CAREER: Understanding the Routinization of Mathematics Language Routines in Middle and High Schools

职业：了解初中和高中数学语言常规的常规化

• 批准号: 2144027
• 负责人: Sarah Roberts
• 金额: $ 110.28万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2144027&HistoricalAwards=false
• 关键词: CAREER; Understanding; Routinization; Mathematics; Language

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). This is also funded by the Discovery Research preK-12 program (DRK-12) which seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models and tools. Interpreting and analyzing data provides students problems that apply mathematics and statistics to their own experiences. Mathematics teaching about data needs to include opportunities for students to discuss and explain their mathematical reasoning and problem solving. Particularly for data science, students need opportunities to communicate and discuss their ideas. Mathematics teaching requires the ability to support that learning experience with multilingual students and learn to engage all students in mathematical problem solving. Data science presents a context for middle and high school students to examine data that is personally meaningful and relevant. The project would develop and investigate mathematics language routines focused on data science topics in middle and high school. The study will investigate teachers’ use of mathematics language routines and a professional development model to support teachers’ learning. The educational integration plan in the project will build mathematics teacher expertise and create video cases to support teacher professional development. Graduate and undergraduate researchers will also have opportunities to learn about education research and mathematics teaching. The research focuses on core practices of mathematics teaching that are regularly incorporate in teachers’ interactions with students. The project uses a studio day model for teachers to have a professional learning experience closely connected to their work and guided by a district instructional specialist. The central concept of the study is the enactment of mathematics language routines (MLRs) in data science. The three research objectives are: (1) to develop professional learning materials organized around MLR studio day cycles for four grade-level vertically articulated professional learning communities; (2) to execute and study professional learning materials organized for grade-level vertically articulated professional learning communities; (3) to understand and document students’ and teachers’ day-to-day classroom enactments of MLRs to understand their adaptive expertise related to MLRs; and (4) to create educational video case materials for in-service and preservice teachers on the use of MLRs. The research activities focus on studying how teachers develop adaptive expertise as they use MLRs, to be able to flexibly use the MLRs. The data collection focuses primarily on qualitative analysis of the professional learning communities, studio days, classroom observations, and artifacts of the mathematics learning routines. The data analysis uses an adaptive expertise framework to interpret and describe the teachers’ learning and enactment of the mathematics language routines. The educational activities attend to video case materials developed using research materials.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项全部或部分根据2021年美国救援计划法案（公法117-2）资助。这也是由发现研究preK-12计划（DRK-12），旨在通过研究和创新资源，模型和工具的开发，显着提高preK-12学生和教师的科学，技术，工程和数学（STEM）的学习和教学资助。解释和分析数据为学生提供了将数学和统计学应用于自己经验的问题。关于数据的数学教学需要包括学生讨论和解释他们的数学推理和解决问题的机会。特别是对于数据科学，学生需要有机会交流和讨论他们的想法。数学教学需要有能力支持多语种学生的学习经验，并学会让所有学生参与数学问题的解决。数据科学为中学生提供了一个背景，让他们检查对个人有意义和相关的数据。该项目将开发和研究专注于初中和高中数据科学主题的数学语言例程。本研究将探讨教师使用数学语言程式及专业发展模式以支援教师学习的情况。该项目的教育整合计划将建立数学教师的专业知识，并创建视频案例，以支持教师的专业发展。研究生和本科生研究人员也将有机会了解教育研究和数学教学。本研究的重点是数学教学的核心实践，经常纳入教师与学生的互动。该项目采用工作室日模式，让教师在地区教学专家的指导下，获得与其工作密切相关的专业学习经验。该研究的中心概念是在数据科学中制定数学语言例程（MLR）。本研究的三个目标是：（1）围绕MLR工作室日周期为四个年级水平的垂直衔接专业学习社区开发专业学习材料;（2）执行和研究为年级水平的垂直衔接专业学习社区组织的专业学习材料;（3）了解和记录学生和教师在课堂上的日常教学活动，以了解他们与最低限度标准有关的适应性专门知识;及（4）为在职及在职教师制作有关使用多媒体学习资源的教育录像个案资料。研究活动的重点是研究教师如何发展适应性的专业知识，因为他们使用MLR，能够灵活地使用MLR。数据收集主要集中在定性分析的专业学习社区，工作室天，课堂观察，和文物的数学学习例程。数据分析采用自适应专家框架来解释和描述教师的数学语言惯例的学习和制定。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。