This study aims to identify the causes of delays in public construction projects in Thailand, a developing country. Increasing construction durations lead to higher costs, making it essential to pinpoint the causes of these delays. The research analyzed 30 public construction projects that encountered delays. Delay causes were categorized into four groups: contractor-related, client-related, supervisor-related, and external factors. A questionnaire was used to survey these causes, and the Relative Importance Index (RII) method was employed to prioritize them. The findings revealed that the primary cause of delays was contractor-related financial issues, such as cash flow problems, with an RII of 0.777 and a weighted value of 84.44%. The second most significant cause was labor issues, such as a shortage of workers during the harvest season or festivals, with an RII of 0.773. Additionally, various algorithms were used to compare the Relative Importance Index (RII) and four machine learning methods: Decision Tree (DT), Deep Learning, Neural Network, and Naïve Bayes. The Deep Learning model proved to be the most effective baseline model, achieving a 90.79% accuracy rate in identifying contractor-related financial issues as a cause of construction delays. This was followed by the Neural Network model, which had an accuracy rate of 90.26%. The Decision Tree model had an accuracy rate of 85.26%. The RII values ranged from 68.68% for the Naïve Bayes model to 77.70% for the highest RII model. The research results indicate that contractor financial liquidity and costs significantly impact construction operations, which public agencies must consider. Additionally, the availability of contractor labor is crucial for the continuity of projects. The accuracy and reliability of the data obtained using advanced data mining techniques demonstrate the effectiveness of these results. This can be efficiently utilized by stakeholders involved in construction projects in Thailand to enhance construction project management.

This research quantitatively examines how technology-mediated formative assessment techniques affect student learning outcomes in middle school education. The research investigates the correlation between instructors’ technology use, attitudes, and student performance in several academic disciplines using surveys and evaluations conducted with teachers and students. Results show strong positive connections between how often technology is used, the specific digital tools used, how effective technology-mediated formative assessment is judged to be, and the results of student learning. On the other hand, obstacles to implementation were shown to have a negative relationship with student accomplishment. The research emphasizes that technology-mediated formative assessment is more successful in some subjects, emphasizing the necessity to customize teaching methods for each subject’s requirements. The study revealed a positive correlation between student learning outcomes and the frequency of technology use, the types of digital tools used, and the perceived effectiveness of technology-mediated formative assessment. These results suggest ways to improve the use of technology and formative assessment in middle school instruction.

The digital era has ushered in significant advancements in Generative Artificial Intelligence (GAI), particularly through Generative Models and Large Language Models (LLMs) like ChatGPT, revolutionizing educational paradigms. This research, set against the backdrop of Society 5.0 and aimed at sustainable educational practices, utilizes qualitative analysis to explore the impact of Generative AI in various learning environments. It highlights the potential of LLMs to offer personalized learning experiences, democratize education, and enhance global educational outcomes. The study finds that Generative AI revitalizes learning methodologies and supports educational systems’ sustainability by catering to diverse learning needs and breaking down access barriers. In conclusion, the paper discusses the future educational strategies influenced by Generative AI, emphasizing the need for alignment with Society 5.0’s principles to foster adaptable and sustainable educational inclusion.

This study aimed to examine the impact of digital leadership among school principals and evaluate the mediating effect of Professional Learning Communities (PLCs) on enhancing teachers’ innovation skills for sustainable technology integration, both in traditional classroom settings and e-learning environments. Employing a quantitative approach with a regression design model, Structural Equation Modelling (SEM) and Partial Least Squares (PLS-SEM) were utilized in this research. A total of 257 teachers from 7 excellent senior high schools in Makassar city participated in the study, responding to the questionnaires administered. The study findings indicate that while principal digital leadership does not directly influence teachers’ innovation skills in technology integration, it directly impacts Professional Learning Communities (PLCs). Moreover, PLCs themselves have a significant influence on teachers’ innovation skills in technology integration. The structural model presented in this study illustrates a noteworthy impact of principal digital leadership on teachers’ innovation skills for technology integration through Professional Learning Communities (PLCs), with a coefficient value of 47.4%. Principal digital leadership is crucial in enhancing teachers’ innovation skills for sustainable technology integration, primarily by leveraging Professional Learning Communities (PLCs). As a result, principals must prioritize the creation of supportive learning environments and implement programs to foster teachers’ proficiency for sustainable technology integration. Additionally, teachers are encouraged to concentrate on communication, collaboration, and relationship-building with colleagues to exchange insights, address challenges, and devise solutions for integrating technology, thereby contributing to sustained school improvement efforts. Finally, this research provides insights for school leaders, policymakers, and educators, emphasizing the need to leverage PLCs to enhance teaching practices and student outcomes, particularly in sustainable technology integration.

The purpose of this study was to assess rural students’ computational thinking abilities. The following proofs were observed: (1) Students’ abstraction affected algorithmic thinking skills; (2) Students’ decomposition influenced algorithmic thinking skills; (3) Students’ abstraction impacted evaluation skills; (4) Students’ algorithmic thinking affected evaluation skills; (5) Students’ abstraction impacted generalization skills; (6) Students’ decomposition impacted generalization skills; (7) Students’ evaluation affected generalization skills. Gender differences were observed in the relationship among the computational thinking factors of junior high school students. This included the abstraction-generalization skills; evaluation-generalization skills; and decomposition-generalization skills relationships, which were moderated by the gender of the students. 258 valid surveys were collected, and they were utilized in the study. Conducting the descriptive, reliability, and validity analyses used SPSS software, and the structural equation modeling (SEM) was also conducted through Smart PLS software to assess the hypothetical relationships. There were gender disparities in the correlation among computational thinking components of the junior high school students’ studying in rural areas. Research has shown that male and female students may have different abstractions, evaluations, and generalizations related to computational thinking, with females being more strongly associated than males in non-programming learning contexts. These results are expected to provide relevant information in subsequent analyses and implement a computational thinking curriculum to overcome the still-existing gender gaps and promote computational thinking skills.

This research explores the advancement of Artificial Intelligence (AI) in Occupational Health and Safety (OHS) across high-risk industries, highlighting its pivotal role in mitigating the global incidence of occupational incidents and diseases, which result in approximately 2.3 million fatalities annually. Traditional OHS practices often fall short in completely preventing workplace incidents, primarily due to limitations in human-operated risk assessments and management. The integration of AI technologies has been instrumental in automating hazardous tasks, enhancing real-time monitoring, and improving decision-making through comprehensive data analysis. Specific AI applications discussed include drones and robots for risky operations, computer vision for environmental monitoring, and predictive analytics to pre-empt potential hazards. Additionally, AI-driven simulations are enhancing training protocols, significantly improving both the safety and efficiency of workers. Various studies supporting the effectiveness of these AI applications indicate marked improvements in risk management and incident prevention. By transitioning from reactive to proactive safety measures, the implementation of AI in OHS represents a transformative approach, aiming to substantially reduce the global burden of occupational injuries and fatalities in high-risk sectors.