The Tiger Teachers’ model of knowledge: what’s missing?

“If all else fails for Michaela at least we’re going to do a great line in radical evangelical street preachers.” Jonathan Porter, Head of Humanities at the Michaela Community School was referring to an impassioned speech from Katharine Birbalsingh, the school’s head teacher at the recent launch of their book, Battle Hymn of the Tiger Teachers: The Michaela Way.

Michaela Community School’s sometimes blistering critique of the English education system, coupled with its use of pedagogical methods abandoned by most schools decades ago, has drawn acclaim, criticism and condemnation. There’s a strong, shared narrative about the Michaela Way amongst the contributors to Battle Hymn. If I’ve understood it correctly, it goes like this:

There’s a crisis in the English education system due to progressive ideas that have dominated teacher training since the 1960s. Child-centred methods have undermined discipline. Poor behaviour and lack of respect makes it impossible for teachers to teach. Subject knowledge has been abandoned in favour of higher-level skills wrongly claimed to be transferable. The way to combat the decline is via strict discipline, teacher authority, a knowledge-based curriculum and didactic teaching.

Knowledge is power

“Knowledge is power” is the Michaela motto. Tiger Teachers are required to have extensive knowledge of their own subject area in order to teach their pupils. Pupils are considered to be novices and as such are expected to acquire a substantial amount of factual knowledge before they can develop higher-level subject-specific skills.

Given the emphasis on knowledge, you’d expect the Tiger Teachers to apply this model not only to their pupils, but to any subjects they are unfamiliar with.   But they don’t. It appears to apply only to what pupils are taught in school.

A couple of years ago at a ResearchEd conference, I queried some claims made about memory. I found myself being interrogated by three Tiger Teachers about what I thought was wrong with the model of memory presented. I said I didn’t think anything was wrong with it; the problem was what it missed out. There are other examples in Battle Hymn of missing key points. To illustrate, I’ve selected four. Here’s the first:

Rousseau

Rousseau is widely recognised as the originator of the progressive educational ideas so derided in the Michaela narrative.   If you were to rely on other Tiger Teachers for your information about Rousseau, you might picture him as a feckless Romantic philosopher who wandered the Alps fathering children whilst entertaining woolly, sentimental, unrealistic thoughts about their education.   You wouldn’t know that he argued in Émile, ou de L’Éducation not so much for the ‘inevitable goodness’ of children as Jonathan Porter claims (p.77), but that children (and adults) aren’t inherently bad – a view that flew in the face of the doctrine of original sin espoused by the Geneva Calvinism that Rousseau had rejected and the Catholicism he (temporarily) converted to soon after.

At the time, children were often expected to learn by rote factual information that was completely outside their experience, that was meaningless to them. Any resistance would have been seen as a sign of their fallen nature, rather than an understandable objection to a pointless exercise. Rousseau advocated that education work with nature, rather than against it. He claimed the natural world more accurately reflected the intentions of its Creator than the authoritarian, man-made religious institutions that exerted an extensive and often malign influence over people’s day-to-day lives.   Not surprisingly, Émile was promptly banned in Geneva and Paris.

Although Jonathan Porter alludes to the ‘Enlightenment project’ (p.77), he doesn’t mention Rousseau’s considerable influence in other spheres. The section of Émile that caused most consternation was entitled ‘The Creed of a Savoyard Priest’. It was the only part Voltaire thought worth publishing. In it, Rousseau tackles head-on Descartes’ proposition ‘I think, therefore I am’. He sets out the questions about perception, cognition, reasoning, consciousness, truth, free will and the existence of religions, that perplexed the thinkers of his day and that cognitive science has only recently begun to find answers to. I’m not defending Rousseau’s educational ideas, I think Voltaire’s description “a hodgepodge of a silly wet nurse in four volumes” isn’t far off the mark, but to draw valid conclusions from Rousseau’s ideas about education, you need to know why he was proposing them.

Battle Hymn isn’t a textbook or an academic treatise, so it would be unreasonable to expect it to tackle at length all the points it alludes to. But it is possible to signpost readers to relevant issues in a few words. There’s nothing technically wrong with the comments about Rousseau in Battle Hymn, or Robert Peal’s Progressively Worse (a core text for Tiger Teachers) or Daisy Christodoulou’s Seven Myths about Education (another core text); but what’s missed out could result in conclusions being drawn that aren’t supported by the evidence.

Teacher qualifications

Another example is teacher qualifications. Michaela teachers don’t think much of their initial teacher training (ITT); they claim it didn’t prepare them for the reality of teaching (p.167),  it indoctrinates teachers into a ‘single dogmatic orthodoxy’ (p.171), outcomes are poor (p.158), and CPD in schools is ‘more powerful’ (p.179). The conclusion is not that ITT needs a root-and-branch overhaul, but that it should be replaced with something else; in-school training or … no qualification at all. Sarah Clear says she’s “an unqualified teacher and proud” (p.166) and argues that although the PGCE might be a necessary precaution to prevent disaster, it doesn’t actually do that (p.179), so why bother with it?

