Search Results (1,331)

Student course performance evaluation serves as a critical pedagogical tool for diagnosing learning gaps and enhancing educational outcomes, yet conventional assessments often suffer from rigid single-metric scoring and ambiguous causality. This study proposes an integrated analytic framework addressing these limitations by synergizing pedagogical expertise with data-driven diagnostics through four key measure: (1) Interval Analytic Hierarchy Process (I-AHP) to derive criterion weights reflecting instructional priorities via expert judgment; (2) K-means clustering to objectively stratify students into performance cohorts based on multidimensional metrics; (3) Random Forest classification and SHAP value analysis to quantitatively identify key discriminators of cluster membership and interpret decision boundaries; and (4) attribution-guided interventions targeting cohort-specific deficiencies. Leveraging a dual-channel ecosystem across pre-class, in-class, and post-class phases, we established a hierarchical evaluation system where I-AHP weighted pedagogical sub-criteria to generate comprehensive student scores. Full article

Artificial intelligence (AI) is rapidly transforming rheumatology, particularly in imaging and laboratory diagnostics where data complexity challenges traditional interpretation. This narrative review summarizes current evidence on AI-driven tools across musculoskeletal ultrasound, radiography, MRI, CT, capillaroscopy, and laboratory analytics. A structured literature search (PubMed, Scopus, Web of Science; 2020–2025) identified 90 relevant publications addressing AI applications in diagnostic imaging and biomarker analysis in rheumatic diseases, while twelve supplementary articles were incorporated to provide contextual depth and support conceptual framing. Deep learning models, notably convolutional neural networks and vision transformers, have demonstrated expert-level accuracy in detecting synovitis, bone marrow edema, erosions, and interstitial lung disease, as well as in quantifying microvascular and structural damage. In laboratory diagnostics, AI enhances the integration of traditional biomarkers with high-throughput omics, automates serologic interpretation, and supports molecular and proteomic biomarker discovery. Multi-omics and explainable AI platforms increasingly enable precision diagnostics and personalized risk stratification. Despite promising performance, widespread implementation is constrained by significant domain-specific validation gaps, data heterogeneity, lack of external validation, ethical concerns, and limited workflow integration. Clinically meaningful progress will depend on transparent, validated, and interoperable AI systems supported by robust data governance and clinician education. The transition from concept to clinic is under way—AI will likely serve as an augmenting rather than replacing partner, standardizing interpretation, accelerating decision-making, and ultimately facilitating precision, data-driven rheumatologic care. Full article

The present study pursued three objectives: (i) to examine whether self-access web-based instruction could significantly improve EFL students’ ability to formulate pragmatically appropriate email requests; (ii) to determine whether L2 proficiency influenced students’ pragmatic performance and their gains following instruction; and (iii) to explore changes in learners’ confidence when evaluating the appropriateness of their own email requests. Sixty-eight first-year English Studies students at a Spanish university completed a five-week intervention integrated into their curriculum. Their L2 proficiency was assessed using the Oxford Placement Test, which categorized them into B1 (n = 22), B2 (n = 23), and C1 (n = 23) levels. Using a pre–post-test design, learners’ performance was assessed through email tasks varying in imposition, and their confidence was measured via Likert-scale ratings. Results showed statistically significant improvements across all dimensions of an analytic rubric—particularly in request appropriateness and organization—indicating that self-access instruction effectively enhanced learners’ pragmatic competence. Gains were similar across the three proficiency groups, with B2 students showing slightly higher though not statistically significant improvements. Participants also reported significantly increased confidence in evaluating their own email appropriateness post-intervention. Findings support the integration of self-access pragmatic resources into EFL curricula to develop academic communication skills in higher education contexts. Full article

