A Linked Meta-analysis on Teacher Knowledge

本研究的目标是建立一个教师知识模型，将其作为一种可修改的特征，调节其他教师属性（如人口统计学和能力倾向）的影响，通过教师专业发展和教师教育等干预措施进行修改，并对学生和教师在数学和科学方面的成果产生潜在影响。本研究将采用定量研究综合的方法。Becker， Kennedy和O'Reilly，以及他们的员工和顾问Jakubowski和Southerland，将进行多部分综合，包括1)教师特征之间关系的研究，重点是教师知识，2)教师专业发展和教师教育的研究，以及3)将教师资格和知识与数学和科学教育领域的成果相关的研究。部分合成工作已经开始，这要归功于现有的两个由美国国家科学基金会资助的项目，由贝克尔和肯尼迪（佛罗里达州立大学和密歇根州立大学）以及奥赖利（阿伯特联合公司）领导。研究人员将在这些项目中增加一项关于教师知识的预测和修正的新综合研究，并关注干预措施和其他教师特征的作用。该项目的这一部分将包括在专业发展干预背景之外考察教师学习的研究。这种学习将包括教师教育项目（教师职前教育）等方面的学习。这些文献将通过建立作为两个正在进行的项目的一部分开发的现有数据库，使用关联元分析的工具进行合并。将这些项目的研究结果联系起来，将使研究人员能够在整个专业发展连续体中建立新的教师学习模型，从职前学习到在职学习。本研究的智力贡献还将包括规范教师学习知识库中的差距，这将通过检查教师知识发展过程中的每个环节（以及可能影响该过程的其他因素）的研究来确定。实证模型将通过多个关联元分析的方式构建，也称为模型驱动的元分析。Becker开发了模型驱动的元分析方法，并对离婚家庭的儿童结果、运动成绩结果和科学成就的性别差异的预测进行了模型驱动的元分析。最终，从这一综合中获得的知识可以在教师学习专业发展干预的背景下进行测试，尽管这项工作不是本具体建议的一部分。

The goal of this research is to develop a model of teacher knowledge as a modifiable characteristic that mediates the effects of other teacher attributes (such as demographics and aptitudes), is modifiable due to interventions such as teacher professional development and teacher education, and has potential impacts on student and teacher outcomes in math and science. The methods of quantitative research synthesis will be used in this effort. Becker, Kennedy and O'Reilly, along with their staff and consultants Jakubowski and Southerland, will conduct a multi-part synthesis involving 1) studies of relationships among teacher characteristics, with a focus on teacher knowledge, 2) studies of teacher professional development and teacher education, and 3) studies relating teacher qualifications and knowledge to outcomes in the areas of math and science education. Parts of the synthesis work are already underway thanks tobuild on two existing NSF-funded projects, headed by Becker and Kennedy (at Florida State University and Michigan State University) and O'Reilly (Abt Associates). The researchers will add to these projects a new synthesis of studies concerning the prediction and modification of teacher knowledge, with attention to the roles of both interventions and other teacher characteristics. This part of the project will include studies that examine teacher learning outside the context of professional development interventions. Such learning will include learning in teacher education programs (teacher preservice education) and the like. These literatures will be merged by building on existing databases developed as a part of the two ongoing projects, using the tools of linked meta-analysis. Linking the findings of these projects will enable the researchers to build new models of teacher learning across the professional development continuum, from pre-service through in-service learning. The intellectual contribution of this research also will include the specification of gaps in the knowledge base concerning teacher learning, which will be identified by examining research on each link in the process of teacher knowledge development (as well as other factors that may impinge upon that process). The empirical models will be constructed by way of multiple linked meta-analyses, also known as model-driven meta-analysis. Becker has developed methods for model-driven meta-analysis and also conducted model-driven meta-analyses on the prediction of child outcomes in divorcing families, sport performance outcomes, and the prediction of gender differences in science achievement. Eventually, the knowledge garnered from this synthesis can be tested in the context of teacher learning in professional development interventions, though that work is not a part of this specific proposal.