Prospects for Scientific Advances in
Learning from the Perspective of Work in
Education & Technology
Roy Pea
Stanford University
February 28, 2013
Sciences of Learning Workshop
National Science Foundation
Educate for Broader Competencies
• Preliminary classification of skills, abilities
• Importance of Deeper Learning, 21st Century Skills
• Perspectives on Deeper
Learning
• Deeper Learning of ELA,
Math, Science
• Teaching & assessing for
transfer
• Systems to support Deeper
Learning
Three Broad Domains of
Competencies
• Cognitive
• Cognitive processes and strategies
• Knowledge
• Creativity
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Critical thinking
Information literacy
Reasoning
Innovation
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Flexibility
Initiative
Appreciation for diversity
Metacognition
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Communication
Collaboration
Responsibility
Conflict resolution
• Intrapersonal
• Intellectual openness
• Work ethic and conscientiousness
• Positive core self-evaluation
• Interpersonal
• Teamwork and collaboration
• Leadership
Why broader competencies?
• Important for broadening STEM participation
• More than cognitive processes and strategies
at stake in educational participation
• Interpersonal issues such as stereotype
threat, social belonging, difficulties in
communication and collaboration
• Intrapersonal issues such as ‘academic
tenacity’ — motivation, self-beliefs,
disciplinary identity, self-regulation
• All are consequential for selecting and
maintaining STEM learning pathways
Hyperconnected World
• 125 Mil US consumer smartphones
• Up 29% in a year, 99% in two years
• 50 Mil US tablets
• US 8 to 18 yr olds: 11 hrs media use a day (in 7.5
hrs!) - increasing media multitasking - three
screens: TV, computers and mobile phones
Next decade of technology-enhanced
learning opportunities combines…
• Very-low cost “always-on” networked smart mobiles
for on-demand learning resources
• Elastic cloud computing & “software-as-services”
platforms
• Increasingly open educational resources, tagged to
learning standards and learning maps
• More accessible open platforms for developing
learning and educational tools for use 24/7
• Participatory media culture
• Ubiquitous sensors (GPS+) and location-aware
services for learning-in-the world
• Increasingly accessible data visualization
• Immersive worlds and games – for learning, too
• Social networks used for learning and education
Grand Challenge Problem #1
• “Design and validate an integrated system that
provides real-time access to learning experiences
tuned to the levels of difficulty and assistance that
optimize learning for all learners, and that
incorporates self-improving features that enable it
to become increasingly effective through
interaction with learners.”
• Such integrated systems should:
• Discover appropriate learning resources…
• Configure those resources with forms of representation
and expression appropriate for the learner’s age, prior
knowledge, reading ability, and language.
• Select appropriate paths and scaffolds for moving the
learner through the learning resources with the optimal
level of challenge and support.
Grand Challenge Problems: History
• A grand challenge defines a
commitment by a scientific community
to work together towards a common
goal - valuable and achievable within a
predicted timescale.
• Predecessor: Hilbert’s 1900 address to
International Congress of Mathematicians on
23 major mathematical problems to be studied
for the next century.
• “Grand Challenges”: major problems of
science and society whose solutions require
1000-fold or greater increases in the power
and speed of supercomputers and their
supporting networks, storage systems,
software and virtual environments:
• U.S. High Performance Computing and
Communications program (HPCC, 1991)
Larry Smarr, NCSA
Director, c. 1989
Personalized Learning GCP
Criteria
1)
Understandable, with Significance.
Inspiration
Clearly stated compelling case for
contributing to long term benefits for science,
industry and society.
2)
Challenging, and Timely.
Hard problems within conceivable reach in
15-20 years with concerted coordinated
efforts.
3)
Clearly useful, in terms of Impact
and Scale, if problem is solved.
Contributes to long term benefits for many
people at large, and with international scope.
4)
Metrics: Testable and Incremental.
Can measure progress, incremental
milestones.
Jim Gray:
Director,
Microsoft
Research Lab,
San Francisco
Jim Gray (2003).
What Next? A
Dozen InformationTechnology
Research Goals.
Journal of the ACM,
50(1), 41–57.
Learning Analytics is about collecting
traces that learners leave behind and
using those traces to improve learning.
