2026 ASEE Annual Conference & Exposition

Although numerous supports have been put in place to different extents at the state and institutional levels, such as transferable core courses and articulation agreements, to smooth the transition from community college to four-year institutions, engineering transfer students continue to face barriers when searching for details about their transfer. These challenges reflect potential information asymmetries, where critical information is difficult to find or interpret, and are compounded by disparities in transfer student capital (TSC), which refers to the accumulation of knowledge and strategies students have relative to the transfer process. One of the primary sources of information that students use to gather transfer information is the university website. Still, these websites are expansive, making the exact information potentially difficult to locate - even for well-informed students. To explore how students actually navigate these sites, we posed the following research question: “How do website navigation metrics correlate with the likelihood of successfully locating transfer-related information?”

We sampled 22 pre-transfer students for think-aloud interviews, recruiting them through social media posts and snowball sampling. Students interacted with two institutional websites that were unfamiliar to them. On each site, students were instructed to find specific pieces of transfer information, including general admissions, program-specific requirements, course equivalencies, transfer credits, GPA calculation, degree requirements, and credit evaluation timing. Their browsing sessions were modeled as directed graphs using the igraph package in R by extracting each page they visited and associated verbalizations. We computed navigation metrics to characterize their information search quantitatively, which included the total pages visited, the number of transitions, the average in-degree and out-degree, the average path length, and the number of cycles. Logistic regression was employed at the task level, with task success (1 = found correct information, 0 = not found) as the dependent variable. Predictors included the network metrics described above as well as task type. Logistic regression was employed at the task level, with task success (1 = found correct information, 0 = not found) as the dependent variable. Predictors included the network metrics described above as well as task type.

Our task completion data reveal that students demonstrated moderate success in navigating university websites to locate transfer-related information, achieving an overall success rate of 44.5%. Across prompts, students performed well on tasks such as general admissions (78%) and degree requirements (76%), but struggled substantially with GPA calculation (21%), equivalencies (29%), and credit evaluation timing (21%). These lower success rates occurred primarily on tasks that required cross-referencing multiple pages, suggesting greater navigational complexity and potential information asymmetries, as evidenced in our logistic regression results to be shared in the full paper.

We expect this study to provide insight into how engineering pre-transfer students engage with university websites, which will help inform better communication of key transfer information. Moreover, this study contributes methodologically by using network analysis to compare pre-transfer students’ navigation patterns quantitatively. It sets the stage for further explorations of how transfer student capital can mitigate issues with access to transfer-related information, providing early insights into this interaction.