Investigating How Predictive Analytics and Student Data Modeling Influence Interventions, Curriculum Design, and Educational Policy

Abstract

This research analyzes the influence of predictive analytics and student data modeling on instructional interventions, curriculum development, and education policy formulation in colleges and universities. With a quantitative approach, responses were gathered through a Likert-scale survey from 300 respondents across faculty, administrators, data analysts, and student support staff in public and private colleges and universities. The study points to the extensive use of predictive analytics to predict students at risk, enable early intervention, and enhance decision-making strategies. It also points to the importance of data-driven information in curriculum design and instructional strategy to match the needs of the students. Furthermore, the study points to the use of predictive analytics in enhancing the effectiveness and inclusivity of education policies. The study verifies predictive analytics centers have improved student retention, academic achievement, and technology usage. The study concludes by presenting suggestions to maximize the use of predictive analytics, reduce the complexity of faculty training, and make inter-disciplinary collaboration and data privacy simpler while ensuring inclusivity. Future implications are that predictive analytics will continue to define the education profession by focusing on personalized learning, equity, and long-term student attainment.