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Cross-reality environments in smart buildings to advance STEM cyberlearning

  • Raffaele De AmicisEmail author
  • Mariapaola Riggio
  • Arash Shahbaz Badr
  • Jason Fick
  • Christopher A. Sanchez
  • Eric Andrew Prather
Original Paper

Abstract

Real time data associated with the Building Information Model plays a critical role in the interpretation of the built environment, which is particularly relevant as an increasing number of education facilities and institutions promote sustainable engineering practices and monitoring data available to the public. However, it is challenging for non-technical audiences to fully comprehend or use information concealed in scientific data related to the performance of structures and materials. It is especially difficult for them to connect these concepts to physical contexts and phenomena. In this paper, we present how cross-reality paradigms in Architecture, Engineering, and Construction, coupled with multimodal representation techniques, enhance data literacy in both professionals and laypeople alike. In particular, we present the design of a learning environment where cutting-edge holographic interfaces and display technologies are combined with sonified and visual data to create a more immersive environment for data analysis and exploration, empowering users with situated data awareness and new ways of understanding real-time data.

Keywords

Augmented reality Cyberlearning Cross-reality environments Data literacy Structural health monitoring Smart building Virtual reality 

Notes

Acknowledgements

The Living Lab @ Peavy Hall project is conducted through the TallWood Design Institute with funding by the U.S. Department of Agriculture’s Agricultural Research Service (USDA ARS Agreement No. 58-0202-5-001). The material presented in this contribution is also based upon work that is supported by the National Institute of Food and Agriculture, U.S. Department of Agriculture, McIntire Stennis Project Under 1009740. The author Eric Andrew Prather was supported by AFRI ELI Grant No. 2018-67032-27704, from the USDA National Institute of Food and Agriculture. Findings and conclusions are those of the Authors and do not reflect opinions or views of the supporting agencies.

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Authors and Affiliations

  1. 1.Electrical Engineering and Computer Science, College of EngineeringOregon State UniversityCorvallisUSA
  2. 2.Wood Science and Engineering, College of ForestryOregon State UniversityCorvallisUSA
  3. 3.Music Technology and Production, College of Liberal ArtsOregon State UniversityCorvallisUSA
  4. 4.School of Psychological Science, College of Liberal ArtsOregon State UniversityCorvallisUSA

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