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 ...

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: ...

Researchers have used machine learning to more accurately identify children with learning difficulties who, until now, have either been misdiagnosed, or have gone under the radar of education authorities. Scientists at the Medical Research Council (MRC) Cognition and Brain Sciences Unit at the University of Cambridge said by using data from hundreds of children who struggle at school, they were able to identify new clusters of learning difficulties that did not match the previous diagnoses some children had been given. 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 experiencing problems at school. The team build up a machine learning algorithm with a range of cognitive testing data from each child, including measures of listening skills, spatial reasoning, problem-solving, vocabulary, and memory. Based on this data, the algorithm suggested that the children best fitted into four clusters of difficulties.

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.