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STEM refers to four fields of study and occupation: science, technology, engineering, and mathematics. But STEM has taken on social and political meaning far beyond the sum of its component parts. Public and policy discussions of STEM, whether in education or employment, rest on a startling lack of clarity about what counts as STEM. Most studies of postsecondary STEM education focus on students’ programs of study as the measure of STEM education, but we find this metric leads to substantial mismeasurement. Instead, we argue that examining STEM course taking is a more accurate measure of STEM preparation among college students. This descriptive study establishes conceptual and operational definitions of STEM coursework and uses nationally representative college student transcript data to develop a more accurate measure STEM course taking. Finally, we analyze the extent of potential mismeasurement and estimate STEM course taking using this revised classification system. Among bachelor’s degree students, we find wide variation in the number of STEM courses completed by students both within and between programs of study. Moreover, we find that many students in non-STEM programs of study complete substantial amounts of STEM coursework at levels comparable to that of many STEM students. 

The education and training of students and workers for careers in STEM fields is a longstanding concern of educators, development practitioners, analysts, and policymakers around the world. This chapter focuses on STEM workforce development in the United States in the context of global education migration and global enterprises that employ STEM graduates. It begins by addressing the politicized history of STEM workforce development, finding the STEM crisis theme is a perennial policy favorite in the US, appearing every few years as an urgent concern in the nation's competition with whatever other nation is ascendant, or as the cause of whatever problem is ailing the domestic economy. Turning to the measurement of STEM supply and demand, we find it is fraught with difficulty and inconsistency. The entry concludes by considering the need for, and the obstacles to increasing the supply of STEM students at US colleges and universities. Overall, we find that STEM policy is often a response to broader anxieties and politics—whether about international threats or domestic economic crises—and is seldom based on substantial empirical analysis. DOI: https://doi.org/10.1016/B978-0-12-818630-5.13065-9 

STEM acronymically refers to four areas of inquiry – Science, Technology, Engineering, and Mathematics. But as its use has become ubiquitous, STEM has taken on social and political meaning far beyond the sum of its component parts. In this paper, we take a first step in clarifying the analytic categories of STEM in education. This, we propose, is a necessary first building block for STEM analysis – to understand what constitutes STEM coursework, the constituent element of a STEM education. We first review the STEM definitional problems we have identified in the process of examining two sets of NCES nationally-representative data, provide analysis of the extent of potential mismeasurement, and estimates of impact. We then outline an approach to resolving the mismeasurement problems in nationally-representative postsecondary student surveys. DOI: https://doi.org/10.7282/00000318 

Mathematics is an important and hotly contested aspect of U.S. postsecondary education. Its importance for academics and careers and the extent and impact of math achievement disparities are all subject of longstanding debate. Yet there is surprisingly little research into how much and what types of mathematics courses are taken by U.S. undergraduates and the extent of math achievement differentials among students. This article advances the understanding of math course taking by developing course-taking metrics for a nationally representative cohort of bachelor’s graduates. Using NCES transcript data to construct consistent measures of mathematics and quantitative course taking, our analysis finds large variability both within and between STEM/non-STEM majors and a large population of non-STEM graduates earning mathematics credits comparable to their peers in STEM fields. Mathematics course taking differs substantially from course taking in other subjects. We also find that often-observed gender differentials are a function of major, not gender, with females in the most mathematics-intensive programs earning as many or more mathematics credits than their male peers.