The STEM Teacher Pipeline in Washington State: A Comprehensive Analysis of Preservice Predictors of STEM Teacher Career Paths and Effectiveness

初中和高中的STEM教师短缺，人们担心如何提高STEM教师的效率。尽管针对现有STEM教师队伍的干预措施投入了大量资金，但我们国家在确定具体的干预措施方面几乎没有取得进展，这些干预措施可以提高STEM教师的效率，或者增加被培训为STEM教师的个人实际进入公共教学队伍的可能性。通过专门关注STEM教师素质的职前指标，其中许多指标是政策可操纵的（例如，TEPs要求的课程或未来教师分配到学生教学岗位），该项目旨在为教师教育计划和政策制定者提供有意义的证据，证明可以提高STEM教师队伍质量的职前干预措施。它还将提供有关职前经历的证据，预测哪些未来的STEM教师最终会进入教师队伍，以及没有进入教师队伍的未来STEM教师的收入，并将为瞄准和招聘下一代STEM教师的努力提供信息。这种研究模式也可以在其他州复制，以帮助我们加深对全国教师教育和劳动力的了解。该项目由教育和人力资源核心研究计划支持，该计划资助STEM学习和学习环境的基础研究，扩大STEM参与和STEM劳动力发展。该项目将是华盛顿州第一个全州范围内的STEM教师管道分析，将利用独特的纵向数据集。该数据集的独特之处在于，它将有关职前教师教育经历的详细信息（如成绩单和学生教学数据）与华盛顿州特定教师教育项目中所有接受过教师培训的个人的结果联系起来。该项目将这些数据与华盛顿州新P-20数据仓库的纵向数据以及教师教育教师和学区管理人员的调查数据合并，以调查三个广泛的研究问题：(1)职前教师教育的哪些方面可以预测未来STEM教师进入公共教学队伍的概率和时间？(2)从未进入公共教学队伍的未来STEM教师最终在哪里就业，他们在这些职位上的起薪相对于州单一工资表中的起薪是多少？(3)哪些职前经验和特征（包括内容领域准备、教学知识、临床经验以及候选人的教师教育经验、学生教学经验和早期职业教学经验之间的一致性或“匹配”）可以预测未来STEM教师的有效性？描述性分析使用分裂人口模型和增值模型来调查第一个和第三个研究问题。对于第二个研究问题，分析将集中在描述性统计上，比较那些从事和不从事教学的教师候选人的结果，关注工资和工资等级等中介特征。

There is a shortage of STEM teachers in middle and high schools and a concern about improving the efficacy of STEM teachers. Despite a tremendous investment in interventions targeted to the existing STEM teacher workforce, our nation has made little progress in identifying specific interventions that can improve STEM teacher effectiveness or increase the probability that individuals trained to be STEM teachers actually enter the public teaching workforce. By focusing exclusively on preservice indicators of STEM teacher quality, many of which are policy manipulable (e.g., the curriculum required by TEPs or the assignment of prospective teachers to student teaching positions), this project seeks to provide meaningful evidence to teacher education programs and policymakers about preservice interventions that could improve the quality of the STEM teacher workforce. It will also provide evidence about the preservice experiences that predict which prospective STEM teachers ultimately enter the teaching workforce and the earnings of prospective STEM teachers who do not, and will inform efforts to target and recruit the next generation of STEM teachers. This research model can also be replicated in other states to help enhance our understanding of teacher education and workforce nationwide. This project is supported by the Education and Human Resources Core Research Program, which funds fundamental research in STEM learning and learning environments, broadening participation in STEM, and STEM workforce development. This project will be the first statewide analysis of the STEM teacher pipeline in Washington State that will leverage a unique longitudinal dataset. This dataset is unique in that it will connect detailed information about preservice teacher education experiences - such as transcript and student teaching data - to outcomes for all individuals trained to be teachers within specific teacher education programs in Washington State. The project will merge these data to longitudinal data from Washington State's new P-20 data warehouse and survey data of teacher education faculty and school district administrators to investigate three broad research questions: (1) What aspects of preservice teacher education are predictive of the probability and timing of public teaching workforce entry for prospective STEM teachers?; (2) Where do prospective STEM teachers who never enter the public teaching workforce end up employed, and what are their starting salaries in these positions relative to starting teaching salaries in the state's single salary schedule?; and (3) What preservice experiences and characteristics (including measures of content-area preparation, pedagogical knowledge, clinical experiences, and the alignment or "match" between a candidate's teacher education experiences, student teaching experiences, and early-career teaching experiences) are predictive of future STEM teacher effectiveness? The descriptive analyses use split-population models and value-added models to investigate the first and third research question. For the second research question, the analyses will focus on descriptive statistics that compare outcomes for those teacher candidates who do and do not enter teaching, attending to mediating characteristics such as salaries and pay scales.