Computational Linear Algebra to identify potential students with ADHD: pairing data compression and K-means

Attention deficit hyperactivity disorder (ADHD) causes several difficulties that can lead to academic failure. We aim to use information technology to improve the performance of these students. Our first step is to assist the teacher in identifying ADHD students in the classroom, which is the object of this work. We will use linear algebra techniques to achieve the objective: data compression and k-means. Data analysis is done manually and is based on the theoretical framework. We got the necessary information by compressing it into one level, using two clusters to identify a binary response, making the information faster and visually accessible.