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Analytics: EDUCAUSE


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Presented to EDUCAUSE, October 21, 2011

Published in: Education, Technology
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Analytics: EDUCAUSE

  1. Transforming learning through analyticsGeorge SiemensEDUCAUSEOctober 21, 2011Philadelphia
  2. Questions? Ask here:
  3. American intelligence communities areinterested in your YouTube video, flickruploads, tweets -- even your online bookpurchases -- and for over a year theyve beenlaying down some serious cash to get abetter look at all of them. a-software-firm-that-monitors-your-online-activity-2011-7
  4. dashboard solution with geographicaland predictive analysis to help identify whereand how resources should be deployed toreduce crime.
  5. 4576486330882679982.html
  6. Next-Generation Analytics. Analytics is growing along three key dimensions:From traditional offline analytics to in-line embedded analytics. This has been the focus formany efforts in the past and will continue to be an important focus for analytics.From analyzing historical data to explain what happened to analyzing historical and real-timedata from multiple systems to simulate and predict the future.Over the next three years, analytics will mature along a third dimension, from structured andsimple data analyzed by individuals to analysis of complex information of many types(text, video, etc…) from many systems supporting a collaborative decision process that bringsmultiple people together to analyze, brainstorm and make decisions.
  7. Big Data. The size, complexity of formats and speed of delivery exceeds the capabilities oftraditional data management technologies; it requires the use of new or exotic technologiessimply to manage the volume alone. Many new technologies are emerging, with the potentialto be disruptive (e.g., in-memory DBMS). Analytics has become a major driving application fordata warehousing, with the use of MapReduce outside and inside the DBMS, and the use ofself-service data marts. One major implication of big data is that in the future users will notbe able to put all useful information into a single data warehouse.
  8. Who is going to get sick?
  9. Analytics need corrective capacity
  10. Why?
  11. If your daughter shops on your account…
  12. Yeah, maybe not.
  13. or
  14. Recommenders gone badThisDOES NOTequal this
  15. Empowering end-users: access to,and means of interrogating, data
  16. Data revealsour sentiments,our attitudes,our social connections,our intentions,and what we might do next.
  17. Learning analytics is themeasurement, collection, analysis andreporting of data about learners and theircontexts, for purposes of understanding andoptimizing learning and the environments inwhich it occurs. Society of Learning Analytics Research
  18. Anywhere/time data is produced in education an analytics opportunity exists
  20. Learner successUse of university resources/servicesAt-risk learners (predictive capacity)Help-seeking behaviorAlertsInterventionLearner dashboardPath of concept developmentReal-time analytics
  22. What are NOT learning analytics?Classroom optimizationStaff allocationWeb analyticsThese are important analytics activities foruniversities, but they are not learning analytics
  23. Analytics must berooted in learning sciences
  24. Analytics produce patterns, not insight
  25. Analytics should do more than only evaluate what we are doing in our existing system.
  26. They should help to change the system.
  27. An LMS pulls things together by emphasizingone spaceAnalytics/monitoring tools pull things togetherby evaluating relatedness outside of a centralspaceEach new node amplifies value
  28. Let’s avoid the mistakes of previous educational technology adoption.
  29. Let’s start with open.and learnersand educatorsand researchers
  30. Proposal:Open Learning Analytics ArchitectureIntegratedModularizedExtensible
  31. 51
  32. Privacy, security, ethics
  33. April 29-May 2, 2012 Vancouver
  34. Open online course: Learning Analytics January 23 - March 17, 2012
  36. Twitter: gsiemens Analytics & Knowledge 2012: Vancouver