education reform: evaluating the evidence

Over the last few weeks I’ve found myself moving from being broadly sympathetic to educational ‘reform’ to being quite critical of it. One comment on my blog was “You appear to be doing that thing where you write loads, but it is hard to identify any clear points.” Point taken. I’ll see what I can do in this post.

my search for the evidence

I’ve been perplexed by the ideas underpinning the current English education system since my children started encountering problems with it about a decade ago. After a lot of searching, I came to the conclusion that the entire system was lost in a constructivist wilderness. I joined the TES forum to find out more, and discovered that on the whole, teachers weren’t – lost, that is. I came across references to evidence-based educational research and felt hopeful.

Some names were cited; Engelmann, Hirsch, Hattie, Willingham. I pictured a growing body of rigorous research and searched for the authors’ work. Apart from Hattie’s, I couldn’t find much. Willingham was obviously a cognitive psychologist but I couldn’t find his research either. I was puzzled. Most of the evidence seemed to come from magazine articles and a few large-scale studies – notorious for methodological problems. I then heard about Daisy Christodoulou’s book Seven Myths about Education and thought that might give me some pointers. I searched her blog.

In one post, Daisy cites work from the field of information theory by Kirschner, Sweller & Clark, Herb Simon and John Anderson. I was familiar with the last two researchers, but couldn’t open the Simon papers and Anderson’s seemed a bit technical for a general readership. I hadn’t come across the Kirschner, Sweller and Clark reference so I read it. I could see what they were getting at, but thought their reasoning was flawed.

Then it dawned on me. This was the evidence bit of the evidence-based research. It consisted of some early cognitive science/information theory, some large-scale studies and a meta-analysis, together with a large amount of opinion. To me that didn’t constitute a coherent body of evidence. But I was told that there was more to it, which is why I attended the ResearchED conference last weekend. There was more to it, but the substantial body of research didn’t materialise. So where does that leave me?

I still agree with some points that the educational reformers make;

• English-speaking education systems are dominated by constructivist pedagogical approaches
• the implementation of ‘minimal guidance’ approaches has failed to provide children with a good education
• we have a fairly reliable, valid body of knowledge about the world and children should learn about it
• skills tend to be domain-specific
• cognitive science can tell us a lot about how children learn
• the capacity of working memory is limited
• direct instruction is an effective way of teaching.

But I have several reservations that make me uneasy about the education reform ‘movement’.

1. the evidence.

Some is cited frequently. Here’s a summary.

If I’ve understood it correctly, Engelmann and Becker’s DISTAR programme (Direct Instruction System for Teaching Arithmetic and Reading) had far better outcomes for basic maths and reading, higher order cognitive skills (in reading and maths) and responsibility and self-esteem than any other programme in the Project Follow-Through evaluation carried out in 1977.

At around the same time, ED Hirsch had realised that his students’ comprehension of texts was impaired by their poor general knowledge, and in 1983 he published an outline of his concept of what he called ‘cultural literacy’.

A couple of decades later, Daniel Willingham, a cognitive psychologist, started to apply theory from cognitive science to education.

In 2008, John Hattie published Visible Learning: A Synthesis of Over 800 Meta-Analyses Relating to Achievement – the result of 15 years’ work. The effect sizes Hattie found for various educational factors are ranked here.

Kirschner, Sweller and Clark’s
2006 paper Why Minimal Guidance During Instruction Does Not Work: An Analysis of the Failure of Constructivist, Discovery, Problem-Based, Experiential, and Inquiry-Based Teaching is also often cited. John Sweller developed the concept of ‘cognitive load’ in the 1980s, based on the limited capacity of working memory.

2. the conclusions that can be drawn from the evidence

The DISTAR programme, often referred to as Direct Instruction (capitalised), is clearly very effective for teaching basic maths and literacy. This is an outcome not to be sniffed at, so it would be worth exploring why DISTAR hasn’t been more widely adopted. Proponents of direct instruction often claim it’s because of entrenched ideological opposition; it might also be to do with the fact that it’s a proprietary programme, that teacher input is highly constrained, and that schools have to teach more than basic maths and literacy.

ED Hirsch’s observation that students need prior knowledge before they can comprehend texts involving that knowledge is a helpful one, but has more to say about curriculum design than pedagogy. There are some major issues around all schools using an identical curriculum, who controls the content and how children’s knowledge of the curriculum is assessed.

Daniel Willingham
has written extensively on how findings from cognitive science can be applied to education. Cognitive science is clearly a rich source of useful information. The reason I couldn’t find his research (mainly about procedural memory) appears to be because at some point he changed his middle initial from B to T. I’d assumed it was by someone else.

Although I have doubts about Kirschner Sweller and Clark’s paper, again the contribution from cognitive science is potentially valuable.

John Hattie’s meta-analyses provide some very useful insights into the effectiveness of educational influences.

The most substantial bodies of evidence cited are clearly cognitive science and Hattie’s meta-analyses, which provide a valuable starting point for further exploration of the influences he ranks. Those are my conclusions.

But other conclusions are being drawn – often that the evidence cited above supports the view that direct instruction is the most effective way of teaching and that traditional educational methods (however they are defined) are superior to progressive ones (however they are defined). Those conclusions seem to me to be using the evidence to support beliefs about educational methods, rather than deriving beliefs about educational methods from the evidence.

3. who’s evaluating the evidence?

A key point made by proponents of direct instruction is that students need to have knowledge before they can do anything effective with it. Obviously they do. But this principle appears to be being overlooked by the very people who are emphasizing it.

If you want to understand and apply findings from a meta-analysis you need to be aware of common problems with meta-analyses, how reliable they are, what you need to bear in mind about complex constructs etc. You don’t need to have read everything there is to read about meta-analyses, just to be aware of potential pitfalls. If you want to apply findings from cognitive science, it would help to have at least a broad overview of cognitive science first. That’s because, if you don’t have much prior knowledge, you have no way of knowing how reliable or valid information is. If it’s from a peer-reviewed paper, there’s a good chance it’s reliable because the reviewers would have looked at the theory, the data, the analysis and conclusions. How valid it is (ie how well it maps on to the real world) is another matter. I want to look at some of what ED Hirsch has written to illustrate the point.

Hirsch on psychology and science

Hirsch’s work is often referred to by education reformers. I think he’s right to emphasise the importance of students’ knowledge and I’m impressed by his Core Knowledge framework. There’s now a UK version (slightly less impressive) and his work has influenced the new English National Curriculum. But when I started to check out some of what Hirsch has written I was disconcerted to find that he doesn’t seem to practice what he preaches. In an article in Policy Review he sets out seven ‘reliable general principles’ derived from cognitive science to guide teachers. The principles are sound, even if he has misconstrued ‘chunking’ and views rehearsal as a ‘disagreeable need’.

But Hirsch’s misunderstanding of the history of psychology suggests that not everything he says about psychology might be entirely reliable. He says;

Fifty years ago [the article is dated 2002] psychology was dominated by the guru principle. One declared an allegiance to B.F. Skinner and behaviorism, or to Piaget and stage theory, or to Vygotsky and social theory. Today, by contrast, a new generation of “cognitive scientists,” while duly respectful of these important figures, have leavened their insights with further evidence (not least, thanks to new technology), and have been able to take a less speculative and guru-dominated approach. This is not to suggest that psychology has now reached the maturity and consensus level of solid-state physics. But it is now more reliable than it was, say, in the Thorndike era with its endless debates over “transfer of training.””

