Big-data visualisations are increasingly prevalent in scientific research, media, and industry. Unlike traditional graphs and charts, big-data visualisations often present large, complex, and dynamic datasets using multidimensional representations, colour gradients, and interactive features. Developing students’ ability to navigate and interpret these representations is critical for fostering data literacy and informed decision-making. However, how secondary students come to understand, analyse, and extract meaning from big-data visualizations remains an open research question.
We are currently collecting data on how Year 6 through Year 12 students engage with and reason about real, modern big-data visualisations. Students are being asked to think aloud as they explore visualisations that vary based on key attributes. They are also completing measures of spatial reasoning and fluid reasoning. Given that big-data visualizations often rely on spatial representations of abstract concepts, strong spatial reasoning skills may aid students in recognizing patterns, understanding scale, and mentally transforming visual information. Fluid reasoning may help students adapt flexibly, integrate multiple sources of information, and make inferences beyond surface-level data interpretation.
The current study will present on how a Fulbright scholarship has brought together mathematics education and cognitive science research to complete this project. In this talk, we will present on how we are analysing a rich video dataset to characterise students’ approaches, misconceptions, and strengths when making sense of big-data visualisations. We will also present preliminary data exploring the potential roles of spatial reasoning and fluid reasoning.