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ResearchEd 2017 National Conference - This is the new m*th!


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It’s good that education is talking about evidence and preventing myths to take hold. However, in the advent of this mythbusting naive adoption of counter-research is creating new myths. Use a maths formula, stick a ‘neuroscientific’ image on it or suggest it is all about cognitive science and you are ready to go! This talk will give examples how naive interpretations of educational (e.g. with PISA), econometric, neuroscientific and psychological (e.g. ‘less load is better’) research are creating new myths.

Published in: Education
  • Thanks for sharing this Christian. I think that this is a very important topic - the dangers of neo-phillia are ever present.
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ResearchEd 2017 National Conference - This is the new m*th!

  1. 1. THIS IS THE NEW M*TH! Christian Bokhove September 9th 2017
  2. 2. Who am I ? • Christian Bokhove • From 1998-2012 teacher maths and computer science, secondary school Netherlands • PhD from Utrecht University • Lecturer at University of Southampton • Maths education • Technology use • Large-scale assessment • Computer Science stuff
  3. 3. Purpose and disclaimers • We run the risk of creating more myths • I want to frame myths and mechanisms first • …and then give some examples I encountered in media • Choice those you probably agree with most • … don’t make the mistake of thinking because I’m critical of A I’m against A or trying to debunk A. • Not meant as exhaustive review of the research • Tried to add most of the references at the end • Ironically, a presentation like this simplifies, which is a risk in communicating concepts and ideas
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  6. 6. Howard-Jones (2014) “In 2002, the Brain and Learning project of the UK’s Organization of Economic Co-operation and Development (OECD) drew attention to the many misconceptions about the mind and brain that arise outside of the medical and scientific communities.” (Howard-Jones, 2014, p. 817)
  7. 7. Myths begin “examples of cases in which entrepreneurs have knowingly set out to mislead educators are difficult to find.” (Howard- Jones, 2014, p. 817) “more likely that such interventions originate from uninformed interpretations of genuine scientific facts and are promoted by victims of their own wishful thinking who hold a “sincere but deluded fixation on some eccentric theory that the holder is absolutely sure will revolutionize science and society” (Howard-Jones, 2014, p. 817)
  8. 8. Perpetuated • Cultural conditions e.g. differences in terminology and language • Also check Lilienfeld et al. (2015, 2017) for lists with psychological terms to avoid and pairs of confusion • Counter-evidence difficult to access • Untestable • Biases • Complex
  9. 9. Macdonald et al. (2017)
  10. 10. Rekdal (2014) • Case example of urban legend spinach and iron • Not significantly more iron • Not first food if iron deficient • The truth is too simple
  11. 11. The truth is too simple But in fact Larsson cited Hamblin (1981) • Treasure hunt • Can add more references but sometimes back to one source
  12. 12. Hamblin (1981) But the decimal point claim has no reference.
  13. 13. Irony Frontline of the fight against bad science and academic carelessness. Sutton (2010) argued other possible causes. Pointed to another person, Bender etc etc
  14. 14. Hamblin to Sutton on his website
  15. 15. “Follies and fallacies in medicine”
  16. 16. Robin-Garcia et al. (2017) • Dentistry • “tweeting about scholarly articles represents curating and informing about state-of-the-art appears not to be realized in practice.” • “Simplistic and naïve use of social media data risks damaging the scientific enterprise, misleading both authors and consumers of scientific literature.” • (Now check the paper, it says much more, and it has its own limitations)
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  18. 18. “At the same time, I think that there are real dangers in popularizing science. One danger comes from oversimplifying core concepts where people may come to believe they understand a key concept better than they actually do. A potentially bigger danger comes from overhyping new science.” “When scientists want to make recommendations for how people might live their lives differently based on studies, then, we ought to wait about 15 years before giving those recommendations. Otherwise, we run the risk of giving bad advice that we have to walk back later. Having to take back our advice can undermine the public’s faith in the science.”
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  21. 21. Carrell et al. (2016) • Glances over disruption v domestic violence • Economics paper: sig testing with 10% w/ large N • Most importantly: reported vs unreported
