Constructivism is a theory of learning, not a theory of pedagogy. Neuroscience explains why this is important

Unsurprisingly, teachers are very interested in the brain, and excited about the potential for psychology and neuroscience evidence to be used in improving what they do (see for example the responses to this survey). It’s a shame, then, that so little time in many teacher training routes has traditionally been given over to psychology and the science of learning. Especially if you trained five or more years ago (as I did), it seems to have been very rare to have received more than a cursory introduction to the subject… apart from one notable exception. However little psychology gets covered otherwise, one mandatory inclusion on many teacher training courses is constructivism — the idea that every individual constructs their own understanding and knowledge of the world, based on their own unique experiences. As a result, constructivist ideas are undoubtedly the most widely held ‘folk-psychological’ belief about learning amongst current teachers (See Torff, 1999, and Partick & Pintrich, 2001 for more on the content of teacher training and changes in trainee teachers’ beliefs about learning).

In this post I am going to argue that this state of affairs is dangerous. Importantly, though, the problem is not that constructivism is wrong. Constructivism works well as a theory of learning. The problem comes, however, when it is assumed that the theory of learning implies a particular pedagogical approach (‘educational constructivism’, or sometimes the closely related approach of ‘constructionism‘). Using evidence from neuroscience, I will try to show that there is a great deal of support for constructivism the learning theory, and a good deal less for constructivism the pedagogical approach.

Some of the misapplications of constructivism to pedagogy have been well documented before. Phillips (1995) described ‘The good, the bad, and the ugly‘ of constructivist pedagogy, and Hyslop-Margison & Strobel (2007) put a slightly more nuanced slant on the same topic. Much of their focus is on the potential for more radical constructivist views to lead to a relativistic approach to knowledge in which, because knowledge is individually constructed, there is ‘your knowledge’ and ‘my knowledge’ and little scope for any external validation.

My focus here, however, is on the extent to which the operations of the brain, especially during development, can help us to see why constructivism the learning theory does not entail the pedagogy. I’ll use the evidence underpinning the theory of ‘Neuroconstructivism’ (Mareschal et al., 2007), one of the most popular theories of developmental cognitive neuroscience, to help me with this.

Partial representations, and context-specificity

A central feature of neuroconstructivism is that the development of our brains and the storage of information in them is hugely context-dependent. At any one moment, activity in the brain is a reflection of the context that the organism finds itself in. Mareschal et al., provide four different levels where the activity of the brain is constrained by the context in which it occurs: neural, network, bodily and social. We’ll look at examples of context-specificity for each of these in turn in a minute. The important point, though, is that brain activity reflects the precise state of the organism at the time of the event, so any new piece of ‘learning’ will be encoded completely within the context of the learning experience, rather than reflecting any general underlying feature of it. In the terminology of the theory, we are only ever able to create ‘partial representations’; representations of the world which capture some, but not all of it. Partial representations are, by their very nature, completely context-dependent; that is, they reflect the features of the world (and of the brain) which were the case when the information was originally stored. Let’s then look at the four levels which can lead to the creation of such ‘partial’ representations. This section is heavily paraphrased from a previous description of the theory, but I reproduce it here for clarity.

  1. Neural context – ‘encellment

The cellular neighbours of a neuron exert a large influence over its eventual function as a processor of information. The characteristics of its response and the way in which it connects and influences other neurons is in turn dependent on the type and amount of activity that the neuron itself receives. One classic example is that “cells that wire together fire together” — the more that cells communicate with each other, the more that their connections are strengthened, and the greater influence that a preceding cell exerts over the activity of subsequent cell.

However, the context-dependence of neural activity is not limited to the simple co-operative strengthening of connections. They can also compete. Hubel and Wiesel’s Nobel Prize-winning work on vision in cats involved looking at what happened if a part of the brain called the visual cortex was understimulated (because they had covered up the eye which sent information to it). They found that after 2-3 months, the nerve cells in the understimulated area began to switch functions and process information from the uncovered eye instead.

So what?

The activity of any one neuron is context-specific. This context is constantly changing due to a number of different factors: the ever-changing strengths of connections to potentially thousands of other inputs, competition (or co-operation) between neighbouring cells, or a progressive specialisation of the cell’s function. Therefore a signal from a neuron can only be interpreted as representing that cell’s response to a particular set of circumstances at that specific time; the neural context.

Another consequence of the reliance of each neuron’s activity on so many of its neighbours is that this means that any information that is encoded by the neuron is likely to be done so in a distributed fashion, across large groups of neurons. Such ‘distributed representations’, whilst more robust on the face of damage and brain changes, are also far more likely to be ‘partial representations’, relying as they do on numerous small contributions from different neural sources. They will never capture a concept or an idea in its entirety. Instead, they record a blurred snapshot of some of the key details approximating the concept, a partial representation.

 

2. Network context – ‘embrainment’

Just as individual neurons can be affected by the context in which they find themselves, so entire brain areas can co-operate, compete and change function as a result of their context within the brain as a whole. On a larger scale than that noticed by Hubel and Wiesel, Cohen et al (1997) found that in people who have been blind from an early age, ‘visual’ cortex begins to take on other functions entirely, such as touch when reading braille. Similarly, if you re-route visual information into a ferret auditory cortex (an area that would normally deal with processing sounds), the area will begin to respond to visual patterns instead (Sur and Leamey, 2001)! In less drastic fashion, maturation in the brain involves the progressive specialisation of many different brain areas, which gradually take over sole control of functions that previously relied on wider networks. Again this process can be categorised by competition, with one area gradually coming to exert a dominant influence over a particular kind of processing. Good examples of these sorts of processes have been found in the pre-frontal cortex (PFC) during adolescence, with different sections of that brain area becoming ‘responsible’ for particular functions.  (see Dumontheil, 2016 for some examples).

So what?

Most formal education is taking place during periods of rapid brain development and maturation. Brain areas are progressively specialising and refining their functions, dependent on their relationship to other brain areas and input from the outside world. In this context, the distributed and partial representations that we build of the world are likely to be highly context-dependent, not only on the particular pattern of inputs, but also on the time and stage of development in which the information was learned.

 

3. Bodily context – ‘embodiment

The brain does not sit in glorious isolation from the rest of the body. Some hard-wired nervous behaviours, such as reflexes, can in fact form the basis for the beginnings of brain development. Infants make spontaneous reaching movements from an early age and even new-born infants will move their limbs to block a light beam (Van der Meer et al, 1995). These kinds of behaviour initiate the beginnings of feedback mechanisms between the visual and motor areas of the brain and eventually allow for the development of complex visually-guided behaviour. Of equal importance, the design of some parts of the body can constrain brain development by ensuring that it does not need to develop certain skills; cricket ears are designed to respond preferentially to male phonotaxis (a sound made by rubbing one wing against the other). The cricket brain has no such specialisation for making this distinction, because the job has already been done (Thelen et al., 1996). In human cognition, examples of ‘embodiment’ might include state-dependent memory; the finding that we recall information more successfully in a similar ‘state’ to when we learned it, for example after exercise (Miles and Hardman, 1984) or even when drunk (Goodwin et al., 1969).

So what?

