This document discusses learning analytics at the intersection of student support, privacy, agency, and institutional survival in higher education. It notes increasing competition and constraints that universities face, and the need for data and evidence to demonstrate student retention, success, and throughput. However, it also discusses concerns about educational triage, focusing only on certain students, and the lack of transparency around algorithmic decision making. The document calls for consideration of student privacy, agency, and the moral implications of admission practices and levels of support provided.

1. Learning analytics: At the intersections between
student support, privacy, agency and institutional
survival
Paul Prinsloo (University of South Africa, Unisa) @14prinsp
Sharon Slade (Open University, OU) @sharonslade
Imagecredit:https://www.flickr.com/photos/haydnseek/2534088367

2. Image credit: http://www.yourtango.com/201168184/facebook-relationship-status-
what-does-its-complicated-mean

3. HIGHER EDUCATION
Image credits:
Survivor image: Adapted from https://commons.wikimedia.org/wiki/File:Survivor_Bermuda_Logo.jpeg
Murky middle: https://s-media-cache-ak0.pinimg.com/736x/5c/58/07/5c58072b5f003d7a69b129cb6f8055b6.jpg
Triage: https://en.wikipedia.org/wiki/Triage
Surveillance camera: http://www.governmentnews.com.au/wp-content/uploads/2014/02/cctv_camera.jpg

4. HIGHER EDUCATION
• Increasing competition, changing contexts, internationalisation
• Rankings and quality regimes/criteria
• Increasing funding constraints and austerity measures
• Funding follows performance rather than preceding it – the need for
evidence
• Persisting concerns about student retention, failure and dropout
• History of well-intentioned but often bang-bang approaches to increasing
student retention and success
• The mandate and fiduciary duty of higher education
• Optimising the student experience, ensuring student success/throughput
Survivor – the Higher
Education series (new
rules, new contestants,
better than ever)

5. • Determine criteria/characteristics
• Calculate cost of care/intervention/return on
investment
• Implementation – educational triage
• Evaluation
Moving the murky middle/drowning the
bunnies

6. Engaging with (some) assumptions &
practices re the need for (more) data
• Our (mis)understanding of student
retention, success and failure
• Can we assume that knowing more, per
se, results in understanding and care; that
more data will necessarily contribute to
better teaching and learning?
• The danger of context collapse and the need to ensure context integrity
when data collected from disparate sources and for a variety of purposes
are combined
• The inherent biases, dangers and potential of algorithmic decision-
making
• The scope of students’ right to privacy

7. Educational triage in practice
• School league tables can lead to a
focus on key boundaries
– Evidence that the ‘murky middle’
overlooked in favour of those most
able to support achievement of
key results
• Traditional classroom-based
universities
– potential focus on ‘seen’ or
perceived need
– often driven by individual subject
tutors
• Distance learning institutions
– largely reliant on student data to
direct support
– often driven by available data and
assumed patterns

8. Analytics in practice at the Open University
Framework for consistent support
– Drives minimum set of proactive
interventions through curriculum focused
Student Support Teams to all students
– Additional core interventions target students
based on characteristics (potentially ‘at risk’)
and/or study behaviours
– Large number of possible proactive
interventions (e.g., missed milestones, etc)
– Prioritising interventions is complex: which
characteristics/milestones/behaviours/
modules take precedence? Who decides?
– Results in non-standard support largely not
transparent to students and driven by
available staff resource

9. Some considerations…
• We cannot ignore the reality of ‘Survivor: Higher Education’
• The impact of funding, resources and contexts on the ‘murky middle’
• The moral implications of our admission requirements: admission without a
reasonable chance of success? The cost of support to make ‘success’ happen?
• Educational triage’s potential to exclude students from access/support based
on criteria that disregard context, structural inequalities and inter-
generational debt
• The need for transparency re rationales for inclusion/exclusion & decisions
made
• The scope of students’ agency: can students refuse advice/support provided
they understand the consequences of their opting out?

10. Image credit: http://www.yourtango.com/201168184/facebook-relationship-status-
what-does-its-complicated-mean

11. Thank you
Prof 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
Personal blog:
http://opendistanceteachingandlearning
.wordpress.com
Twitter profile: @14prinsp
Dr Sharon Slade
Senior Lecturer
Faculty of Business and Law
The Open University, Walton Hall,
Milton Keynes, MK7 6AA, United
Kingdom
T: +44 (0) 1865 486250
sharon.slade@open.ac.uk
www.linkedin.com/profile/view?id=53
123496&trk=tab_pro
Twitter profile: @sharonslade

12. References and additional reading
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13. References and additional reading
(cont.)
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References and additional reading
(cont.)

15. References and additional reading
(cont.)
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16. References and additional reading
(cont.)
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17. References and additional reading
(cont.)
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18. References and additional reading
(cont.)
Open University. (2014). Policy on ethical use of student data for learning analytics.
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use-student-data-learning-analytics-policy
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19. References and additional reading
(cont.)
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
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20. References and additional reading
(cont.)
Prinsloo, P., & Slade, S. (2016). Here be dragons: Mapping student responsibility in
learning analytics, in Mark Anderson and Collette Gavan (eds.), Developing Effective
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21. References and additional reading
(cont.)
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22. References and additional reading
(cont.)
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