How neuroscience can help struggling pupils

It’s a field that offers us a chance to better understand the brain processes involved in learning, but further research is needed on the application of neuroscience in schools. Victoria Knowland and Cathy Rogers explain the potential it has for helping pupils with developmental disorders such as dyslexia and ADHD
27th August 2021, 12:05am
How Neuroscience Research Can Help Schools To Support Struggling Pupils

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How neuroscience can help struggling pupils

https://www.tes.com/magazine/teaching-learning/general/how-neuroscience-can-help-struggling-pupils

On paper, educational neuroscience sounds like a field of research that every teacher should know more about. The logic here is clear: education is about effective learning, and cognitive neuroscience tries to understand the brain processes involved in learning.

However, in practice, things are a lot more complicated; the journey from “brain scan” to “lesson plan” can be long, even hazardous.

Critics point to the risk of teachers being derailed by “neuromyths” - pseudoscientific ideas that have taken hold in popular understanding - and the fact that neuroscientific data is too impractical, imprecise and remote from the classroom.

Others claim that cognitive neuroscience really just tells us what we already know but in different words.

So, does cognitive neuroscience really have anything useful to add to the conversation about effective learning?

The potential of neuroscience in schools

We argue that, yes, it does - as long as we look at the right areas of research.

Specifically, research into developmental disorders, such as developmental dyslexia, provide us with a blueprint for how studies of brain activity can help us to better support children to thrive in the classroom.

Here are three areas in which it would pay for teachers to be aware of the developing research.

Neuro-prognosis: predicting whether an intervention might help

It’s very difficult to predict, using only behavioural measures, how well the reading skills of children with impairments will progress over their time at school. This makes it difficult to know which children are likely to need extra support.

One US research team, led by Fumiko Hoeft, set out to see if brain-based measures could predict reading progress any better. They followed a group of 25 14-year-olds with dyslexia and their peers with no reading difficulties over two and a half years.

At the start of the study, the team scanned children’s brains while they performed a phonologically demanding rhyming task, and looked at changes in blood flow through the brain. They found that while behavioural measures of reading did not predict reading gains over time in the dyslexic group, gains were predicted with 72 per cent accuracy by the brain scan results.

Another US team, led by Katherine Aboud, took this idea one step further, looking at whether brain measures could predict which children would respond to intervention. The brains of a group of children with dyslexia, aged 8 to 14, were scanned and they were then given a short but intense period of one-to-one reading intervention. They were split into two groups depending on whether they responded to the intervention or not, based on tests of reading ability afterwards.

The children who responded were more likely, before the intervention, to show strong connections between frontal areas of the brain (which support attention) and reading areas of the brain.

So, it seems that patterns of activity in the brain could be a useful predictor of which children might not need intervention at all and which might respond well to intervention. Similar work suggests that this could be a promising approach for children with other developmental disorders, such as autism.

This has the potential to allow teachers to better target support.

Neuro-intervention: developing new techniques to stimulate the brain

Brain-based interventions that support students’ progress in schools can be something of an ethical grey area. However, there are interventions currently being developed that may offer options to parents and teachers in the future.

One example is transcranial electrical stimulation (tES). This involves applying a small electrical impulse to the surface of the skull over the brain area responsible for whatever observed behaviour a child has difficulty with. The idea is to increase or decrease activity in those brain areas and, consequently, alter behaviour. For example, in children with dyslexia, this would involve stimulating reading areas of the brain.

Giulia Lazzaro and colleagues used this technique to look at the effect of stimulating the visual word form area (VWFA, an area in the left temporoparietal cortex crucial for decoding text), compared with stimulating an area on the opposite side of the brain.

After 20 minutes of tES stimulation of the VWFA for a group of 10 children with dyslexia, between the ages of 10 and 16, researchers found that reading accuracy and word recognition speed improved only when the VWFA was stimulated. This supports the idea that behaviour relevant to dyslexia can be manipulated in the short term by directly exciting areas of cortex.

The same technique has also been trialled, with some success, with children with attention deficit hyperactivity disorder (ADHD) (where it was associated with improvements in clinical symptoms and cognitive deficits).

While we are a long way off using such techniques in schools, it is useful for teachers to be aware of the developments in treatment that are on the horizon.

Neuro-diagnosis: earlier identification of children who need support

We now have evidence that the brain can provide what we call “biomarkers” of developmental disorders. A biomarker is any reliable, physical, objectively measurable indicator of disorder.

These can be found in the blood, the brain or the genes, as in Down syndrome. In the brain, biomarkers could be patterns of activity or physical features.

One promising study, led by Bettina Serrallach, looked at the structure and function of the auditory cortex (where sounds are processed) in children with dyslexia.

The shape and size of this area, and how it responded to musical tones, were highly characteristic in children with dyslexia, compared with children with attentional difficulties and children with no developmental concerns.

Why is this particularly interesting for schools? Biomarkers can be a better means of diagnosis than behavioural symptoms, as different biomarkers can indicate different origins of a disorder and therefore point to different interventions.

That’s certainly true in the case of dyslexia, where reading can be difficult for many different reasons.

So biomarkers have the potential to not only allow earlier identification and intervention but also support a distinction between disorders that may be otherwise ambiguous, such as ADHD and ADD (attention deficit disorder).

This could help teachers to better understand how best to support individual pupils rather than relying on generic interventions designed to offer a blanket approach to supporting all learners with a particular disorder.

What does this mean for the classroom?

In the short term, radical change to classroom practice is unlikely to be brought about by educational neuroscience. Much of the science of learning is currently focused on understanding why the methods that teachers already know work do so.

However, when we consider the potential support we can offer to students with developmental disorders, it becomes much easier to see the practical implications of this field of research.

Not only can educational neuroscience help us to better understand what we already know, it can also give us the means to do things that we hadn’t even considered.

Victoria Knowland is a lecturer in speech and language sciences at Newcastle University. Cathy Rogers is a PhD student at the Centre for Brain and Cognitive Development at Birkbeck, University of London

This article originally appeared in the 27 August 2021 issue under the headline “How neuroscience could lend support to struggling pupils”

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