The development of our brains is constrained and uniquely differentiated by our nervous systems and by the body in which we find ourselves. Again, this is not just the case between individuals but also within individuals as they develop over time, and as they pass through the myriad different internal states which characterise our existence. The representations that we have of the world will reflect these changing embodiments, and will be ‘partial representations’ in that they are formed, and linked to, this embodied context. This therefore provides further scope for learning to be constrained by the situation (in the widest possible sense) in which the information was initially encountered. The Goodwin et al. paper is particularly relevant here, as it tested two outcomes; recognition and transfer. They found that, whilst recognition memory was not significantly affected by the a change in states between learning and recall, the ability to transfer the information was. Transfer, as a more complicated cognitive procedure than simple recall, is as a result even more susceptible to being restricted by the context in which it occurs.

 

4. Social context – ‘ensocialment

The concept of ensocialment, the idea that the social context for any act of learning is crucial to shaping the learning that takes place, will be the most familiar of these four levels of analysis to educators. Vygotsky’s social constructivist theories are probably the most famous educational application of this sort of idea. People learn from others with more skills than them; with the more knowledgeable mentors using language and guidance to ‘scaffold’ the learner’s interactions with the world in the most productive manner. The concept of scaffolding; a supportive structure which is gradually removed as the learner gains in ability, is used to one degree or another by almost every major educational approach.

So what?

The type of scaffolding that is used may become inextricably linked to the solution that is produced, to the point where the ‘partial representation’ that we have of the solution is not accessed when the problem is framed differently. In one famous example, children working in Brazilian markets were able to demonstrate mathematical strategies on their stalls that they could not do in the classroom. Knowing how to do something in one context is no guarantee of being able to demonstrate the same skill in another.

 

Neuroconstructivism and constructivist learning

This brief survey has aimed to show how our experiences in the world can only ever lead to context-specific, partial representations of these events in our brains. It should hopefully be immediately clear that this evidence fits very well with many of the main ideas of constructivism as a theory of learning. The constructivist idea that meaning and knowledge are created by the individual in response to their specific experiences and ideas clearly fits very comfortably with the notion of partial representations. Given that each individual will have their own unique neural, network, bodily and social context at any one moment, even the same environmental stimulus is likely to lead to different partial representations of that stimulus in different people.

The neuroscientific evidence also complements the ‘constructive’ aspect of learning very nicely: the building up of ever more complex schemas through the gradual formation of multiple, overlapping partial representations of the world. It also appears to explain the focus of at least some constructivist theories (e.g. Piaget and Dewey) on the individual, highlighting the unique individual context in which any act of learning takes place. Indeed, the individual context is so important that, as we have seen, learning will often not transfer to contexts (even seemingly very similar tasks and environments) which do not share enough of the original features.

 

Neuroconstructivism and constructivist pedagogy

So far so good. Unfortunately, it has been common practice in education to go a step further, and to assume that the theory of learning implies/favours a particular pedagogical approach. Because constructivism emphasises the unique individual context in which learning takes place (and the individual’s role in the construction of that knowledge), so constructivist pedagogy places the onus on the individual student to construct their own understanding of the world. Teachers are therefore encouraged to design learning environments through which students are able to learn for themselves, sometimes facilitating the learning, but generally providing limited explicit guidance. Because constructivism emphasises the ‘active construction’ of knowledge, so constructivist pedagogy often places increased value on hands-on, ‘active’ or experiential learning (such as by experiment, project or solving real-world problems). This will, naturally, be entirely familiar to any teachers reading this post. If your teacher training was anything like mine, it will have been exactly how you were taught to teach.

Unfortunately, the leap from learning theory to pedagogy is not justified. Constructivism, as conceived purely as a theory of learning by Piaget, was not designed to be associated with any specific pedagogical approach. More importantly, the raft of neuroscientific evidence supporting the theory of ‘neuroconstructivism’ actually, in my view, provides strong evidence to suggest the opposite, that constructivist pedagogies are unlikely to be the most effective approaches to learning, at least until schemas are well developed.

 

Partial representations, and constructivist pedagogy 

Let us return to ‘partial representations’, those context-dependent neural traces which constitute how the brain changes in response to experience. As we have seen, these traces are:

  • partial – in that they reflect not the underlying structure of the knowledge but the whole neural, network, bodily and social context in which the knowledge was formed.
  • distributed – in that they are made up of numerous small contributions from neurons distributed across brain areas. No one piece of information therefore resides in any one place, and any reactivation of that knowledge will be an approximate reconstruction, rather than a video-tape playback.
  • context-dependent – as the knowledge contained in our partial representations often does not transfer to situations.

What relevance does this have for pedagogy? Well, let us take a constructivist-inspired pedagogical approach, which involves minimal explicit guidance being given to a student. Imagine a child over the course of a lesson discovering – through some trial and error and some careful teacher-facilitation in a thoughtfully structured learning environment – the formula for Pythagoras’ theorem. It sounds like a lovely educational experience for both student and teacher. Unfortunately, at the end of this process we have (time-consumingly) produced but a single partial representation, which is likely to be highly susceptible to context-dependency.

What the neuroscience of learning tells us is that in order to increase the likelihood of them being more easily accessed subsequently, students require multiple, overlapping partial representations, which are strengthened through repeated access. In his book ‘The Hidden Lives of Learners‘, Graham Nuthall wrote that students need to encounter information three or four times to learn it, and with the idea of multiple, overlapping partial representations we can see why such repeated exposure to information is so important. From this perspective, it is not the discovery of the strategy which is important for subsequent success, but the repeated exposure to it, and practice at accessing it, multiple times and in multiple different ways. There is therefore nothing wrong with the student learning about Pythagoras in the way described above, provided that they are subsequently afforded repeated opportunities to revisit and practise their new knowledge in different contexts. The problem is that, all too often, demonstration of a skill in the learned context is taken as indicating mastery of the skill in general, so lessons move on after a limited number of demonstrations of any new idea. Neuroconstructivism clearly shows that under such circumstances, we are unlikely to form enough overlapping partial distributions to be able to transfer our knowledge to a new context.

 

‘Neuroconstructivist pedagogy’: Multiple, overlapping, partial representations

What I have tried to show above is that our representations of the world, by their very nature, are only ever ‘partial’ representations. Given that, it makes sense for educators to work to create as many of them, and to strengthen them, wherever possible. So is there a particular pedagogical approach that is specifically favoured by the neuroscientific evidence? No. I should be clear I do not think that neuroscience findings can ever be used to directly conclude that any one pedagogical method is better than others. I do, however, think that the evidence underpinning neuroconstructivism provides some important considerations which different pedagogical approaches can all benefit from taking into account. These considerations are encapsulated in the statement:

Students require multiple, overlapping, partial representations of knowledge in order to transfer it to new contexts.

