Setting Learning Analytics in Context: Overcoming the Barriers to Large-Scale Adoption


  • Rebecca Ferguson The Open University
  • Leah P. Macfadyen The University of British Columbia
  • Doug Clow The Open University
  • Belinda Tynan The Open University
  • Shirley Alexander University of Technology, Sydney
  • Shane Dawson University of South Australia, Adelaide



Administration, Policy, change management, higher education, implementation, learning analytics, ROMA, teaching, TEL, Technology Enhanced Learning


A core goal for most learning analytic projects is to move from small scale research towards broader institutional implementation, but this introduces a new set of challenges because institutions are stable systems, resistant to change. To avoid failure and maximize success, implementation of learning analytics at scale requires explicit and careful consideration of the entire TEL technology complex: the different groups of people involved, the educational beliefs and practices of those groups, the technologies they use and the specific environments within which they operate. It is crucial to provide not only the analytics and their associated tools, but also to begin with a clear strategic vision, to critically assess institutional culture, to identify potential barriers to adoption, to develop approaches to overcome these and to put in place appropriate forms of support, training and community building. In this paper, we provide tools and case studies that will support educational institutions in deploying learning analytics at scale with the goal of achieving specified learning and teaching objectives. The ROMA Framework offers a step-by-step approach to the institutional implementation of learning analytics and this approach is grounded by case studies of practice from the UK and Australia.




How to Cite

Ferguson, R., Macfadyen, L. P., Clow, D., Tynan, B., Alexander, S., & Dawson, S. (2014). Setting Learning Analytics in Context: Overcoming the Barriers to Large-Scale Adoption. Journal of Learning Analytics, 1(3), 120-144.



Special section: LAK'14 selected and invited papers

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