Her view doesn’t quite square with Dani Quinn’s perspective on professional qualifications. Dani advocates competition in schools because there’s competition in the professional world. She says; “Like it or not, when it comes to performance, it is important to know who is the best” and cites surgeons and airline pilots as examples (p.133). But her comparison doesn’t quite hold water. Educational assessment tends to be norm-referenced (for reasons Daisy Christodoulou explores here) but assessments of professional performance are almost invariably criterion-referenced in order to safeguard minimum standards of technical knowledge and skill. But neither Dani nor Sarah mention norm-referenced and criterion-referenced assessment – which is odd, given Daisy Christodoulou’s involvement with Michaela. Again, there’s nothing technically wrong with what’s actually said about teacher qualifications; but the omission of relevant concepts increases the risk of reaching invalid conclusions.

Replicating Michaela

A third example is from the speech given by Katharine Birbalsingh at the book launch. It was triggered by this question: “How would you apply Michaela in primary? Could you replicate it in coastal areas or rural areas and how would that work?”

Katharine responds: “These are all systems and values that are universal. That could happen anywhere. Of course it could happen in a primary. I mean you just insist on higher standards with regard to the behaviour and you teach them didactically because everyone learns best when being taught didactically … You would do that with young children, you would do that with coastal children and you would do that with Yorkshire children. I don’t see why there would be a difference.” She then launches into her impassioned speech about teaching and its social consequences.

You could indeed apply Michaela’s systems, values, behavioural expectations and pedagogical approach anywhere. It doesn’t follow that you could apply them everywhere. Implicit in the question is whether the Michaela approach is scalable. It’s not clear whether Katharine misunderstood the question or answered the one she wanted to answer, but her response overlooks two important factors.

First, there’s parent/pupil choice. Brent might be one of the most deprived boroughs in the country, but it’s a deprived borough in a densely populated, prosperous city that has many schools and a good public transport system. If parents or pupils don’t like Michaela, they can go elsewhere. But in rural areas, for many pupils there’s only one accessible secondary school – there isn’t an elsewhere to go to.

Then there’s teacher recruitment. If you’re a bright young graduate, as most of the Michaela staff seem to be, the capital offers a vibrant social life and  a wide range of interesting career alternatives should you decide to quit teaching. In a rural area there wouldn’t be the same opportunities.  Where I live, in a small market town in a sparsely populated county, recruitment in public sector services has been an ongoing challenge for many years.

Coastal towns have unique problems because they are bounded on at least one side by the sea. This makes them liminal spaces, geographically, economically and socially. Many are characterised by low-skilled, low-paid, seasonal employment and social issues different to those of an inner city. For teachers, the ‘life’ bit of the work-life balance in London would be very different from what they could expect in out-of-season Hartlepool.

Of course there’s no reason in principle why a replica Michaela shouldn’t transform the educational and economic prospects of coastal or rural areas.   But in practice, parent/pupil choice and teacher recruitment would be challenges that by definition Michaela hasn’t had to face because it’s a classic ‘special case’.  And it’s not safe to generalise from special cases. Again, there’s nothing technically wrong with what Katharine said about replicating Michaela; it’s what she didn’t say that’s key.  The same is true for the Tiger Teachers’ model of cognitive science, the subject of the next post.

References

Birbalsingh, K (2016).  Battle Hymn of the Tiger Teachers: The Michaela Way.  John Catt Educational.

Christodoulou, D (2014).  Seven Myths about Education.  Routledge.

Peal, R (2014).  Progressively Worse: The Burden of Bad Ideas in British Schools.  Civitas.

Rousseau, J-J (1974/1762).  Émile.  JM Dent.

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Kieran Egan’s “The educated mind” 2

The second post in a two-part review of Kieran Egan’s book The Educated Mind: How Cognitive Tools Shape our Understanding.

For Egan, a key point in the historical development of understanding was the introduction by the Greeks of a fully alphabetic representation of language – it included symbols for vowels as well as consonants. He points out that being able to represent speech accurately in writing gives people a better understanding of how they use language and therefore of the concepts that language represents. Egan attributes the flowering of Greek reasoning and knowledge to their alphabet “from which all alphabetic systems are derived” (p.75).

This claim would be persuasive if it were accurate. But it isn’t. As far as we know, the Phoenicians – renowned traders – invented the first alphabetic representation of language. It was a consonantal alphabet that reflected the structure of Semitic languages and it spread through the Middle East. The Greeks adapted it, introducing symbols for vowels. This wasn’t a stroke of genius on their part – Semitic writing systems also used symbols for vowels where required for disambiguation – but a necessary addition because Greek is an Indo-European language with a syllabic structure. The script used by the Mycenaean civilisation that preceded the Greeks was a syllabic one.

“a distinctive kind of literate thinking”

Egan argues that this alphabet enabled the Greeks to develop “extended discursive writing” that “is not an external copy of a kind of thinking that goes on in the head; it represents a distinctive kind of literate thinking” (p.76). I agree that extended discursive writing changes thinking, but I’m not convinced that it’s distinctive nor that it results from literacy.

