Search Results (530)

Teaching in a globalised world offers opportunities to reduce barriers, enhance understanding, and expand access for diverse learners. Blended approaches, combining in-person and online delivery, can encourage learning communities across geographical boundaries. However, disparities in access to technology, internet reliability, and conducive study environments highlight inequalities and varied learner experiences. While digital networks may support identity and belonging, some students report feeling distracted or disengaged in online settings. This study explores the experiences of first-year doctoral candidates completing the final taught module of their Doctorate in Business Administration (DBA) at the University of Gloucestershire, UK. Participants, mostly international students now based in the UK, are engaged in both in-person classes and online staff-led webinars. Data was gathered through four in-person focus groups. Engeström’s Cultural-Historical Activity Theory was applied as an analytical framework, conceptualising teaching and learning as an activity system mediated by Tools, Rules, Community, and Division of Labour. This enabled a comparison of students’ experiences in online and in-person contexts. The findings revealed contradictions within the system, identifying barriers to engagement and adaptation, and offering insights into the evolving pedagogical demands of blended doctoral education. Full article

An adaptive fault-tolerant containment control approach is developed for nonlinear multi-agent systems to address issues related to both communication link and actuator faults. This approach achieves fault-tolerant containment control through the introduction of a convex hull signal estimator and a fault compensation mechanism. First, a leader–follower network model with communication link faults is constructed, and distributed containment errors are established. The proposed framework involves three key components: the design of an adaptive backstepping control law, the introduction of a nonlinear filter for boundary error elimination, and the application of a radial basis function neural network (RBFNN) for the approximation of unknown nonlinear terms. Meanwhile, an adaptive convex hull estimator is designed to estimate the signals formed by the leaders, and an actuator fault estimator is constructed to compensate for fault signals online. Additionally, Lyapunov stability analysis demonstrates that all containment errors remain uniformly bounded. To support simulation teaching and validation, numerical simulations and autonomous underwater vehicle (AUV) simulations are used to not only to confirm the efficacy of the presented control technique but also to provide illustrative cases for educational purposes. Full article

The sustainable development of agrifood systems is a pressing global challenge, highlighting the need for frameworks that guide responsible investment and community engagement. The Principles for Responsible Investment in Agriculture and Food Systems (CSA-IRA), approved by the Food Security Council in 2014, provide such a framework. Recognizing this opportunity, the FAO selected the Gesplan Research Group of the Polytechnic University of Madrid in 2016 to promote these principles in Latin America, the Caribbean, and Spain, leveraging the expertise of PhD graduates in Projects and Planning for Sustainable Rural Development. The main objective of this research was to explore how teaching, research, and civil society engagement can be integrated to operationalize CSA-IRA principles and foster sustainable development. To achieve this, the study applied the “Working with People” model across multiple countries and contexts, using university–business collaborations to implement practical, socially responsible initiatives. Over nine years, the approach generated a network of 46 universities and 52 agrifood companies across 12 countries, demonstrating effective multi-stakeholder collaboration. The accumulated experience led to the proposal of the Metauniversity—a “university of universities”—as an innovative instrument to scale knowledge transfer, research, and community engagement. These findings highlight that structured, collaborative networks can translate CSA-IRA principles into tangible actions, offering a replicable model for sustainable agrifood development globally Full article

In the digital age, generative artificial intelligence (GenAI) development has brought about structural transformations in higher education. This study examines how students’ regular use of artificial intelligence tools brings a new active player into the educational process. This is an “uninvited assistant” that changes traditional models of teaching and learning. This study was conducted using the following standard methods: bibliometric analysis, student survey using an electronic questionnaire, primary processing and graphical visualization of empirical data, calculation of statistical indicators, t-statistics, and z-statistics. As the results of the bibliometric analysis show, the evolution in the perception and integration of artificial intelligence within higher education discussions, as evidenced by the comparison of network visualizations from 2020 to the present, reveals a significant transformation. Based on a quantitative survey of 1197 undergraduate students in five Eastern European countries, this paper proposes a conceptual shift from the classic two-dimensional (2D) model of higher education services based on university teacher–student interactions to a three-dimensional (3D) model that includes artificial intelligence as a functional third player (an uninvited assistant). Statistical hypothesis testing confirms that students need AI and regularly use it in the learning process, facilitating the emergence of this new player. Based on empirical data, this study presents a hypothetical 3D model (X:Y:Z), where the Z-axis reflects the intensity of AI use. This model challenges traditional didactic frameworks and calls for updating educational policies, ethical standards, and higher education governance systems. By merging digital technologies and social change, the results provide a theoretical and practical basis for rethinking pedagogical relationships and institutional roles in the digital age. Full article

