2026 ASEE Annual Conference & Exposition

This empirical research brief presents preliminary quantitative results from a longitudinal (two-year), large (>1000 actors), mixed-methods study that explored relationships between the social interactions of engineering undergraduates and their academic success (i.e., grade point average [GPA,] and retention in engineering) using Social Network Analysis (SNA) techniques.

Increasingly, engineering education researchers are interested in exploring the ways that undergraduate engineering students’ interactions with others may influence their academic outcomes. Within social cognitive perspectives of learning, Social Cognitive Theory posits that student learning and behavior are mediated by individual factors and social interactions. Individual factors include self-efficacy and sense of belonging. Social interactions include observation of students’ peers (i.e., other students and friends), as well as interactions with institutional (e.g., faculty, staff, and administrators), and external (e.g., family members, work associates, etc.) actors. Concurrent with the interest in the role of interactions in engineering student learning, SNA has grown as a method to quantify interactions between individuals for subsequent visual and statistical analysis.

To understand and design extra/curricular activities that emphasize the types of social interactions that support positive academic outcomes for engineering undergraduates, existing EER has employed SNA within single undergraduate engineering course contexts to correlate student interactions with indicators of student success. To further investigate and potentially extend current findings to a broader, more holistic student support network that exists within, across, and outside of single undergraduate courses, we conducted a two-year, longitudinal, mixed-methods study of the interactions of first- and second-year undergraduate engineering students at a large, land grant university in the western United States. Primary results revealed statistically significant relationships between participants’ overall connectedness and their academic success. Specifically, the results indicate that the measures of overall participant connectivity, indicated by the number of members in their network, were positively related to an increased cumulative engineering GPA and a higher likelihood of retention. The analysis also identified statistically significant relationships between the types of connections that participants made and their GPA. Specifically, participants who received study support from institutional actors, namely professors, and certain types of external actors such as job supervisors and coworkers, were more likely to earn a higher GPA than those who received study support from other categories of external actors including dating partners, roommates, and friends not currently in an engineering program.

Overall, preliminary results highlight key considerations for designing social interactions into engineering programs on an institutional scale. Findings not only support those of prior single-course and cross-sectional studies but further suggest that engineering students’ selection of study support connections may directly affect course achievement measured through GPA. These findings reinforce previous recommendations from studies based on single-course networks; social integration into the academic community consisting of students, faculty, and staff plays an important role in engineering student success. Thus, as future interventions and strategies are proposed and developed to increase engineering student engagement across the major, college, and institution, curricular designers should consider how best to incorporate the mechanisms that positively influence students’ connections with vital institutional and external actors.