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Analytics: Improving teaching and learning

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Presented to UTS, Sydney, Australia. November 21, 2011

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Analytics: Improving teaching and learning

  1. 1. Using analytics to improve theteaching and learning environment George Siemens November 21, 2011 Data Intensive University Forum Sydney, Australia
  2. 2. “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.”
  3. 3. We’re living in data.We’re all doing analytics.
  4. 4. Next-Generation Analytics. Analytics is growing along three key dimensions:(1) From traditional offline analytics to in-line embedded analytics. Thishas been the focus for many efforts in the past and will continue to be an important focus foranalytics.(2)From analyzing historical data to explain what happened toanalyzing historical and real-time data from multiple systems to simulate andpredict the future.Over the next three years, analytics will mature along a third dimension,(3) from structured and simple data analyzed by individuals to analysisof complex information of many types (text, video, etc…) from many systemssupporting a collaborative decision process that brings multiple people together to analyze,brainstorm and make decisions.
  5. 5. Data revealsour sentiments,our attitudes,our social connections,our intentions,and what we might do next.
  6. 6. Roots of learning analytics Statistical methods Intelligent EDM Tutors Big Data Personalization Business Learning AdaptiveIntelligence learning Analytics
  7. 7. Analytics processesData Sources Repositories Tools and Analytics Permissions Monitoring MethodsLMS, library, Data warehouse Dashboards, Predictive, Admin, faculty,social media, (institutional, visualization, course-path, learners,support services, national) query & drill social network, reportingmobiles, profile, down, data mining, agenciesattendance automated learner profile monitoring, “quantified self” monitoring
  8. 8. Siemens, Long, 2011. EDUCUASE Review
  9. 9. 1. Data Trails
  10. 10. 2. Machine-human readable content
  11. 11. 3. Learner Profile Development
  12. 12. 4. Analytics tools and Methods
  13. 13. 5. Prediction & Intervention
  14. 14. 6. Adapting and personalizing
  15. 15. Siemens, Long, 2011. EDUCAUSE Review
  16. 16. Open Learning Analytics
  17. 17. Challenge: Organizationalcapacity building for analytics deployment and use
  18. 18. Why invest in analytics?1. Unbox the “black box of learning”2. Identify students at the margins3. Adapt teaching process to context/learners4. Target support resources to those who need it
  19. 19. 5. Personalize and adapt content6. More effective planning and allocation of institutional resources7. (in the future) Restructure education processes to account for the architecture of information today: social, network, fragmented participatory
  20. 20. “Knewton analyzes learning materials based on thousandsof data points—concepts, structure, difficulty level, mediaformat—and uses sophisticated algorithms to piecetogether the perfect bundle of content for eachstudent, every day.The more students who use the platform, the moreaccurate it becomes.”
  21. 21. Check my activity Predictive Analytics Reporting
  22. 22. Open online course: Learning Analytics January 23 - March 17, 2012 Simon Shane Dawson Erik Duval Dragan Gasevic George SiemensBuckingham Shum
  23. 23. Twitter: gsiemens Analytics & Knowledge 2012: Vancouver
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Presented to UTS, Sydney, Australia. November 21, 2011


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