This document summarizes some common myths and misconceptions about education, neuroscience, and psychology. It discusses how myths can begin from misinterpretations of scientific facts rather than intentional deception. Myths are then perpetuated through cultural differences in language, limited access to counter-evidence, complexity of topics, and cognitive biases. Some specific examples of myths discussed include overstating the effects of disruptive students on classmates' achievement, oversimplifying cognitive load theory, and overhyping new scientific findings before adequate research has been conducted. The document emphasizes the importance of carefully checking original sources, acknowledging limitations and complexity, and educating others to mitigate reductive explanations.

1. THIS IS THE NEW M*TH!
Christian Bokhove
September 9th 2017

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. 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

4. WHAT CAN WE SAY
ABOUT MYTHS

5. https://en.oxforddictionaries.com/definition/myth

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. 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. 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. Macdonald et al. (2017)

11. 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

12. The truth is too simple
But in fact Larsson cited Hamblin (1981)
• Treasure hunt
• Can add more references but sometimes back to one
source

13. Hamblin (1981)
But the decimal point claim has no reference.

14. Irony
Frontline of the fight against bad science and academic
carelessness.
Sutton (2010) argued other possible causes.
Pointed to another person, Bender etc etc

15. Hamblin to Sutton on his website

16. “Follies and fallacies in medicine”

17. 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)

18. https://www.psychologytoday.com/blog/ulterior-motives/201709/the-pitfalls-popularizing-new-science?amp

19. “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.”
https://www.psychologytoday.com/blog/ulterior-motives/201709/the-pitfalls-popularizing-new-science?amp

20. SOME EXAMPLES

21. https://www.mercatornet.com/family_edge/view/disruptive-kids-can-cost-classmates-a-lot/18494

22. Carrell et al. (2016)
• Glances over disruption v domestic violence
• Economics paper: sig testing with 10% w/ large N
• Most importantly: reported vs unreported
https://bokhove.net/2016/06/29/unpicking-economic-papers-a-paper-on-behaviour/

23. 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)

24. 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
https://bokhove.net/2016/07/04/unpicking-economic-papers-a-paper-on-direct-instruction/

25. 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

26. Rest of slide Intentionally left blank to not impose too much load.
Scale originates from Paas (1992)

27. Or over-state
what research
in CLT domain
says
For example
expertise
reversal (which
also happened
in own
research)
“no place”

28. 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.
https://bokhove.net/2017/04/26/the-case-of-france/

29. 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)

30. WRAPPING UP

31. • 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)

32. 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

33. 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, https://doi.org/10.3389/fpsyg.2017.01314
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. https://doi.org/10.1371/journal.pone.0183551
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.