This paragraph is riddled with misconceptions. Skinner was indeed an influential psychologist, but behaviourism was controversial – Noam Chomsky was a high profile critic. Piaget was influential in educational circles – but children’s cognitive development formed one small strand of the wide range of areas being investigated by psychologists. Vygotsky’s work has also been influential in education, but it didn’t become widely known in the West until after the publication in 1978 of Mind in Society – a collection of his writings translated into English – so he couldn’t have had ‘guru’ status in psychology in the 1950s. And to suggest that cognitive scientists are ‘duly respectful’ of Skinner, Piaget and Vygotsky as ‘important figures’ in their field, suggests a complete misunderstanding of the roots of cognitive science and of what matters to cognitive scientists. But you wouldn’t be able to question what Hirsch is saying if you had no prior information. And in this article, Hirsch doesn’t support his assertions with references, so you couldn’t check them out.

In a conference address that also forms a chapter in book entitled The Great Curriculum Debate, Hirsch attributes progressive educational methods to the Romantic movement and in turn to religious beliefs, completely overlooking the origins in psychological research of ‘progressive’ educational methodologies and, significantly, the influence of Freud’s work.

In the grand scheme of things, of course, Hirsch’s view of psychology in the 1950s, or his view of the origins of progressive education don’t matter that much. What does matter is that Hirsch himself is seen as something of a guru largely because of his emphasis on students needing to have sound prior knowledge, but here he clearly hasn’t checked out his own.

What’s more important is Hirsch’s view of science. In the last section of the article on classroom research and cargo cults, entitled ‘on convergence and consensus’, in which he compares classroom research with that from cognitive psychology, he says “independent convergence has always been the hallmark of dependable science“. That’s true in the sense that if several researchers approaching a problem from different directions all come to the same conclusion, they would be reasonably confident that their conclusion was a valid one.

Hirsch illustrates the role of convergence using the example of germ theory. He says “in the nineteenth century, for example, evidence from many directions converged on the germ theory of disease. Once policymakers accepted that consensus, hospital operating rooms, under penalty of being shut down, had to meet high standards of cleanliness.” What’s interesting is that Hirsch slips, almost imperceptibly, from ‘convergence’ into ‘consensus’. In scientific research, convergence is important, but consensus can be extremely misleading because it can be, and often has been, wrong. Ironically, not long before high standards of cleanliness were imposed on hospitals, the consensus had been that cross-contamination theory was wrong, as Semmelweis discovered to his cost. Reliable findings aren’t the same as valid ones.

Hirsch then goes on to say “What policymakers should demand from the [education] research community is consensus.” No they shouldn’t. Consensus can be wrong. What policymakers need to demand from education research is methodological rigour. We already have the relevant expertise, it just needs to be applied to education. Again, if you have no frame of reference against which you can evaluate what Hirsch is saying, you’d be quite likely to assume that he’s right about convergence and consensus – and you’d be none the wiser about the importance of good research design.

what the teachers say

I’m genuinely enthusiastic about teachers wanting to base their practice on evidence. I recognize that this is a work in progress and it’s only just begun. I can quite understand why someone whose teaching has been transformed by a finding from cognitive science might want to share that information as widely as possible. But ironically, some of the teachers involved appear to be doing exactly the opposite of what they recommend teachers do with students.

If you’re not familiar with a knowledge domain, but want to use findings from it, it’s worth getting an overview of it first. This doesn’t involve learning loads of concrete facts, it involves getting someone with good domain knowledge to give you an outline of how it works, so you can see how the concrete facts fit in. It also involves making sure you know what domain-specific skills are required to handle the concrete facts, and whether or not you have them. It also means not making overstated claims. Applying seven principles from cognitive science means you are applying seven principles from cognitive science. That’s all. It’s important to avoid making claims that aren’t supported by the evidence.

What struck me about the supporters of educational reform is that science teachers are noticeable by their absence. Most of the complaints about progressive education seem to relate to English, Mathematics and History. These are all fields that deal with highly abstracted information that is especially vulnerable to constructivist worldviews, so they might have been disproportionately influenced by ‘minimal guidance’ methods. It’s a bit more difficult to take an extreme constructivist approach to physics, chemistry, biology or physical geography because reality tends to intervene quite early on. The irony is that science teachers might be in a better position than teachers of English, Maths or History to evaluate evidence from educational research. And psychology teachers and educational psychologists would have the relevant domain knowledge, which would help avoid reinventing the wheel. I’d recommend getting some of them on board.

“waiter’s memory”

At the ResearchED conference last Saturday, when I queried the usefulness of the diagram of working memory that was being used, I was asked two questions. Here’s the first:

What’s wrong with Willingham’s model of working memory?

Nothing’s wrong with Willingham’s model. As far as I can tell, the diagram of working memory that was being used by teachers at the ResearchED conference had been simplified to illustrate two key points; that working memory has limited capacity and that information can be transferred from working memory to long-term memory and vice-versa.

My reservation about it is that if it’s the only model of working memory you’ve seen, you won’t know what Willingham has left out, nor how working memory fits into the way the brain processes information. And over-simplified models of things, if unconstrained by reality, tend to take on a life of their own which doesn’t help anyone. The left-brain right-brain mythology is a case in point. An oversimplified understanding of the differences between right and left hemispheres followed by a process of Chinese whispers ended up producing some bizarre educational practices.

The second question was this:

What difference would it make if we knew more about how information is processed in the brain?

It’s a good question. The short answer is that if you rely on Willingham’s diagram for your understanding of working memory, you could conclude, as some people have done, that direct instruction is the only way students should be taught. As I hope I showed in my previous post, the way information is processed is more complex than the diagram suggests. I think there are three key points that are worth bearing in mind.

Long-term memory is constantly being updated by incoming sensory information.

Children are learning all the time. They learn implicitly, informally and incidentally from their environment as well as explicitly when being taught. It’s well worth utilising that ability to learn from ‘background’ information. Posters, displays, playground activities, informal conversations, and dvds and books used primarily for entertainment, can all exploit implicit, informal and incidental learning that will support and extend and reinforce explicit learning.

We’re not always aware that we are learning.

I only need two or three exposures to an unfamiliar place, or face or song before I can recognise it again, and I don’t need to actively pay attention to, or put any effort into recalling, the place, face or song in order to do so. I would have reliably learned new things, but my learning would be implicit. I wouldn’t be able to give accurate directions, describe the face so that someone else would recognise it, or hum the tune. (Daniel Willingham suggests that implicit memory doesn’t exist, but he’s talking about the classification rather than the phenomenon.)

Peter Blenkinsop and I found that we were using different definitions of learning. My definition was; long-term changes to the brain as a result of incoming information. His was; being able to explicitly recall information from long-term memory. Both definitions are valid, but they are different.

Working memory is complex.

George Miller’s paper ‘The magical number seven, plus or minus two’ is well worth reading. What’s become clear since Miller wrote it is that his finding that working memory can handle only 7±2 bits of information at once applies to the loops/sketchpads/buffers in working memory. At first, it was assumed there was only one loop/sketchpad/buffer. Since then more have been discovered. In addition, due to information being chunked, the amount of information in the loops/sketchpads/buffers can actually be quite large. On top of that, the central executive is simultaneously monitoring information from the environment, the body and long-term memory. That’s quite a lot of information flowing through working memory all the time. We don’t actively pay attention to all of it, but it doesn’t follow that anything we don’t pay attention to disappears forever. In addition to working memory capacity there are several other things the brain does that make it easier, or harder, for people to learn.

