Illustration of networks in the brain.
What are the real-world applications of neuroscience, cognitive neuroscience and the emerging field of educational neuroscience?

Learning agility: cognitive neuroscience in education

Dr Peter Thomas, writing with David Simpson, take a look at the future of cognitive neuroscience in education.

Written while Peter Thomas was founding director of HaileyburyX and David Simpson was Head of Science in Haileybury’s Middle School.

If you are a professional educator, cognitive neuroscience may be a term that is just hovering on the periphery of your awareness.

Cognitive, we all understand. But unless you have a background in psychology or the biological sciences, the neuroscience part may be more opaque. The combination of both may be much less meaningful — as are its applications in learning and teaching in the form of educational neuroscience.

A growing field that is spreading out to encompass more interests and phenomena, neuroscience has as its basis the empirical study of the brain and nervous system. A subject that neuroscience might be concerned with is, for example, the changes in the brain’s structure and connectivity as we age.

Cognitive neuroscience is the study of the biological processes that underlie human cognition. As with the broader field of neuroscience, cognitive neuroscience uses technologies — such as functional neuroimaging — that measure brain activity and provide insights into how biological processes drive cognition, behaviour and action.

One of the reasons that the approach is interesting is that looking at cognitive processes at a granular level can generate new insights that are notoriously hard to pin down using traditional research methods such as experiments and self-reporting. At the very least, they can help us refine concepts such as ‘motivation’ (which we discuss later) and possibly provide new empirically anchored accounts of them.

And in educational terms, since the brain and its associated processes are what allow us to adapt and learn, educational neuroscience is about understanding what learning is and how our neural processes, genetic makeup, and environmental factors influence how we learn.

Educational neuroscience is shedding light on topics such as why certain types of learning are more rewarding than others; the plasticity of the brain and what happens when we learn new skills at different ages; ways of enhancing our ability to learn, and the role of digital technologies in learning, along with many others.

What are the real-world applications of neuroscience, cognitive neuroscience and the emerging field of educational neuroscience? Is it merely of theoretical interest? Is it properly the domain of the biological sciences rather than professional educators? And will we just see more dubiously-founded ‘brain boosting’ techniques that claim to improve how much we can learn? Will the neuromyths become more widespread?

In this story, we’ll look at some of these questions — and more.

We’re going to frame them in terms of understanding, and helping support, what we call learning agility: the more we understand at a fundamental level the processes that underlie learning, the better we can support students to become more agile in how they learn and so improve their learning.

We’ll review some of the basic concepts in neuroscience, cognitive neuroscience and educational neuroscience. We’ll take a look at some examples of studies about topics like plasticity and its implications and look at some of the limitations (or, rather, as yet unfulfilled aims) of these areas of study.

And, most importantly, we’ll look at what we might expect to see as practical outcomes that can inform real-world teaching.

Some concepts in cognitive neuroscience

Although we don’t claim that this list is exhaustive or authoritative, here are some elements of cognitive neuroscience that have implications for learning agility at the current time that form part of an educational neuroscience. The research base is huge and growing.

Possibly the most well-known and most widely accepted finding from neuroscience is neuroplasticity.

Also known as neural plasticity or brain plasticity, it describes the ability of the nervous system to change the structure, functions, or connections of the neurons in the brain change in response to stimuli.

While the volume and complexity of findings about neurons and how they are organised are beyond this story, it is clear that the ability of neurons to modify how they communicate with each other using chemical neurotransmitters across synapses is a fundamental property of nervous systems.

According to Demarin, Morović, and Béne (2014), there are two types of neuroplasticity: structural neuroplasticity, in which the strength of the connections between neurons changes, especially in the developing brain; and functional neuroplasticity, which describes permanent changes in the relationship between synapses brought about by learning.

The upshot of all of this is that the brain is not composed of fired wiring — it changes itself.

This is not a new idea.

The implications have been explored for decades in areas such as how the brain reorganises itself after trauma and the effects of stress on how the brain works. An understanding of neuroplasticity offers both an explanation of why when you practice something consistently, like learning how to play a musical instrument, you get better: it’s because you rewire your brain based on experience. As is often said, “cells that fire together, wire together”: if you perform a task or recall some information, that causes connected neurons to activate. It strengthens the connections between those neurons. Practice, it turns out, makes permanent.

An understanding of neuroplasticity also offers a DIY basis for helping you rewire it yourself.

Neuroplasticity has formed the basis for many ‘brain training’ techniques in anything from mindfulness, meditation, therapy for ADHD, OCD and autism, transcranial magnetic stimulation to treat chronic pain and the use of tasks and games to improve memory or cognitive abilities. As Norman Doidge, the author of the most well-known popular science book on neuroplasticity, The Brain That Changes Itself (Doidge 2007), says, “Within the lab, within science, within neurophysiology, neuroplasticity is established fact — nobody is challenging it.”

