Measuring and Visualizing STEM Pathways

美国各地都对了解学生如何进入、退出和坚持科学、技术、工程和数学（STEM）学位和职业有浓厚的兴趣。然而，对于学生们是如何进入STEM领域的，他们是如何坚持的，以及为什么他们会遵循某些途径获得这些技术领域的学位或职业，我们仍然没有很强的理解。本研究旨在增加我们对学生如何在两个关键领域进行的认识：高中课程学习和高等教育到职业过渡。此外，该项目旨在创建数据工具，使教育工作者、研究人员和政策制定者更容易将路径可视化，并确定追求特定路径的个人群体，并通过简单的界面和直观的可视化与公众分享关键发现。这项研究将使用由国家教育统计中心（NCES）和国家科学与工程统计中心（NCSES）收集的学生和劳动力数据。这些数据集将用于识别高中学生课程模式的当前和过去趋势，并允许根据这些模式预测将在学院和大学学习STEM的学生人数。通过使用联邦政府收集的具有全国代表性的数据，根据年龄、性别、少数民族地位和残疾状况对不同群体的这些问题进行调查，结果将提供对途径差异进行关键比较的能力，并突出可能的干预机会。完成的数据工具模块将作为nsf资助的Sci2Tool的一部分提供和维护。

There is a strong interest across the U.S. to understand how students enter, exit and persist in pathways toward science, technology, engineering and mathematics (STEM) degrees and careers. However, we still do not have a very strong understanding of how students enter into STEM, how they persist and why they follow certain paths to a degree or career in these technical fields. This study proposes to add to our knowledge of how students proceed in two key areas: high school course taking and the higher education to career transition. Additionally, the project seeks to create data tools that will make it easier for educators, researchers and policymakers to visualize pathways and identify groups of individuals pursuing specific paths and to share key findings with the public through a simple interface and intuitive visualizations. The research will use data on students and the workforce collected by the National Center for Education Statistics (NCES) and the National Center for Science and Engineering Statistics (NCSES). These datasets will be used to identify current and past trends in high school student course-taking patterns and allow for making a prediction about the number of students who will pursue STEM in college and university based on these patterns. By using nationally representative data collected by the federal government to investigate these issues across groups based on age, sex, minority status and disability status, the results will provide the ability for critical comparisons of differences in pathways and highlight possible opportunities for intervention. The completed data tool modules will be made available and maintained as part of the NSF-funded Sci2Tool.