Things that make learning easier (and harder)

1. Pre-existing information

People learn by extending their existing mental schemata. This involves extending neural networks – literally. If information is totally novel to us, it won’t mean anything to us and we’re unlikely to remember it. Because each human being has had a unique set of life experiences, each of us has a unique set of neural networks and the way we structure our knowledge is also unique. It doesn’t follow that everybody’s knowledge framework is equally valid. The way the world is structured and the way it functions are pretty reliable and we know quite a lot about both. Students do need to acquire core knowledge about the world and it is possible to teach it. Having said that, there are often fundamental disagreements within knowledge domains about the nature of that core knowledge, so students also need to know how to look at knowledge from different perspectives and how to test its reliability and validity.

Tapping into children’s existing schemata, not just those relating to what they are supposed to be learning in school but what they know about the world in general, can provide hooks on which to hang tricky concepts. Schemata from football, pop culture or Dr Who can be exploited, not in order to make learning ‘fun’, but to make sense of it. That doesn’t mean that teachers have to refer to pop culture, or that they should do so if it’s likely to prove a distraction.

2. Multi-sensory input

Because learning is about the real world and takes place in the real world, it usually involves more than one sensory modality – human beings rely most heavily on the visual, auditory and tactile senses. Neural connections linking information from several sensory modalities make things we’ve learned more secure because they can be accessed via several different sensory routes. It also makes sense to map the way information is presented as accurately as possible onto what it relates to in the real world. Visits, audio-visuals, high quality illustrations and physical activities can convey information that chalk-and-talk and a focus on abstract information can’t. Again, the job of multi-sensory vehicles for learning isn’t to make the learning ‘fun’ (although they might do that) or to distract the learner, but to increase the amount of information available.

3. Trial-and-error

The brain relies on trial-and-error feedback to fine-tune skills and ensure that knowledge is fit for purpose. We call trial-and-error learning in young children ‘play’. Older children and adults also use play to learn – if they get the opportunity. In more formal educational settings, formative assessment that gives feedback to individual students is a form of trial-and-error learning. It’s important to note that human beings tend to attach greater weight to the risk of failure and sanctions than they do to opportunities for success and reward. This means that tasks need to be challenging but not too challenging. Too many failures – or too many successes – can reduce interest and motivation.

4. Rehearsal

Willingham emphasises the importance of rehearsal in learning. The more times neural networks are activated, the stronger the connections become within them, and the more easily information will be recalled. Rehearsal at intervals is more effective than ‘cramming’. That’s because the connections between neurons have to be formed, physically, and there’s no opportunity for that to happen if the network is being constantly activated by incoming information. There’s a reason why human beings need rest and relaxation.

5. Problem-solving

Willingham is often quoted as saying ‘the brain is not designed for thinking’. That’s true in the sense that our brains default to quick-and-dirty solutions to problems rather than using logical, rational thought. What’s also true is what Willingham goes on to say; ‘people like to solve problems, but not to work on unsolveable problems’ (p.3). The point he’s making is that our problem-solving capacity is limited. Nonetheless, human technology bears witness to the fact that human beings are problem-solvers extraordinaire, and the attempts to resolve problems have resulted in a vast body of knowledge about how the world works. It’s futile to expect children to do all their learning by problem-solving, but because problem-solving involves researching, re-iterating, testing and reconfiguring knowledge it can be an effective way of acquiring new information and making it very memorable.

6. Writing things down

Advocates of direct instruction place a lot of emphasis on the importance of long-term memory; the impression one gets is that if factual information is memorised it can be recalled whenever it’s needed. Unfortunately, long-term memory doesn’t work like that. Over time information fades if it’s not used very often and memories can become distorted (assuming they were accurate in the first place). If we’ve acquired a great deal of factual information, we won’t have time to keep rehearsing all of it to keep it all easily accessible. Memorising factual information we currently need makes sense, but what we need long-term is factual information to hand when required, and that’s why we invented writing. And books. And the internet, although that has some of the properties of long-term memory. Recording information enormously increases the capacity and reliability of long-term memory.

grover

In a classic Sesame Street sketch, Mr Johnson the restaurant customer suggests that Grover the waiter write down his order. Grover is affronted: “Sir! I am a trained professional! I do not need to write things down. Instead, I use my ‘waiter’s memory’.” Waiters are faced with an interesting memory challenge; they need to remember a customer’s order for longer than is usually possible in working memory, but don’t need to remember the order long-term. So they tend to use technical support in the form of a written note. Worth watching the sketch, because it’s a beautiful illustration of how a great deal of information can be packed into a small timeframe, without any obvious working memory overload. (First time round most children would miss some of it, but Sesame Street repeats sketches for that reason.)

Conclusion

It won’t have escaped the attention of some readers that I have offered evidence from cognitive science to support educational methods lumped together as ‘minimal guidance’ and described as ‘failing’ by Kirschner, Sweller and Clark; constructivist, discovery, problem-based, experiential, and inquiry-based teaching. A couple of points are worth noting in relation to these approaches.

The first is that they didn’t appear suddenly out of the blue. Each of them has emerged at different points in time from 150 years of research into how human beings learn. We do learn by experiencing, inquiring, discovering, problem-solving and constructing our knowledge in different ways. There is no doubt about that. There’s also no doubt that we can learn by direct instruction.

The second point is that the reason why these approaches have demonstrably failed to ensure that all children have a good knowledge of how the world works, is because they have been extended beyond what George Kelley called their range of convenience.

In other words they’ve been applied inappropriately. You can’t just construct your own understanding of the world and expect the world to conform to it. Trying to learn everything by experience, discovery, inquiry or problem-solving is a waste of effort if someone’s already experienced, discovered or inquired about it, or if a problem’s already been solved. Advocates of direct instruction are quite right to point out that you usually need prior knowledge before you can solve a problem, and a good understanding of a knowledge domain before you know what you need to inquire about, and that many failures in education have come about because novices have been expected to mimic the surface features of experts’ behavior without having the knowledge of experts.

Having said that, relying on an oversimplified model of working memory introduces the risk of exactly the same thing happening with direct instruction. The way the brain processes information is complex, but not so complex it can’t be summarised in a few key principles. Human beings acquire information in multiple ways, but not in so many ways we can’t keep track of them. Figuring out what teaching approaches are best used for what knowledge might take a bit of time, but it’s a worthwhile investment, and should help to avoid the one-size-fits-all approach that has bedevilled the education system for too long.

Acknowledgements

Image of Grover from Muppet Wiki http://muppet.wikia.com/wiki/Grover

there’s more to working memory than meets the eye

I’ve had several conversations on Twitter with Peter Blenkinsop about learning and the brain. At the ResearchEd conference on Saturday, we continued the conversation and discovered that much of our disagreement was because we were using different definitions of learning. Peter’s definition is that learning involves being able to actively recall information; mine is that it involves changes to the brain in response to information.

working memory

Memory is obviously essential to learning. One thing that’s emerged clearly from years of research into how memory works is that the brain retains information for a very short time in what’s known as working memory, and indefinitely in what’s called long-term memory – but that’s not all there is to it. I felt that advocates of direct instruction at the conference were relying on a model of working memory that was oversimplified and could be misleading. The diagram they were using looked like this;

simple model of memory

simple model of memory

This model is attributed to Daniel Willingham. From what the teachers were saying, the diagram is simpler than most current representations of working memory because its purpose is to illustrate three key points;

• the capacity of working memory is limited and it holds information for a short time
• information in long-term memory is available for recall indefinitely and
• information can be transferred from working memory to long-term memory and vice versa.

