Big Data Applications in Special Education Policy and Decision Making

Abstract

The combination of big data analytics and artificial intelligence (AI) has been an important shift in the contemporary educational system, providing a novel opportunity to improve the learning process, as well as assist with data-driven decision-making. The application of big data is significant in improving instructional practices and policymaking in the field of special education, where students must be approached to learning using personalized and adaptive approaches. The paper gives a step-by-step example of how one can use the Big Data to inform the policy and decision-making in special education, encompassing a wide range of data sets such as student record, assistive technologies and behavioral measurements using intricate analytical tools.The proposed framework will be based on the techniques, such as predictive analytics, machine learning, and data mining, which will simplify the state of affairs with early identification of learning disabilities, designing an individualized education plan (IEP), and simplifying the resource allocation process. The results indicate the improvement of student academic outcome, interaction and intervention efficacy to a great extent compared to the traditional methods. Statistical and graphical results show that the average test scores have significantly increased and that the performance variability has decreased, which demonstrates that the framework can be used to foster fair learning outcomes. The framework could also help in the fair decision-making process at the policy level by providing real-time data and rationale of the evidence-based approaches. Although it has its benefits, issues like data privacy, ethical issues, and limitations on infrastructure need to be overcome to have a responsible approach.