Classifying the suitability of educational videos for attention deficit hyperactivity disorder students with deep neural networks

Abstract

This paper presents a comprehensive deep learning-based system to evaluate the educational videos' suitability for students with attention deficit hyperactivity disorder (ADHD). Current methods frequently ignore important instructional elements that are necessary for improving learning experiences for students with ADHD, such as instructor hand movements, video length, object variety, and audio-visual quality. We emphasize two key issues for how to address these difficulties, first, we present the ADHD online instructor (AOI) dataset, a particular benchmark for assessing instructional hand movement in video suitability to solve the absence of a reference dataset for classifying educational videos relevant to ADHD. Second, the system includes creating an enhanced multitask deep learning model that increases classification accuracy by using task-specific enhancements and optimized architectures. This solves the requirement for a strong model that can distinguish between suitable and unsuitable instructional content. Comprehensive tests using pretrained convolutional neural network (CNN) models indicate that the enhanced VGG16 model outperforms baseline methods by achieving a highest accuracy of 97.84%. The results highlight the value of integrating deep learning methods with structured benchmark datasets, exposing up the path to more resilient and flexible instructional materials designed for students with ADHD.