1. Balancing Standardization and Personalization in Education
Keynote at “Framing the Future of Higher Education” Symposium
11 July 2014
Austin, Texas
Norma Ming
Co-Founder & Director of Learning Design
@mindmannered
Standardization & Personalization

2. COST
VALUE
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3. What should students learn?
How should we facilitate that learning?
How should we assess that learning?
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4. Standardization: What should stay constant?
Knowledge
Entrance and exit standards
Articulation of prerequisites
Definitions of mastery
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5. Define learning by knowledge, not time.
Measure knowledge and learning, not time.
Competency-based learning, badges.
Blurring the lines between formal and informal learning.
With OER and learning resources everywhere, need mechanisms for integrating easily across them.
High standards and consistent standards. Not necessarily universal, uniform standards.
Standardization & Personalization

6. Standardization: What should stay constant?
Knowledge
Entrance and exit standards
Articulation of prerequisites
Definitions of mastery
Data
For sharing and comparing information
Across students
Across institutions
For better analytics to assess, evaluate, and improve
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7. Which data, and how? Collect everything.
Not just inputs and outputs, but also:
Formative assessment
Data on instructional processes
Shared conventions and formats.
Metrics of success
Common Education Data Standards
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8. Standardization: What should stay constant?
Knowledge
Entrance and exit standards
Articulation of prerequisites
Definitions of mastery
Data
For sharing and comparing information
Across students
Across institutions
For better analytics to assess, evaluate, and improve
Practices
Operational: For consistency, efficiency, economy
Instructional: For quality
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9. Successful instructional practices
Pellegrino, Chudowsky, & Glaser (2001)
Bransford, Brown, & Cocking (2000)
Bain (2004)
1. Prior Knowledge Affects Learning
2. How the Way Students Organize Knowledge Affects Learning
3. What Motivates Students to Learn?
4. How Students Develop Mastery
5. Practice and Feedback
6. Climate
7. Self-Directed Learners
Ambrose, Bridges, DiPietro, Lovett, & Norman (2010)
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Standardization & Personalization

10. Why personalize? Equity Economy Meaningful learning
Two interpretations of “economy”:
Labor market and economics (need more specialization)
Economical use of resources (consider opportunity cost)
Inert knowledge won’t transfer and can’t be applied; need to make personal sense of it.
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Standardization & Personalization

11. Personalization: What should vary?
Knowledge taught / expected
Goals
Entry and exit points
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12. Past, present, & future knowledge vary.
Graphic: map of hiking trail or mountain to climb
Flexible entry points accommodate different prior knowledge.
Flexible exit points accommodate different goals.
Flexible paths accommodate different learning rates.
Multiple routes to success
Modular experiences
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Standardization & Personalization

13. Personalization: What should vary?
Knowledge taught / expected
Goals
Entry and exit points
Assessment
What
When
How
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14. Assessment: Beyond standardized testing
“Collect and analyze everything.”
Naturalistic, unstructured assessment
Different resources, contexts, audiences, products
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15. Predictive analytics to learning analytics
Examine student behaviors, past backgrounds, test scores, grades, interests, income, employment, resource utilization, etc.
Learning analytics:
Provide real-time feedback to students on learning
Provide feedback to instructors
How is the whole class performing?
What is the knowledge and learning of an individual student?
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Standardization & Personalization

16. Assessing knowledge in discussions
3-D projection
Each point = 1 thread
Discussion content converged:
over time (ROYGBIV)
across classes
Economics course
~1000 students
~70,000 posts
3-D topic-space projection
Each point = one thread
ROYGBIV progression of discussion threads over time
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Standardization & Personalization

17. Unstructured assessment maps to grades.
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18. Personalization: What should vary?
Knowledge taught / expected
Goals
Entry and exit points
Assessment
What
When
How
Instruction
Needs, strengths, preferences
Constraints, resources
Support networks
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Standardization & Personalization

19. Adaptive learning Adapt, but don’t pander. Present: Past: Future:
Learning styles?
Student-as-consumer?
Just-in-time learning?
Present:
Past:
Future:
Prior knowledge
Extent / nature of scaffolding
Motivation for learning
Patterns of errors
Response to feedback
Self-regulation support
Real-life constraints
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Standardization & Personalization

20. Personalized instruction
Adaptive (machine) + Personalized (human) intelligence
Personalize, don’t individualize.
People learn from other people, because they are different.
Create common ground.
Build upon cohorts and communities.
Incorporate instructors’ expertise.
Decouple roles, learning activities, time:
Different activities at same time
Same activities at different time
Different roles in same activity at same time
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Standardization & Personalization

21. Personalization demands self-directed learning.
Autonomy
Purpose
Mastery
Passive consumers of information get lost and left behind.
Greater need for assessment feedback and self-regulation.
This is what employers are looking for.
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Standardization & Personalization

22. Personalize instruction of self-directed learning.
How do you scaffold a growth mindset?
“Your hard work paid off!”
“What could you do differently?”
“Just keep swimming…”
Mismatched and generic feedback won’t work.
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Standardization & Personalization

23. Standardization & Personalization
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Standardization & Personalization

24. Meta-questions: Do we need standards for meta-learning?
How should we assess meta-learning?
Which brings me back full circle—
Not typically what people mean by “closing the loop”…
Is the main reason why employers seek meta-learning because they can’t trust the measures of content learning? Or is meta-learning really more important, especially given the fast-changing nature of today’s information age / needs of labor market?
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Standardization & Personalization

25. Discuss.