Circular economy learning fosters competencies in sustainable resource management and environmental protection, which have been recognized by the OECD (Organization for Economic Cooperation and Development) to be essential for cross-curricular training and higher education. However, implementing gamification techniques through mobile applications remains challenging, as their effectiveness depends on students’ willingness to adopt them. This study evaluated acceptance of a gamified mobile application for circular economy learning among university students in Peru, analyzing the relationships between the constructs of the Technology Acceptance Model (TAM). A quantitative correlational case study involving 76 students was conducted. The results showed a moderate-to-high acceptance rate of 73.69%, with significant correlations identified between the TAM constructs. This study contributes to closing gaps in empirical evidence on the acceptance of technology for sustainability education in diverse contexts. Future studies should integrate generative artificial intelligence into gamified apps to deliver personalized feedback and employ learning analytics tools for progress tracking, supporting global efforts toward SGD 4 (Quality Education) and SDG 12 (Responsible Production and Consumption) for the transition to circular economies. Full article

The purpose of this work is twofold: first, it aims to derive an exact analytical form of the Pareto index based on the already developed model of the economy as a scale-free network comprising a given amount of either wealth or income (total number of links, each link representing a non-zero amount or quantum of income or wealth) distributed among its variable number of actors (nodes), all of whom have equal access to the system), and second, it aims to employ the derived analytical form of the Pareto index to determine the degree to which the observed inequality in wealth and in income as measured by the respective empirical values of the Pareto index is inherent in the economic system rather than the result of externally imposed factors invariably reflecting a lack of equal access. The derived analytical form of the Pareto index for wealth or for income is described by an exponential function whose exponent is the inverse of the average number of wealth or of income per actor (one-half of the average number of links per node) in the economic model. This exponent features prominently in the scale-free model of the economy and has a numerical value of 0.69 when the Pareto index attains a numerical value of 2, which signifies the optimal, albeit still unequal, distribution of wealth or of income in the economy under the condition of equal access. Because of the correspondence of the scale-free model of the economy to a physical system comprising quantum particles such as photons in thermodynamic equilibrium or state of maximum entropy in accordance with the laws of statistical mechanics, the inverse of the exponent is proportional to the temperature of the economic system, and a new parameter introduced to describe in a comprehensible manner the deviation in the economic system from its optimal distribution of wealth or income. A comparison of the empirical wealth and income Pareto indexes based on economic data for the four largest economies in the word, i.e., USA, China, Germany, and Japan, which account for over 50% of the global GDP, versus the corresponding optimal values per the scale-free model of the economy reveals interesting trends that can be explained away by the prevailing degrees of equal access, as manifested by inadequate education, health care, and housing, as well as the existence of rules and institutions favoring certain actors over others, particularly with regard to the accumulation of wealth. It has also been determined that the newly introduced parameters in the scale-free model of the economy of temperature as well as the quanta of wealth and of income should be expressed in power purchase exchange rates for meaningful comparisons among national economies over time. Full article

In Korea, rural regions increasingly function as peri-urban zones integrated into urban systems. To assess vulnerabilities in these transitional areas characterized by mixed land use and uneven access to infrastructure, this study developed a three-tiered peri-urban livability vulnerability framework by integrating the analytic hierarchy process and the entropy weight method. The results indicated that medical facilities, childcare and education centers, and village communities consistently emerged as key indicators, linking peri-urban livability directly to the stability of settlement environments and the quality of life of residents. Contrastingly, expert evaluations and data-driven outcomes related to road networks and agricultural infrastructure displayed substantial discrepancies, revealing gaps between perceived importance and actual provision levels. Such differences highlight the risk of underestimating infrastructure-related vulnerabilities when subjective assessments are employed exclusively. By synthesizing subjective and objective weights, this study advances urban and environmental analysis and supports evidence-based decision-making for policy prioritization. The findings demonstrate that peri-urban vulnerability is shaped less by productive capacity than by social infrastructure and community stability. This conclusion offers crucial insights for enhancing livability and guiding urban planning strategies. Full article

