Distributed Interaction
Design
A Ph.D. Dissertation
How can we reach out and include anyone in the world in the design process of new technologies?
How can we utilize machine learning to gain design insights?
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I answer these questions and more in my Ph.D. dissertation:
DISTRIBUTED INTERACTION DESIGN (D.X.D.)
Conducting user studies in a lab has a number of drawbacks: 1. The number of users is limited. 2. Only users who are able and willing to come to a lab participate in the studies. 3. Limited to geographically close participants.
Analyzing study data requires time and effort.
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Stepping out of the lab
To overcome the physical limitations of a lab, I translated the process of conducting a user study online by building a web-based tool called Crowdlicit.
Using machine learning
To increase the efficiency of analyzing the results of user studies, I created Crowdsensus.
Stepping out of the lab
Crowdlicit enables me to conduct user studies with
in
as traditional lab-based studies.
using machine learning
Crowdsensus enables me to analyze study data
as fast as traditional approaches.
Crowdlicit
read the paper
watch the talk
crowdsensus
read the paper
watch the talk
Both of these project were folded into the CROWDDESIGN engine
