Abstract

Background: Higher education institutions (HEIs) in the United States face increasing pressure to improve instructional administration in response to complex academic demands and growing student expectations. The traditional manual approach generates operational delays which result in student service delivery delays and staff employment disparities.

Methods: The research study employed a mixed-methods approach which included five American universities that consisted of Harvard University and Stanford University and University of Michigan and University of Texas at Austin and Arizona State University. The researchers examined data which came from enrollment records and faculty workload reports and student satisfaction surveys that spanned from 2018 to 2023. The study gathered survey and interview data from 237 participants who included administrators and faculty members and staff personnel.

Results: Optimization frameworks brought major success to the organization through their execution. The administrative response speed improved by 34 to 36 percent while faculty work distribution became more balanced by 25 to 26 percent and student contentment rose by 14 percent. The predictive modeling approach achieved better course demand forecasts which improved prediction accuracy from 68% to 90-92%. The evaluation between different institutions showed that all institutions received advantages yet the level of impact differed according to their digital preparedness and available resources.

Conclusion: Data-driven process optimization brings substantial changes to educational management systems which operate throughout American universities. The use of analytics and predictive tools helps Higher Education Institutions achieve better operational efficiency and enhanced fairness and improved student services.

Keywords: Data-driven optimization, instructional administration, higher education, process mining, predictive analytics