This briefing paper is written for managers and early adopters in further and higher education who are thinking about how they can build capability in their institution to make better use of data that is held on their IT systems about the organisation and provision of the student experience. It will be of interest to institutions developing plans, those charged with the provision of analytical data, and administrators or academics who wish to use data to inform their decision making. The document identifies the capabilities that individuals and institutions need to initiate, execute, and act upon analytical intelligence.
For the purpose of this paper, the term Learning Analytics (LA) is used to cover these activities using the definition of:
Analytics is the process of developing actionable insights through problem definition and the application of statistical models and analysis against existing and/or simulated future data. (CETIS, 2012)
The proposition behind learning analytics is not new. In the school sector particularly, good teaching practice has long involved record keeping with pen and paper and the analysis and reflection on this data to inform courses of action, and more recently using technology. Similarly, in different ways, all higher education (HE) and further education (FE) institutions use data to inform their decision making in assessment boards and course committees. However, as institutions increasingly use technology to mediate, monitor, and describe teaching, learning and assessment through Virtual Learning Evironments (VLEs) and other systems, it becomes possible to develop ‘second generation’ learning analytics. The large data sets being acquired are increasingly amenable to new techniques and tools that lower the technical and cost barrier of undertaking analytics. This allows institutions to experiment with data to gain insight, to improve the student learning experience and student outcomes, and identify improvements in efficiencies and effectiveness of provision.