Data in Space and Time: Supporting Learners in Understanding and Analyzing Spatiotemporal Data
Effective Years: 2022-2025
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.