Online citizen science projects have demonstrated their usefulness for research, however little is known about the potential benefits for volunteers. We conducted 39 interviews (28 volunteers, 11 researchers) to gain a greater understanding of volunteers' motivations, learning and creativity (MLC). In our MLC model we explain that participating and progressing in a project community provides volunteers with many indirect opportunities for learning and creativity. The more aspects that volunteers are involved in, the more likely they are to sustain their participation in the project. These results have implications for the design and management of online citizen science projects. It is important to provide users with tools to communicate in order to supporting social learning, community building and sharing. AbstractCitizen science; Informal learning; Public engagement with science and technology Keywords

Citizen science is a growing field of research and practice, generating new knowledge and understanding through the collaboration of citizens in scientific research. As the field expands, it is becoming increasingly important to consider its potential to foster education and learning opportunities. Although progress has been made to support learning in citizen science projects, as well as to facilitate citizen science in formal and informal learning environments, challenges still arise. This paper identifies a number of dilemmas facing the field—from competing scientific goals and learning outcomes, differing underlying ontologies and epistemologies, diverging communication strategies, to clashing values around advocacy and activism. Although such challenges can become barriers to the successful integration of citizen science into mainstream education systems, they also serve as signposts for possible synergies and opportunities. One of the key emerging recommendations is to align educational learning outcomes with citizen science project goals at the planning stage of the project using co-creation approaches to ensure issues of accessibility and inclusivity are paramount throughout the design and implementation of every project. Only then can citizen science realise its true potential to empower citizens to take ownership of their own science education and learning.

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Citizen science is a promising field for educational practices and research. However, it is also highly heterogeneous, and learning happens in diverse ways, according to project tasks and participants’ activities. Therefore, we adopt a sociocultural view of learning, in which understanding learning requires a close analysis of the situation created both by the project tasks and the dynamics of engagement of the participants (volunteers, scientists, and others). To tackle the complexity of the field, this chapter maps learning in citizen science into six territories, according to where learning might take place: formal education (schools and universities); out-of-school education (science and nature clubs, summer camps, outdoor education, etc.); local and global communities (neighbourhood associations, activist associations, online communities, etc.); families; museums (science museums, art museums, zoos, and botanic gardens); and online citizen science. For each territory, we present key findings from the literature. The chapter also introduces our six personal journeys into the field of learning and citizen science, displaying their variety and the common lessons, challenges, and opportunities. Finally, we present four key tensions arising from citizen science projects in educational settings and look at training different stakeholders as a strategy to overcome some of these tensions.

This paper focuses on an unexplored dimension of Citizen Science: the potential of Volunteer Computing (VC) for informal learning. VC has been one of the most popular forms of Citizen Science since its beginnings in 1997, when the first VC platforms, such as SETI@home, were created. Participation in VC is based on volunteers donating their idle computer resources to contribute to large-scale scientific research. So far, this has often been considered as a rather passive form of participation, compared to other online Citizen Science (or citizen cyberscience) projects, since volunteers are not involved in active data collection, data analysis or project definition. In this paper we present our research, which was conducted in 2013-2014 with the BOINC Community "Alliance Francophone", and demonstrate that some of the volunteers in Distributed Computing research projects are not at all passive. We show that the dynamism of BOINC greatly relies on community-led gamification and that participation may lead to important learning outcomes. These include extending one's scientific interests and network of people who share similar interests, and progressing within the fields of communication, computing and Internet literacy. Also, as demonstrated by our recent ILICS survey research (2015), these latest learning outcomes are experienced by all categories of participants according to their level of engagement irrespective of their level of formal education, which is an interesting finding for lifelong education policies. Altogether, VC projects engage volunteers emotionally, far beyond the simple use of their computer time and power, and may have a personal and educational value. For a minority of very active volunteers, these projects become real "Windows of Opportunity" for making friends, gaining skills and benefiting from new experiences, which would not easily happen otherwise in their normal everyday environment.

The chapter gives an account of both opportunities and challenges of human–machine collaboration in citizen science. In the age of big data, scientists are facing the overwhelming task of analysing massive amounts of data, and machine learning techniques are becoming a possible solution. Human and artificial intelligence can be recombined in citizen science in numerous ways. For example, citizen scientists can be involved in training machine learning algorithms in such a way that they perform certain tasks such as image recognition. To illustrate the possible applications in different areas, we discuss example projects of human–machine cooperation with regard to their underlying concepts of learning. The use of machine learning techniques creates lots of opportunities, such as reducing the time of classification and scaling expert decision-making to large data sets. However, algorithms often remain black boxes and data biases are not visible at first glance. Addressing the lack of transparency both in terms of machine action and in handling user-generated data, the chapter discusses how machine learning is actually compatible with the idea of active citizenship and what conditions need to be met in order to move forward – both in citizen science and beyond.

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