The relationship between this fundamental concept in cognitive neuroscience and learning is obvious: learning creates new pathways in the brain. Each time you learn something, your brain changes.

Of course, there is a multitude of pedagogical theories, frameworks and approaches that have dictated how teachers teach and how learners learn.

The most well-known and most widely applied is Bloom’s taxonomy which generations of teachers have used. Of course, there is a whole range of differing, and differently motivated, educational theories and philosophies including higher-order thinking, mastery learning, learning styles or reflective practice. What can cognitive neuroscience tell us about how learning happens and how we can develop students’ learning agility that supports, challenges, strengthens, or enhances educational theory?

As reflected in Bloom’s taxonomy, it may depend on what kind of learning we are talking about.

Simple non-associative learning is different to much more complex social learning; and those, in turn, are different from skill learning. But in each case, given appropriate practice, we do know that humans improve on every kind of task — whether that’s perceptual learning, motor learning or cognitive training.

It may also depend on how long the learning takes. There is a distinction between fast stage learning that happens as someone becomes initially familiar with a task, and slow stage learning that takes much longer.

And there is the question of whether learning is specific or can be transferred. Research suggests that the transfer of learning from a trained task to even another very similar task is the exception rather than the rule, even though the skills acquired in video game training, musical training, or athletic training do seem to transfer.

Learning is complex. But for now, we will assume that in a classroom context (and for ‘classroom’ we can read a variety of blended, online, hybrid or flex learning settings) the brains of students respond to the complexities of the setting, including the teachers, the instructional materials and their peers.

Neuroplasticity — the creation of new neural pathways — relies on learners encountering activities that are patterned, repeated and done with moderate stress (often known as eustress or beneficial stress), that forces neurons to fire and so neural networks to expand and consolidate, when supported by scaffolding. Scaffolding is important because of the power of neuroplasticity: once something has been learned, it is often difficult to unlearn it. Bad habits are hard to break, and bad learning is hard to unlearn, and so the process of inquiry-based learning needs to be supported by a process of guided inquiry.

When they teach, teachers do pattern complexity by creating sequences that are meaningful to students and which help students make connections. Ultimately this relies on neuroplasticity.

While it’s fascinating to think about the workings of the brain, and neuroscience research contributes to more general understandings of brain function and structure, these are not just issues of academic interest for education.

The questions of how students learn and so how should we teach are pressing and immediate.

Take the current discussion of Australian PISA results that show a decline in maths outcomes. One in five Australian 15-year-olds fail to achieve the international baseline level. One suggested solution is to emphasise ‘problem-solving skills’ under the assumption that such skills will transfer and allow students to solve unknown problems, thus equipping them for ‘real-world’ mathematical challenges rather just to be competent users of set procedures”.

This may or may not be the solution, but it should be clear that, from the discussion of neuroplasticity, learning is complex and there may be other relevant influences.

For example, just as neuroplasticity means that the brain reshapes its neuronal connections based on experience, it is now accepted that negative experiences can inhibit learning — just as positive ones can help reinforce it. Research by Hattie (2018) highlights the importance of affective-emotional influences on learning, such as teachers’ expectations of their students’ competence.

And these too, have neurological underpinnings: neuronal circuits in the prefrontal cortex interact with subcortical structures like the basal ganglia and the hippocampus and highlight the influence of motivation, which we will discuss next, on learning.

Learning is necessarily an emotional process — whether that emotion is positive or negative — and neglecting that in favour of purely cognitive aspects of learning is not to see the whole picture, and one place where understanding the neuroscience of learning is incredibly useful.

Finally, and as a segue into a discussion about the neuroscience of motivation, Carol Dweck’s concept of ‘growth mindset’ may also be seen through a neuroscience lens.

Dweck proposes that a person who has a growth mindset believes that their most basic abilities can be developed through dedication and hard work. She says that this “creates a love of learning and a resilience that is essential for great accomplishment.” The implication is that for those who believe they can change and improve, change and improvement is more likely to happen.

But is growth mindset a neurological phenomenon?

The limited neuroscientific research on the neural mechanisms of growth mindset indicates it is associated with the dorsal regions of the brain. In contrast, intrinsic motivation is associated with the midbrain regions. The common brain areas related to both growth mindset and intrinsic motivation are ACC and ventral striatum. There is similarly limited (although promising) research into the mechanisms of ‘grit’.

Equipped with this knowledge, we may be better able to pin down neurological underpinnings and, coupled with a better understanding of the neuroscientific aspects of growth mindset and its and relationship to motivation, and it may provide deeper insights into learning agility.

Motivation is important, certainly to decision-making, and certainly to learning.

