Shared neurodevelopmental spectrum could link ADHD, autism and dyslexia to later outcomes

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A paper published in Molecular Psychiatry, led by researchers from Queen Mary University of London and Royal Holloway, University of London, challenges the way we think about neurodevelopmental conditions.

The study suggests it would be better for experts to focus on a broad constellation of traits (a “neurodevelopmental spectrum”) to identify potential challenges and support children and young people, rather than looking at individual conditions like autism, ADHD and dyslexia in isolation.

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Reading interventionist among first graduates of UWA’s new dyslexia therapy degree program

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When the University of West Alabama launched its master’s degree in dyslexia therapy in fall 2024, Rachel Walters saw an opportunity to gain the specialized training she wished had been available when her brother struggled with dyslexia as a child. Walters saw firsthand how few resources Alabama offered students with learning differences.

Now a reading interventionist at Briarwood Christian School in Birmingham, Walters works with students with language delays, attention-deficit/hyperactivity disorder and other reoccurring diagnoses, including dyslexia. Prior to enrolling in UWA’s program, she relied primarily on intervention strategies.

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Assessing early dyslexia screening using the Rasch model for secondary school students

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Early identification of dyslexia is crucial for mitigating academic challenges and ensuring timely interventions for students at risk. The existing research fails to resolve the psychometric issues for the screening instrument developed. Consequently, this study evaluates the psychometric properties of an early dyslexia screening instrument for secondary school students using the Rasch Measurement Model. 

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Adaptive Learning Platform for Dyslexic Students

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Adaptive learning systems are a significant area of research in personalized education, especially for students with dyslexia, as structured and responsive instructional support can greatly enhance learning outcomes. Many conventional rule-based methodologies are not readily adaptable for personalized instruction or real-time modification. To overcome this limitation, this paper introduces a lightweight adaptive learning system that utilizes a machine learning model to produce instructional recommendations. The system employs a Decision Tree Classifier trained on structured quiz-response features to assess a learner’s proficiency and recommend the most appropriate learning stages. The platform is not meant to be a diagnostic tool; instead, it is meant to be a post-identification instructional support system.

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