Art as a Context for Data Science: Exploring Fourth-Grade Students' Data Visualization Practices

• In Event: Exploring Data Science Across the Curriculum and Across Grade Levels

Abstract

Socializing, working, and even teaching and learning are increasingly impacted by data. For students, work with data can serve as a cross-curricular practice (Lee & Wilkerson, 2018) that is powerful in terms of students to reason about the world (Lehrer & Schauble, 2015). While research has defined approaches to support student work with data (Lehrer & Romberg, 1996), less attention has been paid to how activities such as recording and analyzing data can build upon the important assets and interests of students and their communities (Civil, 2007), especially at the elementary level.

In our study, we sought to support students to both create and analyze data and communicating findings through data visualizations that they create. The following research questions guided our work:

Within an art and statistics integrated pedagogical approach, what mathematics and statistical concepts do participants learn?
What aesthetic features do the data visualizations students create depict?

To answer these questions, we designed an art and statistics integrated pedagogical approach and explored its impacts with 14 10-11-year-old students in an elementary school in the Southeast United States. The project included eight sessions that took place from January – April of 2019. Early in the project, students created, together, a data representation that reflected their individual and collective interests. Students then used a survey to understand the interests of other 4th-grade students and analyzed the data from the survey to create data visualizations using visual methodologies (Grodoski, 2018) individually or in pairs (see Figure 4 for an example of student visualizations). Work samples from students’ data visualizations and reflective notes following each class session were examined to identify relevant themes across students (Saldaña, 2015). Students’ data visualizations were analyzed through the use of a framework, the grammar of graphics, for the components of effective data visualizations (Wilkinson, 2005).

Findings reveal that students are able to explain the rationale behind their visualizations, and most of these explanations are grounded in their data or data summaries. Students’ visualizations reflected important mathematical ideas of additive and multiplicative thinking. Students’ visualizations regularly depicted data through a number of components of effective visualizations to represent amount and proportions through, particularly area, shape, colors, labels, and scale. Relationships between amounts as well as distributions of or variability in quantities were less-frequently represented.

Our findings suggest that students can consider the design and aesthetic properties of their data visualizations without the mathematical and statistical ideas becoming ignored, a key challenge for data analysts and data scientists at all levels (Wilke, 2019). The use of the grammar of graphics to highlight what appeared (and did not appear) in students’ data visualizations may serve as a helpful analytic tool for scholars researching data visualization in other contexts, particularly as this frame has been used to develop a popular statistical graphics library for creating data visualizations (ggplot2; Wickham, 2016).

Authors

• Joshua Michael Rosenberg, The University of Tennessee - Knoxville

• Lynn L. Hodge, The University of Tennessee

• Joy Gaulden Bertling, The University of Tennessee - Knoxville

• Shande King, Trevecca Nazarene University