Folk wisdom usually takes the number of participants as an often necessary and sufficient condition for identifying a crowd. This assumption has diffused among academics interested in crowdsourcing and collective intelligence as well. Yet, consider 50 people located in a small area such as the counter of a kiosk in a park. One could arrive at a threshold effect of the density of the number of people per square meter that would give the impression of having a crowd. Consider now 50 participants in an idea competition discussing online. In this case, the density of crowding could be related to the number of contributions generated by the crowd, and the ability of the organization to handle the contributions of the crowd in terms of the number of data scientists, the storage capacity of database management systems, as well as the availability of analytics tools. As pointed out by [Viscusi and Tucci 2015] high density within provisional boundaries together with other characteristics such as growth rate, equality among members, goal orientation, and seriality of the interactions distinguish a crowd from groups or communities, with consequently different implications for their management and planning of related initiatives. The purpose of this study is to explore the emergence of different types of crowd dynamics in small teams. An interpretive framework based on a typology of crowds considering the above-mentioned characteristics guides the analysis. To this end, we discuss a summary of the results from a pilot study of a crowdsourcing exercise in higher education.