Search Results (1,104)

This study examines how students perceive factors contributing to their sense of belonging (SoB) in an online project-based master’s course in social innovation, incorporating collaboration with external stakeholders and structured around real-world challenges from external partners. SoB has been shown to impact academic outcomes positively, but online teamwork and collaboration outside the institution walls present unique challenges. The study adopts a qualitative approach and an interpretivist standpoint to find meaningful aspects and generate an understanding of positions among the students. Data was gathered through interviews, allowing students to express their unique experiences and perspectives on SoB in the context of a master’s course, with participants from several countries. Thematic analysis of the interview data identified the following seven categories related to students’ perceived belonging: team formation and trust, personal introductions, group continuity, access to teachers, use of informal communication channels, shared tools, and digital fluency. Furthermore, working towards a common challenge or goal seems to promote SoB among team members. The results indicate that SoB was shaped by multiple interrelated factors, with team-based collaboration and structured group dynamics playing a central role. The study contributes to ongoing research on student belonging by identifying specific practices that may support SoB in digitally supported, team-oriented learning environments. Full article

Teacher education plays a key role in promoting inclusion and educational equity, especially in contexts characterised by increasing socio-cultural diversity and technological advancement. In this framework, Universal Design for Learning (UDL) and digital technologies are presented as complementary and innovative strategies to create accessible, flexible, and motivating learning environments for all students. The study analysed the impact of UDL-focused learning activities and integrated Information and Communication Technologies (ICT). A comparative tool was applied before and after the intervention to measure the level of knowledge, perception, and digital competence of prospective teachers. Statistical analyses were carried out to evaluate the changes obtained. Findings reveal significant improvements in knowledge about UDL, as well as positive perceptions of ICT as a resource for inclusion. Participants demonstrated a greater understanding of UDL principles and strengthened their digital competences to design educational proposals adapted to diversity. The research confirms the value of integrating UDL and ICT in teacher training, fostering inclusive educational practices. It highlights the need to strengthen training programmes that respond to the current challenges of the education system. Full article

This research aims to understand the evolving role of academic libraries, focusing on student perceptions of current services and their vision for the future. Data was collected using a survey at a midwestern research university in the United States. The survey contained both quantitative and qualitative questions. The objective of the survey was to understand the current utilization of library services and students’ future visions for academic libraries. Qualitative and quantitative analysis techniques were utilized as part of the study. Thematic analysis was employed as part of the qualitative analysis, while descriptive and inferential analysis techniques were utilized in the quantitative analysis. The findings reveal that many students use libraries for traditional functions such as studying and accessing resources. There is also an inclination toward digitalization due to convenience, accessibility, and environmental sustainability; however, print materials remain relevant as well. Another finding was a lack of awareness among some students regarding available library services, indicating a need for better marketing and communication strategies. Students envision future libraries as technology-driven spaces integrating artificial intelligence (AI), augmented reality (AR), virtual reality (VR), and innovative collaborative environments. Ethical considerations surrounding AI, including privacy, bias, and transparency, are crucial factors that must be addressed. Some of the actionable recommendations include integrating ethical AI, implementing digital literacy initiatives, conducting ongoing usability and user experience (UX) research within the library, and fostering cross-functional collaboration to enhance library services and student learning. Full article

Inclusive music education refers to the use of musical experiences and practices as tools for promoting participation, equity, and meaningful engagement among all learners, including those with Special Educational Needs (SEN). Music education has long been recognized not only for its value in emotional expression and cultural transmission but also for its cognitive and relational benefits. This entry examines the inclusive and transformative potential of music, highlighting how it can foster equitable, accessible, and culturally relevant learning environments. Drawing from pedagogy, neuroscience, and educational technology, the entry explores how music contributes to cognitive, emotional, and social development, with a focus on learners with SEN. It emphasizes the importance of early exposure to music, the strong connections between music and language acquisition, and the need to challenge persistent misconceptions about innate musical talent. The findings demonstrate that when supported by digital tools and educational games, music education becomes a powerful driver of inclusion, enhancing participation, relational dynamics, and cognitive engagement. The entry concludes by advocating for a reimagining of music not as a secondary subject, but as a foundational component of holistic and inclusive education, capable of building more empathetic, connected, and equitable societies. Full article

