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.)
01/04
MOTIVATION
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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02/04
Work
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.
03/04
Results
Stepping out of the lab
Crowdlicit enables me to conduct user studies with
X3
Participants
in
1/2
time
as traditional lab-based studies.
using machine learning
Crowdsensus enables me to analyze study data
as fast as traditional approaches.
X4
times
04/04
explore more
Crowdlicit
read the paper
watch the talk
crowdsensus
read the paper
watch the talk
Both of these project were folded into the CROWDDESIGN engine