Given that having knowledge that we are able to transfer to a new context is pretty much the point of education itself, I think this is an important message. Again, however, I am not arguing here in favour any one pedagogical approach. I have my personal preferences, but I want to keep this separate from what I think the neuroscience evidence objectively tells us. The creation of multiple, overlapping, partial representations could conceivably be achieved through numerous pedagogical approaches, as long as students have the opportunity for repeated exposure to information and multiple chances to apply new knowledge to different contexts. It could be achieved through more explicit or direct instruction methods, through dialogic approaches, through well-structured co-operative or group strategies, even (if you have enough time spare in your curriculum) through repeated overlapping inquiry-style investigations such as the one described earlier. What teachers need to consider is, in their own context, which (combination of) teaching methods is most likely to produce repeated exposure to knowledge, and practice at applying it to different contexts. What this looks like in each classroom is a decision for each individual teacher.

So there we go. Some people are sceptical that neuroscience has anything to offer education. I agree that the neuroscientific evidence doesn’t provide support for any one concrete strategy of instruction (indeed, I don’t think it ever could). I think it does, however, create a simple and powerful question that I continue to ask myself as I evaluate how I teach. It is also a question that I wish I had been asked right at the start of my teaching career. So… how are you going to ensure that you form multiple, overlapping, partial representations?

 

 

Constructivism is a theory of learning, not a theory of pedagogy. Neuroscience explains why this is important

It is more useful for teachers to see attention as an effect not a cause

We frequently urge our students (and ourselves!) to “pay attention”, but what do we really mean by this phrase? The notion of ‘paying attention to’ an object creates an impression of attention as a resource which we have at our disposal, ready to be deployed (or not) at our command. This is reinforced by the numerous metaphors we have for the concept (a filter, a spotlight, a zoom lens, even a glue). In all of these metaphors attention is cast in the role of a ‘tool’ for us to use. Going a level deeper, such metaphors all take for granted a reified concept of attention, i.e. that attention is a real, measurable ‘thing’. Increasingly in the academic study of attention, however, there is some opposition to these traditional notions of what attention is. These can generally be summarised as an effort to recast attention as an effect rather than a cause (e.g. here, here and here, amongst others).

I think that this seemingly niche academic debate actually has some interesting implications for educators. Thinking of attention as an effect rather than a cause can throw a new light on the understudied problem of attention in schools, and what teachers can do about it.

What’s the problem?

There are two main problems with the metaphors which reify attention as a resource, one logical and one practical. The logical one (which is of less relevance perhaps to educators, but still useful for fans of logical validity), is that the evidence for these models of memory often rest on circular arguments. For example, if we take the metaphor of attention as a flashlight, imagine the following dialogue, taken from this paper by Vince Di Lollo 

Person A: a stimulus flashed at a location just ahead of a moving object is perceived more promptly and more accurately. 

Person B: why is that? 

A: because the attentional spotlight is deployed to that location, and stimuli presented at an attended location are processed more promptly and more accurately. 

B: and how do we know that attention has been deployed to that location? 

A: we know it because stimuli presented at that location are perceived more promptly and more accurately. 

The practical problem is that metaphors such as this place the burden for the control of attention firmly on the student themselves, to deploy as their preferences or abilities allow. Now I am in no way arguing that students are not capable of exercising control over their attention, nor that teachers should be held responsible when a student’s attention wanes; indeed I would strongly repudiate this. I do think, however, that a model which casts attention as a resource of the student is unhelpful to teachers. Teachers looking to improve student performance using this model are left with few options, other than perhaps brain training (so far unimpressive) or vague appeals to a student’s better nature (“direct your attention towards this pleeeease”).

Attention as an effect not a cause

Far more productive for educators would be the discussions arising from seeing attention as an effect, rather than a cause. This reconceptualisation naturally invites the consideration of

  1. which conditions are most likely to engender the effect of focused attention?

and importantly…

  1. which information should we create these conditions for?

Again, this suggestion in no way denies that students are causal agents in their own behaviour, merely that teachers will be more empowered by a focus on the conditions that they can create whereby attention emerges as an effect.

I will look at each of these two questions in turn

Which conditions are most likely to engender the effect of focused attention?

Decades of careful psychological work in dark laboratories has helped to confirm a lot of common sense notions about how attention can be captured (e.g. by bright colours, unexpected shapes or other features which stand out, stimuli which move, or loom, motivation, meaning, reward and so on.) If these features are present in the task, we are generally more able to focus on the task. If they are present in a stimulus which is not part of the task, then we are more likely to be distracted. 

So far so good; as teachers we need to try to make our stimuli as salient as possible and reduce other distractions. However this apparent simplicity leads us on to the second, less commonly considered implication of considering attention as an effect; if we can create the conditions to direct student attention, what information exactly do we want to create these conditions for? In other words (returning to the old metaphor for simplicity’s sake), what do we want student attention to be focused on?

Which information should we create these conditions for? Selecting the target of focused attention

Where attention is discussed in education, it tends to be focused on the ideas above, in terms of strategies for capturing attention. Indeed, the goal of my teacher training on this topic was entirely this, the creation of an attentive, engaged class. What exactly they should be engaged by seemed less of a concern. If attention could be attracted by the teacher, then learning was assumed to be an inevitability.

Sadly this position is mistaken. I have written before of the limited capacity ‘bottleneck’ of attention. This limited capacity means that only a tiny fraction of information arriving into our perceptual systems will ever be processed to a meaningful degree. Therefore whilst an attentive class can clearly be a step in the right direction, they will only be learning efficiently if we as educators ensure that their attention is focused precisely on the stimuli that we want it to be. Just as becoming distracted by low level disruption from other students may inhibit learning, so will engaging with superfluous material presented by the teacher. If attention is the result of creating the right conditions, then we need to be very clear about exactly what is worth creating those conditions for.

Take the case of powerpoint slides. I spent many hours early in my teaching career crafting aesthetically pleasing powerpoint slides. My backgrounds were salient, full of nice bright colours. Some of the words zoomed in from the side of the screen. Text was usually accompanied with a picture (or even a gif if I was feeling particularly creative), usually humorous and tangentially related to the main information. Looking back, especially through the lens of considering attention as an effect and questioning where I was encouraging that effect to occur, is sobering. My salient features were either surface level features (the movement of the text rather than its content) or entirely irrelevant to what I wanted the students to know (the background and the pictures). I was actively inviting attention to be directed away from that which I thought was most important. This is why important and potentially impactful strategies such as dual coding, which combine visual and verbal materials (and which has been valuably popularised recently by figures such as Oliver Caviglioli), need to be treated with caution.  Bad dual coding is not just ineffective, it leads to split attention or outright distraction.

Deciding on the right targets for our students’ attention is a challenge that cuts right across education, from the pedagogical issue of how best to deliver information to the curricular decisions required to identify precisely what it is that we want students to know in the first place. I have been delighted to see an increased focus on curriculum amongst teacher networks as a result (e.g. here, here and many great posts here for example, though to my knowledge the specific link between the importance of curriculum design and attention has not be explored either in blogs or research).

A new way to view attention in the classroom

Viewing attention as an effect makes us value it (and the contribution that we can make to it) more. It makes us consider more carefully how to attract attention, but crucially also what we want to attract attention to. Capturing attention is not in itself the aim. The goal is to provide the optimal conditions so that attention is captured by the exact stimuli that we have identified as most valuable. I have tried to argue here that this process may be assisted if we define attention less as a cause of student behaviour and more as an effect of the conditions that we put in place. 

