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

华盛顿大学正在进行一项跨学科研究项目，该项目使用来自华盛顿和德克萨斯州的全州纵向数据以及学士学位及以上（B B）纵向研究数据集分析数学和科学教师的职业道路。项目目标有三个：（1）通过研究那些离开教学的人的长期职业道路，为改善数学和科学教师队伍的循证政策和实践提供信息，（2）建立一个基础设施，使用“大数据”在两个并行的全州纵向数据系统中进行严格和政策相关的教育研究，（3）建立一个多元化的学者群体，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-跟踪学校和教育到其他经济部门的教师-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来描述K-12教师职业发展路径和流失，包括COVID-19大流行导致的流失，并为劳动力政策提供信息。研究人员将使用职业选择的罗伊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的非教师劳动力市场提供了几个预测。本项目使用的数据集将使用德克萨斯大学奥斯汀分校教育研究中心和华盛顿教育研究数据中心提供的原始数据以及B B数据集生成。为该项目进行的分析将包括对每个州的单独但相同的平行分析。 研究问题考察了（1）数学和科学教师流动的模式沿着三个维度，（2）现有教师在离开教学岗位后获得更高工资的程度，（3）工资和感知的工作条件与教师职业道路决定之间的关系。 将使用描述性统计、绘图软件（用于构建每个州的交互式地图）和回归分析数据。 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学习和学习环境的基础研究，扩大STEM参与，以及STEM劳动力发展的工作。该奖项反映了NSF的法定使命，并通过使用基金会的智力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.