Contextualizing Institutional Data with Lived Experience: Collaborative mapping to catalyze data science learning and engagement

• In Event: Community-Informed STEM Learning

Abstract

I design and evaluate learning environments that help communities recognize how socio-cultural systems are embedded in local landscapes. My research investigates how this recognition, grounded in shared space, can foster collaborative, consequential learning. In this roundtable, I wish to explore how technology can support a community to engage with and learn from the data that is used to characterize their neighborhood (Wilkerson and Polman, 2020). Grounded in the theories and practices of critical data science, my work focuses on: (1) connecting everyday lived experiences (rich sources of neighborhood-level personal knowledge) with institutional data about a given landscape; and (2) how layering these data using educational technology might support data engagement and data science learning.

My roundtable contribution will draw from a collaborative project between learning sciences and urban planning. Using a collaborative mapping methodology co-designed with members of a rural community in support of climate education (Author 2, 2025), we engaged 17 adult residents aged 30s to 90s, in a medium-sized Northeastern city. Using the ArcGIS StoryMap platform, we are co-designing with participants a rich narrative of the past, present, and future of their neighborhood within a public, online map. Our approach integrates:
1. Personal knowledge – including audio recordings of community members recollections and participant-supplied photographs.
2. Institutional data – such as census data, redlining maps, community health data, and data on shifting infrastructure

Throughout the design experience, which involved four workshops lasting 2 hours each over six weeks, participants were asked to share their personal knowledge and recollections of the neighborhood. Researchers presented institutional data regarding the neighborhood and participants were asked to engage with the neighborhood between workshop meetings by taking photos and gathering material and recollections from family, friends, and neighbors. Researchers compiled this material into participant-determined categories that were then co-constructed into layered, hyperlocal, place-based educational data narratives that could be shared via the interactive map.

For example, one map waypoint aligned a participant’s recollection of buying a six cent donut from the Jewish-owned bakery across from her school during the 1950s with a contemporary photo the participant took of the building, documents highlighting the historic sociocultural significance of Jewish-owned businesses in the neighborhood, and redlining maps and census data. Combined, this information presented a narrative about neighborhood demographic shifts. Another map waypoint layered participant recollections of excellent black doctors who had historically lived and worked in the neighborhood with data relating to past and current neighborhood health trends. By integrating hyper-local personal knowledge with relevant institutional data sources, our project aims to co-create with participants robust, engaging, data-rich narratives that invite community member interaction and catalyze neighborhood-wide data science learning.

Author

• Heather Killen, University at Buffalo - SUNY