Human Computer Interaction Ph.D. Thesis Proposal

  • Gates Hillman Centers
  • Reddy Conference Room 4405
  • Ph.D. Student
  • Human-Computer Interaction Institute
  • Carnegie Mellon University
Thesis Proposals

Supporting Sensemaking through Data Flows

The internet has become the de-facto information source for individuals — from finding a new cookie recipe, to learning how a transistor works. Tools for helping users find answers to their questions have grown tremendously in response; a user can get an answer in their search results for when the next Pirates game is, or get a list of facts about their favorite movie actress. However, for many questions, such as buying a car, there isn't one right answer — the best choice for an individual depends on their particular set of circumstances and personal preference. For these situations, it’s up to the user to make sense of the answer space: accumulate what options are available, what the differences and features of these options are, and eventually choose between them. 

This process, sensemaking, is a highly iterative and cyclical process, where information is constantly being found, incorporated, restructured, summarized, and generalized. As users continue to collect new information, they need to adjust and restructure their existing information, while incorporating the key known points of the new information into their understanding of the problem. This constant adjustment of both the data and structure surrounding the data puts a significant mental burden on users, and often requires them to resort to external means to track and manage this information. In the context of online sensemaking, this can be done in notepads, tabs, word processors, spreadsheets, kanban boards, or even emails. As users proceed to move along with their data in this process, they need to manually update and transfer data between these tools, which might often be more trouble than its worth.

In this work, I explore interactive systems which provide support for the transitions between phases of the process, or dataflows. I theorize that tools which are able to support the underlying mechanisms occurring during these transitions, through either computation or interaction, will decrease the cognitive load on users and allow for support of other sensemaking requirements, such as easy resumption and refinding. I developed an initial concept system using crowdworkers to power the different phases as a proof of concept. Then in three systems, Bento, Siphon and Distil, I investigate naturally supporting collecting sources, extracting information, and organizing content in an individual setting. In my proposed work, I will explore the final portion of this workflow: evaluating and decision making with the structured information. 

Thesis Committee:
Niki Kittur (Chair)
Brad Myers
Adam Perer
Jaime Teevan (Microsoft Research)

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