2023 ASEE Annual Conference & Exposition

In this full student-led research paper, we examine how collaborative learning impacts the social networks of engineering students. We believe this is important because it will provide us with insight into how collaborative learning can affect feelings of social connectedness, which is known to impact students’ academic success.

A survey was designed and sent to senior undergraduate engineering students at a mid-sized, Mid-Atlantic university. The survey included demographic questions (gender, race/ethnicity, year, etc.), questions about the student's perceptions of their social connectedness and their instructors tendency to promote collaborative learning, and a series of questions designed to elicit the student’s social network. These questions used the affective approach for eliciting social networks, which asked students to name up to 10 of their closest friends at the university and provide basic demographic information for these friends (the friend’s major, race/ethnicity, and gender) as well as information about how the student interacts with each friend. Finally, participants indicated which of the identified close friends know each other , and to what degree (strangers, moderate friends, or close friends). From this, an ego network (network of direct ties) was generated for each student participant.

Data analysis using the network analysis software OraLite and Excel were used to explore ego network homophily, social capital, and clustering coefficient. Ego network homophily, or the tendency of individuals to form close ties with others like them, may be greater in instances where instructors promote teamwork. Similarly, students may have greater social capital (i.e., access to more resources through their social network) when collaboration is encouraged—for example, students in collaborative environments may work with their closest friends on homework assignments and team projects. The clustering coefficient is a measure of the overall connectedness of one’s network (how many of your friends know each other?), and can be used to infer the overall interconnectedness of a student’s social network. Taken together, these analyses can describe the ways in which collaborative learning may shape a students’ social networks and perceptions of social connectedness.

The results of this study indicate that, in instances where students perceived that their instructors implemented collaborative learning more often, a student’s social network became more densely interconnected. Additionally, the number of friends a student chooses to work with is positively correlated to how often said student works or studies in a group setting. We also found a correlation between social connectedness within a department and the competitiveness of the department. These findings can be used to inform instructor’s pedagogical approaches and provide additional support for the benefits of collaborative learning.