Assistant Vice Provost, Analytics & AI Virginia Tech BLACKSBURG, Virginia, United States
Session Abstract: In higher education, where resources are constrained and demands constantly evolve, making informed decisions regarding program offerings, resource allocation, and strategic planning is paramount. At Virginia Tech, we have developed a data-driven analytic framework for evaluating academic programs called Program Economics Analysis (PEA), consisting of four key elements: instructional cost, workload measure, program revenue, and program economics. This session discusses PEA's methodologies, integration into decision-making frameworks, demos on our University DataCommons, and future directions. By leveraging PEA, institutions can illuminate curricular inefficiencies, optimize resource allocation, and enhance faculty productivity. Ultimately, PEA equips institutions to navigate challenges, optimize resources, and foster academic excellence.
Keywords: program economics, decision-making, resource allocation, instructional cost, workload measure
