1. Using analytics to improve the teaching and learning environment George Siemens November 21, 2011 Sydney, Australia Data Intensive University Forum

2. “A university where staff and students understand data and, regardless of its volume and diversity, can use it and reuse it, store and curate it, apply and develop the analytical tools to interpret it.”

3. We’re living in data. We’re all doing analytics.

7. Next-Generation Analytics. Analytics is growing along three key dimensions: (1) From traditional offline analytics to in-line embedded analytics. This has been the focus for many efforts in the past and will continue to be an important focus for analytics. (2)From analyzing historical data to explain what happened to analyzing historical and real-time data from multiple systems to simulate and predict the future. Over the next three years, analytics will mature along a third dimension, (3) from structured and simple data analyzed by individuals to analysis of complex information of many types (text, video, etc…) from many systems supporting a collaborative decision process that brings multiple people together to analyze, brainstorm and make decisions.

8. Data reveals our sentiments, our attitudes, our social connections, our intentions, and what we might do next.

9. Learning Analytics Business Intelligence Big DataEDM Statistical methods Intelligent Tutors Personalization Adaptive learning Roots of learning analytics

10. Analytics processes Data SourcesRepositoriesTools and Monitoring Analytics Methods Permissions LMS, library, social media, support services, mobiles, profile, attendance Data warehouse (institutional, national) Dashboards, visualization, query & drill down, automated monitoring, “quantified self” monitoring Predictive, course-path, social network, data mining, learner profile Admin, faculty, learners, reporting agencies

11. Siemens, Long, 2011. EDUCUASE Review

13. 1. Data Trails

14. 2. Machine-human readable content

15. 3. Learner Profile Development

16. 4. Analytics tools and Methods

17. 5. Prediction & Intervention

19. 6. Adapting and personalizing

20. Siemens, Long, 2011. EDUCAUSE Review

21. Open Learning Analytics

23. Challenge: Organizational capacity building for analytics deployment and use

24. Why invest in analytics? 1.Unbox the “black box of learning” 2.Identify students at the margins 3.Adapt teaching process to context/learners 4.Target support resources to those who need it

25. 5.Personalize and adapt content 6.More effective planning and allocation of institutional resources 7.(in the future) Restructure education processes to account for the architecture of information today: social, network, fragmented participatory

26. “Knewton analyzes learning materials based on thousands of data points—concepts, structure, difficulty level, media format—and uses sophisticated algorithms to piece together the perfect bundle of content for each student, every day. The more students who use the platform, the more accurate it becomes.”

27. Predictive Analytics Reporting Check my activity

28. Open online course: Learning Analytics January 23 - March 17, 2012 http://www.solaresearch.org/ Simon Buckingham Shum Shane Dawson Erik Duval Dragan Gasevic George Siemens

29. change.mooc.ca Twitter: gsiemens www.elearnspace.org/blog http://www.solaresearch.org/ Learning Analytics & Knowledge 2012: Vancouver http://lak12.sites.olt.ubc.ca/