1. Innovation: Big Data and Analytics George Siemens, PhD NEASC December 9, 2015

2. This system is being unbundled & rebundled, creating new power and influence structures

5. We don’t have the data or the models for understanding how dramatic changes now occurring will impact higher education

6. Lack of data-informed decision making culture Macfadyen, L., & Dawson, S. (2012). Numbers Are Not Enough. Why e-Learning Analytics Failed to Inform an Institutional Strategic Plan. Educational Technology & Society, 15(3), 149-163.

8. Siemens, Long, 2011. EDUCUASE Review

9. The WHY of learning analytics

10. “If the ladder of educational opportunity rises high at the doors of some youth and scarcely rises at the doors of others, while at the same time formal education is made a prerequisite to occupational and social advance, then education may become the means, not of eliminating race and class distinctions, but of deepening and solidifying them.” President Truman, 1947

11. Pell Institute, 2015

14. McKinsey Quarterly, 2012

15. Student profiles Diversifying (OECD) Less than 50% now full time (US Census Bureau) http://www.oecd.org/edu/skills-beyond-school/EDIF%202013-- N%C2%B015.pdf http://www.census.gov/prod/2013pubs/acsbr11-14.pdf

16. Favours women over men More learners as % (up to 60%) Average entrance age increasing Top three countries for entering students: China, India, USA Traditional science courses waning in popularity Greater international student OECD 2013

17. Enrolment: “perfect storm of challenges ahead” University Business, January 2015

19. To understand what tomorrow’s education system will look like, we have to understand the architecture of information today: how is it created how is it shared how is it iterated how is it controlled?

24. Parallel developing partners: Adaptive and personalized learning PlatformPublisher KnewtonPearson Smart SparrowMcGraw-Hill Desire2Learnadaptcourseware LoudCloudCMU OLI

25. Knowledge development, learning, is (should be) concerned with learners understanding relationships, not simply memorizing facts. i.e. naming nodes is “low level” knowledge activity, understanding node connectivity, and implications of changes in network structure, consists of deeper, coherent, learning

26. Granularization of assessment Cracking the credit hour (New America Foundation) Badges (Mozilla & others) http://newamerica.net/publications/policy/cracking_the_credit_hour http://openbadges.org/

27. Educational Quality through Innovative Partnerships (EQUIP)

28. Certificates Fastest growing form of credentialing (800% increase in 30 years) Industry-facing Carnevale, Rose, Hanson 2012

29. Competencies Competency-based degrees (Chronicle, 2014) Prior learning assessment (Insider Higher Ed, 2012) http://chronicle.com/article/Competency-Based-Degrees-/144769/ http://www.insidehighered.com/news/2012/05/07/prior-learning- assessment-catches-quietly

30. Knowledge in pieces diSessa, 1993

31. “The world is one big data problem” Gilad Elbaz

32. The WHAT of learning analytics

35. In: Siemens, Gasevic, & Dawson (eds), 2015

36. Learning analytics is the measurement, collection, analysis, and reporting of data about learners and their contexts, for the purposes of understanding and optimizing learning and the environments in which it occurs. LAK11 Conference

37. Learning analytics is about learning Gašević, D., Dawson, S., Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64-71.

38. Once size fits all does not work in learning analytics Gašević, D., Dawson, S., Rogers, T., Gašević, D. (2016). Learning analytics should not promote one size fits all: The effects of course-specific technology use in predicting academic success. The Internet and Higher Education, 26, 68–84.

39. “a team at Google couldn’t decide between two blues, so they’re testing 41 shades between each blue to see which one performs better” Douglas Bowman

40. What will LA do for learning science & education Add a new research layer Personalization Optimization (move from negative orientation) Organizational insight Improved decision making New models of learning Increase competitiveness Improve marketing/promotion/recruitment

41. Blending physical and digital spaces Wearables Ambient computing IoT …biometric/physiological data needed to answer complex questions around social and affective being and learning

42. The HOW of learning analytics

52. This system is being unbundled & rebundled, creating new power and influence structures