Salt Lake City, Utah
June 23, 2018
June 23, 2018
July 27, 2018
Educational Research and Methods
Using Social Network Analysis to Study the Social Structures of Inclusion
The purpose of this research paper is to understand how diverse students are incorporated into the social structure of a large enrollment first-year engineering design course. Despite previous work demonstrating the benefits of diverse individuals in engineering, little work has examined how diverse students are incorporated into the social networks that exist within engineering classrooms. Social interactions are one of the most influential sources for integration into communities of practice. Through understanding how students interact and the structure of these interactions, we can elucidate how the engineering community includes members of underrepresented populations. Previous social network analysis (SNA) studies have scrutinized student classroom interactions. These studies typically attempt to link classroom interactions to academic outcomes (i.e., grades). In this study, we start to shift the focus away from connecting student interactions to academic outcomes and examine how the structure of student interactions can encourage an inclusive environment in a formal engineering environment. SNA data was collected via an online survey (n = 502, 74% response rate) one month into the semester at a Western land-grant institution. The survey asked first-year engineering students to indicate with whom they had interacted using a pre-populated list of the class roster and open-ended questions. The number of students that were mentioned by a participant (out-degree) is interpreted as a proxy of their sociableness. Whereas, the number of times a student was mentioned by others (in-degree) is interpreted as popularity. We posit that in an inclusive network structure the social behaviors (i.e., in and out-degree) will be independent of students’ demographic characteristics (e.g., race and gender). Nonparametric hypothesis testing (i.e., Kruskal-Wallis and Dunn’s test) was used to investigate the effects of gender and race on both in and out-degree. Results indicate that the social structure of the first-year engineering community is inclusive of both gender and race. Specifically, results indicated no significant differences for in-degree based on measures of race and gender, for students who provided race and gender information. Out-degree was not significantly different based on race. However, women did demonstrate significantly higher out-degree scores (i.e., greater sociableness) than their peers. Building on previous SNA literature, the increased connections expressed by women may lead to increased learning gains or performance within engineering.
Results indicated that the social structure of this first-year engineering course, as indicated by in-degree and out-degree, is not significantly different for underrepresented groups. This result begins to illustrate a more complex story than the existing literature has documented of engineering as an unwelcoming environment for underrepresented students. Future work will explore how these structures do or do not persist over time and how individuals develop attitudes towards diverse individuals as a result of these interactions. We hope that the results of this work will provide practical ways to improve engineering climate for underrepresented students.
Pearson, N. S., & Major, J. C., & Godwin, A., & Kirn, A. (2018, June), Using Social Network Analysis to Study the Social Structures of Inclusion Paper presented at 2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah. https://peer.asee.org/31211
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