A Survey on the Machine Learning For E-Learning System and Dyslexia - Kala, V; Vimala, S (International Journal of Computer Sciences and Engineering, 2020)
Learning disabilities like dysgraphia, dyslexia, dyspraxia, etc. interfere with academic achievements yet have likewise long terms consequences beyond academic time. It is widely admitted that between 5% to 10% of the total population is subject to this kind of disability. For assessing such disabilities in early childhood, children have to solve a battery of tests. Human experts score these tests and decide whether the children require specific education strategies based on their marks. The assessment can be lengthy, exorbitant, and emotionally difficult. Dyslexia is a learning disorder characterized by a lack of reading and/or composing skills, trouble in fast word naming and likewise poor in spelling. Dyslexic people have great trouble to read and interpret words or letters. Research work is carried out to order dyslexic from non-dyslexics by different approaches, for example, machine learning, image processing, understanding the cerebrum behaviour through brain science, contemplating the differences in life systems of mind. In recent years, e-learning systems have played an increasingly significant role in higher education and, specifically, in enhancing learning experiences for people who have learning difficulties. However, huge numbers of the people involved in the development and implementation of e-learning instruments overlook the needs of dyslexic students. In this paper, a detailed literature survey is carried on the machine techniques for the prediction of dyslexia students and e-learning for learning and cognitive disabilities.