Learning through augmented reality (AR) and virtual reality (VR) experiences has become a valuable approach in modern robotics education. This study evaluated this approach and investigated how 99 first-year industrial engineering students explored robot systems through such online experiences while staying at home. The objective was to examine learning in the AR/VR environment and evaluate its contribution to understanding the robot systems and to fostering integrative thinking. During the AR experiences that we developed using Vuforia Studio, the students learned about TurtleBot2 and RACECAR MN robots while disassembling and modifying their models and by obtaining information about their components. In the VR experience with the RacecarSim simulator, the students explored sensor-based robot navigation. Quizzes were used to assess understanding of robot systems, and a post-workshop questionnaire evaluated the workshop’s contribution to learning about the robots and to training integrative thinking skills. The data indicate that the students gained understanding of the robot systems, appreciated the contribution of the augmented and virtual reality apps, and widely used integrative thinking throughout the practice. Our study shows that AR apps and virtual simulators can be effectively used for experiential learning about robot systems in online courses. However, these experiences cannot replace practice with real robots.

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Student engagement has been described as active involvement in a learning activity that significantly affects learning achievement. This study investigated student engagement in robotics education, considering it as an instant emotional reaction on interaction with the teacher, the peers, and the robotic environment. The objective was to characterize engagement in high school robotics courses through the lenses of preparation for academic and technical careers. Students who participated in this study (N = 41), all of whom were in the eleventh grade, belonged to either School A (n 1 = 20) or School B (n 2 = 21). School A students studied only one subject at an advanced level—mechatronics, while each student in School B studied engineering systems as well as one of the following three subjects at an advanced level: computer science, a natural science subject, or mathematics. Data were collected via structured classroom observations, interviews, and a questionnaire. From the analysis of the collected data, we identified 23 engagement structures in total, 12 of which were already known in the literature, and 11 of which were novel. The two groups of students shared nine known structures, and no novel structures. Unlike previous studies of engagement structures, this study was based on an entire year of observations. Additionally, it is one of the first studies of high school student engagement in robotics education. Our findings and conclusions contribute to understanding of student engagement in robotic education, allowing robotics teachers to tailor their instruction more effectively.

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