The integration of Soft Open Points (SOPs) into distribution networks has been an essential method for enhancing operational flexibility and efficiency. But simultaneous optimization of network reconfiguration and SOP scheduling constitutes a difficult mixed-integer nonlinear programming (MINLP) problem that is likely to suffer from premature convergence with standard metaheuristic solvers, particularly in large power networks. This paper proposes a novel hybrid algorithm, hTLBO–CO, which synergistically integrates the exploitative capability of Teaching–Learning-Based Optimization (TLBO) with the explorative capability of the Cheetah Optimizer (CO). One of the notable contributions of our framework is an in-depth problem formulation that enables SOP locations on both tie and sectionalizing switches with an efficient constraint-handling scheme, preserving topo-logical feasibility through a minimum spanning tree repair scheme. The evolved hTLBO–CO algorithm is systematically validated across IEEE 33-, 69-, and 119-bus test feeders with differential operational scenarios. Results indicate consistent dominance over established metaheuristics (TLBO, CO, PSO, JAYA), showing significant efficiency improvement in power loss minimization, voltage profile enhancement, and convergence rate. Remarkably, in a situation with a large-scale 119-bus power grid, hTLBO–CO registered a significant 50.30% loss reduction in the single-objective reconfiguration-only scheme, beating existing state-of-the-art approaches by over 15 percentage points. These findings, further substantiated by comprehensive statistical and multi-objective analyses, confirm the proposed framework’s superiority, robustness, and scalability, establishing hTLBO–CO as a robust computational tool for the advanced optimization of future distribution networks. Full article

In the context of digital school development, the leadership practices of school leadership teams play a significant role. If leadership teams want to enact leadership practices effectively, they require strong connections to the entire teaching staff as well as close contact with other key actors in the digital process. Since little is known about these connection patterns of school leadership teams, this study aims to uncover them. The aim is to provide practical advice to school administrators and schools regarding digital school development, and to derive concrete recommendations for action concerning their relationships and management. To this end, we examined the social networks of the teaching staff of 13 German secondary schools (N = 817 teachers) by asking all the teachers to complete a questionnaire about their contacts in relation to digital school development. We conducted a social network analysis and extracted various network metrics pertaining to the school leadership teams of these institutions, considering not only their integration within the overall network but also their connections with a pivotal stakeholder: the digital coordinator. To contextualize our findings, we compared the network metrics of the two different professional target groups using t-tests. The results reveal significant variability in the connection patterns of school leadership teams across different schools. Furthermore, our analysis indicates that digital coordinators consistently exhibit higher levels of connectedness within the realm of digital school development than the members of the school leadership teams. These findings highlight the importance of close collaboration between school leadership teams and the digital coordinator in order to advance digital school development. It is also suggested that school leadership teams should consider delegating more responsibilities to the digital coordinator, particularly those necessitating close collaboration with the teaching staff. Full article

Primary school teaching buildings represent a typical category of densely populated public architecture, where the safe evacuation of occupants is essential to ensuring their safety. The spatial organizational structure plays a pivotal role in determining overall evacuation efficiency. However, systematic research linking spatial organization with evacuation performance remains limited. This study addresses this gap by analyzing 102 real-world cases of primary school teaching buildings, identifying common spatial organizational patterns, and developing a spatial structural framework based on fundamental units and their organizational relationships. A hybrid methodology integrating weighted network analysis and evacuation simulation is employed to quantitatively evaluate the relationship between spatial organization types and evacuation performance, ultimately proposing three design principles—Integrity, Balance, and Stability—to guide evacuation efficiency optimization. The findings provide a methodological reference for evacuation research in public buildings and offer practical design guidance for optimizing primary school facility layouts. Full article

