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Balancing Standardization and Personalization in EducationKEYNOTE AT “FRAMING THE FUTURE OF HIGHER EDUCATION”
SYMPOSIUM11 JULY 2014
AUSTIN, TEXAS
Norma MingCo-Founder & Director of Learning Design
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COST
VALUE
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What should students learn?
How should we facilitate that
learning?
How should we assess that learning?
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Standardization:What should stay constant?
o Knowledgeo Entrance and exit standardso Articulation of prerequisiteso Definitions of mastery
Define learning by knowledge, not time.
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Standardization:What should stay constant?
o Knowledgeo Entrance and exit standardso Articulation of prerequisiteso Definitions of mastery
o Datao For sharing and comparing information
o Across studentso Across institutions
o For better analytics to assess, evaluate, and improve
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Collect everything.◦ Not just inputs and outputs, but also:
◦ Formative assessment◦ Data on instructional processes
Which data, and how?
Shared conventions and formats.◦ Metrics of success◦ Common Education Data Standards
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Standardization:What should stay constant?
o Knowledgeo Entrance and exit standardso Articulation of prerequisiteso Definitions of mastery
o Practiceso Operational: For consistency,
efficiency, economyo Instructional: For quality
o Datao For sharing and comparing information
o Across studentso Across institutions
o For better analytics to assess, evaluate, and improve
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Successful instructional practices
Bain (2004)
Ambrose, Bridges, DiPietro, Lovett, &
Norman (2010)Bransford, Brown, & Cocking (2000)
Pellegrino, Chudowsky, & Glaser (2001)
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Why personalize?
◦ Equity
◦ Economy
◦ Meaningful learning
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Personalization:What should vary?
o Knowledge taught / expectedo Goalso Entry and exit points
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◦ Multiple routes to success◦ Modular experiences
Past, present, & future knowledge vary.
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Personalization:What should vary?
o Knowledge taught / expectedo Goalso Entry and exit points
o Assessmento Whato Wheno How
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“Collect and analyze everything.”◦ Naturalistic, unstructured assessment◦ Different resources, contexts, audiences, products
Assessment: Beyond standardized testing
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Predictive analytics to learning analytics
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Assessing knowledge in discussions
❑ 3-D projection❑ Each point = 1
thread
❑ Discussion content converged:❑ over time
(ROYGBIV)❑ across classes
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Unstructured assessment maps to grades.
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Personalization:What should vary?
o Knowledge taught / expectedo Goalso Entry and exit points
o Instructiono Needs, strengths, preferenceso Constraints, resourceso Support networks
o Assessmento Whato Wheno How
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Adapt, but don’t pander.◦ Learning styles?◦ Student-as-consumer?◦ Just-in-time learning?
Adaptive learning
Past:◦ Prior knowledge◦ Patterns of errors
Present:◦ Extent / nature of
scaffolding◦ Response to feedback◦ Self-regulation support◦ Real-life constraints
Future:◦ Motivation for learning
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Personalized instruction
Personalize, don’t individualize.◦ People learn from other people, because they are different.◦ Create common ground.◦ Build upon cohorts and communities.◦ Incorporate instructors’ expertise.
Adaptive (machine) + Personalized (human) intelligence
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Personalization demands self-directed learning.
Autonomy
Purpose
Mastery
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How do you scaffold a growth mindset?
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Personalize instruction of self-directed learning.
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Meta-questions:
Do we need standards for meta-learning?
How should we assess meta-learning?
