Title: Understanding Problem-Solving and Collaboration in an Open-Ended Scientific-Discovery Game
Advisor: Zoran Popovic
Supervisory Committee: Zoran Popovic (Chair), Amy Ko (GSR, iSchool), Steve Tanimoto, and Dan Weld
Abstract: Countless human pursuits depend upon creative problem solving, especially in complex, open-ended domains. As part of the growing technological support for such problem solving in digital environments, an opportunity exists to advance the understanding and design of these systems. The vast increase in the scale and richness of data on problem-solving behavior afforded by digital environments opens up new avenues for the study of this behavior. Digital environments also offer the possibility of building systems that actively guide and facilitate individual and collaborative problem solving toward the most productive outcomes. These systems could scaffold effective solving strategies for novices, intervene in the solving process to suggest areas of focus, or take the form of layers of machine intelligence that schedule individual and group work and dynamically adapt environmental parameters to achieve increased solution quality.
Few of these innovations will be possible, however, without a deep understanding of the problem-solving process in the domain of interest. Such an understanding would need to address the full space of strategies solvers employ, how they fit together and change over time, and how they contribute to both success and failure. In this presentation, I propose to develop one component of a deep understanding of the problem-solving process in the scientific-discovery game Foldit by addressing the question what makes groups and individuals successful problem solvers in Foldit? As solvers in Foldit are tasked with real-world, open-ended problems without known solutions, it is highly-suitable domain in which to study complex problem-solving behavior. I present my overall approach to understanding problem-solving in Foldit based on analysis of visual representations of the problem-solving process and exploration of the features of individual and group behavior associated with high performance. I show that patterns of strategic behavior can be identified in these representations and discuss how the visualizations and other analysis can be further developed to capture the full strategic picture of solving behavior.