Introduction: The educational inclusion of students with Autism Spectrum Disorder (ASD) in Spain has been promoted through regulations such as LOMCE and LOMLOE. However, its effective implementation depends on teachers’ attitudes and perceptions. This study analyzes teachers’ feelings, attitudes, and concerns regarding the inclusion of students with ASD. Methods: A quantitative, descriptive, and cross-sectional study was conducted with a sample of 2310 teachers from different educational stages in Spain. The SACIE-R and INTEA questionnaires were used to assess teachers’ perceptions of inclusion. ANOVA tests and Spearman correlations were applied for statistical analysis. Results: The results show that the variable “Attitudes” follows a normal distribution, indicating a stable perception of inclusion. In contrast, the variables “Feelings” and “Concerns” present an inverse relationship: the greater the concern, the fewer positive feelings toward inclusion. Significant differences were found based on gender, type of school, educational stage, and teaching specialty. Discussion: Positive attitudes toward inclusion are associated with greater training and specialization in diversity. Special Education teachers show better perceptions, while in Secondary Education, concerns and negative feelings prevail. Continuous training emerges as a key factor in improving teachers’ perceptions. Conclusions: The study highlights the importance of training programs and support strategies to promote effective inclusion. Strengthening support networks and teacher training is recommended to improve attitudes toward the inclusion of students with ASD. Full article

Integrating digital technologies into mathematics teaching remains a complex task for teachers, despite its recognized potential to enhance learning. Research on teacher professional development (TPD) for integrating technology in the mathematics classroom is therefore essential to inform the design of effective TPD programs. This narrative review aims to support researchers by providing an overview of theoretical frameworks employed in the literature on TPD for teaching mathematics with technology. Specifically, we address the following research questions: what theoretical frameworks are employed in research that focuses on TPD for teaching mathematics with technology? In what ways are these theoretical frameworks used? Drawing from a set of studies identified in a recent systematic review, we identify ten theoretical frameworks and classify their use into three categories: design tools, analytical tools, and instructional tools. While most frameworks are used to inform TPD design and data analysis, their use as reflective tools with teachers is less common. This review contributes to clarifying the role of theoretical frameworks in the field and suggests future research directions, including the exploration of their metacognitive potential in TPD settings and the comparison of multiple frameworks through networking. Full article

Flow past bluff bodies (e.g., circular cylinders) forms a canonical context for teaching external flow separation, vortex shedding, and the coupling between surface pressure and hydrodynamic forces in offshore engineering. Conventional laboratory implementations, however, often fragment local and global measurements, delay data feedback, and omit intelligent modeling components, thereby limiting the development of higher-order cognitive skills and data literacy. We present a low-cost, modular, data-enabled instructional hydrodynamics platform that integrates a transparent recirculating water channel, multi-point synchronous circumferential pressure measurements, global force acquisition, and an artificial neural network (ANN) surrogate. Using feature vectors composed of Reynolds number, angle of attack, and submergence depth, we train a lightweight AI model for rapid prediction of drag and lift coefficients, closing a loop of measurement, prediction, deviation diagnosis, and feature refinement. In the subcritical Reynolds regime, the measured circumferential pressure distribution for a circular cylinder and the drag and lift coefficients for a rectangular cylinder agree with empirical correlations and published benchmarks. The ANN surrogate attains a mean absolute percentage error of approximately 4% for both drag and lift coefficients, indicating stable, physically interpretable performance under limited feature inputs. This platform will facilitate students’ cross-domain transfer spanning flow physics mechanisms, signal processing, feature engineering, and model evaluation, thereby enhancing inquiry-driven and critical analytical competencies. Key contributions include the following: (i) a synchronized local pressure and global force dataset architecture; (ii) embedding a physics-interpretable lightweight ANN surrogate in a foundational hydrodynamics experiment; and (iii) an error-tracking, iteration-oriented instructional workflow. The platform provides a replicable pathway for transitioning offshore hydrodynamics laboratories toward an integrated intelligence-plus-data literacy paradigm and establishes a foundation for future extensions to higher Reynolds numbers, multiple body geometries, and physics-constrained neural networks. Full article

In this paper, an adaptive sliding mode fault-tolerant control (FTC) scheme is proposed for small Quadrotor Unmanned Aerial Vehicles (QUAVs) used in education and teaching formation in the presence of systematic unknown external disturbances with actuator failures. A radial basis function neural network (RBFNN) is employed to handle the nonlinear interaction function, and a fault-tolerant-based NN extended state observer (NNESO) is designed to estimate the unknown external disturbance. Meanwhile, an adaptive fault observer is developed to estimate and compensate for the fault parameters of the system. To achieve satisfactory trajectory tracking performance for the QUAV, an adaptive sliding mode control (SMC) strategy is designed. This strategy mitigates the strong coupling effects among the design parameters within the QUAV formation. The stability of the closed-loop system is rigorously demonstrated by Lyapunov analysis, and the controlled QUAV formation can achieve the desired tracking position. Simulation results verify the effectiveness of the proposed control method. Full article

