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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? 

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. 

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

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