Supporting Reasoning with Multidimensional Datasets: Leveraging Student Intuitions Through Collaborative Data Production

使用多维数据集支持推理：通过协作数据生产利用学生的直觉

• 批准号: 2201177
• 负责人: Lynn Stephens
• 金额: $ 137.98万
• 依托单位: Concord Consortium
• 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: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2201177&HistoricalAwards=false
• 关键词: Supporting; Reasoning; Multidimensional; Datasets; Leveraging

It is increasingly vital that people be able to make sense of scientific data and extract information from public datasets in order to inform their decisions about everything from ballot initiatives on climate policy to personal choices about vaccines. The project has a long-term goal of broadening participation in STEM by making data literacy attainable by more students. The project will develop instructional design supports for high school students that build on their novice intuitions for visualizing and interacting with complex datasets. The project will also develop design principles to guide technology developers, curricular developers, and researchers in creating environments more conducive to promoting data literacy for all learners, including those who are not confident math learners and those interested in further work in STEM. These results will inform future efforts aimed at helping students better understand how to interact with data. The project will also produce working examples of open-source software and technological supports in CODAP (Common Online Data Analysis Platform) based on the design principles it develops.Project research will explore two broad conjectures about how technological and instructional supports for interrogating multidimensional data can improve students’ abilities to make sense of their world and empower them to use data personally and professionally. First, the project envisions that providing students with resources to represent and visualize multidimensional data in ways that build on novice intuitions will allow them more agency in transforming data structures to answer their own questions. Second, the project posits that working collaboratively to build a multidimensional dataset can help students develop rich associations, which contribute to robust and flexible mental models of data structure that students can then apply to datasets more broadly. Research methods include think-aloud interviews and instructional sessions with small groups of students to explore their intuitive notions about data structure and how these intuitive notions can be leveraged to offer support for visualizing and transforming data. Project research will result in a) theoretical insights into how novices intuitively represent and interact with multidimensional data; b) design principles for constructing user interfaces and educational experiences that can support student understanding and use of multidimensional datasets; and c) tested examples of software user interfaces and instructional activities that exemplify the design principles.This project is supported by NSF's EHR Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development. The program supports the accumulation of robust evidence to inform efforts to understand, build theory to explain, and suggest intervention and innovations to address persistent challenges in education.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.

越来越重要的是，人们能够理解科学数据并从公共数据集中提取信息，以便为他们的决策提供信息，从关于气候政策的投票倡议到关于疫苗的个人选择。该项目有一个长期目标，即通过让更多的学生能够获得数据素养来扩大对STEM的参与。该项目将为高中生开发教学设计支持，以他们的新手直觉为基础，可视化并与复杂的数据集互动。该项目还将制定设计原则，以指导技术开发人员、课程开发人员和研究人员创造更有利于促进所有学习者的数据素养的环境，包括那些没有信心的数学学习者和那些对STEM进一步工作感兴趣的人。这些结果将为未来旨在帮助学生更好地了解如何与数据交互的努力提供参考。该项目还将根据其开发的设计原则，制作开放源码软件和CODAP(公共在线数据分析平台)技术支持的工作实例。项目研究将探索两个广泛的猜想，即询问多维数据的技术和教学支持如何提高学生理解他们世界的能力，并使他们能够个人和专业地使用数据。首先，该项目设想，为学生提供资源，以建立在新手直觉基础上的方式来表示和可视化多维数据，将使他们在转换数据结构以回答自己的问题方面有更多的代理。其次，该项目认为，合作构建多维数据集可以帮助学生建立丰富的关联，这有助于建立稳健而灵活的数据结构心理模型，然后学生可以更广泛地应用于数据集。研究方法包括有声思考访谈和与一小群学生的教学会议，以探索他们对数据结构的直观概念，以及如何利用这些直观概念为可视化和转换数据提供支持。项目研究将导致a)对新手如何直观地表示多维数据并与之交互的理论见解；b)构建用户界面和教育体验的设计原则，以支持学生对多维数据集的理解和使用；以及c)软件用户界面和教学活动的测试实例，以例证设计原则。该项目由NSF的EHR核心研究(ECR)计划支持。ECR计划强调基础STEM教育研究，以产生该领域的基础知识。在基本、广泛和持久的关键领域进行投资：STEM学习和STEM学习环境，扩大STEM的参与，以及STEM劳动力发展。该计划支持积累强有力的证据，为理解、构建理论以解释并建议干预和创新以应对教育中持续存在的挑战的努力提供信息。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。