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Award ID contains: 2215004

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  1. This study aims to investigate the design considerations and tensions in developing data visualization activities that integrate multi-disciplinary, critical, and community-centered approaches to data learning. We do so through conjecture mapping and qualitative analysis in the context of a design-based research study of an informal education program focused on data visualization. We found multiple design considerations tied to each approach, with critical approaches often deprioritized and related to tensions with the other approaches. 
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    Free, publicly-accessible full text available June 10, 2026
  2. MDPI (Ed.)
    In the recent K-12 educational literature, arts-based data visualization has been positioned as a compelling means of rendering data science and statistical learning accessible, motivating, and empowering for youth, as data users and producers. However, the only research to attend carefully to youth’s data-based, artistic storytelling practices has been limited in scope to specific storytelling mechanisms, like youth’s metaphor usage. Engaging in design-based research, we sought to understand the art and design decisions that youth make and the data-based arguments and stories that youth tell through their arts-based data visualizations. We drew upon embodied theory to acknowledge the holistic, synergistic, and situated nature of student learning and making. Corresponding with emerging accounts of youth arts-based data visualization practices, we saw regular evidence of art, storytelling, and personal subjectivities intertwining. Contributing to this literature, we found that these intersections surfaced in a number of domains, including youth’s pictorial symbolism, visual encoding strategies, and data decisions like manifold pictorial symbols arranged to support complex, multilayered, ambiguous narratives; qualitative data melding community and personal lived experience; and singular statements making persuasive appeals. This integration of art, story, agency, and embodiment often manifested in ways that seemed to jostle against traditional notions of and norms surrounding data science. 
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    Free, publicly-accessible full text available June 1, 2026