There’s been some discussion amongst teachers recently about the claim that committing facts to long-term memory mitigates the limitations of working memory. Thorough memorisation of information certainly helps – we can recall it quickly and easily when we need it – but we can still only juggle half-a-dozen items at a time in working memory. The pre-literate and semi-literate civilisations that preceded the Greeks relied on long-term memory for the storage and transmission of information because they didn’t have an alternative. But long-term memory has its own limitations in the form of errors, biases and decay. Even people who had memorisation down to a fine art were obliged to develop writing in order to have an accurate record of things that long-term memory isn’t good at handling, such as what’s in sealed sacks and jars and how old it is. Being able to represent spoken language in writing takes things a step further. Written language not only circumvents the weaknesses of long-term memory, it helps with the limitations of working memory too. Extended discursive writing can encompass thousands of facts, ideas and arguments that a speaker and a listener would find it impossible to keep track of in conversation. So extended discursive writing doesn’t represent “a distinctive kind of literate thinking” so much as significantly extending pre-literate thinking.

the Greek miracle

It’s true that the sudden arrival in Greece of “democracy, logic, philosophy, history, drama [and] reflective introspection… were explainable in large part as an implication of the development and spread of alphabetic literacy” (p.76). But although alphabetic literacy might be a necessary condition for the “Greek miracle”, it isn’t a sufficient one.

Like all the civilisations that had preceded it, the economy of the Greek city states was predominantly agricultural, although it also supported thriving industries in mining, metalwork, leatherwork and pottery. Over time agricultural communities had figured out more efficient ways of producing, storing and trading food. Communities learn from each other, so sooner or later, one of them would produce enough surplus food to free up some of its members to focus on thinking and problem-solving, and would have the means to make a permanent record of the thoughts and solutions that emerged. The Greeks used agricultural methods employed across the Middle East, adapted the Phoenician alphabet and slavery fuelled the Greek economy as it had previous civilisations. The literate Greeks were standing on the shoulders of pre-literate Middle Eastern giants.

The ability to make a permanent record of thoughts and solutions gave the next generation of thinkers and problem-solvers a head start and created the virtuous cycle of understanding that’s continued almost unabated to the present day. I say almost unabated, because there have been periods during which it’s been impossible for communities to support thinkers and problem-solvers; earthquakes, volcanic eruptions, drought, flood, disease, war and invasion have all had a devastating and long-term impact on food production and on the infrastructure that relies on it.

language, knowledge and understanding

Egan’s types of understanding – Somatic, Mythic, Romantic, Philosophic and Ironic – have descriptive validity; they do reflect the way understanding has developed historically, and the way it develops in children. But from a causal perspective, although those phases correlate with literacy they also correlate with the complexity of knowledge. As complexity of knowledge increases, so understanding shifts from binary to scalar to systematic to the exceptions to systems; binary classifications, for example, are characteristic of the way people, however literate they are, tend to categorise knowledge in a domain that’s new to them (e.g. Lewandowski et al, 2005).

Egan doesn’t just see literacy as an important factor in the development of understanding, he frames understanding in terms of literacy. What this means is that in Egan’s framework, knowledge (notably pre-verbal and non-verbal knowledge) has to get in line behind literacy when it comes to the development of understanding. It also means that Egan overlooks the key role of agriculture and trade in the development of writing systems and of the cultures that invented them. And that apprenticeship, for millennia widely used as a means of passing on knowledge, is considered only in relation to ‘aboriginal’ cultures (p.49). And that Somatic understanding is relegated to a few pages at the end of the chapter on the Ironic.

non-verbal knowledge

These are significant oversights. Non-verbal knowledge is a sine qua non for designers, artisans, architects, builders, farmers, engineers, mariners, surgeons, physiotherapists, artists, chefs, parfumiers, musicians – the list goes on and on. It’s true that much of the knowledge associated with these occupations is transmitted verbally, but much of it can’t be transmitted through language, but acquired only by looking, listening or doing. Jenny Uglow in The Lunar Men attributes the speed at which the industrial revolution took place not to literacy, but to the development of a way to reproduce technical drawings accurately.

Egan appears sceptical about practical people and practical things because when

those who see themselves as practical people engaging in practical things [who] tend not to place any value on acquiring the abstract languages framed to deal with an order than underlies surface diversity” are “powerful in government, education departments and legislatures, pressures mount for an increasingly down-to-earth, real-world curriculum. Abstractions and theories are seen as idle, ivory-tower indulgences removed from the gritty reality of sensible life.” (p.228)

We’re all familiar with the type of people Egan refers to, and I’d agree that the purpose of education isn’t simply to produce a workforce for industry. But there are other practical people engaging in practical things who are noticeable by their absence from this book; farmers, craftspeople, traders and engineers who are very interested in abstractions, theories and the order that underlies surface diversity. The importance of knowledge that’s difficult to verbalise has significant implications for the curriculum and for the traditional academic/vocational divide. Although there is clearly a difference between ‘abstractions and theories’ and their application, theory and application are interdependent; neither is more important than the other, something that policy-makers often find difficult to grasp.