Every teacher knows that if students are motivated, they will learn more effectively and recall more of what they learned.

Although motivation is complex — for example, the difference between mastery goals (which are about competence) and performance goals (which are about performance compared to others) — motivation matters, whether intrinsic or extrinsic motivation. Both affect learning.

What light can cognitive and educational neuroscience shed on motivation?

In terms of intrinsic motivation — a spontaneous curiosity to seek out challenges and exercise and develop new skills and knowledge — many studies have found intrinsic motivation to be a predictor of better learning, better performance, more creativity and psychological balance.

Although intrinsic motivation is itself a complex cognitive, affective, and behavioural phenomenon, researchers in neuroscience, such as Ryan and Di Domenico (2016), have started to examine it to integrate behavioural studies with neuroscience findings.

It seems that the neurotransmitter dopamine is an important part of intrinsic motivation. This is because dopamine plays a role in one of the fundamental systems in mammalians brains, a general-purpose SEEKING system that keeps them in a state of exploratory engagement important for, amongst other things, finding food and mates. Dopamine also plays a part in cognitive flexibility, creativity and persistence.

Neuroscientific studies, such as that by de Manzano et al. (2013), used positron emission tomography to examine the correlation between intrinsic motivation and how dopamine is processed in the brain. Others, looking at EEG waveforms, aim to shed light on intrinsic motivation and learning. Along with studies looking at how motivation might be part of large-scale neural networks in the brain, the aim is to understand how motivation works and how it is related to other neurological processes such as working memory.

In terms of extrinsic motivation, studies indicate that rewards (for example, money) enhance learning through the operation of a reward network in the brain. But as in all things to do with how our brains work, the reality is complicated. It seems that it might also be the case that rewards reduce learning.

Murayama, Matsumoto, Izuma and Matsumoto (2010) assigned subjects to a reward group (play for money) or a control group (play for fun) and asked them to play a game while being scanned in an fMRI machine. Studies of the brain activity of those in the reward group suggested that they were less engaged — had less intrinsic motivation — because an area of the reward network in the brain was less active.

What can these types of studies (of which there are many) tell us about motivation? And how might they help educators as they deal with the task of helping students to be more agile learners?

One thing they do is to indicate that the relationship between intrinsic and extrinsic motivation is complex.

People typically overestimate the power of intrinsic rewards, especially for complex or boring tasks — they say that their intrinsic desire to learn will be enough. But it turns out that people are also motivated by seeing others enjoying doing things — generating a kind of anticipatory intrinsic motivation that makes them think they will enjoy doing something.

Educators need to consider the complex balance between intrinsic and extrinsic motivation. While trying to design learning that stimulates intrinsic motivation is important, other factors — including fostering student agency, autonomy, and creating opportunities for performance-related feedback — can influence intrinsic motivation.

But didn’t we know this already — either from observation or from educational research?

While the neuroscience of motivation is still in its infancy, neuroscience methods such as fMRI can measure a learner’s neural responses to a specific learning task. If we examined, for example, growth mindset and how it relates to motivation and learning, we might find, as Ng (2018) did, that:

“…there is an undermining effect of monetary reward on intrinsic motivation; that is, one’s intrinsic motivation is undermined when extrinsic reward is no longer promised. Neuroscience findings suggest that there are connections between the striatum and the prefrontal cortex in determining the outcome; decreased activation of the striatum and midbrain when the subjects do not get the task value, as well as decreased activation of the lateral prefrontal cortex (LPFC) when they are not motivated to show cognitive engagement with the task. Since growth mindset is a belief system that favors hard work and performance monitoring, a learner’s subjective belief in determining the outcome may modulate activity of the striatum, in response to cognitive feedback that nurtures growth mindset. Hence, neuroscientific evidence may provide insights into the learning and motivational processes that could be helpful for teachers and practitioners in improving their learning and teaching practices, thus supporting student learning and motivation.”

We suggest that role of cognitive neuroscience is to help shed light on not just how motivation works in the brain but to understand how it relates to other processes and to untangle those relationships at a granular level. It can also provide a better understanding of processes like ‘reward’ — of which intrinsic and extrinsic motivation is a part — by looking at how the reward system in our brain functions.

And if we better understand how neurological reward systems work, we may be able to use that understanding to support more agile learning.

Memory may be one of the capacities that uniquely identifies us as humans. Without it would be impossible for language, relationships, or identity to develop or for learning to happen.

Yet, over 100 years of research in psychology, biology, or neuroscience hasn’t arrived at a single understanding of memory and how it works.

It’s widely accepted that we can distinguish between short-term memory and long-term memory. Short-term memory is a buffer where a limited amount of information can be held briefly — such as a string of meaningless numbers you are asked to remember.

