Enabling student success
This guide discusses the role of activity data in supporting student success by identifying students at risk who may benefit from positive interventions to assist in their success.
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 two factors: Large student numbers and as teaching and learning becomes more ‘virtualised’ and the effects of the current economic climate on funding.
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.
Evidence is accumulating that activity data can be used to identify patterns of patterns of activity that indicate ‘danger signs’ and sub-optimal practice. Many students at risk can be automatically identified by matching them on indicators of poor performance. Typically such systems provide alerts or some kind of indicator (eg a traffic light using a green-amber-red metaphor). This approach forms part of the field of ‘learning analytics’, which is becoming increasingly popular in North America.
Well-chosen indicators do not imply a cause and effect relationship, rather they provide a means to single out individuals using automatically collected activity data, and perhaps based on co- occurring indicators (e.g. Students who do not visit the library in Term 1 and 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. Examples from the JISC Activity Data programme appear in the 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
- More about Learning Analytics appears 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