Search Results (434)

The transformative function of data-driven leadership in higher education institutions (HEIs) is becoming crucial for advancing sustainable development. By integrating data-driven decision-making with Sustainable Development Goals (SDGs), particularly SDG4 (quality education) and SDG10 (reduced inequalities), EIs can improve the efficacy, inclusivity, and employability of their graduates. To examine this influence, this study implements a systematic literature review (SLR) that adheres to the PRISMA standards and integrates empirical and theoretical insights regarding data-driven leadership in HEI governance, teaching, and learning strategies. The results indicate that combining data analytics into decision-making processes improves institutional efficacy, aligns curricula with the market demands, strengthens student outcomes, and cultivates an inclusive and sustainable academic environment. Moreover, this study introduces a conceptual model connecting sustainable development and data-driven decision-making, offering a structured framework for HEIs to navigate digital transformation responsibly. In addition, this model also emphasizes the importance of balancing technology, ethics, and human-centric leadership in developing educational institutions that are prepared for the future. Ultimately, these insights provide practical advice for academic leaders and policymakers aligning HEI strategies with global sustainability objectives. By advocating for innovative, inclusive, and data-driven leadership, HEIs can promote long-term societal transformation and higher education excellence. Full article

Environmental literacy plays a crucial role in promoting sustainable behavior and increasing public participation in environmental protection. This study investigates the environmental literacy of the general public in Chiayi County, Taiwan, focusing on five key dimensions: environmental awareness, knowledge, attitudes, action skills, and behavior. A cross-sectional survey was conducted using a structured questionnaire, with data analyzed through SPSS, including descriptive statistics, factor analysis, reliability testing, hierarchical regression analysis, and moderation analysis. The results indicate that while respondents demonstrate high awareness of environmental issues and positive attitudes toward sustainability, there are significant gaps in environmental knowledge and action skills. Furthermore, demographic factors such as education, age, gender, and occupation moderate the relationships between these dimensions. These findings highlight the need for targeted educational initiatives and policy interventions to bridge the gap between awareness and actual environmental behavior. This study provides empirical insights for environmental education programs, emphasizing the importance of practical skill development, community engagement, and policy-driven support. By refining environmental education strategies, Taiwan can foster a more environmentally responsible society, contributing to long-term sustainability goals. Full article

Background: Children with disabilities, particularly in low- and middle-income countries (LMICs), face heightened risks of vaccine-preventable diseases due to a range of systemic and social barriers. Although immunization is a fundamental human right and a proven public health intervention, this vulnerable group is often overlooked in policy and practice. Understanding the factors compromising vaccine equity for these children is critical to reducing zero-dose prevalence and improving health outcomes. Methods: This scoping review examined peer-reviewed, gray literature from 2010 to 2024. Searches were conducted in PubMed, Google Scholar, and relevant organizational reports (WHO, UNICEF). Studies addressing children with disabilities and focusing on immunization barriers, interventions, or lessons learned were selected. English-language publications were screened in title/abstract and full-text stages. Key data extracted included population, barriers, and immunization outcomes. Since this review focused on articles in English, this is a key limitation. Results were synthesized thematically to identify recurring patterns and to guide improved interventions and policies. Results: Twelve articles met the inclusion criteria. Key barriers identified were inadequate healthcare infrastructure, insufficient provider training, limited follow-up services in rural regions, societal stigma, and pervasive misconceptions around both disability and vaccines. Factors such as maternal education, logistical support for caregivers, and using low-sensory, inclusive vaccination settings were consistently linked with better outcomes. Effective strategies included mobile vaccination units, tailored interventions (e.g., distraction or sedation techniques), school-based immunization programs, and robust community engagement to address stigma. Lessons learned underscored the importance of flexible, individualized care plans and empowering families through transparent communication. Conclusions: Children with disabilities continue to experience significant gaps in immunization coverage, driven by intersecting barriers at the individual, health system, and societal levels. Scaling tailored interventions, inclusive policies, strengthened infrastructure, and ongoing research can help ensure these children receive equitable access to life-saving vaccinations. Full article

