Building A Teacher Knowledge Base For The Implementation Of High-quality Instructional Resources Through The Collaborative Investigation Of Video Cases

通过视频案例协同调研构建教师知识库实施优质教学资源

基本信息

  • 批准号:
    1908311
  • 负责人:
  • 金额:
    $ 61.77万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2019
  • 资助国家:
    美国
  • 起止时间:
    2019-07-01 至 2024-06-30
  • 项目状态:
    已结题

项目摘要

This is a Late-Stage Design and Development collaborative effort submitted to the teaching strand of the Discovery Research PreK-12 (DRK12) Program. This project will address the pressing national need to generate shared, practice-based knowledge about how to implement freely available, high-quality instructional resources (mathematics formative assessment lessons) that have been shown to produce significant gains in student learning outcomes. It will expand a professional development model (Analyzing Instruction in Mathematics using the Teaching for Robust Understanding Framework (AIM-TRU)) that supports teacher learning about effective lesson implementation. The backbone of AIM-TRU is a growing, open repository of video cases available to teachers and teacher educators across the U.S. who use or are interested in using the lessons. The repository will include tools such as a facilitator's guide to support teachers and teacher educators to engage in the model and collaboratively investigate the video cases. Consequently, the work will have the potential to engage teachers and teacher educators in improving mathematics education at scale. Because the video cases will capture implementation and ideas for improving instruction in schools serving populations who are underrepresented in mathematics, AIM-TRU will serve to improve mathematics education equitably.Research questions focus on what teachers learn about high-quality mathematics instruction and instructional materials within a community of practice, and how that learning influences their teaching. In AIM-TRU, teachers engage in the collaborative investigation of video cases utilizing a shared repertoire that includes questioning protocols adapted from the Teaching for Robust Understanding (TRU) framework. This framework articulates five dimensions of classroom instruction that are necessary and sufficient to support students in becoming powerful mathematical thinkers. This affords teachers opportunities to use the TRU dimensions as lenses to diagnose common problems of practice that arise in implementation, and propose innovations and theories for improving instruction that can be tested in real classrooms and documented in new video cases. Analytic tools will be used from frame analysis to produce empirical evidence of what teachers are learning about instruction and instructional materials along the five dimensions of TRU. These data will be mapped to a random sample of video recordings of participating teachers' instruction, scored using the TRU Math Rubric, in order to link learning outcomes from the professional development to changes in instruction. Addressing these research questions will provide a deeper understanding and empirical evidence of learning within teacher collectives, the pressing national need to develop mechanisms to produce collective professional knowledge for teaching, and further efforts to understand the types of knowledge required for effective teaching.The DRK-12 Program 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. Projects in the program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects.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.
这是一个后期设计和开发合作的努力提交给发现研究PreK-12 (DRK12)项目的教学链。该项目将解决国家在如何实施免费提供的高质量教学资源(数学形成性评估课程)方面产生共享的、基于实践的知识方面的迫切需求,这些资源已被证明在学生学习成果方面取得了显著成果。它将扩展一种专业发展模式(利用稳健理解教学框架(AIM-TRU)分析数学教学),支持教师学习如何有效地实施课程。AIM-TRU的骨干是一个不断增长的、开放的视频案例库,可供美国各地使用或有兴趣使用这些课程的教师和教师教育工作者使用。该存储库将包括一些工具,比如辅助者指南,以支持教师和教师教育工作者参与模型并协作调查视频案例。因此,这项工作将有可能使教师和教师教育工作者参与大规模改善数学教育。由于视频案例将捕捉到改善学校教学的实施和想法,为那些在数学方面代表性不足的人群提供服务,AIM-TRU将有助于公平地改善数学教育。研究问题集中于教师在实践社区中对高质量数学教学和教学材料的了解,以及这种学习如何影响他们的教学。在AIM-TRU中,教师利用共享的曲目参与视频案例的合作调查,其中包括从稳健理解教学(TRU)框架改编的提问协议。这个框架阐明了课堂教学的五个维度,这些维度是必要的,足以支持学生成为强大的数学思考者。这为教师提供了使用TRU维度作为透镜来诊断实践中出现的常见问题的机会,并为改进教学提出创新和理论,这些创新和理论可以在真实教室中进行测试,并记录在新的视频案例中。分析工具将从框架分析中使用,以产生经验证据,证明教师沿着TRU的五个维度学习教学和教学材料。这些数据将被映射到参与教师教学的随机视频记录样本中,使用TRU数学标准进行评分,以便将专业发展的学习成果与教学变化联系起来。解决这些研究问题将为教师集体学习提供更深入的理解和经验证据,国家迫切需要发展为教学产生集体专业知识的机制,并进一步努力了解有效教学所需的知识类型。DRK-12项目旨在通过研究和开发创新资源、模型和工具,显著提高pre -12学生和教师对科学、技术、工程和数学(STEM)的学习和教学。该计划中的项目建立在STEM教育的基础研究和先前的研究和开发努力的基础上,为拟议的项目提供理论和实证依据。该奖项反映了美国国家科学基金会的法定使命,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。

项目成果

期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Methodological Advancements for Analyzing Teachers’ Learning in a Community of Practice
实践社区中教师学习分析方法的进步
  • DOI:
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Leonard, Helene;Bonaccorso, Victoria;DiNapoli, Joseph;Murray, Eileen
  • 通讯作者:
    Murray, Eileen
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David Jabon其他文献

Achieving I/O improvements in a mass spectral database
在质谱数据库中实现 I/O 改进
  • DOI:
    10.1145/1217728.1217739
  • 发表时间:
    2006
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Eric Puryear;Jennifer Van Puymbrouck;D. Angulo;K. Drew;Lee Ann Hollenbeck;Dominic Battré;A. Schilling;David Jabon;G. Laszewski
  • 通讯作者:
    G. Laszewski
Ecological research can augment restoration practice in urban areas degraded by invasive species—examples from Chicago Wilderness
生态研究可以增强因入侵物种而退化的城市地区的恢复实践——来自芝加哥荒野的例子
  • DOI:
    10.1007/s11252-008-0057-8
  • 发表时间:
    2009
  • 期刊:
  • 影响因子:
    2.9
  • 作者:
    L. Heneghan;Lauren G. Umek;Bradley M. Bernau;K. Grady;Jamie Iatropulos;David Jabon;Margaret Workman
  • 通讯作者:
    Margaret Workman
Implementing Spectral Similarity Algorithms for Protein Identification
实施蛋白质识别的光谱相似性算法
  • DOI:
  • 发表时间:
    2006
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Hussam Ala Eldeen;D. Angulo;K. Drew;Dominic Battré;A. Schilling;Eric Puryear;Jennifer Van Puymbrouck;David Jabon;G. Laszewski
  • 通讯作者:
    G. Laszewski
A Batch Import Module for an Empirically Derived Mass Spectral Database
用于经验推导的质谱数据库的批量导入模块
  • DOI:
  • 发表时间:
    2006
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Jennifer Van Puymbrouck;D. Angulo;K. Drew;Lee Ann Hollenbeck;Dominic Battré;A. Schilling;David Jabon;G. Laszewski
  • 通讯作者:
    G. Laszewski

David Jabon的其他文献

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