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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Reading difficulties, like dyslexia, are common and often affect achievement and outcomes during school and later in life. A new study, published in Current Biology, reports that current methods used to test for reading disabilities in young children may be missing a certain subgroup of children whose reading issues stem from visual-processing difficulties.

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The first step to solving a reading delay is knowing it exists.

Decades of research have found that all children learn to read by developing the same core skills. This applies to children with dyslexia, English language learners and those reading well beyond grade level. Per the science of reading, some kids just need more targeted and explicit instruction — more time building whichever specific reading skill (and there are several) isn’t growing as fast.

But to do that, teachers need to figure out which skill is lacking. It takes a particular kind of training and the right tools — and until recently, many Connecticut schools lacked both.

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