Search Results (87)

Physical educators can incorporate mathematics and technology into their curriculum. The challenge is how to do this without sacrificing the core learning central to physical education (PE). The aim of this study was to examine the impact of an intentionally designed interdisciplinary six-week program called Maths on the Move (MOTM), specifically designed to integrate mathematics and PE. The study participants included two middle school PE teachers and two mathematics teachers. Within PE lessons, students wore a human activity monitor (HAM) that recorded step counts and acceleration to allow students to gather their personalized data for use in their mathematics lessons on statistics and probability. While the teachers applied our interdisciplinary approach, the challenges and complexities of interdisciplinary methods were observed. We demonstrated how the integration of PE and mathematics can enrich students’ learning experiences, illustrating MOTM as a versatile integrated approach. Despite the results, a gap between pedagogical content knowledge, teacher connectiveness, and practical application was found. In conclusion, this study underlined the value and possibilities of integrating PE and mathematics through a teacher-centered approach, setting the stage for future research to enhance the effectiveness of interdisciplinary education. Full article

Background and Objectives: Ultrasound (US)-guided peripheral regional anesthesia (pRA) is gaining increasing importance in emergency medicine as an effective, low-ridsk alternative to general anesthesia (GA), procedural sedation (PS), or opioid therapy. By enabling rapid, direct pain management in the emergency department (ED), pRA can help preserve scarce surgical and anesthetic resources and, in some cases, avoid inpatient admissions. The aim of this study was to analyze the indications, techniques, and clinical impact of pRA in the orthopedic-focused ED of an affiliated hospital. Materials and Methods: All pRA and PS procedures performed over a six-year period were retrospectively reviewed among 35,443 orthopedic-trauma emergency patients. pRA was carried out under US guidance with standardized monitoring. Diagnoses, block techniques, effectiveness, and complications were analyzed descriptively. Results: A total of 1292 patients (3.7%) underwent either pRA (n = 1117; 3.2%) or PS (n = 175; 0.5%). pRA was performed in 22% of cases for interventions such as reductions or extensive wound management. In 78%, pRA was applied for analgesia, for example, in the diagnostic work-up and treatment of non-immediately operable fractures, lumbago, or arthralgia. The most common pRA techniques were brachial plexus blocks (54%) and femoral nerve blocks (25%). Fascial plane blocks (6.1%) and paravertebral blocks (1.5%) were rarely used. PS was performed in 175 of 1292 patients (13%), although pRA would have been feasible in 159 of these cases. No complications of pRA were observed, and GA could routinely be avoided. Conclusions: US-guided pRA proved to be an effective and safe alternative to PS, GA, or systemic analgesia for selected indications, allowing immediate treatment without the need for operative capacities. To ensure safe application, these techniques should be an integral part of the training curriculum for ED personnel. Full article

This research investigates various stakeholder perspectives on AI-powered teacher education, focusing on its potential benefits, strengths, and limitations for integrating this promising technology into a sustainable educational future. It was designed as an exploratory mixed-methods study. It involved five distinct groups: curriculum developers in teacher-training institutions, artificial intelligence experts, department heads and deans in education faculties, private sector managers in teacher-training companies, and over 500 pre-service teachers. The findings reveal promising smart opportunities that AI offers for reimagining teacher training, contributing to the social and long-term institutional sustainability of teacher education. Key components of AI-powered teacher education identified include “Intended use of AI in teacher education context,” “Machine learning with data monitoring,” “AI-human interaction in teacher training,” “AI-powered feedback for better faculty management,” and critically, “Digital vision, risks, and AI ethics for responsible and sustainable implementation.” Prominently stressed codes within these themes include “AI readiness, automated teacher education curriculums, a new recruitment system, designing AI-guided smart faculties, measuring on-entry skills, identifying risky pre-service teachers, improving teachers’ assessment capacity, creating smart content, and criticisms over its value.” The results of the multiple regression analysis demonstrate that curiosity about AI use has the strongest impact on pre-service teachers’ openness and readiness for AI-empowered teacher education, followed by institutional AI support. The research concludes by implicitly calling for a holistic and ethical strategy for leveraging AI to prepare educators to successfully navigate the demands of a sustainable future. Full article

Citizen science is not only a participatory means of contributing to scientific knowledge but also an effective approach to addressing a wide range of societal challenges. Integrating citizen science with sustainability entails leveraging public engagement in scientific research to promote sustainable practices and advance the United Nations 2030 Agenda for Sustainable Development Goals (SDGs). The degree of public participation can influence the learning outcomes achieved. This study investigated the benefits and limitations of a co-creation citizen science approach implemented in a school context for monitoring species distribution on rocky shores, aligned with SDGs 4, 13, and 14. A mixed-methods design was applied, combining questionnaires administered to students (n = 100); participant observations of students, teachers, and researchers; and the analysis of observations submitted by one class (C2) to the iNaturalist platform. Students recorded 21 valid observations representing 13 different taxa, and developed skills such as critical thinking, problem-solving, collaboration, and interpersonal communication. They also recognised the potential of co-creation as a means of addressing scientific questions. However, teachers reported constraints in implementing the project, notably the breadth of the school curriculum and the lack of local support. This study reinforces the potential of co-creation citizen science projects to foster sustainable education. Full article

