Leaping the chasm:
moving from buzzwords to
implementation of learning analytics
George Siemens
Technology Enhanced Knowledge Research Institute
(TEKRI)
Athabasca University
February 1, 2012
Slides (with citations and links)
http://www.slideshare.net/gsiemens/educause2012
1. Roots of learning analytics and context of
deployment
2. Becoming at data-intensive university
1. Roots of learning analytics and context of
deployment
2. Becoming at data-intensive university
Won’t make the argument for why analytics are
growing
“Imagination no longer comes as cheaply as it
did in the past. The slightest move in the virtual
landscape has to be paid for in lines of code.”
Latour (2007)
What’s different today?
volume (apparently, there’s lots of data)
velocity (processing capacity)
variety (internet of things, social media)
variability (meaning variance)
“Analytics, and the data and research that fuel it,
offers the potential to identify broken models
and promising practices, to explain them, and to
propagate those practices.”
Grajek, 2011
http://www.dataqualitycampaign.org/
A different way of thinking and functioning
EMC: Data Science Revealed: A Data-Driven Glimpse into the Burgeoning New Field
Reading a book (or any interaction with data) is analytics
Check my activity
Predictive Analytics Reporting
Methods, techniques & evidence
Metrics,
or analytics on analytics,
are hard (and contextual)
What is the impact of effective use of data?
Argument: “more precise and accurate
information should facilitate greater use of
information in decision making and therefore
lead to higher firm performance.”
Brynjolfsson, Hitt, Kim (2011)
LA resources, publications, archive:
Student success/completion
Astin (1996)
Tinto (1993)
Distributed, multi-level analytics
Suthers & Rosen (2011)
Attention metadata
Duval (2011)
Learning networks, crowds, communities
Haythornthwaite (2011)
Discourse analysis (automated and manual)
De Liddo & Buckingham Shum (2011)
Social learning analytics
Buckingham Shum & Ferguson (2011)
Participatory learning and reputation
Clow & Makriyannis (2011)
Early warning
Macfayden & Dawson (2010)
Campbell et al (2006)
Semantic Web to Social Machines
“People do the creative work and the machine
does the administration”
Web=unlimited scaling of info
Web should=unlimited social interaction
Hendler & Berners-Lee (2010)
1. Roots of learning analytics and context of
deployment
2. Becoming at data-intensive university
We collect enough data.
We need to focus on connecting.
Multiple data sources:
Social media
University help resources
LMS
Student information system
Course progression, etc
Privacy as a transactional entity
Share my data to improve learning support from
the university (school)
“All-embracing technique is in fact the
consciousness of the mechanized world.
Technique integrates everything. It avoids shock
and sensational events”
Ellul, 1964
Analytics as a complex system:
multiple interacting entities,
more meaningful when connected
Challenges:
Broadening scope of data capture
- data outside of the current model of LMS
- sociometer: Choudhury & Pentland (2002)
- classroom/library/support services,
- quantified self
Timeliness of data (real-time analytics)
Three communities that don’t
communicate
Systems/enterprise level
Researchers
Educators (cobbling)
What does a data-intensive university
look like?
Kron, et al (2011)
Acquisition: how do we get the data – structured
and unstructured?
Storage: how do we store large quantities?
Cleaning: how do we get the data in a working
format
Integration: How do we “harmonize” varying data
sets together
Analysis: which tools and methods should be used?
Representation/visualization: tools and methods to
communicate important ideas
“A university where staff and students
understand data and, regardless of its volume
and diversity, can use it and reuse it, store and
curate it, apply and develop the analytical tools
to interpret it.”
43
Principles of a systems-wide analytics
tool
1. Algorithms should be open, customizable for
context
2. Students should see what the organization sees
3. Analytics engine as a platform: open for all
researchers and organizations to build on
4. Connect analytics strategies and tools: APIs
5. Integrate with existing open tools
6. Modularized and extensible
Learning Analytics & Knowledge 2012:
Vancouver
http://lak12.sites.olt.ubc.ca/
Open online course:
http://lak12.mooc.ca/
Twitter/randomly popular social media: gsiemens
www.learninganalytics.net
http://www.solaresearch.org/