The expectations for the (re)use of open government data (OGD) are high. However, measuring their impact remains challenging, as their effects are not solely economic but also long-term and spread across multiple domains. To accurately assess these impacts, we must first understand where they occur. This research presents a structured approach to developing a taxonomy for open government data (OGD) impact areas using machine learning-driven topic modeling and iterative taxonomy refinement. By analyzing a dataset of 697 OGD use cases, we employed various machine learning techniques—including Latent Dirichlet Allocation (LDA), Non-Negative Matrix Factorization (NMF), and Hierarchical Dirichlet Process (HDP)—to extract thematic categories and construct a structured taxonomy. The final taxonomy comprises seven high-level dimensions: Society, Health, Infrastructure, Education, Innovation, Governance, and Environment, each with specific subdomains and characteristics. Our findings reveal that OGD’s impact extends beyond governance and transparency, influencing education, sustainability, and public services. Our approach provides a scalable and data-driven methodology for categorizing OGD impact areas compared to previous research that relies on predefined classifications or manual taxonomies. However, the study has limitations, including a relatively small dataset, brief use cases, and the inherent subjectivity of taxonomic classification, which requires further validation by domain experts. This research contributes to the systematic assessment of OGD initiatives and provides a foundational framework for policymakers and researchers aiming to maximize the benefits of open data. Full article

This study presents a multi-user virtual reality (VR) tool designed to enhance hands-on learning in engineering education through real-time sensorized digital twins. The motivation stems from the limitations of traditional laboratory settings, such as time constraints and restricted access to physical equipment, which can hinder practical learning. The developed environment allows multiple students, wearing VR headsets, to interact simultaneously with a real-time synchronized virtual model of an engine, replicating its physical counterpart at the Mechanical Engineering Laboratory of the Pontificia Universidad Católica de Valparaíso, Chile. This novel integration of VR and digital twin technology offers students a unique opportunity to observe engine behavior in operation within a safe, controlled virtual space. By bridging theoretical knowledge with practical experience, this approach deepens understanding of complex mechanical concepts while fostering the development of key technical skills. Additionally, the use of real-time data visualization and digital twins provides a safer, more interactive, and efficient alternative to traditional laboratory practices, overcoming constraints like time limitations and equipment availability. This innovative method introduces students to Industry 4.0 principles, encouraging data-driven analysis and informed decision making. Full article

The transition of global agriculture from yield-driven production to quality-driven systems has gained urgency, where premium pricing strategies offer pathways to enhance farmer incomes and promote sustainable practices. As a critical staple crop, rice exemplifies the challenges of aligning producer standards with consumer preferences to realize market premiums. This study systematically evaluates determinants of consumers’ willingness to pay (WTP) for premium rice, integrating analyses of attribute preferences, cognition perception, and purchasing experience. Utilizing survey data from 1714 consumers across four Chinese cities, we employ principal component analysis to identify key quality dimensions and ordered logit models to quantify their impacts. Hedonic pricing theory informs the estimation of implicit prices for specific attributes. The results reveal that intrinsic characteristics (like nutrition) and extrinsic cues (like the brand), along with consumers’ nutritional awareness, knowledge, and perceptions of quality-price correlation, jointly drive premium WTP. The mean acceptable premium reaches 4.52 yuan/500 g, with nutritional attention enhancements commanding the highest valuation (0.171 yuan/500 g). The findings underscore the necessity of standardized quality grading systems aligned with consumer preferences and targeted interventions to bridge information asymmetries. Policymakers are recommended to improve supply-side quality signaling through enhanced packaging and certification systems while strengthening demand-side nutrition education to facilitate value chain coordination and sustainable, high-quality development in agriculture. Full article