Traditional lecture-based teaching often struggles to foster student engagement, active participation, and deep learning in large-class general education courses. As class sizes grow, students may become passive learners, limiting their ability to develop essential skills such as self-directed learning and teamwork. Innovative instructional strategies are needed to address these challenges and create a more interactive, student-centered learning environment. Team-Based Learning (TBL) has emerged as a practical pedagogical approach that promotes collaboration, critical thinking, and student accountability. This study investigates the influence of TBL on Self-Directed Learning (SDL) and Team Dynamics (TD) through a quasi-experimental design. One class was classified as the experimental group (TBL), while the other was classified as the control group (traditional lecture-based teaching). Data were analyzed using independent-samples one-way ANCOVA and the Johnson–Neyman method to examine the impacts of TBL on SDL and TD. The results indicate that the experimental group adopting TBL outperformed the control group in both SDL and TD. The ANCOVA results revealed that TBL had a significant positive impact on the self-monitoring factor of SDL after controlling for pre-test scores. Furthermore, the Johnson–Neyman analysis demonstrated that the effect of TBL varied across different pre-test levels, suggesting that the influence of TBL on SDL and TD was more pronounced under certain conditions. Overall, this study supports the effectiveness of TBL as a pedagogical strategy in large-class general education courses, highlighting its potential to enhance students’ SDL and TD. These findings provide valuable insights for future teaching practices and curriculum design, emphasizing the need for more interactive, student-centered learning approaches in higher education. Full article

In this paper, we report how digital resources support engineering students in the early stages of mathematical modelling within a Challenge-Based Education (CBE) course. The study was conducted in a second-year engineering course involving mathematics, physics, and ethics. Through a case study of two student teams, we analyze how a digital curriculum resource—specifically, a dashboard designed for feedback and progress monitoring—helped students identify, define, and begin modelling a real-world problem related to crowd flow on train platforms. Using the instrumental approach, we examined the dual processes of instrumentation (integration of resources) and instrumentalization (adaptation and repurposing of tools). Results show that the Dashboard played a central role in fostering self-regulated learning, interdisciplinary collaboration, and the iterative refinement of guiding questions. Students used data analysis, simulations, and modelling techniques to build and validate mathematical representations in answer to the guiding questions. Our findings contribute to ongoing discussions on how mathematics education in engineering can be enhanced through activity-based learning and targeted use of digital tools. We argue that digital feedback systems like dashboards can bridge the gap between abstract mathematical content and its meaningful application in engineering contexts, thus fostering engagement, autonomy, and authentic learning. Full article

Timely, objective pain recognition in dairy cattle is essential for welfare assurance, productivity, and ethical husbandry yet remains elusive because evolutionary pressure renders bovine distress signals brief and inconspicuous. Without verbal self-reporting, cows suppress overt cues, so automated vision is indispensable for on-farm triage. Although earlier systems tracked whole-body posture or static grimace scales, frame-level detection of facial micro-expressions has not been explored fully in livestock. We translate micro-expression analytics from automotive driver monitoring to the barn, linking modern computer vision with veterinary ethology. Our two-stage pipeline first detects faces and 30 landmarks using a custom You Only Look Once (YOLO) version 8-Pose network, achieving a 96.9% mean average precision ($mAP$) at an Intersection over the Union (IoU) threshold of 0.50 for detection and 83.8% Object Keypoint Similarity (OKS) for keypoint placement. Cropped eye, ear, and muzzle patches are encoded using a pretrained MobileNetV2, generating 3840-dimensional descriptors that capture millisecond muscle twitches. Sequences of five consecutive frames are fed into a 128-unit Long Short-Term Memory (LSTM) classifier that outputs pain probabilities. On a held-out validation set of 1700 frames, the system records 99.65% accuracy and an F1-score of 0.997, with only three false positives and three false negatives. Tested on 14 unseen barn videos, it attains 64.3% clip-level accuracy (i.e., overall accuracy for the whole video clip) and 83% precision for the pain class, using a hybrid aggregation rule that combines a 30% mean probability threshold with micro-burst counting to temper false alarms. As an early exploration from our proof-of-concept study on a subset of our custom dairy farm datasets, these results show that micro-expression mining can deliver scalable, non-invasive pain surveillance across variations in illumination, camera angle, background, and individual morphology. Future work will explore attention-based temporal pooling, curriculum learning for variable window lengths, domain-adaptive fine-tuning, and multimodal fusion with accelerometry on the complete datasets to elevate the performance toward clinical deployment. Full article

