This project is rooted in the need for a mobile device to use outside of my future science classroom to measure, collect, and compile weather and water data. Incorporating this device into a secondary school classroom will not only allow students to use the weather station, they will also integrate engineering practices into their science education by building the weather station in small teams. Intelligent weather stations have been around for many years, often functioning on a large scale to collect weather, wind, and solar data to be used for forecasts, energy management, agriculture, and in the planning of clean energy projects. These tools have also been named automatic weather stations, they have been developed to function wirelessly and to remotely send their collected data to be stored or displayed for research and development purposes. Weather stations typically consist of an array of sensors to measure temperature, humidity, wind speed and direction, solar radiation, and atmospheric pressure. They also include a battery to power the station and a base that processes collected data and has the capabilities to send and/or store the data. Currently, the Python 3 code to implement individual sensors is complete. The device is able to upload data to the cloud using ThingSpeak. In addition, I have begun prototyping and testing possible circuit designs for the final model. Due to limitations of the Raspberry Pi's ability to supply power to multiple sensors in parallel, device calls must be done sequentially and with regard to time. The design, development, and use of a wireless weather station device can be used in science, technology, engineering, and math (STEM) experiences to introduce secondary students to the science and engineering practices laid out in the Next Generation Science Standards (NGSS). After the completion of this project in my future science class, students will have experience using engineering practices and problem-solving skills to assemble a simple IoT device. Thus far, I have encountered obstacles that students will likely face, such as limitations in the required power of all sensors, and I am now better equipped to assist them when these problems arise.
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Bowen et al. (2024) studied this question.
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