Citizen-led Work using Social Computing and Procedural Guidance
Online platforms enable people to interact with friends, family, and the world at large. How might people go beyond sharing stories and ideas to building and testing theories in the real world? While many are motivated to dig deeper into their lived experience, limited expertise and lack of platform support make complex activities like experimentation dauntingly hard. Novices benefit greatly from expert guidance: this thesis advocates baking the guidance into the interface itself.
This dissertation introduces procedural guidance to build just-in-time expertise for difficult tasks. Procedural guidance has multiple advantages: it is minimal, leverages teachable moments, and can be ability-specific. This dissertation instantiates this insight of procedural guidance through a sequence of increasingly complex social computing systems: Gut Instinct for curating ideas, Docent for generating hypotheses, and Galileo for citizen-led experiments.
Gut Instinct hosts online learning materials and enables people to collaboratively brainstorm potential influences on people’s microbiome. Docent explicitly teaches people to create hypotheses by combining personal insights and online learning with task-specific scaffolding. Finally, Galileo reifies experimentation in the software, provides multiple roles for contribution, and automatically manages interdependencies. Multiple evaluations—controlled experiments and field deployments with online communities including American Gut participants—demonstrate that procedural guidance enables people to transform intuitions to hypotheses and structurally-sound experiments. By enabling people to draw on lived experience, this dissertation harbingers a future where people can convert their intuitions to actionable plans and implement these plans with online communities. This dissertation concludes by discussing opportunities for complex work using social computing platforms.