Evaluating an Immersive Learning Environment for Robotics Training

Autor: Eric Peterson, Biayna Bogosian, Shahin Vassigh
Rok vydání: 2022
Zdroj: Training, Education, and Learning Sciences.
ISSN: 2771-0718
DOI: 10.54941/ahfe1002400
Popis: In Spring 2021, an interdisciplinary team of researchers at Florida International University (FIU) designed a virtual reality (VR) training prototype for novices to learn how to work with industrial robots. Developed with the support of a grant from the National Science Foundation (NSF) by a team of architecture and computer science faculty, the Robotics Academy immersive learning environment prototype leverages advanced technologies to teach robotics in a fully immersive VR environment. This paper will describe the learning environment, the introductory lesson prototype, the learning evaluation tools, and the comparative outcomes of testing this learning prototype with a test group and a control group.As robotic automation continues to transform manufacturing, construction, and other industries, VR may offer a solution for training the labor force for more technically demanding jobs. VR provides computer-generated simulations of the real or an imagined environment that can serve as a rich and engaging space for learning (Mantovani et al., 2003). Recent research demonstrates that immersive environments can facilitate learning (and the assessment of learning) by providing a safe and low-cost setting for practice and rehearsal (Beck, 2019). Training workers to operate robots in a traditional classroom setting often relies on low teacher to student ratios as a means for accommodating individualized or small group coaching using a dedicated training robot. This pedagogical method can be both costly and time consuming. Meanwhile, on-the-job training can both slow down production and expose inexperienced trainers and trainees to potentially hazardous conditions. Immersive virtual learning environments offer a potential solution to reduce the cost of traditional training and mitigate exposure to hazardous conditions while learning how to operate industrial robots.The design team for the Robotics Academy created an immersive learning environment with simulated robots and input devices while the curriculum team developed both a script introducing the fundamentals of industrial robotic safety and a series of self-directed activities for learning how to operate an industrial robot. To measure the effectiveness of our VR learning tool the evaluation team offered 45 minutes of self-directed learning using a VR headset to a test group of twenty-one second year architecture students with no prior knowledge or experience working with industrial robots. A control group of twenty-one second year architecture students with similar background received training using the same script paired with an image-based slide lecture in a traditional classroom setting, but they were not provided access to the VR training tool or practice time to work with a robot. Both groups were tested with a short quiz to assess their retention of key concepts from the script and a practicum test using a teach pendant input device for controlling an industrial robot. Finally, students were asked to rate their own level of confidence, self-reliance, and readiness to proceed to the next level of training. On the written test students showed similar rates of retention of key concepts from the training script with a modestly higher average score for in-person training over the VR training tool. However, in a series of timed exercises, students who used the VR training tool demonstrated higher levels of task accomplishment with fewer errors and faster completion times for practicum testing. Finally, those who used the VR training tool reported higher levels of self-confidence. While more learning outcome testing is necessary, these initial results indicate that immersive learning environments like our VR tool may be an effective method for educating the labor force for jobs that involve automation with technology such as industrial robots.
Databáze: OpenAIRE