A Toolkit for Synchronized Physical-Virtual Visualization of Network Graphs for STEM Education Via Integration of Augmented Reality and Embedded Electronics

open_access
Open Access
Author:
Kim, Julian
Area of Honors:
Mechanical Engineering
Degree:
Bachelor of Science
Document Type:
Thesis
Thesis Supervisors:
Guhaprasanna Manogharan, Thesis Supervisor
Anne Elizabeth Martin, Thesis Honors Advisor
Keywords:
Network graph visualization
Embedded electronics
augmented reality
STEM education
Abstract:
Network graphs are prevalent across many disciplines because they provide a general abstraction for representing interconnected systems. As such, developing network analysis skills is critical for Science, Technology, Engineering, & Mathematics (STEM) students. However, current network visualization tools present a steep learning curve to students due to unique query languages and visual clutter in dense graphs. This study introduces a novel immersive toolkit grounded in multimedia learning and embodied cognition theories to improve network graph comprehension in undergraduate STEM students. The toolkit integrates embedded electronics with Augmented Reality (AR) to create dynamic visualizations of complex networks featuring: (1) visual attributes to communicate network properties, (2) tactile interaction through physical network models, (3) local and global views of network graphs, and (4) contextual AR overlays. The toolkit utilizes a Microsoft HoloLens 2 headset to project the overlay onto a physical network model constructed from transparent 3D-printed polyhedron and Polyvinyl Chloride (PVC) tubing. Through-hole Light Emitting Diodes (LEDs), WS2812b LED strips, and ESP32 microcontroller modules were embedded in the physical network to drive its behavior. The toolkit was deployed in an upper-level undergraduate statistics class (N=25). Results yielded statistically significant improvements in learning outcomes following the intervention (p<0.001), with the largest gains observed among students with low initial confidence. These findings demonstrate the potential of immersive, multimodal visualization tools to augment cognition in complex analytical tasks and support more effective teaching of network-based concepts.