Egan acknowledges that there’s a problem with emphasising the importance of non-verbal knowledge in circles that assume that language underpins understanding. As he points out “Much modernist and postmodernist theory is built on the assumption that human understanding is essentially languaged understanding” (p.166). Egan’s framework elbows aside language to make room for non-verbal knowledge, but it’s a vague, incoherent “ineffable” sort of non-verbal knowledge that’s best expressed linguistically through irony (p.170). It doesn’t appear to include the very coherent, concrete kind of non-verbal knowledge that enables us to grow food, build bridges or carry out heart-transplants.

the internal coherence of what’s out there

Clearly, bodies of knowledge transmitted from person to person via language will be shaped by language and by the thought-processes that produce it, so the knowledge transmitted won’t be 100% complete, objective or error-free. But a vast amount of knowledge refers to what’s out there, and what’s out there has an existence independent of our thought-processes and language. What’s out there also has an internally coherent structure that becomes clearer the more we learn about it, so over time our collective bodies of knowledge more accurately reflect what’s out there and become more internally coherent despite their incompleteness, subjectivity and errors.

The implication is that in education, the internal coherence of knowledge itself should play at least some part in shaping the curriculum. But because the driving force behind Egan’s framework is literacy rather than knowledge, the internal coherence of knowledge can’t get a word in edgeways. During the Romantic phase of children’s thinking, for example, Egan recommends introducing topics randomly to induce ‘wonder and awe’ (p.218), rather than introducing them systematically to help children make sense of the world. To me this doesn’t look very different from the “gradual extension from what is already familiar” (p.86) approach of which Egan is pretty critical. I thought the chapter on Philosophic understanding might have something to say about this but it’s about how people think about knowledge rather than the internal coherence of knowledge itself – not quite the same thing.

the cherries on the straw hat of society

The sociologist Jacques Ellul once described hippies as the cherries on the straw hat of society* meaning that they were in a position to be critical of society only because of the nature of the society of which they were critical. I think this also serves as an analogy for Egan’s educational framework. He’s free to construct an educational theory framed solely in terms of literacy only because of the non-literate knowledge of practical people like farmers, craftspeople, traders and engineers. That brings me back to my original agricultural analogy; wonder and awe, like apple blossom and the aroma of hops, might make might make our experience of education and of agriculture transcendent, but if it wasn’t for coherent bodies of non-verbal knowledge and potatoes, swedes and Brussels sprouts, we wouldn’t be in a position to appreciate transcendence at all.

References

Lewandowski G, Gutschow A, McCartney R, Sanders K, Shinners-Kennedy D (2005). What novice programmers don’t know. Proceedings of the first international workshop on computing education research, 1-12. ACM New York, NY.

Uglow, J (2003). The Lunar Men: The Friends who made the Future. Faber & Faber.

Note
*I can’t remember which of Ellul’s books this reference is from and can’t find it quoted anywhere. If anyone knows, I’d be grateful for the source.

Kieran Egan’s “The educated mind” 1

I grew up in a small hamlet on the edge of the English Fens. The clay soil it was built on retains nutrients and moisture, so, well-drained, it provides an ideal medium for arable farming. Arable crops aren’t very romantic. The backdrop to my childhood wasn’t acres of lacy apple blossom in spring or aromatic hops in summer, although there were a few fields of waving golden wheat. I grew up amongst potatoes, swedes and Brussels sprouts. Not romantic at all, but the produce of East Anglia has long contributed to the UK population getting through the winter.

A few weeks ago on Twitter Tim Taylor (@imagineinquiry) asked me what I thought about Kieran Egan’s book The Educated Mind: How Cognitive Tools Shape our Understanding. This book is widely cited by teachers, so I read it. It reminded me of the sticky clay and root vegetables of my childhood – because sticky clay, root vegetables and other mundane essentials are noticeable by their absence from Egan’s educational and cultural framework. For Egan, minds aren’t grounded in the earth, but in language. To me the educational model he proposes is the equivalent of clouds of apple blossom and heady hops; breathtakingly beautiful and dizzying, but only if you’ve managed to get through the winter living on swedes and potatoes. My agricultural allusion isn’t just a simile.

recapitulation

Egan begins by claiming there’s a crisis in mass education systems in the West due to their being shaped by three fundamentally incompatible ideas; socialisation, Plato’s concept of reason and knowledge, and Rousseau’s focus on the fulfilment of individual potential. To resolve this inherent conflict, Egan proposes an alternative educational framework based on the concept of recapitulation. Recapitulation was a popular idea in the 19th century, fuelled by the theory of evolution and the discovery that during gestation human embryos go through phases that look remarkably like transformations from simple life forms to more complex ones. As Ernst Haeckel put it ‘ontogeny recapitulates phylogeny’.

