The Bloom's Deception
Cognitive load theory and a possible explanation for why we don't understand how much we know
There are two Bloom’s taxonomies. The first is the actual taxonomy developed by Benjamin Bloom and colleagues in 1956. A taxonomy is a naming system and what Bloom’s taxonomy names is different types of learning objective. The taxonomy consists of three domains - affective, psychomotor and cognitive - but it is the cognitive domain that most people tend to focus on. Fatefully, Bloom’s taxonomy arranges the learning objectives within each domain in a hierarchy. In the cognitive domain, this hierarchy proceeds from knowledge through comprehension, analysis, synthesis and finally to evaluation.
Critics have noted that this is not based on any particular experimental evidence. A generous interpretation is perhaps that the lower levels of the hierarchy must be mastered prior to achieving the upper levels. Unfortunately, a common interpretation - the second version of Bloom’s taxonomy - is that the lower levels are somehow inferior to the upper ones (for instance, this ASCD article identifies knowledge with ‘shallow processing’) and that teachers need to spend far more time assessing the so called ‘higher order’ objectives. That’s when Bloom’s taxonomy becomes damaging.
And yet it is probably not the fault of the taxonomy itself. Instead, what we see in its (mis)interpretation may be a result of a more fundamental psychological process that dismisses factual learning as rote, trivial and old-fashioned - a process that underpins everything from the nineteenth century educational progressivist movement to more contemporary ideas that are often expressed under the banner of constructivism. So, it is an interesting question as to what is at the root of this process.
A fairly straightforward answer, and one I have given in the past, is the ‘curse of knowledge’. Essentially, teachers and other relative experts underestimate their own reserves of knowledge and therefore underestimate the key role of this knowledge in expert performance. It can be thought of as a lack of empathy - it is hard for a person with knowledge to understand what it is like to lack that knowledge. The best example I can give from my own experience is when I was in Grade 10 and a student teacher took over my science class. He wrote the title of the lesson on the board: “Surfactants and surface-active agents.” And he lost us.
However, this is a description of a phenomenon and not an explanation for it. It is perhaps even tautological: Why do experts lack an appreciation of what it’s like to be a novice? Because they lack an appreciation of what it’s like to be a novice, of course.
In my view cognitive load theory has the potential to explain what is going on.
Most cognitive scientists would accept that knowledge is not stored in long-term memory in the same way we store files in a filing cabinet or books in a library. Instead, concepts tend to be stored semantically - by their meaning. One approach to describing the way knowledge is organised is schema theory, which pictures concepts being stored in webs, with each concept a node and links made to related concepts, a little like you might see on a concept map diagram.
Crucially, there appears to be no particular size or scale to these schemas. A topic such as ‘dinosaurs’ could be conceived as both a schema - feathered dinosaurs, Jurassic versus Cretaceous, predators versus herbivores etc. - or as a node in a schema such as ‘vertebrates’.
Cognitive load theory posits that when encountering new academic information, we first have to process it in our working memory which is extremely limited (Cowan suggests we can process about four items at a time). However, this limit falls away completely when dealing with schemas stored in long-term memory. We can effortlessly process these in parallel with any new items.
Working memory is roughly equivalent to the thoughts we are conscious of thinking. And so, to a relative expert with plenty of relevant knowledge, he or she will be conscious of thinking about analysing or evaluating a problem and not the deployment of their vast reserves of knowledge. If you ask them what they are thinking about, this is what they will tell you.
Which is one reason why professional mathematicians are so poor at giving advice about the teaching of school maths. Real mathematicians spend time posing conjectures and solving problems, they will claim, and not on drill. Well no, that’s because they don’t need to. But school students do.
Our intuitions about learning are therefore misleading. It is these intuitions that lead us into the error of interpreting Bloom’s taxonomy as lower level = bad, higher level = good and not a phenomenon that originates with the taxonomy itself.
Whenever our intuitions are wrong, we have a powerful tool to put us back on the right track: science. Yet the sad irony of education is that pretty much everyone, including professional scientists and mathematicians, are happy to talk off the top of their heads about education without drawing on much, if any, empirical evidence.