Neural Networks for Assessing Reading Disabilities in School-Aged Children

Reading, or the ability to infer meaning from printed words in order to correctly interpret relevant information, is the most fundamental component of education. To recognize letters, letter strings, and words, one must possess the ability to decode abstract graphemes accurately and fluently into their corresponding phonemes. Additionally, processing text requires the capacity to read and comprehend text with both fluency and accuracy. Thus, reading requires a variety of cognitive abilities, including effective processing speed, phonological awareness, syntactic processing, auditory and visual word recognition, and phonological awareness. The present study reports the outcomes of a research that evaluated the identification of students with reading disabilities using an artificial neural network. The neural network consisted of structured tasks aiming at a) reading, b) distinguishing words and pseudowords and c) reading two texts. Participants were 235 children attending grades from third to sixth class. Audio is converted into a spectrogram and students with disabilities are identified using machine learning algorithms and auditory analysis. The outcome of the present study suggests that a neural network that is comprised from three tasks can identify the reading abilities and classify the school aged children between typical achievers and reading disabled. Furthermore, including the mAP scores, the results show that the model is highly effective in identifying and classifying reading difficulties in real-time, offering a promising avenue for future research and practical applications in educational settings.