Examining the Factors that Influence Retention and Effectiveness of Computer Science Teachers in High-Need Middle and High Schools

该项目旨在满足国家在高需求学校准备和保留有效的计算机科学（CS）教师的需求。获得CS教育的机会在公立学校中分布不成比例。高需求的学校有更多的学生来自计算领域代表性不足的群体;他们也更有可能有教师流动，不太可能有经验丰富的教师和CS机会。本研究项目将探讨教师准备计划如何提高CS教师在高需求学校的保留率，并准备教师提供有效，公平的CS教学。这是必不可少的，因为对STEM教师的需求不断增加，特别是在高需求的学校，以及对K-12 CS教师的呼吁，他们准备让来自代表性不足的群体的学生参与进来。随着流动率特别高的中学CS教师，目标是生成信息，可用于增加长期保留CS教师在高需求的学校。该项目还将确定课程的特点，使教师能够提供有效，公平的CS教学。（教育，培训，研究）与纽约大学，加州州立大学弗雷斯诺和路易斯安那理工大学及其周围的纽约市公立学校，弗雷斯诺联合，中央联合，桑格联合学区的三个诺伊斯资助的项目合作伙伴，比恩维尔教区、卡多教区、林肯教区、沃希托教区、里奇兰教区和温教区。项目目标包括解决研究问题，围绕什么样的个人，人际和机构因素发挥了作用，教师准备计划如何可以增加CS教师在高需求学校的保留。本研究还旨在调查教师准备计划如何帮助教师在初中和高中课堂上提供有效和公平的CS教育。研究将以社会生态和社会正义教学内容知识理论框架为指导。一个初步的猜想地图，文件研究为基础的假设，关于如何准备教师坚持和从事有效的CS教学将引导工作。这项研究的目的是招募多达80名教师谁正在准备教CS在高需求的初中和高中。他们将参加他们的职前准备计划和他们的2年在职教学要求，以及超出此教学要求。将采用混合方法收集调查、访谈、观察和机构数据。这些数据将被分析，以描述教师的CS知识和社会正义教学法的发展，他们的经验，作为一个学生的教师和新手带头教师，他们的社区的支持，他们的身份作为一个CS教师，以及准备程序和学校网站的功能。使用设计研究方法，该团队将使用这些数据来迭代地完善猜想图，并在每个机构的项目级别进行更改。该项目有可能有助于了解课程如何帮助教师以公平和包容的方式提供CS内容，包括确定有前途的准备策略和支持，以解决影响高需求学校保留的因素。它的目的是导致一个理论模型，可用于通知程序的设计，可以增加CS教师保留和支持公平的CS教学在初中和高中。诺伊斯项目的地理分布将增加结果与一系列人口和环境相关的可能性。一个由计算机科学教育、教师准备和计算公平专家组成的咨询委员会将监测项目里程碑并提供反馈，并为招聘、数据收集和数据分析的设计贡献他们的专业知识。研究结果将通过出版物，演示文稿和CS教师专业发展工具包传播给研究人员和从业人员。这个轨道4：诺伊斯研究项目是通过罗伯特诺伊斯教师奖学金计划（诺伊斯）的支持。诺伊斯计划支持有才华的STEM本科专业和专业人士成为有效的K-12 STEM教师和经验丰富的模范K-12教师，成为高需求学区的STEM硕士教师。它还支持对高需求学区的K-12 STEM教师的有效性和保留的研究。该奖项反映了NSF的法定使命，并且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 project aims to serve the national need of preparing and retaining effective computer science (CS) teachers in high-need schools. Access to CS education is disproportionately distributed across public schools. High-need schools have greater numbers of students from groups that are underrepresented in computing fields; they are also more likely to have teacher turnover and less likely to have experienced teachers and CS opportunities. This research project will investigate how teacher preparation programs can increase CS teacher retention in high-need schools and prepare teachers to deliver effective, equitable CS instruction. This is essential due to the increasing demand for STEM teachers, particularly in high-need schools, and the call for K-12 CS teachers who are prepared to engage students from underrepresented groups. With turnover particularly high among secondary CS teachers, the goal is to generate information that can be used to increase the longer-term retention of CS teachers in high-need schools. The project will also identify the features of programs that prepare teachers to deliver effective, equitable CS instruction.This project is led by ETR (Education, Training, Research) Associates in partnership with three Noyce-funded projects at New York University, California State University Fresno, and Louisiana Tech University and their surrounding school districts of New York City Public Schools, Fresno Unified, Central Unified, Sanger Unified, Bienville Parish, Caddo Parish, Lincoln Parish, Ouachita Parish, Richland Parish, and Winn Parish. Project goals include addressing research questions around what individual, interpersonal and institutional factors play a role in how teacher preparation programs can increase the retention of CS teachers in high-need schools. The research also intends to investigate how teacher preparation programs can prepare teachers to deliver effective and equitable CS education in middle and high school classrooms. The research will be guided by socio-ecological and social justice pedagogical content knowledge theoretical frameworks. An initial conjecture map that documents research-based hypotheses about how to prepare teachers to persist and engage in effective CS instruction will steer the work. The study aims to recruit up to 80 teachers who are being prepared to teach CS in high-need middle and high schools. They will participate during their pre-service preparation program and their 2-year in-service teaching requirement, as well as beyond this teaching requirement. Mixed methods will be used to collect survey, interview, observation, and institutional data over time. The data will be analyzed to describe teachers’ development of CS knowledge and social justice pedagogy, their experience as a student teacher and a novice lead teacher, their community of support, their identity as a CS teacher, as well as features of the preparation program and school sites. Using a design-research approach, the team will use the data to iteratively refine the conjecture map and make changes at the program level at each institution. This project has the potential to contribute to understanding of how programs can prepare teachers to deliver CS content in an equitable and inclusive way, including identifying promising preparation strategies and supports to address factors that affect retention in high-need schools. It is intended to result in a theoretical model that can be used to inform the design of programs that can increase CS teacher retention and support equitable CS instruction in middle and high school. The geographic dispersion of Noyce projects will increase the likelihood that the results will be relevant to a range of populations and settings. An advisory board of experts on computer science education, teacher preparation, and equity in computing will monitor and provide feedback on project milestones, as well as contribute their expertise to the design of recruitment, data collection, and data analyses. Study findings will be disseminated to researchers and practitioners through publications, presentations, and a CS teacher professional development toolkit. This Track 4: Noyce Research project is supported through the Robert Noyce Teacher Scholarship Program (Noyce). The Noyce program supports talented STEM undergraduate majors and professionals to become effective K-12 STEM teachers and experienced, exemplary K-12 teachers to become STEM master teachers in high-need school districts. It also supports research on the effectiveness and retention of K-12 STEM teachers in high-need school districts.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.