Search Results (7,070)

Educational sustainability necessitates a holistic development paradigm where academic resilience and physical literacy are mutually reinforcing. Within the framework of the United Nations Sustainable Development Goals (specifically SDG 3: Good Health and Well-being and SDG 4: Quality Education), this study investigates the predictive capacity of kinesthetic profiles—encompassing both intelligence and learning styles—on sports attitudes and academic achievement among adolescents. Employing a quantitative cross-sectional design, data were collected from a substantial sample of 695 adolescents. The regression analyses revealed a critical pedagogical distinction: unlike kinesthetic intelligence, the kinesthetic learning style (β = 0.612), emerged as the paramount predictor of sports attitudes explaining a substantial 42.3% of the total variance. Furthermore, a significant positive correlation was identified between kinesthetic traits and academic performance, challenging the traditional dichotomy between physical and cognitive development. These findings advocate for a strategic paradigm shift from “one-size-fits-all” instruction to kinesthetic-based pedagogies that align with students’ sensory preferences. Consequently, integrating movement-oriented strategies into curricula is proposed not merely as an instructional choice, but as a vital sustainable education strategy to foster both academic excellence and the lifelong physical and mental well-being of the next generation. Full article

The maturation of induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) remains a major translational bottleneck in regenerative cardiac medicine, as current differentiation platforms yield electrophysiologically and metabolically immature phenotypes. This review explores emerging strategies to engineer “smart” biomaterial interfaces that actively instruct iPSC-CM maturation through synergistic biophysical and metabolic reprogramming. By integrating nanotopographical patterning, mechanoelectric coupling, and tunable substrate stiffness with metabolic interventions such as mitochondrial substrate optimization and fatty acid oxidation induction, the literature reveals consistent links between cell–matrix crosstalk, sarcomeric organization, calcium handling, and oxidative metabolism. Recent advances in bioactive scaffolds and extracellular vesicle (EV)-functionalized hydrogels are highlighted as platforms capable of approximating key features of the myocardium’s native electromechanical and bioenergetic environment. Across two- and three-dimensional culture systems, this review identifies recurring maturation patterns, persistent gaps in metric standardization and long-term phenotype stability, and ongoing limitations related to scalability and translational implementation. Collectively, the findings synthesized here indicate that convergence between biomaterial engineering and metabolic programming represents a critical design principle for advancing iPSC-CMs toward functionally mature, clinically relevant phenotypes. This integrated approach enhances the fidelity of iPSC-CMs for disease modeling, drug screening, and regenerative cardiac therapies. Full article

This Systematic Quantitative Literature Review (SQLR) examines instructional content (the what) and instructional strategies (the how) that contribute to overall reading performance for students in mainstream English-speaking primary classes. Drawing on 163 peer-reviewed studies published over four and a half decades, the authors examine instructional content and strategies aligned with six interrelated foundational elements of reading development: phonological awareness, phonics, vocabulary, fluency, comprehension, and oral language. In response to the proliferation of reading research and the limitations of narrative reviews, the five iterative phases of the SQLR method enable rigorous selection, coding, and synthesis of studies reporting quantitative evidence of the contribution of instructional content and strategies to students’ overall reading performance. The second part of the paper focuses on phonics instruction, an element of the teaching of reading central to ongoing public, educational, and political debate. The authors identify significant variation in terms of the scale, duration, and year-levels of the reported research, and foreground the complex roles of teacher professional learning, teachers’ pedagogical decision-making, and implementation fidelity in shaping the research projects. The paper finishes by synthesizing evidence that concludes that while phonics instruction can contribute to overall reading performance, its effects are variable and contingent on specific instructional and contextual conditions. Full article

