data intensive university

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  • 1.The data-intensive universityGeorge Siemens, PhDJuly 27, 2012Presented to:American Association of State and College UniversitiesSan Francisco, CA

2. Assumptions 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. probably indicates that these sectors face strong systemic barriers to increasingproductivity 5. Kron, et al (2011) 6. higher education finds itself on the verge ofdiving deeply into the analytical end of theeducation transformation pool Wagner & Ice 2012 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. http://www.dataqualitycampaign.org/A different way of thinking and functioning 9. What is a data-intensive university? 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. Siemens, Long, 2011. EDUCUASE Review 12. Limited efficiency and productivity gainsthrough piecemeal solutions 13. StrategyPlanning & Metrics & Capacity Systemic resources toolsdevelopment change allocationData inventory Data/Analytics Analytics goals Faculty/StaffCourse team & target areasPD models?Role of data Data sourcesEducator-Student access Self-directedProblem or controlledlearningopportunitytoolsStakeholders BudgetEnterprise Learning Automated(IR, Academic, toolsdesign discoveryAdmin) PrioritiesIterativeProcessStudentAccess developmentmapping andmodels of algorithmsevaluationGovernance Stages of Visualization Intelligent deploymentcurriculumCompliance Policy development 14. StrategyPlanning & Metrics & Capacity Systemic resources toolsdevelopment change allocationData inventory Data/Analytics Analytics goals Faculty/StaffCourse team & target areasPD models?Role of data Data sourcesEducator-Student access Self-directedProblem or controlledlearningopportunitytoolsStakeholders BudgetEnterprise Learning Automated(IR, Academic, toolsdesign discoveryAdmin) PrioritiesIterativeProcessStudentAccess developmentmapping andmodels of algorithmsevaluationGovernance Stages of Visualization Intelligent deploymentcurriculumCompliance Policy development 15. StrategyPlanning & Metrics & Capacity Systemic resources toolsdevelopment change allocationData inventory Data/Analytics Analytics goals Faculty/StaffCourse team & target areasPD models?Role of data Data sourcesEducator-Student access Self-directedProblem or controlledlearningopportunitytoolsStakeholders BudgetEnterprise Learning Automated(IR, Academic, toolsdesign discoveryAdmin) PrioritiesIterativeProcessStudentAccess developmentmapping andmodels of algorithmsevaluationGovernance Stages of Visualization Intelligent deploymentcurriculumCompliance Policy development 16. StrategyPlanning & Metrics & Capacity Systemic resources toolsdevelopment change allocationData inventory Data/Analytics Analytics goals Faculty/StaffCourse team & target areasPD models?Role of data Data sourcesEducator-Student access Self-directedProblem or controlledlearningopportunitytoolsStakeholders BudgetEnterprise Learning Automated(IR, Academic, toolsdesign discoveryAdmin) PrioritiesIterativeProcessStudentAccess developmentmapping andmodels of algorithmsevaluationGovernance Stages of Visualization Intelligent deploymentcurriculumCompliance Policy development 17. StrategyPlanning & Metrics & Capacity Systemic resources toolsdevelopment change allocationData inventory Data/Analytics Analytics goals Faculty/StaffCourse team & target areasPD models?Role of data Data sourcesEducator-Student access Self-directedProblem or controlledlearningopportunitytoolsStakeholders BudgetEnterprise Learning Automated(IR, Academic, toolsdesign discoveryAdmin) PrioritiesIterativeProcessStudentAccess developmentmapping andmodels of algorithmsevaluationGovernance Stages of Visualization Intelligent deploymentcurriculumCompliance Policy development 18. StrategyPlanning & Metrics & Capacity Systemic resources toolsdevelopment change allocationData inventory Data/Analytics Analytics goals Faculty/StaffCourse team & target areasPD models?Role of data Data sourcesEducator-Student access Self-directedProblem or controlledlearningopportunitytoolsStakeholders BudgetEnterprise Learning Automated(IR, Academic, toolsdesign discoveryAdmin) PrioritiesIterativeProcessStudentAccess developmentmapping andmodels of algorithmsevaluationGovernance Stages of Visualization Intelligent deploymentcurriculumCompliance Policy development 19. We collect enough data. We need to focus on connecting.Multiple data sources:Social mediaUniversity help resourcesLMSStudent information systemCourse progression, etc 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. 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. 37 23. Siemens, Long, 2011. EDUCAUSE Review 24. http://edfuture.net/ October 8-November 16, 2012 25. http://lakconference.org 26. gsiemens @gmailTwitterSkypeFBWhereverwww.elearnspace.orgwww.connectivism.cawww.learninganalytics.net