Predictive Analytics for Identifying Academic Risks in Students with Disabilities

Abstract

The innovative approach to risk identification in the academic performance of students with disabilities is the concept of predictive analytics because it enables the possibility to detect possible risks early in their development and offer individual help. The complete machine learning model is created that incorporates the scholarly, behavioral and disability specific characteristics to boost the accuracy of prediction. Various algorithms, such as Decision Trees, Random Forest, Support Vector Machines, and Gradient Boosting are analyzed, and ensemble algorithms prove to be more effective. The framework also uses explainable AI techniques to promote transparency and enable informed decision-making. Experiment findings suggest that the presence of contextual and accessibility-related features greatly enhance risk prediction ability. It is also responsible in terms of ethical considerations of fairness, data privacy, and inclusiveness. The suggested system offers a viable and scalable approach to enhancing the academic performance and decreasing the chances of dropping out of school in students with disabilities.