Taxonomy blues

It’s too darn hot. The thermostat has hit 30. Science says – yes, all of it – that  high temperatures can lead to distorted thinking. So, let’s address one question before I melt into a puddle on the floor. Are taxonomies useful in education?

In the natural sciences, taxonomies are an attempt to classify the world, to map out who is most closely related to whom. A duck-billed platypus may have a bill that looks like, well, a duck but that does not make it a duck. It is an example of convergent evolution and sits in an entirely separate branch of the evolutionary tree.  Very few natural scientists that I’ve met have ever argued that classifying the world is not a useful exercise, even if they had disagreed about how things are classified. Is it equally the case in education?

I would argue that taxonomies play two principal roles: to spark a debate about something and to make complex things more understandable. Debating is good, its teaches us to present and hone arguments, listen to counter-arguments and helps to ensure that whatever agreement is reached is more robust. A lack of debate can lead to bad things, like being swallowed by a whale. Making things more understandable is also good, to a point. The world is an impossibly complex place, and it seems a reasonable thing to create ways in which to navigate through this infinite mound of stuff with our sanity’s intact. However, do taxonomies such as the SOLO example help us to do this?

Firstly, let’s address the labelling. The names are awful. Surface and deep learning, threshold concepts and rhizomatic learning make some immediate sense to us because they use a commonly understood metaphor to help us understand the point they are making (in the same way as online retailers use the notion of a ‘basket’ to help us understand how the transaction will progress). The SOLO labels are not so intuitive. Pre-structural, Unistructural, Multistructural, Relational and Extended Abstract are terms that obfuscate rather then illuminate. They act as a barrier to understanding. Contrast that with the labels used with Bloom’s taxonomy (Remembering, Understanding, Applying…) which can be more readily understood.

Secondly, there is a danger that some people may use these taxonomies in a literal way. Instead of using them merely as a reflective tool, people may assume a literal progression between one state and the next. No such progression exists, at least in my view. We can not observe someone ascending from the multistructural to the relational, nor can be definitively prove that such distinct states exist. However if someone taken by the model uses it in dogmatic ways (in the same way that Mumford’s learning styles have been misused) then it could have the opposite effect (i.e. encouraging people to follow – and accept – the model rather than think for themselves).

For now, I think I’m going to stick with surface and deep learning as my taxonomy of choice. It is simple to explain, simple to understand and simply profound.




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