Unlocking Math Potential: EDSense - A Personalized Intervention Tool for Children with Dyscalculia

Math difficulties, such as dyscalculia, can hinder children’s ability to acquire strong mathematical problem-solving capabilities, limiting their potential for success. Early identification and intervention are essential in addressing these challenges. This research paper introduces EDSense, a specialized Bayesian Knowledge Tracing (BKT) model developed for math skill assessment in children who may have dyscalculia. The system employs a Directed Acyclic Graph (DAG) to represent the dependencies between probabilities and incorporates the prior probability of mastery, slip rate, and guessing rate to update the student’s knowledge state. The case study findings revealed challenges in math comprehension, multiplication, and symbol identification among children who may have dyscalculia, highlighting the need for targeted interventions and instructional strategies. The personalized and data-driven approach of EDSense contributes to the field of dyscalculia intervention, providing a promising tool for math skill assessment in children who may have dyscalculia. The development of EDSense has the potential to enhance early identification and intervention for children with dyscalculia, leading to improved math outcomes and better support for students with this specific learning disorder.