DIGITAL SENSOR LABORATORIES AS A TOOL FOR TEACHING THE PHYSICAL FOUNDATIONS OF IOT SYSTEMS
DOI:
https://doi.org/10.31110/2616-650X-vol14i3-019Keywords:
physical foundations of IoT systems, teaching aids, digital laboratories, microcontroller kits, smartphone as a sensor platform, measurement error and uncertainty, IT training, physics education, vocational educationAbstract
The article explores the possibilities of using digital sensor laboratories as technical teaching aids for developing an understanding of the physical foundations of IoT systems. The focus is placed on two of the most accessible instrumental configurations: microcontroller kits (Arduino/ESP32 with sensor modules) and the smartphone as a sensor platform (IMU/microphone/camera, etc.). The review is conducted in the format of a conceptually oriented scoping review, which allows integrating engineering and pedagogical sources with meteorological standards and data transmission protocols. The main result of the study is a correspondence matrix “physical concept – type of teaching tool – data artifact – IoT interpretation,” which systematizes how, through the mentioned instrumental configurations, sensor principles, error and uncertainty, calibration and drift, noise and filtering, discretization and quantization, dynamic range, timestamps and synchronization, transmission channel reliability, and energy consumption trade-offs are addressed. The scientific novelty of the study lies in presenting these practices in a matrix suitable for designing lessons with a predetermined logic of transition from measurement physics to systemic consequences for IoT data (quality, stability, latency, and losses), without appealing to empirical verification of effectiveness. Future research should focus on empirically testing how the proposed matrix performs in real-world educational settings: it would be useful to assess whether it improves the quality of students’ explanations and their ability to justify conclusions based on data rather than on intuitive judgments; It is worth analyzing how this influences the development of students’ critical attitude toward data quality (whether real-world measurements on smartphones help students better appreciate the role of environmental conditions, and whether the controllability of measurements on microcontrollers helps them better understand the procedure and limits of accuracy).
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