Abstract
AbstractMaximizing the flow of students through the science, technology, engineering, and math (STEM) pipeline is important to promoting human capital development and reducing economic inequality. A critical juncture in the STEM pipeline is the highly cumulative sequence of secondary school math courses. Students from disadvantaged schools are less likely to complete advanced math courses. Here, we conduct an analysis of how the math pipeline differs across schools using student polygenic scores, which are DNA-based indicators of propensity to succeed in education. We integrated genetic and official school transcript data from over 3000 European-ancestry students from U.S. high schools. We used polygenic scores as a molecular tracer to understand how the flow of students through the high school math pipeline differs in socioeconomically advantaged versus disadvantaged schools. Students with higher education polygenic scores were tracked to more advanced math already at the beginning of high school and persisted in math for more years. Analyses using genetics as a molecular tracer revealed that the dynamics of the math pipeline differed by school advantage. Compared to disadvantaged schools, advantaged schools buffered students with low polygenic scores from dropping out of math. Across all schools, even students with exceptional polygenic scores (top 2%) were unlikely to take the most advanced math classes, suggesting substantial room for improvement in the development of potential STEM talent. These results link new molecular genetic discoveries to a common target of educational-policy reforms.
Funder
John Templeton Foundation
Jacobs Foundation
Ford Foundation
Publisher
Springer Science and Business Media LLC
Subject
Developmental Neuroscience,Education
Reference64 articles.
1. Board, N. S. Revisiting the STEM workforce: A Companion to Science and Engineering Indicators 2014 (National Science Foundation VA, 2015).
2. Aughinbaugh, A. The effects of high school math curriculum on college attendance: Evidence from the NLSY97. Econ. Educ. Rev. 31, 861–870 (2012).
3. Long, M. C., Conger, D. & Iatarola, P. Effects of high school course-taking on secondary and postsecondary success. Am. Educ. Res. J. 49, 285–322 (2012).
4. Sadler, P. M. & Tai, R. H. The two high-school pillars supporting college science. Science 317, 457–458 (2007).
5. Rose, H. & Betts, J. R. Math Matters: The Links Between High School Curriculum, College Graduation, And Earnings. (Public Policy Institute of CA 2001).
Cited by
34 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献