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

\bibitem{asee_peer_38122}
  Elliott, J., \& Minichiello, A., \& Marquit, J. D. (2021, July), \emph{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

Jack Elliott, Angela Minichiello P.E., and Joshua D. Marquit.  "Work in Progress: An Investigation of the Influences of Peer Networks on Engineering Undergraduate Performance Outcomes".  2021 ASEE Virtual Annual Conference Content Access, Virtual Conference, 2021, July.  ASEE Conferences, 2021.  https://peer.asee.org/38122 Internet.  10 Jul, 2024

\bibitem{asee_peer_38122}
  Jack Elliott, Angela Minichiello P.E., and Joshua D. Marquit.
  "Work in Progress: An Investigation of the Influences of Peer Networks on Engineering Undergraduate Performance Outcomes".
  \emph{2021 ASEE Virtual Annual Conference Content Access, Virtual Conference, 2021, July}.
  ASEE Conferences, 2021.
  https://peer.asee.org/38122 Internet.  10 Jul, 2024

@INPROCEEDINGS{asee_peer_38122
author = "Jack Elliott, Angela Minichiello P.E., and Joshua D. Marquit"
title = "Work in Progress: An Investigation of the Influences of Peer Networks on Engineering Undergraduate Performance Outcomes"
booktitle = "2021 ASEE Virtual Annual Conference Content Access"
year = "2021"
month = "July"
address = "Virtual Conference"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/38122}
number = {10.18260/1-2--38122}
}

TY  - CPAPER
AB  - 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.  

AU  - Jack Elliott
AU  - Angela Minichiello P.E.
AU  - Joshua D. Marquit
CY  - Virtual Conference
DA  - 2021/07/26
PB  - ASEE Conferences
TI  - Work in Progress: An Investigation of the Influences of Peer Networks on Engineering Undergraduate Performance Outcomes
UR  - https://peer.asee.org/38122
DO  - 10.18260/1-2--38122
ER  -