Data Connections: Developing a Coherent Picture of Mathematics Teaching and Learning

数据连接：建立数学教学的连贯图景

• 批准号: 1050667
• 负责人: Walter Stroup
• 金额: $ 121.35万
• 依托单位: University of Nebraska-Lincoln
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-06-15 至 2015-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1050667&HistoricalAwards=false
• 关键词: Data; Connections; Developing; Coherent; Picture

The purpose of this study is to develop statistical models to create a coherent model of the effects of teacher professional development in mathematics on student learning. The project works closely with two partnerships of the NSF Math and Science Partnership (MSP) program, the Math in the Middle Institute Partnership and NebraskaMATH, and with the Lincoln Public Schools. Utilizing data already collected through these MSP partnerships, the study builds newly developed models to help these and other MSP partnerships and their evaluators interpret student and teacher data in statistically productive and meaningful ways. Specifically, the project focuses on layered value-added models to estimate teacher effects, and how those models can be adapted for use with messy data, for example when different tests are administered in different grade levels at different times of year. The project is investigating the use of Z-scores, parallel processing, and binning by quantile to address issues arising with available student achievement data. The scope of the project research includes: 1) methods to estimate student achievement trajectories over time; 2) methods for best connecting these trajectories to measures of teaching quality; 3) connecting measures of student and teacher attitudes to each other and to measures of student achievement and teaching quality; and 4) estimating the impact of MSP interventions on all of the above.MSP projects, school districts and States are grappling with how to evaluate the effects of their professional development programs for teachers on student learning. Few studies have addressed how to use value-added models to analyze achievement data that are not on a single developmental scale, and even fewer have discussed how to use information from multiple instruments in a single year that are on different scales. In addition to this work, the research findings will be disseminated widely, through presentations at national conferences, articles published in peer-reviewed journals, and a dissemination conference targeting MSP project and evaluation personnel. Utilizing value-added models increases the potential to provide evidence of high quality teacher impact in high-need schools. By helping MSP partnerships use the methods developed in this study, the project is building build their capacity to inform the nation of how their MSPs impact teaching and learning.

本研究的目的是开发统计模型，以创建数学教师专业发展对学生学习影响的连贯模型。 该项目与 NSF 数学和科学合作伙伴 (MSP) 计划的两个合作伙伴关系，即中学数学研究所合作伙伴关系和内布拉斯加州数学学会，以及林肯公立学校密切合作。该研究利用通过这些 MSP 合作伙伴关系收集的数据，构建了新开发的模型，以帮助这些和其他 MSP 合作伙伴关系及其评估者以统计上有效且有意义的方式解释学生和教师数据。具体来说，该项目重点关注分层增值模型来估计教师效果，以及如何调整这些模型以适应混乱的数据，例如在一年中的不同时间在不同年级进行不同的测试。该项目正在研究 Z 分数、并行处理和按分位数分箱的使用，以解决可用学生成绩数据出现的问题。该项目研究的范围包括：1）评估学生随时间的成绩轨迹的方法； 2）将这些轨迹与教学质量衡量标准联系起来的最佳方法； 3) 将学生和教师态度的衡量标准以及学生成绩和教学质量的衡量标准联系起来； 4) 估计 MSP 干预措施对上述所有方面的影响。 MSP 项目、学区和州正在努力解决如何评估其教师专业发展计划对学生学习的影响。很少有研究讨论如何使用增值模型来分析不属于单一发展尺度的成就数据，更很少有研究讨论如何使用一年内不同尺度的多个工具的信息。除了这项工作之外，研究成果还将通过在全国会议上的演讲、在同行评审期刊上发表的文章以及针对 MSP 项目和评估人员的传播会议来广泛传播。利用增值模型可以增加为高需求学校提供高质量教师影响力的证据。通过帮助 MSP 合作伙伴使用本研究中开发的方法，该项目正在培养他们的能力，以向全国通报他们的 MSP 如何影响教学和学习。