Developing the STEM Workforce: Career Pathways of Math and Science Teachers in Texas and Washington after the COVID-19 Pandemic

华盛顿大学正在开展一项跨学科研究项目，利用华盛顿州和德克萨斯州以及Baccalaureate and Beyond （B&amp；B）纵向研究数据集的纵向数据，分析数学和科学教师的职业道路。该项目有三个目标：(1)通过研究离开教学的教师的长期职业发展路径，为改善数学和科学教师队伍提供基于证据的政策和实践；(2)建立一个基础设施，利用“大数据”在两个平行的全州纵向数据系统中开展严谨和政策相关的教育研究；(3)建立一个多元化的学者社区，开展数据密集型研究。调查人员将实施一系列研究，探索数学和科学教师的职业道路——跟踪学校的教师，从教育到其他经济部门——以更深入地了解影响STEM教师发展的因素。该项目将提供经验证据，描述K-12教师的职业道路和人员流失，包括COVID-19大流行造成的人员流失，并为劳动力政策提供信息。研究者将使用罗伊的职业选择模型来指导研究问题和分析方法。该模型为教师劳动力市场和竞争激烈的非教师劳动力市场提供了几种预测。项目使用的数据集将通过德克萨斯大学奥斯汀教育研究中心和华盛顿教育研究数据中心以及B&amp；B数据集提供的原始数据生成。为项目进行的分析将包括对每个州进行独立但相同的并行分析。研究问题考察了(1)数学和科学教师在三个维度上的流动模式，(2)现有教师离职后获得更高工资的程度，以及(3)工资与感知工作条件和教师职业道路决策之间的关系。数据将使用描述性统计、制图软件在每个州构建交互式地图和回归分析。调查结果将广泛传播给包括国家决策者在内的主要利益攸关方。该项目由EHR核心研究（ECR）计划资助，该计划支持推进STEM学习和学习环境基础研究、扩大STEM参与和STEM劳动力发展的工作。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The University of Washington is conducting an interdisciplinary research project that analyzes math and science teacher career pathways using longitudinal statewide data from Washington and Texas and the Baccalaureate and Beyond (B&B) Longitudinal Study dataset. There are three project objectives: (1) To inform evidence-based policies and practices for improving the math and science teacher workforce by studying the long-term career paths of those who leave teaching, (2) To build an infrastructure for using “big data” to conduct rigorous and policy-relevant education research across two parallel statewide longitudinal data systems, and (3) establish a diverse community of scholars to carry out data-intensive research. The investigators will implement a set of studies that explore math and science teacher career paths – tracking teachers across schools and from education into other sectors of the economy – to provide a deeper understanding of the factors that affect the development of STEM teachers. The project will provide empirical evidence to describe K-12 teacher career pathways and attrition, including attrition resulting from the COVID-19 pandemic, and inform workforce policies.Investigators will use the Roy model of occupational choice to guide the research questions and analytic approach. The model offers several predictions for the teacher labor market and competing non-teacher labor markets. The dataset used for the project will be generated using raw data provided through the University of Texas at Austin Educational Research Center and the Washington Education Research Data Center and the B&B datasets. Analyses conducted for the project will include separate but identical parallel analyses for each state. The research questions examine (1) patterns in math and science teacher mobility along three dimensions, (2) the extent to which existing teachers earn higher salaries after leaving teaching, and (3) the relationships between salaries and perceived working conditions and teachers’ career path decisions. Data will be analyzed using descriptive statistics, mapping software to construct interactive maps in each state, and regression. The findings will be disseminated broadly to key stakeholders including state policy makers. This project is funded by the EHR Core Research (ECR) program, which supports work that advances fundamental research on STEM learning and learning environments, broadening participation in STEM, and STEM workforce development.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.