The theory of recapitulation has been largely abandoned by biologists, but is still influential in other domains. Egan applies it to the intellectual tools – the sign systems that children first encounter in others and then internalise – that Vygotsky claimed shape our understanding of the world. Egan maps the historical ‘culturally accumulated complexity in language’ onto the ways that children’s understanding changes as they get older and proposes that what children are taught and the way they are taught should be shaped by five distinct, though not always separate, phases of understanding:

Somatic; pre-linguistic understanding
Mythic; binary opposites – good/bad, strong/weak, right/wrong
Romantic; transcendent qualities – heroism, bravery, wickedness
Philosophic; the principles underlying patterns in information
Ironic; being able to challenge philosophic principles – seeing alternatives.

At first glance Egan’s arguments appear persuasive but I think they have several fundamental weaknesses, all originating in flawed implicit assumptions. First, the crisis in education.

crisis? what crisis?

I can see why a fundamentally incoherent education system might run into difficulties, but Egan observes:

“…today we are puzzled by the schools’ difficulty in providing even the most rudimentary education to students”… “the costs of…social alienation, psychological rootlessness and ignorance of the world and possibilities of human experience within it, are incalculable and heartbreaking.” (p.1)

Wait a minute. There’s no doubt that Western education systems fail to provide even the most rudimentary education for some students, but those form a tiny minority. And although some school pupils could be described as socially alienated, psychologically rootless or ignorant of the world and possibilities of human experience within it, that description wouldn’t apply to many others. So what exactly is the crisis Egan refers to? The only clue I could find was on page 2 where he describes ‘the educational ineffectiveness of our schools’ as a ‘modern social puzzle’ and defines ‘modern’ as beginning with the ‘late nineteenth century development of mass schooling’.

To claim an educational system is in crisis, you have to compare it to something. Critics often make comparisons with other nations, with the best schools (depending on how you define ‘best’) or with what they think the education system should be like. Egan appears to fall into the last category, but to overlook the fact that prior to mass schooling children did well if they manage to learn to read and write at all, and that girls and children with disabilities often didn’t get any education at all.

Critics often miss a crucial point. Mass education systems, unlike specific schools, cater for entire populations, with all their genetic variation, socio-economic fluctuations, dysfunctional families, unexpected illnesses and disruptive life events. In a recent radio interview, Tony Little headmaster of Eton College was asked if he thought the very successful Eton model could be rolled out elsewhere. He pointed out, dryly, that Eton is a highly selective school, which might just be a factor in its academic success. One obvious reason for the perceived success of schools outside state systems is that those schools are not obliged to teach whichever children happen to live nearby. Even the best education system won’t be problem-free because life is complex and problems are inextricably woven into the fabric of life itself. I’m not suggesting that we tolerate bad schools or have low aspirations. What I am suggesting is that our expectations for mass education systems need to be realistic, not based on idealised speculation.

incompatible ideas

Speculation also comes into play with regard to the incompatibility of the three ideas Egan claims shape mass education in the West. They have certainly shaped education historically and you could see them as in tension. But the ideas are incompatible only if you believe that one idea should predominate or that the aims inherent in each idea can be perfectly met. There’s no reason why schools shouldn’t inculcate social values, teach reason and knowledge and develop individual potential. Indeed, it would be difficult for any school that taught reasoning and knowledge to avoid socialisation because of the nature of schools, and in developing reasoning and knowledge children would move towards realising their potential anyway.

If, as Egan argues, Western mass education systems have been ineffective since they started, his complaint appears to be rooted in assumptions about what the system should be like rather than in evidence about its actual potential. And as long as different constituencies have different opinions about the aims of the education system, someone somewhere will be calling ‘Crisis!’. That doesn’t mean there is one. But Egan believes there is, hence his new framework. The framework is based on the development of written language and its impact on thinking and understanding. For Egan, written language marked a crucial turning point in human history.

why write?

There’s no doubt that written language is an important factor in knowledge and understanding. Spoken language enables us to communicate ideas about things that aren’t right here right now. Written language enables us to communicate with people who aren’t right here right now. The increasing sophistication of written language as it developed from pictograms to syllabaries to alphabets enabled increasingly sophisticated ideas to be communicated. But the widely held belief that language is the determining factor when it comes to knowledge and understanding is open to question.

The earliest known examples of writing were not representations of language as such but records of agricultural products; noting whether it was wheat or barley in the sacks, wine or oil in the jars, when the produce was harvested and how many sacks and jars were stored where. Early writing consisted of pictograms (images of what the symbols represent) and ideograms (symbols for ideas). It was centuries before these were to develop into the alphabetic representations of language we’re familiar with today. To understand why it took so long, we need to put ourselves in the shoes (or sandals) of the early adopters of agriculture.

food is wealth

Farming provides a more reliable food supply than hunting and gathering. Farming allows food that’s surplus to requirements to be stored in case the next harvest is a bad one, or to be traded. Surplus food enables a community to support people who aren’t directly involved in food production; rulers, administrators, artisans, traders, scribes, teachers, a militia to defend its territory. The militia has other uses too. Conquering and enslaving neighbouring peoples has for millennia been a popular way of increasing food production in order to support a complex infrastructure.