It is more useful for teachers to see attention as an effect not a cause

The Multidisciplinary Gardner

We all need to be multidisciplinary these days. University students are confidently told by their tutors that the future lies away from narrow specialisation; the true twenty-first century student will be able to traverse the arbitrary boundaries of specific subject categories with ease. This is all fine, but our enthusiasm for the polymathic can, I worry, have some serious dangers in a world where even achieving specialisation in one discipline is so challenging.

The application of psychology and neuroscience findings to education seems in many ways an archetypal example of such a modern multidisciplinary project, one to which increasing numbers of teachers and researchers are contributing. This interest is naturally welcome, given that I am a firm believer in the potential gains for education of taking a more scientifically informed approach to learning. However, the increasing numbers of academic researchers looking to make connections between their work and educational practice does raise some concerns about what it truly means to be ‘multidisciplinary’ in such a new and expanding field. Specifically, I would suggest that the following maxim would be a useful one for both researchers considering their own results, and also teachers looking to evaluate the potential contribution of research to their practice:

Multi-disciplinarity doesn’t mean simply applying from your discipline into another one

Whilst this is an issue that has niggled at me for some time, the straw which broke the camel’s back this week was reading ‘The Gardener and the Carpenter’ by developmental psychologist Alison Gopnik. The book is an attempt to show why developmental psychology evidence demonstrates that the modern fashion for ‘parenting’ (the ‘carpenter’ of the title, in which parents desperately try to shape their child into a particular pre-defined adult form), is misguided. I really wanted to like the book, and given that I can’t stand ‘how to’ parenting books, and find developmental psychology fascinating, I thought I would be onto a pretty sure thing. And in parts I did enjoy it. The parts describing the developmental psychology research were superb; delightfully, almost whimsically, creative experiments which produce fascinating results. Gopnik is a world-leading expert in the development of young children’s (e.g. under 7 or so) reasoning skills and I read these sections with the confidence and excitement that comes from receiving a guided tour from a true specialist. It would have been a much shorter book had it stopped there, but I would have been a happier reader.

The basic message of the evidence presented is that young children are remarkably adept and efficient at learning from the adults around them, but also from their environment. This combination allows them to both absorb the wisdom and traditions of previous generations, but also to make new discoveries themselves. Too much of one of these two strands can be detrimental to the other; too much didactic adult instruction can impede natural curiosity and discovery. So far so good, for experiments on children who are mostly of pre-school age or just above.

Unfortunately, Gopnik cannot resist extrapolating the findings of these studies into suggestions for educational policy. Not just primary schools, but even secondary schools, would apparently benefit from a redesign to allow learning to occur in a more observational manner, with greater emphasis on learning by ‘apprenticeship’, where children develop ‘mastery’ from extended interactions with experts. Inquiry and discovery learning should therefore replace a school system which only teaches children ‘to learn how to go to school’ and ‘become experts at test-taking’. Is there evidence to support these conjectures? Does Gopnik have the same level of experience in the field of educational design as in developmental reasoning? Certainly, the continual references to studies dry up entirely in these sections. Instead, statements such as,

“Many of the most effective teachers, even in modern schools, use elements of apprenticeship. Ironically, these teachers are more likely to be found in the ‘extracurricular’ classes than in the required ones. The stern but beloved baseball coach or the demanding but passionate music teacher let children learn this way”

seem to suggest that her evidence is gathered mainly from film plots. In actual fact, such ‘minimal guidance’ instruction has been found to be generally much less effective as a form of instruction, unless the students are already knowledgeable about a topic.

Gopnik is undoubtedly a multidisciplinary figure (she also writes philosophy papers), but I’m not sure that the educational system is one of those disciplines. As a philosopher, she will presumably be aware of Hume’s distinction that what ‘is’ does not imply what ‘ought’. The fact that children do have a great ability (sometimes better than adults) to learn through their own experimentation and interaction with the world, doesn’t imply that they ought to always do this to acquire their knowledge. Let’s say, for the sake of argument, that her ‘apprenticeship and inquiry’ model of education was the most effective one available. Even in this case, a range of other factors also govern judgements of ‘value’ in education, such as the time taken, resources, teacher capacity, motivation and many others1. All these need to be weighed into the mix as well. As Dylan William has written, “Big effects may not be worth pursuing if they cost too much to secure. And very small effects may be important if they are inexpensive to implement.” Goodness knows how much it would cost in terms of hiring and training new teachers if we are to redesign schools around a small group apprenticeship model. We’d need to be unbelievably confident that it was the right thing to do. As we saw above, however, there is good reason not even to think that it is the most effective way to learn in many situations.

In terms of crimes against multidisciplinarity, I do not by any means think that Gopnik is alone (or even the worst offender). Indeed I have noted before that I am often uncomfortable with many so-called ‘Educational Neuroscience’ studies being used to make unwarranted and premature applications to the classroom. To me, the role of educational neuroscience is not at all about the creation of new sparkly teaching methods, but too often this seems to be the aim (off the back of a few brain scans). Multidisciplinarity offers potentially huge benefits to learning and education, but in order to reap those rewards, and to avoid cul-de-sacs and wasted effort, we must be as confident as we can be that our ideas will stand up to scrutiny in the new field. We must all ensure that, as researchers, we understand enough about the discipline we are applying into to be able to objectively evaluate our ideas (or to work with others who can). If we don’t, then we aren’t really being multidisciplinary, and we probably aren’t making progress.

References:

Kirschner, P. A., Sweller, J., & Clark, R. E. (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 (2): 75, 41(2), 75–86. Retrieved from http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Work+:+An+Analysis+of+the+Failure+of+Constructivist+,+Discovery+,+Problem-Based+,+Experiential+,+and+Inquiry-Based+Teaching#8

Footnotes:

  1. Incidentally, the is-ought distinction is also one that I think is useful for explaining why the issue of the role of genetics in learning (as ably summarised by Annie Brookman-Byrne recently) should not be as controversial as it often is. The (unarguable) fact that genetics does play a role in academic learning does nothing to imply anything normative about how people should be treated as a result of this.
The Multidisciplinary Gardner

Pay attention! Why I think it is important to study attention in school children

cell_phoneAlthough I have written before about how attention develops throughout adolescence, it took reading this tweet this week to make me realise that I had never actually recorded why I think the study of attention in school-age children is important (and seemingly nor have many other people, given the tweet). Education is a hugely complex pursuit, with many variables contributing to any one outcome, so clearly any educational research programme needs to focus on a number of these strands concurrently. Despite this, I think that attention skills (specifically, the ability to control the focus of attention and resist distraction) are deserving of being a major strand of any such programme, for four main reasons:

  1. Attention is the gateway to cognition
  2. Attention directly impacts school attainment across the whole spectrum – not just at the lowest end
  3. Attention may mediate other key variables which contribute to school success
  4. Attention skills likely impact on our happiness

 

1. Attention is the gateway to cognition

Attention is a fascinating cognitive ability to study because it straddles the boundaries between low-level and higher-level brain processes. Whilst the exact relationship between attention and other cognitions such as working memory and intelligence is still being mapped out, the fact that attention can operate so early in the processing stream makes it inevitable that it will have effects on any attempt to use the information further down the line. We cannot process what we don’t let in. Although a little outdated now, the Multi-Store Model of Memory (Atkinson & Shiffrin, 1968) is a useful visual illustration of this attentional bottleneck into our memory systems.

