Writing Data Stories: Integrating Computational Data Investigations into the Middle School Science Classroom

编写数据故事:将计算数据调查融入中学科学课堂

基本信息

  • 批准号:
    1900606
  • 负责人:
  • 金额:
    $ 236.98万
  • 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:
    2019-02-15 至 2024-06-30
  • 项目状态:
    已结题

项目摘要

As a result of the powerful innovation and application of computing in STEM disciplines, the STEM+C program addresses an urgent need for real-world, interdisciplinary, and computational preparation of students from the early grades through high school (preK-12). Preparing today's students to work with data fluently is critical to ensuring a scientifically literate and empowered citizenry. But most efforts to incorporate data analysis into the K-12 curriculum are limited to short, isolated activities; basic skills development; or, are introduced as new courses devoted specifically to data and computing, limiting both the potential benefits and the audience for such efforts. This project seeks to integrate computational data analysis into the middle school science curriculum in a longitudinal, interdisciplinary way - drawing from the computer and data sciences, literacy studies, statistics, and science education to saturate the classroom with relevant tools, resources, and support. Middle school classrooms will analyze and draw conclusions about publicly available scientific datasets using a free, innovative, computational data analysis platform called the Common Online Data Analysis Platform (CODAP). Units will be designed specifically with Dual Language Learners (DLL) in mind, inviting students to share their investigations by writing multimodal texts that blend both familiar and academic modes of expression to explain and contextualize their data analysis processes. Units that build on one another in difficulty and complexity will be introduced throughout the academic year, and participating teachers will receive significant training opportunities. Overall the project is anticipated to directly impact approximately 2,500 students and 20 teachers in the greater San Francisco Bay area, from predominantly high needs schools.The project will provide a research context to address the following questions: How do students learn, over time, to use computational tools to structure, calculate, filter, and transform data for scientific inquiry? What patterns of engagement in scientific practices are supported by the integration of computational data analysis and visualizations into the science curriculum? And, what new literacy practices might support DLL and learners with limited access to technology or who are still developing academic literacy in constructing oral and written arguments and explanations using data and visualizations as evidence? Specifically, it brings together and seeks to extend three complementary research constructs. Data moves are the computational actions analysts take to transform and analyze datasets. Syncretic texts are specialized texts that blend academic discourse, such as the formulae and statistical language needed to explain data analysis, with the familiar modes of expression (including home languages and alternative forms of expression such as video, art, animation, etc.), which has been found to invite a wide range of marginalized students to experiment with and develop academic language. Finally, a community of learners approach allows different student groups in a classroom to conduct and share the results of different investigations with the same dataset, making the exploration of large and complex data more feasible in everyday classrooms. Analyses will make use of video, CODAP log data, students' written texts, and pre-post assessments to investigate learning and participation longitudinally over the course of an entire academic year.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.
由于STEM学科中计算的强大创新和应用,STEM+C程序解决了从低年级到高中(preK-12)学生对现实世界,跨学科和计算准备的迫切需求。 让今天的学生做好熟练使用数据的准备,对于确保科学素养和赋权公民至关重要。但是,大多数将数据分析纳入K-12课程的努力仅限于短期,孤立的活动;基本技能发展;或者作为专门用于数据和计算的新课程引入,限制了这些努力的潜在好处和受众。该项目旨在将计算数据分析以纵向,跨学科的方式整合到中学科学课程中-从计算机和数据科学,扫盲研究,统计和科学教育中汲取经验,使课堂充满相关工具,资源和支持。中学课堂将使用一个名为通用在线数据分析平台(CODAP)的免费,创新的计算数据分析平台来分析和得出有关公共科学数据集的结论。单元将专门为双语学习者(DLL)设计,邀请学生通过编写多模态文本来分享他们的调查,这些文本融合了熟悉的和学术的表达模式,以解释和情境化他们的数据分析过程。在整个学年中,将引入在难度和复杂性方面相互建立的单元,参与教师将获得重要的培训机会。总的来说,该项目预计将直接影响大约2,500名学生和20名教师在大旧金山弗朗西斯科湾区,从主要是高需求schools.The项目将提供一个研究背景下,以解决以下问题:学生如何学习,随着时间的推移,使用计算工具的结构,计算,过滤和转换数据的科学探究?将计算数据分析和可视化整合到科学课程中,可以支持哪些科学实践参与模式?还有,什么新的识字实践可以支持DLL和学习者有限的技术访问或谁仍在发展的学术素养,在构建口头和书面的论点和解释使用数据和可视化作为证据?具体而言,它汇集并寻求扩展三个互补的研究结构。数据移动是分析人员为转换和分析数据集而采取的计算操作。融合文本是将学术话语(如解释数据分析所需的公式和统计语言)与熟悉的表达方式(包括母语和其他表达形式,如视频,艺术,动画等)融合在一起的专业文本,它被发现邀请了广泛的边缘化学生来尝试和发展学术语言。最后,学习者社区方法允许教室中的不同学生群体使用相同的数据集进行不同的调查并分享调查结果,从而使在日常课堂中探索大型复杂数据变得更加可行。分析将利用视频,CODAP日志数据,学生的书面文本,和前后评估,调查学习和参与纵向整个学年的过程中。这个奖项反映了NSF的法定使命,并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