Long-term memory is a huge — and potentially unlimited — store of knowledge about past events that we build up through experience and learning. And, as Lovell (2020) proposed, we can also think about the world as a long-term external memory store: the accumulated information stored on the internet, for example, is effectively an extension of our long-term memory that we can access at any time.

Working memory is a refinement of the idea of short term memory to account for findings about how the brain processes information in short term memory rather than just briefly storing it.

The currently accepted understanding is that working memory consists of three different types of storage: the phonological loop (which stores auditory information by silently rehearsing sounds or words in a continuous loop); the visuo-spatial sketchpad (which stores visual and spatial information), the multimodal episodic buffer (which links information to form meaningful sequences) and finally the central executive (which channels information to the three other stores).

The amount of information that can be maintained in working memory is limited but, according to recent studies, it can be expanded through training, and the expanded capacity can be transferred between trained and non-trained tasks. An individual’s working memory capacity also seems to predict performance in higher cognitive abilities (Klingberg 2010). Research suggests that the neurological handling of attention can affect working memory capacity, which is especially pertinent to learners with ADHD and the educators who teach them. Even if training does not expand working memory capacity, reducing the amount of task-irrelevant information may free more capacity is available to process learning (McNab and Klingberg 2008).

Research in this area is complex, multidisciplinary and, as in all areas of psychological and neuroscientific research, new findings are emerging all the time that propose refinements of the working memory model. For example, Matthey, Bays and Dayan (2015) propose the existence of complex processes that allocate memory resources depending on the nature of the information and what was previously stored in parts of working memory.

Our interest in this story is the cognitive neuroscience of memory and how it can inform educational neuroscience and contribute to learning agility. There must, of course, be a set of neurological processes that underlie memory. As we know, memory can be improved due to neuroplasticity, and we can use an understanding of those processes to shed light on topics in learning.

Cognitive neuroscience is interested in how information and mental experiences are coded and represented in the brain. It has been suggested that, since there are different types of memory — such as topographical (recognition of places), flashbulb (memories of highly emotional events) and episodic (memories tied to a time and place), amongst others — there may be interrelated neural networks, in different brain areas, that store these memories and interact with each other for recall. These networks may interact in complex ways which are currently little understood.

In terms of working memory, the encoding of working memory involves the activation of individual neurons by sensory inputs, which persist even after the sensory input disappears. Imaging studies detected working memory signals in the medial temporal lobe (MTL) — a brain area associated with long-term memory and the prefrontal cortex — suggesting a relationship between working memory and long-term memory. The MTL also contains structures related to cognitive and emotional functions, such as the amygdala, which drives emotional responses and interacts with regions like the hippocampus to encode emotions into memory.

And in terms of shifting the contents of working memory into long-term memory, cognitive neuroscientists have suggested that encoding episodic memory involves changes in molecular structures that alter how neurons communicate (Jensen and Lisman 2005). For recall of memories, it has also been suggested that memories are altered as they are retrieved, rather than just being carbon copies, through complex processes involving the MTL. What you remember may not be what you remembered.

But what does this mean?

We can ask the same question as we have earlier in this story — what can these types of studies (of which there are many) tell us about memory and learning? And how might it help educators as they deal with the task of supporting students to be more agile learners?

One clue is provided by the suggestion we saw earlier by Lovell — that the world is also a memory store.

If what we do when we are learning is commit information to our internal working memory, partly based on information we find in the world and then transfer it to our long term memory, how do we make that process as easy as possible? What is the most agile way to learn?

Lovell proposes that working memory is a bottleneck. It is limited, typically new information takes up more working memory than familiar information, and one way to get around this is by a process of chunking.

Lovell demonstrates this by the example of a child first learning to read who sees the letter H for the first time. For the first time, it’s just a collection of lines but later, when processed in long term memory, it becomes a single chunk — the letter H. When the child sees it again, it’s a single element, not separate lines. So then the process of reading, or writing, H becomes automatic and effortless.

This chunking process can be combined with another idea: cognitive load.

Intrinsic cognitive load is the knowledge that we want students to acquire (like the alphabet). Extrinsic cognitive load is the materials educators provide to help students acquire that knowledge. Intrinsic cognitive load is where we want students to devote their limited working memory resources; we shouldn’t clog it up with materials that get in the way.

So in the example of learning to read, sound-letter combinations are intrinsic load (the sounds /d/ /ɒ/ /ɡ/ for dog), whereas a picture of a dog would be an extrinsic load. Lovell’s proposal is that extrinsic load needs to be reduced through good instructional design — which allows students to use their limited capacity working memory more effectively.

Although cognitive load theory is just that — another theory of learning and so will be open to refinement — it does indicate how an understanding of underlying neurological processes can be useful.

If we better understand, for example, the process involved in attention in learning, we may be able to better understand how working memory resources can be used, trained and extended.

