The Voice from Our Community: Appalachian Youth Crafting Data Visualizations for Social Good

• In Event: Data Science as a Tool for Engaging in Civic Education

Abstract

Because of data science education’s contextualized and inherently interdisciplinary nature (Jiang et al., 2022; Wilkerson & Polman, 2020), this study draws on arts-based research (Blumenfeld-Jones, 2016) and data-art inquiry (Matuk et al., 2022). We designed and implemented a four-cycle program called MVP. In this program, secondary-level youth from local urban and rural high schools in Eastern Tennessee were recruited, whom we call “data artists.” In each program, these Appalachian youths identified community issues, collected community data, and created artistic visualizations to communicate those issues to people from their community. Literature shows that engaging with data activities can help youth develop prosocial mindsets and raise awareness of global challenges (Harris et al., 2020; Wilkerson & Laina, 2018). This process exemplifies self-transcendent learning, or the belief that learning contributes positively to others and has a broader social impact (Yeager et al., 2014; Yeager & Bundick, 2009). Inspired by proactive data activism, this study involves youth using data to advocate for social good. In the MVP program, we encouraged our data artists to actively engage in communal topic discussions and produce visualizations that can reflect community concerns. Framing our work through proactive data activism, we ask: How do Appalachian youth develop self-transcendence in a data-art inquiry program?

In this study, data were collected during the third and fourth MVP cycles, in Spring and Summer 2024. The spring program included 13 90-minute weekly sessions and 3 community learning events, and the summer program was a 2-week summer camp with 1 community learning event. We analyzed three types of data: post-program interviews, youth-crafted data visualizations, and the artist statements that accompany each final project to explain the visualizations and their intended messages. Thematic analysis (Braun & Clarke, 2012) was used to analyze the data–beginning with familiarization, initial coding for self-transcendent ideas, and grouping codes into themes. Three types of self-transcendence were discovered: concern for others’ well-being, community issues, and broader societal challenges. First, the concern for others’ well-being is often associated with personal experiences and escalates while youth collecting community data. For example, Dakota noticed her sleep issues due to AP test preparation and then surveyed others about sleep and visualized results in a dreamcatcher (Figure 1a) showing how schoolwork and shift work impacted sleep quality. Second, some data artists presented common community concerns. Ja Mari, a data artist from the summer camp, used geographical data to show the issue of flooding in local neighborhoods (Figure 1b). Third, some data visualizations aim to address broader issues. For instance, Pamela explored misinformation and public trust. Her collage (Figure 2.1) conveyed resilience and civic engagement. She said, “Even if it feels overwhelming, we still have to participate and make a difference.” These findings suggest that through expressing and presenting the concern for others’ well-being, common community issues, and broader societal challenges, our data artists could not only build up data literacy but were also empowered to use data toward a better society, which is key for self-transcendent learning.

Authors

• Yilang Zhao, University at Buffalo - SUNY

• Lynn L. Hodge, University of Tennessee