So far, so good.

My reservation about the diagram is that if it’s the only diagram of working memory you’ve ever seen, you might get the impression that it shows the path information follows when it’s processed by the brain. From it you might conclude that;

• information from the environment goes directly into working memory
• if you pay attention to that information, it will be stored permanently in long-term memory
• if you don’t pay attention to it it will be lost forever, and
• there’s a very low limit to how much information from the environment you can handle at any one time.

But that’s not quite what happens to information coming into the brain. As Peter pointed out during our conversation, simplifying things appropriately is challenging; you want to simplify enough to avoid confusing people, but not so much that they might misunderstand.

In this post, I’m going to try to explain the slightly bigger picture of how brains process information, and where working memory and long-term memory fit in.

sensory information from the external environment

All information from the external environment comes into the brain via the sense organs. The incoming sensory information is on a relatively large scale, particularly if it’s visual or auditory information; you can see an entire classroom at once and hear simultaneously all the noises emanating from it. But individual cells within the retina or the cochlea respond to tiny fragments of that large-scale information; lines at different angles, areas of light and dark and colour, minute changes in air pressure. Information from the fragments is transmitted via tiny electrical impulses, from the sense organs to the brain. The brain then chunks the fragments together to build larger-scale representations that closely match the information coming in from the environment. As a result, what we perceive is a fairly accurate representation of what’s actually out there. I say ‘fairly accurate’ because perception isn’t 100% accurate, but that’s another story.

chunking

The chunking of sensory information takes place via networks of interconnected neurons (long spindly brain cells). The brain forms physical connections (synapses) between neighbouring neurons in response to novel information. The connections allow electrical activation to pass from one neuron to another. The connections work on a use-it-or-lose-it principle; the more they are used the stronger they get, and if they’re not used much they weaken and disappear. Not surprisingly, toddlers have vast numbers of connections, but that number diminishes considerably during childhood and adolescence. That doesn’t mean we have to keep remembering everything we ever learned or we’ll forget it, it’s a way of ensuring that the brain can process efficiently the types of information from the environment that it’s most likely to encounter.

working memory

Broadly speaking, incoming sensory information is processed in the brain from the back towards the front. It’s fed forward into areas that Alan Baddeley has called variously a ‘loop’, ‘sketchpad’ and ‘buffer’. Whatever you call them, they are areas where very limited amounts of information can be held for very short periods while we decide what to do with it. Research evidence suggests there are different loops/sketchpads/buffers for different types of sensory information – for example Baddeley’s most recent model of working memory includes temporary stores for auditory, visuospatial and episodic information.

Baddeley's working memory model

Baddeley’s working memory model

The incoming information held briefly in the loops/sketchpads/buffers is fed forward again to frontal areas of the brain where it’s constantly monitored by what’s called the central executive – an area that deals with attention and decision-making. The central executive and the loops/sketchpads/buffers together make up working memory.

long-term memory

The information coming into working memory activates the more permanent neural networks that carry information relevant to it – what’s called long-term memory. The neural networks that make up long-term memory are distributed throughout the brain. Several different types of long-term memory have been identified but the evidence points increasingly to the differences being due to where neural networks are located, not to differences in the biological mechanisms involved.

Information in the brain is carried in the pattern of connections between neurons. The principle is similar to the way pixels represent information on a computer screen; that information is carried in the patterns of pixels that are activated. This makes computer screens – and brains – very versatile; they can carry a huge range of different types of information in a relatively small space. One important difference between the two processes is that pixels operate independently, whereas brain cells form physical connections if they are often activated at the same time. The connections allow fast, efficient processing of information that’s encountered frequently.

For example, say I’m looking out of my window at a pigeon. The image of the pigeon falling on my retina will activate the neural networks in my brain that carry information about pigeons; what they look like, sound like, feel like, their flight patterns and feeding habits. My thoughts might then wander off on to related issues; other birds in my garden, when to prune the cherry tree, my neighbour repairing her fence. If I glance away from the pigeon and look at my blank computer screen, other neural networks will be activated, those that carry information about computers, technology, screens and rectangles in general. I will no longer be thinking about pigeons, but my pigeon networks will still be active enough for me to recall that I was looking at a pigeon previously and I might glance out of the window to see if it is still there.

Every time my long-term neural networks are activated by incoming sensory information, they are updated. If the same information comes in repeatedly the connections within the network are strengthened. What’s not clear is how much attention needs to be paid to incoming information in order for it to update long-term memory. Large amounts of information about the changing environment are flowing through working memory all the time, and evidence from brain-damaged patients suggests that long-term memory can be changed even if we’re not paying attention to the information that activates it.

the central executive

Information from incoming sensory information and from long-term memory is fed forward to the central executive. The function of the central executive is a bit like the function of a CCTV control room. According to Antonio Damasio it monitors, evaluates and responds to information from three main sources;

• the external environment (sensory information)
• the internal environment (body states) and
• previous representations of the external and internal environments (carried in the pattern of connections in neural networks).

One difference is that loops/sketchpads/buffers and the system that monitors them consist of networks of interconnected neurons, not TV screens (obviously). Another is that there isn’t anybody watching the brain’s equivalent of the CCTV screens – it’s an automated process. We become aware of information in the loops/sketchpads/buffers only if we need to be aware of it – so we are usually conscious of what’s happening in the external environment or if there are significant changes internally or externally.

The central executive constantly compares the streams of incoming information. It responds to it via networks of neurons that feed back information to other areas of the brain. If the environment has changed significantly, or an interesting or threatening event occurs, or we catch sight of something moving on the periphery of our field of vision, or experience sudden discomfort or pain, the feedback from the central executive ensures that we pay attention to that, rather than anything else. It’s important to note that information from the body includes information about our overall physiological state, including emotions.

So a schematic general diagram of how working memory fits in with information processing in the brain would look something like this:

Slide1

It’s important to note that we still don’t have a clear map of the information processing pathways. Researchers keep coming across different potential loops/sketchpads/buffers and there’s evidence that the feedback and feed-forward pathways are more complex than this diagram shows.

I began this post by suggesting that an over-simplified model of working memory could be misleading. I’ll explain my reasons in more detail in the next post, but first I want to highlight an important implication of the way incoming sensory information is handled by the brain.

pre-conscious processing

A great deal of sensory information is processed by the brain pre-consciously. Advocates of direct instruction emphasise the importance of chunking information because it increases the capacity of working memory. A popular example is the way expert chess players can hold simultaneously in working memory several different configurations of chess pieces, chunking being seen as something ‘experts’ do. But it’s important to remember that the brain chunks information automatically if we’re exposed to it frequently enough. That’s how we recognise faces, places and things – most three year-olds are ‘experts’ in their day-to-day surroundings because they have had thousands of exposures to familiar faces, places and things. They don’t have to sit down and study these things in order to chunk the fragments of information that make up faces, places and things – their visual cortex does it automatically.

