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The Graduate Research Identity Development program (GRID) is an initiative in the College of Engineering at North Carolina A&T State University, sponsored by the National Science Foundation since 2019. The program offers seminar-type lectures supplemented with activities designed to help graduate students develop critical skills for research-based careers. The program is focused on graduate engineering students but is open to graduate students from all programs. Students also choose mentors from within and outside the university with the goal of increasing their sense of belonging to the field and their identities as research engineers. As part of this program, a pilot study is in progress, aimed at performing a full-scale network analysis of student interactions. A web-based survey was administered to collect information about students in and outside the College of Engineering who participate in the GRID program sessions. The survey was designed to collect information on the relationship networks (or lack thereof) that students are involved in as they matriculate through their graduate program. It assesses things such as how and where the students interact with one another, members of faculty and staff, and with contacts from intramural and extramural organizations. Several items are also used to assess students’ perceptions of themselves as research engineers. In this paper, we focus on the interactions of students in the classroom. More specifically, we form networks based on the student answers about the classes they have taken in different departments. We then analyze the resultant networks and contrast certain graph theoretic properties to students’ scores on the research engineer identity items. Do students that are in the periphery, or students that have more connections attain higher research engineer identity scores? Do students that form complete subnetworks (cliques) or core-periphery structures (induced stars) have higher scores than others? This paper presents the findings from this pilot study from the network analysis on this cohort of students. In summary, we find that students with high eigenvector centrality scores and those who form larger cliques possess significantly higher research engineer identity scores.
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This content will become publicly available on February 12, 2026
Experience Report: Using the FABRIC Testbed to teach a Graduate Computer Networking course
The curriculum for a graduate Computer Networking course in Computer Science typically includes activities that help students gain a variety of practical skills that complement the theoretical knowledge they learn during the course. These skills are developed through exercises that present students with scenarios in which they are to understand or cause specific communication behavior over a network. These exercises are constrained by the computer resources that students use for learning. Ideally those resources can be tuned to increase the fidelity of the network that a student is managing—and ultimately allow each student to fully control their own network. This paper describes the motivation, process, and challenges of delivering a graduate course in networking using resources on FABRIC—a publicly-funded, international testbed for research in networking. The paper analyzes the experience of teaching three graduate courses on networking, and reflects on using FABRIC to (1) ensure that students have equal access to a high-quality network environment (rather than rely on students’ individual laptops or self-managed school equipment), and (2) exploit the research testbed’s flexibility to develop a rich range of exercises for students. We discuss our lessons learned and share advice for other instructors.
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- Award ID(s):
- 2346499
- PAR ID:
- 10601210
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400705311
- Page Range / eLocation ID:
- 1246 to 1252
- Format(s):
- Medium: X
- Location:
- Pittsburgh PA USA
- Sponsoring Org:
- National Science Foundation
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