Volume 20, No. 3, 2023
Machine Learning Based Application For Improving Learning Disabilities In Children
Yogita Alone , Dr. G. R. Bamnote
Abstract
Learning disorders like dysgraphia, dyslexia, dyspraxia, and others hinder academic progress and have lasting implications beyond the academic realm. It is widely recognized that these disabilities affect approximately 5% to 10% of the population. Early in their lives, children must undergo a series of tests to be evaluated for such conditions. Human professionals score these assessments and determine whether specialized educational strategies are necessary based on the results. Unfortunately, this evaluation process can be time-consuming, expensive, and emotionally draining. Dyslexia, for example, is a learning disability characterized by difficulties in reading, writing, word identification, and spelling. Dyslexics struggle to comprehend words and letters, making reading a challenging task. Researchers use various methodologies, such as machine learning, image processing, brain science to study cerebrum behavior, and analyzing brain anatomy differences, to distinguish dyslexics from non-dyslexics. In recent years, e-learning technologies have gained significant importance in higher education, especially in enhancing learning experiences for individuals with learning disabilities. However, many professionals involved in creating and implementing e-learning tools often overlook the specific needs of dyslexic students. This research aims to conduct a comprehensive literature review focusing on machine learning algorithms for dyslexia prediction and e-learning solutions catering to learning and cognitive disorders.
Pages: 25-32
Keywords: Deep learning, Learning disability, Machine learning.