Washington: Scientists using machine learning – a type of artificial intelligence – with data from hundreds of children who struggle at school, identified clusters of learning difficulties.
Researchers from the Medical Research Council Cognition and Brain Sciences Unit at the University of Cambridge said that this reinforces the need for children to receive a detailed assessment of their cognitive skills to identify the best type of support.
The study recruited 550 children who were referred to a clinic because they were struggling at school.
The scientists said that much of the previous research into learning difficulties have focused 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.
“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,” said the study’s lead author, Dr Duncan Astle.
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 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 mathematics. 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.
“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,” said Dr Astle.
“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,” added senior author Dr Joni Holmes.
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 behavioral difficulties, which had not been included in the machine learning.
The study appeared in the Journal of Developmental Science.