as-multi
The Multi-Store Model of Memory

At its most basic then, ‘attention’ describes the ability to select and process information from the surrounding environment. To illustrate how this affects real-life performance, individuals found to display good visual and working memory capacity have been shown to be more efficient at actively suppressing salient distractors (Gazzaley, 2011; Zanto & Gazzaley, 2009). In contrast, the inability to filter out such competing stimuli predicts low working memory capacity (Gaspar et al., 2016). To relate this to the picture above, if we are allowing in irrelevant information through the attentional bottleneck, then our ability to process and manipulate the information in the way that we want to will be hindered by the increased presence of competing distractors. The beneficial effects of an efficient attention system can be seen very early on in development. Attention skills as an infant predict intelligence as an adolescent (e.g. Bornstein & Sigman, 1986; McCall & Carriger, 1993), creating discrepancies of up to 20 IQ points (Sigman, Cohen & Beckwith, 1997) in combination with environmental differences.

Most of the goals of education involve much ‘higher-level’ cognitive processes, such as reasoning, creativity and long-term memory integration. For this reason, it could seem reasonable to focus all of our research attention on these skills as well. However, this is to neglect the fact that if we ever want to understand or improve the functioning of a higher level process, we need to ensure that all the supporting lower-levels are working properly first.

2. Attention directly impacts school attainment for ALL students

It’s hardly news that distracted students are a common bugbear amongst teachers, and few would dispute the general sentiment that attention is important to education. Although the terminology varies in different sources (e.g “distractibility”, “concentration”, “engagement” “cognitive control”,“executive control” etc.), the idea of students being able to resist distraction is a key component of many educational theories (e.g. Caldwell, 2007; Fredricks, Blumenfeld & Paris, 2004; Hidi, 1995; Posner & Rothbart, 2005). This makes it all the stranger, then, that serious investigation into precisely when and how attention skills impact students in the classroom has often been lacking, especially outside of children diagnosed with ADHD.

In addition to the general acknowledgement of its importance, this lack of a specific focus on attention skills is strange, because there is good evidence that attention skills (or lack of them) strongly predict academic attainment for all students, not just those with an ADHD diagnosis (Breslau et al., 2009; Duncan et al., 2007; Merrell et al., 2016), with the effects of inattention potentially becoming more detrimental the further students progress through education (Merrell and Tymms, 2001). Breslau et al. (2009), for example, used teacher ratings of attention at age six to predict reading and Maths achievement at age 17. Attention remained a significant unique predictor, even when controlling for potential confounding variables such as such as IQ, socio-economic status, parental education or other emotional or behavioural problems.

Importantly, these effects are by no means isolated to the tail of the distribution; they are felt well beyond the confines of the clinical ‘ADHD’ boundary. The plot below from Merrell et al. (2016), for example, demonstrates a linear relationship between inattention scores at age 5 and Key Stage 2 performance in Maths and English; even relatively minor decreases in attention skills have a measurable impact on school attainment.

Screen Shot 2017-10-05 at 15.53.36
Box and whisker plot showing relatively linear decreases in attainment at age 11 by number of criteria met relating to inattention at age 5. Taken from Merrell et al. (2016)

3. Attention may mediate other key variables which contribute to school success

Although hardly surprising, the finding that individual differences in attention skills can affect educational outcomes in school age children (especially above and beyond IQ and other background variables) suggests that they are worthy of further investigation. In addition, however, there is evidence that attention may be an important mediating factor in a number of other key skills needed for school readiness. Barriga et al. (2002) examined a range of psychological and behavioural complaints in teenagers (withdrawal, somatic complaints, delinquent behaviour, and aggressive behaviour) and found that attention significantly mediated the relationship between all of these and academic achievement. In other words, attention skills (or lack thereof) played a significant role in determining whether these factors actually did end up having a detrimental effect on students: the better developed their attention skills, the less they were affected.

Also, whilst we know that ADHD will often co-occur with other executive deficits such as self-monitoring and working memory, it is less widely known that this relationship is also present in non-clinical samples (Gathercole et al., 2008), so even relatively minor problems of attention can be magnified through their relationship to other crucial skills. Admittedly the direction of causality in this case is not as clear-cut, but again this is at least indicative of the the broad importance of attention skills to general school readiness and success.

 

4. Attention skills impact on happiness

In addition to the direct effects of distraction on educational attainment, there are other important social and emotional consequences of everyday inattention. Emerging evidence from a number of fields suggests that the ability to control the focus of one’s attention and to resist distraction may be an important factor in people’s experience of general well-being and happiness.

Firstly, people who are distracted often report reduced happiness. For example, distraction by social media has also been found to negatively affect people’s ratings of their happiness, both under experimental conditions using questionnaires (Brooks, 2015) and in more naturalistic settings using experience sampling techniques such as by sending the participant regular text messages to assess in-the-moment changes in focus and mood (Kross et al., 2013). Being distracted by your own thoughts is also increasingly implicated in negative mood changes. ‘Spontaneous’ mind wandering (the unintentional drifting of one’s thoughts from a focal task toward other, task-unrelated thoughts; Seli, Risko & Smilek, 2016) is associated with reduced happiness – a finding that has been noted in both laboratory (Smallwood, Fitzgerald, Miles & Phillips, 2009) and real world contexts (Killingsworth & Gilbert, 2010).

Secondly, people who are sad often report increased distractibility. Distractibility is commonly recognised as a symptom in depression and other affective disorders (e.g. Mialet, Pope & Yurgelun-Todd, 1996), and artificially lowering participants’ mood during an experiment has been shown to lead to an increase in their distractibility (Pacheco-Unguetti & Parmentier, 2014).

Clearly, it is likely that these findings are at least partially related to the other two points above. If you have the requisite attention skills for educational success then it is likely that you will also be happier for lots of other reasons as well. However, lab experiments of distraction which have measured mood usually find that the effect on mood (for most participants) is relatively short lived. This suggests that, rather than simply reflecting general life circumstances, there is often something inherently unpleasant about the act of being distracted from a main focus, which therefore affects our mood accordingly1.

We know it’s important… but that’s pretty much it for studies in schools

The three points above aimed to establish why I think attention skills are worthy of greater focus from both researchers and schools. If we accept the argument so far, then a range of other questions present themselves. What are the specific differences between children with ‘good’ and ‘bad’ attention control? How do problems like distractibility manifest themselves at different ages? How long are children of different ages able to focus their attention productively in different academic situations? Are there any reliable early signs of approaching inattention that can be identified in students? What conditions make distraction more likely? And perhaps most importantly of all: what, if anything, can be done to reduce inattention in the classroom?

