DIagnosys: An Analytical Framework for the Identification of Elementary School Students with Intellectual Disability

Abstract

The identification of mild Intellectual Disability (ID) usually occurs late when the demands intrinsic to literacy reveal the typical signs to the educators. The study had two phases. The first phase aimed at developing a computation system (framework), named DIagnosys, an instrument designed to help educators identify students with characteristics compatible with ID, and to describe the operational, tactical, and strategic levels. The second phase verified the framework predictive sensitivity, using an artificial intelligence algorithm. For that purpose, the framework was applied in 51 teachers and their 1,758 students of 2nd and 4th grade, and their respective parents. We collected data using a checklist of signs compatible with ID, the Brief Problem Monitor (teacher and parent versions), the Wechsler Abbreviated Scale of Intelligence, and medical evaluation. The statistical analysis using a Confusion Matrix showed an accuracy of 82 and 95% for teacher and parent checklists, respectively. The decision-making model showed high indexes of sensitivity, providing evidence that teachers can be protagonists of the teaching-learning process mobilizing the parents to use the health care services.