Virtual Conference
July 26, 2021
July 26, 2021
July 19, 2022
Educational Research and Methods
12
10.18260/1-2--38122
https://peer.asee.org/38122
289
Jack Elliott is a concurrent M.S. in Engineering (mechanical) and Ph.D. in Engineering Education student at Utah State University. His M.S. research is in experimental fluid dynamics including the application of PIV, and his Ph.D. work examines student collaboration in engineering education.
Angela Minichiello is an assistant professor in the Department of Engineering Education at Utah State University (USU) and a registered professional mechanical engineer. Her research examines issues of access, diversity, and inclusivity in engineering education. In particular, she is interested in engineering professional formation, problem-solving, and the intersections of online learning and alternative pathways for adult, nontraditional, and veteran undergraduates in engineering.
Dr. Marquit is an Associate Teaching Professor and Program Liaison of the Psychology Department at Penn State Brandywine in Media, Pennsylvania. He has a doctoral degree in experimental and applied psychological science. He teaches courses in statistics, research methods, environmental psychology, industrial-organizational psychology, and psychology and climate change; and has recently won The Distinguished Teacher/Excellence in Teaching Award in 2019. He has conducted collaborative research in a broad arena of topics in environmental, social, health, sports, organizational, educational, gender, space, and clinical psychology. The results of his research have been presented to government agencies and at professional conferences at the local and international levels and published in peer-reviewed journals.
This work in progress paper describes an ongoing study aimed at improving the current understanding of group work and the influence that formal, informal, and social peer networks have on engineering college student performance outcomes over time. An ability to work effectively in teams to solve problems and design solutions is a required outcome for all ABET accredited engineering programs. Furthermore, the potential for peer collaboration to impact engineering students’ learning has theoretical foundations in social learning theory. Research suggests that when collaborative learning (i.e., working with peers on course work) is implemented within undergraduate engineering courses, students show increased engagement with course material and improved academic performance. Despite these promising results, most collaborative learning research in engineering education has employed data collection techniques at the level of a single course or at a single point in time. Thus, previous studies may not fully capture the range of students’ networks, or the potential effects of these networks on student performance over multiple courses and time. Additionally, existing research has often compared generalized interactions (i.e., who a student studies with for a particular course without distinguishing interaction types) to net performance (i.e. average grades or performance on a single assessment). This relationship may not adequately capture the varying and nuanced interaction strategies used by individual students. Therefore, we argue that additional research is needed to wholly understand peer network formation and the impact these networks have on students’ performance outcomes over time. To this end, the proposed study will investigate relationships between students’ formal, informal, and social peer interactions and performance outcomes (i.e., grade point average and attrition) over a period of two years. The study design consists of quantitative data collection, (i.e., periodic online student questionnaires) and analyses of the peer and social interactions between first- and second-year undergraduates enrolled in the engineering college at a mid-size, predominantly white, land grant institution. Social Network Analysis (SNA) is a robust technique for characterizing and comparing interpersonal interactions and is increasingly utilized in education research because of its’ ability to examine qualitative student interactions through application of quantitative network matrices. The researchers will analyze these matrices to develop network and node traits known as SNA measures (e.g. centrality—the connectedness of an actor—and density—the network’s connectedness as a whole) and to map these interactions using network plots called sociograms. In sum, the findings of this study will identify how group interactions form and evolve over time among engineering students with varied interaction preferences and how these interactions affect student performance in engineering. Ultimately, new understandings will better equip engineering educators to incorporate collaborative learning activities such as group work into their curricula in ways that are effective, lasting, and responsive to authentic student learning practices in engineering.
Elliott, J., & Minichiello, A., & Marquit, J. D. (2021, July), Work in Progress: An Investigation of the Influences of Peer Networks on Engineering Undergraduate Performance Outcomes Paper presented at 2021 ASEE Virtual Annual Conference Content Access, Virtual Conference. 10.18260/1-2--38122
ASEE holds the copyright on this document. It may be read by the public free of charge. Authors may archive their work on personal websites or in institutional repositories with the following citation: © 2021 American Society for Engineering Education. Other scholars may excerpt or quote from these materials with the same citation. When excerpting or quoting from Conference Proceedings, authors should, in addition to noting the ASEE copyright, list all the original authors and their institutions and name the host city of the conference. - Last updated April 1, 2015