Culturally Relevant Data Science: How Youth Interpret Data Regarding Native Language Use and Preservation

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

Abstract

In today’s data-rich world, preparing youth to navigate, interpret, and act on data is crucial (Lee & Wilkerson, 2018; Wilkerson & Polman, 2020), particularly for those historically marginalized in data science fields. This study focuses on Pacific Islanders, including students from the Micronesian region, who remain profoundly underrepresented in data science and other STEM fields (Kerr et al., 2018), with absence from relevant curriculum and practice (Spencer et al., 2020). This underrepresentation limits both inclusivity in the field and its capacity to address social and environmental issues through diverse epistemologies. This presentation addresses this need by reporting on the progress of the Amplifying Indigenous Micronesian Intelligence for Data Science (MINDS) with Culturally Relevant Data project, which engages high school students in Guam and Saipan in culturally relevant data science education. Grounded in culturally relevant pedagogy (Ladson-Billings, 1995), this project leverages Indigenous Micronesian perspectives to co-develop data science curriculum modules centered on students’ cultural identities and lived realities. In collaboration with Indigenous and non-Indigenous math teachers and political scientists, lessons engage students in analyzing data tied to topics such as island sustainability, land dispossession, and language revitalization—the main topic of this presentation. By situating learning within social, historical, and environmental contexts, the project fosters students’ critical data literacy and sociopolitical engagement as they connect data to struggles for equity and justice.

This study used a multiple-case study design (Yin, 2017) to explore how Micronesian youth engaged with culturally relevant data science activities and how this shaped their views on native language preservation. Participants were five high school students from Guam and Saipan who engaged in a data science lesson foregrounding CHamoru (the native language of Guam and Saipan) language preservation—a critical concern, as CHamoru is classified as endangered. Students collected data over 24 hours on how often they encountered CHamoru, including at home, school, in the community, and even in dreams. They then created personal data visualizations to represent frequency counts. These visual artifacts served both analytical and reflective purposes, helping students explore their lived experiences with the CHamoru language. The project demonstrated how data science can serve as a tool for restorative justice by visualizing language loss and initiating conversations about cultural sustainability. Data sources included the students’ visualizations, written reflections, and a focus group interview. Thematic analysis was used to identify patterns of how students linked data science to cultural identity and language preservation.

Findings show the potential of this project to foster sociopolitical awareness among Micronesian youth. First, student visualizations revealed generational CHamoru use, prompting reflection on language loss and continuity. Students noted older generations used the language more, while youth used only fragments. Second, students used their visualizations as storytelling and cultural expression, with designs shaped like flowers, coconut trees, and meals. This rehumanizing approach integrated creativity, cultural symbols, and narrative. Third, students critically reflected on equity and representation, questioning omissions and how data visualizations can obscure or illuminate identity dimensions. These reflections highlight data’s power and the ethical responsibility of visualizing community realities.

Author

• Richard Carlos L. Velasco, University of Florida