Background/Objectives: Pain is highly prevalent among hospitalized patients, and suboptimal pain assessment and management remain common in clinical practice. Nurses are central to timely pain recognition and intervention, yet knowledge and attitudinal gaps can hinder evidence-based pain care. Therefore, this study aimed to evaluate the impact of an evidence-based instructional program on nurses’ knowledge and attitudes toward pain management at a tertiary hospital in Saudi Arabia. Methods: A one-group pretest–posttest quasi-experimental study was conducted at King Fahad Military Medical Complex, Dhahran, Saudi Arabia (January–July 2025). Registered nurses providing direct patient care (N = 226) completed a researcher-developed questionnaire assessing pain management knowledge (30 items) and attitudes (10 items, 5-point Likert scale) immediately before and one week after a structured three-hour evidence-based educational program. Data were analyzed using descriptive statistics, paired-sample t-tests, and Pearson correlation coefficients (SPSS v30), with p < 0.05 considered statistically significant. Results: Baseline findings indicated moderate knowledge (mean of total scores = 15.54 ± 4.32) and generally positive attitudes toward pain management (mean = 3.83 ± 0.60). Knowledge scores increased significantly after the intervention to become moderate to high (pretest: 15.54 ± 4.32 vs. posttest: 18.65 ± 3.83; p < 0.001). Attitude scores also improved significantly following the program (p < 0.001). Knowledge and attitudes showed a significant positive correlation both preintervention (r = 0.241, p < 0.001) and postintervention (r = 0.435, p < 0.001). Conclusions: A brief evidence-based educational program yielded measurable improvements in nurses’ pain management knowledge and attitudes. Integrating structured pain education into continuing professional development may strengthen patient-centered pain care and support more consistent evidence-based practice in tertiary settings. Full article

Rapid access to building intelligence is critical for emergency response, yet paper fire safety instructions (FSi) often provide limited utility under stress. This structured narrative review addresses the “information gap” between unit arrival and decision-making by analyzing the legal admissibility, technological requirements, and security risks of digital FSi across Poland, Germany, France, Belgium, and Hungary. While no explicit prohibition of digital forms was identified, enforcement practices prioritize paper as the evidentiary master. Consequently, we propose a hybrid model: a paper master for compliance and redundancy, supplemented by a digital operational overlay accessed via “zero-install” offline-first progressive web apps (PWA). The review defines a minimum operational dataset (MOD)—prioritizing critical data like utility shut-offs and hazards over full documentation—and addresses cybersecurity threats, specifically QR-phishing (“quishing”). We conclude that the hybrid model minimizes legal and operational risks while significantly reducing time-to-information, provided that strict content identity and change management protocols are maintained. Full article

The objective of this paper is to evaluate the training efficiency of AR-based versus paper-based instruction methods with different levels of task complexity in terms of knowledge retention (i.e., short-term memory and long-term memory). Two maintenance operations were selected (i.e., low-demand (check all seals of the pump housing) and high-demand maintenance tasks (repair a gearbox of the piston pump and check all seals of the gearbox)). Twenty-eight healthy males were recruited and randomly divided into two groups. Each participant performed the maintenance task with assistance (i.e., AR-based or paper-based instruction) and repeated it without instructions twice (30 min apart and then two weeks later). Knowledge retention was measured using the total task completion time without guidance, error number, the number of self-corrected errors (SCEs), and the number of times help was sought from the researcher. The results show that using AR-based instruction enhanced the participants’ performance, especially in the highly demanding task, by reducing task completion time and errors in both short- and long-term retention tasks. Participants who were guided through a paper-based instruction method showed more errors, more instances of seeking help from the researcher, and a longer time to complete the long-term retention task, compared with those guided through the AR-based instruction method. Overall, using AR instruction to guide maintenance workers increased training transfer by 33.61%. Full article

