Collaborative Research: Impacts of State Policy on Computer Science Participation and Teacher Preparation

人们对扩大计算机科学（CS）教育的参与以提高学生个人和整个国家的学术和经济成果产生了广泛的兴趣。为了实现这一目标，各州越来越多地采用和投资于政策，以扩大CS课程的可用性，鼓励学生参与CS课程，并确保合格的CS教师的可用性。然而，这些政策在多大程度上取得了成功，还需要证据。通过将各州采用CS教育政策的数据与高中和高等教育机构的数据相结合，该项目旨在提供证据，说明这些政策如何影响决策者感兴趣的结果。该项目的结果将提供证据，指出具体的政策杠杆，是有效的扩大CS教育和解决不平等，已被充分记录在CS教育。该项目将是一个国家的CS教育政策的影响的全国性分析，并代表伊利诺伊大学香槟分校和堪萨斯州立大学之间的研究伙伴关系。它将使用2000-2022年的一套纵向数据。这个数据集在两个方面是独一无二的。首先，它将包括一个关于各州采用特定CS教育政策的时间的新数据集（例如，要求所有高中提供CS或创建副CS教师准备计划）。其次，政策采用数据将与CS高中课程的纵向管理数据相结合，本科招生，目前的高中教师和教师认证，可公开或在国家数据仓库。一旦结合起来，这个数据集将允许特定的国家政策在统计上与特定的CS教育成果联系起来。具体而言，该项目将回答四个研究问题：在多大程度上做国家CS教育政策影响（1）在高中课程;（2）在CS和CS相关专业的招生和毕业;（3）CS教师的生产;和（4）高中CS教师的资格和特点？研究这些问题将利用不同的州在不同的时间采取不同的政策这一事实，并将涉及“事件研究”模型的最新进展，该模型允许对政策的短期和长期影响进行因果推断。这将提供迄今为止最严格的因果证据，说明政策对扩大参与社区服务的影响以及这些影响如何随时间变化。这些结果将通过会议演讲，期刊文章，论文在实践和政策导向的网点，并与国家和地方决策者和学校管理人员的会议参与传播。该项目通过CS for All：Research and RPPs计划获得资助。该奖项反映了NSF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。

There is widespread interest in broadening participation in computer science (CS) education to improve academic and economic outcomes for individual students and for the country as a whole. To accomplish this, states have increasingly adopted and invested in policies to expand the availability of CS courses, to encourage student participation in CS courses, and to ensure the availability of qualified CS teachers. However, evidence is needed on the extent to which these policies have succeeded. By combining data on states' adoptions of CS education policies with data on high schools and institutions of higher education, this project aims to provide evidence on how those policies affect outcomes of interest to policymakers. Results from this project will provide evidence pointing to specific policy levers that are effective for expanding CS education and for addressing inequities that have been well documented in CS education.This project will be a nationwide analysis of the effects of states' CS education policies and represents a research partnership between the University of Illinois Urbana-Champaign and Kansas State University. It will use a longitudinal data set spanning the years 2000-2022. This data set will be unique in two ways. First, it will include a novel data set on the timing of states' adoptions of specific CS education policies (e.g., requiring that all high schools offer CS or creating preservice CS teacher preparation programs). Second, policy adoption data will be combined with longitudinal administrative data on CS high school course taking, undergraduate enrollments, current high school teachers, and teacher certification that is available either publicly or in state data warehouses. Once combined, this data set will allow specific state policies to be statistically linked to specific CS educational outcomes. Specifically, the project will answer four research questions: To what extent do state CS education policies affect (1) course taking in high school; (2) enrollment and graduation in CS and CS-related majors; (3) the production of CS teachers; and (4) the qualifications and characteristics of high school CS teachers? Answering these questions will take advantage of the fact that different states adopted different policies at different times and will involve recent advancements in "event study" modeling that allows for causal inferences about both the short- and long-term impacts of policies. This will provide the most rigorous causal evidence to date about the impacts of policies on broadening participation in CS and how those impacts vary over time. These results will be disseminated through conference presentations, journal articles, essays in practice- and policy-oriented outlets, and engagement with meetings of state and local policymakers and school administrators. This project is funded through the CS for All: Research and RPPs program.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.