A broad view is taken of analytics for Learning and Teaching applications in Higher Education. In this we discriminate between learning analytics and academic analytics: uses for learning analytics are concerned with the optimisation of learning and teaching per se, while uses of educational analytics are concerned with optimisation of activities around learning and teaching, for example, student recruitment.
Some exemplars of the use of analytics for Learning and Teaching are to:
- Identify students at risk so as to provide positive interventions designed to improve retention.
- Provide recommendations to students in relation to reading material and learning activities.
- Detect the need for, and measure the results of, pedagogic improvements.
Tailor course offerings.
- Identify teachers who are performing well, and teachers who need assistance with teaching methods.
- Assist in the student recruitment process.
Conclusions drawn in this paper include:
- Learning and academic analytics are underutilised in UK Higher Education.
- There are risks to adoption of analytics, including that measures that are not useful are revealed through analytics, and that staff and students lack the knowledge to use analytics. However, there are visualisation techniques that help significantly in non-specialist adoption.
- There is also a risk for institutions that delayed introduction of analytics may lead to missed opportunities and lack of competitiveness in the UK Higher Education market. Our judgment is that this is the most compelling risk to amortise.
- Institutions vary in analytics readiness and maturity, and may to a greater or lesser extent be ready for the introduction of analytics or increases in the use of analytics.
- We stress that there are different scales of analytics projects, from small projects that can be undertaken under a limited budget and time frame, to large projects that may involve, say, creating a data warehouse and employing experienced analytics staff to build complex models.
- A good way of starting is to undertake small limited-scope analytics projects. These enable institutions to develop staff skills and/or raise the profile of analytics in the institution.
- Large-scale analytics may involve the activities of staff who may variously be characterized as analysts, modellers or data scientists. These staff are often in short supply, but reference to local departments of statistics may provide expert help.
- There are commercial solutions that work in conjunction with commonly adopted Virtual Learning Environments and student information systems. Some of these are ‘plug-and-play’ and do not require analyst input. However, before purchase, they should be evaluated for suitability, particularly with respect to intuitional pedagogic approaches and concerns.