Should learning facts by rote be central to education?

Michael Gove is reported as saying that ‘Learning facts by rote should be a central part of the school experience’, a philosophy which apparently underpins his shakeup of school exams. Arguing that "memorisation is a necessary precondition of understanding", he believes that exams that require students to memorize quantities of material ‘promote motivation, solidify knowledge, and guarantee standards’.

Let’s start with one sturdy argument: "Only when facts and concepts are committed securely to the working memory, so that it is no effort to recall them and no effort is required to work things out from first principles, do we really have a secure hold on knowledge.”

This is a great point, and I think all those in the ‘it’s all about learning how to learn’ camp should take due notice. On the other hand, the idea that memorizing quantities of material by rote is motivating is a very shaky argument indeed. Perhaps Gove himself enjoyed doing this at school, but I’d suggest it’s only motivating for those who can do it easily, and find that it puts them ‘above’ many other students.

But let’s not get into critiquing Gove’s stance on education. My purpose here is to discuss two aspects of it. The first is the idea that rote memorization is central to education. The second is more implicit: the idea that knowledge is central to education.

This is the nub of the issue: to what extent should students be acquiring ‘knowledge’ vs expertise in acquiring, managing, and connecting knowledge?

This is the central issue of today’s shifting world. As Ronald Bailey recently discussed in Reason magazine, Half of the Facts You Know Are Probably Wrong.

So, if knowledge itself is constantly shifting, is there any point in acquiring it?

If there were simple answers to this question, we wouldn’t keep on debating the issue, but I think part of the answer lies in the nature of concepts.

Now, concepts / categories are the building blocks of knowledge. But they are themselves surprisingly difficult to pin down. Once upon a time, we had the simple view that there were ‘rules’ that defined them. A dog has four legs; is a mammal; barks; wags its tail … When we tried to work out the rules that defined categories, we realized that, with the exception of a few mathematical concepts, it couldn’t be done.

There are two approaches to understanding categories that have been more successful than this ‘definitional’ approach, and both of them are probably involved in the development of concepts. These approaches are known as the ‘prototypical’ and the ‘exemplar’ models. The key ideas are that concepts are ‘fuzzy’, hovering around a central (‘most typical’) prototype, and are built up from examples.

A child builds up a concept of ‘dog’ from the different dogs she sees. We build up our concept of ‘far-right politician’ from the various politicians presented in the media.

Some concepts are going to be ‘fuzzier’ (broader, more diverse) than others. ‘Dog’, if you think about St Bernards and Chihuahuas and greyhounds and corgis, has an astonishingly diverse membership; ‘banana’ is, for most of us, based on a very limited sample of banana types.

Would you recognize this bright pink fruit as a banana? Or this wild one? What about this dog? Or this?

I’m guessing the bananas surprised you, and without being told they were bananas, you would have guessed they were some tropical fruit you didn’t know. On the other hand, I’m sure you had no trouble at all recognizing those rather strange animals as dogs (adored the puli, I have to say!).

To the extent that you’ve experienced diversity in your category members, the concept you’ve built will be a strong one, capable of allowing you to categorize members quickly and accurately.

In my article on expertise, I list four important differences between experts and novices:

  • experts have categories

  • experts have richer categories

  • experts’ categories are based on deeper principles

  • novices’ categories emphasize surface similarities.

How did experts develop these deeper, richer categories? Saying, “10,000 hours of practice”, may be a practical answer, but it doesn’t tell us why number of hours is important.

One vital reason the practice is important is because it grants the opportunity to acquire a greater diversity of examples.

Diverse examples, diverse contexts, this is what is really important.

What does all this have to do with knowledge and education?

Expertise (a word I use to cover the spectrum of expertise, not necessarily denoting an ‘expert’) is rooted in good categories. Good categories are rooted in their exemplars. Exemplars may change — you may realize you’ve misclassified an exemplar; scientists may decree that an exemplar really belongs in a different category (a ‘fact’ is wrong) — but the categories themselves are more durable than their individual members.

I say it again: expertise is rooted in the breadth and usefulness of your categories. Individual exemplars may turn out to be wrong, but a good category can cope with that — bringing exemplars in and out is how a category develops. So it doesn’t matter if some exemplars need to be discarded; what matters is developing the category.

You can’t build a good category without experiencing lots of exemplars.

Although, admittedly, some of them are more important than others.

Indeed, every category may be thought of as having ‘anchors’ — exemplars that, through their typicality or atypicality, define the category in crucial ways. This is not to say that they are necessarily ‘set’ exemplars, required of the category. No, your anchors may well be different from mine. But the important thing is that your categories have such members, and that these members are well-rooted, making them quickly and reliably accessible.

Let’s take language learning as an example (although language learning is to some extent a special case, and I don’t want to take the analogy too far). There are words you need to know, basic words such as prepositions and conjunctions, high-frequency words such as common nouns and verbs. But despite lists of “Top 1000 words” and the like, these are fewer than you might think. Because language is very much a creature of context. If you want to read scientific texts, you’ll want a different set of words than if your interest lies in reading celebrity magazines, to take an extreme comparison.

What you need to learn is the words you need, and that is specific to your interests. Moreover, the best way of learning them is also an individual matter — and by ‘way’, I’m not (for a change) talking about strategies, which is a different issue. I’m talking about the contexts in which you experience the words you are learning.

For example, say you are studying genetics. There are crucial concepts you will need to learn — concepts such as ‘DNA’, ‘chromosomes’, ‘RNA’, epigenetics, etc — but there is no such requirement concerning the precise examples (exemplars) you use to acquire those concepts. More importantly, it is much better to cover a number of different examples that illuminate a concept, rather than focus on a single one (Mendel’s peas, I’m looking at you!).

Genetics is changing all the time, as we learn more and more. But that’s an argument for learning how to replace outdated information (an area of study skills sadly neglected!), not an argument for not learning anything in case it turns out to be wrong.

To understand a subject, you need to grasp its basic concepts. This is the knowledge part. To deal with the mutability of specific knowledge, you need to understand how to discard outdated knowledge. To deal with the amount of knowledge relevant to your studies and interests, you need skills in seeing what information is important and relevant for your studies and interests and in managing the information so that it is accessible when needed.

Accessibility is key. Whether you store the information in your own head or in an external storage device, you need to be able to lay hands on it when you need it. And here’s the nub of the problem: you need to know when you need it.

This problem is the primary reason why internal storage (in your own memory) is favored by many. It’s only too easy to file something away in external storage (physical files; computer documents; whatever) and forget that it’s there.

But what all this means is that what we really need in our memory is an index. We don’t need to remember what a deoxyribose sugar is if we can instantly look it up whenever we come across it.

Or do we?

This is the point, isn’t it? If you want to study a subject, you can’t be having to look up every second word in the text, you need to understand the concepts, the language. So you do need to have those core concepts well understood, and the technical vocabulary mastered.

So is this an argument for rote memorization?

No, because rote memorization is a poor strategy, suitable only for situations where there can be no understanding, no connection.

We learn by repetition, but rote repetition is the worst kind of repetition there is.

To acquire the base knowledge you need to build expertise, you need repetition through diverse examples. This is the art and craft of good instruction: providing the right examples, in the right order.

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