Reflecting on my original aims for attending LAK 2012 I’ve put together some points for JISC to consider for the Analytics Reconnaissance piece and future work structured on the LAK Summit day and including my own observations

1. Culture
a. Awareness raising
b. Fostering the reflective institution

2. Data
a. Security
b. Access
c. Ownership
d. Anonymity
e. Fostering big collections
f. Governance for analytics vs transactional

3. Stakeholders
a. Needs analysis
b. Questions required
c. Institutional needs vs Individual needs
d. Learning analytics should affect actor behaviour

4. Skills
a. Longer term provision for future work force
b. Digital Literacies of existing workforce

5. Opportunities
a. Influence researchers
b. Bridge between research and practice
c. Ethics (for research) and Policy (for practice)
d. Personalisation
e. Content identification, surfacing and re-use
f. Assessment and feedback
g. Course advertising

6. Retention, Progression, Completion

7. Accountability
i. Societal
ii. Parental
iii. Lender
iv. Funder
v. Employer

8. Selling points
i. Destinations of leavers / alumni

9. Definitions
a. Common vocabulary

10. Optimising student success (See Donald Norris / Educause study of 40 HEIs and appropriate vendors from LAK12, very good)
a. Managing the student pipeline
b. Eliminating impediments to success
c. Dynamic query, alerts and interventions, at-risk behaviour
d. Learner relationship management systems
e. Personalised learning system environments and learning analytics

11. Educause Gaps
a. Talent gap lack of analytics expertise and experience
b. The need for professional development
c. Enhancements in tools, apps, solutions, services
d. Educause will be articulating organisational capacity indicators and a match up tool – tools and approaches to institutional aims and goals

12. Educause and Analytics
a. Analytics is integrated into their 20 conferences
b. Analytics featured in Educause research vehicles
c. Future ideas
i. Topical
1. Student swirl
2. Longitudinal data systems
3. Policy impediments
4. Student perceptions of analytics
ii. Surveys
1. Student and state perceptions of learning analytics
iii. CPD
1. Online short courses
2. Analytics literacy

13. Gates Foundation
a. How to build capacity to move learning analytics to mainstream in HEIs
b. Analytics for what? Do we have the solution to widely felt problems here – get the message out
c. The important issues aren’t technological, they’re organisational and people centric
d. Gates are keen to undertake collaborative funding. I have the contact detail.

14. On fire issues
a. Social Analytics (OU)
b. George Siemens Key Note day 3
c. Purdue Signals
d. Department of Education Analytics paper and contact
i. OU paper and contacts (Simon BS,
ii. Alignment of data mining and analytics
iii. Wider academic analytics
iv. Map (learning) analytics opportunities to the student lifecycle
v. Look to other disciplines to fill the gaps eg social science for social analytics
vi. Katy and Tony Hirst for visualization analytics
vii. Educause Keynote describing the next steps from their recon (revisit it to expand HEI interviews as well as making various ‘offers’)
viii. SOLAR Open Analytics Platform (Think WordPress openness) – actively seeking partners
ix. SOLAR joint event with JISC / SURF input, see Flares etc
x. Commercials; cloud content across best of breed instances / installations allow big data analytics avoiding cleanliness and interoperability issues
xi. Recommendation services
xii. JISC / Educause internal webinar between analytics planners and content authors

15. Quick wins
i. Awareness raising
ii. Analytics to benefit JISC
iii. Purdue approach
iv. Best of breed user groups for cloud big data opportunities
v. Bridging between SOLAR, Data Mining and embedding in practice
vi. Course Data, XCRI and CAP
vii. Spotting the actionable, moving from knowledge to intervention
viii. Lever the personalization and retention agendas
ix. Lever the competitive edge and unique selling point agendas
x. Lever the implications of doing nothing in this space
xi. The repercussions of student behaviour traits and making students aware of these
xii. Work with HESA and UCAS and other national data collections to educate them
xiii. Longitudinal case studies and evidence – start them now!
xiv. Are we collecting the right data?
xv. Start the ‘question’ surfacing activity via stakeholders
xvi. Profile the SICT for analytics / press on with the BI maturity and the Emerging Practices Initiative idea. Go through SOLAR for expertise and Advance for packaging
xvii. Analytics capability audit (organisational and individual). See how low education is according to McKinsey
xviii. Analytics to triage interventions. OU prioritise human interventions to at risk students by early on analytics. This student with these types of characteristics and behaviours need no input, others need an appointment / regular support with a tutor
xix. Interview / site visit OU for potential via Simon BS, Rebecca Fergusson, Doug Clow
xx. Pooling of de-identified data
xxi. Integrate into related conferences etc (don’t make more)
xxii. Watch the Educause section so we don’t duplicate but compliment
xxiii. Senior management persuasion tools in natural language; analytics animation
xxiv. Articulate the economic benefits
xxv. Document the ethical decisions (what to collect, what not to collect, what to clean, to what extent etc)

Slides from LAK 12 are available here