Measuring and Visualizing STEM Pathways

美国各地都有浓厚的兴趣了解学生如何进入，退出和坚持通往科学，技术，工程和数学（STEM）学位和职业的途径。 然而，我们仍然没有很好地了解学生如何进入STEM，他们如何坚持以及为什么他们遵循某些路径获得这些技术领域的学位或职业。这项研究建议增加我们的知识，学生如何进行在两个关键领域：高中课程和高等教育的职业过渡。此外，该项目还寻求创建数据工具，使教育工作者、研究人员和决策者更容易通过简单的界面和直观的可视化方式将路径可视化，并确定追求特定路径的个人群体，并与公众分享关键发现。 这项研究将使用国家教育统计中心（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生课程学习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全国代表性数据，根据年龄、性别、少数民族地位和残疾状况调查不同群体的这些问题，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.