Educating the Scientific Brain and Mind: Insights from The Science of Learning & Educational Neuroscience
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9 January 2015, Friday 12:45 pm – 2:00 pm RMS 101, 1/F., Runme Shaw Bldg., HKU by Professor Kevin Niall DUNBAR, College of Education, University of Maryland, College Park, US http://sol.edu.hku.hk/educating-scientific-brain-mind-insights-science-learning-educational-neuroscience/
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Educating the Scientific Brain and Mind: Insights from The Science of Learning & Educational Neuroscience
- 1. Kevin Niall Dunbar Director, Laboratory for Thinking, Reasoning & Educational Neuroscience Department of Human Development & Quantitative Methodology University of Maryland College Park
- 2. Science of Learning & Educational NeuroscienceHow do we learn? Can we identify Underlying mechanisms of learning complex concepts? Especially Science? Use Naturalistic & Experimental Methods
- 4. Our approachKey questions What facilitates Conceptual change? What inhibits conceptual change Underlying Mechanisms of conceptual change
- 5. Our approaches Methods Naturalistic studies Controlled experiments Families in science museums Neuroimaging studies Students in undergraduate laboratory courses Key questions What facilitates Conceptual change? What inhibits conceptual change Underlying Mechanisms of conceptual
- 6. Multiple Domains: Science, Performing Arts Politics! Multiple Locations: Lab, Museum, School, Grand Canyon! Scientific Thinking across the Spectrum Analogy Causality Creativity Multiple Methods: Verbal protocols, RT, Neuroimaging, Naturalistic, Experimental
- 7. Early work: Goals-and conceptual change Students given short training in molecular biology Shown how to design experiments to investigate functions of genes Students’ goals can block conceptual change Let them achieve their goal then will consider alternate Relations even if more complex (Dunbar 1993) kndunbar@umd.edu
- 8. kndunbar@umd.edu Our Naturalistic Work Molecular Biology Labs Science Museums Undergraduate Science labs
- 9. 2 Immunology & Molecular Bio Molecular Bio of Bacteria Molecular Bio of Parasites Molecular Bio of HIV More recently 5 labs in Canada 1 lab in Italy 2 Laboratories Investigated
- 10. 2 Immunology & Molecular Bio Molecular Bio of Bacteria Molecular Bio of Parasites Molecular Bio of HIV 2 Laboratories Investigated
- 11. 3 Three Labs, 28 Research projects 417 Outcomes 223 Inconsistent194 Consistent Consistent Inconsistent ≈88% Time Reasoning about data
- 12. Leading Labs • Lab A. I saw the results and wanted to throw myself off a bridge • Explained + a new theory • in 10 minutes
- 13. A sample lab meeting
- 14. Gender Analyses Analogical Reasoning – No differences Social interactions – No differences – Women no less likely to challenge Unexpected findings – Men more likely to assume know the cause – Women more likely to determine cause – Goals block consideration of alternate Concepts
- 15. In One Lab, over 100 unexpected findings Gender & Unexpected Findings
- 16. Conceptual Change: Graduate Students & Post Doctoral Students Use key reasoning strategies Attend To Anomalies Generate new relations Generate Analogies Causal Thinking Generate new Concepts Distributed Relational Thinking Do allow current goals to block conceptual change kndunbar@umd.edu
- 17. What is Analogy?
- 18. EXAMPLE ANALOGY “YOU KNOW, JUST BECAUSE YOU CAN SEE TEN MOLECULES, THAT STILL ISN'T WORKING IN MY BOOK. A MONKEY WILL EVENTUALLY TYPE SHAKESPEARE, GIVEN THE OPPORTUNITY. PCR IS NOT UNLIKE THAT. YOU DO IT A BILLION TIMES, AND YOU PROBABLY WILL FIND ONE THING THAT HAPPENED TO BE RIGHT.”
- 19. Analogy Results Range 6-25, – Mean Number of Analogies – Per Meeting – For 3 Labs 0. 2.3 4.5 6.8 9. Lab A Lab B Lab C
- 20. Back to lab Experiments Show Goals critical • Depending on goal – People use structural Or superficial Features – (Blanchette & Dunbar 2000) 0 20 40 60 80 Remind Generate Superficial Structural
- 21. Typical analogy results People bad at analogy But scientists show they canuse analogy What can explain the difference? Generation of the analogies Goals of the reasoner
- 22. Analogy in science • In standard analogy research subjects dont GENERATE analogies. REMINDING is most common task. Tasks encourage use of superficial features • Analogy like all forms of reasoning is influenced by goals and context. • People are successful with meaningful real-world analogs. • Goals are critical
- 23. Conclusion on analogy • People can and do use analogy • Involves working memory and abstraction and categorization • fMRI shows frontopolar and DLPFC involved • fNIRS reveals that frontopolar activated early followed by Dorsolateral PFC • Indicates that a key issue is preparing people to map from source to target, preparing a mental set
- 24. Neuroimaging the scientific mind kndunbar@umd.edu
- 26. Is analogy present? fMRI Green et al 2009
- 27. Brain blocks generation of new relations when inconsistent kndunbar@umd.edu
- 28. 12 Data inconsistent with a theory recruits error detection networks blocks memory Particularly when data is inconsistent with a plausible theory What happens with tasks when data is inconsistent with your theory?
- 29. Newtonian Theories of Motion Really difficult to learn and teach What happens when data are inconsistent with persons preconceptions?
- 36. Conceptual Blocking How do we overcome blocksUMD Students Eccentric Poet Rigid Librarian But, Context & expectations are key educational tools facilitating relational thought Can we use stereotypes to induce conceptual change kndunbar@umd.edu
- 38. What do we know now? Conceptual change in science Analogy, causality and the unexpected Goals often block conceptual change but reframing allows new goals and conceptual change Neuroimaging and behavioral converge on ways to overcome conceptual blocking
- 39. Collaborators Leslie Atkins Denis Dumas Eve Forster Jonathan Fugelsang Adam Green Kaja Jasinska Ada Le Anthony Naimi Laura-Ann Petitto Matthew Roser