Future of Learning: Digital, distributed, and data-driven

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  • After going through it, I disagree with a Top-Down approach where people are turned into data. The individual is missing from this model. The data ABOUT the individual, averaged together to provide generalized emergent patterns can be USEFUL for developing curriculum but I don't agree with the Big Data approach if it involves grueling testing procedures, constant data collection that interferes with teacher creativity, learning preferences and such. Too much Homogeneous even if it's in the 'name of' individualization. So... sorry, I can't agree with the model to drive education but I can see it being useful as a periodic (but not excessive) measuring tool.
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  • Hope you don't mind George: I get restlesss clicking through slideshows but I really wanted the information, so I set it to music and put it on youtube. https://www.youtube.com/watch?v=iTzP_qKi7Mk
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  • I'm reading this now. Thank you for putting it together.
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Future of Learning: Digital, distributed, and data-driven

  1. 1. The future of learning: Digital, distributed, and data-driven George Siemens, PhD Trondheim, Norway May 12, 2016
  2. 2. The future that I envision To enable all students to achieve an education that enables quality of life and meaningful employment through a) exceptional quality research and; b) sophisticated data collection and; c) advanced machine learning & human learning analysis/support.
  3. 3. What I’ll talk about: A bit of context Digital Data Distributed Imagining our future
  4. 4. A bit of context Digital Data Distributed Imagining our future
  5. 5. What does it mean to be human in a digital age?
  6. 6. 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?
  7. 7. A few LINK Research Lab projects
  8. 8. Projects - dLRN $1.6M Bill and Melinda Gates Foundation (PI) linkresearchlab.org/dlrn
  9. 9. Projects - Smart Science Network $5.2M Bill and Melinda Gates Foundation (Co-PI) linkresearchlab.org/research
  10. 10. Projects - BCC: Community and Capacity for Educational Discourse Research $254K NSF (Co-PI) linkresearchlab.org/research
  11. 11. Projects - BIGDATA: Collaborative Research $1.6M NSF (Co-PI) linkresearchlab.org/research
  12. 12. DALMOOC: multi-pathway learning linkresearchlab.org/dalmooc
  13. 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
  14. 14. Emerging Technologies and their Practical Applications in K12 Teaching and Learning MOOC goo.gl/w9Bkdx
  15. 15. The new landscape
  16. 16. World Economic Forum: Future of Jobs, 2016
  17. 17. “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
  18. 18. Complexification of learning needs Learning needs are complex, ongoing Simple singular narrative won’t suffice going forward The idea of learning is expanding and diversifying
  19. 19. Learning & Knowledge Framework
  20. 20. Existing educational practices addresses these quadrants
  21. 21. The future of learning is in these quadrants
  22. 22. The worst idea to happen to education in the past 50 years
  23. 23. educational technology hasn’t really improved student learning
  24. 24. School rankings Need to focus on innovation, not sorting and categorizations of students, schools, and teachers. Promote wellness, quality of life, happiness index, over routine testing and mechanization of learning.
  25. 25. My research interests in K-12
  26. 26. Questions that we are currently asking: (We being: Catherine Spann, Dragan Gasevic, Shane Dawson, Danijela Gasevic, Andy Berning)
  27. 27. Australia Study
  28. 28. What are the effects of screen time, physical activity, weight status, and social support on school performance?
  29. 29. Are there observed gender and ethnic differences in the association of barriers to physical activity with physical activity, screen time, weight status and overall health?
  30. 30. What is the relationship between neighbourhood walkability (determined by walk score based on child's postal code) and safety and their association with physical activity, screen time, weight status and overall health?
  31. 31. What is the between neighbourhood walkability (determined by walk score based on child's postal code) and safety and overall academic performance?
  32. 32. What is the association between screen time (e.g., use of computer for games) and wellbeing and is there a mediation effect of social supports and extracurricular activities in that association
  33. 33. UTA/LINK and School Districts
  34. 34. What are the associations between academic catastrophes and attendance, well-being, achievement, student behavior?
  35. 35. Can these associations be used to predict learner drop out, increase academic performance and reduce related challenges for both individuals and the school systems?
