Learning traits and capture mode of learning disability with classification in e-learning for detecting learning disability using machine learning

Abstract

Learning Disabilities (LD) are a type of disability that affects people of normal or above-average intelligence. The ability to learn is harmed, and this could last a lifetime. Some children may have a single learning problem, while others may have multiple learning disorders that overlap. Learning disability may include disabilities in various areas related to reading, language and mathematics. Learning disabled children are a broad collection of kids who may face challenges in a variety of areas. For example, one child with a learning disability may have major reading challenges, whereas another may have no reading difficulty at all but struggles with written communication. Learning difficulties are developmental abnormalities that commonly appear during the course of a child’s schooling. These limitations cause a considerable gap between an individual’s genuine potential and day-to-day performance. The purpose of this study is to provide a taxonomy of the many learning qualities of LD, as well as the types of characteristics that cater to which learning disabilities, and to identify the modalities in which a particular learning disability can be captured. Based on these characteristics , we design an e-learning system to detect the presence of learning disability using machine learning.