Attention is our ability to selectively process stimuli. The neurological processes that underlie attention are critical — they allow us to maintain focus and complete tasks.

Some of the key questions that cognitive neuroscience can shed light on are the influence of practice on attention (can we learn to be more attentive?); whether attention is an epigenetic trait; the influence of reward on attention (does extrinsic motivation matter?); or whether social interaction is important to how we selectively pay attention to stimuli. Cognitive neuroscience methods such as neurostimulation, functional neuroimaging pattern analysis and the evaluation of oscillatory brain activity can uncover the brain mechanisms of attention.

Here’s another example. We spend as much as 50 per cent of our time hours engaged in mind-wandering and researchers are working to understand brain mechanisms behind it.

Research by Kucyi et al. (2017) looked at how what happens in the brain when people are ‘in the zone’ — focused on the task at hand — and ‘out of the zone’ using functional MRI. They found that shifts in the attention were mirrored in major changes in neural networks activity, communication within and between these networks, or ‘dynamic functional connectivity.

Independent, agile learners are aware of — and take control of — their learning.

Of course, ‘self-regulation’ isn’t a term one would often associate with the thoughts, behaviours and actions of adolescents. Young people in this stage of development are more likely to act on impulse, misread or misinterpret social cues and emotions, get involved in accidents and engage in dangerous or risky behaviour. This is true for boys especially — in Australia, the rate of hospitalisation due to injury is 1.5 times as likely for teenage boys as it is for girls (Australian Institute of Health and Welfare 2020).

Self-regulation in learning requires learners to take deliberate action to moderate between negotiating distractions, receiving input from others in the classroom and achieving successful learning outcomes. It’s a goal-driven process in which learners monitor and regulate their internal abilities (what they can do) and their responses to external environments (what happens to them).

Zimmerman (2000) and Zimmerman and Moylan (2009) suggest three cyclical phases in self-regulated learning: a forethought phase, a performance phase and a self-reflection phase. The forethought phase requires task analysis, planning and activation of learning through self-awareness and control of underlying motivations. The performance phase involves students implementing and remaining aware of the strategies they use and regulating their behaviour and learning strategies to stay on track or achieve a goal. The self-reflection phase is concerned with evaluating and judging performance against standards set in the forethought phase.

Research by Heatherton and Wagner (2011) suggests that successful self-regulation is dependent on top-down control from the prefrontal cortex over subcortical regions involved in reward and emotion. Neuroimaging findings suggest that a failure to self-regulate happens whenever the balance between the subcortical and prefrontal cortex tips towards the subcortical areas, due to particularly strong impulses or when prefrontal cortex function is impaired or underdeveloped.

This has implications for all adolescent learners, not just those with social or mental health concerns. A student’s learning agility will be negatively impacted if their prefrontal regions cannot regulate, or balance, the subcortical regions involved in representing reward incentives, emotions, or attitudes.

But beyond understanding how the brain works in self-regulation, what is the practical outcome? Can self-regulatory behaviours can be explicitly taught to students?

Bjork, Dunlosky and Kornell (2013) highlight the role of psychological biases and how they impair self-regulated learning. These include misunderstanding the role of errors and mistakes in learning, over-attributing differences in achievement to innate ability and overconfidence and in general, adolescent students’ ability to accurately assess the state of their learning.

It might seem, then, that coaching students to overcome the limitations of their still-developing neurology might not be a simple matter. However, Dunlosky et al. (2007) and Bjork (2013) identify teachable strategies — such as spaced retrieval rather than massed cramming, interleaving rather than blocked sessions on topics, and generating information or procedures rather than looking them up.

In this area, as in the others in this story, educational neuroscience can help unravel some of the intertwined strings in developing learning agility — neuroplasticity, motivation, memory and self-regulation.

As our understanding of the neurological basis for learning and development mature, along with advances in technology and neuroimaging capabilities, the value of insights from the hard science of neurology could amplify our understanding of learning and teaching.

Where to from here?

As we have said throughout this story, while the huge amount of work going on in cognitive neuroscience is fascinating — after all, what could be more intriguing than uncovering what our brains do to produce the experience of being human — a discipline of educational neuroscience could have huge practical ramifications for learning and teaching.

But there is a vast amount yet to discover. And correlation does not imply causation: accurately determining which regions of the brain are responsible, and to what extent, for cognitive behaviours is an ongoing challenge, although technology is coming along in leaps and bounds in this space.

Beyond doing more — and more detailed — research, where do we go from here? How can cognitive neuroscience emerge as educational neuroscience with practical implications? How can educational neuroscience increase the learning agility of students?

At least at the moment, one of the inhibiting factors is the prevalence of the neuromyths (Dekker et al. 2012) that we mentioned earlier.

