This project explores the possibility of using Unmanned Aerial System (UAS) technology to support learning in construction engineering and management courses. Today, construction projects are becoming increasingly more complex. Students can learn about how projects unfold through field trips to build sites, but expense, timing, and safety precautions make it hard for students to see how a variety of construction projects unfold in time and space. This project will help students learn about construction projects by using unmanned aerial vehicles to record video of build sites over time; students and their teachers will be able to select important project aspects to view, and recorded video will help establish a case library for future students to see how the realities of job sites differ from contstruction documents.
This project focuses on facilitating the integration of procedural and configurational knowledge in construction and engineering management by supporting teaching of spatio-temporal constraints through case-based reasoning. The CyberEye system will be built and refined through iterative design of two components: first, the remote video and image generation component, which will integrate the aerial vehicle, a ground control station, and a communications and control platform to allow the cameras to capture desired aspects of the construction site; and second, a system to support learner access to the video, which will support creation of instructive cases, allow instructor and student planning and input of aerial missions (both synchronously and asynchronously), and archiving and reflective viewing for learners. The system will study both the learning outcomes and propose design principles using a four-iteration design-based research methodology implemented in college-level construction engineering courses. Semi-structured interviews and participant observation will be used to support formative evaluation, while pre-post experimental comparison will gauge the impact of the CyberEye intervention on student learning and especially spatial reasoning within the CEM domain for each iteration.