期刊论文数量(10)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Data Literacies and Social Justice: Exploring Critical Data Literacies through Sociocultural Perspectives
  • DOI:
    10.22318/icls2020.406
  • 发表时间:
    2020-06
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Golnaz Arastoopour Irgens;Simon Knight;A. Wise;T. Philip;Maria C. Olivares;Sarah Van Wart;Sepehr Vakil;J. Marshall;Tapan S. Parikh;M. L. Lopez;Michelle Wilkerson;Kris D. Gutiérrez;Shiyan Jiang;J. Kahn
  • 通讯作者:
    Golnaz Arastoopour Irgens;Simon Knight;A. Wise;T. Philip;Maria C. Olivares;Sarah Van Wart;Sepehr Vakil;J. Marshall;Tapan S. Parikh;M. L. Lopez;Michelle Wilkerson;Kris D. Gutiérrez;Shiyan Jiang;J. Kahn
Storytelling “in theory”: Re-imagining computational literacies through the lenses of syncretism and translanguaging
讲故事——理论上——:通过融合和跨语言的视角重新想象计算能力
  • DOI:
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Vogelstein, Lauren;McBride, Cherise;Ma, Jasmine Y.;Wilkerson, Michelle;Vogel, Sara;Barrales, Wendy;Ascenzi-Moreno, Laura;Hoadley, Christopher;Gutiérrez, Kris
  • 通讯作者:
    Gutiérrez, Kris
Contextualizing, historicizing, and re-authoring data-as-text in the middle school science classroom
在中学科学课堂上将数据情境化、历史化和重新创作为文本
Student Participation in Sociocritical Data Literacy: Shapes, Trends, and Future Directions From a Middle School Science Unit
学生参与社会批判数据素养:中学科学单元的形状、趋势和未来方向
Paths through Data: Successes and Future Directions in Supporting Student Reasoning about Environmental Racism
数据路径:支持学生推理环境种族主义的成功和未来方向
  • DOI:
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Reigh, Emily V.;Escudé, Meg;McBride, Cherise;Wei, Xinyu;Bakal, Michael;Rivero, Edward;Roberto, Collette;Wilkerson, Michelle H.;Gutiérrez, Kris
  • 通讯作者:
    Gutiérrez, Kris
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Michelle Wilkerson其他文献

Teachers, Students, and After-School Professionals as Designers of Digital Tools for Learning
教师、学生和课外专业人士作为数字学习工具的设计者
  • DOI:
  • 发表时间:
    2017
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Michelle Wilkerson
  • 通讯作者:
    Michelle Wilkerson
Youth Reasoning With Interactive Data Visualizations: A Preliminary Study
交互式数据可视化的青少年推理:初步研究
Designed and Emergent Pedagogical Supports for Coordinating Quantitative and Agent-Based Descriptions of Complex Dynamic Systems.
为协调复杂动态系统的定量和基于代理的描述提供设计和紧急的教学支持。
Storytelling as a Support for Collective Constructionist Activity
讲故事作为集体建构主义活动的支持
  • DOI:
    10.7551/mitpress/12091.003.0028
  • 发表时间:
    2020
  • 期刊:
  • 影响因子:
    0
  • 作者:
    N. Holbert;M. Berland;Y.;Kafai;Michelle Wilkerson;B. Gravel
  • 通讯作者:
    B. Gravel
Integrating Computational Artifacts into the Multi-representational Toolkit of Physics Education
将计算工件集成到物理教育的多表示工具包中
  • DOI:
    10.1007/978-3-319-58914-5_3
  • 发表时间:
    2017
  • 期刊:
  • 影响因子:
    3.3
  • 作者:
    B. Gravel;Michelle Wilkerson
  • 通讯作者:
    Michelle Wilkerson

Michelle Wilkerson的其他文献

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{{ truncateString('Michelle Wilkerson', 18)}}的其他基金

CAP: Data Science, Learning and Youth: Connecting Research and Creating Frameworks
CAP:数据科学、学习和青年:连接研究和创建框架
  • 批准号:
    1645559
  • 财政年份:
    2016
  • 资助金额:
    $ 236.98万
  • 项目类别:
    Standard Grant
CAREER: DataSketch: Exploring Computational Data Visualization in the Middle Grades
职业:DataSketch:探索中年级的计算数据可视化
  • 批准号:
    1660576
  • 财政年份:
    2016
  • 资助金额:
    $ 236.98万
  • 项目类别:
    Continuing Grant
CAP: Data Science, Learning and Youth: Connecting Research and Creating Frameworks
CAP:数据科学、学习和青年:连接研究和创建框架
  • 批准号:
    1541676
  • 财政年份:
    2015
  • 资助金额:
    $ 236.98万
  • 项目类别:
    Standard Grant
CAREER: DataSketch: Exploring Computational Data Visualization in the Middle Grades
职业:DataSketch:探索中年级的计算数据可视化
  • 批准号:
    1350282
  • 财政年份:
    2014
  • 资助金额:
    $ 236.98万
  • 项目类别:
    Continuing Grant

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Collaborative Research: Critical Data Stories: Co-Designing Remixing Tools with Teachers to Support Critical Data Literacy with Middle School Youth
合作研究:关键数据故事:与教师共同设计混音工具,以支持中学生的关键数据素养
  • 批准号:
    2302659
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