This study investigates consumer satisfaction in e-learning services by addressing a specific gap in the literature: the limited integration of sustainability principles and behavioral modeling in understanding satisfaction drivers in online education. While existing studies have explored engagement and usability, few have considered how sustainability-related factors influence satisfaction in digital learning environments. Based on a conceptual model involving system quality, service quality, motivation, and cognitive engagement, we applied structural equation modeling (WarpPLS) to a sample of 312 university students from Romania, using mainstream learning management systems (LMS). Data were collected from students at the Bucharest University of Economic Studies using a convenience sampling method. The results show that service quality and cognitive engagement are the strongest predictors of satisfaction. This study offers practical recommendations for improving sustainable digital marketing strategies in e-learning, such as enhancing support services and aligning platform features with eco-conscious consumer expectations. Full article

Digital transformation has profoundly impacted engineering education, demanding new pedagogical approaches that ensure effective and sustainable learning. Educational psychology plays a fundamental role in strategically integrating educational technologies, fostering more inclusive, interactive, and efficient learning environments. This article explores the intersection of technological innovation, engineering education, and educational psychology, analyzing how digital tools such as Artificial Intelligence, virtual reality, gamification, and remote laboratories can optimize the teaching–learning process. It also examines the psychopedagogical impact of these technologies, addressing challenges like cognitive load, student motivation, digital accessibility, and emotional well-being. Finally, the article presents guidelines for sustainable implementation aligned with the Sustainable Development Goals (SDGs), promoting efficient, equitable, and student-centered education. As a theoretical and exploratory study, it also points to directions for future empirical investigations and practical applications. The insights provided offer strategic guidance for academic managers and educational policymakers seeking to implement sustainable, inclusive, and pedagogically effective digital innovation in engineering education. Full article

The integration of artificial intelligence (AI) into educational environments is fundamentally transforming the learning process, raising new questions regarding student engagement and motivation. This empirical study investigates the relationship between AI-based learning support and the experience of flow, defined as the optimal state of deep attention and intrinsic motivation, among university students. Building on Csíkszentmihályi’s flow theory and current models of technology-enhanced learning, we applied a validated, purposefully developed AI questionnaire (AIFLQ) to 142 students from two Hungarian universities: the Ludovika University of Public Service and Eszterházy Károly Catholic University. The participants used generative AI tools (e.g., ChatGPT 4, SUNO) during their academic tasks. Based on the results of the Mann–Whitney U test, significant differences were found between students from the two universities in the immersion and balance factors, as well as in the overall flow score, while the AI-related factor showed no statistically significant differences. The sustainability of the flow experience appears to be linked more to pedagogical methodological factors than to institutional ones, highlighting the importance of instructional support in fostering optimal learning experiences. Demographic variables also influenced the flow experience. In gender comparisons, female students showed significantly higher values for the immersion factor. According to the Kruskal–Wallis test, educational attainment also affected the flow experience, with students holding higher education degrees achieving higher flow scores. Our findings suggest that through the conscious design of AI tools and learning environments, taking into account instructional support and learner characteristics, it is possible to promote the development of optimal learning states. This research provides empirical evidence at the intersection of AI and motivational psychology, contributing to both domestic and international discourse in educational psychology and digital pedagogy. Full article

(1) Background: As digitalisation transforms society, digital competences are increasingly essential. Yet students’ digital competences often vary significantly, largely influenced by socio-economic background. Some schools—termed “resilient schools”—effectively counter these disadvantages and foster high digital competence. This study investigates the prevalence of such schools and examines how they differ from others. (2) Methods: Drawing on representative ICILS 2023 data, quantitative secondary analyses—including descriptive statistics, t-tests, and multiple regression—were conducted. Following the identification of resilient schools, in-depth analyses focus on those countries with substantial proportions of resilient schools above 10 percent (Austria, Italy, and Portugal), as these countries seem to have effective strategies to foster school resilience. (3) Results: The findings highlight considerable cross-national variation, indicating that school resilience is context-dependent. Resilient schools consistently emphasise student learning-related factors—such as ICT-related attitudes and educational aspirations—while the influence of home environments is less pronounced than in other schools. (4) Conclusions: Strengthening student learning-related factors appears to be crucial for building digital resilience. These insights can inform policy and practice aimed at fostering educational equity and closing the digital divide, particularly in socio-economically disadvantaged contexts. Full article

