Taxonomy – David Didau

Didau’s Taxonomy

Taxonomy is the science of classification. As such it’s useful for ordering items within a domain into different categories. Contrary to popular understanding, although taxonomies can be hierarchical, they don’t have to be so. In education, the word ‘taxonomy’ is most closely associated with the prefix, ‘Bloom’s’. As every teacher knows, Bloom’s Taxonomy is a triangle with ‘knowledge’ at its base and ‘evaluation’ or ‘creativity’ at its apex. In fact, Bloom’s Taxonomy is not just a triangular diagram, it’s actually an attempt to classify different thinking skills. The triangle has simply come to represent the taxonomy.

The educational psychologist, Benjamin Bloom, who developed the first iteration of his taxonomy in 1956, saw knowledge as the basis, the foundation, of all thinking, not as a category of thinking skill but as “the necessary precondition for putting these skills and abilities into practice.” This is presumably why, in the diagram it is placed at the base of the triangle and given an areas larger than all the other areas.

The problem is that when we see a triangle, a trick of the brain forces us to see the apex as representing something higher and more sought after than the base. This fallacious understanding has led educationalists to conceive of those skills at the bottom of the triangle to represent ‘lower order’ thinking whereas those at the top are seen as ‘higher order’. The consequences of the misapprehension have been dire.

Over the past six decades, ‘mere’ knowledge has been denigrated as something to be rushed past and superseded as soon as possible, and analysis, synthesis, evaluation and creativity have been lauded as the aims of any right-thinking educator. While this was no doubt well-intentioned, the rush to develop students’ analytic and creative skills has had the unintended consequence of making them less knowledgeable. The problem is that thinking skills cannot be meaningful practised in the absence of something to think about.

My contention is this: You cannot think about something you don’t know and the more you know, the better you can think. It’s certainly true that raw reasoning ability – sometimes referred to as fluid intelligence – exists without prior knowledge. As such, we can apply our fluid intelligence to problems in the environment about which we have no knowledge. But, the ability to apply what we know – or crystallised intelligence – trumps fluid intelligence.

Let’s consider an example. Consider two individuals: Sarah has high fluid intelligence but knows nothing about quantum physics and Tony has lower fluid intelligence but knows a lot about quantum physics. If we were to expect Sarah to analyse or synthesise different aspects of the quantum physics she would have to rely on her working memory to hold all the new information in mind while simultaneously trying to think about it. There’s no doubt that Sarah would be able to process new information more quickly that Tony if they were more equally ignorant of the subject, but because Tony knows a lot about this particular subject he can access long-term memories to overcome the limits of working memory which will mean he has more room in his consciousness to think analytically and creatively. If both Tony and Sarah were given the same task, Tony’s performance would be superior.

Because thinking skills require knowledge, they don’t exist generically. There’s no such thing as the generic ability to be analytical or creative; you can only analyse some thing or be creative in a particular filed. It therefore makes sense to re-imagine Bloom’s taxonomy so that it better represents the types of thinking we might want our students to be able to perform.

I read Doug Lemov’s post on the problems with the way Bloom’s taxonomy is perceived and saw he’d proposed a new way of representing the taxonomy.

Inspired, I thought I’d have a go at formulating my own post-Bloom taxonomy, taking in some of what we’ve learned from cognitive science in the intervening decades:

Long-term memory contains what we know. I’ve separated this into things we know we know – declarative knowledge – and things we don’t know we know – non-declarative knowledge. The ‘pillars’ flowing between long-term and working memory represent our different cognitive abilities. They’re also, you may have noticed, the trendy 21st century skills we here so much about. Blue pillars are – I think – mostly declarative, while the green are mostly non-declarative. The turquoise pillars are either a bit of both, or ones I wasn’t sure about. Working memory – our capacity to hold new information from the environment and process it with information retrieved from long-term memory – is the point of the triangle because, well, working memory capacity is constrained. It also suggests that this is the point at which we act on the world. Long-term memory is the inverted base of the triangle to reflect the idea that long-term memory – and with it our crystalised intelligence – expands as we know more. No doubt this needs some work, and I’m certain it won’t please everyone, but I think it might be a small improvement on the triangle. At the very least it clearly represents how different ways of thinking interact with what we know.

Even if you think this diagram has descriptive power, please, please resist the temptation to put it in a lesson plan. This sort of thing should never be prescriptive.

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