Data in Space and Time: Supporting Learners in Understanding and Analyzing Spatiotemporal Data

时空数据：支持学习者理解和分析时空数据

• 批准号: 2201154
• 负责人: Chad Dorsey
• 金额: $ 149.65万
• 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: 2022-10-15 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2201154&HistoricalAwards=false
• 关键词: Data; Space; Time; Supporting; Learners

Many of society’s biggest dilemmas and grandest opportunities involve extensive interpretation of complex data that vary across both space and time. Such spatio-temporal (ST) data stand at the forefront of the most critical decisions across practically all sectors of society, from making sense of changes in the climate and responses to the causes of socioeconomic differences to the understanding of global economic changes. Over the past few decades, analyzing and interpreting ST data has moved from the purview of niche domains to a necessary skill for citizens and workers alike. Hence, the need to prepare learners to work with such data has grown to the same level of urgency. Skills at analyzing and interpreting ST data cannot be left to begin in undergraduate study or learned during workplace training. However, despite the growing importance of such data in industry and society, the STEM education field's understanding of how learners come to make sense of ST data remains severely limited. Fortunately, emerging research and techniques offer promise for improving this understanding. Drawing upon existing research into visual and spatial understanding, cognitive interpretation of time, and technology-based tools and techniques, this project will identify how learners approach and make sense of ST data. In doing so, the project will produce a guiding framework outlining fruitful directions for future research and actionable principles for the development of curricula and instructional materials that aim to engage learners in exploring ST data.Three objectives guide this project as it aims to understand how secondary school learners make sense of spatio-temporal data. First is to compile an inventory of existing knowledge about learners’ understanding of ST data and analyzing students’ approaches to ST data. Second is to develop and test supports and affordances in an iterative process that addresses identified challenges and opportunities. Third, and finally, is to define and disseminate a framework identifying cognitive challenges and related supports for learning with and about ST data. The project will conduct use-inspired basic research to examine learners’ approaches and sense-making via three related lines of investigation: 1) What strategies learners use to make sense of the data and what challenges different data types pose? 2) How learners come to identify and understand patterns and relationships within such data and what challenges different pattern types pose? 3) What understandings do learners construct when engaging with ST data and in what ways technology-based affordances can help support learners in analyzing or constructing understanding from such data? Adopting a design-based research approach employing a combination of think-aloud protocols, retrospective interviews, and data skills assessment, the project will create and disseminate a framework that identifies struggles faced by learners confronting varying types of ST datasets, highlights user interface affordances and data visualization approaches with potential for addressing these struggles, and draws actionable connections between the two.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.

社会上许多最大的困境和最大的机遇都涉及对复杂数据的广泛解释，这些数据在空间和时间上都不同。从理解气候变化和对社会经济差异原因的反应，到理解全球经济变化，这种时空(ST)数据站在几乎所有社会部门最关键决策的前沿。在过去的几十年里，分析和解释ST数据已经从利基领域的范围转变为公民和工人的一项必要技能。因此，让学习者做好使用这些数据的准备的必要性也变得同样紧迫。分析和解释科技数据的技能不能从本科生学习开始，也不能在工作场所培训中学习。然而，尽管这类数据在工业和社会中的重要性与日俱增，STEM教育领域对学习者如何理解ST数据的理解仍然严重有限。幸运的是，新兴的研究和技术为改善这种理解提供了希望。利用现有的视觉和空间理解、对时间的认知解释以及基于技术的工具和技术的研究，本项目将确定学习者如何处理和理解ST数据。在这样做的过程中，该项目将产生一个指导框架，概述未来研究的富有成效的方向，以及制定旨在吸引学生探索科技数据的课程和教材的可行原则。三个目标指导该项目，因为它旨在了解中学生如何理解时空数据。首先是编制一份关于学习者对ST数据的理解的现有知识清单，并分析学生对ST数据的处理方法。第二，在应对已确定的挑战和机遇的迭代过程中开发和测试支助和负担。第三，也是最后一点，是定义和传播一个框架，确定利用和关于科技数据学习的认知挑战和相关支持。该项目将通过三个相关的调查路线，进行受使用启发的基础研究，以检验学习者的方法和意义的形成：1)学习者使用什么策略来理解数据，不同的数据类型构成了什么挑战？2)学习者如何识别和理解这些数据中的模式和关系，不同的模式类型构成了什么挑战？3)学习者在接触ST数据时建立了什么理解，基于技术的启示可以以什么方式帮助学习者从这些数据中分析或构建理解？该项目采用一种基于设计的研究方法，采用有声思考协议、回溯性访谈和数据技能评估相结合的方法，将创建和传播一个框架，确定学习者面对不同类型的ST数据集所面临的挣扎，突出用户界面承受能力和潜在的数据可视化方法，并在两者之间建立可行的联系。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。