Common Online Data Analysis Platform (CODAP)

这个项目旨在让学生参与有意义的科学数据收集、分析、可视化、建模和解释。它的目标是9-12年级的科学教学。这项拟议的研究提出了这样一个问题：“学习者如何构思经验数据并与之互动，特别是当它具有一个层次结构，其中参数和结果在一个级别，而原始数据在另一个级别时？”随着处理数据成为学生在STEM课程中学习不可或缺的一部分，理解学生如何构思数据变得越来越重要。这是特别及时的，因为科学变得越来越数据驱动。该团队将开发和测试一个通用在线数据分析平台(CODAP)。STEM课程开发已经转向在线，但为学生进行数据分析的工具开发尚未跟进。因此，在线课程开发项目往往被迫开发自己的数据分析工具，满足于桌面工具，或者没有。在与协和联盟、教育发展中心和明尼苏达大学的NSF资助项目的合作下，项目团队正在开发一个在线、开源的数据分析平台，不仅可供这三个项目使用，而且随后还可供其他项目使用。这项拟议的研究在需要调查的问题和方法上都开辟了新的天地。这些调查建立在学生对数据表示的理解、中心和分布的测量以及数据建模的先前研究的基础上，以更密切地观察学生对数据结构的理解，特别是当它们出现在真实的科学情景中时。基于三个不同的STEM项目的协作设计将产生一个灵活的数据分析环境，可供今后几年的其他项目采用。这样的设计过程增加了CODAP不仅仅是一个独立工具的可能性，而且可以有意义地整合到在线课程中。CODAP的首要目标是改善学生充分参与日益数据驱动的社会的准备。它建议通过改善一个关键的基础设施来做到这一点：即，希望将学生参与数据纳入内容学习的课程开发人员可以使用课堂友好的数据分析工具。

This project aims to engage students in meaningful scientific data collection, analysis, visualization, modeling, and interpretation. It targets grades 9-12 science instruction. The proposed research poses the question "How do learners conceive of and interact with empirical data, particularly when it has a hierarchical structure in which parameters and results are at one level and raw data at another?" As working with data becomes an integral part of students' learning across STEM curricula, understanding how students conceive of data grows ever more important. This is particularly timely as science becomes more and more data driven.The team will develop and test a Common Online Data Analysis Platform (CODAP). STEM curriculum development has moved online, but development of tools for students to engage in data analysis has yet to follow suit. As a result, online curriculum development projects are often forced to develop their own data analysis tools, settle for desktop tools, or do without. In a collaboration with NSF-funded projects at the Concord Consortium, Educational Development Center, and University of Minnesota, the project team is developing an online, open source data analysis platform that can be used not only by these three projects, but subsequently by others. The proposed research breaks new ground both in questions to be investigated and in methodology. The investigations build on prior research on students' understanding of data representation, measures of center and spread, and data modeling to look more closely at students' understanding of data structures especially as they appear in real scientific situations. Collaborative design based on three disparate STEM projects will yield a flexible data analysis environment that can be adopted by additional projects in subsequent years. Such a design process increases the likelihood that CODAP will be more than a stand-alone tool, and can be meaningfully integrated into online curricula. CODAP's overarching goal is to improve the preparation of students to fully participate in an increasingly data-driven society. It proposes to do so by improving a critical piece of infrastructure: namely, access to classroom-friendly data analysis tools by curriculum developers who wish to integrate student engagement with data into content learning.