Personalizing Math Instruction at Scale: A Meta-Analytic and Cost-Effectiveness Analysis of Math Tutoring Programs

大规模个性化数学教学：数学辅导项目的元分析和成本效益分析

• 批准号: 2100334
• 负责人: Matthew Kraft
• 金额: $ 81.71万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2100334&HistoricalAwards=false
• 关键词: Personalizing; Math; Instruction; Scale; Meta

This project will identify the most cost-effective and scalable methods of delivering mathematics tutoring to K-12 public school students for the purpose of accelerating learning. While there is a notably large body of rigorous research documenting that high-dosage tutoring produces large academic gains for children who have fallen behind academically, less is known about the features of effective programs needed to guide program design. Additionally, the widespread adoption of tutoring programs has been hampered by the high upfront costs of intensive individualized instructional programs. This project will therefore isolate the key features of the most effective tutoring programs and the costs associated with these programs to identify program models with the potential to deliver the greatest benefits at the lowest cost. Program features could relate to tutor characteristics, the dosage of tutoring delivered, the timing of tutoring sessions, the training and support provided to tutors, tutor-student ratios, the content of tutoring sessions, and more. The project is particularly timely given policymakers and educators are currently looking to invest in affordable strategies to support students’ learning in mathematics given early evidence that Covid-19-related educational disruptions have negatively affected mathematics achievement. This project is funded by the EHR Core Research (ECR) program, which supports work that advances fundamental research on STEM learning and learning environments, broadening participation in STEM, and STEM workforce development.The project will include a thorough meta-analysis of all existing studies of mathematics tutoring interventions that use quasi-experimental or experimental methods designed to isolate the causal effects of the tutoring program in question. More specifically, the research team will search for, collect, and code all studies based on the features of the intervention under examination, the size of the effects the intervention had on student outcomes, as well as information that will allow them to calculate costs for each intervention. Researchers will then pool estimates across studies to identify the average effects of mathematics tutoring on student achievement and average per-pupil costs and to examine how the effects and costs of tutoring programs vary by design and program features. Finally, the researchers will examine which features of interventions predict the largest academic gains for students and calculate cost-benefit ratios for each intervention. Meta-analytic and cost-effectiveness reviews are a critical step in developing new scientific knowledge by generating conclusions with greater precision and external validity and accounting for publication bias which can skew perceptions about the overall effectiveness of an intervention when only well-known or successful programs are highlighted in the literature. This project will provide a comprehensive review across all existing studies, ultimately allowing the research team to accurately identify those mathematics tutoring models with the greatest promise for both impact and scalability.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.

该项目将确定最具成本效益和可扩展的方法，为K-12公立学校学生提供数学辅导，以加速学习。虽然有大量严谨的研究证明，高剂量的辅导会为那些在学业上落后的孩子带来巨大的学业收益，但人们对指导课程设计所需的有效课程的特点知之甚少。此外，辅导计划的广泛采用受到密集的个性化教学计划的高昂前期成本的阻碍。因此，该项目将隔离最有效的辅导计划的关键功能和与这些计划相关的成本，以确定有可能以最低的成本提供最大的利益的计划模型。项目特征可能与导师特征、提供的辅导剂量、辅导课程的时间、为导师提供的培训和支持、导师与学生的比例、辅导课程的内容等有关。鉴于早期证据表明，与2019冠状病毒病相关的教育中断对数学成绩产生了负面影响，政策制定者和教育工作者目前正在寻求投资于负担得起的战略，以支持学生学习数学，因此该项目尤为及时。该项目由EHR核心研究（ECR）计划资助，该计划支持推进STEM学习和学习环境的基础研究，扩大STEM参与，以及STEM劳动力发展。该项目将包括对所有现有的数学辅导干预研究进行彻底的荟萃分析，这些研究使用准实验或实验方法，旨在隔离辅导计划的因果影响。更具体地说，研究小组将根据所研究的干预措施的特点，干预措施对学生成绩的影响大小，以及使他们能够计算每次干预措施成本的信息，搜索，收集和编码所有研究。然后，研究人员将汇总各项研究的估计，以确定数学辅导对学生成绩的平均影响和平均每名学生的成本，并研究辅导计划的效果和成本如何因设计和计划特点而异。最后，研究人员将研究干预措施的哪些特征预测学生的最大学术收益，并计算每种干预措施的成本效益比。荟萃分析和成本效益审查是开发新科学知识的关键步骤，通过产生更高精度和外部有效性的结论，并考虑出版偏倚，当文献中只强调知名或成功的项目时，出版偏倚可能会扭曲对干预措施整体有效性的看法。该项目将对所有现有研究进行全面审查，最终使研究团队能够准确地识别出那些具有最大影响力和可扩展性的数学辅导模型。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。