Intelligent approaches for early prediction of learning disabilities in children using learning patterns: A survey and discussion

Abstract

Learning disabilities in children occur in early childhood age. These disabilities include dyslexia, dysgraphia, dyscalculia, ADHD, etc. These children face difficulty in academic progress in life. Difficulties include reading, writing, and spelling words, despite these students possessing normal or above-average intelligence. The learning gap between these students and others increases with time. As a result, these students become less motivated, find it difficult to progress in life, and struggle with employment opportunities. Children with these symptoms often have emotional consequences, including frustration and low self-esteem. These disabilities range around 10 to 15% of the total population, which is considerably high. There is an immense need for early diagnosis to provide them with remedial education and special care. Researchers have proposed a diverse range of approaches to detect learning disorders like dyslexia, one of the most common learning disorders. These approaches include the detection of LD using eye tracking, electroencephalography (EEG) scan, detection using handwritten text, the use of a gaming approach, audiovisual approaches, etc. This paper critically analyses recent contributions of intelligent technique-based dyslexia prediction and provides a comparison. Among the mentioned techniques, it is found that detection using eye tracking, EEG, and MRI are costly, complex, and non-scalable. In contrast, detection using handwritten text and a gaming approach is scalable and cost-effective. A character-based approach is presented as word formation is difficult for children for whom English is a second language. Also, in early childhood, children make fewer mistakes in character writing. An experimental setup for handwritten text-based detection is done using the CNN model, and future opportunities for learning disabilities detection are discussed in this paper.