The integration of digital serious games into speech learning (rehabilitation) has demonstrated significant potential in enhancing accessibility and inclusivity for children with speech disabilities. This review of the state of the art examines the role of serious games, Artificial Intelligence (AI), and Natural Language Processing (NLP) in speech rehabilitation, with a particular focus on interaction modalities, engagement autonomy, and motivation. We have reviewed 45 selected studies. Our key findings show how intelligent tutoring systems, adaptive voice-based interfaces, and gamified speech interventions can empower children to engage in self-directed speech learning, reducing dependence on therapists and caregivers. The diversity of interaction modalities, including speech recognition, phoneme-based exercises, and multimodal feedback, demonstrates how AI and Assistive Technology (AT) can personalise learning experiences to accommodate diverse needs. Furthermore, the incorporation of gamification strategies, such as reward systems and adaptive difficulty levels, has been shown to enhance children’s motivation and long-term participation in speech rehabilitation. The gaps identified show that despite advancements, challenges remain in achieving universal accessibility, particularly regarding speech recognition accuracy, multilingual support, and accessibility for users with multiple disabilities. This review advocates for interdisciplinary collaboration across educational technology, special education, cognitive science, and human–computer interaction (HCI). Our work contributes to the ongoing discourse on lifelong inclusive education, reinforcing the potential of AI-driven serious games as transformative tools for bridging learning gaps and promoting speech rehabilitation beyond clinical environments. Full article

This study investigates medical students’ intentions to adopt AI-powered Natural Language Processing (NLP) tools (e.g., ChatGPT, Copilot) within educational contexts aligned with the perceived requirements of digital sustainability. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT), data were collected from 301 medical students in Saudi Arabia and analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The results indicate that Performance Expectancy (PE) (β = 0.65), Effort Expectancy (EE) (β = 0.58), and Social Influence (SI) (β = 0.53) collectively and significantly predict Behavioral Intention (BI), explicating 62% of the variance in BI (R² = 0.62). AI awareness did not significantly influence students’ responses or the relationships among constructs, possibly because practical familiarity and widespread exposure to AI-NLP tools exert a stronger influence than general awareness. Moreover, BI exhibited a strong positive effect on perceptions of digital sustainability (PDS) (β = 0.72, R² = 0.51), highlighting a meaningful link between AI adoption and sustainable digital practices. Consequently, these findings indicate the strategic role of AI-driven NLP tools as both educational innovations and key enablers of digital sustainability, aligning with global frameworks such as the Sustainable Development Goals (SDGs) 4 and 9. The study also concerns AI’s transformative potential in medical education and recommends further research, particularly longitudinal studies, to better understand the evolving impact of AI awareness on students’ adoption behaviours. Full article

Higher-education institutions are increasingly embracing digital transformation to meet the evolving expectations of students, academics, and administrators. The smart campus paradigm offers a strategic framework for this shift, yet most existing assessment models originate from high-income contexts and remain largely untested in the Global South, where infrastructural and technological conditions differ substantially. This study addresses this gap by evaluating the contextual relevance of a comprehensive smart campus assessment framework at the Papua New Guinea University of Technology (PNGUoT). A questionnaire survey of 278 participants—students and staff—was conducted using a 5-point Likert scale to assess the perceived importance of performance indicators across four key dimensions: Smart Economy, Smart Society, Smart Environment, and Smart Governance. A hybrid methodology combining the Best–Worst Method (BWM) and Public Opinion (PO) data was used to prioritise framework components. The research hypothesises that contextual factors predominantly influence the framework’s relevance in developing countries and asks: To what extent is the smart campus assessment framework relevant and adaptable in the Global South? The study aims to measure the framework’s relevance and identify contextual influences shaping its application. The findings confirm its overall applicability while revealing significant variations in stakeholder priorities, emphasising the need for context-sensitive and adaptable assessment tools. This research contributes to the refinement of smart campus frameworks and supports more inclusive and responsive digital transformation strategies in developing country higher education institutions. Full article

The digital transformation of the Architecture, Engineering and Construction sector is accelerating the adoption of digital platforms as critical enablers of data integration, stakeholder collaboration and process optimization. This paper presents a systematic review of 125 peer-reviewed journal articles (2015–2025), selected through a PRISMA-guided search using the Scopus database, with inclusion criteria focused on English-language academic literature on platform-enabled digitalization in the built environment. Studies were grouped into six thematic domains, i.e., artificial intelligence in construction, digital twin integration, lifecycle cost management, BIM-GIS for underground utilities, energy systems and public administration, based on a combination of literature precedent and domain relevance. Unlike existing reviews focused on single technologies or sectors, this work offers a cross-sectoral synthesis, highlighting shared challenges and opportunities across disciplines and lifecycle stages. It identifies the functional roles, enabling technologies and systemic barriers affecting digital platform adoption, such as fragmented data sources, limited interoperability between systems and siloed organizational processes. These barriers hinder the development of integrated and adaptive digital ecosystems capable of supporting real-time decision-making, participatory planning and sustainable infrastructure management. The study advocates for modular, human-centered platforms underpinned by standardized ontologies, explainable AI and participatory governance models. It also highlights the importance of emerging technologies, including large language models and federated learning, as well as context-specific platform strategies, especially for applications in the Global South. Full article

