloo, in review)
Just because we can, does not mean we have
to, and if we do, who will provide oversight?

31. Collecting, analysing and using student data:
towards an ethics of care
1. Do no harm. Repeat after me. Do no harm
2. They have a right to know. If not, then this research
resembles surveillance and spying, and not research
3. Make it clear what data are collected, when, for what
purpose, for how long it will be kept and who will have
access and under what circumstances
4. Provide students access to information and data held
about them, to verify and/or question the conclusions
drawn, and where necessary, provide context
5. Provide access to a neutral ombudsperson
(See Prinsloo & Slade, 2015)

32. Collecting, analysing and using student data:
towards an ethics of care (2)
6. Context matters. Downstream use for purposes other than
the original purpose for the collection of data compromises
the contextual integrity of data
7. Involve students in the meaning-making. They are not data
points on a PowerPoint at a conference. They have
contexts, histories. They are infinitely more than their data.
8. Who will we hold accountable for algorithms?
9. What are the benefits for students? For you? For the
institution? Be transparent.
(See Prinsloo & Slade, 2015)

33. (In)conclusions
I am not your data, nor am I your vote bank,
I am not your project, or any exotic museum object,
I am not the soul waiting to be harvested,
Nor am I the lab where your theories are tested,
I am not your cannon fodder, or the invisible worker,
or your entertainment at India habitat centre,
I am not your field, your crowd, your history,
your help, your guilt, medallions of your victory,
I refuse, reject, resist your labels,
your judgments, documents, definitions,
your models, leaders and patrons,
because they deny me my existence, my vision, my space,
your words, maps, figures, indicators,
they all create illusions and put you on pedestal,
from where you look down upon me,
So I draw my own picture, and invent my own grammar,
I make my own tools to fight my own battle,
For me, my people, my world, and my Adivasi self! ~Abhay Xaxa
Source: http://www.adivasiresurgence.com/i-am-not-your-data/

34. THANK YOU
Paul Prinsloo
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

35. Bibliography and additional reading
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41. 41
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Willis, J. E., Slade, S., & Prinsloo, P. (in review). Ethical oversight of student data in learning analytics: A typology
derived from a cross-continental, cross-institutional perspective. Submitted to special issue of Educational
Technology Research and Development (Exploring the Relationship of Ethics in Design and Learning Analytics:
Implications for the Field of Instructional Design and Technology), guest edited by M. Tracey and D.
Ifenthaler.
Bibliography and additional reading (cont.)