But for surplus food to be turned into wealth, storage and trade are required. Storage and trade require written records and writing is labour-intensive. While scribes are being trained and are maintaining records they can’t do much farming; writing is costly. So communities that can’t predict when a series of bad harvests will next result in them living hand-to-mouth, will focus on writing about things that are difficult to remember – what’s in a sealed container, when it was harvested etc. They won’t need to keep records of how to grow food, look after animals, histories, myths, poems or general knowledge if that information can be transmitted reliably from person to person orally. It’s only when oral transmission stops being reliable that written language as distinct from record-keeping, starts to look like a good idea. And the more you trade, the more oral transmission gets to be a problem. Travellers might need detailed written descriptions of people, places and things. Builders and engineers using imported designs or materials might need precise instructions.

Spoken language wasn’t the only driving force behind the development of written language – economic and technical factors played a significant role. I don’t think Egan gives these factors sufficient weight in his account of the development of human understanding nor in his model for education, as I explain in the next post.

truth and knowledge

A couple of days ago I became embroiled in a long-running Twitter debate about the nature of truth and knowledge, during which at least one person fell asleep. @EdSacredProfane has asked me where I ‘sit’ on truth. So, for the record, here’s what I think about truth and knowledge.

1. I think it’s safe to assume that reality and truth are out there. Even if they’re not out there and we’re all experiencing a collective hallucination we might as well assume that reality is real and that truth is true because if we don’t, our experience – whether real or imagined – is likely to get pretty unpleasant.

2. I’m comfortable with the definition of knowledge as justified true belief. But that’s a definition of an abstract concept. The extent to which people can actually justify or demonstrate the truth of their beliefs (collectively or individually) varies considerably.

3. The reason for this is the way perception works. All incoming sensory information is interpreted by our brains, and brains aren’t entirely reliable when it comes to interpreting sensory information. So we’ve devised methods of cross-checking what our senses tell us to make sure we haven’t got it disastrously wrong. One approach is known as the scientific method.

4. Science works on the basis of probability. We can never say for sure that A or B exists or that C definitely causes D. But for the purposes of getting on with our lives if there’s enough evidence suggesting that A or B exists and that C causes D, we assume those things to be true and justified to varying extents.

5. Even though our perception is a bit flaky and we can’t be 100% sure of anything, it doesn’t follow that reality is flaky or not 100% real. Just that our knowledge about it isn’t 100% reliable. The more evidence we’ve gathered, the more consistent and predictable reality looks. Unfortunately it’s also complicated, which, coupled with our flaky and uncertain perceptions, makes life challenging.

seven myths about education: a knowledge framework

In Seven Myths about Education Daisy Christodoulou refers to Bloom’s taxonomy of educational objectives as a metaphor that leads to two false conclusions; that skills are separate from knowledge and that knowledge is ‘somehow less worthy and important’ (p.21). Bloom’s taxonomy was developed in the 1950s as a way of systematising what students need to do with their knowledge. At the time, quite a lot was known about what people did with knowledge because they usually process it actively and explicitly. Quite a lot less was known about how people acquire knowledge, because much of that process is implicit; students usually ‘just learned’ – or they didn’t. Daisy’s book focuses on how students acquire knowledge, but her framework is an implicit one; she doesn’t link up the various stages of acquiring knowledge in an explicit formal model like Bloom’s. Although I think Daisy makes some valid points about the educational orthodoxy, some features of her model lead to conclusions that are open to question. In this post, I compare the model of cognition that Daisy describes with an established framework for analysing knowledge with origins outside the education sector.

a framework for knowledge

Researchers from a variety of disciplines have proposed frameworks involving levels of abstraction in relation to how knowledge is acquired and organised. The frameworks are remarkably similar. Although there are differences of opinion about terminology and how knowledge is organised at higher levels, there’s general agreement that knowledge is processed along the lines of the catchily named DIKW pyramid – DIKW stands for data, information, knowledge and wisdom. The Wikipedia entry gives you a feel for the areas of agreement and disagreement involved. In the pyramid, each level except the data level involves the extraction of information from the level below. I’ll start at the bottom.



Data

As far as the brain is concerned, data don’t actually tell us anything except whether something is there or not. For computers, data are a series of 0s and 1s; for the brain data is largely in the form of sensory input – light, dark and colour, sounds, tactile sensations, etc.

Information
It’s only when we spot patterns within data that the data can tell us anything. Information consists of patterns that enable us to identify changes, identify connections and make predictions. For computers, information involves detecting patterns in all the 0s and 1s. For the brain it involves detecting patterns in sensory input.

Knowledge
Knowledge has proved more difficult to define, but involves the organisation of information.

Wisdom
Although several researchers have suggested that knowledge is also organised at a meta-level, this hasn’t been extensively explored.

The processes involved in the lower levels of the hierarchy – data and information – are well-established thanks to both computer modelling and brain research. We know a fair bit about the knowledge level largely due to work on how experts and novices think, but how people organise knowledge at a meta-level isn’t so clear.