Folk-beliefs, based on dubious science, such as VAK (visual, auditory, kinaesthetic) learning styles, BrainGym-style ‘education kinesiology’ programmes, or left-brain/right-brain still persist — perhaps because they offer a plausible, simple and comforting set of explanations. As we have seen, cognitive neuroscience is now uncovering the incredible complexity of the neurological mechanisms that underlie cognition, learning, and human behaviour.

Cognitive neuroscience has to deal with complex phenomena using complex and sophisticated methodologies. Its explanations may not be easily accessible to educators, educational leaders, or education policymakers. Educational neuroscience needs to be accessible to those without a PhD in psychology, neurology or cognitive science and five years of postdoctoral research experience.

As Tandon and Singh (2016) point out, quoting Bruer, “educational applications of brain sciences may come eventually, but as of now neuroscience has little to offer teachers in terms of informing classroom practice”. But on the other hand, Galaburda (2010) says, “Knowledge from neuroscience also lends itself to applications to education and I would hypothesise that the predictive value of neuroscience data to learning is apt to be greater than that of genetic data”. There seems to be little consensus, as yet, on what educational neuroscience can and might do.

But perhaps one of the critical problems is the same one shared by any scientific discipline that starts in the controlled environment of the laboratory and tries to emerge into the real and messy world of people. It’s what Willingham (2012) calls the vertical problem:

“we can’t take lab findings and pop them right into the classroom. To use my favorite painfully obvious example, lab findings consistently show that repetition is good for memory. But you can’t mindlessly implement that in schools — “keep repeating this til you’ve got it, kids.” Repetition is good for memory, but terrible for motivation.”

A second problem is what Willingham calls the horizontal problem:

“…in schools, the outcomes we care about are behavioral; reading, analysing, calculating, remembering. These are the ways we know the child is getting something from schooling. At the end of the day, we don’t really care what her hippocampus is doing, so long as these behavioral landmarks are in place […] likewise, most of the things that we can change are behavioral […] For neuroscience to be useful in the classroom we’ve got to translate from the behavioral side to the neural side and then back again.”

The horizontal problem is really about ‘looping in’ neuroscience to education and creating a two-way relationship between what we are coming to understand about the brain through cognitive neuroscience and what happens in learning settings. How do we do that?

Teachers learning about educational neuroscience might be a good start (Hook and Farah 2012).

There are now many courses aimed at practising teachers (such as the Professional Certificate in Educational Neuroscience at Melbourne University, amongst many others worldwide). These typically sit within the label ‘Mind, Brain and Education’ (MBE) studies, which aims to

“bring together biology, cognitive science, development, and education to create a strong research foundation for education [a…] a strong base in research based on the collaboration of researchers and practitioners will lead to many major improvements in education. Evidence will lead to better choices of ways to teach and to facilitate learning, including specification of different learning pathways for different learners.” (Fisher 2009).

A delphi survey of educators (Tokuhama-Espinosa 2017) found that “increased knowledge and insights about the learning and developing brain”, “awareness and debunking of neuromyths” and “improvement in evidence-based educational practices” were seen as the most valuable contributions of research to educational practice. The authors also suggest that:

“…more work is needed to nurture a new type of professional at the crossroads not only of mind (psychology), brain (neuroscience) and education, but also at the intersection of research and teacher practice.” (Tokuhama-Espinosa 2017)

Assuming we could create this type of educational neuroscience teacher-practitioner, what are some of the educational neuroscience interventions we might see?

What would be desirable is that edtech companies use established educational neuroscience findings to build their products. That might be products that provide learning support (most LMSs, for example, are designed with almost no regard to neurological insights into how learning takes place) or products that play a complementary role to that of the teacher.

Even though ‘braingym’-like edtech tools have been debunked, there is a growing neurotech industry — what for education could be called neuroedtechs — that draw on findings from neuroscience.

For example, in the attention space the startup focus@will (Mossbridge 2016) uses streamlined music to reduce exogenous attention and improve endogenous attention. They say say “streamlined music can have a beneficial impact on cognition without any obvious costs, while at the same time it may potentially boost mood”. Or, take a look at memoryOS, which uses Microlearning, gamification and spaced repetition — along with the ‘Mind Palace’ technique and claims to improve dramatically the ability to remember.

According to a review by The Education Endowment Foundation (Wellcome 2014), there is also evidence that some neurofeedback technology may be beneficial to learning.

A study investigating EEG neurofeedback concluded that it produced improvements in the performance ability of music students suggested that self-induced changes in neural rhythms can produce detectable changes in neural function. Other studies, such as that by Kosmyna and Maes (2019), show how EEG neurofeedback could be used to maintain attention levels during learning. And a study by Szafir and Mutlu (2012) describes an adaptive artificial agent designed to recapture diminished attention using verbal and nonverbal cues, significantly improving student recall of learning content.

