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

社会上许多最大的困境和最大的机遇都涉及对跨越时空变化的复杂数据的广泛解释。这些时空（ST）数据站在几乎所有社会部门最关键决策的前沿，从理解气候变化和对社会经济差异原因的反应到对全球经济变化的理解。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必要技能。因此，让学习者准备好处理这些数据的需求也变得同样紧迫。分析和解释ST数据的技能不能留给本科学习或在职培训中学习。然而，尽管这些数据在工业和社会中的重要性日益增加，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。利用现有的关于视觉和空间理解、时间认知解释以及基于技术的工具和技术的研究，该项目将确定学习者如何处理和理解ST数据。在此过程中，该项目将制定一个指导框架，概述未来研究的富有成效的方向，以及制定旨在吸引学习者探索ST数据的课程和教学材料的可行原则。三个目标指导这个项目，因为它旨在了解中学学习者如何理解时空数据。首先是编制一份关于学习者对ST数据理解的现有知识清单，并分析学生使用ST数据的方法。第二是在处理确定的挑战和机会的迭代过程中开发和测试支持和支持。第三，也是最后，是定义和传播一个框架，确定认知挑战和相关支持，以学习和了解ST数据。该项目将进行使用启发的基础研究，通过三个相关的调查线来检查学习者的方法和意义构建：1)学习者使用什么策略来理解数据，不同的数据类型会带来什么挑战？2)学习者如何识别和理解这些数据中的模式和关系，不同的模式类型会带来什么挑战？3)学习者在使用ST数据时构建了什么样的理解？基于技术的支持以何种方式帮助学习者分析或从这些数据中构建理解？采用基于设计的研究方法，结合有声思考协议、回顾性访谈和数据技能评估，该项目将创建和传播一个框架，确定学习者面对不同类型的ST数据集所面临的困难，突出用户界面的可用性和有可能解决这些困难的数据可视化方法，并在两者之间建立可操作的联系。本项目由美国国家科学基金会EHR核心研究（ECR）项目支持。ECR项目强调在该领域产生基础知识的基础STEM教育研究。投资在至关重要、广泛和持久的关键领域：STEM学习和STEM学习环境，扩大STEM参与，以及STEM劳动力发展。该项目支持积累有力的证据，为理解、构建理论进行解释提供依据，并提出干预和创新建议，以应对教育中持续存在的挑战。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.