LEARNING 'SCIENCE' IN AN AIR QUALITY MONITORING COMMUNITY?

Previous research argues that Citizen Science (CS) initiatives (where volunteers engage with scientists and/or experts in scientific and monitoring activities) have the potential to create learning opportunities for members of the public. Especially the initiatives where members of the public participate in scientific research. This study will look at an online CS initiative, Luftdata.se, where volunteers assemble and deploy digital and internet of things connected sensors to monitor particulate matter. It stems from the maker-movement and hence have a focus on the technology. What type of 'science' is produced in this type of initiative where technology, and not science is the main focus? A previous study reviled that participants in Luftdata.se move beyond the technology focused activities provided by the CS-initiative when interacting in the initiatives Facebook group. Such shifts in behavior was also seen by researchers when examining group interactions through the lens of Communities of Practice in the first year of participation in CS initiatives. Another way is to focus social learning opportunities by exploring how learning unfolds when looking at exploratory talk in the conversations. In this study, content analysis was used to explore the online social learning practices in the conversations in the Facebook group of the CS initiative. Conversations of the Facebook group of the CS initiative were observed for over a year. Data from 104 posts were also collected during a three-month period in spring 2019. The content of these conversations was coded using the 'learning in the wild' coding schema for learning through social media by Haythornthwaite et al (2018) in order to understand how knowledge, ideas and resources are shared in the online discussion forum of the Luftdata.se initiative. In this study it is argued that meaning is negotiated in the conversations of the Facebook group when solving technical problems with the sensors and in discussions on how to best monitor particulate matter. The monitor- and sensor specific conversations are about the assembly and deployment of technology. The conversations involve information seeking, providing resources, and being social by e.g. expressing gratitude to the help received. Newcomers learn by asking questions and by following conversations (lurking). The members also talk about the data and are reasoning about possible interpretations. Environmental monitoring experts seem to follow these conversations and are providing explanations or interpretations when needed. Traces of coordination and exploratory talk were hence recognized, and the posts in the Facebook group are establishing a communicative connection. The findings also reveal that sociocultural differences and discontinuities seem to be present since interest groups are identified (experts in environmental monitoring and other members more interested in the technology). People are coming from different contexts, but the differences and discontinuities are not seen as a boundary between two activity systems in this context. Rather, the people in this Facebook group is functioning as a community. The active members in the Facebook group are sharing knowledge and expertise, and they are learning from each other on how to assemble and connect the monitors.