Adaptive Educational Intervention System Integrating Machine Learning, Embedded Electronics, and Management Analytics for Children with Learning Disabilities

Abstract

An adaptive intervention learning system based on machine learning, embedded electronics, and management analytics for enhancing the instructional responsiveness of children with learning disabilities is developed in this study. However, a significant deficiency exists as most of the existing intervention frameworks typically dissociate the process of observing the learner, decision-making concerning instructional adaptation, and planning education within an institution. This deficiency is addressed in this proposed framework by embedding electronic observation within classroom engagement, prediction of the need for intervention based on machine learning, and management analytics for instructional adaptation within a holistic educational framework. Validation is achieved through statistical measures such as multivariate variance analysis, evaluation of prediction reliability, and significance testing based on education-performance parameters that are sensitive to disability status. The findings reveal enhanced accuracy in interventions, minimised instructional lag, and enhanced educational consistency relative to conventional fragmented intervention frameworks.