Leaping the chasm: moving from buzzwords to implementation of learning analytics
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Transcript of Leaping the chasm: moving from buzzwords to implementation of learning analytics
Leaping the chasm: moving from buzzwords to
implementation of learning analytics
George SiemensTechnology Enhanced Knowledge Research Institute (TEKRI)Athabasca UniversityFebruary 1, 2012
Slides (with citations and links)
http://www.slideshare.net/gsiemens/educause2012
“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
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)
Semantic Web to Social Machines
“People do the creative work and the machine does the administration”Web=unlimited scaling of infoWeb should=unlimited social interaction
Hendler & Berners-Lee (2010)
We collect enough data. We need to focus on connecting.
Multiple data sources:
Social mediaUniversity help resourcesLMSStudent information systemCourse 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
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)
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 formatIntegration: How do we “harmonize” varying data sets togetherAnalysis: 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.”
Principles of a systems-wide analytics tool
1. Algorithms should be open, customizable for context2. Students should see what the organization sees3. Analytics engine as a platform: open for all researchers and organizations to build on4. Connect analytics strategies and tools: APIs5. Integrate with existing open tools6. 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/