Block 4 Activity 17: Why analytics may be ignored.

This activity is based on Macfadyen and Dawson (2012), Numbers are not enough.

Identify areas for lack of uptake of use of learning analytics:



  • Need support of the faculty and staff if trying to create a vision or plan for change.
  • Although learning analytics are being used to highlight areas within LMS that could benefit the learners,  they are having little motivating power. Perceived by institutional management as being  too difficult for faculty and staff to use, and time consuming with rewards that don’t appear to be beneficial enough.
  • Organisational culture that supports change by adding resources rather than strategically reallocating resources


  • In this example faculties tend to take on an agrarian approach seeing their hierarchical management structures as interfering.
  • Any policy or change seen to impinge on faculty autonomy in teaching is usually  resisted.
  • Learning is tightly regulated in a cohort/semester system.
  • Incentives for implementing/using learning analytics in faculties are low.
  • Consensus governance.



  • Resistant to change that just appears into their curriculum or practice. Is this a capacity issue?
  • Will look for the advantage this change will bring to them – whether its compatible with existing practice, values and whether it  will be difficult to use.
  • Existing academic workload is already high without having to learn how to use learning analytics on top.
  • The potential for using learning technologies to enhance teaching and learning may not be fully understood.


Learning analytics alone cannot bring about social and cultural change (habits, practices and behaviours) within a university LMS.  Change brings about resistance unless the benefits of the change fit in with individual beliefs and values.  If a university sees value in learning analytics and want to effectively introduce them into their systems then  accessibility and presentation of the learning analytics process is important to ensure data has the capacity to motivate and affect a positive behavioural change.  Any data analysis needs to be looked at through informed and contextualised interpretation by the user(s). Shared values, vision, beliefs and communication across all levels is paramount.


Macfadyen, L.P. and Dawson, S. (2012) ‘Numbers are not enough. Why e-learning analytics failed to inform an institutional strategic plan’, Educational Technology & Society, vol. 15, no. 3, pp. 149–63; also available online at libraryservices/ resource/ article:106516&f=28635 (accessed 22 July 2106).




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