Data Intensive University

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  • http://www.businessinsider.com/the-cia-just-put-a-ton-of-cash-into-a-software-firm-that-monitors-your-online-activity-2011-7
  • Source: McKinsey Report: Big Data: The Next Frontier for Innovation, Competition, and Productivity
  • http://sloanreview.mit.edu/feature/achieving-competitive-advantage-through-analytics/
  • http://www.educause.edu/EDUCAUSE+Review/EDUCAUSEReviewMagazineVolume46/ResearchandDataServicesforHigh/238391
  • http://alumni.media.mit.edu/~tanzeem/TR-554.pdf
  • Data Intensive University

    1. 1. The data-intensive university George Siemens, PhD July 27, 2012 Presented to: American Association of State and College Universities San Francisco, CA
    2. 2. Assumptions
    3. 3. American intelligence communitiesare interested in your YouTubevideo, flickr uploads, tweets --even your online book purchases --and for over a year theyve beenlaying down some serious cash toget a better look at all of them.
    4. 4. “…probably indicates that these sectors face strong systemic barriers to increasingproductivity”
    5. 5. Kron, et al (2011)
    6. 6. “higher education finds itself on the verge ofdiving deeply into the analytical end of theeducation transformation pool” Wagner & Ice 2012
    7. 7. “Analytics, and the data and research that fuelit, offers the potential to identify broken modelsand promising practices, to explain them, and topropagate those practices.” Grajek, 2011
    8. 8. http://www.dataqualitycampaign.org/A different way of thinking and functioning
    9. 9. What is a data-intensive university?
    10. 10. “A university where staff and studentsunderstand data and, regardless of its volumeand diversity, can use it and reuse it, store andcurate it, apply and develop the analytical toolsto interpret it.”
    11. 11. Siemens, Long, 2011. EDUCUASE Review
    12. 12. Limited efficiency and productivity gainsthrough piecemeal solutions
    13. 13. Strategy Planning & Metrics & Capacity Systemic resources tools development change allocationData inventory Data/Analytics Analytics goals Faculty/Staff Course team & target areas PD models?Role of data Data sources Educator- Student access Self-directedProblem or controlled learningopportunity toolsStakeholders Budget Enterprise Learning Automated(IR, Academic, tools design discoveryAdmin) Priorities Iterative Process StudentAccess development mapping and models of algorithms evaluationGovernance Stages of Visualization Intelligent deployment curriculumCompliance Policy development
    14. 14. Strategy Planning & Metrics & Capacity Systemic resources tools development change allocationData inventory Data/Analytics Analytics goals Faculty/Staff Course team & target areas PD models?Role of data Data sources Educator- Student access Self-directedProblem or controlled learningopportunity toolsStakeholders Budget Enterprise Learning Automated(IR, Academic, tools design discoveryAdmin) Priorities Iterative Process StudentAccess development mapping and models of algorithms evaluationGovernance Stages of Visualization Intelligent deployment curriculumCompliance Policy development
    15. 15. Strategy Planning & Metrics & Capacity Systemic resources tools development change allocationData inventory Data/Analytics Analytics goals Faculty/Staff Course team & target areas PD models?Role of data Data sources Educator- Student access Self-directedProblem or controlled learningopportunity toolsStakeholders Budget Enterprise Learning Automated(IR, Academic, tools design discoveryAdmin) Priorities Iterative Process StudentAccess development mapping and models of algorithms evaluationGovernance Stages of Visualization Intelligent deployment curriculumCompliance Policy development
    16. 16. Strategy Planning & Metrics & Capacity Systemic resources tools development change allocationData inventory Data/Analytics Analytics goals Faculty/Staff Course team & target areas PD models?Role of data Data sources Educator- Student access Self-directedProblem or controlled learningopportunity toolsStakeholders Budget Enterprise Learning Automated(IR, Academic, tools design discoveryAdmin) Priorities Iterative Process StudentAccess development mapping and models of algorithms evaluationGovernance Stages of Visualization Intelligent deployment curriculumCompliance Policy development
    17. 17. Strategy Planning & Metrics & Capacity Systemic resources tools development change allocationData inventory Data/Analytics Analytics goals Faculty/Staff Course team & target areas PD models?Role of data Data sources Educator- Student access Self-directedProblem or controlled learningopportunity toolsStakeholders Budget Enterprise Learning Automated(IR, Academic, tools design discoveryAdmin) Priorities Iterative Process StudentAccess development mapping and models of algorithms evaluationGovernance Stages of Visualization Intelligent deployment curriculumCompliance Policy development
    18. 18. Strategy Planning & Metrics & Capacity Systemic resources tools development change allocationData inventory Data/Analytics Analytics goals Faculty/Staff Course team & target areas PD models?Role of data Data sources Educator- Student access Self-directedProblem or controlled learningopportunity toolsStakeholders Budget Enterprise Learning Automated(IR, Academic, tools design discoveryAdmin) Priorities Iterative Process StudentAccess development mapping and models of algorithms evaluationGovernance Stages of Visualization Intelligent deployment curriculumCompliance Policy development
    19. 19. We collect enough data. We need to focus on connecting.Multiple data sources:Social mediaUniversity help resourcesLMSStudent information systemCourse progression, etc
    20. 20. Challenges:Broadening scope of data capture - data outside of the current model of LMS - sociometer: Choudhury & Pentland (2002) - classroom/library/support services, - quantified selfTimeliness of data (real-time analytics)
    21. 21. Principles of a systems-wide analytics tool1. Algorithms should be open, customizable forcontext2. Students should see what the organization sees3. Analytics engine as a platform: open for allresearchers and organizations to build on4. Specific analytics strategies and tools: APIs5. Integrate and connect with existing open tools6. Modularized and extensible
    22. 22. 37
    23. 23. Siemens, Long, 2011. EDUCAUSE Review
    24. 24. http://edfuture.net/ October 8-November 16, 2012
    25. 25. http://lakconference.org
    26. 26. gsiemens @ gmail Twitter Skype FB Whereverwww.elearnspace.orgwww.connectivism.cawww.learninganalytics.net

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