(Eric Duval, U. Leuven, Belgium, LAK 2012)
• LA informs personalized, adaptive learning pathways
through online learning systems that can better support
learning for everyone
• Recommended learning resources
• More engaging and inspiring 24/7 learning: games,
projects, badges for competencies
• Can we identify students’ difficulties early and provide the
kinds of support needed for success?
• Suggests continuously improvable curricula: Learning
networks getting smarter with every click
Towards Personalized Learning
• Vannevar Bush’s Memex
• Danny Hillis @ Oscon July 2012
Towards Personalized Learning
• Danny Hillis story; Oscon July 2012
Towards Personalized Learning
• Danny Hillis story; Oscon July 2012
Towards Personalized Learning
• Danny Hillis story; Oscon July 2012
Towards Personalized Learning
• Danny Hillis story; Oscon July 2012
Towards Personalized Learning
• Danny Hillis story; Oscon July 2012
http://www.lrmi.net/
Towards Personalized Learning
Shared Learning Collaborative: http://slcedu.org
Active in Learning Analytics:
.com., org, .edu
Bringing broader competencies
into the learning mapping
• Needed: studies of intertwined relationships during
learning & human development between cognitive,
intrapersonal and interpersonal categories of
competencies
• Needed: studies of learning progressions to inform
learning maps for K-12 beyond mathematics
• Needed: college-level research on these topics
Use Richer Pedagogical Models
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Project-based learning
Problem-based learning
Complex system modeling
Cognitive apprenticeships
Knowledge building communities
Computer games, sims for learning
Communities of practice
Learning in virtual worlds
Immersive and embodied learning
Learning by argumentation
We need Education Data
Scientists to Make Progress on
Many Issues
• Modeling challenges: User Knowledge, User
Behavior & User Experience modeling, User
Profiling; Domain modeling; Learning
component analysis; Social learning capital
• Challenges in adaptation, personalization,
recommendations, group learning
• Interactive data visualization systems for “runtime” guidance for learners, teachers, leaders:
enabling learners to see directly how their
effort improves their success.
Challenge #2: Multi-modal learning
science and technology development
• Need: to capture, integrate and make
systematic inquiries into large-scale,
multimodal data streams of socially and
contextually-situated learning & teaching
interactions in-situ…
• Learners and teacher in a classroom
• Collaborators in a project-based learning
environment after-school
• Learning on the move with mobile devices
• Out-of-MOOC interactions among peers face
to face or using other social media platforms
• Onsite workplace learning outside classes
Why? Missing vital data
• Human learning is a complex multi-sensory affair of
embodied semiotic interactions
• The production and perception of meaning in context
engages the full range of sensory modalities for
learning; and context is continually being re-created
• Increasing challenges associated for inquiry into how
learning is occurring within and across formal and
informal settings, as learners and educational
systems exploit increasingly pervasive mobile
learning devices and online educational applications
and resources such as MOOCs, OER, Wikipedia,
web search, and digital curricula, games and
simulations
‘Sensorium’: Quantified Self for Learning
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“Sensing” of learning contexts and recognition of the people, gestures, discourse
patterns, activities, and bio-behavioral responses in them will be an important
complement to “learning analytics” and “educational data-mining”, enabling new
kinds of predictions and learning-relevant resources and supports.
Can exploit ‘lifelogging’ technologies: memory augmentation, medical
diagnostics, behavior change; data privacy challenges
Multimodal Learning Sciences:
Sense-making technologies
• Needed: technologies and associated
methodologies for integrating diverse data
types from sensor streams and human-coded
data, and multimodal data stream
‘workbenches’, incorporating analytic tools for
sense-making and pattern detection with rich
interactive data visualization capabilities for
examining data stream inter-relationships
• Audio, video, bio-sensors, GPS, digital pen
inputs, clickstreams…
Grow Interdisciplinary
Collaboratories
• Inter-disciplinary collaboratories:
• Social sciences, computer sciences, electrical
engineering, statistics, learning sciences,
neurosciences, educational research, disciplinary
domain expertise
• Better coordination across PIs, Labs, Industry
• Greater cumulativity of approaches, theories,
findings, tools & platforms, open data
Prospects for Scientific Advances in
Learning from the Perspective of Work in
Education & Technology
Roy Pea
Stanford University
February 28, 2013
Sciences of Learning Workshop
National Science Foundation