But as with all research in this area — and probably all research in human behaviour and cognition — it’s wise to treat it with caution.

Beyond the neuroedtechs, there are many areas where educational neuroscience can inform practice.

To take just one example, again drawn from the Education Endowment Foundation review, creativity — now a huge concern to educators, and prized as desirable in many subjects from mathematics to business — can be seen in terms of educational neuroscience.

Researchers looking at ‘creative neurocognition’ have provided insights into individual differences in creativity and strategies for fostering creativity. EEG studies have shown, for example, how individual differences can be explained in terms of an individual’s resting state of attention (whether they are more broadly or narrowly focused). fMRI techniques can validate and explore strategies considered to foster creativity, such as how sharing ideas with others may boost creative output by reducing the tendency to suppress our automatic associations. And researchers have suggested that incorporating unrelated stimuli into creative activities can increase neural function in brain regions related to creative effort. While there are no systematic studies that have tested these strategies in the classroom, those that do exist report positive impacts on creativity when strategies that broaden the attention of young children are used.

So, to wrap up, let’s take a final quote from the Education Endowment Foundation review:

“The demand for neuroscience-informed education comes from both directions, with neuroscientists emphasising the potential of their work to improve education and educators being keen to learn what neuroscience has to offer. This enthusiasm does, however, mean that the topic needs to be approached with care, to ensure that neuroscience ideas are not adopted at too early a stage or before they have been properly translated for classroom use. This enthusiasm also means that interventions with a ‘neuro tag’, whether or not they are linked to neuroscience or indeed have any evidence of educational impact, are likely to propagate within education and be welcomed by schools and teachers — raising the importance of both dispelling myths and accurately disseminating evidence.”

“Dispelling myths and accurately disseminating evidence” would seem a good place to start if we are to encourage, support and enhance the learning agility of our students through educational neuroscience.

Readings and references

Australian Institute of Health and Welface (2020) Australia’s children. DOI: 10.25816/5ebca4d0fa7dd

Adams T (2015) Norman Doidge: the man teaching us to change our minds. The Observer, February 2015.

Baddeley A (2000) The episodic buffer: a new component of working memory? Trends in Cognitive Science. Volume 4, Issue 11, 1 November 2000, Pages 417–423.

Baddeley A and Hitch G (1974) Working memory. In Bower G H (ed) The psychology of learning and motivation: Advances in research and theory (Vol. 8, pp. 47–89). New York: Academic Press.

Bernard S (2010) Neuroplasticity: Learning Physically Changes the Brain.

Brookman-Byrne A (2017) Bringing scientific evidence to the classroom: The rise of educational neuroscience. The Jacobs Foundation Blog on Learning and Development.

Dekker S, Lee N, Howard-Jones P and Jolles J (2012) Neuromyths in Education: Prevalence and Predictors of Misconceptions among Teachers. Frontiers in psychology, 3, 429.

Demarin V, Morovic S and Béné R (2014) Neuroplasticity. Periodicum Biologorum, 116, 209–211.

Doidge N (2007) The Brain That Changes Itself: Stories of Personal Triumph from the Frontiers of Brain Science.

de Manzano Ö, Cervenka S, Jucaite A, Hellenäs O, Farde L, and Ullén F (2013) Individual differences in the proneness to have flow experiences are linked to dopamine D2-receptor availability in the dorsal striatum. Neuroimage 67, 1–6. DOI: 10.1016/j.neuroimage.2012.10.072

Di Domenico SI and Ryan RM (2017) The Emerging Neuroscience of Intrinsic Motivation: A New Frontier in Self-Determination Research. Front. Hum. Neurosci. 11:145.

Dunlosky J, Serra M and Baker JMC (2007) Metamemory applied. In Durso FT, Nickerson R S, Dumais ST, Lewandowsky S and Perfect TJ (eds) Handbook of Applied Cognition, pp. 137–59. New York: Wiley.

Dweck C (2007) Mindset: The New Psychology of Success. Ballantine Books.

Fernandes MA, Wammes JD and Meade ME (2018) The surprisingly powerful influence of drawing on memory. Current Directions in Psychological Science 27(5): 302–308.

Fischer KW (2009) Mind, Brain, and Education: Building a Scientific Groundwork for Learning and Teaching.

Galaburda AM, Battro AM, Dehaene S, Singer WJ, (eds) (2010) Neuroscience, education, and learning disabilities. Human Neuroplasticity and Education. 2010:151.

Geake J (2008) Neuromythologius in Education. Educational Research 50(2),123–133.

Goswami U (2004) Neuroscience and Education. British Journal of Educational Psychology 1–14.