It is notable how few answers we have to these questions for school age children. We are closer to some answers for studies using university students, often in lecture settings, but it is not always clear how these findings should be applied to younger children and school settings. In lectures we know, for example, that attention often measurably decreases through the duration of a lecture. Students take fewer notes, fidget and look around more, and even show reduced heart rates towards the end of a lecture (see Wilson & Korn, 2007, for a discussion of all of these). Students in lectures also mind wander significantly more in the second half of lectures (Risko et al., 2012) and report becoming bored more easily (Mann & Robinson, 2009). We also know some of the conditions under which students report greater levels of distraction in lectures, including background noise (Zeamer & Fox Tree, 2013) and non-work related laptop use (not only measurably distracting for the user, but also for others in their vicinity! See Sana, Weston and Cepeda, 2013).

We also have some early hints at ideas which might help to reduce levels of inattention, again from university student samples. For example, interpolating lecture content with regular short quizzes has been found to both improve recall and reduce mind wandering rates (Spuzner, Khan & Schachter, 2013), although to my knowledge this remains to be tested against other, external distractions. Also, there is emerging evidence that pitching the difficulty of the task correctly (i.e. challenging, but not impossible) may act as a ‘shield’ against distraction (Halin et al., 2014). Again, however, exactly how these ideas play out in the school classroom as opposed to the lecture theatre or laboratory testing room remains to be established.

In conclusion, given the widespread acknowledgement and evidence regarding the importance of attention skills to school success, it is surprising how little we know regarding the specifics of attention and distraction in the classroom. I am optimistic that a greater understanding of such processes over the next few years could lead to a number of relatively simple pedagogical or environmental strategies to improve attention in the classroom. If, that is, we pay a little more attention to them.

 

 

1. The fact that the effect of distraction on happiness is short-lived does not diminish its potential to have longer-term impacts. A short term effect that is being experienced on a very regular basis can quickly develop into something much more serious. In this way, a person experiencing very frequent distraction may begin to experience more severe and longer-lasting mood changes.

 

References

  • Bornstein, M. H., & Sigman, M. D. (1986). Continuity in mental development from infancy. Child development, 251-274.
  • Caldwell, J. E. (2007). Clickers in the large classroom: Current research and best-practice tips. CBE Life Sciences Education, 6, 9-120.
  • Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: potential of the concept, state of the evidence. Review of Educational Research, 74, 59–109.
  • Hidi, S. (1995). A re-examination of the role of attention in learning from text. Educational Psychology Review, 7, 323–350
  • Posner, M. I., & Rothbart, M. K. (2005). Influencing brain networks: implications for education. Trends in Cognitive Sciences, 9, 99–103
  • Breslau, J., Miller, E., Breslau, N., Bohnert, K., Lucia, V. C., & Schweitzer, J. (2009). The Impact of Early Behavior Disturbances on Academic Achievement in High School. Pediatrics, 123(6), 1472–1476. http://doi.org/10.1542/peds.2008-1406 
  • Duncan, G. J., Dowsett, C. J., Claessens, A., Magnuson, K., Huston, A. C., Klebanov, P., … Zill, N. (2007). School Readiness and Later Achievement. Developmental Psychology, 43(6), 1428–1446. Retrieved from http://dx.doi.org/10.1037/[0012-1649.43.6.1428].supp
  • Halin, N., Marsh, J. E., Hellman, A., Hellström, I., & Sörqvist, P. (2014). A shield against distraction. Journal of Applied Research in Memory and Cognition, 3(1), 31–36. http://doi.org/10.1016/j.jarmac.2014.01.003
  • Mann, S., & Robinson, A. (2009). Boredom in the lecture theatre: an investigation into the contributors, moderators and outcomes of boredom amongst university students. British Educational Research Journal, 35(2), 243–258. http://doi.org/10.1080/01411920802042911
  • Merrell, C., Sayal, K., Tymms, P., & Kasim, A. (2016). A longitudinal study of the association between inattention, hyperactivity and impulsivity and children’s academic attainment at age 11. Learning and Individual Differences. http://doi.org/10.1016/j.lindif.2016.04.003
  • Merrell, C., & Tymms, P. B. (2001). Inattention, hyperactivity and impulsiveness: their impact on academic achievement and progress. British Journal of Educational Psychology, 71(1), 43-56.
  • Barriga, A. Q., Doran, J. W., Newell, S. B., Morrison, E. M., Barbetti, V., & Robbins, B. D. (2002). Relationships Between Problem Behaviors and Academic Achievement in Adolescents The Unique Role of Attention Problems. Journal of Emotional and Behavioral Disorders10 (4), 233-240. 
  • Gaspar, J. M., Christie, G. J., Prime, D. J., Jolicœur, P., & McDonald, J. J. (2016). Inability to suppress salient distractors predicts low visual working memory capacity. Proceedings of the National Academy of Sciences. http://doi.org/10.1073/pnas.1523471113
  • Gathercole, S. E., Alloway, T. P., Kirkwood, H. J., Elliott, J. G., Holmes, J., & Hilton, K. A. (2008). Attentional and executive function behaviours in children with poor working memory. Learning and Individual Differences, 18(2), 214–223. http://doi.org/10.1016/j.lindif.2007.10.003
  • Gazzaley, A. (2011). Influence of early attentional modulation on working memory. Neuropsychologia, 49(6), 1410–24. http://doi.org/10.1016/j.neuropsychologia.2010.12.022
  • Brooks, S. (2015). Does personal social media usage affect efficiency and well-being? Computers in Human Behavior, 46, 26–37. http://doi.org/10.1016/j.chb.2014.12.053
  • Kross, E., Verduyn, P., Demiralp, E., Park, J., Lee, D. S., Lin, N., … Ybarra, O. (2013). Facebook Use Predicts Declines in Subjective Well-Being in Young Adults. PLoS ONE, 8(8), 1–6. http://doi.org/10.1371/journal.pone.0069841
  • Seli, P., Risko, E. F., & Smilek, D. (2016). On the Necessity of Distinguishing Between Unintentional and Intentional Mind Wandering. Psychological Science. http://doi.org/10.1177/0956797616634068
  • Smallwood, J., Fitzgerald, A., Miles, L. K., & Phillips, L. H. (2009). Shifting moods, wandering minds: negative moods lead the mind to wander. Emotion (Washington, D.C.), 9(2), 271–6. http://doi.org/10.1037/a0014855
  • Killingsworth, M. A., & Gilbert, D. T. (2010). A wandering mind is an unhappy mind. Science (New York, N.Y.), 330, 932. http://doi.org/10.1126/science.1192439
  • McCall, R. B., & Carriger, M. S. (1993). A meta‐analysis of infant habituation and recognition memory performance as predictors of later IQ. Child development, 64(1), 57-79.
  • Mialet, J.-P., Pope, H. G., & Yurgelun-Todd, D. (1996). Impaired attention in depressive states: a non-specific deficit? Psychological Medicine, 26(05), 1009. http://doi.org/10.1017/S0033291700035339
  • Pacheco-Unguetti, A. P., & Parmentier, F. B. R. (2014). Sadness Increases Distraction by Auditory Deviant Stimuli. Emotion, 14(1), 203–213.  http://doi.org/10.1037/a0034289
  • Risko, E. F., Anderson, N., Sarwal, A., Engelhardt, M., & Kingstone, A. (2012). Everyday Attention: Variation in Mind Wandering and Memory in a Lecture. Applied Cognitive Psychology, 26(2), 234–242. http://doi.org/10.1002/acp.1814 
  • Sana, F., Weston, T., & Cepeda, N. J. (2013). Laptop multitasking hinders classroom learning for both users and nearby peers. Computers & Education, 62, 24–31. http://doi.org/10.1016/j.compedu.2012.10.003 
  • Sigman, M., Cohen, S. E., & Beckwith, L. (1997). Why does infant attention predict adolescent intelligence? Infant Behavior and Development, 20(2), 133–140. http://doi.org/10.1016/S0163-6383(97)90016-3
  • Szpunar, K. K., Khan, N. Y., & Schacter, D. L. (2013). Interpolated memory tests reduce mind wandering and improve learning of online lectures. Proceedings of the National Academy of Sciences, 110(16), 6313–6317. http://doi.org/10.1073/pnas.1221764110
  •  Zanto, T. P., & Gazzaley, A. (2009). Neural Suppression of Irrelevant Information Underlies Optimal Working Memory Performance. Journal of Neuroscience, 29(10), 3059–3066. Retrieved from http://gazzaleylab.ucsf.edu/wp-content/uploads/2014/09/Zanto2009-Neural-suppression-of-irrelevant-information-underlies-optimal-working-memory-performance.pdf
  • Zeamer, C., & Fox Tree, J. E. (2013). The process of auditory distraction: Disrupted attention and impaired recall in a simulated lecture environment. Journal of Experimental Psychology: Learning, Memory, and Cognition, 39(5), 1463–1472. http://doi.org/10.1037/a0032190
Pay attention! Why I think it is important to study attention in school children