This means that a large amount of information going through young children’s working memory is already chunked. We don’t know to what extent the central executive has to actively pay attention to that information in order for it to change long-term memory, but pre-conscious chunking does suggest that a good deal of learning happens implicitly. I’ll comment on this in more detail in my next post.

A tale of two Blobs

The think-tank Civitas has just published a 53-page pamphlet written by Toby Young and entitled ‘Prisoners of The Blob’. ‘The Blob’ for the uninitiated, is the name applied by the UK’s Secretary of State for Education, Michael Gove, to ‘leaders of the teaching unions, local authority officials, academic experts and university education departments’ described by Young as ‘opponents of educational reform’. The name’s not original. Young says it was coined by William J Bennett, a former US Education Secretary; it was also used by Chris Woodhead, first Chief Inspector of Ofsted in his book Class War.

It’s difficult to tell whether ‘The Blob’ is actually an amorphous fog-like mass whose members embrace an identical approach to education as Young claims, or whether such a diverse range of people espouse such a diverse range of views that it’s difficult for people who would like life to be nice and straightforward to understand all the differences.

Young says;

They all believe that skills like ‘problem-solving’ and ‘critical thinking’ are more important than subject knowledge; that education should be ‘child-centred’ rather than ‘didactic’ or ‘teacher-led’; that ‘group work’ and ‘independent learning’ are superior to ‘direct instruction’; that the way to interest children in a subject is to make it ‘relevant’; that ‘rote-learning’ and ‘regurgitating facts’ is bad, along with discipline, hierarchy, routine and anything else that involves treating the teacher as an authority figure. The list goes on.” (p.3)

It’s obvious that this is a literary device rather than a scientific analysis, but that’s what bothers me about it.

Initially, I had some sympathy with the advocates of ‘educational reform’. The national curriculum had a distinctly woolly appearance in places, enforced group-work and being required to imagine how historical figures must have felt drove my children to distraction, and the approach to behaviour management at their school seemed incoherent. So when I started to come across references to educational reform based on evidence, the importance of knowledge and skills being domain-specific, I was relieved. When I found that applying findings from cognitive science to education was being advocated, I got quite excited.

My excitement was short-lived. I had imagined that a community of researchers had been busily applying cognitive science findings to education, that the literatures on learning and expertise were being thoroughly mined and that an evidence-based route-map was beginning to emerge. Instead, I kept finding references to the same small group of people.

Most fields of discourse are dominated by a few individuals. Usually they are researchers responsible for significant findings or major theories. A new or specialist field might be dominated by only two or three people. The difference here is that education straddles many different fields of discourse (biology, psychology sociology, philosophy and politics, plus a range of subject areas) so I found it a bit odd that the same handful of names kept cropping up. I would have expected a major reform of the education system to have had a wider evidence base.

Evaluating the evidence

And then there was the evidence itself. I might be looking in the wrong place, but so far, although I’ve found a few references, I’ve uncovered no attempts by proponents of educational reform to evaluate the evidence they cite.

A major flaw in human thinking is confirmation bias. To represent a particular set of ideas, we develop a mental schema. Every time we encounter the same set of ideas, the neural network that carries the schema is activated. The more it’s activated, the more readily it’s activated in future. This means that any configuration of ideas that contradicts a pre-existing schema, has, almost literally, to swim against the electromagnetic tide. It’s going to take a good few reiterations of the new idea set before a strongly embedded pre-existing schema is likely to be overridden by a new one. Consequently we tend to favour evidence that confirms our existing views, and find it difficult to see things in a different way.

The best way we’ve found to counteract confirmation bias in the way we evaluate evidence is through hypothesis testing. Essentially you come up with a hypothesis and then try to disprove it. If you can’t, it doesn’t mean your hypothesis is right, it just means you can’t yet rule it out. Hypothesis testing as such is mainly used in the sciences, but the same principle underlies formal debating, the adversarial approach in courts of law, and having an opposition to government in parliament. The last two examples are often viewed as needlessly combative, when actually their job is to spot flaws in what other people are saying. How well they do that job is another matter.

It’s impossible to tell at first glance whether a small number of researchers have made a breakthrough in education theory, or whether their work is simply being cited to affirm a set of beliefs. My suspicion that it might be the latter was strengthened when I checked out the evidence.

The evidence

John Hattie conducted a meta-anlaysis of over 800 studies of student achievement. My immediate thought when I came across his work was of the well-documented problems associated with meta-analyses. Hattie does discuss these, but I’m not convinced he disposed of one key issue; the garbage-in-garbage-out problem. A major difficulty with meta-analyses is ensuring that all the studies involved use the same definitions for the constructs they are measuring; and I couldn’t find a discussion of what Hattie (or other researchers) mean by ‘achievement’. I assume that Hattie uses test scores as a proxy measure of achievement. This is fine if you think the job of schools is to ensure that children learn what somebody has decided they should learn. But that assumption poses problems. One is who determines what students should learn. Another is what happens to students who, for whatever reason, can’t learn at the same rate as the majority. And a third is how the achievement measured in Hattie’s study maps on to achievement in later life. What’s noticeable about the biographies of many ‘great thinkers’ – Darwin and Einstein are prominent examples – is how many of them didn’t do very well in school. It doesn’t follow that Hattie is wrong – Darwin and Einstein might have been even greater thinkers if their schools had adopted his recommendations – but it’s an outcome Hattie doesn’t appear to address.

Siegfreid Engelmann and Wesley C Becker developed a system called Direct Instruction System for Teaching Arithmetic and Reading (DISTAR) that was shown to be effective in Project Follow-Through – a evaluation of a number of educational approaches in the US education system over a 30 year period starting in the 1960s. There’s little doubt that Direct Instruction is more effective than many other systems at raising academic achievement and self-esteem. The problem is, again, who decides what students learn, what happens to students who don’t benefit as much as others, and what’s meant by ‘achievement’.

ED Hirsch developed the Core Knowledge sequence – essentially an off-the-shelf curriculum that’s been adapted for the UK and is available from Civitas. The US Core Knowledge sequence has a pretty obvious underlying rationale even if some might question its stance on some points. The same can’t be said of the UK version. Compare, for example, the content of US Grade 1 History and Geography with that of the UK version for Year 1. The US version includes Early People and Civilisations and the History of World Religion – all important for understanding how human geography and cultures have developed over time. The UK version focuses on British Pre-history and History (with an emphasis on the importance of literacy) followed by Kings and Queens, Prime ministers then Symbols and figures – namely the Union Jack, Buckingham Palace, 10 Downing Street and the Houses of Parliament – despite the fact that few children in Y1 are likely to understand how or why these people or symbols came to be important. Although the strands of world history and British history are broadly chronological, Y4s study Ancient Rome alongside the Stuarts, and Y6s the American Civil War potentially before the Industrial Revolution.

Daniel Willingham is a cognitive psychologist and the author of Why don’t students like school? A cognitive scientist answers questions about how the mind works and what it means for the classroom and When can you trust the experts? How to tell good science from bad in education. He also writes for a column in American Educator magazine. I found Willingham informative on cognitive psychology. However, I felt his view of education was a rather narrow one. There’s nothing wrong with applying cognitive psychology to how teachers teach the curriculum in schools – it’s just that learning and education involve considerably more than that.

Kirschner, Sweller and Clark have written several papers about the limitations of working memory and its implications for education. In my view, their analysis has three key weaknesses; they arbitrarily lump together a range of education methods as if they were essentially the same, they base their theory on an outdated and incomplete model of memory, and they conclude that only one teaching approach is effective – explicit, direct instruction – ignoring the fact that knowledge comes in different forms.