This study examined quality of life and deep learning among 109 veterinary medicine students across three cohorts (2021–2023) in an integrated modular curriculum in Portugal. Quality of life was measured three times per academic year using the WHOQOL-BREF, whereas deep learning was assessed twice yearly via assessment tests. The results revealed consistently low scores in the psychological domain of the quality-of-life assessment and a noticeable decline in both quality of life and assessment tests during the second year. These findings highlight the need to monitor student well-being and adapt teaching strategies to sustain motivation and academic success. Full article

The design of materials with programmable degradation profiles presents a fundamental challenge in pattern recognition across molecular space, requiring the identification of complex structure–property relationships within an exponentially large chemical domain. This paper introduces a novel physics-informed deep learning framework that integrates multi-scale molecular sensing data with reinforcement learning algorithms to enable intelligent characterization and prediction of polymer degradation dynamics. Our method combines three key innovations: (1) a dual-channel sensing architecture that fuses spectroscopic signatures from Graph Isomorphism Networks with temporal degradation patterns captured by transformer-based models, enabling comprehensive molecular state detection across multiple scales; (2) a physics-constrained policy network that ensures sensor measurements adhere to thermodynamic principles while optimizing the exploration of degradation pathways; and (3) a hierarchical signal processing system that balances multiple sensing modalities through adaptive weighting schemes learned from experimental feedback. The framework employs curriculum-based training that progressively increases molecular complexity, enabling robust detection of degradation markers linking polymer architectures to enzymatic breakdown kinetics. Experimental validation through automated synthesis and in situ characterization of 847 novel polymers demonstrates the framework’s sensing capabilities, achieving a 73.2% synthesis success rate and identifying 42 structures with precisely monitored degradation profiles spanning 6 to 24 months. Learned molecular patterns reveal previously undetected correlations between specific spectroscopic signatures and degradation susceptibility, validated through accelerated aging studies with continuous sensor monitoring. Our results establish that physics-informed constraints significantly improve both the validity (94.7%) and diversity (0.82 Tanimoto distance) of generated molecular structures compared with unconstrained baselines. This work advances the convergence of intelligent sensing technologies and materials science, demonstrating how physics-informed machine learning can enhance real-time monitoring capabilities for next-generation sustainable materials. Full article

Effective resource planning in higher education requires anticipating student demand for courses, especially when dealing with elective programs. Monitoring student preference is a recurring topic in the literature; however, to the authors’ knowledge, no simple methods for estimating student preferences when choosing courses in higher education have been proposed. This study develops and explores the use of a simple questionnaire to capture patterns in student course preferences within a university context. The research is developed in the context of the nine Cross-Over modules offered as part of the curriculum of the master’s programs (MSc) of the Faculty of Civil Engineering and Geosciences of Delft University of Technology (The Netherlands). No prior registration is required far in advance for these courses, making an accurate estimation of student numbers critical for the planning and allocation of educational resources. The developed questionnaire is applied three times in two different academic years to the students’ choice of Cross-Over modules. The questionnaire was shared in 2021, with 225 responses out of 339 students, in 2022, with 159 responses out of 365 students, and in 2024, with 94 responses out of 272 students. Student enrollment in the academic year 2023/2024 is used to assess the performance of the questionnaire. The questionnaire is able to capture general preferences of the students, providing fair estimates of the number of students per course; larger differences are observed in courses with a lower number of students. In addition, some patterns were identified in student preferences: there is a relationship between the first and second choices, and students usually choose modules closer to their own disciplines. The developed questionnaire provides with a reasonable first estimation of the expected number of students in courses, allowing for better planning and allocation of educational resources beforehand. Full article

In response to challenges around student engagement and teacher technological proficiency, this paper looks at the impact of gamification on students’ mathematical resilience whilst monitoring their mathematical anxiety plus investigating teachers’ experiences, willingness, and professional development ambitions to utilise gamified instructional tools in the mathematics classroom. Drawing on strategies to motivate students, the aim of this paper is to unbundle gamification in enacting curriculum change and the role of teacher professional development in using the pedagogical approach in mathematics in Ireland. Ireland is currently experiencing second-level curriculum reforms that are placing particular emphasis on digital competence and technological fluency from both teachers and students. With teachers highlighting the gap in educators’ pedagogical skills for the smooth roll out of recent curriculum reform due to the lack of knowledge and competency in technological teaching strategies, this study is both relevant and timely. Games have been used in multiple industries aiming to motivate participants and increase engagement on a particular matter. However, the term “gamification” has been coined by Pelling as the use of games in a non-gaming context. Current students are very technologically savvy due to the exposure of software applications from a young age and the integration of technological appliances in all walks of life. Traditional teaching and learning strategies are potentially seen as monotonous and somewhat boring to today’s students. Utilising game-based design such as leaderboards, points, and badges encourages motivation and enhances engagement of students. With this in mind, and the rate of change in mathematics curricula globally in recent years, there is a significant emphasis on the necessity of professional development initiatives to adapt at the same rate. Full article

