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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.
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Technology Enhanced Knowledge Research Institute (TEKRI)
February 1, 2012
volume (apparently, there’s lots of data)
velocity (processing capacity)
variety (internet of things, social media)
variability (meaning variance)
A different way of thinking and functioning
Predictive Analytics Reporting
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)
Suthers & Rosen (2011)
De Liddo & Buckingham Shum (2011)
Buckingham Shum & Ferguson (2011)
Clow & Makriyannis (2011)
Macfayden & Dawson (2010)
Campbell et al (2006)
“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)
Multiple data sources:
University help resources
Student information system
Course progression, etc
Share my data to improve learning support from the university (school)
multiple interacting entities,
more meaningful when connected
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)
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
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
Open online course: