Data Visualizations for Social Betterment: Youth Developing Self-transcendence in a Data-art Inquiry Program

• In Event: Beyond the Numbers: Youth Interpreting and Acting on Data for Social Change

Abstract

Data science education is both highly contextualized and inherently interdisciplinary (Jiang et al., 2022; Wilkerson & Polman, 2020). Drawing from arts-based research (Blumenfeld-Jones, 2016) and data-art inquiry (Matuk et al., 2022), we designed and implemented a four-cycle program called MVP. We invited secondary-level youth—members of a local afterschool club and students at a rural high school in Eastern Tennessee—whom we refer to as “data artists,” to identify community issues, gather data, and create artistic visualizations to communicate those issues to community members. Research has shown that engaging with data can influence youth to develop prosocial mindsets (Harris et al., 2020; Wilkerson & Laina, 2018) and raise awareness of global challenges, such as COVID-19 (Calabrese Barton et al., 2021). 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). This study is guided by proactive data activism, which involves using data to advocate for change—e.g., by mapping crises or visualizing injustices to raise awareness and spur action (Gutiérrez, 2018). In MVP, we encouraged data artists to engage local topics and produce visualizations reflecting community concerns. Framing our work through proactive data activism, we ask: What types of self-transcendence do youth develop in a data-art inquiry program?

Data were collected during the second and third MVP cycles, in Fall 2023 and Spring 2024. Two data sources were used: post-program interviews and artist statements. We conducted nine interviews with 14 middle schoolers (Fall 2023) and 16 high schoolers (Spring 2024). Artist statements accompanied each final project to explain the visualizations and their intended messages. We used thematic analysis (Braun & Clarke, 2012), beginning with familiarization, initial coding for self-transcendent ideas, and grouping/refining codes into themes. We identified three types of self-transcendence: concern for others’ well-being, community issues, and broader societal challenges. First, several students began with personal experiences, then expanded to broader calls for awareness. Dakota, a high school junior, focused on her sleep issues due to AP test prep, then surveyed others about sleep and visualized results in a dreamcatcher (Figure 1a) showing how schoolwork and shift work impacted sleep quality. Second, youth raised community concerns. Dante, a middle schooler, addressed food waste, stating, “I wanted people to think about how what they do every day affects everyone else.” His visualization (Figure 1b) used emotional imagery to prompt reflection on waste and shared responsibility. Third, some addressed broader issues. Pamela explored misinformation and public trust. Her collage (Figure 1c) conveyed resilience and civic engagement. She said, “Even if it feels overwhelming, we still have to participate and make a difference.” These findings suggest data stories alone may be insufficient to foster deep, self-transcendent learning. While storytelling builds data literacy, data-art inquiry helps youth express data in ways that are personally and socially resonant. Inspired by data activism, educators should support youth in using data toward a better society.

Authors

• Yilang Zhao, University at Buffalo - SUNY

• Lynn L. Hodge, University of Tennessee