Collaborative Research: Modern Meta-Analysis Research Institute

改善美国年轻人在科学、技术、工程和数学(STEM)领域的教育是一项有充分证据、得到广泛支持的联邦政策优先事项。为了应对这一经济现实，出现了几项公共和私人倡议，包括为幼儿园前到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。这一方法的第一部分是审查近几十年来制定和评估的大量STEM教育干预措施。重要的是要了解哪些干预措施有效，对谁有效，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，并探索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天的面对面密集研讨会和多个研讨会后机会，因为参与者正在开发STEM教育研究元分析项目。通过这一活动时间表，该项目旨在提高STEM教育研究人员进行现代元分析的方法技能，从而更好地了解STEM教育干预措施如何影响学习结果。该项目将使参与者能够进行元分析，通过探索效果大小异质性的方法，探索哪些STEM干预措施有效，对谁有效，在什么条件下有效。研究所将支持学员开发针对异质性的元分析研究问题，扩大他们使用包括MetaReview和R在内的现代元分析软件应用程序的技能，开展元分析，利用元回归模型探索和解释效果大小的异质性，并适当地解释和交流元分析的结果。研究所前讲习班活动将使学员熟悉系统审查的步骤和使用R进行统计分析的步骤。为期五天的面对面研讨会将侧重于如何进行系统审查和荟萃分析的初步阶段，借鉴讲师团队进行大规模审查的经验。研讨会的日程安排还将花费大量时间教授如何使用R计划进行荟萃分析和荟萃回归，以及探索不同研究之间的效果异质性的方法。该研究所旨在向学员提供必要的知识和技能，以领导和进行基本的元分析，并在STEM教育研究的背景下对已发表的元分析进行批判性评价。该项目得到了NSF的STEM教育研究EDU核心研究能力建设计划(ECR：BCSER)的支持，该计划旨在建设研究人员开展高质量STEM教育研究的能力。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.