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This paper focuses on VEXcode VR, a programming environment for educational robotics without the need for the physical presence of robots. VEXcode VR is based on the VEXcode programming language, which is powered by Scratch Blocks. Besides the graphical programming of virtual robots, the users can also employ text-based Python programming interface, and a combination of both. The programs generated contain similar commands as those used with VEX 123, GO, IQ, and V5 robots, thus making VEXcode VR a supplement to instruction with physical robots. VEXcode VR can be easily and freely accessed through a web browser, making robot programming accessible to a large population of students, teachers, and mentors. At the center of the programming environment are the surroundings, called playgrounds, a variety of scenes including a grid map, an art canvas, and a walled maze. Pre-built 4-wheeled robotic rovers are programmed to complete challenges which are categorized as sensor, movement, geometry, algorithm, or advanced types. These challenges include tasks like robot movements, picking and placing of objects, detecting color and location of lines as the robots navigate a maze, cross numbers of a grid path, or collect trash at the Mangrove coral reef. The virtual robots are equipped with drive trains, sensors, pen features, and an electromagnetic end-effector to manipulate the objects within the environment. Location and orientation of the robot as well as the status of its sensors are available in a dashboard interface during the execution of programs. VEX Robotics education experts created an educator certification program, curriculum, and easy to follow activities which are also available in Google Docs, thus teachers can add to or edit these activities. In addition, various efforts were made to present this environment to the educational community including webinars by the company and summer workshops by one of its educational partners. This paper concludes with the details of these efforts including an ONLINE workshop conducted during the pandemic. The structure and feedback from these efforts as well as their future are also included in the conclusions.
Sirinterlikci, A., & McKenna, J., & Lin, Y., & Oravec, R., & Harter, L. (2022, August), Learning Robot Programming Anywhere: VEXcode VR (Other) Paper presented at 2022 ASEE Annual Conference & Exposition, Minneapolis, MN. https://peer.asee.org/41259
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