Digital marketing has become a game-changer by combining cutting-edge technologies, insights into how customers behave, and applicability across industries to change how businesses plan and how they interact with customers. Digital marketing is a key part of being competitive, sustainable, and innovative in a world where more and more people are using the internet and social media. Even though this subject is important, the study of it is still scattered, which shows that there is a need to systematically map out its intellectual structure. This research utilizes a bibliometric and topic modeling methodology, analyzing 4722 publications sourced from the Scopus database, including the string “Digital Marketing”. The authors employed Latent Dirichlet Allocation (LDA), a method from Natural Language Processing, to discern latent study themes and Vosviewer 1.6.20 for bibliometric analysis. The results explore ten main thematic clusters, such as digital marketing and blockchain, applications in the health and food industries, higher education and skill enhancement, machine learning and analytics, small and medium-sized enterprises (SMEs) and sustainability, emerging trends and ethics, sales transformation, tourism and hospitality, digital media and audience perception, and consumer satisfaction through service quality. These clusters show that digital marketing is becoming more interdisciplinary and is becoming more connected to ethical and technological issues. The report finds that digital marketing research is changing quickly because of artificial intelligence (AI), blockchain, immersive technology, and reflect it with a digital business environment. Future directions encompass the expansion of analyses to new economies, the implementation of advanced semantic models, and the navigation of ethical difficulties, thereby guaranteeing that digital marketing fosters both business progress and public welfare. Full article

The heavy environmental impact of the construction industry—responsible for 39% of world CO₂ emissions and consuming over 40% of natural resources—supports the need for evidence-based decision-making tools for sustainable material selection balancing environmental, economic, and social considerations. This research develops and evaluates an integrated decision support system that couples cradle-to-grave lifecycle assessment (LCA) with various multi-criteria decision-making (MCDM) methods to optimize climate-resilient material selection for schools. The methodology is an integration of hybrid Analytic Hierarchy Process–Technique for Order of Preference by Similarity to Ideal Solution (AHP-TOPSIS) and VIKOR techniques validated with eight case studies in hot-arid, hot-humid, and temperate climates. Environmental, economic, social, and technical performance indices were evaluated from primary experimental data and with the input from 22 international experts with climate change assessment expertise. Ten material options were examined, from traditional, recycled, and bio-based to advanced composite systems throughout full building lifecycles. The results indicate geopolymer–biofiber composite systems achieve 42% reduced lifecycle carbon emissions, 28% lower cost of ownership, and 35% improved overall sustainability performance compared to traditional equivalents. Three MCDM techniques’ cross-validation demonstrated a satisfactory ranking correlation (Kendall’s τ = 0.87), while Monte Carlo uncertainty analysis ensured framework stability across 95% confidence ranges. Climate-adaptive weighting detected dramatic regional optimization contrasts: thermal performance maximization in tropical climates and embodied impact emphasis in temperate climates. Three case studies on educational building projects demonstrated 95.8% accuracy in validation of environmental performance and economic payback periods between 4.2 and 6.8 years in real-world practice. Full article

Attention and brain focus are essential in human activities that require learning. In higher education, a popular means of acquiring knowledge and information is through serious games. The need for integrating digital learning tools, including serious games, into university curricula has been demonstrated by the students’ preferences that are oriented more towards engaging and interactive alternatives than traditional education. This study examines real-time attention measurement in serious games using wearable brain–computer interfaces (BCIs). By capturing electroencephalography (EEG) signals non-invasively, the system continuously monitors players’ cognitive states to assess attention levels during gameplay. The novel approach proposes adaptive attention measurements to investigate the ability to maintain attention during cognitive tasks of different durations and intensities, using a single-channel EEG system—NeuroSky Mindwave Mobile 2. The measures have been achieved on ten volunteer master’s students in Computer Science. Attention levels during short and intense tasks were compared with those recorded during moderate and long-term activities like watching an educational lecture. The aim was to highlight differences in mental concentration and consistency depending on the type of cognitive task. The experiment was designed following a unique protocol applied to all ten students. Data were acquired using the NeuroExperimenter software 6.6, and analytics were performed in RStudio Desktop for Windows 11. Data is available at request for further investigations and analytics. Experimental results demonstrate that wearable BCIs can reliably detect attention fluctuations and that integrating this neuroadaptive feedback significantly enhances player focus and immersion. Thus, integrating real-time cognitive monitoring in serious game design is an efficient method to optimize cognitive load and create personalized, engaging, and effective learning or training experiences. Beta and attention brain waves, associated with concentration and mental processing, had higher values during the gameplay phase than in the lecture phase. At the same time, there are significant differences between participants—some react better to reading, while others react better to interactive games. The outcomes of this study contribute to the design of personalized learning experiences by customizing learning paths. Integrating NeuroSky or similar EEG tools can be a significant step toward more data-driven, learner-aware environments when designing or evaluating educational games. Full article