Active learning has increasingly been adopted as an evidence-aligned approach to improving learning quality in university physics—a domain characterized by high conceptual abstraction, persistent misconceptions, and substantial variability in student performance. Evidence from physics education research indicates that active-learning designs can outperform lecture-dominant instruction in conceptual learning and student engagement; however, reported effects vary substantially across instructional settings and implementation models. Here, empirical studies and review-level syntheses are integrated to delineate (i) the instructional components that most reliably underpin successful active learning, (ii) the mechanisms through which these components influence learning processes and outcomes, and (iii) the boundary conditions that moderate effectiveness across higher-education contexts. The synthesis is further situated within sustainability-oriented higher education by linking physics active-learning designs to competence development relevant to quality education, climate literacy, and collaborative problem solving. Evidence spanning flipped classroom implementations, peer instruction, collaborative problem solving, inquiry- and project-based approaches, and technology-enhanced formats is organized into a component–mechanism–outcome framework structured along cognitive, affective, and behavioral pathways. Two deliverables are advanced: an integrative mechanism model connecting instructional components to mediating processes, learning outcomes, and sustainability-aligned competencies, and an operational toolbox that translates the evidence into actionable design heuristics, measurement options, and scaling considerations. By redirecting attention from “which strategy works” to “which components work, how, and under what conditions,” the review aims to support instructors, departments, and institutions seeking scalable, evidence-aligned active learning in university physics. Full article

Document-level Event Extraction (DEE) requires identifying complex event records and arguments dispersed across unstructured texts. However, applying general Large Language Models (LLMs) to DEE is intrinsically hindered by their lack of inductive bias for rigid structural constraints, often leading to schema violations and suboptimal performance in complex structural prediction tasks. To address this, we propose the S tructure-Aware Lightweight DEE, termed SALE, which leverages the structural reasoning potential of Code-Based LLMs (Code-LLMs) as a favorable inductive preference. We leverage the natural isomorphism between event schemas and programming object definitions, formulating event extraction as a Python 3.9 class instantiation task to bridge the gap between semantic understanding and structural adherence. Specifically, SALE employs a novel two-stage training paradigm: First, a Structure-Aware Fine-tuning stage injects general structural knowledge via diverse code-style instruction tasks derived from broad Information Extraction (IE) datasets; second, an Event Extraction Alignment stage utilizes a reward-based alignment loss—optimized via policy gradient—to adapt this capability to document-level intricacies. The effectiveness of SALE stems from the synergy between its structure-aware prompting and the specialized alignment stage built on a code-oriented backbone. Extensive experiments on established news-domain benchmarks (RAMS and WikiEvents) demonstrate that our approach significantly outperforms representative supervised and general LLM baselines in cross-task zero-shot and few-shot transfer settings (e.g., surpassing supervised baselines by over 7% in F1 score). Furthermore, SALE maintains a highly efficient inference profile and parameter-efficient footprint, offering a practical and scalable solution for vertical domain applications. Full article

Deploying Large Language Models (LLMs) on Maritime Autonomous Surface Ships (MASS) entails a critical trade-off between reasoning depth, inference latency, and hardware constraints. To fill the existing gap, we introduce MARTIAN (Maritime Agent for Real-time Tactical Inference And Navigation), a 14B-parameter decision support agent engineered for edge deployment on standard vessel hardware (e.g., the NVIDIA Jetson AGX Orin). Central to our approach is the Cognitive Core architecture, which utilizes a verified dataset of 21,800 Chain-of-Thought (CoT) instruction–response pairs to align general linguistic capabilities with maritime procedural logic. Empirical evaluations demonstrate that MARTIAN achieves an overall accuracy of 73.23% (SFT only) and 81.16% (SFT + RAG) on the Bilingual Maritime Multiple-Choice Questionnaire (BM-MCQ), a standardized assessment dataset constructed based on Officer of the Watch (OOW) competencies. Notably, the SFT-only configuration attains 78.53% on pure-logic-intensive COLREG tasks—surpassing the 72B-parameter Qwen-2.5 foundation model in this domain—while maintaining a real-time inference latency of 22.4 ms/token. Crucially, our ablation studies support a nuanced Interference Hypothesis: while RAG significantly enhances factual recall in knowledge-intensive domains (boosting total accuracy from 73.23% to 81.16%), it concurrently introduces semantic noise that degrades performance in pure logic reasoning tasks (e.g., COLREG maneuvering accuracy decreases from 78.53% to 77.36%). On the basis of this finding, we identify and empirically motivate a decoupled cognitive design principle that separates procedural reflexes (via SFT) from declarative knowledge (via RAG). While the full implementation of an adaptive routing mechanism is deferred to future work, the ablation results presented herein offer a validated, cost-effective reference architecture for deploying transparent and regulation-compliant AI on resource-constrained merchant vessels. Full article

