Data Analytics in Citizen Cyberscience: Evaluating Participant Learning and Engagement with Analytics

Oula Abu Amsha, Daniel K. Schneider, Jose Luis Fernandez-Marquez, Julien Da Costa, Brian Fuchs, Laure Kloetzer

Abstract


Citizen Cyberscience (CCS) projects are online projects that engage participants with no necessary prior scientific experience in online tasks of very varied types and that contribute to the scientific research in different domains. Many research studies confirm the usefulness of CCS projects to researchers while less has been done to explore their added-value for the participants. Specifically, we are interested to know to what extent CCS projects help participants learn while participating through typically small-sized and very specific tasks.

We propose in this work to include another source of quantitative data to the research toolbox usually used to evaluate learning in informal learning contexts as the context of citizen science. This data source is learning analytics that makes use of the already very ubiquitous web analytics and that is heavily used in varied online learning environments. Based on our experience with two CCS pilot projects, we created a framework to help CCS project designers properly implement learning analytics in their project in order to make the full use of these analytics and integrate them with other sources of quantitative data related to the user experience. We apply the proposed framework to explore the interaction between learning and engagement in two pilot CCS projects of different types: volunteer thinking and gaming. We conclude with a number of recommendations to avoid pitfalls and proposals for best practice based on our experience. 


Keywords


Citizen cyberscience, analytics, engagement, learning, framework

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References


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DOI: http://dx.doi.org/10.15346/hc.v3i1.5

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