seven myths about education – what’s missing?




concepts and evidence


concepts but no evidence


evidence but no concepts





burden of proof







Developmental Disorders. Developmental Psychology, 45, 56–63.


seven myths about education: finally…


concepts from cognitive psychology





migrating models




why criticise the reformers?









cargo-cult science




the MUSEC briefings and Direct Instruction

Yesterday, I got involved in a discussion on Twitter about Direct Instruction (DI). The discussion was largely about what I had or hadn’t said about DI. Twitter isn’t the best medium for discussing anything remotely complex, but there’s something about DI that brings out the pedant in people, me included.

The discussion, if you can call it that, was triggered by a tweet about the most recent MUSEC briefing. The briefings, from Macquarie University Special Education Centre, are a great idea. A one-page round-up of the evidence relating to a particular mode of teaching or treatment used in special education is exactly the sort of resource I’d use often. So why the discussion about this one?

the MUSEC briefings

I’ve bumped into the briefings before. I read one a couple of years ago on the recommendation of a synthetics phonics advocate. It was briefing no.18, Explicit instruction for students with special learning needs. At the time, I wasn’t aware that ‘explicit instruction’ had any particular significance in education – other than denoting instruction that was explicit. And that could involve anything from a teacher walking round the room checking that students understood what they were doing, to ‘talk and chalk’, reading a book or computer-aided learning. The briefing left me feeling bemused. It was packed with implicit assumptions and the references, presented online presumably for reasons of space, included one self-citation, a report that reached a different conclusion to the briefing, a 400-page book by John Hattie that doesn’t appear to reach the same conclusion either, and a paper by Kirschner Sweller and Clark that doesn’t mention children with special educational needs, The references form a useful reading list for teachers, but hardly constitute robust evidence for support the briefing’s conclusions.

My curiosity piqued, I took a look at another briefing, no.33 on behavioural optometry. I chose it because the SP advocates I’d encountered tended to be sceptical about visual impairments being a causal factor in reading difficulties, and I wondered what evidence they were relying on. I knew a bit about visual problems because of my son’s experiences. The briefing repeatedly lumped together things that should have been kept distinct and came to different conclusions to the evidence it cites. I think I was probably unlucky with these first two because some of the other briefings look fine. So what about the one on Direct Instruction, briefing no.39?

Direct Instruction and Project Follow Through

Direct Instruction (capitalized) is a now commercially available scripted learning programme developed by Siegfried Engelmann and Wesley Becker in the US in the 1960s that performed outstandingly well in Project Follow Through (PFT).

The DI programme involved the scripted teaching of reading, arithmetic, and language to children between kindergarten and third grade. The PFT evaluation of DI showed significant gains in basic skills (word knowledge, spelling, language and math computation); in cognitive-conceptual skills (reading comprehension, math concepts, math problem solving) and in affect measures (co-operation, self-esteem, intellectual achievement, responsibility). A high school follow-up study by the sponsors of the DI programme showed that was associated with positive long-term outcomes.

The Twitter discussion revolved around what I meant by ‘basic’ and ‘skills’. To clarify, as I understand it the DI programme itself involved teaching basic skills (reading, arithmetic, language) to quite young children (K-3). The evaluation assessed basic skills, cognitive-conceptual skills and affect measures. There is no indication in the evidence I’ve been able to access of how sophisticated the cognitive-conceptual skills or affect measures were. One would expect them to be typical of children in the K-3 age range. And we don’t know how long those outcomes persisted. The only evidence for long-term positive outcomes is from a study by the programme sponsors – not to be discounted, but not a reliable enough to form the basis for a pedagogical method.

In other words, the PFT evaluation tells us that there were several robust positive outcomes from the DI programme. What it doesn’t tell us is whether the DI approach has the same robust outcomes if applied to other areas of the curriculum and/or with older children. Because the results of the evaluation are aggregated, it doesn’t tell us whether the DI programme benefitted all children or only some, or if it had any negative effects, or what the outcomes were for children with specific special educational needs or learning difficulties – the focus of MUSEC. Nor does it tell us anything about the use of direct instruction in general – what the briefing describes as a “generic overarching concept, with DI as a more specific exemplar”.

the evidence

The briefing refers to “a large body of research evidence stretching back over four decades testifying to the efficacy of explicit/direct instruction methods including the specific DI programs.” So what is the evidence?

The briefing itself refers only to the PFT evaluation of the DI programme. The references, available online consist of:

• a summary of findings written by the authors of the DI programme, Becker & Engelmann,
• a book about DI – the first two authors were Engelmann’s students and worked on the original DI programme,
• an excerpt from the same book on a commercial site called,
• an editorial from a journal called Effective School Practices, previously known as Direct Instruction News and published by the National Institute for Direct Instruction (Chairman S Engelmann)
• a paper about the different ways in which direct instruction is understood, published by the Center on Innovation and Improvement which is administered by the Academic Development Institute, one of whose partners is Little Planet Learning,
• the 400-page book referenced by briefing 18,
• the peer-reviewed paper also referenced by briefing 18.

The references, which I think most people would construe as evidence, include only one peer-reviewed paper. It cites research findings supporting the use of direct instruction in relation to particular types of material, but doesn’t mention children with special needs or learning difficulties. Another reference is a synthesis of peer-reviewed studies. All the other references involve organisations with a commercial interest in educational methods – not the sort of evidence I’d expect to see in a briefing published by a university.

My recommendation for the MUSEC briefings? Approach with caution.

seven myths about education: traditional subjects



traditional subjects






criteria for the curriculum





dead white males



Core Knowledge sequence




knowledge itself





why subjects?





the curriculum in context








seven myths about education: deep structure

deep structure and understanding




deep structure and small numbers of facts




• first-order information – letters and words (chunked)
• knowledge – the deep structure/schema underlying each problem.


visual vs verbal representations





deep structure of knowledge domains



Daisy’s model of cognition












seven myths about education: facts and schemata



a simple theory of cognition








I think Daisy assumes that the principle Anderson found pertaining to low-level ‘units of knowledge’ applies to all units of knowledge at whatever level of abstraction. It doesn’t. Before considering why it doesn’t, it’s worth noting a problem with the use of the word ‘facts’ when describing data. Some researchers have equated data with ‘raw facts’. The difficulty with defining data as ‘facts’ is that by definition a fact has to be valid (true) and not all data is valid, as the GIGO (garbage-in-garbage-out) principle that bedevils computer data processing and the human brain’s often flaky perception of sensory input demonstrate. In addition, ‘facts’ are more complex than raw (unprocessed) data or raw (unprocessed) sensory input.


chunking revisited


understanding facts














chess experts







Edited for clarity 8/1/17.

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.


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.

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

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

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.

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.
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.