Matching individual attributes with task types in collaborative citizen science.

Shinnosuke Nakayama,Oded Nov,Marina Torre,Maurizio Porfiri

doi:10.7717/peerj-cs.209

Abstract

In citizen science, participants’ productivity is imperative to project success. We investigate the feasibility of a collaborative approach to citizen science, within which productivity is enhanced by capitalizing on the diversity of individual attributes among participants. Specifically, we explore the possibility of enhancing productivity by integrating multiple individual attributes to inform the choice of which task should be assigned to which individual. To that end, we collect data in an online citizen science project composed of two task types: (i) filtering images of interest from an image repository in a limited time, and (ii) allocating tags on the object in the filtered images over unlimited time. The first task is assigned to those who have more experience in playing action video games, and the second task to those who have higher intrinsic motivation to participate. While each attribute has weak predictive power on the task performance, we demonstrate a greater increase in productivity when assigning participants to the task based on a combination of these attributes. We acknowledge that such an increase is modest compared to the case where participants are randomly assigned to the tasks, which could offset the effort of implementing our attribute-based task assignment scheme. This study constitutes a first step toward understanding and capitalizing on individual differences in attributes toward enhancing productivity in collaborative citizen science.

Highlights

Productivity is imperative to success in citizen science, yet retaining participants is a challenge (Chu, Leonard & Stevenson, 2012)
We evaluated the proposed task assignment scheme by comparing the productivity resulting from attribute-based task assignment against that from random assignment, using the empirical data collected in the experiment
That could predict task performance based on empirical evidence: the experience in playing action video games would explain the output of the task that required processing visual information with quick judgment (West et al, 2008; Dye, Green & Bavelier, 2009; Chisholm et al, 2010; Green, Pouget & Bavelier, 2010), and the level of intrinsic motivation would explain the output of the task that required engagement for a prolonged time (Eveleigh et al, 2014; Nov, Arazy & Anderson, 2014; Nov, Laut & Porfiri, 2016; Zhao & Zhu, 2014)

Summary

Introduction

Productivity is imperative to success in citizen science, yet retaining participants is a challenge (Chu, Leonard & Stevenson, 2012). Low engagement limits the scope and quality of data (Cox et al, 2015), by hindering the ability of researchers to aggregate data generated by multiple participants (Hines et al, 2015; Swanson et al, 2015). A great effort is required to increase participation (Segal et al, 2015) and data volume (Sprinks et al, 2017), especially when the projects focus on specific topics that may not appeal to broad audiences (Prestopnik & Crowston, 2012). A new approach is in need to leverage the effort of limited pools of participants (Roy et al, 2015) and maximize their potential productivity. Matching individual attributes with task types in collaborative citizen science.

Methods

Results

Discussion

Conclusion