The document discusses how analytics can be applied to learning and knowledge. It notes that with big data and social media, the world is becoming more data-driven and our actions are increasingly leaving digital traces. It questions how organizations can understand how information and power structures influence knowledge sharing, activities, and innovation. It explores how people can make sense of abundance through social processes and networks to achieve coherence and growth of knowledge.

1. Analytics in Learning and
Knowledge
George
Siemens
June 8, 2011
Milan, Italy
Presented to:

2. Critical Challenge:
Understand how power and control
structures influence information flow,
person-level activity, and innovation in an
organization

3. http://www.scribd.com/doc/48586092/KPCB-Top-10-Mobile-
Trends

4. Blurring the physical and virtual
worlds (blame mobile devices)

5. Intentions, interests: Explicit

6. All the world is data. And so are we.
And all of our actions.
http://www.hoganphoto.com/batsto_grist_mill.htm

8. Crazy abundance

9. More is different, but not new
1550-1750
http://muse.jhu.edu/journals/journal_of_the_history_of_ideas/toc/jhi64.1.html

10. BIG
Data

11. What is different about information?
Quantity: how many people can talk to (share
with) each other
Type: social, sensors & information
Representation: the “thing that points to another
thing” is not human readable anymore
(i.e. a cave painting of an animal=the animal it
represents. Binary is abstracted representation for
machines, not humans)
Relationships: (power & control)
(more on that in a bit)

12. How do we cope?

13. Through social means?

14. Social
media?

15. Social sensemaking and wayfinding in
abundance and complexity

16. Social spaces and knowledge
growth

17. but

18. “Peak Social”

20. UNSTRUCTURED
Daily
Crisis
Sensemaking
Mapping to
New
Existing
Knowledge
Knowledge
STRUCTURED
INDIVIDUAL COLLECTIVE

21. Social
and/versus
big-data, machine-driven,
patterning [inset buzzword]
sensemaking

22. Event-
Continual flow centred
of information pattern
recognition

25. Talk-o-meter

26. http://research.uow.edu.au/learningnetworks/seeing/snapp/
index.html

27. Information Information
Quantity Type
Coherence
through technical and
social networks
and techniques
Information
Representation

28. Each new node amplifies the value
of the entire network…and
produces
lock-in

36. Learning Analytics & Knowledge 2012:
http://lak12.sites.olt.ubc.ca/
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