The key concept in this framework is information. Used in this context, ‘information’ tells you whether something has changed or not, whether two things are the same or not, and identifies patterns. The DIKW hierarchy is sometimes summarised as; information is information about data, knowledge is information about information, and wisdom is information about knowledge.

a simple theory of complex cognition

Daisy begins her exploration of cognitive psychology with a quote by John Anderson, from his paper ACT: A simple theory of complex cognition (p.20). Anderson’s paper tackles the mystique often attached to human intelligence when compared to that of other species. He demonstrates that it isn’t as sophisticated or as complex as it appears, but is derived from a simple underlying principle. He goes on to explain how people extract information from data, deduce production rules and make predictions about commonly occurring patterns, which suggests that the more examples of particular data the brain perceives, the more quickly and accurately it learns. He demonstrates the principle using examples from visual recognition, mathematical problem solving and prediction of word endings.

natural learning

What Anderson describes is how human beings learn naturally; the way brains automatically process any information that happens to come their way unless something interferes with that process. It’s the principle we use to recognise and categorise faces, places and things. It’s the one we use when we learn to talk, solve problems and associate cause with effect. Scattergrams provide a good example of how we extract information from data in this way.

Scatterplot of longitudinal measurements of total brain volume for males (N=475 scans, shown in dark blue) and females (N=354 scans, shown in red).  From Lenroot et al (2007).

Scatterplot of longitudinal measurements of total brain volume for
males (N=475 scans, shown in dark blue) and females (N=354 scans,
shown in red). From Lenroot et al (2007).

Although the image consists of a mass of dots and lines in two colours, we can see at a glance that the different coloured dots and lines form two clusters.

Note that I’m not making the same distinction that Daisy makes between ‘natural’ and ‘not natural’ learning (p.36). Anderson is describing the way the brain learns, by default, when it encounters data. Daisy, in contrast, claims that we learn things like spoken language without visible effort because language is ‘natural’ whereas we need to be taught ‘formally and explicitly’, inventions like the alphabet and numbers. That distinction, although frequently made, isn’t necessarily a valid one. It’s based on an assumption that the brain has evolved mechanisms to process some types of data e.g. to recognise faces and understand speech, but can’t have had time to evolve mechanisms to process recent inventions like writing and mathematics. This assumption about brain hardwiring is a contentious one, and the evidence about how brains learn (including the work that’s developed from Anderson’s theory) makes it look increasingly likely that it’s wrong. If formal and explicit instruction are necessary in order to learn man-made skills like writing and mathematics, it begs the question of how these skills were invented in the first place, and Anderson would not have been able to use mathematical problem-solving and word prediction as his examples of the underlying mechanism of human learning. The theory that the brain is hardwired to process some types of information but not others, and the theory that the same mechanism processes all information, both explain how people appear to learn some things automatically and ‘naturally’. Which theory is right (or whether both are right) is still the subject of intense debate. I’ll return to the second theory later when I discuss schemata.

data, information and chunking

Chunking is a core concept in Daisy’s model of cognition. Chunking occurs when the brain links together several bits of data it encounters frequently and treats them as a single item – groups of letters that frequently co-occur are chunked into words. Anderson’s paper is about the information processing involved in chunking. One of his examples is how the brain chunks the three lines that make up an upper case H. Although Anderson doesn’t make an explicit distinction between data and information, in his examples the three lines would be categorised as data in the DIKW framework, as would be the curves and lines that make up numerals. When the brain figures out the production rule for the configuration of the lines in the letter H, it’s extracting information from the data – spotting a pattern. Because the pattern is highly consistent – H is almost always written using this configuration of lines – the brain can chunk the configuration of lines into the single unit we call the letter H. The letters A and Z also consist of three lines, but have different production rules for their configurations. Anderson shows that chunking can also occur at a slightly higher level; letters (already chunked) can be chunked again into words that are processed as single units, and numerals (already chunked) can be chunked into numbers to which production rules can be applied to solve problems. Again, chunking can take place because the patterns of letters in the words, and the patterns of numerals in Anderson’s mathematical problems are highly consistent. Anderson calls these chunked units and production rules ‘units of knowledge’. He doesn’t use the same nomenclature as the DIKW model, but it’s clear from his model that initial chunking occurs at the data level and further chunking can occur at the information level.

The brain chunks data and low-level units of information automatically; evidence for this comes from research showing that babies begin to identify and categorise objects using visual features and categorise speech sounds using auditory features by about the age of 9 months (Younger, 2003). Chunking also occurs pre-consciously (e.g. Lamme 2003); we know that people are often aware of changes to a chunked unit like a face, a landscape or a piece of music, but don’t know what has changed – someone has shaved off their moustache, a tree has been felled, the song is a cover version with different instrumentation. In addition, research into visual and auditory processing shows that sensory information initially feeds forward in the brain; a lot of processing occurs before the information reaches the location of working memory in the frontal lobes. So at this level, what we are talking about is an automatic, usually pre-conscious process that we use by default.

knowledge – the organisation of information

Anderson’s paper was written in 1995 – twenty years ago – at about the time the DIKW framework was first proposed, which explains why he doesn’t used the same terminology. He calls the chunked units and production rules ‘units of knowledge’ rather than ‘units of information’ because they are the fundamental low-level units from which higher-level knowledge is formed.

