Rural Computing

[Funding Agency: NSF #1845964]

The primary goal of this project is to develop the theories and designs needed to inform the development of data-driven technologies to support and share the knowledge and values of rural America. The methodology includes multi-sited ethnographic fieldwork, design of data-driven technologies, and creation of a theoretical framework to inform development of systems for rural populations. Existing research shows that rural residents feel left behind and marginalized due to rapid socio-economic changes often driven by urban dwellers. This project aims to move beyond contrasting the challenges of living in rural areas, such as lack of broadband access, in comparison with urban areas. Rather, it builds on the conviction that knowledge work by rural members is undervalued, and that research to make visible the innovative knowledge and deeply-held values of rural populations can prove beneficial for users as a whole. The topic of this Faculty Early Career Development project naturally supports the plan to deliver STEM research and education to rural communities and students, cooperate with local government agencies and organizations to create outreach programs which attract participation and understanding of rural activities, and distribute created technologies to bridge rural and non-rural areas.

This work seeks to establish an action research agenda investigating, conceptualizing, and supporting the intersection of rural and urban knowledge and values. This project consists of three interlocking strands: (1) Identification of expertise and values in rural populations: The researchers will conduct multi-sited ethnographic studies to identify the current data-driven practices and challenges of rural populations in two subcultures: small farmers and outdoor recreationalists; (2) Design and evaluation of reflective technologies: Scoped by findings of the multi-sited ethnography, this strand will employ participatory design activities with rural and non-rural members to iteratively create data-driven designs; (3) Development of theoretical frameworks: Synthesizing findings from the first two strands on both subcultures, we will develop a transferable theoretical framework outlining strategies for designing data-driven technologies for sharing knowledge and values. This framework will offer guidelines on designing technologies with rural populations. The results of this work will assist human-computer interaction researchers and practitioners who design for diverse populations where distribution of expertise is unequal and values may clash.

Notable outputs: Papers at CHI 2017, CSCW 2018, DIS 2019; workshops at CSCW 2018 and CHI 2019