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

Concord Consortium, Concord MA

Investigators

Abstract

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.