‘Liking’ vs ‘wanting’. A neuroscientific view on classroom motivation

One way in which educational neuroscience research can have an immediate and direct effect on modern education is through evaluating whether the theories and techniques which are currently used in schools are plausible, given the neuroscientific and cognitive evidence. This rather humble, constraining role is in contrast to the popular image of neuroscience as a shiny new tool with which to revolutionise classroom instruction (and as a result is far less likely to attract any funding). Still, I see it as a crucial first step in providing practical applications linking the lab and the classroom coherently. As an example, let’s take the case of motivation. Do common ideas about motivation in the classroom coherently reflect what we know about motivation in the brain?

I came across the picture below this week, with a quote attributed to educational consultants Gayle Gregory and Carolyn Chapman.

motivation-and-fishing

Whist I have struggled to find the specific reference given in the picture, Gregory and Chapman are a pretty prolific publishing duo, so it wasn’t difficult to find other similar material. For example, in ‘Differentiating Instruction with Style’, Gregory (2005) writes that motivation to learn was increased when students:

“chose to learn the topic, had fun learning, got a sense of personal satisfaction from the experience, were able to use the learning to enhance their lives and enjoyed working with their instructor.”

Gregory and Chapman’s fishing analogy and the quote above taps into a natural intuition that motivation and enjoyment are intrinsically linked. If we find something enjoyable, then presumably we will want to do it again. The obvious conclusion for educators to draw is that if we want motivated students, we must focus our efforts on making learning as enjoyable as possible for them.

Here, then, is an intuitive and seemingly common-sense psychological theory. It is also one that is hugely prevalent across all levels of education. Indeed, trainee teachers are taught this very concept; teacher training courses will often cover intrinsic motivation, where the satisfaction of performing the action itself provides the motivation to repeat it. Maslow’s Hierarchy of Needs is the classic example of this idea, though there have been many other adaptations since (see e.g. Csikszentmihalyi, 2000; Glasser, 1990, 1998; Ryan & Deci, 2000). All these theories assume a close, even necessary, connection between liking something and wanting to repeat it. But is this assumption supported by what we know about the neuroscience of motivation? I would argue that it is not.

Liking is not the same as wanting

Evidence emerging over the last 20 years of research into the neuroscience of motivation has begun to strongly suggest that merely finding something pleasurable may not actually be enough to generate a motivational state; in fact, liking something and wanting to repeat it may be dissociable. In an excellent review of neuroscientific models of motivation and their relevance to education, Kim (2013) writes:

This means that a state of liking for a specific object or activity cannot be understood as a motivational state and that liking is not a prerequisite for generating motivation. From this perspective, liking refers to an emotional state whereas wanting has more to do with motivation and decision utility (Berridge and Aldridge, 2008). 

A good deal of the careful work unpacking the various different aspects of what makes an experience pleasurable has come from the lab of Kent BerridgeFor example, whilst liking and wanting have previously both been associated with a region of the brain called the nucleus accumbens (NAcc), Berridge (2003) found that they are actually processed by distinct, anatomically separate NAcc regions which can operate independently of one another. In addition, liking and wanting may involve different neurotransmitters, as artificially suppressing dopamine release can reduce wanting behaviour towards a stimulus without reducing the degree of liking for it (Berridge and Robinson, 2003). Berridge concluded that dopamine was only important for increasing the ‘incentive salience’  ̶  the degree of wanting  ̶   of a stimulus, and in turn therefore producing a motivational state to repeat it, rather than for regulating the liking of the stimulus itself.

Whilst this distinction between liking and wanting may seem initially counter-intuitive, it is actually one that we are all pretty familiar with in our everyday lives. Many of us will recognise that it is perfectly possible to be highly motivated to perform an action, without finding the action itself intrinsically pleasurable. An obvious example for many people might be our jobs, but even within the realm of activities which we freely choose to do this distinction is still surprisingly common. Take exercise, for example. Many people have strong desire to exercise (exercise has a high ‘incentive salience’) and are therefore motivated to exercise regularly. For a good proportion of these people, however, the actual process of exercise, the in-the-moment sensory experience of it, is not in itself pleasurable. Indeed, it may sometimes be actively unpleasant; the first football game after buying new boots was always an agonising ordeal, but there was no way I was actually going to stop playing. Why, then, do we continue? Because we have some higher goal (or stimulus of very high incentive salience) which motivates us, overriding the temporary experience of pain, tiredness or discomfort.

runner-in-pain-article
Many ‘hobbies’ may not in themselves be pleasurable at the time. Piano practice was far worse than this.

A less wholesome example of the same process is drug abuse. Drug addicts show a stark dissociation between liking and wanting. They may come to hate the drug itself, but the incentive salience is such that they crave it nonetheless (Berridge & Robinson, 1995). Animals too will continue to self-administer a drug long after they appear to find the experience pleasurable (Berridge & Valenstein, 1991), even to the point of complete exhaustion or death (Olds and Milner, 1954).

Explaining the difference: hedonia and eudaimonia

Identifying different components of happiness is by no means a new idea. Aristotle distinguished between hedonia (pure sensory pleasure) and eudaimonia (a life well-lived or ‘human flourishing’), and this ancient division is actually remarkably useful in helping us to interpret modern day neuroscientific findings. Hedonia represents ‘liking’, whilst eudaimonia provides the ‘wanting’ or incentive salience (as well as higher cognitive influences such as goal setting). Whilst in most conceptions of eudaimonia it is assumed to be a positive force, it is important to note the corollary, overly intense ‘wanting’ can lead to unhappiness and addiction (Kringelbach & Berridge, 2009). Whilst the brain systems governing hedonic and eudaimonic experience are complex, and extend beyond simply different areas of the NAcc mentioned above, they are again clearly distinguishable in the brain, involving different regions and neurotransmitters (Kringelbach & Berridge, 2009).