This scoping review maps AI-based approaches used to infer or measure attention and emotion in technology-enhanced learning (TEL), with a particular focus on tertiary (higher) education and learning analytics-enabled digital environments supporting online and hybrid instruction. Although artificial intelligence (AI) promises personalized digital education, many systems still respond poorly to students’ attentional and emotional fluctuations. We therefore examined the extent to which the literature converges on jointly measuring attention and emotion through AI in educational contexts, especially in virtual and distance-learning settings. Following PRISMA-ScR, we searched Scopus and Web of Science and identified 39 eligible studies. We conducted a methodological quality appraisal using Joanna Briggs Institute tools, a keyword co-occurrence bibliometric analysis, and a narrative synthesis. The evidence shows a rapidly expanding field and a wide range of AI-based techniques, but emotion and attention are typically operationalized and modelled in isolation. Both the bibliometric and narrative results indicate persistent conceptual fragmentation and limitations in the validity of measurement metrics. Overall, the field has not yet established a unified paradigm that integrates attention and emotion within AI-driven educational systems, constraining their adaptive potential. This evidence highlights the need for theory-informed and operational frameworks that enable genuinely holistic, student-centred pedagogical adaptation. Full article

Calls for equity-focused professional development (PD) for graduate teaching assistants (GTAs) have grown as GTAs play an essential role in large-enrollment undergraduate courses and as future faculty members. However, there is a dearth of research on specific curricular design aspects that would be productive for GTAs. This study addresses this gap by examining mathematics GTAs’ perspectives on the curricular aspects of a PD program designed to support equitable and inclusive teaching practices. Building off Wiggins and McTighe’s curricular design framework, we employed surveys, interviews, exit tickets, and PD artifacts collected from 56 GTAs across three universities, and conducted a thematic analysis combining inductive and deductive coding. Four themes consistently emerged regarding the design features that most influenced GTAs’ learning: (a) Community and Collaboration; (b) Awareness Through Discovery; (c) Concrete Ideas and Examples Connected to Theory; and (d) Practicality and Actionable Takeaways. These findings highlight design principles for equity-oriented PD that resonate with GTAs that extend the curricular design framework. We discuss implications for developing sustainable, context-sensitive PD that cultivates equitable mathematics instruction and supports GTAs’ pedagogical growth. Full article

Background: COVID-19 can have adverse effects on individuals’ lung functions for up to 6 months or more after the episode. As a result, people may be reluctant to exercise, and this can have further adverse effects on their lung capacity and fitness. This study aimed to examine the effectiveness of a telemedicine program designed to increase the exercise participation of young adults after COVID-19 and evaluate the changes in lung function after exercise training. Methods: The quasi-experimental pre–post study recruited sixty university students who had suffered from COVID-19 within the past 12 months. Four pulmonary outcomes were compared: forced expiratory volume in one second (FEV1), forced vital capacity (FVC), peak expiratory flow rate (PEFR), and the ratio of FEV1 to FVC. The telemedicine exercise (TE) group (n = 36) received an intervention to carry out regular stepping exercise (up to 10,000 steps) via online video instruction and frequent WhatsApp reminder messages. The control group (n = 24) only received an initial WhatsApp message to carry out regular stepping exercise, with no further follow-up. Results: The FVC, FEV1, and FEV1/FVC ratio revealed significant overall improvement both within groups and between groups (p < 0.001), with moderate effect sizes. PEFR showed a significant improvement within groups (p = 0.007) but not between groups (p = 0.533). The TE group recorded a significant increase in daily step count (from 7165 to 9733, p < 0.001) after 4 weeks of training. The control group showed a significant reduction in step count (from 6975 to 6442, p = 0.049). Conclusions: The results confirmed the beneficial effects of the telemedicine exercise program in contributing to increased exercise participation and improved lung functions. Full article