This study investigates the perceptions of artificial intelligence (AI) among undergraduate construction management students who are poised to become future leaders in the construction industry. As the industry increasingly adopts AI technologies to enhance project planning, design, site management, and safety, understanding students’ attitudes toward these innovations becomes crucial. This research employs a mixed-methods approach, combining quantitative survey data with qualitative data to capture the students’ insights on AI’s potential applications, benefits, and challenges within the construction sector. Findings indicate that while students recognize AI’s transformative potential to improve efficiency and safety in construction processes, they also express concerns regarding ethical implications, job displacement, and the necessity of new skills to effectively integrate AI into their future careers. Additionally, this study reveals a significant gap in students’ knowledge about AI technologies and their applications in the construction industry. These insights underscore the importance of incorporating AI-focused curricula in construction management programs to better prepare students for the evolving landscape of the industry. Ultimately, this research contributes to the understanding of how the next generation of construction professionals perceives AI and highlights the need for educational institutions to adapt their programs to equip students with the competencies required for a technology-driven future. Full article

Advances in telecommunications and artificial intelligence (AI) are reshaping modern educational spaces. Drawing upon diverse resources, this systematic literature review examines how these new advances including 5G, Internet of Things (IoT), and AI-based analytics can transform conventional classrooms into adaptive, secure, and highly interactive environments. Real-time data collection and personalized feedback systems are found to significantly enhance engagement and accessibility for diverse learner populations. Furthermore, emerging security architectures, such as zero-trust frameworks and AI-driven intrusion detection, mitigate cyber threats and strengthen data confidentiality. Nevertheless, it is found that broader adoption is limited due to practical hurdles, which include budget allocation, professional development, and regulatory compliance. In response, strategic recommendations are provided to guide the planning and implementation of intelligent telecommunications in different educational contexts while noting the need for responsible data governance and equitable access. By illustrating how AI-assisted connectivity can enhance personalized instruction while safeguarding learner privacy, this study offers a forward-looking perspective on modern pedagogical approaches which can balance technological innovation with ethical considerations. Full article

The integration of artificial intelligence in education has the potential to revolutionize personalized learning by adapting instructional methods, content, and pace to the individual needs of students. This systematic review investigates the integration of artificial intelligence into personalized learning within higher education. An extensive literature search was conducted across multiple databases, yielding 17,899 records from which 45 studies met the inclusion criteria. The risk of bias was assessed using a standardized ranking system. This systematic review follows the PRISMA guidelines to ensure transparency in study selection, data extraction, and synthesis. The findings of the review are synthesized to examine how AI-driven solutions enhance adaptive learning, improve student engagement, and streamline administrative processes. The results indicate that AI technologies can significantly optimize educational outcomes by tailoring content and feedback to individual learner needs. However, several challenges persist, such as ethical concerns, data privacy issues, and the necessity for effective teacher training to support technology integration. This analysis reveals considerable potential for AI to transform educational practices, while also emphasizing the importance of establishing standardized evaluation frameworks and conducting longitudinal studies. The implications of these findings are critical for educators, policymakers, and university administrators aiming to leverage AI for educational innovation and sustainable transformation. Full article

Most people in the world now live in urban areas and their shared quest for better cities is embodied in several Sustainable Development Goals of the United Nations. These indicate that successful cities need jobs, adequate housing stock, effective governance, and other support systems. At the most basic level, they need a basket of core public works services like clean water and efficient transit, among others. These must be provided to improve public trust in government by addressing equity and affordability while also improving operational and cost efficiency. These targets are moving as transitions are occurring from stove-piped to integrated services, even while social contracts between government and the private sector are also shifting. Essential tools to improve cities include urban planning and infrastructure development, but applying them effectively faces challenges like climate change, inequality, social disorder, and even armed conflicts. This paper focuses on seven core public works services for drinking water, wastewater, stormwater, trash collection, mass transit, streets and traffic control, and disaster management. It reviews how these have evolved in the US, how they are organized under the federalism system, and how the goal of integrated management is being pursued. Challenges to integrated approaches include increasing responsibilities but lack of funding, political stress, and rule-driven and internally oriented management. Methods for performance assessment are explained under legacy systems based on methods like indicators and benchmarking applied to public works systems. Current methods focus on regulatory targets and the details; information has been shallow and not always timely. This paper projects how the performance assessment of core public works systems can be broadened to address goals like those of the SDGs and assesses why it is difficult to rate major systems. Examples of the activities of NGOs are given and an example of how progress toward SDG6 is included to show why performance management of integrated management applied to linked systems is needed. Performance dashboards with open government are currently the most common pathways, but emerging methods based on data analytics and visualization offer new possibilities. Reviewing the status of public works management shows that it is an important branch of the field of public administration, and it can be presented as a professional field with its own identity. The findings will support educators and researchers as well as provide policy insights into public works and stakeholder engagement. Full article