Hedonia and Eudaimonia in education

So what relevance has this neuroscientific distinction between eudaimonia and hedonia for education? I would say quite a lot. If we accept that the incentive salience of an object is not intrinsically linked to our liking of it, then suddenly the rationale behind many teaching strategies is thrown into question. As Kim (2013) concludes:

There is a need for careful reconsideration of the argument in which the school activity should be enjoyable to generate motivation because pleasure and enjoyment may not automatically lead to motivation.

When considering the happiness of students in lessons, we have a natural tendency to think in terms of hedonic experience, prioritising the immediate gratification of an enjoyable activity and assuming that this will create a motivational engagement. Instead, the component of happiness which has the strongest impact on motivational processes is eudaimonia. This raises a challenge, as it much easier to see how one might create a hedonic experience for students than a eudaimonic one. Uncovering which techniques promote a eudaimonic educational environment is a question for classroom research rather than the lab1, but the answers are likely to lie in approaches which eschew short-term emotional gratification in favour of challenge and student satisfaction over a longer time frame.

So how can neuroscience influence education?

Much of the debate around the potential impact of neuroscience on education surrounds its potential (or otherwise) to create revolutionary, novel teaching techniques. I wrote last week about why I thought that this was an unnecessarily restrictive approach. The application of the neuroscience of motivation to the classroom is a great example of how neuroscience (and cognitive psychology) research can be used to critically appraise and fine-tune what we do already, rather than re-invent the wheel. Maybe neuroscience never will revolutionise the way that information is delivered in schools (I wouldn’t be at all surprised if it didn’t). But providing teachers with a reasoned and evidence-based justification for resisting the pressure to prioritise cheap emotional gains at the expense of long-term challenge and eudaimonic satisfaction, whilst also reassuring them that this is more likely to produce motivated students, rather than less? That’s not bad for starters, is it?

Footnotes:

  1. An ongoing programme looking at this very issue is the Sci-Napse project run by Paul Howard-Jones from Bristol University and funded by the EEF and the Wellcome Trust. The study is based on lab findings that the dopamine responses in brain areas associated with creating incentive motivations are stronger when rewards are provided in an uncertain or inconsistent fashion. This makes sense; uncertain rewards have been known to be highly motivating to behaviour ever since Skinner’s experiments with rats and pigeons from the 1930s. Some teachers may have ethical qualms about student learning being influenced through targeting the same circuits that were hijacked to produce the uncontrolled, addictive behaviours produced in Skinner’s pigeons, but it’s an interesting approach.
  2. Of course, the most effective methods are likely be ones which are able to produce both hedonic and eudaemonic experiences. The interaction between the two produces stronger responses than either individual system (Smith & Berridge, 2007). A combination of eudaimonia and hedonic also more strongly predicts positive work outcomes (Turban & Yan, 2016). I focus here on the importance of eudaimonia because of its specific relationship to motivation and also because of its tendency to be neglected in the classroom.

References:

Berridge, K. C., & Robinson, T. E. (1995). The mind of an addicted brain: neural sensitization of wanting versus liking. Current Directions in Psychological Science, 4(3), 71-75.

Berridge, K. C., and Valenstein, E. S. (1991). What psychological process mediates feeding evoked by electrical stimulation of the lateral hypothalamus? Behav. Neurosci. 105, 3–14.

Berridge, K. C., and Robinson, T. E. (2003). Parsing reward. Trends Neurosci. 26, 507–513.

Berridge, K. C., and Aldridge, J. W. (2008). Decision utility, the brain and pursuit of hedonic goals. Soc. Cogn. 26, 621–646.

Csikszentmihalyi, M. (2000). Happiness, flow, and human economic equality. Am. Psychol. 55, 1163–1164.

Gregory, G. H., & Chapman, C. (2012). Differentiated instructional strategies: One size doesn’t fit all. Corwin press.

Gregory, G. H. (Ed.). (2005). Differentiating instruction with style: Aligning teacher and learner intelligences for maximum achievement. Corwin Press.

Kim, S. I. (2013). Neuroscientific model of motivational process. Frontiers in Psychology, 4(98), 2.

Kringelbach, M. L., & Berridge, K. C. (2009). Towards a functional neuroanatomy of pleasure and happiness. Trends in Cognitive Sciences, 13(11), 479–487. http://doi.org/10.1016/j.tics.2009.08.006

Olds, J., & Milner, P. (1954). Positive reinforcement produced by electrical stimulation of septal area and other regions of rat brain. Journal of comparative and physiological psychology47(6), 419.

Ryan, R. M., and Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am. Psychol. 55, 68–78

Smith, K. S., & Berridge, K. C. (2007). Opioid limbic circuit for reward: interaction between hedonic hotspots of nucleus accumbens and ventral pallidum. Journal of Neuroscience27(7), 1594-1605.

Turban, D. B., & Yan, W. (2016). Relationship of eudaimonia and hedonia with work outcomes. Journal of Managerial Psychology31(6), 1006-1020.

‘Liking’ vs ‘wanting’. A neuroscientific view on classroom motivation

My NPJ Science of Learning Interview – ‘Educational implications of attention and distraction in teenagers’

The Nature partner journal ‘Science of Learning’ website is another useful addition to the increasing number of resources encouraging a more scientific approach to education and learning.

It’s also just gone up in my estimations greatly (!) as they’ve published an interview with me about my PhD work. Read it here. If you’re a teacher or researcher and any of this sounds interesting to you, please feel free to get in contact.

 

My NPJ Science of Learning Interview – ‘Educational implications of attention and distraction in teenagers’

Psychology for teachers

There are many superb blogs on teaching, and some which focus specifically on the links between teaching and the psychology of learning. What I feel is sometimes not available to teachers are short and accessible introductions to some key ideas regarding how we learn.

This section consists of a series of short blogs designed to introduce teachers to research findings about how students learn, with suggestions for how these ideas could influence practice and links etc for further reading. I hope that you enjoy reading them and find them useful. If anyone has any suggestions for other topics which could be added, please let me know!

It is important to realise that none of these strategies is a magic ticket on their own! Instead, they are a foundation, from which each teacher can experiment and adjust their practice as best suits their teaching style and their school environment. The ‘suggestions for practice’ are simply that – suggestions. You may be able to think of much more effective ways of incorporating a particular piece psychology into your lessons. Feel free to try out new things and to experiment, but use these evidence-based ideas as a starting point. Why not use one or more of these as the basis for a new scheme of work or learning policy at your school? Or arrange an internal CPD day to share ideas and resources?

If you find a particularly effective method that seems to improve student progress, why not contact a Psychology or Education department at a university and see if you can arrange for a larger scale trial of the idea. In fact I would encourage all teachers and schools to take part in research projects into what works in education. The more teachers and schools that can become the driving force behind research (and key partners in it), the more progress we will make in discovering what techniques really work in schools.

The ‘Psychology for Teachers’ section currently contains introductions to (in alphabetical order):

Psychology for teachers