The rapid integration of Industry 4.0 technologies into non-computer engineering curricula presents a significant pedagogical challenge: avoiding a “one-size-fits-all” approach. While Project-Based Learning (PBL) is widely advocated for teaching Internet of Things (IoT), little research addresses how students from different engineering branches—specifically Industrial, Environmental, and Electrical—respond to identical technical requirements. This study evaluates the deployment of ESP32-based IoT solutions for local agriculture and beekeeping problems in the Peruvian Andes, analyzing the performance and perception of three distinct student cohorts (Total N = 95). Results indicate a significant divergence in learning outcomes and satisfaction. The cohort predominantly composed of Industrial Engineering students (NRC-33563) demonstrated lower adherence to technical code modularization (88% vs. 97%) and lower overall course recommendation rates compared to the mixed cohorts (NRC-33562/33561), who reported higher engagement with the hardware implementation. These findings suggest that while Environmental and Electrical engineering students naturally align with the sensing and actuation layers of IoT, Industrial engineering students may require a curriculum that emphasizes process optimization and data analytics over raw firmware development. We propose a differentiated pedagogical framework to maximize engagement and competency acquisition across diverse engineering disciplines.
Guevara et al. (2026) studied this question.