Gamification has emerged as a powerful strategy in digital education, enhancing engagement, motivation, and learning outcomes. However, most research lacks theoretical grounding and often applies multiple and uncontextualized game elements, limiting its impact and replicability. To address these gaps, this study introduces a Systemic Gamification Theory (SGT)—a comprehensive, human-centered model for designing and evaluating inclusive and effective gamified educational environments. Sustained in Education, Human–Computer Interaction, and Psychology, SGT is structured around four core principles, emphasizing the importance of integrating game elements (1—Integration) into cohesive systems that generate emergent outcomes (2—Emergence) aligned synergistically (3—Synergy) with contextual needs (4—Context). The theory supports inclusivity by accounting for individual traits, situational dynamics, spatial settings, and cultural diversity. To operationalize SGT, we developed two tools: i. a set of 10 Heuristics to guide and analyze effective and inclusive gamification; and ii. a Framework for designing and evaluating gamified systems, as well as comparing research methods and outcomes across different contexts. These tools demonstrated how SGT enables robust, adaptive, and equitable gamified learning experiences. By advancing theoretical and practical development, SGT fosters a transformative approach to gamification, enriching multimedia learning through thoughtful system design and reflective evaluation practices. Full article

The escalating impacts of climate change pose significant threats to global agriculture, necessitating a rapid development of climate-resilient crop varieties. The integration of multi-omics technologies—such as genomics, transcriptomics, proteomics, metabolomics, and phenomics—has revolutionized our understanding of the intricate molecular networks that govern plant stress responses. Coupled with advanced predictive modeling approaches such as machine learning, deep learning, and multi-omics-assisted genomic selection, these integrated frameworks enable accurate genotype-to-phenotype predictions that accelerate breeding for augmented stress tolerance. This review comprehensively synthesizes the current strategies for multi-omics data integration, highlighting computational tools, conceptual frameworks, and challenges in harmonizing heterogeneous datasets. We examine the contribution of digital phenotyping platforms and environmental data in dissecting genotype-by-environment interactions critical for climate adaptation resilience. Further, we discuss technical, biological, and ethical challenges, encompassing computational bottlenecks, trait complexity, data standardization, and equitable data sharing. Finally, we outline future directions that prioritize scalable infrastructures, interpretability, and collaborative platforms to facilitate the deployment of multi-omics-guided breeding in diverse agroecological contexts. This integrative approach possesses transformative potential for the development of resilient crops, ensuring agricultural sustainability amidst increasing environmental volatility. Full article

Modern database systems require autonomous CPU scheduling frameworks that dynamically optimize resource allocation across heterogeneous workloads while maintaining strict performance guarantees. We present a novel hierarchical deep reinforcement learning framework augmented with graph neural networks to address CPU scheduling challenges in mixed database environments comprising Online Transaction Processing (OLTP), Online Analytical Processing (OLAP), vector processing, and background maintenance workloads. Our approach introduces three key innovations: first, a symmetric two-tier control architecture where a meta-controller allocates CPU budgets across workload categories using policy gradient methods while specialized sub-controllers optimize process-level resource allocation through continuous action spaces; second, graph neural network-based dependency modeling that captures complex inter-process relationships and communication patterns while preserving inherent symmetries in database architectures; and third, meta-learning integration with curiosity-driven exploration enabling rapid adaptation to previously unseen workload patterns without extensive retraining. The framework incorporates a multi-objective reward function balancing Service Level Objective (SLO) adherence, resource efficiency, symmetric fairness metrics, and system stability. Experimental evaluation through high-fidelity digital twin simulation and production deployment demonstrates substantial performance improvements: 43.5% reduction in p99 latency violations for OLTP workloads and 27.6% improvement in overall CPU utilization, with successful scaling to 10,000 concurrent processes maintaining sub-3% scheduling overhead. This work represents a significant advancement toward truly autonomous database resource management, establishing a foundation for next-generation self-optimizing database systems with implications extending to broader orchestration challenges in cloud-native architectures. Full article