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  1. Professional Development Intensive Workshop Participatory Applied Research on Enhancing Learning: a Spectrum of Small Change to Transformation.Eric Hamilton, Pepperdine Universityeric.hamilton@pepperdine.eduhttp://tinyurl.com/tuskegee-par-jan2013

  2. thank you! • honor to be here • goal is for us to be more effective at enhancing student learning • workshop is participatory • so is the work • 3 parts – a) intro and planning, then divide into b) media authoring or model-eliciting activities

  3. topics & keywords • introductions • key challenges we share • future learning environments, action research, complex reasoning, creativity, passion and immersion in learning; intergenerational collaboration; media-making; tablets; participatory content development

  4. Other notes • finding ways or tools to change. • our models are insufficient • what we do was already insufficient • getting better at what we used to will still be insufficient for what comes next • now at an intense historical nexus • and a tantalizing and breathtaking precipice of tools and possibilities

  5. what is different? • about what you are teaching • who you are teaching • the culture of our teaching

  6. Pop culture … • … is so easy to ridicule • But sometimes expresses compelling truths • Schooling and testing is largely a memory fade race. • We try to leverage long-term knowledge by getting students to experience memory fade after the test rather than before. • How can we not only get learners to develop but sustainably use new ideas? • the quest is for disciplinary learner engagement and immersion

  7. A vision for the future • In every aspect of life (except schools!) what it means to learn has changed from the pre-digital era. • The nature of complex thinking and the competencies required for functioning in society have changed. • We rely more on social interaction and intelligence in our own learning journeys • The fields of learning science and human and social dynamics help focus on the possibilities of for future educational environments

  8. ten principles of future learning environments

  9. ten principles of future learning environments, con't

  10. A complex system • Each principle insinuates itself into each of these others and derives meaning from the others • These are all inputs into and outputs from each other • Together, they form a complex system with amazing and infinite combinatoric possibilities how we shape student experience and intellectual development

  11. it's all about engagement • if you don't learn the way I teach, I must learn to teach the way you learn • the principles – and each of our personalized adaptation of them – are integral to a quest for engaging • engagement mediates learning • (notes on a continuum of engagement to flow)

  12. It’s all about me • Flow is highly phenomenological • Autotelic functioning is personalized • The research is on the individual • Michael Jordan in the zone, Game 2, 1992 Finals against the Blazers

  13. What are the analogs to group flow? • Not Jordan in the 1992 Finals but the Horace Grant dish to John Paxson to close out the 1993 finals • An orchestra that loses a sense of time while maintaining a perfect sense of timing

  14. Interactional bandwidth • Consider two human activities • Hand-raising in a classroom • Multi-tasking in electronically-mediated activities like video games or television • Human attentional & interactional capacity is significantly higher than hand-raising patterns of typical classrooms suggest! • Future learning environments, with higher bandwidth, will enable greater cognitive/affective density and layering. 110.. 300.. 1200..4800.. 14400.. 56K... 256.. 10Mbs .. 100Mbs • Summary of principles

  15. Sightlines • See structure in • Cognition • Relationships • Content • Featural elements of knowledge • Take me to the wizard • Visualization and simulation systems let us see more, and see more accurately Summary of principles

  16. Connectivity • We nourish each other • We impart not only knowledge one to another, but meaning • The cognitive neuroscience of learning in social contexts is markedly different than isolated learning Summary of principles

  17. Modeling • Systems thinking becomes more salient than factual accretion • Retention is a different in part because synaptic connectivity is richer and has more pathways

  18. Fluid contextual transitions Greater emphasis on heterogeneous competencies and hybrid functionality • Between virtual and IRL • Social and solitary, analytic and immersive, drill and flow • Examining objects to being an object • Emulating an agent and being emulated • Analytic, reflective, immersive • More interoperability of individual-social-machine knowledge forms. Summary of principles

  19. Individualization • Imagine a world where schools conformed to youngsters rather than vice versa • Take a moment to inventory the human and social tragedy that ascends from only a small fraction of learners forming natural fits with the schooling environment • Alienation • Disengagement Summary of principles

  20. Four grand challenges • Break-out role influencing society innovative tools and ways of thinking about collaboration. • Agility in learning through the life cycle • Ultra-deep model collaboration: sharing human experience • Unlocking group “flow” in the science of collaboration.

  21. Eric Hamilton, Pepperdine introduction to meas

  22. Common questions • What are they? What aren’t they? • Do they “work” ? • Can they be assessed? • Is this problem-based learning? • Brad Pitt and Oakland CA

  23. One way to think of them • Tools to help understand how knowledge and complex reasoning evolve and grow • Tools to help knowledge and complex reasoning evolve and grow • MEAs, as much as anything, are about how to interpret what is seen

  24. Two important axioms • Knowledge and competencies are structure and interconnected – they are not additive. • Knowledge and competencies can represented with models that are expressed orally, visually, or in countless other ways

  25. The most important tool a [researcher][professor] has • …is to understand the models that learners possess.

  26. elicitation • The best way to understand or work with models is to elicit them, to draw them out • Not the same as pounding them in • Sort of a “reverse polarity”

  27. Elicitation part 2 • Elicitation – drawing out – is essential for understanding cognition. And it needs to be done carefully, in a way that preserves structure. • Years of research produced the six principles for elicitation. • Not just elicitation though…but testing, revision and transformation

  28. A funny, symmetrical thing happened • …on the road to understanding conceptual evolution • Elicitation implies translation and representation • Testing implies representational manipulaton and adaptation • Revision implies re-adaptation

  29. Model Eliciting Activities (MEAs) • Developed by math educators (Lesh) • Client-driven, open-ended problems designed to be model eliciting and thought revealing • Require students to mathematizeinformation and structure in context • e.g., quantify, organize, dimensionalize • Adapted to engineering (Diefes-Dux)