Examining relationships among teacher professional learning and associated teacher and student outcomes in math and science: A meta-analytic approach to mediation and moderation

本研究综合研究回顾了专业学习干预的效果，并将促进STEM教育工作者对教师专业学习（PL），教师知识和实践以及平均学生效果之间至关重要的关系的理解。了解这些关系将使该领域能够为教师设计更好的PL体验，真正有利于学生的学习。该项目利用主要文献中因果影响研究的可用性和元分析技术的进步，以前所未有的方式揭示PL功能，教师知识和实践以及学生成绩之间的关系。为了确保研究对其他研究人员尽可能有用，该团队将公开分享我们的数据和分析脚本，允许其他元分析师在我们的努力基础上再接再厉。此外，研究人员将为其他主要研究人员提供基于网络的数据分析工具，用于检查研究数据，进行先验功率分析，并帮助主要研究人员在已经完成的研究背景下更好地解释研究结果。本综合研究使用元分析来研究PL干预与数学和科学的学生和教师的结果的研究的影响。研究团队使用具有稳健方差估计的元回归以及元分析结构方程建模。更广泛的影响是由于数据和分析工具可以被许多社区的研究人员，高等教育教师和政策制定者使用。该社区包括教师教育工作者、教育研究人员、地区PL提供者、STEM教育政策制定者沿着数学和科学教师和学生。该研究项目得到了NSF EHR核心研究（ECR）计划的支持。ECR计划强调基础STEM教育研究，产生该领域的基础知识。投资是在关键领域是必不可少的，广泛的，持久的：干学习和干学习环境，扩大参与干，干劳动力发展。该计划支持积累强有力的证据，为理解，建立理论解释，并建议干预和创新，以解决STEM兴趣，教育，学习和参与方面的持续挑战提供信息。 该项目还得到了Discovery Research preK-12计划（DRK-12）的资助，该计划旨在通过研究和开发创新资源，模型和工具，显着提高preK-12学生和教师对科学，技术，工程和数学（STEM）的学习和教学。DRK-12项目中的项目建立在STEM教育的基础研究以及为拟议项目提供理论和经验依据的先前研究和开发工作的基础上。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。

This research synthesis study reviews the effects of professional learning interventions and will advance STEM educators' understanding of the critically important relationships among teacher professional learning (PL), teacher knowledge and practice, and average student effects. Understanding these relationships will allow the field to design better PL experiences for teachers that truly benefit student learning. The project leverages the availability of causal impact studies in the primary literature and advancements in meta-analytic techniques to uncover relationships among PL features, teacher knowledge and practice, and student outcomes in a manner that has not been done before. To ensure that the research is as useful as possible to other researchers, the team will publicly share our data and analysis scripts, allowing other meta-analysts to build on our efforts. In addition, the researchers will generate web-based data analysis tools for other primary researchers to use to examine the data from the study, conduct a priori power analyses, and to help primary researchers better interpret findings in the context of research that has already been done. This synthesis research uses meta-analysis to study the effects from studies of PL interventions with math and science student and teacher outcomes. The research team uses meta-regression with robust variance estimation as well as meta-analytic structural equation modeling. Broader impacts are addressed as the data and analyses tools can be used by many communities of researchers, higher education faculty, and policy makers. The communities include teacher educators, education researchers, district PL providers, STEM education policy makers, along with math and science teachers and students.This research project is supported by NSF's EHR Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad, and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development. The program supports the accumulation of robust evidence to inform efforts to understand, build theory to explain, and suggest intervention and innovations to address persistent challenges in STEM interest, education, learning and participation. The project is also funded by the Discovery Research preK-12 program (DRK-12), which seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models and tools. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects. 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.