Although Anderson’s model of information processing for low-level units still holds true, what has puzzled researchers in the intervening couple of decades is why that process doesn’t scale up. The way people process low-level ‘units of knowledge’ is logical and rational enough to be accurately modelled using computer software, but when handling large amounts of information, such as the concepts involved in day-to-day life, or trying to comprehend, apply, analyse, synthesise or evaluate it, the human brain goes a bit haywire. People (including experts) exhibit a number of errors and biases in their thinking. These aren’t just occasional idiosyncrasies – everybody shows the same errors and biases to varying extents. Since complex information isn’t inherently different to simple information – there’s just more of it – researchers suspected that the errors and biases were due to the wiring of the brain. Work on judgement and decision-making and on the biological mechanisms involved in processing information at higher levels has demonstrated that brains are indeed wired up differently to computers. The reason is that what has shaped the evolution of the human brain isn’t the need to produce logical, rational solutions to problems, but the need to survive, and overall quick-and-dirty information processing tends to result in higher survival rates than slow, precise processing.

What this means is that Anderson’s information processing principle can be applied directly to low-level units of information, but might not be directly applicable to the way people process information at a higher-level, the way they process facts, for example. Facts are the subject of the next post.

References
Anderson, J (1996) ACT: A simple theory of complex cognition, American Psychologist, 51, 355-365.
Lamme, VAF (2003) Why visual attention and awareness are different, TRENDS in Cognitive Sciences, 7, 12-18.
Lenroot,RK, Gogtay, N, Greenstein, DK, Molloy, E, Wallace, GL, Clasen, LS, Blumenthal JD, Lerch,J, Zijdenbos, AP, Evans, AC, Thompson, PM & Giedd, JN (2007). Sexual dimorphism of brain developmental trajectories during childhood and adolescence. NeuroImage, 36, 1065–1073.
Younger, B (2003). Parsing objects into categories: Infants’ perception and use of correlated attributes. In Rakison & Oakes (eds.) Early Category and Concept development: Making sense of the blooming, buzzing confusion, Oxford University Press.

systems complexity: what we learn in school

More years ago than I care to remember I taught, briefly, at a parent controlled school – along the lines of Michael Gove’s free schools, but in those days parents had to stump up the cash themselves. One of my first tasks was to draft a curriculum. The experience stood me in good stead when I found myself educating both my children at home.

What had concerned me most about my children’s education at school was not so much what they knew or didn’t know but what they understood about the world they live in. As my eldest put it; “We were taught about the Egyptians, the Greeks and the Romans, but I never understood why, or what they had to do with each other.”

After some trial-and-error (the standard school timetable was a non-starter) we adopted the history of the universe as a narrative spine for our learning. We started with the Big Bang and proceeded from there. We made a timeline of the universe that stretched the length of the house. The periodic table filled one wall of our dining room and the rest of our home was festooned with posters from the excellent Edugraphics. We found out what life must have been like for the young Mendeleev and for the inhabitants of Darmstadt during WWII. We studied evolution and creation stories, unearthed skulls with Leakey and watched our distant ancestors farm and develop city-states. My youngest returned to school just after the fall of the Roman Empire. I must remember to let him know what happened next.

Few teachers would think of introducing an eight year-old with special educational needs to sub-atomic theory, but for my son, that knowledge made sense of everything. Once you have a basic deep structure understanding of the connection between energy and matter, how elements interact, what DNA does, how brains process information and how people tend to behave, you have a broad framework into which all new surface features of knowledge fit. So new knowledge, whatever it is, makes sense.

But the school curriculum (in the UK at least) tends not to start from first principles. It usually begins – understandably and justifiably – with building on young children’s existing knowledge (My Family, Our Town, sand and water play). It’s later dominated by the requirements of academia. What undergraduates are required to know largely determines the content of A level courses, which in turn determines what is learned at GCSE level and so on. Add to the mix what politicians or other interested parties believe children should learn and you have a curriculum that is derived neither from the deep structure of knowledge nor from how children learn.

Using deep structure as a starting point has a number of advantages. It enables you to understand:

-how everything is related to everything else (however distantly)
-how skills and knowledge are related
-the importance and relevance of different skills and different types of knowledge

Schools have always had a problem with non-academic skills like plumbing or painting and decorating, partly because they are non-academic skills but also because of their social status. Because fewer people have the skills needed to become lawyers or doctors, these professions command high salaries and high status. Schools tend to measure their success by the number of their graduates who go into high status professions. Not on how happy those graduates are with their work or how useful they are to their communities.

We are frequently reminded that our knowledge about the world is growing at an exponential rate and that specialists can’t hope to keep on top of their own field, never mind others. This has led to increasing specialization and as a consequence there is pressure on the school curriculum to become fragmented and unconnected. Increased specalisation might be inevitable but it doesn’t follow that economists don’t need to understand human behaviour, or that doctors don’t need to grasp the principles of nutrition or that journalists don’t need to know how the brain works. Nor that it’s OK for politicians to understand only politics and not the principles that link everything together.