Moving beyond Access with a Data Collection Ecosystem to optimize Underrepresented Minority Student Persistence in STEM

This presentation uses data-driven insights to understand how students transition in STEM. Schlossberg's Transition Theory provides the framework to explore how students move-in, through and out of STEM courses and disciplines. The historical STEM data is analyzed through Schlossberg's 4 "S" system to thoroughly unpack multiple situations, guide the design of institutional strategy, expands how the student make sense of the self (learning and understanding), as well as, identify resources for effective support. To maximize support, completion structures must begin with a backward design. The data output guides the development of the ecosystem. Within the complex network is the interconnectedness of high school STEM culture and grade point average (gpa). High school gpa is a standard request on the admissions application and data analysis; however, the diagnosis of culture is not. The predictive and descriptive admissions' data is juxtaposed with five years of college cohort data for an institutional strategy that diminishes misalignment between high school and college STEM cultures, the identification of key priorities, and coordinated academic enrichment activities for students.