1,093
Views
56
CrossRef citations to date
0
Altmetric
CURRICULUM AND INSTRUCTION

Pilot Study Using the Augmented Reality Sandbox to Teach Topographic Maps and Surficial Processes in Introductory Geology Labs

, , , &
Pages 199-214 | Received 19 Nov 2015, Accepted 23 May 2016, Published online: 13 Jun 2018
 

ABSTRACT

Spatial thinking is often challenging for introductory geology students. A pilot study using the Augmented Reality sandbox (AR sandbox) suggests it can be a powerful tool for bridging the gap between two-dimensional (2D) representations and real landscapes, as well as enhancing the spatial thinking and modeling abilities of students. The AR sandbox involves a real box of sand with virtual contour lines and a water flow model created using a three-dimensional (3D) scanning camera, visualization software, and a projector. It was used in undergraduate, physical geology courses to teach topographic maps and surficial features and processes. The instructor demonstrated topographic concepts (contour lines, topographic profiles, etc.), and students engaged in model building of coastal and fluvial environments (drainage basins, cutoffs, longshore transport, sea-level rise, spits, flooding, etc.). The virtual water flow model was used to illustrate water flow dynamics on surface features. With the AR sandbox connected to a computer monitor, students could simultaneously see 3D landscapes in the sandbox and their corresponding 2D images on the monitor. Students used camera phones to capture landscape models they built and submitted them via e-mail for grading. Exit surveys indicated students were overwhelmingly positive (97%) in their perception of how the AR sandbox improved their understanding of learning objectives. They also preferred AR sandbox activities when compared to traditional laboratories that used only topographic maps. Effective classroom use of the AR sandbox required developing student-modeling exercises that took advantage of real-time feedback, virtual water, and physical modeling activities. While data are limited and more research is needed, real-time feedback on student models by both the students and the instructor suggests sandbox models are particularly useful for dispelling student misconceptions.

Acknowledgments

The ECU authors thank the Department of Geological Sciences for funding this project and Rob Howard for invaluable computer assistance and the donation of a camera and video card. Students in these summer 2015 courses contributed significantly with their enthusiastic participation in the exercises and their thoughtful comments. The following students also helped with developing exercises and making the videos: Mark Akland, Kristen Daniel, Devon Reed, Caroline Smith, Luke Stevenson, and Jeremy Robbins. The AR sandbox was created as part of a project called LakeViz3D, which is supported by the National Science Foundation under grant 1114663. The development of the AR sandbox software was also supported by the National Science Foundation under grant 1135588. The LakeViz3D-affiliated authors (S.R. and S.H.) gratefully acknowledge members and advisers of the LakeViz3D project, a collaboration of scientists, science educators, evaluators, museum professionals, and media developers from UC Davis TERC, UC Davis KeckCAVES, UC Berkeley LHS, ECHO Leahy Center for Lake Champlain, and Audience Viewpoints Consulting. Finally, the comments of reviewers and editors for the Journal of Geoscience Education significantly improved this manuscript.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 53.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 102.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.