What is Activity Data useful for?

We have considered the nature of activity data and the types of analysis for which it may be particularly useful. These apply to a range of domains, processes, key performance indicators and problem spaces in the work of an educational institution, just as they do in a supermarket or an insurance company. Historically, it is interesting to note the range of business domains identified in a 2005 North American survey in which 213 users of ‘educational analytics’ reported their data sources (http://www.educause.edu/ers0508 ). There is strong emphasis on administrative systems rather than academic systems, with a VLE- proxy making only 6 th place and no mention of Library systems.
System
% of respondents using the system
Student information system
93.0%
Financial system
84.5%
Admissions
77.5%
HR system
73.7%
Advancement
36.2%
Course management system
29.5%
The JISC Activity Data programme is based on the premise that new opportunities for capturing and exploiting activity data have opened up in the Web 2.0 world, not least involving student and researcher facing systems. Here are 10 example uses, many of which are exemplified in the projects and themes introduced in this synthesis website with relevant projects listed:
Student Facing
  • Student recruitment
  • Student retention
  • Student choice and progression
Academic Performance
Process Related
  • Business Process improvement
  • IT Systems optimisation
  • Scholarly Resource management
What are the dangers?
The thematic explorations and project uses that are the focus of this synthesis website indicate both the significant opportunity and the range of issues to be addressed by institutions, domain practitioners and IT professionals dealing with activity data in any of its guises in the educational setting.
Firstly, this is ‘big data’. It can involve very large numbers of records, sometimes diverse in format and potentially from many systems - even within a single domain such as a library. Use of activity data therefore raises challenges of:
Secondly, this is potentially dangerous data. Implementers, analysts and users must remain vigilant regarding the following issues that are explored in detail in this synthesis:
  • Quality of data - are there issues of veracity and completeness?
  • Critical mass of data - is it at least statistically significant?
  • Applications involved - do they act as a distraction therefore not telling the real story of service use?
  • Aggregation - is the story best told locally or through aggregation (eg at faculty, institution, UK sector levels)
  • Not least, legal compliance - are obligations regarding Data Protection and privacy being met?