  22. 22. Seductive allure • Popular papers e.g. McCabe & Castel (2008) • Farah & Hook (2013) “little empirical support for the claim that brain images are inordinately influential.” • “an alternative explanation is that this effect is representative of a more general bias in judging explanations.” (Hopkins et al., 2016, p. 67)
  23. 23. Schwerdt & Wupperman (2010) • Popular paper re ‘traditional teaching’ • TIMSS 2003 data (often happens, use of fairly old data. At this point 2007 was available) • Complex data sets: incorrect use is risky (Bokhove, 2014) • Answer: no
  24. 24. Cognitive Load Theory • A very good summary of Cognitive Load Theory appeared just recently. • Does not contain the newest work • Mentions limitations but imo a bit underplaying what it means • Role germane load (schemas) New South Wales Centre for Education and Statistics
  25. 25. Rest of slide Intentionally left blank to not impose too much load. Scale originates from Paas (1992)
  26. 26. Or over-state what research in CLT domain says For example expertise reversal (which also happened in own research) “no place”
  27. 27. Case of France (Hirsch) • Careful when interpreting • When dive deeper behind the (French) data • Some categories seem to miss • A new edition, not known at time writing, shows gaps stable • Take-away message: ideally, trace down origins, update and monitor your ideas.
  28. 28. Dismiss the ‘classics’ ? • Discard Piaget, Vygotsky, Bruner, Bloom ? • No, empirical advances mean we now know more ? • But many ideas still relevant • I compare this with for example Newton: respect but not for his pseudoscience and Leibniz’s integrals ‘won’ • Not too easy not too hard: optimise and manage ‘load’? • Scaffolding, guidance (Renkl) • Types of knowledge (Bloom) • Conflict (Kapur)
  29. 29. WRAPPING UP
  30. 30. • Follow-up sources • But… it can be very time-consuming • Read, read and read (and sometimes refrain from a position until read up a bit) • Beware of over-simplifications • Note that fact some over-complicate things, does not mean ‘simple is best’. Demand someone ‘prove’ their alleged dichotomy. • Nuance is ok, and not ‘evading debate’. • 15yr rule might be a bit too much but add scope and disclaimers to claims. • Educate, e.g. “These results suggest that further training in science may help people to better understand what makes something a good explanation, possibly mitigating the reductive allure effect.” (Hopkins et al., 2016, p. 75) • Accept ‘a lesser form of knowledge’ (Labaree, 1998)
  31. 31. QUESTIONS How can we best retain such a level of criticality without: • Needing to study for years first? • Without antagonising each other? • Calling everything a myth beforehand? • What myth have I now perpetuated? @cbokhove
  32. 32. Selected references Bokhove, C. (2014) Demonstrating the consequences of not taking into account sampling designs with TIMSS 2011 data. Paper presented at the Fourth Meeting of the European Association for Research on Learning and Instruction (EARLI) SIG Educational Effectiveness, Southampton, GB, 27 – 29 Aug 2014. Carrell, S E, M Hoekstra and E Kuka (2016) “The long-run effects of disruptive peers”, NBER Working Paper 22042. link. Farah, M.J., & Hook, C.J. (2013). The Seductive Allure of “Seductive Allure”. Perspectives on Psychological Science, 8(1), 88-90. Hopkins, E.J., Weisberg, D.S., & Taylor, J.C.V. (2016). The seductive allure is a reductive allure: People prefer scientific explanations that contain logically irrelevant reductive information. Cognition, 155, 67- 76. Howard-Jones, P. (2014). Neuroscience and education: myths and messages. Nature Reviews Neuroscience, 15(12), 817-824. Labaree, D.F. (1998). Educational Researchers: Living With a Lesser Form of Knowledge. Educational Researcher, 27(8), Macdonald, K., Germine, L., Anderson, A., Christodoulou, J., & McGrath, L.M. (2017). Dispelling the myth: Training in education or neuroscience decreases but does not eliminate beliefs in neuromyths, Frontiers in Psychology, McCabe, D.P., & Castel, A.D. (2008). Seeing is believing: The effect of brain images on judgments of scientific reasoning. Cognition, 107, 343-352. Paas, F. (1992). Training strategies for attaining transfer of problem-solving skill in statistics: A cognitive-load approach. Journal of Educational Psychology, 84(4), 429-434. Rekdal, O.B. (2014). Academic urban legends. Social Studies of Science, 44(4), 638-654. Robinson-Garcia, N., Costas, R., Isett, K., Melkers, J., & Hicks, D. (2017). The unbearable emptiness of tweeting—About journal articles. PLOS one. Schwerdt G., & Wupperman A. C. (2010). Is traditional teaching really all that bad? A within-student between-subject approach. Economics of Education Review, 30(2), 365–379.