Labour migration from Central and Eastern Europe plays a significant role in the labour market of the European Union, yet few studies examine the direction and extent of occupational mobility triggered by migration. This study introduces a new analytical tool, the Career Benefit Index, which measures the direction of change in occupational status between the labour markets of the country of origin and the host country. The tool also enables the assessment of sociological factors that explain these changes. The index was developed using data from Hungarian workers living in Austria and Germany. The analysis revealed that educational attainment has no significant impact on career mobility. In contrast, demographic factors such as gender, age, and particularly very high-level German language proficiency strongly influence career trajectories. The index demonstrates that labour market capacities play a limited role in shaping migrants’ career paths, as the host labour markets tend to “evaluate” migrant workers primarily based on their linguistic and demographic attributes. The index and the findings contribute to a deeper understanding of labour market integration among Central and Eastern European migrants and may offer new directions for migration and employment policy analysis. Full article

Artificial Intelligence (AI), particularly Generative AI (GenAI) and Large Language Models (LLMs), is rapidly reshaping higher education by transforming teaching, learning, assessment, research, and institutional management. This entry provides a state-of-the-art, comprehensive, evidence-based synthesis of established AI applications and their implications within the higher education landscape, emphasizing mature knowledge aimed at educators, researchers, and policymakers. AI technologies now support personalized learning pathways, enhance instructional efficiency, and improve academic productivity by facilitating tasks such as automated grading, adaptive feedback, and academic writing assistance. The widespread adoption of AI tools among students and faculty members has created a critical need for AI literacy—encompassing not only technical proficiency but also critical evaluation, ethical awareness, and metacognitive engagement with AI-generated content. Key opportunities include the deployment of adaptive tutoring and real-time feedback mechanisms that tailor instruction to individual learning trajectories; automated content generation, grading assistance, and administrative workflow optimization that reduce faculty workload; and AI-driven analytics that inform curriculum design and early intervention to improve student outcomes. At the same time, AI poses challenges related to academic integrity (e.g., plagiarism and misuse of generative content), algorithmic bias and data privacy, digital divides that exacerbate inequities, and risks of “cognitive debt” whereby over-reliance on AI tools may degrade working memory, creativity, and executive function. The lack of standardized AI policies and fragmented institutional governance highlight the urgent necessity for transparent frameworks that balance technological adoption with academic values. Anchored in several foundational pillars (such as a brief description of AI higher education, AI literacy, AI tools for educators and teaching staff, ethical use of AI, and institutional integration of AI in higher education), this entry emphasizes that AI is neither a panacea nor an intrinsic threat but a “technology of selection” whose impact depends on the deliberate choices of educators, institutions, and learners. When embraced with ethical discernment and educational accountability, AI holds the potential to foster a more inclusive, efficient, and democratic future for higher education; however, its success depends on purposeful integration, balancing innovation with academic values such as integrity, creativity, and inclusivity. Full article

Ritual prayer (ṣalāh) is one of the most central and enduring practices in Islam, widely recognized for its spiritual significance. However, its educational and formative role in shaping the Muslim’s inner self and moral identity remains insufficiently explored in contemporary scholarship. This paper aims to examine ritual prayer as a core pedagogical tool within Islamic education, focusing on its transformative power in the processes of self-purification (tazkiyah) and identity formation. The study seeks to analyze the ethical and psychological dimensions of ṣalāh, drawing on classical Islamic sources, as well as integrating insights from contemporary critical philosophy—particularly Byung-Chul Han’s Vita Contemplativa—and Islamic virtue ethics, including perspectives such as those advanced by Elizabeth Bucar. Through this framework, the paper explores how prayer shapes inner dispositions like humility, mindfulness, sincerity, patience, and submission, reinforcing both spiritual awareness and communal belonging. Employing a descriptive-analytical methodology, the study engages Qur’anic verses, prophetic traditions, and traditional pedagogical literature to investigate how ṣalāh functions as a lived and repeated experience that cultivates the soul and molds ethical behavior. The discussion highlights how regular performance of prayer integrates belief with action and contributes to the formation of a reflective and morally grounded Muslim identity. This paper contributes to the field of Islamic Practical Theology by demonstrating how ritual prayer operates as a dynamic and holistic model for moral and spiritual development. It provides educators and scholars with a theoretical and applied vision for incorporating ṣalāh-based character education into Islamic curricula. Future research may explore how prayer interacts with modern lifestyles, digital spiritual practices, and intergenerational transmission of religious identity in diverse contexts. Full article