Conclusions

I agree with some of the points made by the reformers:
• I agree with the idea of evidence-based education – the more evidence the better, in my view.
• I have no problem with children being taught knowledge. I don’t subscribe to a constructivist view of education – in the sense that we each develop a unique understanding of the world and everybody’s worldview is as valid as everybody else’s – although cognitive science has shown that everybody’s construction of knowledge is unique. We know that some knowledge is more valid and/or more reliable than other knowledge and we’ve developed some quite sophisticated ways of figuring out what’s more certain and what’s less certain.
• The application of findings from cognitive science to education is long overdue.
• I have no problem with direct instruction (as distinct from Direct Instruction) per se.

However, some of what I read gave me cause for concern:
• The evidence-base presented by the reformers is limited and parts of it are weak and flawed. It’s vital to evaluate evidence, not just to cite evidence that at face-value appears to support what you already think. And a body of evidence isn’t a unitary thing; some parts of it can be sound whilst other parts are distinctly dodgy. It’s important to be able to sift through it and weigh up the pros and cons. Ignoring contradictory evidence can be catastrophic.
• Knowledge, likewise, isn’t a unitary thing; it can vary in terms of validity and reliability.
• The evidence from cognitive science also needs to be evaluated. It isn’t OK to assume that just because cognitive scientists say something it must be right; cognitive scientists certainly don’t do that. Being able to evaluate cognitive science might entail learning a fair bit about cognitive science first.
• Direct instruction, like any other educational method, is appropriate for acquiring some types of knowledge. It isn’t appropriate for acquiring all types of knowledge. The problem with approaches such as discovery learning and child-led learning is not that there’s anything inherently wrong with the approaches themselves, but that they’re not suitable for acquiring all types of knowledge.

What has struck me most forcibly about my exploration of the evidence cited by the education reformers is that, although I agree with some of the reformers’ reservations about what’s been termed ‘minimal instruction’ approaches to education, the reformers appear to be ignoring their own advice. They don’t have extensive knowledge of the relevant subject areas, they don’t evaluate the relevant evidence, and the direct instruction framework they are advocating – certainly the one Civitas is advocating – doesn’t appear to have a structure derived from the relevant knowledge domains.

Rather than a rational, evidence-based approach to education, the ‘educational reform’ movement has all the hallmarks of a belief system that’s using evidence selectively to support its cause; and that’s what worries me. This new Blob is beginning to look suspiciously like the old one.

Daisy debunks myths: or does she?

At the beginning of this month, Daisy Christodolou, star performer on University Challenge, CEO of The Curriculum Centre and a governor of the forthcoming Michaela Community School, published a book entitled Seven Myths about Education. Daisy has summarised the myths on her blog, The Wing to Heaven. There are few things I like better than seeing a myth debunked, but I didn’t rush to buy Daisy’s book. In fact I haven’t read it yet. Here’s why.

Debunking educational ‘myths’ is currently in vogue. But some of the debunkers have replaced the existing myths with new myths of their own; kind of second-order myths. The first myth is at least partly wrong, but the alternative proposed isn’t completely right either, which really doesn’t help. I’ve pointed this out previously in relation to ‘neuromyths’. One of the difficulties involved in debunking educational myths is that they are often not totally wrong, but in order to tease out what’s wrong and what’s right, you need to go into considerable detail, and busy teachers are unlikely to have the time or background knowledge to judge whether or not the criticism is valid.

Human beings have accumulated a vast body of knowledge about ourselves and the world we inhabit, which suggests strongly that the world operates according to knowable principles. It’s obviously necessary to be familiar with the structure and content of any particular knowledge domain in order to have a good understanding of it. And I agree with some of Daisy’s criticisms of current approaches to learning. So why do I feel so uneasy about what she’s proposing to put in its place?

Daisy’s claims

Daisy says she makes two claims in her book and presents evidence to support them. The claims and the evidence are:

Claim one: “that in English education, a certain set of ideas about education are predominant…” Daisy points out that it’s difficult to prove or disprove the first claim, but cites a number of sources to support it.

Claim two: “that these ideas are misguided”. Daisy says “Finding the evidence to prove the second point was relatively straightforward” and lists a number of references relating to working and long-term memory.

Daisy’s reasoning

The responses to claim one suggest that Daisy is probably right that ‘certain ideas’ are predominant in English education.

She is also broadly right when she says “it is scientifically well-established that working memory is limited and that long-term memory plays a significant role in the human intellect” – although she doesn’t define what she means by ‘intellect’.

She then says “this has clear implications for classroom practice, implications which others have made and which I was happy to recap.”

Her reasoning appears to follow that of Kirschner, Sweller & Clark, who lump together ‘constructivist, discovery, problem-based, experiential, and inquiry-based teaching’ under the heading ‘minimal instruction’ and treat them all as one. The authors then make the assumption that because some aspects of ‘minimal instruction’ approaches might impose a high cognitive load on students, that they should be discarded in favour of ‘direct instruction’ that takes into account the limitations of working memory.

This is the point at which I parted company with Daisy (and Kirschner, Sweller & Clark). Lumping together a set of complex and often loosely defined ideas and approaches to learning is hardly helpful, since it’s possible that some of their components might overload working memory, but others might not. I can see how what we know about working and long-term memory demonstrates that some aspects of the predominant ‘certain set of ideas’ might be ‘misguided’, but not how it demonstrates that they are misguided en masse.

The nature of the evidence

I also had reservations about the evidence Daisy cites in support of claim two.

First on the list is Dan Willingham’s book Why Don’t Students Like School? Willingham is a cognitive psychologist interested in applying scientific findings to education. I haven’t read his book either*, but I’ve yet to come across anything else he’s written that has appeared flawed. Why Don’t Students Like School? appears to be a reliable, accessible book written for a wide readership. So far, so good.

Next, Daisy cites Kirschner, Sweller and Clark’s paper “Why minimal guidance during instruction does not work: an analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching”. This paper is obviously harder going that Willingham’s book, but is published in Educational Psychologist, so would be accessible to many teachers. I have several concerns about this paper and have gone through its arguments in detail.

My main reservations are;
• the simplistic way in which the pedagogical debate is presented,
• what’s left out of the discussion
• why a model of memory that’s half a century out of date is referred to.

That last point could apply to the next three items on Daisy’s list; two papers by Herb Simon, a Nobel prizewinner whose ideas have been highly influential in information theory, and one by John Anderson on his Adaptive Character of Thought model. Simon’s papers were published in 1973 and 1980 respectively, and Anderson’s in 1996 although his model dates from the 1970s.

Another feature of these papers is that they’re not easy reading – if you can actually get access to them, that is. Daisy’s links were to more links and I couldn’t get the Simon papers to open. And although Anderson’s paper is entitled ‘A simple theory of complex cognition’, what he means by that is that an apparently complex cognitive process can be explained by a simple information processing heuristic, and not that his theory is easy to understand. He and Simon both write lucidly, but their material isn’t always straightforward.

I completely agree with Daisy that the fundamentals of a knowledge domain don’t date – as she points out elsewhere, Pythagoras and Euripides have both stood the test of time. There’s no question that Simon’s and Anderson’s papers are key ones – for information scientists at least, and that the principles they set out have stood the test of time. But quite why she should cite them and not more accessible material that takes into account several further decades of research into brain function, is puzzling.

