Most recent research on teacher evaluation examines evaluation’s measurement properties and accountability uses. Less research studies how evaluation data can improve teaching and student learning. In other contexts, researchers have examined how teachers use data to improve their practice. From general research on teachers’ data use, we apply the data-driven decision-making (DDDM) framework to synthesize research on teacher evaluation since 2009. We illustrate how evaluation data are collected, analyzed, and synthesized to inform instruction and improve student learning. Most research focused on teachers’ use of observation data, not their use of student data. We find that the teachers’ use of evaluation data involves more social learning than the DDDM model implies. The effects of observation on instruction and student learning are often weak, apparently because observers lack the time and knowledge to support teachers’ thorough analysis and synthesis of evaluation data. Implications for policy and further research are offered.

中文翻译：

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作为数据使用的教师评估：最近的研究表明

最近关于教师评价的研究考察了评价的测量属性和问责制用途。研究评估数据如何改善教学和学生学习的研究较少。在其他情况下，研究人员研究了教师如何使用数据来改进他们的实践。从对教师数据使用的一般研究出发，我们应用数据驱动决策 (DDDM) 框架来综合自 2009 年以来对教师评价的研究。我们说明了如何收集、分析和综合评价数据以通知教学和改善学生学习. 大多数研究集中在教师对观察数据的使用上，而不是他们对学生数据的使用上。我们发现教师对评估数据的使用比 DDDM 模型所暗示的涉及更多的社会学习。观察对教学和学生学习的影响往往很弱，显然是因为观察者缺乏时间和知识来支持教师对评估数据的全面分析和综合。提供了对政策和进一步研究的影响。