Link: CETIS Analytics Series Vol 1, No 3. Analytics for Learning and Teaching (pdf)
Link: CETIS Analytics Series Vol 1, No 3. Analytics for Learning and Teaching (MS Word .docx)
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.
Link: CETIS Analytics Series Vol 1, No 2. Analytics for the Whole Institution: Balancing Strategy and Tactics (pdf)
Link: CETIS Analytics Series Vol 1, No 2. Analytics for the Whole Institution: Balancing Strategy and Tactics (MS Word .docx)
The benefits afforded by the longitudinal collection and analysis of key institutional data are not new to enterprise IT managers nor to senior management more generally. Data warehousing and Business Intelligence (BI) dashboards are integral to the modern management mindset and part of the ‘enterprise IT’ architecture of many Higher Education Institutions and Further Education Colleges.
However some things are changing that pose questions about how business intelligence and the science of analytics should be put to use in customer facing enterprises:
- The demonstration by online services ranging from commodity sales to social networks of what can be done in near-real time with well-connected data.
- The emphasis brought by the web to the detection, collection and analysis of user activity data as part of the BI mix, ranging from clicks to transactions.
- The consequent changes in expectations among web users of how services should work, what businesses could do for them, accompanied by shifts in legal and ethical assumptions.
- The availability of new types of tools for managing, retrieving and visualizing very large data that are cheap, powerful and (not insignificantly) accessible to grass roots IT users.
Set against that backdrop, this paper aims to;
- Characterise the educational data ecosystem, taking account of both institutional and individual needs.
- Recognise the range of stakeholders and actors – institutions, services (including shared above-campus and contracted out), agencies and vendors.
- Balance strategic policy approaches with tactical advances.
- Highlight data that may or may not be collected.
- Identify opportunities, issues and concerns arising.
Our focus is therefore not on technology but rather on high value gains in terms of business objectives, the potential for analytics and opportunities for new thinking across the organisation.
Link: CETIS Analytics Series Vol 1, No 1. Analytics; What is Changing and Why Does it Matter? (pdf)
This paper provides a high level overview to the CETIS Analytics Series. The series explores a number of key issues around the potential strategic advantages and insights which the increased attention on, and use of, analytics is bringing to the education sector. It is aimed primarily at managers and early adopters in Further and Higher Education who have a strategic role in developing the use of analytics in the following areas:
- Whole Institutional Issues,
- Ethical and Legal Issues,
- Learning and Teaching,
- Research Management,
- Technology and Infrastructure.