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LiDAR Motion Visualizer: Education

Motion Mapping with Your iPhone or iPad Pro​

This lesson has students explore the shapes of position vs. time graphs resulting from constant velocity and accelerated body motion. Through the "Game" mode of LiDAR Motion Visualizer, learners move back and forth from a flat wall to match pre-made graphs. The student worksheet provides a space for learners to explicitly reflect on what they have learned, including the meaning of position vs. time graph slopes and y-intercepts.

 

Click on the student worksheet below (make a copy from Google Docs versions).

Research Products​

This project aimed to create and test a STEM education tool using iOS’ scanning LiDAR (released March 2020) and Android’s non-scanning LiDAR (released in 2019) technologies for enhanced augmented reality (AR) visualization of position-based physical concepts for remote learning. The innovation makes use of the novel back-facing infrared beam array to significantly increase precision in position measurements and the placement of AR visualizations based on users’ movements and environmental data. Research in the learning sciences entailed a collaboration with STEM educators to develop and test the effectiveness of scenarios for exploration in traditional and remote learning contexts, assessing full-body movement to make sense of motion graphs with a focus on embodied learning and practice with data visualization literacy.

 

The intellectual merit of this project was to answer three core questions about technology and education research:

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  • (RQ1) “To what extent can LiDAR-enhanced AR sense and visualize the relative motion of PMDs and objects in their environments?” 

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  • (RQ2) “How does embodied LiDAR-enhanced AR for the mathematical modeling of motion impact students’ abilities to interpret, predict, and relate various representations of motion and students’ attention to these tasks?”

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To answer these questions, we developed the LiDAR Motion Visualizer mode within Physics Toolbox Sensor Suite and used it in classroom and clinical contexts to determine its effect on student learning.

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Publications

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Chapters

Vieyra, R., Megowan-Romanowicz, C., O’Brien, D. O., Vieyra Cortés, C., & Johnson-Glenberg, M. (2023, 01). Harnessing the digital science education revolution: Smartphone sensors as teaching tools. In S. Asim, J. Ellis, D. Slykhuis, & J. Trumble (Eds.), Theoretical and Practical Teaching Strategies for K-12 Science Education in the Digital Age. IGI Global. Link​

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Journal Articles

Vieyra, R., Megowan Romanowicz, C., O’Brien, D., Vieyra Cortés, C., & Johnson-Glenberg, M. C. (under review). Building embodied intuition for graphs with smartphones. Mathematics Teacher: Learning and Teaching PK-12.

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Vieyra, R., Megowan Romanowicz, C., Johnson-Glenberg, M. C., O’Brien, D., & Vieyra Cortés, C. (2024). Making motion meaningful: Mapping body movements onto graphs. The Science Teacher, 91(3), 57-64. Link

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Megowan-Romanowicz, C., O’Brien, D., Vieyra, R., Vieyra, C., & Johnson-Glenberg, M. (2023). Evaluating learning of motion graphs with a LiDAR-based smartphone application. Proceedings of the Physics Education Research Conference 2023. Link

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Assessment 

The team developed a simple pre- and post-assessment of knowledge about position-time graphs, building on prior validated assessments from physics education research. Contact support@vieyrasoftware.net to see a copy.

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