University of Cambridge researchers used machine learning to attempt to define why children struggle in school. Scientists at the University of Cambridge in the U.K. have used machine learning to identify why children struggle at school, through an analysis of 550 students having difficulty. The team fed their algorithm cognitive testing information from each child, which demonstrated how the children best fit within four clusters of problems. Two clusters identified working memory skills and processing sounds in words as problem areas, while the other two defined broad cognitive difficulties in many areas and typical cognitive tests for the subjects' age. Cambridge's Joni Holmes says the results suggest "children who are finding the same subjects difficult could be struggling for very different reasons, which has important implications for selecting appropriate interventions."

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 ...

Using machine learning - a type of artificial intelligence (AI) - could help develop better predictions of why children struggle at school, scientists say. The researchers from the University of Cambridge in the UK used AI and data from hundreds of children who struggle at school to identify clusters of learning difficulties which did not match the previous diagnosis the children had been given. The finding, published in the journal Developmental Science, reinforces the need for children to receive detailed assessments of their cognitive skills to identify the best type of support. The researchers recruited 550 children who were referred to a clinic because they were struggling at school. 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, they said.

Our understanding of learning difficulties largely comes from children with specific diagnoses or individuals selected from community/clinical samples according to strict inclusion criteria. Applying strict exclusionary criteria overemphasizes within group homogeneity and between group differences, and fails to capture comorbidity. Here, we identify cognitive profiles in a large heterogeneous sample of struggling learners, using unsupervised machine learning in the form of an artificial neural network. Children were referred to the Centre for Attention Learning and Memory (CALM) by health and education professionals, irrespective of diagnosis or comorbidity, for problems in attention, memory, language, or poor school progress (n 530). Children completed a battery of cognitive and learning assessments, underwent a structural MRI scan, and their parents completed behavior questionnaires.

LONDON, Oct 1: Using machine learning – a type of artificial intelligence (AI) – could help develop better predictions of why children struggle at school, scientists say. The researchers from the University of Cambridge in the UK used AI and data from hundreds of children who struggle at school to identify clusters of learning difficulties which did not match the previous diagnosis the children had been given. The finding, published in the journal Developmental Science, reinforces the need for children to receive detailed assessments of their cognitive skills to identify the best type of support. The researchers recruited 550 children who were referred to a clinic because they were struggling at school. 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, they said.

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: ...

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.

Washington DC, [USA] Sep 30 (ANI): 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.