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Matching individual attributes with task types in collaborative citizen science.

Shinnosuke Nakayama1, Marina Torre1, Oded Nov2

  • 1Department of Mechanical and Aerospace Engineering, New York University Tandon School of Engineering, Brooklyn, NY, United States of America.

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Summary
This summary is machine-generated.

Assigning citizen science participants to tasks based on combined individual attributes, like gaming experience and motivation, can modestly boost project productivity. This approach optimizes task allocation for better results.

Keywords:
AptitudeCrowdsourcingData quantityDivision of labor

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Area of Science:

  • Citizen science
  • Human-computer interaction
  • Crowdsourcing

Background:

  • Participant productivity is crucial for citizen science project success.
  • Individual attributes can influence task performance in citizen science.
  • Optimizing task assignment based on participant characteristics is an area of interest.

Purpose of the Study:

  • To investigate the feasibility of a collaborative citizen science approach that enhances productivity by matching participants to tasks based on diverse individual attributes.
  • To explore if combining multiple individual attributes improves task assignment effectiveness compared to single attributes.
  • To assess the impact of attribute-based task assignment on overall participant productivity.

Main Methods:

  • Collected data from an online citizen science project with two distinct task types: image filtering (time-limited) and object tagging (unlimited time).
  • Assigned image filtering tasks to participants with more action video game experience.
  • Assigned object tagging tasks to participants with higher intrinsic motivation.

Main Results:

  • Individual attributes (gaming experience, intrinsic motivation) showed weak predictive power for task performance when considered alone.
  • A greater increase in productivity was observed when participants were assigned tasks based on a combination of these attributes.
  • The productivity increase from attribute-based assignment was modest compared to random assignment.

Conclusions:

  • Combining multiple individual attributes for task assignment in citizen science can lead to modest productivity gains.
  • While promising, the observed gains may not fully justify the effort of implementing complex assignment schemes.
  • This study represents an initial exploration into leveraging individual differences to enhance citizen science productivity.