Amid expanding higher education and enhancing spatial quality, modern university campuses face challenges including inefficient space utilization and a disconnect from human-centered design. We developed a coupled model that integrates the analytic hierarchy process (AHP) with space syntax theory to identify and address functional fragmentation, limited accessibility, and diminished spatial vitality. The Delphi method was employed to determine weights on visual and traffic influence factors. Through spatial quantitative analysis using Depthmap software, we estimated spatial-efficiency discrepancies across 11 component types, including school gates, teaching buildings, and libraries. A case study was conducted at a university located in the hilly terrain of Conghua District, Guangzhou, China which revealed significant contradictions between subjective evaluations and objective data at components, such as the administrative building and gymnasium. These contradictions led to poor visual permeability, excessive path redundancy, and imbalanced functional layouts. Based on the results of this study, targeted optimization strategies were proposed, including permeable interface designs, path network reconfiguration, and the implementation of dynamic functional modules. These interventions were tailored to accommodate the humid subtropical climate, balancing shading, ventilation, and visual transparency. In this study, methodological support for the renovation of existing campus infrastructure was provided as theoretical and technical references for space renewal in tropical and subtropical academic environments and the enhancement of the quality and resilience of campus spaces. The results also broadened the application of interdisciplinary methods in university planning. Full article

This study investigated the professional attitudes of Croatian preservice primary school teachers towards science teaching and their epistemological views on science in the context of the ongoing educational reform. In a quantitative survey conducted at a Croatian university, teachers’ overall attitudes were assessed; it investigated whether participation in a science course influenced these attitudes, and the relationship between their attitudes towards teaching and their epistemological views on science was analyzed. The results showed predominantly positive but nuanced attitudes that combined both traditional and contemporary conceptions of science education. Furthermore, the results showed that participation in the science course had no significant influence on these attitudes, and that professional attitudes appeared to develop independently of epistemological views. The study emphasizes the need to effectively integrate theoretical knowledge and practical experience in teacher education. Furthermore, it emphasizes the importance of inquiry-based learning, reflective teaching practice, promoting gender equality, effective mentoring and maintaining professional networks. Future research should investigate specific curricular interventions aimed at improving trainee teachers’ coherence and confidence in science teaching. Full article

Assessing e-learning students’ satisfaction with lecturers’ interactions in asynchronous forums is essential for enhancing teaching and learning processes. The discussion forum allows students to share comments and ideas with peers or lecturers, stimulating diverse perspectives and improving learning efficacy. However, lecturers’ responses are often similar or redundant to previous students’ comments, limiting feedback depth and potentially reducing students’ perceived value of the interaction. Machine learning classifiers have been widely used to assess satisfaction based on sentiment or semantic similarity. However, integrating sentiment and semantic similarity between students’ comments or opinions and lecturers’ responses in asynchronous online discussion forums has received limited attention and may be improved. Through this research, we propose a novel model called E-learning Satisfaction Assessment using Textual Neural Network (E-SATNet). The E-SATNet model has two main sub-networks. The first sub-network employs a Convolutional Neural Network (CNN) to extract sentiment-related features from students’ reactions to lecturers’ responses. The second sub-network utilizes a Bidirectional Long Short-Term Memory (BiLSTM) to extract semantic features from lecturers’ responses and compute their similarity with the overall discussion content. Evaluation results show that E-SATNet effectively assesses satisfaction, achieving an average F1-score of 88.12. Full article

Accurate detection of student behavior in the classroom helps analyze students’ learning states and contributes to improving teaching effectiveness. We propose the PLA-YOLO11n classroom behavior detection model. We design a novel C3K2_PConv module that integrates partial convolution with modules from the YOLO11 network and apply it to the backbone and neck feature fusion layers. To enhance small-target feature representation, we incorporate a large-kernel self-attention (LSKA) mechanism and replace the SPPF at the end of the backbone with the attention feature integration module (AIFI). We also add a high-resolution detection head. Experimental results on the SCB2 dataset demonstrate that the improved model outperforms the original YOLO11, achieving an increase of 3.8% in mean average precision (mAP@0.5). Full article