future of learning: digital, distributed, and data-driven
TRANSCRIPT
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The future of learning: Digital, distributed, and data-driven
George Siemens, PhDTrondheim, Norway
May 12, 2016
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The future that I envisionTo 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.
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What I’ll talk about:
A bit of contextDigitalDataDistributedImagining our future
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A bit of contextDigitalDataDistributedImagining our future
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What does it mean to be human in a digital age?
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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?
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A few LINK Research Lab projects
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Projects - dLRN
$1.6M Bill and Melinda Gates Foundation (PI)linkresearchlab.org/dlrn
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Projects - Smart Science Network
$5.2M Bill and Melinda Gates Foundation (Co-PI)linkresearchlab.org/research
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Projects - BCC: Community and Capacity for Educational
Discourse Research
$254K NSF (Co-PI)linkresearchlab.org/research
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Projects - BIGDATA: Collaborative Research
$1.6M NSF (Co-PI)linkresearchlab.org/research
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DALMOOC: multi-pathway learning
linkresearchlab.org/dalmooc
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aWEARExpanding data collection to include
broadening scope of data collectionHolistic learningIndividual well-beingPreparing learners for the future of work
and life
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Emerging Technologies and their Practical Applications in K12 Teaching and Learning
MOOC
goo.gl/w9Bkdx
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The new landscape
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World Economic Forum: Future of Jobs, 2016
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“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
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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
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Learning & Knowledge Framework
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Existing educational practices addresses these quadrants
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The future of learning is in these quadrants
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The worst idea to happen to education in the past 50
years
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educational technology hasn’t really improved student learning
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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.
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My research interests in K-12
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Questions that we are currently asking:
(We being: Catherine Spann, Dragan Gasevic, Shane Dawson, Danijela Gasevic, Andy Berning)
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Australia Study
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What are the effects of screen time, physical activity, weight status, and social support on school performance?
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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?
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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?
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What is the between neighbourhood walkability (determined by walk score based on child's postal code) and safety and overall academic performance?
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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
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UTA/LINK and School Districts
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What are the associations between academic catastrophes and attendance, well-being, achievement, student behavior?
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Can these associations be used to predict learner drop out, increase academic performance and reduce related challenges for both individuals and the school systems?
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UTA and institutional learner wellness
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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?
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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?
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A bit of contextDigitalDataDistributedImagining our future
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Self-regulated, self-selected, self-directed learning
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Social media, MOOCs, community knowledge spaces
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Wearables, Ambient, VR, IoT
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Source: Mary Meeker, 2015, Internet Trends
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Source: Mary Meeker, 2015, Internet Trends
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A bit of contextDigitalDataDistributedImagining our future
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Data and AnalyticsBig, sloppy, fuzzy dataInferenceThe 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.
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"I would go so far as to say that he is selling snake oil."
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Lack of data-informed decision making
cultureMacfadyen, 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.
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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.
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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
Machine learning fo
r human
learning
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Emerging methodsPhysiological 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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A bit of contextDigitalDataDistributedImagining our future
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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.
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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
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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
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ExplorationLearning is the exploration of the unknown…
… not just mastery of what is already known.
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Compelling QuestionsHabitable Worlds:Are We Alone?Contagion:Can We Survive?
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Astr
onom
y
Chem
istr
y
Geo
logy
Phys
ics
Biol
ogy
The questions we care about don’t fit in silos
Transdisciplinary
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Smart Courses
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Tempus vs. Hora
Simon, H. A. (1962). The architecture of complexity. Proceedings of the American Philosophical Society, Vol. 106, No. 6. (pp. 457-476).
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Reducing the basic units of education:From courses/workshops/modules to competencies
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Information fragmentation… loss
of narratives of coherence
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The problem: Once we’ve fragmented content and conversation,
we need to stitch them together again so we can act meaningfully
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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
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A bit of contextDigitalDataDistributedImagining our future
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Our definition of the role of education is too narrow. Much of our research reflects this narrowness
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“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)
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1. Moving beyond assumptions based on legacy education system(completion, courses, learning, interaction, i.e. what is unique when
learning@scale?)
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2. Use existing research (Tutors, support structures, cognitive development)
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3. Personalization and adaptivity(Support during learning, assessment)
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Smaller, contextual learning experiences, introduced into work/life/learning processes and networks through social and analytic approaches.
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4. Open learner profile development(Who are the learners? What do they know)
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Personal Knowledge GraphPeople – learners, students, everyone – should have a personal knowledge graph (PKG)
A network model of what we know
Learner-owned
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5. Participatory learning (PBL, social, community, self-organized, pathways)
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6. Granularized learning and assessment(multiple systems, learner owned, competencies)
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Computed curriculum
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7. Use of data and analytics to solve challenges that matter
(in decentralized, open, interconnected systems)
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8. Wholeness, wellness, mindfulness, happiness
All the good ness’s
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The future that I envisionTo 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.
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Questions?