To explore the impact of augmented reality digital picture books in environmental education courses on environmental attitudes and environmental behaviors of children from different cultures.

Introduction: Environmental education has long been closely related to sustainable development. In this study, in response to the United Nations Sustainable Development Goals (SDGs), an augmented reality (AR) digital picture book was created using the unique natural ecosystem of Taiwan's Orchid Island as a source of self-efficacy for science learning. Methods: Interactive environmental education learning through AR drawing was used to determine whether students' science learning self-efficacy and environmental attitudes significantly influenced the environmental behavioral skills of culturally diverse children. In this study, 26 elementary sixth-grade Taiwanese students and 26 elementary sixth-grade Japanese students were invited to participate in an extended reality drawing activity as an environmental education curriculum. Results: Based on the sample size of 52, the survey results were accurate with a sampling error of 3.8% with a confidence level of 95%. A questionnaire survey was administered to the 52 students after the event. After the valid questionnaire samples were collected, a partial least squares structural equation modeling (PLS-SEM) analysis was conducted with Smart PLS 3.0 on the small sample. The results of the study showed that students who had a better self-efficacy in science learning were more likely to engage in conservation actions related to the natural environment in their daily lives. Discussion: In this study, the constructs of environmental behavior were further discussed and the hypothesis model was validated using the quantitative empirical method. The results of the study revealed good reliability, convergent validity, and discriminant validity of the constructs in the hypothesis model, and the hypothesis model itself was validated. In the validated model, students' science learning self-efficacy affects the sustainability of their environmental behaviors, but only through the role of environmental attitudes. However, the environmental attitudes construct plays a fully mediating role in the model.

Immersive Virtual Reality Serious Games With DL-Assisted Learning in High-Rise Fire Evacuation on Fire Safety Training and Research.

In case of fires in high-rise buildings, appropriate safe behaviors for leaving the high floors are the key to reducing injuries and increasing the chance of survival. Traditional training methods are often used to provide knowledge about a fire escape but may become ineffective in terms of knowledge acquisition and internalization. Serious games are an innovative teaching method, aiming at training and educating people in game environments. In recent years, immersive virtual reality has become popular in many educational environments. Various educational training programs are combined with serious games and attract more and more attention because they can make users feel highly involved and promote learning cognition. Therefore, this study proposed the fire safety training of high-rise building fire escape based on virtual reality and invited 140 college students to make explorations through this virtual situation. In addition, deep learning was integrated into the recommended safety training system, so that students could be trained in areas where concepts were ill-defined. According to the results, through the high-rise building fire escape training based on virtual reality, students' fire safety skills were significantly improved and most students could use their behavioral skills in real situations, which has positive effects on promoting the development of fire escape knowledge. Finally, according to the analysis on the results of the DL-assisted learning system, some suggestions were made in this study on behavioral skills training for professional firefighters and researchers.

Children's Digital Art Ability Training System Based on AI-Assisted Learning: A Case Study of Drawing Color Perception.

This study proposed a children's digital art ability training system with artificial intelligence-assisted learning (AI-assisted learning), which was designed to achieve the goal of improving children's drawing ability. AI technology was introduced for outline recognition, hue color matching, and color ratio calculation to machine train students' cognition of chromatics, and smart glasses were used to view actual augmented reality paintings to enhance the effectiveness of improving elementary school students' imagination and painting performance through the diversified stimulation of colors. This study adopted the quasi-experimental research method and designs the pre-test and post-test for different groups. The research subjects are the Grade 4 students of an elementary school in Taitung City, Taiwan. The test tools included an imagination test and an evaluation of painting performance ability. The test results of a total of 30 students before and after the experiment included the experimental group that received the children's digital art ability training system with AI-assisted learning and 30 students in the control group that had not received the teaching were analyzed by covariance. These results were supplemented by the description and interpretation of student feedback, teachers' reflection notes, and other qualitative data to understand the performance of the students in the experimental group in terms of imagination and painting performance.

Exploring Computational Thinking Skills Training Through Augmented Reality and AIoT Learning.

Given the widespread acceptance of computational thinking (CT) in educational systems around the world, primary and higher education has begun thinking about how to cultivate students' CT competences. The artificial intelligence of things (AIoT) combines artificial intelligence (AI) and the Internet of things (IoT) and involves integrating sensing technologies at the lowest level with relevant algorithms in order to solve real-world problems. Thus, it has now become a popular technological application for CT training. In this study, a novel AIoT learning with Augmented Reality (AR) technology was proposed and explored the effect of CT skills. The students used AR applications to understand AIoT applications in practice, attempted the placement of different AR sensors in actual scenarios, and further generalized and designed algorithms. Based on the results of the experimental course, we explored the influence of prior knowledge and usage intention on students' CT competence training. The results show that proposed AIoT learning can increase students' learning intention and that they had a positive impact on problem solving and comprehension with AR technology, as well as application planning and design.

Research on Head-Mounted Virtual Reality and Computational Thinking Experiments to Improve the Learning Effect of AIoT Maker Course: Case of Earthquake Relief Scenes.

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.

An Adaptive Sensor Data Segments Selection Method for Wearable Health Care Services.

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.