Developing a visual IoT environment analysis system to support self-directed learning of students

Applying the Internet of Things (IoT) is a way to link things through the Internet, thus creating work automation advantages. Therefore, program development regarding the IoT is becoming a field that attracts researchers and teachers to explore. However, low-level programming tools are often used in the learning process, which are not always user-friendly for beginners. Therefore, this study proposes a set of visual environmental analysis systems of self-directed learning courses to write instructions by dragging and dropping building blocks to reduce beginners’ learning obstacles. Additionally, this system uses the concept of self-directed learning in the study design. As such, we expect it to assist students in adjusting their learning speed to make learning personalized and reduce the situation of different states during unified learning. Moreover, this system also records all data detected. It presents the charts drawn after data analysis through web pages to increase students’ mastery of environmental changes at various time points. This study designed a complete experiment to explore the differences in the learning effects of this system on students.