- Accepted Paper
Development of a physical-digital learning environment for rotational motion transmission in engineering education
Phys. Rev. Phys. Educ. Res. - Accepted 6 October, 2026
DOI: https://doi.org/10.1103/gddt-c1n3
Phys. Rev. Phys. Educ. Res. - Accepted 6 October, 2026
DOI: https://doi.org/10.1103/gddt-c1n3
This study presents the design and evaluation of a Physical–Digital learning environment for teaching rotational motion transmission in introductory engineering education. The environment integrates SimulaGear, an interactive digital simulation for analyzing gear systems, with a Physical-IoT Experimental System composed of a 3D-printed gear mechanism, infrared sensors, ESP32-based data acquisition, MQTT communication, and real-time visualization through Node-RED dashboards. A quasi-experimental pretest–posttest design was implemented with 37 first-year engineering students enrolled in introductory physics courses. The control group worked exclusively with the SimulaGear digital simulation, whereas the experimental group participated in a Physical–Digital experience that combined SimulaGear with the Physical-IoT Experimental System. Conceptual understanding, student engagement, and technology acceptance were evaluated using a conceptual test, the UWES-S engagement scale, and a survey based on the Technology Acceptance Model. Results showed that both groups improved their conceptual understanding after the intervention, with the group that used the Physical–Digital system showing a specific significant improvement in transmission ratios in multiple-gear systems. In addition, the Physical–Digital experience produced significant gains in vigor and overall engagement, while the digital-only experience did not show significant changes in engagement. Technology acceptance was high in both groups, but significantly higher in the experimental group. These findings suggest that integrating Physical-IoT systems with digital simulations can strengthen students’ engagement and technology acceptance while supporting the interpretation of complex mechanical configurations through the comparison of theoretical predictions and real-time experimental data.
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