Triangulation roadmap

This multi-perspective study investigated the alignment between (1) learning opportunities mentioned by CS project initiators in CS project descriptions available online, (2) learning opportunities shared in tweets from project or platform accounts and, (3) the participants' perceived learning experiences as reflected in survey responses and tweets from individual user accounts. The aim was to identify overlaps and discrepancies between learning opportunities envisioned by project coordinators’ and citizen scientists’ perspectives on learning in CS projects. To this end, we triangulated three datasets - project descriptions (N=94), tweets (N = 216,786) and survey responses (N = 610). The first dataset - a qualitative content analysis of project descriptions stored in the CS Track database – was created in the context of a previous study (cf. section 5.1). The keywords derived from this manual coding of project descriptions were used to conduct an automated analysis of tweets and thus form the basis of the second dataset used in this triangulation study. The third dataset consists of citizen scientists’ responses to the CS Track online survey, which focused primarily on Europe and was distributed for a period of seven months (January-July 2021) through multiple channels. As a second step, we conducted a case study of 11 projects, which allowed us to narrow down the three datasets (project descriptions: N=11, tweets: N=118, survey responses: N=139) and draw conclusions on the level of individual projects. 

The results of both the general comparison and the project-level case study reveal that there is a significant discrepancy between the learning opportunities described by project coordinators and the learning experiences reported by project participants. This gap is particularly evident with regard to skills related to communication and project or research design, but also when it comes to scientific literacy and critical thinking. What our findings also show is that responses vary considerably even among volunteers who participated in the same CS project, which highlights the important role the citizen scientists’ individual backgrounds, interests and motivations play in shaping their learning experiences.

References: M. Oesterheld, V. Schmid-Loertzer, M. Calvera-Isabal, I. Amarasinghe, P. Santos, & Y. Golumbic (2022). Identifying learning dimensions in citizen science projects. In proceedings of Engaging Citizen Science Conference 2022, PoS(CitSci2022) 070. [forthcoming]

More information on this research can be found in D2.2 section 5.2.

Content and grouping: 

• Datasets and documentation related to Oesterheld et al. (2022) study (see section 5.1)

• CS Track survey questions (The CS Track survey data is not currently publicly available. Specific questions regarding survey data (e.g. questions) can be sent to Raija Hämäläinen (raija.h.hamalainen@jyu.fi).

• Triangulation roadmap (juxtaposition of the categories/survey questions we compared)