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Genetic associations with mathematics tracking and persistence in secondary school
Maximizing 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. St...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7002519/ https://www.ncbi.nlm.nih.gov/pubmed/32047651 http://dx.doi.org/10.1038/s41539-020-0060-2 |
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author | Harden, K. Paige Domingue, Benjamin W. Belsky, Daniel W. Boardman, Jason D. Crosnoe, Robert Malanchini, Margherita Nivard, Michel Tucker-Drob, Elliot M. Harris, Kathleen Mullan |
author_facet | Harden, K. Paige Domingue, Benjamin W. Belsky, Daniel W. Boardman, Jason D. Crosnoe, Robert Malanchini, Margherita Nivard, Michel Tucker-Drob, Elliot M. Harris, Kathleen Mullan |
author_sort | Harden, K. Paige |
collection | PubMed |
description | Maximizing 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. |
format | Online Article Text |
id | pubmed-7002519 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-70025192020-02-11 Genetic associations with mathematics tracking and persistence in secondary school Harden, K. Paige Domingue, Benjamin W. Belsky, Daniel W. Boardman, Jason D. Crosnoe, Robert Malanchini, Margherita Nivard, Michel Tucker-Drob, Elliot M. Harris, Kathleen Mullan NPJ Sci Learn Article Maximizing 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. Nature Publishing Group UK 2020-02-05 /pmc/articles/PMC7002519/ /pubmed/32047651 http://dx.doi.org/10.1038/s41539-020-0060-2 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Harden, K. Paige Domingue, Benjamin W. Belsky, Daniel W. Boardman, Jason D. Crosnoe, Robert Malanchini, Margherita Nivard, Michel Tucker-Drob, Elliot M. Harris, Kathleen Mullan Genetic associations with mathematics tracking and persistence in secondary school |
title | Genetic associations with mathematics tracking and persistence in secondary school |
title_full | Genetic associations with mathematics tracking and persistence in secondary school |
title_fullStr | Genetic associations with mathematics tracking and persistence in secondary school |
title_full_unstemmed | Genetic associations with mathematics tracking and persistence in secondary school |
title_short | Genetic associations with mathematics tracking and persistence in secondary school |
title_sort | genetic associations with mathematics tracking and persistence in secondary school |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7002519/ https://www.ncbi.nlm.nih.gov/pubmed/32047651 http://dx.doi.org/10.1038/s41539-020-0060-2 |
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