In the present work, a new analytical model of polytropic flow in constant-diameter pipelines is developed to accurately describe the flow of compressible gases, including natural gas and hydrogen, explicitly accounting for heat exchange between the fluid and the environment. In contrast to conventional models that assume isothermal or adiabatic conditions, the proposed model simultaneously accounts for variations in pressure, temperature, density, and entropy, i.e., it is based on a realistic polytropic gas flow formulation. A system of differential equations is established, incorporating the momentum, continuity, energy, and state equations of the gas. An implicit closed-form solution for the specific volume along the pipeline axis is then derived. The model is universal and allows the derivation of special cases such as adiabatic, isothermal, and isentropic flows. Numerical simulations demonstrate the influence of heat flow on the variation in specific volume, highlighting the critical role of heat exchange under real conditions for the optimization and design of energy systems. It is shown that achieving isentropic flow would require the continuous removal of frictional heat, which is not practically feasible. The proposed model therefore provides a clear, reproducible, and easily visualized framework for analyzing gas flows in pipelines, offering valuable support for engineering design and education. In addition, a unified sensitivity analysis of the analytical solutions has been developed, enabling systematic evaluation of parameter influence across the subsonic, near-critical, and heated flow regimes. Full article

Background: AMR causes a large global health burden, with approximately 4.95 million deaths linked to bacterial AMR in 2019, 1.27 million due to AMR directly. Although Brazil mandated prescriptions for systemic antibiotics in 2010/2011, self-medication and access without prescriptions continue, with community pharmacists playing a vital part in antimicrobial stewardship (AMS). This study examined antibiotic misuse and AMR in Brazil through community pharmacists’ perspectives, emphasising their dual role as professional actors and frontline observers of public behaviour. Methods: We conducted 20 semi-structured interviews with community pharmacists and performed reflexive thematic analysis of their accounts, repeating five independent analytic cycles to confirm thematic robustness. Results: Six themes were consistently identified as recounted by pharmacists in their practice contexts: Access and Self-Medication; Relationships with Healthcare Professionals; Knowledge and Beliefs about Antibiotics; Use and Adherence; Healthcare System Barriers; and Regulation and Enforcement. Pharmacists mentioned regular requests for antibiotics without prescriptions, drug reuse, and significant impact from community, i.e., from relatives, and peers. The common misunderstanding was that antibiotics treat viral illnesses. Structural issues, for instance GP appointment costs and long waits, made patients seek help from pharmacies. Due to regulation being applied inconsistently, pharmacies struggled to refuse unsuitable requests. Conclusions: Framed through pharmacists’ dual vantage as professionals and frontline observers, the findings highlight intertwined factors underpinning inappropriate antibiotic use in Brazil and support a multi-pronged intervention spanning health system strengthening, professional education, economic considerations, and community engagement. Full article

As an important sustainable lifelong learning competence, self-regulated learning involves a continuous process of self-monitoring and self-directing towards a learning goal. This study examined the level of alignment between university students’ self-regulated learning (SRL) profiles using questionnaire data and learning analytics data in flipped classrooms. On the one hand, a hierarchical cluster analysis using the questionnaire data generated two learning profiles of high and low self-regulated (SR) learners. On the other hand, a hierarchical cluster analysis using the questionnaire data produced two learning profiles of active and passive online learners. Although a cross-tabulation analysis showed a significant and positive relationship between students’ learning profiles identified using the questionnaire and learning analytics data, the association was rather weak. Of the high SR learners, there was a significantly higher proportion of active online learners than passive online learners. In contrast, among low SR learners, a significantly lower proportion of active online learners than passive online learners was found. Furthermore, high SR and active online learners and high SR and passive online learners had significantly better academic achievement than low SR and active online learners and low SR and passive online learners, demonstrating the importance of SR in flipped classrooms. Full article