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Work In Progress: Development of a Simplistic Agent-based Model to Simulate Team Progress Within an Innovation-based Learning Course

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Conference

2021 ASEE Virtual Annual Conference Content Access

Location

Virtual Conference

Publication Date

July 26, 2021

Start Date

July 26, 2021

End Date

July 19, 2022

Conference Session

Studies of Student Teams and Student Interactions

Tagged Division

Educational Research and Methods

Page Count

14

Permanent URL

https://peer.asee.org/38142

Download Count

23

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Paper Authors

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Ellen M. Swartz North Dakota State University

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Ellen Swartz is currently pursuing a M.S. degree in Biomedical Engineering at North Dakota State University. Her research interests include STEM education, innovation-based learning, and agent-based modeling of complex adaptive systems. She previously received her B.S. degree from North Dakota State University in Electrical and Computer Engineering.

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Lauren Singelmann North Dakota State University Orcid 16x16 orcid.org/0000-0003-3586-4266

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Lauren Singelmann is a PhD Student in Electrical and Computer Engineering at North Dakota State University. Her research interests are innovation-based-learning, educational data mining, and K-12 Outreach. She works for the NDSU College of Engineering as the K-12 Outreach Coordinator where she plans and organizes outreach activities and camps for students in the Fargo-Moorhead area.

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Ryan Striker P.E. North Dakota State University Orcid 16x16 orcid.org/0000-0001-9058-5636

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Ryan Striker is a life-long learner. Ryan has over a decade of professional experience designing embedded electronic hardware for industrial, military, medical, and automotive applications. Ryan is currently pursuing a PhD in Electrical and Computer Engineering at North Dakota State University. He previously earned his MS in Systems Engineering from the University of Saint Thomas and his BS in Electrical Engineering from the University of Minnesota.

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Mary Pearson North Dakota State University

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Mary is a Ph.D. candidate in biomedical engineering with research focused in the area of bioelectromagnetics, specifically designing electronics that can be used as medical devices. She obtained her B.S. and M.S. degrees at NDSU in electrical and computer engineering. Mary is also interested in STEM education research.

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Enrique Alvarez Vazquez North Dakota State University Orcid 16x16 orcid.org/0000-0002-7257-0817

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Enrique is an experienced Systems Engineer with a demonstrated history of working in the electrical and electronic manufacturing field. Highly skilled in Embedded Devices, Software Engineering, and Electronics. He is a strong information technology professional with two MSc's and working on a Doctor of Philosophy - PhD focused in Electrical Engineering from North Dakota State University.

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Stanley Shie Ng Biola University Orcid 16x16 orcid.org/0000-0003-3021-8206

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Stanley Ng received his BS in Biomedical Engineering from University of California Irvine and MS in Biomedical Diagnostics from Arizona State University. He serves as faculty and director of engineering programs at Biola University. Currently, he is pursuing a Ph.D. in Engineering and STEM Education at North Dakota State University.

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Abstract

This work in progress discusses the development of an agent-based model used to simulate probabilistic team behavior within an Innovation-Based Learning (IBL) course. Innovation-Based Learning is a non-traditional learning model that encourages students to learn and solve complex problems relevant to current societal challenges as defined by local and federal funding opportunities. IBL pushes students to think innovatively, especially on problems with “unknown unknowns” typical of complex systems.

Our research team has implemented the IBL learning model into a cardiovascular engineering course currently offered within four institutions across the United States. When entering the class students are encouraged to define their own learning. They must identify a current relevant problem, form diverse multidisciplinary teams across the multiple institutions involved, and then work collaboratively towards an innovative solution. Throughout the course students are expected to apply the core course concepts they learn into their innovative solutions and track all learning objectives on the course’s online platform. In previous semesters instructors have noticed that many students flourish and perform exceedingly well within the IBL model, but a few students seem to struggle consistently. Understanding the components of what makes teams successful versus unsuccessful is crucial for enhancing the IBL model and scaling it to larger student populations. The complex nature of students, along with the various causes and effects that take place within team dynamics geared our research team towards modeling the IBL course within NetLogo, a multi-agent modeling software.

NetLogo’s modeling software allows complex systems to be broken down into key components or inputs controlled by simple rules that simulate cause-and-effect interactions of various agents and lets researchers assess the agents’ effects on the system as a whole. Creating a model capable of simulating student and group interactions means researchers can introduce various factors into the model and assess how the group dynamics alter as a result. Such results would provide insight into what factors are beneficial or detrimental for group progress. The authors have identified several key components considered influential on student success in the IBL course: group size, time devotion, project connection, team citizenship, and learning outcomes (defined by Webb’s Depth of Knowledge and an Innovative Impact Scale). These inputs were randomly assigned to agents and agent-sets within the modeling system. Simulations were then performed, data collected, and team progress assessed.

To determine the model’s accuracy, core instructors were asked to define these key components for a previous cohort of IBL students. The defined inputs by the instructors were compared and accorded prior to being integrated into the model to remove randomization. The results of team progress in the interpolated model were then analyzed and compared to actual student teams’ progressions within the course. Any results of the models are probabilistic due to the nature and complexity of agent-based modeling. However, the insights into what key factors suggest successful team progress are influential in further advancing a course that harnesses educating students in complex innovative learning environments.

Swartz, E. M., & Singelmann, L., & Striker, R., & Pearson, M., & Alvarez Vazquez, E., & Ng, S. S. (2021, July), Work In Progress: Development of a Simplistic Agent-based Model to Simulate Team Progress Within an Innovation-based Learning Course Paper presented at 2021 ASEE Virtual Annual Conference Content Access, Virtual Conference. https://peer.asee.org/38142

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