UCLA

Educators and researchers often cite the “relevance” of large-scale, externally-sourced quantitative datasets as an important aspect of designing data experiences for students. However, evidence from the learning sciences and beyond has shown that focusing only on the extent to which datasets reflect students’ lived experiences does not fully explain whether or how students build relationships with data. In this study, we present syncretic data storytelling as a pedagogical approach that brings together different types of knowledge—personal experience, patterns from quantitative datasets, and analysis of the social, political, and historical context—to provide multiple pathways for students to build relationships with data. We illustrate this approach by exploring three student cases from a data storytelling workshop on the topic of environmental racism. In each case, the focal student initially did not connect with the provided geospatial dataset, but later used quantitative data in their inquiry as they engaged with other aspects of the curricular design. We show how syncretic data storytelling remediated hierarchies between everyday and school-based knowledge systems to ultimately create conditions for learning that is not just relevant but consequential, meaning that it carries implications for students’ lives.