Hook C and Farah M (2012) Neuroscience for Educators: What are they seeking and what are they finding? Neuroethics. DOI 10.1007/s12152–012–9159–3

Hattie (2018) Hattie Ranking: 252 Influences And Effect Sizes Related To Student Achievement. Visible Learning.

Jensen O and Lisman JE (2005) Hippocampal sequence-encoding driven by a cortical multi-item working memory buffer. Trends Neurosci. 2005 Feb;28(2):67–72. doi: 10.1016/j.tins.2004.12.001. PMID: 15667928.

Kosmyna N and Maes P (2019) AttentivU: An EEG-Based Closed-Loop Biofeedback System for Real-Time Monitoring and Improvement of Engagement for Personalized Learning. Sensors 2019, 19(23), 5200;

Klingberg T (2010) Training and plasticity of working memory. Trends in Cognitive Sciences 14: 317–324.

Kucyi A, Hove M, Esterman M, Hutchison M and Valera E (2017) Dynamic Brain Network Correlates of Spontaneous Fluctuations in Attention. Cerebral Cortex, Volume 27, Issue 3, March 2017, pp 1831–1840

Cognitive Neuroscience Society (2018) Learning What to Learn: Lessons from Cognitive Neuroscience for Education.

Lovell O (2020) Cognitive Load Theory in Action.

Lynch M (2018) For Edtech to Actually Work, it has to Embrace Neuroscience. The TechEdvocate.

Matthey L, Bays PM and Dayan P (2015) A Probabilistic Palimpsest Model of Visual Short-term Memory.

McNab F and Klingberg T (2008) Prefrontal cortex and basal ganglia control access to working memory. Nat Neurosci. 2008 Jan;11(1):103–7. doi: 10.1038/nn2024. Epub 2007 Dec 9. PMID: 18066057.

Mossbridge J (2016) The Influence of Streamlined Music on Cognition and Mood. arXiv preprint arXiv:1610.04255

Murayama K and Kitagami S (2014) Consolidation power of extrinsic rewards: Reward cues enhance long-term memory for irrelevant past events. Journal of Experimental Psychology: General, 143, 15–20. DOI: 10.1037/a0031992

Murayama K (2018) The science of motivation. Psychological Science Agenda.

New maths curriculum should focus on problem-solving — experts. The Educator, 14 April 2021.

Ng B (2018) The Neuroscience of Growth Mindset and Intrinsic Motivation. Brain sciences, 8(2), 20.

Rose N (2018) The role of forgetting as we learn. Ambition Institute.

The Royal Society (2011) Neuroscience: implications for education and lifelong learning.

Ryan RM and Di Domenico SI (2016) Distinct motivations and their differentiated mechanisms: reflections on the emerging neuroscience of human motivation. In Advances in Motivation and Achievement: Recent Developments in Neuroscience Research on Human Motivation, eds S. Kim, J. Reeve and M. Bong (Bingley: Emerald Group Publishing), 349–369.

Szafir D and Mutlu B (2012) Pay attention!: designing adaptive agents that monitor and improve user engagement. In CHI ’12: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems May 2012 Pages 11–20

Tandon PN and Singh NC (2016) Educational Neuroscience: Challenges and Opportunities. Annals of neurosciences, 23(2), 63–65.

Tokuhama-Espinosa TN (2017) International Delphi panel on mind brain, and education science.

Waterhouse L (2006) Multiple intelligences, the Mozart effect, and emotional intelligence: A critical review. Educational Psychologist 41(4), 207–225.

Wellcome (2014) Neuroscience and Education: A Review of Educational Interventions and Approaches Informed by Neuroscience. Education Endowment Foundation, Wellcome.

Willingham D (2008) What will improve a student’s memory? American Educator 32(4): 17–25, 44.

Willingham D (2010) Why Don’t Students Like School? San Francisco, CA: Jossey-Bass.

Willingham D (2012) Neuroscience Applied to Education: Mostly Unimpressive.

Zimmerman B J (2000) Attaining self-regulation: a social cognitive perspective. In Boekaerts M, Pintrich PR and M. Zeidner M (eds) Handbook of Self-Regulation pp 13–40. San Diego, CA: Academic Press. doi: 10.1016/b978–012109890–2/50031–7

Zimmerman BJ and Moylan AR (2009) Self-regulation: Where metacognition and motivation intersect. In Hacker D J, Dunlosky J and Graesser A C (eds) Handbook of metacognition in education, pp. 299–315. New York, NY: Routledge.



Inaugural director of FORWARD at RMIT University | Strategic advisor, QV Systems | Global Education Strategist, Conversation Design Institute | CEO, THEORICA.

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Peter Thomas

Inaugural director of FORWARD at RMIT University | Strategic advisor, QV Systems | Global Education Strategist, Conversation Design Institute | CEO, THEORICA.