Scientists using machine learning – a type of artificial
intelligence – with data from hundreds of children who struggle
at school, identified clusters of learning difficulties which did
not match the previous diagnosis the children had been given.
The researchers from the Medical Research Council (MRC) Cognition
and Brain Sciences Unit at the University of Cambridge say this
reinforces the need for children to receive detailed assessments
of their cognitive skills to identify the best type of support.
The study, published in Developmental Science,
recruited 550 children who were referred to a clinic – the Centre
for Attention Learning and Memory – because they were struggling
at school.
The scientists say that much of the previous research into
learning difficulties has focussed on children who had already
been given a particular diagnosis, such as attention deficit
hyperactivity disorder (ADHD), an autism spectrum disorder, or
dyslexia. By including children with all difficulties regardless
of diagnosis, this study better captured the range of
difficulties within, and overlap between, the diagnostic
categories.
Dr Duncan Astle from the MRC Cognition and Brain Sciences Unit at
the University of Cambridge, who led the study said: “Receiving a
diagnosis is an important landmark for parents and children with
learning difficulties, which recognises the child’s difficulties
and helps them to access support. But parents and professionals
working with these children every day see that neat labels don’t
capture their individual difficulties – for example one child’s
ADHD is often not like another child’s ADHD.
“Our study is the first of its kind to apply machine learning to
a broad spectrum of hundreds of struggling learners.”
The team did this by supplying the computer algorithm with lots
of cognitive testing data from each child, including measures of
listening skills, spatial reasoning, problem solving, vocabulary,
and memory. Based on these data, the algorithm suggested that the
children best fit into four clusters of difficulties.
These clusters aligned closely with other data on the children,
such as the parents’ reports of their communication difficulties,
and educational data on reading and maths. But there was no
correspondence with their previous diagnoses. To check if these
groupings corresponded to biological differences, the groups were
checked against MRI brain scans from 184 of the children. The
groupings mirrored patterns in connectivity within parts of the
children’s brains, suggesting that that the machine learning was
identifying differences that partly reflect underlying biology.
Two of the four groupings identified were: difficulties with
working memory skills, and difficulties with processing sounds in
words.
Difficulties with working memory – the short-term retention and
manipulation of information – have been linked with struggling
with maths and with tasks such as following lists. Difficulties
in processing the sounds in words, called phonological skills,
has been linked with struggling with reading.
Dr Astle said: “Past research that’s selected children with poor
reading skills has shown a tight link between struggling with
reading and problems with processing sounds in words. But by
looking at children with a broad range of difficulties we found
unexpectedly that many children with difficulties with processing
sounds in words don’t just have problems with reading – they also
have problems with maths.
“As researchers studying learning difficulties, we need to move
beyond the diagnostic label and we hope this study will assist
with developing better interventions that more specifically
target children’s individual cognitive difficulties.”
Dr Joni Holmes, from the MRC Cognition and Brain Sciences
Unit at the University of Cambridge, who was senior author on the
study said: “Our work suggests that children who are finding the
same subjects difficult could be struggling for very different
reasons, which has important implications for selecting
appropriate interventions.”
The other two clusters identified were: children with broad
cognitive difficulties in many areas, and children with typical
cognitive test results for their age. The researchers noted that
the children in the grouping that had cognitive test results that
were typical for their age may still have had other difficulties
that were affecting their schooling, such as behavioural
difficulties, which had not been included in the machine
learning.
Dr Joanna Latimer, Head of Neurosciences and Mental Health at the
MRC, said: “These are interesting, early-stage findings which
begin to investigate how we can apply new technologies, such as
machine learning, to better understand brain function. The MRC
funds research into the role of complex networks in the brain to
help develop better ways to support children with learning
difficulties.”
Notes to editors
‘Remapping the cognitive and neural profiles of children who
struggle at school’ by Astle et al is published
in Developmental Science. DOI: https://doi.org/10.1111/desc.12747
