TY - JOUR
T1 - 4X
T2 - A hybrid approach for scaffolding data collection and interest in low-effort participatory sensing
AU - Garg, Kapil
AU - Yongsung, K. I.M.
AU - Gergle, Darren
AU - Zhang, Haoqi
N1 - Funding Information:
We thank all of our participants for testing our application and providing valuable feedback. We also thank the members of the Design, Technology, and Research program, the Delta Lab, and the CollabLab for helpful discussions. Funding for this research was provided by the National Science Foundation under Grant No. IIS-1618096, and by an Undergraduate Research Grant from Northwestern University.
Publisher Copyright:
© 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM.
PY - 2019/11
Y1 - 2019/11
N2 - Participatory sensing systems in which people actively participate in the data collection process must account for both the needs of data contributors and the data collection goals. Existing approaches tend to emphasize one or the other, with opportunistic and directed approaches making opposing tradeoffs between providing convenient opportunities for contributors and collecting high-fidelity data. This paper explores a new, hybrid approach, in which collected data–even if low-fidelity initially–can provide useful information to data contributors and inspire further contributions. We realize this approach with 4X, a multi-stage data collection framework that first collects data opportunistically by requesting contributions at specific locations along users’ routes and then uses collected data to direct users to locations of interest to make additional contributions that build data fidelity and coverage. To study the efficacy of 4X, we implemented 4X into LES, an application for collecting information about campus locations and events. Results from two field deployments (N = 95, N = 18) show that the 4X framework created 34% more opportunities for contributing data without increasing disruption, and yielded 49% more data by directing users to locations of interest. Our results demonstrate the value and potential of multi-stage, dynamic data collection processes that draw on multiple sources of motivation for data, and how they can be used to better meet data collection goals as data becomes available while avoiding unnecessary disruption.
AB - Participatory sensing systems in which people actively participate in the data collection process must account for both the needs of data contributors and the data collection goals. Existing approaches tend to emphasize one or the other, with opportunistic and directed approaches making opposing tradeoffs between providing convenient opportunities for contributors and collecting high-fidelity data. This paper explores a new, hybrid approach, in which collected data–even if low-fidelity initially–can provide useful information to data contributors and inspire further contributions. We realize this approach with 4X, a multi-stage data collection framework that first collects data opportunistically by requesting contributions at specific locations along users’ routes and then uses collected data to direct users to locations of interest to make additional contributions that build data fidelity and coverage. To study the efficacy of 4X, we implemented 4X into LES, an application for collecting information about campus locations and events. Results from two field deployments (N = 95, N = 18) show that the 4X framework created 34% more opportunities for contributing data without increasing disruption, and yielded 49% more data by directing users to locations of interest. Our results demonstrate the value and potential of multi-stage, dynamic data collection processes that draw on multiple sources of motivation for data, and how they can be used to better meet data collection goals as data becomes available while avoiding unnecessary disruption.
KW - Mobile Crowdsourcing
KW - Participatory Sensing
KW - Physical Crowdsourcing
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U2 - 10.1145/3359192
DO - 10.1145/3359192
M3 - Editorial
AN - SCOPUS:85075072083
VL - 3
JO - Proceedings of the ACM on Human-Computer Interaction
JF - Proceedings of the ACM on Human-Computer Interaction
SN - 2573-0142
IS - CSCW
M1 - 90
ER -