Abstract:

As more funding agencies seek to support collaborative efforts between academic institutions and industry, efficiency and effectiveness may appear to be in conflict. However, balancing these two critical components of a funded project designed to:
(1) increase the number of marginalized students in science, technology, engineering, and mathematics (STEM) degree programs and
(2) increase the capacity of minority serving institutions that can prepare an agile and highly qualified workforce, can lead to greater efficacy.

As the external evaluators for a U.S. federally funded program, a comprehensive evaluation was designed to capture data that would inform capacity building, student research opportunities, internships, hiring, and faculty productivity, among other measures. Designing a system of data collection with 90 metrics across 60 academic institutions, and 14 federal laboratories and plants, required macro- and micro-level strategies, multiple methods of data types (qualitative and quantitative), and inclusivity of voice among various stakeholders. This paper will highlight the painstaking challenges of designing a streamlined process for collecting data from different organizations with differing missions, student profiles, and different capacities to inform a common programmatic goal. Specific strategies such as training in data collection protocols, developing standard operating procedures, data verification approaches, and responsiveness to diverse needs will equip the audience with knowing where to start and how to finish creating a set of data that informs the efficacy of an efficient and effective partnership that demonstrates impact on the trajectory of student careers in marginalized communities.

Keywords:

Academic Institutions, Industry, Partnerships, Marginalized Students, Data Collection, External Evaluation.