While deep learning has advanced object detection through hierarchical feature learning and end-to-end optimization, conventional random sampling paradigms exhibit critical limitations in addressing hyperspectral ambiguity and low-distinguishability challenges in ground-based cloud detection. To overcome these limitations, we propose CurriCloud, a loss-adaptive curriculum framework featuring three key innovations: (1) real-time sample evaluation via Unified Batch Loss (UBL) for difficulty measurement, (2) stabilized training monitoring through a sliding window queue mechanism, and (3) progressive sample selection aligned with model capability using meteorology-guided phase-wise threshold scheduling. Extensive experiments on the ALPACLOUD benchmark demonstrate CurriCloud’s effectiveness across diverse architectures (YOLOv10s, SSD, and RT-DETR-R50), achieving consistent improvements of +3.1% to +11.4% mAP50 over both random sampling baselines and existing curriculum learning methods. Full article

To address the challenges of real-time decision-making and resource optimization in multi-agent cooperative interception tasks within dynamic environments, this paper proposes a hierarchical framework for reinforcement learning-based interception algorithm (HFRL-IA). By constructing a hierarchical Markov decision process (MDP) model based on dynamic game equilibrium theory, the complex interception task is decomposed into two hierarchically optimized stages: dynamic task allocation and distributed path planning. At the high level, a sequence-to-sequence reinforcement learning approach is employed to achieve dynamic bipartite graph matching, leveraging a graph neural network encoder–decoder architecture to handle dynamically expanding threat targets. At the low level, an improved prioritized experience replay multi-agent deep deterministic policy gradient algorithm (PER-MADDPG) is designed, integrating curriculum learning and prioritized experience replay mechanisms to effectively enhance the interception success rate against complex maneuvering targets. Extensive simulations in diverse scenarios and comparisons with conventional task assignment strategies demonstrate the superiority of the proposed algorithm. Taking a typical scenario of 10 agents intercepting as an example, the HFRL-IA algorithm achieves a 22.51% increase in training rewards compared to the traditional end-to-end MADDPG algorithm, and the interception success rate is improved by 26.37%. This study provides a new methodological framework for distributed cooperative decision-making in dynamic adversarial environments, with significant application potential in areas such as maritime multi-agent security defense and marine environment monitoring. Full article

Medical imaging is crucial for diagnosing, monitoring, and treating medical conditions. The medical reports of radiology images are the primary medium through which medical professionals can attest to their findings, but their writing is time-consuming and requires specialized clinical expertise. Therefore, the automated generation of radiography reports has the potential to improve and standardize patient care and significantly reduce the workload of clinicians. Through our work, we have designed and evaluated an end-to-end transformer-based method to generate accurate and factually complete radiology reports for X-ray images. Additionally, we are the first to introduce curriculum learning for end-to-end transformers in medical imaging and demonstrate its impact in obtaining improved performance. The experiments were conducted using the MIMIC-CXR-JPG database, the largest available chest X-ray dataset. The results obtained are comparable with the current state of the art on the natural language generation (NLG) metrics BLEU and ROUGE-L, while setting new state-of-the-art results on F1 examples-averaged F1-macro and F1-micro metrics for clinical accuracy and on the METEOR metric widely used for NLG. Full article

This paper presents a conceptual framework for strategic approaches to embedding sustainability in the curriculum at a large research-intensive university. Due to the evolving nature of universities and technology, this journey is never complete, and this paper presents a case study of our approach to driving the work forward. This ambition is part of the institution’s Environmental Policy to ‘monitor and increase the integration with the UN Sustainable Development Goals (SDGs) across our operations, research, and education programmes.’ Our conceptual framework to support embedding in the curriculum guides operationalisation across five key domains: 1. strategy, policy, and principles; 2. institution-wide curriculum change; 3. active and authentic education activities; 4. staff development; and 5. community building. For example, an institution-wide curriculum initiative to redesign the Queen Mary graduate attributes framework was developed to include the attribute ‘Promote socially responsible behaviour for a global sustainable future.’ To gain this attribute means that our graduates are exposed to discussions and knowledge concerning sustainability. Across these five areas, we argue that a strategic approach is necessary for successful and impactful embedding of sustainability in the curriculum. Work across each domain needs to be closely linked and interconnected, and to build links with existing policy, strategy, and frameworks. This approach needs to combine high-level leadership together with support for grass-roots initiatives. Full article