Implementation science (IS) models play a crucial role in translating evidence-based practice (EBP) into sustainable policy reforms. However, the competencies required for nurses to lead these transformations remain poorly defined. Objective: This study develops a framework for implementation lead (IL) nurses, identifying the core competencies needed to drive evidence-based policy transformation within healthcare systems. Method: A secondary data analysis (SDA) was conducted using qualitative data from focus group interviews originally collected, recorded, and transcribed as part of the EQUIP (Evidence-based Quality Improvement Project). The dataset includes insights from 12 IL nurses who participated in PEACE-based training, addressing real-world clinical challenges. Their perspectives were thematically analyzed to generate a competency framework for policy leadership. Findings: The study developed the Implementation Science Competencies for Policy Transformation (ISCPT) framework, which highlights three pillars: (1) evidence appraisal and guideline development, (2) collaborative leadership for policy advocacy, and (3) continuous improvement through data-driven decision-making. Conclusions: Grounded in IL nurses’ perspectives, the ISCPT framework provides a nurse-centric roadmap for policy transformation, integrating interdisciplinary collaboration, adaptive leadership, and evidence-based decision-making into nursing education and practice. While the findings reflect a single healthcare context, the framework offers actionable guidance for preparing nurses to lead policy-driven healthcare improvements. Full article

Science, technology, engineering, and mathematics (STEM) are crucial for economic development and play a significant role in achieving sustainable development goals. Despite this, there is a shortage of skilled STEM professionals and a declining interest in STEM education and careers. The Saudi Vision 2030 goal of economic diversification and sustainable development aims to transform Saudi Arabia into a knowledge-based economy driven by innovation and sustainability. This study investigates factors influencing adolescents’ attitudes toward STEM careers in Saudi Arabia, with comparative insights from Bahrain and Singapore. Structural equation models (SEM) were constructed for each country to analyze the influence of scientific self-concept, school belonging, and teacher effectiveness on students’ choices of science careers. Mediation analysis examined the interest and value of science as mediators in these relationships. Confirmatory factor analysis was conducted to validate model constructs before building SEM models. Data from TIMSS 2019 for eighth-grade students was used to develop model constructs based on relevant items from the student questionnaire. Findings reveal that students’ interest in and value of science significantly influence career decisions, with self-concept and teacher engagement playing crucial roles. Teacher effectiveness had the strongest impact on science interest in Saudi Arabia and Bahrain, while self-concept was most influential in Singapore. These results highlight the importance of fostering teacher engagement and self-concept to encourage students’ career paths in science. To support this, Saudi Arabia should enhance teacher training programs by integrating mentorship, active learning strategies, and technology driven instruction to improve student engagement. Adopting Singapore’s blended learning model can foster self-confidence and independence in STEM education, while hands-on learning and career exposure programs can strengthen students’ self-concept and long-term commitment to STEM fields. Additionally, expanding extracurricular STEM initiatives and industry partnerships will help connect classroom learning to real-world applications. By aligning STEM education reforms with these insights, Saudi Arabia can cultivate a skilled workforce that supports its economic transformation under Vision 2030. Full article

