2025 ASEE Annual Conference & Exposition

Engineering and STEM education opportunities can often hinge on extra-departmental funding opportunities—institutional research centers and external grant competitions. As engineering programs seek to invest in the next generation of engineers, research administrators can operationalize research effort data to identify (1) near-term undergraduate and graduate student experiential opportunities; (2) top-performing teacher-scholars poised to lead student experiences; (3) features of teacher-scholars that can be predictive of early-stage interventions that support their success as fundable grantees. Data visualizations in service to engineering and STEM programs provide a high-context field of opportunity for administrators, faculty, and students, supporting the continued growth of the engineering career pathway. More than just pie-charts, intentional Power BI dashboards can provide a map of the engineering opportunity ‘terrain’ as well as identify the most impactful areas to intervene.

This paper reports on a research institute’s multidimensional regression analysis at Penn State University that was used to support STEM-focused research outcomes and support continued mentoring and development for faculty. Impacting downstream student experiential learning opportunities as well as faculty development, we present this approach as a turnkey operation that is easy to implement in other institutional contexts. The developed research data architecture includes selected variables: scholar h-index; professional rank; collaboration team; interdisciplinarity; prior grant performance; project spending; high impact publications; and external funding outcomes. Selected processes for automating scholar data collection are described.