Mapping the Many Pathways (In and Out) of the Postsecondary STEM Pipeline

University Of Iowa, Iowa City IA

Investigators

Abstract

There is a pressing need to build a skilled and diverse STEM workforce in the United States. Determining how to navigate aspirations and aptitudes during college is a major hurdle for many students. Examining the transcript "stories" of undergraduate cohorts can help postsecondary institutions and future students (1) contextualize the range of course-taking possibilities and performance levels associated with destination majors, and (2) identify key crossroads that students on certain paths will likely encounter. The study helps uncover barriers to STEM degree completion by examining the relationship between course-taking pathway and student characteristics. This project builds on prior knowledge regarding common "leaks" in the STEM pipeline and offers a new framework for assessing the overall health of academic majors relative to each other, taking into account patterns of migration both in and out of STEM. Drawing on social sequence analysis and network science and incorporating student demographic information, this study provides essential information to inform policies and practices for STEM education. College course sequences from eight consecutive semesters of transcript data for two cohorts of undergraduates at a public university are used to document in- and out-migration patterns through STEM and non-STEM majors. As with all campus-specific transcript studies, these data reflect the sorting of a student population that (1) is subject to the same institutional policies, (2) experiences the same exact classroom environment when co-enrolled in a given class, and (3) constitutes true peers in terms of classroom performance hierarchies. Each cohort is first described using an origin-destination typology, which classifies students who start and finish in STEM, students who migrate out of STEM, students who migrate into STEM, and students who start and finish in non-STEM majors. Second, network methodology is employed to quantify the typicality of a student's course-taking sequence relative to other students, an approach that offers insight into the distribution of pathways through academic majors. Finally, course-taking pathways are linked with student characteristics, including gender, race/ethnicity, first-generation student status and ACT scores. These results will be of immediate utility for many stakeholder groups, including students, educators, and policymakers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.