Application of Machine Learning in Learning Problems and Disorders: A Systematic Review

Learning Disorders, which affect approximately 10% of the school population, represent a significant challenge in the educational field. The lack of proper diagnosis and treatment can have profound consequences, triggering psychological problems in those affected by disorders that impact reading, writing, numeracy and attention, among others. Notable among them are Attention Deficit Hyperactivity Disorder (ADHD) and dyslexia. In this context, a literature review focusing on Machine Learning applications to address these educational problems is addressed. The methodology proposed by Barbara Kitchenham guides this analysis, using the online tool Parsifal for the review, generation of search strings, formulation of research questions and management of information sources. The first findings of this research highlight a growing trend in the application of Machine Learning techniques in learning problems and disorders, especially in the last five years, as of 2019. Among the primary sources, the IEEE Digital Library emerges as a key source of information in this rapidly developing field. This innovative approach has the potential to significantly improve early detection, accurate diagnosis and implementation of personalized interventions, thus offering new perspectives in understanding and addressing the educational challenges associated with Learning Disorders.