1. The future of learning: Digital, distributed, and data-driven George Siemens, PhD University of Queensland October 31, 2016

2. George Siemens, 2016 What I’ll talk about: A bit of context Digital Data Distributed Imagining our future

3. George Siemens, 2016 A bit of context Digital Data Distributed Imagining our future

4. George Siemens, 2016 LINK Research Areas linkresearchlab.org/#aboutus How is knowledge created and shared in a digital age? How will we work tomorrow? What is needed for all students to be successful? How will we learn tomorrow?

5. George Siemens, 2016 A few LINK Research Lab projects

6. George Siemens, 2016 Projects - dLRN $1.6M Bill and Melinda Gates Foundation (PI) linkresearchlab.org/dlrn George Siemens, 2016

7. Projects - Smart Science Network $5.2M Bill and Melinda Gates Foundation (Co-PI) linkresearchlab.org/research George Siemens, 2016

8. Projects - BCC: Community and Capacity for Educational Discourse Research $254K NSF (Co-PI) linkresearchlab.org/research George Siemens, 2016

9. Projects - BIGDATA: Collaborative Research $1.6M NSF (Co-PI) linkresearchlab.org/research George Siemens, 2016

10. DALMOOC: multi-pathway learning linkresearchlab.org/dalmooc George Siemens, 2016

11. Emerging Technologies and their Practical Applications in K12 Teaching and Learning MOOC goo.gl/w9Bkdx George Siemens, 2016

12. Projects - INTERlab interlab.me George Siemens, 2016

13. aWEAR Expanding data collection to include broadening scope of data collection Holistic learning Individual well-being Preparing learners for the future of work and life George Siemens, 2016

14. Projects - CIRTL www.cirtl.net George Siemens, 2016

15. World Economic Forum: Future of Jobs, 2016

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18. McKinsey Quarterly, 2012

19. George Siemens, 2016 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

20. George Siemens, 2016 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

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22. SHEF: 2014 State Higher Education Finance

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25. “It's the contented gurgling of Satan's guts after he's eaten a university”

26. George Siemens, 2016 “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

27. George Siemens, 2016 A bit of context Digital Data Distributed Imagining our future

28. George Siemens, 2016 Self-regulated, self-selected, self-directed learning

29. George Siemens, 2016 Social media, MOOCs, community knowledge spaces

30. George Siemens, 2016 Wearables, Ambient, VR, IoT

31. George Siemens, 2016 Source: Mary Meeker, 2015, Internet Trends

32. George Siemens, 2016 Source: Mary Meeker, 2015, Internet Trends

33. George Siemens, 2016 A bit of context Digital Data Distributed Imagining our future

34. George Siemens, 2016 Neuroscience: one lens for insight Plasticity Lobal/regional attributes (i.e. Brain structure) Environmental/nutritional impact Emotional/physical health Developmental stages Numeracy/literacy (biology/experience synergy) Centre for Educational Research and Innovation (2007) Understanding the brain: the birth of a learning science. OECD Publishing:Paris

35. George Siemens, 2016 Relationships between research and practice (“laboratory to the classroom”) underdeveloped Tommerdahl, J. (2010). A model for bridging the gap between neuroscience and education. Oxford Review of Education, 36(1), 97- 109.

36. George Siemens, 2016 “Neurobiological underpinnings of morality, creativity, and culture” Immordino ‐ Yang, M. H., & Damasio, A. (2007). We feel, therefore we learn: The relevance of affective and social neuroscience to education. Mind, brain, and education, 1(1), 3-10.

37. George Siemens, 2016 Need for multi-pathway, multi-discipline models in order to have educational impact Fischer, K. W., Goswami, U., & Geake, J. (2010). The future of educational neuroscience. Mind, Brain, and Education, 4(2), 68-80.

38. George Siemens, 2016 Another approach: Big, sloppy, fuzzy data Inference The benefit of scale “so, follow the data” Halevy, A., Norvig, P., & Pereira, F. (2009). The unreasonable effectiveness of data. Intelligent Systems, IEEE, 24(2), 8-12.

