Programming is considered a skill to arouse and inspire learner's potential. Learning to program is a complex process that requires students to write grammar and instructions. The structure of a programming language does not cause impose problems to students, the real obstacle is how to apply these learned grammars and present them in a complete and correct program code for problem solving. In this study, a deep learning recommendation system was developed, which includes augmented reality (AR) technology, and learning theory, and is provided for study by students in non-major and also from different learning backgrounds. Those students divided into two groups, the students participating in the experimental group were using the AR system with deep learning recommendation and the students participating in the control group were using the AR system without deep learning recommendation. The results show that students in experimental group perform better than the control group with regards to learning achievement. On the other hand, in the part of computational thinking ability, students using a deep learning recommendation based AR system is significantly better than those using non-deep learning recommendation based AR system. Among the various dimensions of computational thinking, creativity, logical computing, critical thinking, and problem-solving skills are significantly different among the two groups of students. The students in experimental group perform better than the control group with regards to the dimensions of computational thinking, creativity, logical computing, critical thinking, and problem-solving skills.INDEX TERMS Deep learning recommendation, computational thinking, AR technology.

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In this study, the head-mounted virtual reality (VR) technology is adpoted for computational thinking teaching in the AIoT Maker course teaching. The earthquake relief situation is designed in the VR in the course scenario, because in the context of situational thinking, pre-emptive training in the face of emergency disasters has been conducted through observation meetings or training courses. Through listening to lecturers or experienced personnel to share experiences, students often have a harder time thinking about real scenes and it is harder to think creatively how to design with the emergency disaster response. In view of this, this research will combine the development and evaluation of earthquake relief training courses for head-mounted VR and computational thinking experiments to explore the use of VR and computational thinking experiments to drive students to create ideas for real disaster relief scenarios. Through computational thinking, students think about different script situations and discuss in each scene to find a suitable maker design of the AIoT project. Finally, this study combined with its modular space program training to develop students' programming skills. According to the experiment, this study is able to strength students' practical learning motivation, and follow-up employ ability training for course learning.

As cloud computing and wearable devices technologies mature, relevant services have grown more and more popular in recent years. The healthcare field is one of the popular services for this technology that adopts wearable devices to sense signals of negative physiological events, and to notify users. The development and implementation of long-term healthcare monitoring that can prevent or quickly respond to the occurrence of disease and accidents present an interesting challenge for computing power and energy limits. This study proposed an adaptive sensor data segments selection method for wearable health care services, and considered the sensing frequency of the various signals from human body, as well as the data transmission among the devices. The healthcare service regulates the sensing frequency of devices by considering the overall cloud computing environment and the sensing variations of wearable health care services. The experimental results show that the proposed service can effectively transmit the sensing data and prolong the overall lifetime of health care services.