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

作为STEM学科中强大的计算创新和应用的结果，STEM+C课程满足了从早期年级到高中(K-12之前)学生对现实世界、跨学科和计算准备的迫切需求。让今天的学生做好流利地使用数据的准备，对于确保公民具备科学素养和能力至关重要。但是，将数据分析纳入K-12课程的大多数努力仅限于短期、孤立的活动；基本技能发展；或者是作为专门针对数据和计算的新课程引入的，限制了此类努力的潜在好处和受众。该项目旨在以纵向、跨学科的方式将计算数据分析整合到中学科学课程中-利用计算机和数据科学、识字研究、统计学和科学教育，使课堂充满相关工具、资源和支持。中学课堂将使用一个免费的、创新的、可计算的数据分析平台，即通用在线数据分析平台(CODAP)，对公开可用的科学数据集进行分析并得出结论。单元的设计将特别考虑到双语学习者(DLL)，邀请学生通过撰写融合熟悉和学术表达方式的多模式文本来分享他们的调查，以解释他们的数据分析过程并将其联系起来。在整个学年中，将引入在困难和复杂程度上相互加强的单元，参与其中的教师将获得大量培训机会。总体而言，该项目预计将直接影响旧金山湾区约2,500名学生和20名教师，他们主要来自高需求学校。该项目将提供一个研究背景，以解决以下问题：随着时间的推移，学生如何学习使用计算工具来构建、计算、过滤和转换用于科学研究的数据？将计算数据分析和可视化整合到科学课程中，支持哪些参与科学实践的模式？以及，哪些新的识字实践可能会支持有限接触技术的DLL和学习者，或者他们在使用数据和可视化作为证据构建口头和书面论点和解释方面仍在发展学术素养？具体地说，它汇集并寻求扩展三个互补的研究结构。数据移动是分析师为转换和分析数据集而采取的计算操作。融合语篇是一种专业语篇，它融合了学术语篇，如解释数据分析所需的公式和统计语言，以及熟悉的表达方式(包括本国语言和视频、艺术、动画等其他表现形式)，这被发现邀请了广泛的边缘化学生尝试和发展学术语言。最后，学习者社区方法允许课堂上的不同学生群体使用相同的数据集进行和共享不同调查的结果，使在日常课堂上探索大型和复杂数据变得更加可行。分析将利用视频、CODAP日志数据、学生的书面文本和事后评估来纵向调查整个学年的学习和参与情况。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.