Knowledge Collaboration Between Professionals and Non-professionals: A Systematic Mapping Review of Citizen Science, Crowd Sourcing and Community-driven Research

This paper presents a mapping review of statuses and trends in citizen science, crowdsourcing and community-driven research from 2013-2018. Understanding these fields is central in relation to the current trend of developing games for citizen science. The review focuses on identifying general themes, trends and gaps regarding knowledge collaboration and specific themes, trends and gaps regarding learning and education. This mapping is central in understanding and developing Citizen Science games, where gamers in science or other professions can collaborate. Two hundred and forty studies were identified through iterative searches and screening processes, and 11 themes were identified through grounded theory-inspired analysis. These themes are: 1. motivation, 2. evaluation, 3. education and learning, 4. man-machine collaboration, 5. participant experience, 6. impact on research, 7. CS technologies, 8. big data, 9. system or project design, 10. social media, and 11. participant development of research. Because our focus was on learning, we defined themes with a focus on traditional educational activity and new forms of learning in the field. The review reveals central discussions on both the potential of technology in citizen science learning and the application of new types of technology. Results related to citizen science learning showed that value is added into knowledge generation by the collective process of a crowd with multiple competences. Specifically, this occurs through two types of processes: social learning and learning from experience. These results indicate that it is important to focus on defining various groups of participant skills when designing citizen science systems, determining what processes users are able to participate in and what additional training or education is needed for participants to contribute to more sophisticated processes. The review also reveals that technology will play an increasing role in crowd sourcing in both research and business. There are central discussions on whether the active input and participation of users will be transformed to more passive input with the involvement of passive sources of data generated by existing and new types of sensor technologies, bots, artificial intelligence and other types of technology. In the context of this review, the IoT development of 'the next generation of crowdsourcing' also raises a number of questions in relation to learning. With a focus on types of participation in learning and educational processes, 'active' versus 'passive' input becomes a challenge that must be addressed. The results presented in this paper are central as a background study regarding the involvement of technology in communities, such as the current trend of developing citizen science games.