This study addresses the rising concerns of technostress experienced by teachers due to the increased reliance on educational technology in both classroom and online settings. Technostress, defined as the adverse psychological effects arising from the use of information communication technologies, has been documented to impact teacher performance and overall well-being. Despite the importance of educational technology in enhancing teaching and learning experiences, many educators report elevated levels of anxiety, stress, and pressures associated with their use of these tools. This study presents practical strategies to help teachers alleviate or prevent technostress while using educational technology. This study used a quantitative approach with a survey conducted among 113 university and schoolteachers. The data analysis included frequency and percentage distribution of categorical variables, Cronbach's alpha for reliability, chi-square test, and exploratory factor analysis to identify strategies for symptom prevention. The results indicated that while many teachers experienced symptoms of technostress due to several factors, some did not. The study concluded with specific strategies, and many teachers agreed highly. The implications of this study are profound for educational institutions, policymakers, and teacher training programs as they underscore the necessity of providing comprehensive training, support, and resources to help educators manage technostress effectively. By integrating these strategies into professional developmental programs and fostering a supportive teaching environment, schools and universities can promote better mental health for teachers, improving students' educational outcomes.

In the current era of globalization, the need arises to train individuals who are spiritually enriched, creatively developed, and culturally grounded through the advancement of education and science, as well as through art and culture. These individuals must be capable of integrating artistic creativity into their professional activities. In this context, the issue of fostering values of historical and cultural significance through virtual reality technologies emerges as a novel area of research. The study aims to reveal the essence of the concept "virtual museum" and test the level of perspective art teachers' readiness for utilizing the virtual museum in their professional activity to foster their cultural values of artic creativity. Employing quantitative and qualitative methods, the study encompassed questionnaires, tests, and assignments administered to 135 university students divided into control and experimental groups. To diagnose students' readiness to utilize virtual museum technology in their professional activities, three components (motivational, cognitive, and operational), criteria, indicators and levels of readiness were identified. Findings indicate that there is a noticeable difference between the experimental group's results before and after completing the authors' elective course titled "Methodology of using the virtual museum". This demonstrates the effectiveness of this course conducted with the experimental group. The study highlights the importance of perspective art teachers' acquisition of knowledge, skills and competences necessary to implement the virtual museum method in their teaching activity through the proposed elective course incorporated into the university educational process in order to foster students' cultural values of artic creativity.

This study, through the method of canonical correlation analysis, revealed significant correlations between various dimensions of learning attitudes of students and various dimensions of teacher knowledge. An analysis of data from a group of 221 high school students showed that teacher knowledge of teaching content, theoretical knowledge, and teaching practice and classroom management significantly impact learning attitudes of students. Specifically, teacher knowledge of teaching content plays a crucial role in promoting students' behavioral inclination to learn chemistry, teachers' theoretical knowledge significantly enhances students' liking for chemistry laboratory courses, while teachers' teaching practice and classroom management have a suppressive effect on students' evaluative beliefs about school chemistry. The results of this study provide effective guidance for both the theory and practice of high school chemistry education.

Sustainability has become increasingly important in recent decades and has become a key concept in various areas of society. The early integration of sustainability principles into education is of crucial importance, as the elementary school years represent a decisive phase in children's development. During this phase, fundamental values, attitudes, and behaviors are formed that will have a significant impact on later lives and the relationship with the environment. Elementary school offer a unique opportunity to reach people from different social backgrounds and thus impart a common basic knowledge that can serve as a basis for shaping a sustainable society. Elementary schools are therefore an ideal place to introduce children to the principles of sustainability and sensitize them to the challenges of the 21st century. The aim of the study is to explore the current state of sustainability education in elementary school. It takes a closer look at whether elementary school students are old enough to be confronted with sustainability, what methods already exist and what the challenges are in implementing sustainability education. The basis for the study is an online survey conducted at 60 different elementary school in the state of Baden-Wuerttemberg in Germany. In conclusion, while there is room for improvement, the survey results suggest a growing awareness of the significance of sustainability education in elementary schools. The findings call for targeted efforts to enhance curriculum integration, teacher training, and resource provision to promote a more sustainable and environmentally conscious generation of students in Baden-Wuerttemberg.

This study comprehensively evaluates the system performance by considering the thermodynamic and exergy analysis of hydrogen production by the water electrolysis method. Energy inputs, hydrogen and oxygen production capacities, exergy balance, and losses of the electrolyzer system were examined in detail. In the study, most of the energy losses are due to heat losses and electrochemical conversion processes. It has also been observed that increased electrical input increases the production of hydrogen and oxygen, but after a certain point, the rate of efficiency increase slows down. According to the exergy analysis, it was determined that the largest energy input of the system was electricity, hydrogen stood out as the main product, and oxygen and exergy losses were important factors affecting the system performance. The results, in line with other studies in the literature, show that the integration of advanced materials, low-resistance electrodes, heat recovery systems, and renewable energy is critical to increasing the efficiency of electrolyzer systems and minimizing energy losses. The modeling results reveal that machine learning programs have significant potential to achieve high accuracy in electrolysis performance estimation and process view. This study aims to contribute to the production of growth generation technologies and will shed light on global and technological regional decision-making for sustainable energy policies as it expands.

The destructive geohazard of landslides produces significant economic and environmental damages and social effects. State-of-the-art advances in landslide detection and monitoring are made possible through the integration of increased Earth Observation (EO) technologies and Deep Learning (DL) methods with traditional mapping methods. This assessment examines the EO and DL union for landslide detection by summarizing knowledge from more than 500 scholarly works. The research included examinations of studies that combined satellite remote sensing information, including Synthetic Aperture Radar (SAR) and multispectral imaging, with up-to-date Deep Learning models, particularly Convolutional Neural Networks (CNNs) and their U-Net versions. The research categorizes the examined studies into groups based on their methodological development, spatial extent, and validation techniques. Real-time EO data monitoring capabilities become more extensive through their use, but DL models perform automated feature recognition, which enhances accuracy in detection tasks. The research faces three critical problems: the deficiency of training data quantity for building stable models, the need to improve understanding of AI's predictions, and its capacity to function across diverse geographical landscapes. We introduce a combined approach that uses multi-source EO data alongside DL models incorporating physical laws to improve the evaluation and transferability between different platforms. Incorporating explainable AI (XAI) technology and active learning methods reduces the uninterpretable aspects of deep learning models, thereby improving the trustworthiness of automated landslide maps. The review highlights the need for a common agreement on datasets, benchmark standards, and interdisciplinary team efforts to advance the research topic. Research efforts in the future must combine semi-supervised learning approaches with synthetic data creation and real-time hazardous event predictions to optimise EO-DL framework deployments regarding landslide danger management. This study integrates EO and AI analysis methods to develop future landslide surveillance systems that aid in reducing disasters amid the current acceleration of climate change.