This document summarizes recent trends in learning analytics (LA) and opportunities for the future. It discusses how in the early years LA lacked evidence for business cases, clear terminology and roles. However, it is now an opportunity for early adopters through collaboration and problem solving. Going forward, the challenges are coordinating an innovation cycle and embedding LA. Success will depend on clearly identifying benefits, co-designing with stakeholders, developing business cases, and addressing readiness at both the organizational and individual level while continuing grassroots innovation.

1. LAK 2013
Presenter or main title…
Recent and Desired Future Trends in LA
Session Title or subtitle…
16.45
Myles Danson
Programme Manager, Technology Enhanced Business Change

2. Jisc Myles and Sheila. What, Who?
 Shared Service

3. Early Characteristics
 New(ish) Field
 Beacons of excellence
 Narrow applications
 Promise of great things
 Little coordination of effort

4. Early Characteristics
 Little evidence for business cases
 Reliance on the implicit
 More holes than net
 New terminology
 New roles
 Intra community excitement
 Extra community confusion

5. Current Opportunities
 Early adopter opportunities and issues
 Grass roots interventions
 Nurturing
 Peer support
 Collaboration
 Shared problem identification & solving
 LAK 13, SOLAR, Educause, Jisc, SURF etc

6. The Challenge
 A Cycle of innovation
 Through to embedding

7. Business Intelligence (BI) comprises evidence-based decision-
making and the processes that gather, present, and use that
evidence base.
It can extend from providing evidence to
support potential students’ decisions whether
or not to apply for a course, through
evidence to support individual faculty and
staff members, teams and departments, to
evidence to support strategic decisions for
the whole organisation.
Analytics is the highest level of BI maturity -
the process of developing actionable insights
through problem definition and the
application of statistical models and analysis
against existing and/or simulated future data

8. Organisational Development
 Utilise readiness / maturity frameworks
 Organisations and Individuals
 Work through representative bodies?
 Shoot high (SMT, Policy, Governance)
 Feed in the innovation

9. Project Reality (Austerity) Check
 Beneficiaries – will your project benefit a sufficiently wide range of people
 Reality of benefit delivery in the timescale
 Reality of sustaining the outputs
 Value to the sector
 Innovativeness and benefits

10. Benefit Examples
• Improved quality and reduced risk (anecdotal and quantitative)
 Improved decision-making (anecdotal)
 Better strategic planning (anecdotal)
 Better risk management (anecdotal)
 Competitive advantage (quantitative)
 Income generation (quantitative)
 Efficiency gains (quantitative)
 Performance benchmarking (anecdotal and quantitative)
 Student satisfaction (quantitative)
 Student retention (quantitative)
 League table ranking (quantitative)
 Cash savings (e.g. from retired software, hardware, redeployed staff)
 Income generation (quantitative)
 Improved speed and efficiency (anecdotal and quantitative)

11. In Summary
 Coordinate an innovation – embedding cycle
 Focus on the benefits (which and to whom and how)
 Co Design and partnerships (include vendors, stakeholder bodies)
 Business case for investmen
 Policy and governance
 Organisational AND individual readiness issues
 Keep up the grass routes innovation