Empirical Benchmarks of Design Parameters for Group Randomized Trials in Teacher Professional Development Intervention Studies

教师专业发展干预研究中分组随机试验设计参数的实证基准

• 批准号: 1405601
• 负责人: Benjamin Kelcey
• 金额: $ 47.6万
• 依托单位: University of Cincinnati Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-15 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1405601&HistoricalAwards=false
• 关键词: Empirical; Benchmarks; Design; Parameters; Group

Professional development of teachers is a strong component in the theory of action of improving student outcomes, yet such efforts are rarely rigorously evaluated. One of the problems of robust evaluation of professional development is that these programs are largely conducted with teachers in their school or district settings rather than being randomly assigned individually to different activities. This requires that the evaluation design be developed around group randomized trials, and the field of educational evaluation lacks important knowledge of how to structure such evaluations. This project is developing estimates of the empirical benchmarks of design parameters to be used to structure evaluations of whole school professional development efforts in mathematics. The project uses the large data sets that have been accumulated using the Mathematical Knowledge of Teaching (MKT) measures to develop estimates of the variation in teacher pedagogical content knowledge in mathematics to provide values that can be used to estimate sample size and other design parameters in evaluation design. Researchers from the College of Education at Wayne State University, the Educational Testing Service and Michigan State University are conducting this research.This project leverages the data collected from multiple professional development projects for mathematics teachers that have used the MKT under the auspices of the researchers that have been collected in an online Teacher Knowledge Assessment System from over 10,000 teachers in over 1150 schools. The data from the MKT is combined with data that is available publically in the Common Core of Data about the schools and districts within which the professional development was conducted and additional data that have been collected by the MKT researchers. The researchers use random intercept hierarchical linear models to estimate the design parameters for four different group random trial designs. They examine the impact of several covariate sets of parameters on the variance components and the corresponding estimates of intraclass correlation. The resulting parameters are reported in association with the conditions of the schools and districts in which the data were generated.The findings from this study are reported in the professional literature on research and evaluation design. In addition, professional development workshops disseminate the findings and the processes used to develop them at national educational research and evaluation meetings. In addition, the researchers include their findings in updates of the Optimal Design software that is used by multiple researchers and evaluators to structure the design of their own studies. Too frequently, researchers do not have sufficient knowledge of the potential contextual issues surrounding the schools and districts in which they work to make accurate estimates of the parameters that they should use to determine important characteristics of their research such as sample size. The findings of this study provide more realistic estimates of the design parameters that researchers and evaluators need to conduct their own studies.

教师的专业发展是提高学生成绩的行动理论的重要组成部分，然而这种努力很少得到严格的评估。对专业发展进行有力评估的问题之一是，这些项目主要是由学校或学区的教师进行的，而不是随机分配到不同的活动中。这就要求评估设计要围绕群体随机试验展开，而教育评估领域缺乏如何构建这种评估的重要知识。这个项目正在开发设计参数的经验基准的估计，用于构建整个学校的数学专业发展努力的评估。该项目使用使用数学教学知识（MKT）措施积累的大型数据集来估计教师在数学教学内容知识方面的变化，以提供可用于估计评估设计中的样本量和其他设计参数的值。来自韦恩州立大学教育学院、教育考试服务中心和密歇根州立大学的研究人员正在进行这项研究。该项目利用了由研究人员主持的多个数学教师专业发展项目收集的数据，这些项目使用了MKT，这些数据收集在一个在线教师知识评估系统中，来自1150多所学校的10,000多名教师。来自MKT的数据与公共核心数据相结合，这些数据是关于进行专业发展的学校和地区的数据，以及MKT研究人员收集的其他数据。研究人员使用随机截距分层线性模型来估计四种不同组随机试验设计的设计参数。他们研究了几个协变量参数集对方差成分和相应的类内相关性估计的影响。所产生的参数与产生数据的学校和地区的条件相关联。本研究结果已发表于研究与评估设计的专业文献中。此外，专业发展讲习班在国家教育研究和评价会议上传播调查结果和用于发展这些结果的过程。此外，研究人员将他们的发现包括在优化设计软件的更新中，该软件被许多研究人员和评估人员用于构建他们自己的研究设计。通常，研究人员对他们工作的学校和学区周围的潜在背景问题缺乏足够的了解，无法准确估计他们应该用来确定研究的重要特征（如样本量）的参数。本研究的发现为研究人员和评估人员进行自己的研究提供了更现实的设计参数估计。