About Us

Over the past five years at NDHU, we have developed related general physics experiments and established a new EMI classroom (物理系特色教室) at NDHU to achieve this goal. The project focuses on the 'General Physics Experiments' course, using it as a foundation to explore innovative teaching methods and materials. The course content has been developed in-house, emphasizing integrating Internet of Things (IoT) concepts into traditional physics experiments. These self-compiled materials span key areas of physics, including mechanics, optics, magnetism, and modern physics. The course introduces students to cutting-edge technology by incorporating IoT principles, offering a contemporary twist on classic physics experiments.

The curriculum is designed to be comprehensive, covering a broad range of topics essential for understanding fundamental and advanced physics concepts. Key areas include IoT basics, where students learn how everyday objects can be connected and controlled via the internet, and 3D printing technology, which allows students to create physical models of theoretical concepts. The mechanics section delves into Newton’s principles, providing a solid grounding in classical physics. In optics, students explore phenomena such as diffraction, refraction, and scattering, using IoT-enabled experiments to visualize and understand these concepts in real time.

The course also covers statistical methods, both classical and modern, giving students the tools to analyze experimental data effectively. Condensed matter physics is another critical area where students explore the physical properties of solids and liquids, using IoT technology to enhance their learning experience. To support the technological aspects of the course, students are introduced to programming with Python, a versatile language widely used in scientific computing, and Arduino, an open-source electronics platform that allows for the creation of interactive electronic devices.

The teaching resources are not limited to traditional methods. Instead, they are integrated into a custom-built 'General Physics Experiments' collaboration platform. This platform is a central hub for students to access various learning materials. These include instructional YouTube videos that guide them through complex experimental procedures and theoretical concepts, ensuring they clearly understand the tasks at hand. The platform also offers interactive features that enable students to collaborate with peers, ask questions, and receive feedback from instructors, fostering a more engaging and supportive learning environment.

In addition to the core content, the project provides supplementary teaching resources tailored to specific aspects of the experiments. For instance, students are given detailed instructions on 3D assembly processes, which are crucial for constructing the experimental setups used in the course. Furthermore, the platform includes resources on IoT circuit theories, ensuring that students thoroughly understand the electronics involved in their experiments. These resources are designed to be easily accessible, allowing students to review them at their own pace and revisit them whenever necessary.

Furthermore, integrating AI tools has introduced a new dimension of data analysis and visualization. In the classroom, students learn to utilize OpenCV or YoLov8 deep learning for image processing and Python for managing and analyzing large datasets. These tools enable real-time visualization of physical phenomena, providing immediate feedback on experimental design and execution. This real-time analysis fosters a more intuitive understanding of the studied concepts, as students can observe the direct effects of their experimental adjustments.

This project employs a comprehensive evaluation approach, integrating practical assessments with authentic evaluations and qualitative data collection, including learning feedback forms and focus group interviews. Assessment rubrics will ensure a thorough and effective evaluation of students' worksheets. The evaluation centers on three key aspects: the ability to identify core problems, the competence in applying experimental data analysis and fitting techniques to resolve experimental challenges, and the capacity to generate insights or propose solutions. By focusing on these areas, the project aims to confirm whether students have developed the necessary skills and attitudes to effectively apply their knowledge in real-world contexts, ensuring that they are not only learning theoretical concepts but are also able to translate them into practical problem-solving abilities. This holistic approach to assessment is designed to foster critical thinking and ensure that students are prepared to use their acquired knowledge effectively in practical applications.