Panel Session at Learning Analytics and Knowledge Conference 2013
Upcoming SlideShare
Loading in...5

Like this? Share it with your network


Panel Session at Learning Analytics and Knowledge Conference 2013

Uploaded on


  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
    Be the first to like this
No Downloads


Total Views
On Slideshare
From Embeds
Number of Embeds



Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

    No notes for slide


  • 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 thatevidence 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