It could be that there simply aren’t any publications that deal specifically with recent findings about memory and apply them to pedagogy. But even if there aren’t, it’s unlikely that most teachers would find Simon and Anderson the most accessible alternatives; Rita Carter’s Mapping the Mind is a beautifully illustrated, very informative description of how the brain works. (It’s worth forking out for the University of California Press edition because of the quality of the illustrations). Stanislas Dehaene’s Reading in the Brain is about reading, but is more recent and explains in more detail how the brain chunks, stores and accesses information.

It looks to me as if someone has given Daisy some key early references about working memory and she’s dutifully cited them, rather than ensuring that she has a thorough grasp of the knowledge domain of which they are part. If this is the case, it’s ironic because having a thorough grasp of a knowledge domain is something Daisy advocates.

So Daisy’s logic is a bit flaky and her evidence base is a bit out of date. So what? The reason Daisy’s logic and evidence base are important because they form the foundation for an alternative curriculum being used by a chain of academies and a high-profile free school.

Implications for curriculum design

Daisy’s name doesn’t appear in the ‘who we are’ or ‘our advisors’ sections of The Curriculum Centre’s (supporting Future Academies) website, although their blog refers to her as their CEO. That might indicate the site simply needs updating. But disappointingly for an organisation describing itself as The Curriculum Centre their ‘complete offer – The Future Curriculum™ – is described as ‘information coming soon’, and the page about the three year KS2 curriculum is high on criticism of other approaches but low on information about itself.

Daisy is also ‘governor for knowledge’ at the Michaela Community School (headteacher Katherine Birbalsingh), a free school that’s already attracted press criticism even though it doesn’t open until September. Their curriculum page is a bit more detailed than that of The Curriculum Centre, but has some emphases that aren’t self-evident and aren’t explained, such as:

 “Our emphasis on traditional academic subjects will provide a solid base on which young people can build further skills and future careers, thus enabling them to grow into thinkers, authors, leaders, orators or whatever else they wish.

One has to wonder why the ‘traditional academic subjects’ don’t appear to be preparing pupils for careers with a more practical bent, such as doctors, economists or engineers.

Michaela recognises that English and Maths are fundamental to all other learning.”

No, they’re not. They are useful tools in accessing other learning, but non-English speakers who aren’t good at maths can be still be extremely knowledgeable.

Michaela Community School will teach knowledge sequentially so that the entire body of knowledge for a subject will be coherent and meaningful. The History curriculum will follow a chronological sequence of events. The English curriculum will follow a similar chronology of the history of literature, and will also build up knowledge of grammar and the parts of speech.”

The rationale for teaching history chronologically is obvious, but history is more than a sequence of events, and it’s not clear why it’s framed in that way. Nor is there an explanation for why literature should be taught chronologically. Nor why other subjects shouldn’t be. As it happens, I’m strongly in favour of structuring the curriculum chronologically, but I know from experience it’s impossible to teach English, Maths, Science, History, Geography, a modern foreign language (French/Spanish), Music and Art chronologically and in parallel because your chronology will be out of synch across the different subject areas. I’ve used a chronological curriculum with my own children and it gave them an excellent understanding of how everything connects. We started with the Big Bang and worked forward from there. But it meant that for about a year our core focus was on physics, chemistry and geography because for much of the earth’s history nothing else existed. I don’t get the impression Michaela or the Curriculum Centre have actually thought through curriculum development from first principles.

Then there was:

The Humanities curriculum at Michaela Community School will develop a chronologically secure knowledge and understanding of British, local and world history and introduce students to the origins and evolution of the major world religions and their enduring influence.”

I couldn’t help wondering why ‘British’ came before local and world history. And why highlight religions and ‘their enduring influence’? It could be that the curriculum section doesn’t summarise the curriculum very well, or it could be that there’s an agenda here that isn’t being made explicit.

I’m not convinced that Daisy has properly understood how human memory works, has used what’s been scientifically established about it to debunk any educational myths, or has thoroughly thought through its implications for classroom practice. Sorry, Daisy, but I think you need to have another go.

References
Carter, R (2010). Mapping the Mind. University of California Press.
Dehaene, S (2010). Reading in the Brain. Penguin.
Willingham, DT (2010). Why Don’t Students Like School? Jossey Bass.

* My bookshelves are groaning under the weight of books I’ve bought solely for the purpose of satisfying people who’ve told me I can’t criticise what someone’s saying until I’ve read their book. Very occasionally I come across a gem. More often than not, one can read between the lines of reviews.

all work and no play will make Jack and Jill bored and frustrated

Another educational dichotomy revealed by a recent Twitter conversation is learning vs play. Although I know people make this distinction, I found myself wondering why, traditionally, work and play have been contrasted, as in the old adage All work and no play makes Jack a dull boy, and when learning might have slipped into the place of work.

The function of play

Hunting and gathering

For many thousands of years, human beings have been hunter-gatherers. Most human infants are capable of gathering (foraging for berries, leaves, shoots etc) before they can walk, although they might need a bit of support and guidance in doing so. Hunting is a more complex skill and needs the dexterity, attentional control, tuition and rehearsal that only older children can handle.

The primary function of typical play in humans, like that seen in other mammals, is to develop the skills required to obtain food and to make sure you don’t become food for anyone else. All that chasing, hiding, running, fighting, climbing, observing, collecting and pulling things apart can make the difference between survival and starvation. Of course human beings are also social animals; hunter-gatherers forage, hunt and eat in groups because that increases everyone’s chances of survival. So humans, like many other mammals, have another characteristic in their play repertoire – mimicry. Copying the behaviour of older children and adults forms the foundation for a wide variety of adult skills not confined to acquiring food.

Hunting and gathering involves effort, but the effort is closely related to the reward of eating. The delay between expending the effort and eating the food is rarely more than a few hours, and in foraging, the food immediately follows the effort. The effort could be described as work, and a child who’s poking an anthill or fighting another child when they should be gathering or hunting could be considered to be playing as opposed to working, but the play of hunter-gatherer children is so closely aligned to their ‘work’, and the consequences of playing rather than working are so obvious, that the distinction between play and work is rather blurred.

Farming

For a few thousands of years, human beings have been farmers. Farming has advantages over hunting and gathering, which is why it’s been so widely adopted. It increases food security considerably, especially in areas that experience cold or dry seasons, because surplus food can be produced and stored for future use. It also reduces,but doesn’t eliminate, the risk of territorial conflict – having to compete for food with another tribe.

In contrast to hunting and gathering, farming involves a great deal of effort that isn’t immediately rewarded. There’s a delay of months or even years before food results from the effort expended to produce it. Human children, like other mammals, aren’t good at delayed gratification. In addition, their default play patterns, apart from mimicry, don’t closely resemble the skills needed to produce food via agriculture. Ploughing, sowing, irrigating, weeding, protecting, harvesting and storing food involve hard, repetitive effort for no immediate reward to an extent that rarely occurs in hunter-gatherer societies. In addition, farming requires a lot of equipment – tools, containers, buildings, furniture etc, also requiring repetitive effort in their manufacture and maintenance. Communities that survive by subsistence farming can do so only if children do some of the work; they don’t have the spare capacity to allow children to spend their childhood only in play. This means that for farming communities, there’s a clear divide between children’s play and the work involved in producing food.