Computer-driven assessment has revolutionized the way educational and professional assessments are conducted. Using artificial intelligence for data analytics, computer-based assessment improves efficiency, accuracy, and optimization of learning across disciplines. Optimizing e-learning requires a structured approach to analyzing learners’ progress and adjusting instruction accordingly. Although learning effectiveness is influenced by numerous parameters, competency-based assessment provides a structured and measurable way to evaluate learners’ achievements. This study explores the application of artificial intelligence algorithms to optimize e-learners’ studying within a generalized e-course framework. A competency-based assessment model was developed using weighted parameters derived from Bloom’s taxonomy. The key contribution of this work is an innovative method for calculating competency scores using weighted attributes and a dynamic assessment parameter, making the optimization process applicable to both learners and instructors. The results indicate that using the weighted attribute method with a dynamic assessment parameter can improve the structuring of e-courses, increase learner engagement, and provide instructors with a clearer understanding of learners’ progress. The proposed approach supports data-driven decision making in e-learning, ensuring a personalized learning experience, and improving overall learning outcomes. Full article

Bringing artificial intelligence (AI) and living intelligence into higher education has the potential to completely reshape teaching, learning, and administrative processes. Living intelligence is not just about using AI—it is about creating a dynamic partnership between human thinking and AI capabilities. This collaboration allows for continuous adaptation, co-evolution, and real-time learning, making education more responsive to individual student needs and evolving academic environments. AI-driven tools are already enhancing the way students learn by personalizing content, streamlining processes, and introducing innovative teaching methods. Adaptive platforms adjust material based on individual progress, while emotionally intelligent AI systems help support students’ mental well-being by detecting and responding to emotional cues. These advancements also make education more inclusive, helping to bridge accessibility gaps for underserved communities. However, while AI has the potential to improve education significantly, it also introduces challenges, such as ethical concerns, data privacy risks, and algorithmic bias. The real challenge is not just about embracing AI’s benefits but ensuring it is used responsibly, fairly, and in a way that aligns with educational values. From a sustainability perspective, living intelligence supports efficiency, equity, and resilience within educational institutions. AI-driven solutions can help optimize energy use, predict maintenance needs, and reduce waste, all contributing to a smaller environmental footprint. At the same time, adaptive learning systems help minimize resource waste by tailoring education to individual progress, while AI-powered curriculum updates keep programs relevant in a fast-changing world. This paper explores the disconnect between AI’s promise and the real-world difficulties of implementing it responsibly in higher education. While AI and living intelligence have the potential to revolutionize the learning experience, their adoption is often slowed by ethical concerns, regulatory challenges, and the need for institutions to adapt. Addressing these issues requires clear policies, faculty training, and interdisciplinary collaboration. By examining both the benefits and challenges of AI in education, this paper focuses on how institutions can integrate AI in a responsible and sustainable way. The goal is to encourage collaboration between technologists, educators, and policymakers to fully harness AI’s potential while ensuring that it enhances learning experiences, upholds ethical standards, and creates an inclusive, future-ready educational environment. Full article

This study explores the application of transformer-based language models for automated Topic Classification in qualitative datasets from interviews conducted in Modern Greek. The interviews captured the views of parents, teachers, and school directors regarding Emergency Remote Teaching. Identifying key themes in this kind of interview is crucial for informed decision-making in educational policies. Each dataset was segmented into sentences and labeled with one out of four topics. The dataset was imbalanced, presenting additional complexity for the classification task. The GreekBERT model was fine-tuned for Topic Classification, with preprocessing including accent stripping, lowercasing, and tokenization. The findings revealed GreekBERT’s effectiveness in achieving balanced performance across all themes, outperforming conventional machine learning models. The highest evaluation metric achieved was a macro-F1-score of 0.76, averaged across all classes, highlighting the effectiveness of the proposed approach. This study contributes the following: (i) datasets capturing diverse educational community perspectives in Modern Greek, (ii) a comparative evaluation of conventional ML models versus transformer-based models, (iii) an investigation of how domain-specific language enhances the performance and accuracy of Topic Classification models, showcasing their effectiveness in specialized datasets and the benefits of fine-tuned GreekBERT for such tasks, and (iv) capturing the complexities of ERT through an empirical investigation of the relationships between extracted topics and relevant variables. These contributions offer reliable, scalable solutions for policymakers, enabling data-driven educational policies to address challenges in remote learning and enhance decision-making based on comprehensive qualitative evidence. Full article