Recommendation learning management system for autism using deep convolutional neural networks and gene expression programming

Autism is a complex condition that affects children at an early stage and interferes with their daily activities in life. People affected by autism have problems with social interaction, communication, and exhibit repetitive behaviors. A personalized recommendation learning system modifies an academic subject, educational program, and the environment in which the student learns according to the individual student’s learning style and interests. This paper creates a personalized recommendation learning system tailored for autistic students affected by Rett and Asperger’s syndromes to meet their needs using Gene Expression Programming and Deep Learning via Convolutional Neural Networks. Gene Expression Programming creates a recommendation-based learning content based on the autistic student’s profile. Deep Convolutional Neural Networks (DCNN) identify the student’s facial emotions and detect disorientation towards the course. If any disorientation is identified, the course is terminated immediately, and an alternate learning style that reduces the disorientation is provided. To evaluate the efficiency of the proposed approach, extensive experiments are conducted. DCNN's ability to predict the student's emotions to avoid challenging courses is 98% effective.