States as STEM Learning Environments: Building an Indicator System to Guide Instructional Improvement at Scale

作为 STEM 学习环境的州：建立指导大规模教学改进的指标体系

• 批准号: 1348528
• 负责人: Mary Kay Stein
• 金额: $ 149.99万
• 依托单位: University of Pittsburgh
• 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: 2013-10-01 至 2019-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1348528&HistoricalAwards=false
• 关键词: States; STEM; Learning; Environments; Building

Investigators are designing a state-wide, empirically based indicator system that aligns with Tennessee's (TN) vision of instructional improvement and is built from data gathered through in-depth study of teaching and learning in a sample of 4th through 8th grade mathematics classrooms. Investigators begin with a set of constructs and measures that are known to predict student learning on high-level mathematics assessments (high-cognitive-demand instruction) and that are associated with teachers' capacity to enact ambitious instruction (teacher social capital). The objective is to distill the measurement of these constructs to a core set of survey indicators that are predictive of student outcomes, can be administered efficiently at scale, and are consequential for state policy makers' decision making. Using a 3-tiered approach - in-depth data collection from an intensive sample of teachers (n=50), less comprehensive data from an intermediate sample (n=100), and survey-based data from a scale sample, a group that will increase in number each year, ending in year 3 with 1000 teachers or approximately 10% of TN's grades 4-8 mathematics teachers - the analyses leverage in-depth qualitative examination of mathematics instruction and teacher social capital in dialogue with survey-based approaches to construct a validity argument (Kane, 2006) that 1) teachers vary on proxy measures of central constructs, 2) measures of these proxies are correlated with other theoretically relevant measures, and 3) these proxies are predictive of student learning and high-quality teaching, respectively, in the scale sample.Previous research on cognitive demand has provided both theoretical and empirical warrants for the connection between teaching and student learning. This project extends that work by establishing proxies for high-cognitive-demand instruction and observing their relationship to student learning across hundreds of classrooms. Previous work on teacher social capital has consisted primarily of theory-building, qualitative studies that have shown relationships between specific features of teacher social capital and level of instruction. This study tests that relationship in larger numbers of classroom and extends it to student learning. Additionally, investigators build a practical theory of how states can support large-scale instructional improvement. While most theorizing and research in this regard has focused on the challenges that states confront in providing professional development for large numbers of teachers, less attention has been paid to the challenge of monitoring the effectiveness and impact of those efforts. This project is based on the idea that just doing more - without systematically learning from what one is doing - is a failing proposition. In the era of current reforms, increasing numbers of states will be devoting substantial resources to improving instruction across their states. By conceptualizing an entire state as a learning environment, this work calls attention to systematic and organized ways to learn from that activity, thereby getting smarter about efficient ways to support large-scale improvement in mathematics.The development of indicators that can be deployed across an entire state is, in and of itself, a sign of broad impact. In addition, the indicator system is portable and, because it is aligned with college and career ready standards adopted by the majority of states, could be taken up by other states, especially those who have committed to common assessments. Furthermore, if leaders in TN and other states use the indicator system to identify where instruction is flagging and where teacher supports are needed, they will be positioned to make better decisions regarding where and how to assist educators across their states. This, in turn, will support the achievement of the societal goal of improved STEM education that will produce more globally competitive high school graduates. Finally, if this study is successful we will have established a longer chain of evidence than heretofore has been assembled. We will show linkages among (a) teachers' access to social resources; (b) teachers' instructional practice; and (c) student performance.

调查人员正在设计一个全州范围内的，基于经验的指标体系，该体系与田纳西州（TN）的教学改进愿景相一致，并通过对4至8年级数学教室样本的教学和学习的深入研究收集数据。调查人员开始与一组结构和措施，是已知的预测学生学习高水平的数学评估（高认知需求指令），并与教师的能力，制定雄心勃勃的指令（教师社会资本）。我们的目标是提取这些结构的测量，以一套核心的调查指标，预测学生的成绩，可以有效地管理规模，并为国家政策制定者的决策是重要的。采用三层方法-从密集的教师样本（n=50）中收集深入的数据，从中间样本（n=100）中收集不太全面的数据，以及从规模样本中收集基于调查的数据，这一群体的人数每年都会增加，在第三年结束时，有1000名教师或大约10%的TN的4-8年级数学教师-分析杠杆-对数学教学和教师社会资本进行深入的定性研究，并与基于调查的方法进行对话，以构建有效性论证（Kane，2006）1）教师在中心结构的代理测量上存在差异，2）这些代理的测量与其他理论相关的测量相关，以往的认知需求研究为教学与学生学习之间的联系提供了理论和实证依据。该项目通过建立高认知需求教学的代理并在数百个教室中观察它们与学生学习的关系来扩展这项工作。以往的工作对教师的社会资本主要是理论建设，定性研究已经表明教师的社会资本和教学水平的具体特征之间的关系。本研究在大量的课堂上测试这种关系，并将其扩展到学生的学习。 此外，调查人员建立了一个实用的理论，国家如何能够支持大规模的教学改进。虽然这方面的大多数理论和研究都集中在国家为大量教师提供专业发展所面临的挑战上，但对监测这些努力的有效性和影响的挑战关注较少。这个项目是基于这样一个想法，即只是做更多-没有系统地学习一个人在做什么-是一个失败的主张。在当前的改革时代，越来越多的州将投入大量资源来改善各州的教学。通过将整个州概念化为一个学习环境，这项工作呼吁人们注意从该活动中学习的系统和有组织的方法，从而更聪明地了解支持大规模数学进步的有效方法。此外，该指标系统是便携式的，因为它与大多数州采用的大学和职业准备标准相一致，可以被其他州采用，特别是那些致力于共同评估的州。此外，如果田纳西州和其他州的领导人使用该指标系统来确定教学在哪里落后，哪里需要教师支持，他们将能够更好地决定在哪里以及如何帮助各州的教育工作者。反过来，这将有助于实现改善STEM教育的社会目标，从而培养出更具全球竞争力的高中毕业生。最后，如果这项研究成功，我们将建立一个比迄今为止收集的证据更长的证据链。我们将展示（a）教师获取社会资源;（B）教师教学实践;（c）学生表现之间的联系。