Teacher teaching engagement is a critical factor influencing teacher development and school quality. Based on survey data from 15,931 high school teachers and 1072 school principals from China, this study explores the impact of principals’ instructional leadership on teacher teaching engagement and its underlying mechanisms. The findings reveal that principals’ instructional leadership significantly enhances teacher teaching engagement, but the strength of its impact varies across different dimensions of teacher teaching engagement. Among these, the influence on teachers’ “differentiated teaching ability” is the most significant. Heterogeneity tests indicate that the effect of principals’ instructional leadership on teacher teaching engagement is stronger in private schools than in public schools, and stronger in non-key schools than in key schools. However, the impact of principals’ instructional leadership on teacher teaching engagement shows little difference between urban and non-urban (county, township, and rural) schools. Mechanism analysis further reveals that school cultural climate plays a significant mediating role between principals’ instructional leadership and teacher teaching engagement. Based on these findings, relevant policy recommendations are provided. Full article

Higher education institutions are expected to cultivate graduates capable of addressing sustainability challenges through innovation, collaboration, and digital competence. However, many business programs struggle to integrate experiential authenticity, intelligent technologies, and collaborative learning into coherent instructional models, limiting students’ intrinsic motivation and sustainability-oriented competence development. This study aims to examine how experiential learning, artificial intelligence-assisted collaborative learning, and team-based learning operate within the Conceive–Design–Implement–Operate instructional framework to influence learning interest and learning satisfaction in a sustainability-oriented business course. Survey data from 217 undergraduate students were analyzed using confirmatory factor analysis, structural equation modeling, and moderated regression analysis. The results indicate that both experiential and AI-supported collaborative learning positively enhance students’ learning interest, which partially mediates their effects on learning satisfaction. Team-based learning strengthens the experiential pathway but does not significantly moderate the AI-assisted pathway. These findings clarify differentiated motivational mechanisms within structured instructional systems and provide theoretical support for designing digitally enhanced sustainability education. Full article

Strengthening key competencies in veterinary preclinical education, such as anatomical identification, spatial–visual reasoning, and anatomical–surgical understanding, is essential for effective preclinical learning. In this context, veterinary preclinical education is undergoing a transformation process in which traditional theoretical–practical approaches show limitations in responding to current educational demands, making it necessary to adopt innovative strategies based on active learning and simulation. This study presents a simulation-based educational approach designed to support competency development within preclinical veterinary education. Using a reproducible and low-cost workflow applied to a real canine cranial case of extra-genital transmissible venereal tumor (TVCT) with frontal bone invasion, used exclusively as a teaching scenario. Fourteen veterinary medicine students from the same institution participated in two instructional conditions: Group A received traditional theoretical instruction (including cadaveric specimens) without the use of 3D-printed models, while Group B participated in simulation-based training supported by virtual planning and a 3D-printed cranial model. Learning outcomes are assessed through structured observation and descriptive analysis. A Likert-type survey was also used to assess satisfaction and engagement among students who participated in the model-supported training, as well as to map competencies across cognitive, visual-spatial, and anatomical-surgical reasoning domains, with evaluation conducted by veterinarians with clinical and teaching experience. Descriptive observations indicated that students participating in the simulation-based training engaged in three-dimensional anatomical exploration of cranial anatomy and case-based anatomical-surgical discussion. In addition, survey responses from Group B indicated high levels of engagement and interest, as well as high perceived usefulness of the model-supported training experience. These findings suggest that simulation-based educational frameworks may offer a safe, transferable, and pedagogically valuable strategy for competency development within preclinical veterinary education. Full article