Educause_2012

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Presented to EDUCAUSE, Feb 1, 2012

Presented to EDUCAUSE, Feb 1, 2012

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  • http://www.bruno-latour.fr/sites/default/files/P-129-THES-GB.pdf
  • See: http://nosql.mypopescu.com/post/6361838342/bigdata-volume-velocity-variability-variety for variability
  • http://www.educause.edu/EDUCAUSE+Review/EDUCAUSEReviewMagazineVolume46/ResearchandDataServicesforHigh/238391
  • SAS/Davenport
  • http://www.emc.com/collateral/about/news/emc-data-science-study-wp.pdf
  • http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1819486
  • http://www.educause.edu/Resources/Browse/LearningAnalytics/39193
  • Astin, A.W. (1996).  Degree attainment rates at American colleges and universities: Effects of race, gender, and institutional type . Report from Higher Education Research Institute. Los Angeles, CA Tinto, V. (1993).  Leaving college: Rethinking the causes and cures of student attrition , 2nd edition. Chicago, IL: The University of Chicago Press.
  • A unified framework for multi-level analysis of distributed learning http://dl.acm.org/citation.cfm?id=2090124&CFID=82269174&CFTOKEN=35344405
  • Attention please!: learning analytics for visualization and recommendation http://dl.acm.org/citation.cfm?id=2090118&CFID=82269174&CFTOKEN=35344405
  • Learning networks, crowds and communities http://dl.acm.org/citation.cfm?id=2090119&CFID=82269174&CFTOKEN=35344405
  • Discourse-centric learning analytics http://dl.acm.org/citation.cfm?id=2090120&CFID=82269174&CFTOKEN=35344405
  • Social Learning Analyticshttp://kmi.open.ac.uk/publications/pdf/kmi-11-01.pdf
  • iSpot analysed: participatory learning and reputation http://dl.acm.org/citation.cfm?id=2090121&CFID=82269174&CFTOKEN=35344405
  • Macfadyen, L.P., & Dawson, S. (2010). Mining LMS data to develop an “early warning system” for educators: A proof of concept.  Computers & Education, 54 (2), 588-599. Campbell, J. P., Collins, W.B., Finnegan, C., & Gage, K. (2006). "Academic analytics: Using the CMS as an early warning system." WebCT Impact 2006. Chicago, IL
  • From the semantic web to social machines: A research challenge for AI on the world wide webhttp://www.sciencedirect.com/science/article/pii/S0004370209001404
  • http://alumni.media.mit.edu/~tanzeem/TR-554.pdf
  • http://sloanreview.mit.edu/feature/achieving-competitive-advantage-through-analytics/

Transcript

  • 1. Leaping the chasm: moving from buzzwords to implementation of learning analytics George Siemens Technology Enhanced Knowledge Research Institute (TEKRI) Athabasca University February 1, 2012
  • 2.
    • Slides (with citations and links)
    • www.slideshare.net/gsiemens
  • 3.
    • 1. Roots of learning analytics and context of deployment
    • 2. Becoming at data-intensive university
  • 4.
    • 1. Roots of learning analytics and context of deployment
    • 2. Becoming at data-intensive university
  • 5.
    • Won’t make the argument for why analytics are growing
  • 6.
    • “ 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)
  • 7. What’s different today?
    • volume (apparently, there’s lots of data)
    • velocity (processing capacity)
    • variety (internet of things, social media)
    • variability (meaning variance)
  • 8.
    • “ 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
  • 9. http://www.dataqualitycampaign.org/ A different way of thinking and functioning
  • 10.  
  • 11. EMC : Data Science Revealed: A Data-Driven Glimpse into the Burgeoning New Field
  • 12.  
  • 13.  
  • 14. Reading a book (or any interaction with data) is analytics
  • 15.  
  • 16.  
  • 17.  
  • 18. Predictive Analytics Reporting Check my activity
  • 19. Methods, techniques & evidence
  • 20. 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)
  • 21.
    • LA resources, publications, archive:
  • 22.
    • Student success/completion
    • Astin (1996)
    • Tinto (1993)
  • 23.
    • Distributed, multi-level analytics
    • Suthers & Rosen (2011)
  • 24.
    • Attention metadata
    • Duval (2011)
  • 25.
    • Learning networks, crowds, communities
    • Haythornthwaite (2011)
  • 26.
    • Discourse analysis (automated and manual)
    • De Liddo & Buckingham Shum (2011)
  • 27.
    • Social learning analytics
    • Buckingham Shum & Ferguson (2011)
  • 28.
    • Participatory learning and reputation
    • Clow & Makriyannis (2011)
  • 29.
    • Early warning
    • Macfayden & Dawson (2010)
    • Campbell et al (2006)
  • 30. 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)
  • 31.
    • 1. Roots of learning analytics and context of deployment
    • 2. Becoming at data-intensive university
  • 32. 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
  • 33. Privacy as a transactional entity
    • Share my data to improve learning support from the university (school)
  • 34.
    • “ All-embracing technique is in fact the consciousness of the mechanized world. Technique integrates everything. It avoids shock and sensational events”
    • Ellul, 1964
  • 35. Analytics as a complex system: multiple interacting entities, more meaningful when connected
  • 36.
    • 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)
  • 37. Three communities that don’t communicate
    • Systems/enterprise level
    • Researchers
    • Educators (cobbling)
  • 38. What does a data-intensive university look like?
  • 39. Kron, et al (2011)
  • 40.
    • A cquisition: how do we get the data – structured and unstructured?
    • S torage: how do we store large quantities?
    • C leaning: how do we get the data in a working format
    • I ntegration: How do we “harmonize” varying data sets together
    • A nalysis: which tools and methods should be used?
    • R epresentation/visualization: tools and methods to communicate important ideas
  • 41.
    • “ 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. ”
  • 42.  
  • 43.
  • 44. 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
  • 45. Learning Analytics & Knowledge 2012: Vancouver http://lak12.sites.olt.ubc.ca/ Open online course: http://lak12.mooc.ca/
  • 46.
    • Twitter/randomly popular social media: gsiemens
    • www.learninganalytics.net
    • http://www.solaresearch.org /