Development of an ESP32-Based Internet of Things Trainer for Sensor and Transducer Learning in Electrical Engineering Education
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Low student motivation and the absence of integrated IoT-based learning media in the Sensor and Transducer Practicum course represent a critical gap that limits students' ability to connect theoretical concepts with industrial practice. This study aimed to realize an ESP32-based IoT Trainer, evaluate its technical performance, and assess its feasibility as a learning medium. The Research and Development method with the ADDIE model was employed. The developed product consists of an integrated trainer unit with nine sensors, a learning module, and jobsheet. Testing involved two content experts, two media experts, and 30 students using a 5-point Likert scale instrument. All trainer components functioned stably across all test conditions. Validation results showed 92.5% feasibility from content experts, 93.75% from media experts, and 90.5% from users, with an overall average of 92.25%, all categorized as "Very Feasible." These findings indicate that the trainer effectively enhances students' hands-on engagement with IoT-integrated sensor systems, bridging the gap between classroom theory and real-world industrial competencies.
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