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Balancing Standardization and Personalization in Education

This keynote presentation by Norma Ming at the “Framing the Future of Higher Education” Symposium delves into the critical balance between standardization and personalization in education. Key themes include what should remain constant in academic standards, the importance of data for comparison and analytics, and the necessity of personalizing learning experiences to ensure equity and meaningful education. The discussion emphasizes adaptive learning strategies, effective assessment methods, and the role of instructors in guiding personalized education.

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Balancing Standardization and Personalization in Education

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  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

  23. @mindmannered

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

  25. Discuss. norma@socos.me

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