Goldstein, M., & Wooldridge, A., & Hale-Lopez, K., & Madiol, M. (2022, August), Modeling in a University-Industry Collaboration:  Deep and Surface Approaches  Paper presented at 2022 ASEE Annual Conference & Exposition, Minneapolis, MN. 10.18260/1-2--41434

\bibitem{asee_peer_41434}
  Goldstein, M., \& Wooldridge, A., \& Hale-Lopez, K., \& Madiol, M. (2022, August), \emph{Modeling in a University-Industry Collaboration:  Deep and Surface Approaches}
  Paper presented at 2022 ASEE Annual Conference \& Exposition, Minneapolis, MN.
  10.18260/1-2--41434

Molly Goldstein, Abigail Wooldridge, Kaitlyn Hale-Lopez, and Michael Madiol.  "Modeling in a University-Industry Collaboration:  Deep and Surface Approaches".  2022 ASEE Annual Conference & Exposition, Minneapolis, MN, 2022, August.  ASEE Conferences, 2022.  https://peer.asee.org/41434 Internet.  18 Jun, 2024

\bibitem{asee_peer_41434}
  Molly Goldstein, Abigail Wooldridge, Kaitlyn Hale-Lopez, and Michael Madiol.
  "Modeling in a University-Industry Collaboration:  Deep and Surface Approaches".
  \emph{2022 ASEE Annual Conference \& Exposition, Minneapolis, MN, 2022, August}.
  ASEE Conferences, 2022.
  https://peer.asee.org/41434 Internet.  18 Jun, 2024

@INPROCEEDINGS{asee_peer_41434
author = "Molly Goldstein, Abigail Wooldridge, Kaitlyn Hale-Lopez, and Michael Madiol"
title = "Modeling in a University-Industry Collaboration:  Deep and Surface Approaches"
booktitle = "2022 ASEE Annual Conference \& Exposition"
year = "2022"
month = "August"
address = "Minneapolis, MN"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/41434}
number = {10.18260/1-2--41434}
}

TY  - CPAPER
AB  - Abstract
Background: Engineering in the workplace often requires interdisciplinary teams to address ill-structured and complex problems (Jonassen et al., 2013), where experienced designers use multiple ways to communicate their design ideas that allow for deep inquiry into a design scenario (Crismond & Adams, 2012). Moreover, successful teams are apt to share “mental models” (Borrego et al., 2013), influencing both design product and design process (Edwards et al., 2006; Mohammed et al., 2010). However, there is a lack of data regarding how successful interdisciplinary teams address a common, urgent design goal.  
 
Purpose: This research investigates how individuals in an interdisciplinary team approach mental and physical models to address a common goal. 
 
Methodology/approach:  
This exploratory study was conducted with a university-industry collaboration at the University of Illinois that designed a mobile laboratory called mobileSHIELD to address the need for COVID-19 testing using a saliva-based, University-developed PCR test during the July 2020-December 2020 timeframe. The mobile component of the lab was the essential differentiator to help increase the utility of the testing by allowing a mobile unit to deploy to remote locations. To accomplish this innovative design goal, the team included interdisciplinary members from the following focus areas or “thrusts”: Lab and testing, Data and IT, Finance, System Design, Community Outreach, and Project Management. 
 
We conducted semi-structured interviews with 18 members of the mobileSHIELD team online via Zoom to better understand their approach to and how they engaged with the design process, including their work system and contributions to the overarching need for increased access to COVID-19 testing. For this exploratory study, we were purposeful in our selection of participants and focused on interviews with seven total members from the Lab and Testing (n=3), Data and IT (n=3), and System Design (n=1) thrusts due to their involvement with both mental and physical models during the planning and implementation processes. The interviews focused on sociotechnical system design, design process and team formation. Each interview was audio recorded through Zoom and transcribed by a professional transcription service. We conducted data analysis using a thematic analysis of interview transcripts in order to distill data into a set of distinct themes within deep and surface modeling.  
  
Findings/Conclusion: Results of the thematic analysis suggest that team members engaged in both deep and surface modeling. Results include team member vignettes in order to illustrate the different cases of approaches to mental and physical models, including themes of understanding the challenge, shared problem frame, understanding feasibility, creating project sub-divisions, shared models, iteration, and prototyping and testing. Understanding how team members share in-depth ideas is impactful for establishing best practices in university-industry collaborations.    


AU  - Molly Goldstein
AU  - Abigail Wooldridge
AU  - Kaitlyn Hale-Lopez
AU  - Michael Madiol
CY  - Minneapolis, MN
DA  - 2022/08/23
PB  - ASEE Conferences
TI  - Modeling in a University-Industry Collaboration:  Deep and Surface Approaches
UR  - https://peer.asee.org/41434
DO  - 10.18260/1-2--41434
ER  -