Learning Analytics: Higher Education
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Learning Analytics: Higher Education Presentation Transcript

  • 1.  Learning analytics inhigher education
    George Siemens
    September 1, 2011
    Pretoria, South Africa
  • 2. We leave data trails everywhere we go
  • 3. and in whatever we do.
  • 4. That data is valuable.
  • 5. It reveals our sentiments, our attitudes,our social connections,our intentions,and what we might do next.
  • 6. We’re giving most of that data away.
  • 7. for free. (or in exchange for some service)
  • 8. American intelligence communities are interested in your YouTube video, flickr uploads, tweets -- even your online book purchases -- and for over a year they've been laying down some serious cash to get a better look at all of them.
    http://www.businessinsider.com/the-cia-just-put-a-ton-of-cash-into-a-software-firm-that-monitors-your-online-activity-2011-7
  • 9.  dashboard solution with geographical and predictive analysis to help identify where and how resources should be deployed to reduce crime.
    http://www.informationbuilders.com/news/press/release/9483
  • 10. http://online.wsj.com/article/SB10001424053111903885604576486330882679982.html
    International Business Machines Corp., which has invested more than $14 billion buying analytics industry companies…since 2005, has teamed up with more than 200 schools…to develop analytics curriculum and training.
  • 11. $3 million:
    Who is going to be admitted into a hospital?
  • 12.
  • 13. Talk-o-meter: who talks the most?
  • 14. “Whether from government transparency initiatives, leaks or Freedom of Information requests, journalists are drowning in more documents than they can ever hope to read.
    We’re building an interactive system where computers do the visualization, while a human guides the exploration.”
    http://overview.ap.org/about/
  • 15.
  • 16.
  • 17. Recommenders are everywhere
  • 18. Recommenders gone bad
    This
    DOES NOT
    equal this
  • 19. So are ‘matching’ tools
    Facial recognition
    http://www.alternet.org/news/152231/5_unexpected_places_you_can_be_tracked_with_facial_recognition_technology
  • 20. Socially-driven suggestions
  • 21. Each new node amplifies the value of the entire network…and produces lock-in
  • 22. Let’s look at this from the lens of teaching and learning
  • 23. Academic Analytics
    “Academic analytics helps address the public’s desire for institutional accountability with regard to student success, given the widespread concern over the cost of higher education and the difficult economic and budgetary conditions prevailing worldwide.”
    http://www.educause.edu/EDUCAUSE+Quarterly/EDUCAUSEQuarterlyMagazineVolum/SignalsApplyingAcademicAnalyti/199385
  • 24. Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.
  • 25. 25
  • 26. http://www.skyrill.com/seatinghabits/
  • 27. Approximate spatiotemporal trajectories of some classes.
    http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0023176
  • 28. http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0023176
    Aggregated networks of daily contacts
  • 29.
  • 30. The plan…designates money to create a "dashboard"—an interactive, online database—to give students, parents, and legislators access to detailed measures of departments' and colleges' productivity and efficiency. Data on individual professors will probably also be included
    http://chronicle.com/article/U-of-Texas-Adopts-Plan-to/128800
  • 31. Let’s avoid the mistakes of previous educational technology adoption.
  • 32. Let’s start with open.and learnersand educatorsand researchers
  • 33. Proposal:Open Learning Analytics Architecture
    Integrated
    Modularized
    Extensible
  • 34. Researchers involved:
    George Siemens & DraganGasevic
    Athabasca University, Canada
    Caroline Haythornthwaite & Shane Dawson
    University of British Columbia, Canada
    Simon Buckingham Shum & Rebecca Ferguson
    Open University, United Kingdom
    Erik Duval & KatrienVerbert
    KatholiekeUniversiteit Leuven, Belgium
    Ryan S. J. d. Baker
    Worcester Polytechnic Institute, United States
  • 35. 35
  • 36. 36
  • 37.
  • 38. Analytics and connectivism
    What is explicit can be connected.
    What can be connected can be analyzed.
  • 39. We want to understand why this happens.
    And what it means when it does.
    Or when it doesn’t.
    And the nature of power and control in enabling/preventingit.
  • 40. change.mooc.ca
    Twitter: gsiemens
    www.elearnspace.org/blog
    Learning Analytics & Knowledge 2012:
    Vancouver
    http://lak12.sites.olt.ubc.ca/