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会都涉及对跨空间和时间变化的复杂数据的广泛解释。这种时空（ST）数据站在社会几乎所有部门最关键决策的最前沿，从了解气候变化和应对社会经济差异的原因到理解全球经济变化。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移到公民和工人的必要技能。因此，让学习者做好使用这些数据的准备的必要性已经达到了同样的紧迫程度。分析和解释ST数据的技能不能从本科学习开始或在工作场所培训中学习。然而，尽管这些数据在工业和社会中的重要性越来越大，但STEM教育领域对学习者如何理解ST数据的理解仍然非常有限。幸运的是，新兴的研究和技术为改善这种理解提供了希望。利用现有的研究到视觉和空间的理解，时间的认知解释，以及基于技术的工具和技术，该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，概述了富有成效的方向，为未来的研究和可操作的原则，旨在让学习者在探索ST data.Three目标的课程和教学材料的发展，指导这个项目，因为它的目的是了解中学学生的时空数据的意义。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，并分析学生对ST数据的理解方式。第二是在一个迭代过程中开发和测试支持和启示，以应对已确定的挑战和机遇。第三，也是最后一点，是确定和传播一个框架，确定认知挑战和相关的支持，学习与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的形成：1）学习者使用什么策略来理解数据，以及不同的数据类型带来了什么挑战？2)学习者如何识别和理解这些数据中的模式和关系，以及不同模式类型带来的挑战？3)学习者在接触科技数据时会构建什么样的理解？基于技术的启示以何种方式帮助学习者分析或构建对这些数据的理解？该项目采用基于设计的研究方法，结合有声思维协议、回顾性访谈和数据技能评估，将创建和传播一个框架，识别学习者面对不同类型的ST数据集时所面临的困难，突出用户界面功能和数据可视化方法，这些方法有可能解决这些困难，该项目得到了美国国家科学基金会EHR核心研究（ECR）项目的支持。ECR计划强调基础STEM教育研究，产生该领域的基础知识。投资是在关键领域是必不可少的，广泛的和持久的：干学习和干学习环境，扩大参与干，干劳动力发展。该计划支持积累强有力的证据，为理解、建立理论解释、提出干预和创新建议以应对教育中持续存在的挑战提供信息。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。