Collaborative Research: Modern Meta-Analysis Research Institute

改善美国青年在科学、技术、工程和数学（STEM）学科方面的教育是一项有充分文件证明、得到广泛认可的联邦政策优先事项。为了解决这一经济现实，已经出现了一些公共和私人举措，包括为从幼儿园到12年级的科学和数学教育制定更严格的标准、课程和评估。解决这个问题需要多管齐下。该方法的第一部分是检查近几十年来开发和评估的大量STEM教育干预措施。重要的是要了解哪些干预措施是有效的，对谁有效，以及在什么条件下有效。现代元分析方法可以检验干预对学习结果的影响，并探讨干预效果在不同研究中的差异。该项目旨在提供一个为期9个月的培训学院，包括在线研讨会前活动，5天的面对面强化研讨会，以及多个研讨会后的机会，参与者将开发一个STEM教育研究荟萃分析项目。通过这一系列活动，该项目旨在提高STEM教育研究人员进行现代荟萃分析的方法技能，从而更深入地了解STEM教育干预措施如何影响学习成果。该项目将使参与者能够进行荟萃分析，通过探索效应大小异质性的方法，探索哪些STEM干预措施对谁有效，以及在什么条件下有效。研究所将支持参与者开发针对异质性的元分析研究问题，扩展他们使用MetaReviewer和R等现代元分析软件应用程序的技能，使用元回归模型进行元分析分析，探索和解释效应大小的异质性，并适当地解释和传达元分析的结果。研究所前讲习班活动将使与会者熟悉系统审查的步骤和使用R进行统计分析。为期五天的面对面研讨会将重点讨论如何进行系统评估和荟萃分析的初步阶段，并借鉴教师团队在进行大规模评估方面的经验。研讨会计划还将投入大量时间来教授使用R程序进行meta分析和meta回归，并使用方法探索研究之间的效应异质性。该研究所旨在为参与者提供必要的知识和技能，以领导和开展基本的元分析，并在STEM教育研究的背景下批判性地评估已发表的元分析。该项目得到了美国国家科学基金会EDU STEM教育研究核心研究能力建设（ECR: BCSER）项目的支持，该项目旨在培养研究者开展高质量STEM教育研究的能力。该奖项反映了美国国家科学基金会的法定使命，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。

Improving the education of America’s youth in the disciplines of science, technology, engineering, and mathematics (STEM) is a well-documented, widely endorsed federal policy priority. Several public and private initiatives have emerged to address this economic reality, including developing more rigorous standards, curricula, and assessments for prekindergarten through grade 12 education in science and mathematics. Addressing this problem requires a multi-pronged approach. The first part of this approach is to examine the extensive number of STEM education interventions that have been developed and evaluated in recent decades. It is important to understand which interventions work, for whom, and under what conditions. Modern meta-analytic methods can examine intervention impacts on learning outcomes and explore how intervention effects vary across studies. This project seeks to deliver a 9-month-long training Institute, with online preworkshop activities, 5 days of an in-person, intensive workshop, and multiple postworkshop opportunities as participants develop a STEM education research meta-analysis project. Through this schedule of activities, the project aims to improve the methodological skill sets of STEM education researchers for conducting modern meta-analyses, resulting in a richer understanding of how STEM education interventions affect learning outcomes. This project will enable participants to conduct meta-analyses that explore which STEM interventions work, for whom, and under what conditions through methods to explore effect size heterogeneity. The Institute will support participants to develop meta-analysis research questions aimed at heterogeneity, to expand their skills for using modern meta-analytic software applications including MetaReviewer and R, to conduct meta-analytic analyses that explore and interpret heterogeneity in effect sizes using meta-regression models, and to interpret and communicate the results of a meta-analysis appropriately. Pre-Institute workshop activities will familiarize participants with the steps of a systematic review and the use of R for statistical analysis. The five-day in-person workshop will focus on how to conduct the preliminary stages of a systematic review and meta-analysis, drawing on the experiences of the instructor team in conducting large-scale reviews. The workshop schedule will also devote significant time to teaching the use of R programs to conduct meta-analysis and meta-regression, with methods to explore effect heterogeneity across studies. The Institute aims to provide participants with the knowledge and skills necessary to lead and conduct basic meta-analyses and to critically evaluate published meta-analyses in the context of STEM education research. The project is supported by NSF's EDU Core Research Building Capacity in STEM Education Research (ECR: BCSER) program, which is designed to build investigators’ capacity to carry out high-quality STEM education research.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.