Balancing Standardization and Personalization in Education

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

2. COST VALUE @mindmannered

3. @mindmannered

4. Standardization:What should stay constant? • Knowledge • Entrance and exit standards • Articulation of prerequisites • Definitions of mastery @mindmannered

5. Define learning by knowledge, not time.

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 @mindmannered

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 @mindmannered

8. Standardization:What should stay constant? • Knowledge • Entrance and exit standards • Articulation of prerequisites • Definitions of mastery • Practices • Operational: For consistency, efficiency, economy • Instructional: For quality • Data • For sharing and comparing information • Across students • Across institutions • For better analytics to assess, evaluate, and improve @mindmannered

9. Successful instructional practices Pellegrino, Chudowsky, & Glaser (2001) Ambrose, Bridges, DiPietro, Lovett, & Norman (2010) Bransford, Brown, & Cocking (2000) Bain (2004) @mindmannered

10. Why personalize? • Equity • Economy • Meaningful learning @mindmannered

11. Personalization:What should vary? • Knowledge taught / expected • Goals • Entry and exit points @mindmannered

12. Past, present, & future knowledge vary. • Multiple routes to success • Modular experiences @mindmannered

13. Personalization:What should vary? • Knowledge taught / expected • Goals • Entry and exit points • Assessment • What • When • How @mindmannered

14. Assessment: Beyond standardized testing • “Collect and analyze everything.” • Naturalistic, unstructured assessment • Different resources, contexts, audiences, products @mindmannered

15. Predictive analytics to learning analytics @mindmannered

16. Assessing knowledge in discussions • 3-D projection • Each point = 1 thread • Discussion content converged: • over time (ROYGBIV) • across classes @mindmannered

17. Unstructured assessment maps to grades. @mindmannered

18. Personalization:What should vary? • Knowledge taught / expected • Goals • Entry and exit points • Instruction • Needs, strengths, preferences • Constraints, resources • Support networks • Assessment • What • When • How @mindmannered

19. Adaptive learning • Adapt, but don’t pander. • Learning styles? • Student-as-consumer? • Just-in-time 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 @mindmannered

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. @mindmannered

21. Personalization demands self-directed learning. @mindmannered

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…” @mindmannered

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24. Meta-questions: • Do we need standards for meta-learning? • How should we assess meta-learning? @mindmannered

25. Discuss. norma@socos.me