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This innovative practice work in progress paper describes an interdisciplinary course, “Industry 4.0 Robotics,” aimed at fostering deep learning and innovation in students across Manufacturing, Robotics, Computer Science, Software Engineering, Networking, Cybersecurity, and Technology Management. The course, jointly taught by faculty from different domains, emphasizes interdisciplinary connections in Industry 4.0 (IN4.0) Robotics through a combination of lectures, real-world insights from industry guest speakers, and hands-on interdisciplinary project-based learning. The contribution of this work lies in its innovative approach that combines proven best practices in education, inspiring deep learning, and an appreciation of interdisciplinary teamwork. The course design builds upon education research on the benefits of leveraging student creativity and requirements engineering practices as learning tools that allow students to develop a deeper understanding. While the benefits of these practices, commonly cited for developing enhanced problem-solving and cognitive flexibility skills, are becoming well understood in many individual disciplines, far less has been published on best practices for achieving this in interdisciplinary thinking. This course design explores this through using hybrid experiential problem based learning and project based learning for students to develop an understanding of interdisciplinary challenges and opportunities. While the benefits of individual educational practices have been studied within specific disciplines, this work extends the understanding of these practices when applied to interdisciplinary challenges, such as those encountered in Industry 4.0 robotics. The course design aims to bridge the gap between the technical aspects of individual disciplines and the social dimensions inherent in interdisciplinary work. This work in progress seeks to share early results showcasing the benefits of interdisciplinary teamwork and problem-solving. By articulating observations of commonalities and differences with prior work within individual disciplines, the paper aims to highlight the unique advantages of this interdisciplinary learning experience, offering insights into the potential impact on student learning. The chosen approach stems from the anticipation of future challenges increasingly necessitating interdisciplinary solutions. The goal of this work is to understand how best practices from individual disciplines can be effectively incorporated into interdisciplinary courses, maximizing student learning, and uncovering unique learning outcomes resulting from this innovative approach. The course design intentionally bridges the gap between the technical aspects of individual disciplines and the social dimensions inherent in interdisciplinary work, to encourage effective communication and collaboration within mixed student teams. While this remains a work in progress, initial observations reveal a heightened interdisciplinary curiosity among students, driving deep learning as they explore the interconnectedness of their own discipline with others within their teams. This curiosity propels self-led exploration and understanding of how their expertise intersects with diverse knowledge areas, creating opportunities for innovative solutions at these disciplinary intersections. This work contributes to the broader landscape of engineering and computing education by offering insights into the practical application of interdisciplinary learning in preparing students for the complex challenges of Industry 4.0.
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Co-Evolution of Communication and System Performance in Engineering Systems Design: A Stochastic Network-Behavior Dynamics Model
The socio-technical perspective on engineering system design emphasizes the mutual dynamics between interdisciplinary interactions and system design outcomes. How different disciplines interact with each other depends on technical factors such as design interdependence and system performance. On the other hand, the design outcomes are influenced by social factors such as the frequency of interactions and their distribution. Understanding this co-evolution can lead to not only better behavioral insights, but also efficient communication pathways. In this context, we investigate how to quantify the temporal influences of social and technical factors on interdisciplinary interactions and their influence on system performance. We present a stochastic network-behavior dynamics model that quantifies the design interdependence, discipline-specific interaction decisions, the evolution of system performance, as well as their mutual dynamics. We employ two datasets, one of student subjects designing an automotive engine and the other of NASA engineers designing a spacecraft. Then, we apply statistical Bayesian inference to estimate model parameters and compare insights across the two datasets. The results indicate that design interdependence and social network statistics both have strong positive effects on interdisciplinary interactions for the expert and student subjects alike. For the student subjects, an additional modulating effect of system performance on interactions is observed. Inversely, the total number of interactions, irrespective of their discipline-wise distribution, has a weak but statistically significant positive effect on system performance in both cases. However, excessive interactions mirrored with design interdependence and inflexible design space exploration reduce system performance. These insights support the case for open organizational boundaries as a way for increasing interactions and improving system performance.
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- PAR ID:
- 10311053
- Date Published:
- Journal Name:
- ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1115/DETC2021-71277
