If we looked at a map of a city, we could circle groups of streets or entire neighborhoods that are known for specific features, characteristics, or cultural groups. For example, there may be a section of the city where all the fashion houses and fabric shops are located. We might also see neighborhoods that have formed around a shared nationality, religious affiliation, or some kind of culture. For example, parts of this unnamed city may be enclaves for hipster culture, while other neighborhoods may be known for their large faith communities.
If you walked into one of these spaces, you would likely see some of the cues that make the common denominator in these communities obvious. For example, in the hipster community, individuals may be dressed in specific ways and espouse certain political and environmental ideologies if you spoke to them. But what if a researcher wanted to find commonalities between the hipster community and some other defined community in our hypothetical city? So for example what might this group have in common with, say, a particular religious community? Furthermore, instead of drawing circles of familiar groups on the map, let’s imagine that the researcher decides to approach the question systematically. So they collect data on all the different communities in the city to see what new groupings bubble to the surface.
Part of our research into the informal STEM learning ecology did just that (what is STEM, you ask? We wrote about that here). In our previous post, we highlighted the various places where people encounter STEM concepts in their everyday lives. As part of the same study, we attempted to find commonalities between the different learning environments based on study participants’ responses. Intuitively, some of these elements seem like they would be connected in people’s minds based on organizations’ goals. For example, zoos and aquariums might be expected to cluster with botanical gardens and national parks given their shared focus on ecological systems. But, as you’ll see shortly, our analysis revealed groupings that in some cases were quite unexpected.
First, a word about what we did. Our survey for this study included several questions designed to capture details such as respondents’ interest in STEM, what they consider to be the social value of STEM, and how they identify with STEM. So that participants didn’t have to answer too many questions per survey, we randomly assigned each individual to answer the aforementioned questions for only one of the four STEM disciplines. We then put this information into a statistical model used to cluster or group different institutions based on respondents’ answers. In each of the clusters, shown in the graphic below, the proximity between locations shows how closely these institutions are related to each other in people’s minds as places to learn STEM content. Furthermore, since our focus is zoos and aquariums, we have highlighted their position in the diagram.
As the graphic shows, zoos and aquariums cluster most closely with back- and front-yards in people’s minds. The data shows that the connective tissue appears to be that people see these environments as places to both learn about animal behavior and observe these behaviors in practice. This emphasis on animal behavior might be why Z/As did not cluster with botanical gardens and national parks for example. Additional topics that respondents associated with the zoo and aquarium cluster include learning about species names, reproduction, and ecosystems. In the next post, we will look in more detail at the specific topics participants reported learning as well as the implications of our findings for STEM learning in zoos and aquariums.