Universities and colleges are focused on supporting students both generally and individually to ensure retention and to assure success. The associated challenges are exacerbated by large student numbers and as teaching and learning becomes more ‘virtualised’. Institutions are therefore looking for indicators that will assist in timely identification of such as ‘at risk’ learners so they can be proactively engaged with the appropriate academic and personal support services.
Whilst computer enabled systems may be part of the problem, they can certainly contribute significantly to the solution through identification of patterns of learning and associated activity that highlight ‘danger signs’ and sub-optimal practice and by the automation of ‘alarms’ (e.g. traffic light indicators, alerts) triggered by one or more indicators. This approach forms part of the field of ‘learning analytics’, which is increasingly popular in North America.
Well-chosen indicators do not necessarily imply a cause and effect relationship, but they do provide a means to single out individuals using automatically collected activity data, typically combining a bundle of indicators (e.g. Students who do not visit the library in Term 1 may be at risk; students who also do not download content from the VLE are highly likely to be at risk).
Taking it further:
Institutions wishing to develop these capabilities may be assisted by this checklist:
- Consider how institutions have developed thinking and methods in such as the JISC Activity Data programme - see resources below
- Identify where log information about learning –related systems ‘events’ are already collected (e.g. Learning, library, turnstile and logon / authentication systems);
- Understand the standard guidance on privacy and data protection relating to the processing and storage of such data
- Engage the right team, likely to include key academic and support managers as well as IT services; a statistician versed in analytics may also be of assistance as this is relatively large scale data
- Decide whether to collect data relating to a known or suspected indicator (like the example above) or to analyse the data more broadly to identify whatever patterns exist
- Run an bounded experiment to test a specific hypothesis
Three projects in the JISC Activity Data programme investigated these opportunities at Cambridge, Huddersfield and Leeds Met universities.
See Activity Data Guide on ‘Data Strategies’ to maximise your potential to identify and track indicators
More about Learning Analytics in the 2011 Educause Horizon Report - http://www.educause.edu/node/645/tid/39193?time=1307689897
Academic Analytics: The Uses of Management Information and Technology in Higher Education, Goldstein P and Katz R, ECAR, 2005 - http://www.educause.edu/ers0508