1. Learning Analytics, New Technologies, and Improved Learning George Siemens Oct 19, 2015 OSU

2. I worry that the social and equitable contribution of universities in society will not remain as they undergo stunning and dramatic restructuring

3. “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

4. The problem space

5. Equity of access Under represented learners Quality of learning Opportunities for success

6. Income inequality: “The defining challenge of our time”

9. 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

10. 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

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

13. The solution space

14. Education Sector Factbook, 2012

15. Ed-tech startups With transformations already underway in news, music, videos/movies, startup gold rush now turning focus to education

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

17. Technology in education

18. This is the background of new teaching/technology use: 1.Growing income inequality 2.New learner profile 3.Systemic change (unbundling & rebundling)

19. Much of it starts in openness (free software, open source, and eventually open education)

20. This idea of building on the accepted foundations of knowledge, the basis of scientific culture, naturally passed into the ethos of the computing world with the advent of the first computer networks in the 60's and 70's. They recognised that they could never fully harness the vast and quickly evolving power of these computers if they worked in isolation. de Grancy et al. 2004

21. “anything artificially put in to stop people from running a program is simply a deliberate bug” Stallman, 1996

22. Four Generations of Educational Technology 1.Basic tech use: CBT, website 2.ERP-type learning systems (LMS/CMS) 3.Fragmentation: Web 2.0, social media, competency, adaptive/personalized 4.Distributed & networked

23. Technology is intertwined with power shifts in society

26. But, the power shift also creates Pareto’s Palace for top companies

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

28. Learners who own their content, spaces, and learning

29. Framework for understanding future technology infrastructure Control between learner and faculty/institution, including structured and unstructured learning activities Ownership of data and content – the learner or the institution Integration – loosely coupled with data exchange happening through APIs and related industry standards or tightly connected with enterprise level systems Structure - centralized and decentralized teaching and learning approaches

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

32. To understand what tomorrow’s education system will look like, the technologies that we will use, and how we will teach, 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?

34. Preparation for future technology use is a mindset shift : experimental, acceptance of ambiguity, recognition of complexity, participatory pedagogy, flexible control points

35. 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

36. Analytics is the process of developing actionable insights through problem definition and the application of statistical models and analysis against existing and/or simulated future data Cooper, 2012

37. 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