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In 1537, a Swiss physician, alchemist, and astrologer named Paracelsus presented a way to create homunculi (Latin for little people). Short but sweet, such a mini-human is created by nourishing a male sperm on human blood in a horse’s womb where a small version of a living human child grows. Sperm were thought to be complete preformed individuals called animalcules, which developed in the woman’s womb into fully formed people. In 1694, Nicolaas Hartsoecker, in his Essai de Dioptrique about what could be seen with the aid of Antoni van Leeuwenhoek’s microscope, wrote of and produced an image of a tiny human form curled up inside a sperm, which he called le petit animal (animalcule) with the human version being le petit l'infant (homunculus). The sperm was a homunculus, identical in all but size to an adult. It is just a matter of growth!

Figure 1.1 Preformation, drawn by Nicolas Hartsoecker (Wellcome Collection, CC BY 4.0)

As odd as this may sound in 2020, there are many people both in education and in the general population who actually think that this same thing is true about the cognitive/intellectual development of a child (and by extension, a novice) into an adult (and by extension, into an expert). A good example of this is discovery learning. The thinking behind this form of education is that since the epistemology of the scientist (i.e., the way an expert knows) is to discover and create new knowledge through experimentation, many mistaken educators and researchers have chosen to apply this approach to the school as a pedagogy for teaching students (i.e., novices who don’t know) (Kirschner, 2009). Said in another way, these misguided educators and policy makers think that the way that a scientist ‘does science’ is also the way that a student should ‘learn science’. To put this in a very down-to-earth focus, such a way of thinking would mean that the way a Formula 1 racing car driver drives in a race is the way that someone without a license should learn to drive a car!

Michelene (Micki) Chi, Robert Glaser, and Paul Feltovich broke with this myth in 1979 showing how experts not only know more than novices, but also think differently. In their search for differences between experts and beginners in solving problems, Micki Chi and her colleagues focused on the very first step that a person takes when when solving a problem, namely reading and interpreting the problem. When dealing with a new problem, the first question is always: what kind of problem is this? To answer this question, you often try to remember similar problems that you’ve encountered before. You search for landmarks; things that look familiar and that you remember. Classifying the problem in a specific category of similar problems is the first step in solving it. The idea is that experts already interpret or categorise problems when reading in a way that’s very different from how beginners do this, and therefore they’re able to solve them more easily, more quickly, in a different way, and better.

How people categorise a problem depends on their previous experiences with similar problems which shapes how they determine what exactly the problem is and, also, the quality of their solutions. Our prior knowledge determines the quality of our problem-solving. As experts have both more knowledge and qualitatively different and better knowledge (this is called deep, conceptual knowledge), the categorisation of problems will give them a head start on beginners. Succinctly stated: Experts have rich knowledge schemata (i.e., they are broad and deep).

Beginners also have schemata, but these are less extensive and profound (i.e., they are poor, narrow, and shallow). The use of these poorer schemata is therefore less effective for them and can sometimes even be counterproductive. Beginners often interpret problems by looking at what’s called surface characteristics such as "a previous problem also dealt with a moving object and this problem does too" and, thus, use the wrong formula (e.g., velocity instead of acceleration). Experts, however, see the underlying concepts of a problem such as "the first problem was about a constant speed, but this is about acceleration, so it’s different". Because of their extensive and qualitatively better schemata, experts know how to quickly and accurately categorise new problems and link them to a correct solution strategy and solution.

When encountering a new problem, experts think in a very solution-oriented way and their prior knowledge is mainly procedural in nature about how to tackle a problem along with a deep conceptual knowledge about the conditions under which the procedures can be applied. The prior knowledge of beginners, on the other hand, consists mainly of descriptions of the physical characteristics of various problems, but does not include the link with possible solutions.

Research that compares the prior knowledge of beginners with that of experts shows that the difference is not only quantitative (i.e., that experts know more) but also qualitative (i.e., their knowledge is also organised differently). This insight brings with it three important implications for education: (1) beginners are not empty vessels that have to be filled, (2) beginners are not 'small' experts, and (3) teaching/instruction should take this into account.