  36. 36. UTA and institutional learner wellness
  37. 37. What is the relationship between mind (stress, negative emotions), body (physical activity and health), and academic performance among nursing students? Can real-time data signals, big learning data, and machine learning models uncover these interrelationships at a deeper level?
  38. 38. How can interventions that challenge physical, cognitive, and emotional dimensions of individuals improve the health, well-being, and academic performance of learners? What is the feasibility of such an intervention on a university campus? Can students adhere to the protocol and does the intervention produce measurable change in health and academic performance?
  39. 39. A bit of context Digital Data Distributed Imagining our future
  40. 40. Self-regulated, self-selected, self-directed learning
  41. 41. Social media, MOOCs, community knowledge spaces
  42. 42. Wearables, Ambient, VR, IoT
  43. 43. Source: Mary Meeker, 2015, Internet Trends
  44. 44. Source: Mary Meeker, 2015, Internet Trends
  45. 45. A bit of context Digital Data Distributed Imagining our future
  46. 46. Data and Analytics 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.
  47. 47. "I would go so far as to say that he is selling snake oil."
  48. 48. 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.
  49. 49. 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.
  50. 50. 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
  51. 51. 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
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  76. 76. A bit of context Digital Data Distributed Imagining our future
  77. 77. Maria Popova in order for us to truly create and contribute to the world, we have to be able to connect countless dots, to cross- pollinate ideas from a wealth of disciplines, to combine and recombine these pieces and build new castles.
  78. 78. 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
  79. 79. Connectivism: 1. Knowledge is networked and distributed 2. The experience of learning is one of forming new neural, conceptual and external networks 3. Occurs in complex, chaotic, shifting spaces 4. Increasingly aided by technology
  80. 80. Exploration Learning is the exploration of the unknown… … not just mastery of what is already known.
  81. 81. Compelling Questions Habitable Worlds: Are We Alone? Contagion: Can We Survive?
  82. 82. Astronomy Chemistry Geology Physics Biology The questions we care about don’t fit in silos Transdisciplinary
  83. 83. Smart Courses
  84. 84. Tempus vs. Hora Simon, H. A. (1962). The architecture of complexity. Proceedings of the American Philosophical Society, Vol. 106, No. 6. (pp. 457-476).
  85. 85. Reducing the basic units of education: From courses/workshops/modules to competencies
  86. 86. Information fragmentation… loss of narratives of coherence
  87. 87. The problem: Once we’ve fragmented content and conversation, we need to stitch them together again so we can act meaningfully
  88. 88. 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
  89. 89. A bit of context Digital Data Distributed Imagining our future
  90. 90. Our definition of the role of education is too narrow. Much of our research reflects this narrowness
  91. 91. “Perhaps in reaching to educate the brain, we should also consider reaching for the heart” (Peer Reviewed (by me) message on Slack, Catherine Spann, 2015)
  92. 92. 1. Moving beyond assumptions based on legacy education system (completion, courses, learning, interaction, i.e. what is unique when learning@scale?)
  93. 93. 2. Use existing research (Tutors, support structures, cognitive development)
  94. 94. 3. Personalization and adaptivity (Support during learning, assessment)
  95. 95. Smaller, contextual learning experiences, introduced into work/life/learning processes and networks through social and analytic approaches.
  96. 96. 4. Open learner profile development (Who are the learners? What do they know)
  97. 97. Personal Knowledge Graph People – learners, students, everyone – should have a personal knowledge graph (PKG) A network model of what we know Learner-owned
  98. 98. 5. Participatory learning (PBL, social, community, self- organized, pathways)
  99. 99. 6. Granularized learning and assessment (multiple systems, learner owned, competencies)
  100. 100. Computed curriculum
  101. 101. 7. Use of data and analytics to solve challenges that matter (in decentralized, open, interconnected systems)
  102. 102. 8. Wholeness, wellness, mindfulness, happiness All the good ness’s
  103. 103. The future that I envision To enable all students to achieve an education that enables quality of life and meaningful employment through a) exceptional quality research and; b) sophisticated data collection and; c) advanced machine learning & human learning analysis/support.
  104. 104. Questions?

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