Industrialisation

In England, subsistence farming was a way of life for thousands of years. As the population increased, pressure was put on land use, and areas of common land used for grazing animals, were increasingly ‘enclosed’ – landowners were given legal rights to take them out of public use. Following the Enclosure Acts of the late 18th/early 19th centuries, thousands of families found they didn’t have access to enough land to sustain themselves. They couldn’t survive by making and selling goods either, because of competition from the mass-production of cheap items in factories, made possible by the invention of the steam engine.

This double-whammy resulted in a mass migration to towns and cities to find work, which often consisted of hard, repetitive, dangerous labour in factories, or, because of the huge increase in demand for coal, in mines. Child labour was in great demand because it was cheap and plentiful, and many families couldn’t survive without their children’s earnings. Working in factories or in coal mines put children’s health in jeopardy. Previous generations of children working on the family smallholding might have found the work boring and repetitive and unpaid, but, poor harvests aside, would have had a reasonably good diet, plenty of fresh air and exercise and free time to pay with their friends. In industrial settings, children were working for twelve hours or more a day in dangerous environments, and, in the case of mines, almost complete darkness. The opportunity to play became a luxury.

Education

The terrible working conditions for children didn’t last that long; a series of Factory Acts in the 19th century were followed by the 1870 Education Act which made education compulsory, and further legislation made it free of charge. Increasing prosperity (as a result of the industrial revolution, ironically) meant that most communities had sufficient resources to allow children to spend their childhood learning rather than working.

Learning vs play

Not everybody saw education in the same light, however. For some at one extreme, education was a means to an end; it produced a literate, numerate workforce that would increase national and individual prosperity. For others, education offered a once-in-a-lifetime opportunity to be archetypally human; to be free of responsibility and engage only in learning and play – what children do naturally anyway. Not surprisingly, many popular children’s authors (popular because of the increase in child literacy) subscribed to the latter view, including Mark Twain, Louisa M Alcott, Lucy M Montgomery, Edith Nesbitt, Enid Blyton and CS Lewis.

Education has essentially been dominated by these two viewpoints ever since; the ‘traditionalists’ on the one hand and the ‘progressives’ on the other. It’s easy to see how the clear distinction between work and play that emerged with the advent of agriculture, and that became even more stark in the industrial revolution, could carry over into education. And how in some quarters, learning might be seen as children’s ‘work’.

In highly developed industrialised societies, the default play patterns of hunter-gatherers bear little resemblance to the skills children will need in later life. But children’s play is very versatile; they observe, mimic and learn from whatever they see around them, they experiment with technology and become skilled in using it. Children are still ‘doing it for themselves’ as they always have done. The informal education they would get if they didn’t attend school would still provide them, as it has for millennia, with the knowledge and skills they would need to survive as adults.

Of course for most people survival isn’t enough. The lives of people in communities that ‘survive’ tend to be nasty, brutish and short, and most people don’t want a life like that. The best way we’ve found to improve our quality of life and standard of living beyond ‘survival’ is to increase the efficiency with which we produce food, goods and services. In theory, at least, this frees up time to find ways of improving our quality of life further. In practice, the costs and benefits of increased efficiency tend to be rather unevenly distributed, with some people bearing most of the costs and others enjoying most of the benefits, but that’s another story.

The best way we’ve found to improve efficiency is for communities to have access to the knowledge we’ve acquired about how the world works. It isn’t necessary for everyone to know all about everything; what is necessary is for people have access to knowledge as and when they need it. Having said that, childhood and adolescence present a golden opportunity, before the responsibilities of adulthood kick in, to ensure that everyone has a good basic knowledge about how the world works.

Learning

A core characteristic of learning is the acquisition of new information in the form of knowledge and/or skills. But human beings aren’t robots; acquiring knowledge isn’t simply a matter of feeding in the knowledge, pressing a button and off we go. We are biological organisms; acquiring knowledge changes our brains via a biological process and it’s a process that takes time and that varies between individuals.

Play

One of the ways in which humans naturally acquire, assimilate and apply new knowledge is through play. A core characteristic of play is that it isn’t directly related to what you do to survive. Play essentially consists of rehearsing and experimentally applying knowledge and skills in a safe environment – one where the outcomes of your rehearsal and experimentation are unlikely to end in disaster.

The amount learning in play varies. Sometimes the play can consist almost entirely of learning – repetition of knowledge or skills until perfect, for example. Sometimes there’s very little learning – the play is primarily for rest and relaxation. And rest and relaxation play can provide the ‘down-time’ the brain needs in order for new information to be assimilated.

Young humans play more than older ones because they have more new knowledge and skills to assimilate and experiment with, and their play tends to incorporate more learning. For very young children all play is learning.

Older humans tend to play for rest and relaxation purposes because they don’t have to acquire so much knowledge. They do learn through play, but it often isn’t recognised as such; it’s ‘kicking an idea around’ or imagining different scenarios, or experimenting with new knowledge in different configurations. In other words learning through play in adults is often seen as a corollary of work – what you get paid to do – not as play per se.

What emerges from this is that construing learning and play as different things and assuming that children and young people must either be learning or playing, is not a valid way of classifying learning and play. Learning can include play and play can include learning. Since play is one of the ways through which human beings learn anyway, it makes sense to incorporate it into learning rather than to see it as something that distracts from learning.

Kirschner, Sweller & Clark: a summary of my critique

It’s important not just to know things, but to understand them, which is why I took three posts to explain my unease about the paper by Kirschner, Sweller & Clark. From the responses I’ve received I appear to have overstated my explanation but understated my key points, so for the benefit of anybody unable or unwilling to read all the words, here’s a summary.

1. I have not said that Kirschner, Sweller & Clark are wrong to claim that working memory has a limited capacity. I’ve never come across any evidence that says otherwise. My concerns are about other things.

2. The complex issue of approaches to learning and teaching is presented as a two-sided argument. Presenting complex issues in an oversimplified way invariably obscures rather than clarifies the debate.

3. The authors appeal to a model of working memory that’s almost half a century old, rather than one revised six years before their paper came out and widely accepted as more accurate. Why would they do that?

4. They give the distinct impression that long-term memory isn’t subject to working memory constraints, when it is very much subject to them.

5. They completely omit any mention of the biological mechanisms involved in processing information. Understanding the mechanisms is key if you want to understand how people learn.

6. They conclude that explicit, direct instruction is the only viable teaching approach based on the existence of a single constraining factor – the capacity of working memory to process yet-to-be learned information (though exactly what they mean by yet-to-be learned isn’t explained). In a process as complex as learning, it’s unlikely that there will be only one constraining factor.

Kirschner, Sweller & Clark appear to have based their conclusion on a model of memory that was current in the 1970s (I know because that’s when I first learned about it), to have ignored subsequent research, and to have oversimplified the picture at every available opportunity.

What also concerns me is that some teachers appear to be taking what Kirschner, Sweller & Clark say at face value, without making any attempt to check the accuracy of their model, to question their presentation of the problem or the validity of their conclusion. There’s been much discussion recently about ‘neuromyths’. Not much point replacing one set of neuromyths with another.

Reference
Kirschner, PA, Sweller, J & Clark, RE (2006). Why Minimal Guidance During Instruction Does Not Work: An Analysis of the Failure of Constructivist, Discovery, Problem-Based, Experiential, and Inquiry-Based Teaching Educational Psychologist, 41, 75-86.