Learning analytics – Challenges, paradoxes and opportunities for mega open distance learning institutions
Learning analytics –Challenges, paradoxes andopportunities for mega opendistance learning institutions Paul Prinsloo, Sharon Slade and Fenella Galpin
Overview of the presentation• Introduction• Learning analytics and the ‘thirdspace’• Pointers from two short case studies• Conclusion
Introduction: What difference will it make (if atall) if we know more about our students?Student success/throughput “is one of the most widely studiedissues in higher education over the past twenty-five years” (Tinto2002:2). Although this research resulted in “an ever moresophisticated understanding of the complex web of events thatshape student leaving and persistence…, most institutions havenot yet been able to translate what we know about studentretention into forms of action that have led to substantial gains instudent persistence and graduation” (Tinto 2006:1, 5).
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).
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
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
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.
THANK YOUDr Paul Prinsloo Dr Sharon Slade Fenella GalpinUniversity of South Open University Open UniversityAfrica firstname.lastname@example.org email@example.com@unisa.ac.za