As stated, beginners have knowledge schemata which are rudimentary, incomplete, shallow, and often contain misconceptions (e.g., naïve hypotheses such as if you kick a ball, then there are still forces working upon it pushing it forward while the only forces working on the ball are gravity and friction which slows it down). New knowledge must be given a place in them, either by hanging the new knowledge in them (i.e., assimilation) or by adapting them to the new knowledge (i.e., accommodation). It’s, thus, extremely important to keep in mind that the novices’ prior knowledge and assumptions are not simply a less extensive version of what the expert knows and assumes, but that it really is different.

As the novice is not a miniature expert, it’s extremely important to realise that what may work for an expert (e.g., discovery learning, problem-based learning, inquiry learning) usually doesn’t work well or is even harmful and counterproductive for the novice (and vice versa).1 This is known as the expertise reversal effect (Sweller, Ayres, Kalyuga, & Chandler, 2003); a reversal of the effectiveness of instructional techniques on learners with differing levels of prior knowledge. While an expert can be given a problem to be solved after having been taught a certain technique or principle, a novice should be given a more structured approach to using that principle for solving the same problem, for example in the form of a worked example. Kalyuga, Chandler, and Sweller(1998) note here that as the learner advances, a fading procedure whereby steps in the solution procedure are gradually left open for the learner to carry out her-/himself, is superior to an abrupt switch from worked example to problems. This slow reduction of guidance as learner expertise increases is an example of the guidance-fading effect; a direct instructional application that is consistent with the expertise reversal effect.

Epistemology is not pedagogy

Takeaways

  • Beginners aren’t ‘little’ experts; they know less and think differently than experts.
  • Children also aren’t small adults. They see the world very differently and therefore have to learn differently.
  • A teaching approach that works well with an expert will most probably not work well with a beginner and can even be detrimental to their learning.
  • The epistemology of the expert is not the proper pedagogy for the learner.
  • Beware the ‘curse of knowledge’:2 a cognitive bias where instructors who are highly knowledgeable in a domain forget the steps they took to acquire that knowledge and can’t understand why novices just don’t ‘get it.’

Notes

1. We’re not taling about a student who knows a little more, but a real expert in a certain area.

2. Kennedy, J. (1995). Debiasing the curse of knowledge in audit judgment. The Accounting Review, 70, 249–273. Available from: https://www.jstor.org/stable/24830


References

Chi, M. T. H., Feltovich, P. J., & Glaser, R. (1979). Categorization and representation of physics problems by experts and novices. Cognitive Science 5, 121-152. https://doi.org/10.1207/s15516709cog0502_2 Available from: https://onlinelibrary.wiley.com/doi/epdf/10.1207/s15516709cog0502_2

De Groot, A. D. (1965). Thought and choice in chess. Den Haag, The Netherlands: De Gruyter Mouton.

Kirschner, P. A. (2009). Epistemology or pedagogy, that is the question. In S. Tobias & T. M. Duffy. Constructivist instruction: Success or failure? (pp. 144-157). New York: Routledge. Available via: http://dspace.ou.nl/bitstream/1820/2326/1/Epistemology%20or%20Pedagogy%20-%20That%20is%20the%20Question.pdf

Kalyuga, S., Chandler, P., & Sweller, J. (1998). Levels of expertise and instructional design. Human factors, 40(1), 1-17. Request from: https://www.researchgate.net/publication/220457696_Levels_of_Expertise_and_Instructional_Design

Kennedy, J. (1995). Debiasing the curse of knowledge in audit judgment. The Accounting Review, 70, 249–273. Available from: https://www.jstor.org/stable/248305

Schneider, W., & Shiffrin, R. M. (1977). Controlled and automatic human information processing: I. Detection, search, and attention. Psychological Review, 84, 1-66. Available from: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.470.2718&rep=rep1&type=pdf

Sweller, J., Ayres, P. L., Kalyuga, S. & Chandler, P. A. (2003). The expertise reversal effect. Educational Psychologist, 38(1), 23-31. https://doi.org/10.1207/S15326985EP3801_4 (Open access)

Wilson, B., & Cole, P. (1991). A review of cognitive teaching models. Educational Technology Research and Development, 39(4), 47-64.