39. George Siemens, 2016 "I would go so far as to say that he is selling snake oil."

40. George Siemens, 2016 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.

41. George Siemens, 2016 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.

42. George Siemens, 2016 Techniques (Baker & Siemens, 2014) Prediction: Infer a single aspect of the data (predicted variable) from some combination of other aspects of the data (predictor variables). Structure discovery: Find structure in the data without an assumption for what should be found. Relationship mining: Discover relationships between large numbers of variables. Discovery with models: Utilize the results of one data mining analysis within another data mining analysis. Distillation of data for human judgment: Visualize data to support basic research and decision-making by practitioners.

43. George Siemens, 2016 Important to know what works where Ineffective to –Scale through humans what should be scaled through technology Inferring and detecting knowledge and other key aspects of learner –Trying to scale through technology what should be scaled by humans Intervening on deep misconceptions or in the face of disengagement

44. George Siemens, 2016 Emerging methods Physiological and physical sensors –Webcam –Skin Conductance Sensor –Environmental observation (Kinect) –Emotion detection –Social sensors –Photoplethysmography Sensor –Heart Rate Sensor –Skin temperature –Posture Sensor –EEG –FMRI

45. George Siemens, 2016 Scaling Across Systems We’ve demonstrated that it is possible to scale LA/affect detectors across hundreds of thousands of students and across populations But right now, we have to rebuild many of our models for new systems Multi-dimensional databases needed to account for multi-device data collection (time locking)

46. George Siemens, 2016 Training the Next-Generation of Learning Analytics Practitioners Over 150 job postings in learning analytics in NY alone Less than 30 MS or PhD students emerging with training in learning analytics worldwide each year

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71. George Siemens, 2016 A bit of context Digital Data Distributed Imagining our future

72. George Siemens, 2016 Reducing the basic units of education: From courses/workshops/modules to competencies

73. George Siemens, 2016 Information fragmentation… loss of narratives of coherence

74. George Siemens, 2016 The problem: Once we’ve fragmented content and conversation, we need to stitch them together again so we can act meaningfully

75. George Siemens, 2016 Agents in a system possess only partial information (Miller and Page 2007) …to make sense and act meaningfully requires connections to be formed between agents

76. George Siemens, 2016 Computed curriculum

77. George Siemens, 2016 Smaller, contextual learning experiences, introduced into work/life/learning processes and networks through social and analytic approaches.

78. George Siemens, 2016 "The traditional scientific method, which is based on analysis, isolation, and the gathering of complete information about a phenomenon, is incapable to deal with such complex interdependencies.” Heylighen et al, 2007

79. George Siemens, 2016 Research as a distributed activity LINK Research Lab approach: Doc/post docs on campus Doc/post docs distributed (Stanford, Columbia, U of Edinburgh) Create networks of specialized and high knowledge capabilities Space and university identity doesn’t matter as much as it has in the past.

80. George Siemens, 2016 A bit of context Digital Data Distributed Imagining our future

81. George Siemens, 2016 What does Alberta have to do to be a leader in digital learning by 2030?

82. George Siemens, 2016 Process Concept paper Small group leader preparation Identification of key topic areas Digital learning Forum (200+ participants, top gov’t officials, including premier) Small group input based on 20 questions Final report

83. George Siemens, 2016 Credentialing – new models/approaches Public and academic oversight Global competitiveness and the role of the private sector Technology development and deployment Infrastructure investment Integration of digital and traditional learning

84. George Siemens, 2016 Recommendations Establish a multi-stakeholder digital learning research center to assess best uses of digital technologies for teaching and learning. Leverage existing investments in digital infrastructure and online learning to create a learning innovation ecosystem. This should include various sector contributors to education, including corporate partners, government agencies, and K-12 systems and universities. Centralize the development and deployment of digital learning provincially

85. George Siemens, 2016 Recommendations Establish a committee to evaluate policy considerations around digital learning, focusing on the policies needed to support Education 2030. Establish a learning and educational analytics research institute with member offices in major Alberta universities and schools. Develop a provincial learning identity profile (controlled and owned by the learner) that enables learners to move into different educational institutions easily, share competencies with corporate employers, and reskill and upgrade their learning. Invest in initiatives to prepare students, faculty, teachers, and administrators to build the capacity to function in complex digital environments. This should be in the form of new academic programs to prepare educators as well as resources for on-demand professional development.