1. Learning analytics –
Challenges, paradoxes and
opportunities for mega open
distance learning institutions
Paul Prinsloo, Sharon Slade and Fenella Galpin

2. Overview of the presentation
• Introduction
• Learning analytics and the ‘thirdspace’
• Pointers from two short case studies
• Conclusion

3. Introduction: What difference will it make (if at
all) if we know more about our students?
Student success/throughput “is one of the most widely studied
issues in higher education over the past twenty-five years” (Tinto
2002:2). Although this research resulted in “an ever more
sophisticated understanding of the complex web of events that
shape student leaving and persistence…, most institutions have
not yet been able to translate what we know about student
retention into forms of action that have led to substantial gains in
student persistence and graduation” (Tinto 2006:1, 5).

4. Unravelling the student departure puzzle:
A theoretical framework: A ‘Thirdspace’
• The concept of ‘Thirdspace’ found in theoretical frameworks
on identity, multiculturalism to describe the temporal space in
which individuals’ previous identities, assumptions, and
beliefs are in flux and fluid. Also referred to as a ‘liminal’ or
‘diasporic’ space (e.g. Bhabha 1994; Brah 1996; Soja 1996).
• In the context of higher education, the 'Thirdspace' provides a
means of understanding the space where students’ identities
are in flux and where they are often labelled as ‘at risk’, or
‘underprepared’ – and potentially blamed for not fitting in in
the dominant discourses and criteria of higher education
(Barnett 1996).

5. STUDENTS Processes and networks:
(Identities, beliefs, lite academic, administrative & social
racies)
A THIRDSPACE: A temporary space shaping Success
Dropout
identities, epistemologies and beliefs
INSTITUTION Processes and networks:
(Admission
requirements, epistemologies, academic, administrative & social
accreditation)
Adapted from Subotzky & Prinsloo, 2011

6. Pointers from two case studies:
• Huge data sets (OU= 250,000 students ; Unisa = 400,000
students) – huge data as constraint or enabler
• Currently, no clear strategy on how the data is used, by
whom, for what purpose
• Although considerable analysis is undertaken, this does not
always flow clearly through the organisation
• Sensemaking happens in isolated pockets
• No clear indication whether current analysis impacts on
institutional practice
• There is a need for integration, coordination and a holistic
approach

7. Conclusion
• The potential for using learning analytics more effectively is
huge.
• Learning analytics can make a huge impact on institutional
choices re pedagogy, assessment strategies.
• An awareness of the student as an evolving participant is
crucial – characteristics that apply on registration of first
module may change
• Faculty use of learning analytics to inform teaching practice
can be constrained by institutional bureaucracies.
• The effective use of learning analytics to inform teaching and
learning praxis in ODL institutions depend on effective
strategies to act on analyses and findings at scale – otherwise
it will remain business as usual.

8. THANK YOU
Dr Paul Prinsloo Dr Sharon Slade Fenella Galpin
University of South Open University Open University
Africa sharon.slade@open.ac.uk fenella.galpin@open.ac.uk
prinsp@unisa.ac.za