As part of a series of technology courses, the school arranged in April a hands-on IoT-related project—the Siemens Separating Station Project—for students who aspire to study computer science in the future. This project represents a form of training that transforms abstract thinking into concrete application. Through systematic process design and the presentation of operational logic, learners are able to convert “logical thinking” into a visualized operational model.
Such training not only strengthens students’ understanding of cause-and-effect relationships and process control, but also directly corresponds to core programming concepts, including conditional statements (if/else), flow control, and event-driven systems.
Furthermore, this type of visualized logic training effectively promotes programming development skills. Learners construct “algorithmic thinking” at a physical or graphical level, allowing them to more efficiently translate concepts into executable code. At the same time, it provides a foundational basis for future AI development capabilities. The training and application of AI models fundamentally rely on clear data flow, decision-making logic, and problem decomposition—skills that are reinforced through this kind of training.
Student Reflection
This learning opportunity was particularly valuable. Compared to previous experiences in learning programming languages, which mainly focused on logic and computation, the uniqueness of this project lies in its direct connection between the LOGO system and real-world industrial technology.
In my view, the most challenging aspect of learning to use LOGO was not the programming itself, but the hardware configuration. The setup and operation of the LOGO system were quite complex. During the process, the teacher repeatedly required us to follow extremely cautious steps, such as “closing the software, disconnecting, and reinitializing.”
When I finally overcame these configuration challenges and observed the simulator executing tasks repeatedly with precision, the sense of achievement was immense. This feeling came from countless attempts, failures, and adjustments. When the program eventually ran as expected, it brought a deep sense of relief.
This experience was not merely about completing a task; it was also a hands-on exploration of the rigorous operational logic required in the field of industrial automation.
