Search Results (230)

This study investigates how student teachers engaged with data science and machine learning (ML) through a collaborative, scenario-based project in a graduate-level online course, AI in STEAM Education. The study focuses on the pilot implementation of the module Responsible AI & Data Science: Ethics, Society, and Citizenship, developed within the EU-funded DataSETUP project. This module introduced student teachers to core data science and AI/ML concepts, with an emphasis on ethical reflection and societal impact. Drawing on qualitative artifacts from the pilot, the analysis applies a five-dimensional framework to examine participants’ thinking across the following dimensions of data engagement: (1) asking questions with data, (2) collecting, cleaning, and manipulating data, (3) modeling and interpreting, (4) critiquing data-based claims, and (5) reasoning about data epistemology. Findings show that student teachers demonstrated growing technical and ethical awareness and, in several cases, made spontaneous pedagogical connections—despite the absence of prompts to consider classroom applications. A supplementary coding lens identified four aspects of emerging pedagogical reasoning: instructional intent, curricular relevance, learning opportunities, and the role of the teacher. These findings highlight the value of integrating critically reflective, practice-based data science education into teacher preparation—supporting not only technical fluency but also ethical, civic, and pedagogical engagement with AI technologies. Full article

Power transformers are critical components in electrical power systems, where failures can cause significant outages and economic losses. Traditional maintenance strategies, typically based on offline inspections, are increasingly insufficient to meet the reliability requirements of modern digital substations. This work presents an integrated multi-sensor monitoring framework that combines online frequency response analysis (OnFRA^® 4.0), capacitive tap-based monitoring (FRACTIVE^® 4.0), dissolved gas analysis, and temperature measurements. All data streams are synchronized and managed within a SCADA system that supports real-time visualization and historical traceability. To enable automated fault diagnosis, a Random Forest classifier was trained using simulated datasets derived from laboratory experiments that emulate typical transformer and bushing degradation scenarios. Principal Component Analysis was employed for dimensionality reduction, improving model interpretability and computational efficiency. The proposed model achieved perfect classification metrics on the simulated data, demonstrating the feasibility of combining high-fidelity monitoring hardware with machine learning techniques for anomaly detection. Although no in-service failures have been recorded to date, the monitoring infrastructure is already tested and validated through laboratory conditions, enabling continuous data acquisition. Full article

Accurate detection of reinforcing bars (rebars) in concrete structures using ground penetrating radar (GPR) is crucial for effective structural evaluation but remains challenging, particularly when asphalt overlays compromise signal clarity. This study evaluates the performance of deep learning-based rebar detection using the You Only Look Once version 8 (YOLOv8) object detection model across three GPR datasets categorized as clear, interfering, and blurry. Models trained on each category were applied across varying conditions to assess generalization and robustness. A filtering algorithm was introduced to eliminate redundant and overlapping detections, thereby significantly improving the accuracy of YOLOv8-based predictions. The YOLOv8 approach outperforms traditional analytical techniques, especially under noisy or complex scenarios. In blurry GPR images where analytical methods fail, the filtered YOLOv8 model accurately detects rebar with a count that closely matches the ground truth. Across different datasets, the YOLOv8 approach demonstrates improved consistency in both location and quantity estimation, with filtered predictions correcting substantial over-detection seen in raw outputs. The study presents a practical framework for applying deep learning to GPR data, enhancing the reliability of rebar detection under diverse field testing and evaluation conditions. The findings highlight the importance of developing tailored training datasets and post-processing strategies when deploying AI tools for in-service bridge inspections. Full article

Accurate monitoring of leakage in urban water supply pipelines is crucial for ensuring the safety of residential water usage. This study proposes a robust physical indicator for identifying leaks in urban water pipelines, grounded in the physical background of leakage noise sources. An integral form of the leakage source noise power spectral density is established, and a rigorous theoretical analysis leads to the development of an effective physical indicator. This indicator addresses the limitation of existing leakage detection methods that overly rely on data-driven features. Experiments were conducted to validate the effectiveness and robustness of the proposed indicator. The results show that the leakage detection models trained with physical features achieved recognition accuracies of 99.89% for Support Vector Machine (SVM) and 99.97% for eXtreme Gradient Boosting (XGBoost) in the experiments. In the field test conducted on an in-service water supply pipeline with a total length of 701 m, the recognition accuracies for SVM and XGBoost were 97.92% and 99.31%, respectively. Full article

A predictive health modeling framework was developed for a family of turbofan engines, focusing on early detection of performance degradation. Turbine Gas Temperature (TGT) was employed as the primary indicator of engine health within the model, due to its strong correlation with core engine performance and thermal stress. The present research uses engine health monitoring (EHM) data acquired from in-service turbofan family engines. TGT is typically measured downstream of the high-pressure turbine stage and is regarded as the key thermodynamic variable of the gas turbine cycle. Three new training approaches were proposed using data segmentation based on time between major overhauls and compared with the conventional train–test split method. Detrending was employed to effectively remove trends and seasonality, enabling the ML-based model to learn more intrinsic relationships. Large generalized models based on the entire engine family were also investigated. Prediction performance was evaluated using selected machine learning (ML) models, including both linear and nonlinear algorithms, as well as a long short-term memory (LSTM) approach. The models were compared based on accuracy and other relevant performance metrics. The prediction accuracies of ML models depend on the selection of data size and segmentation for training and testing. For individual engines, the proposed training approaches predicted TGT with the accuracy of 4 ^∘C to 6 ^∘C in root mean square error (RMSE) by utilizing 65% less data than the train (80%)–test (20%) split method. Utilizing the data of each family engine, the large generalized model achieved similar prediction accuracy in RMSE with a smaller interquartile range. However, the amount of data required was 45–300 times larger than the proposed approaches. The sensitivity of prediction accuracy to the size of the training dataset offers valuable insights into the framework’s applicability, even for engines with limited data availability. Uncertainty quantification showed a coverage width criterion (CWC) between 29 ^∘C and 40 ^∘C, varying with different family engines. Full article

In the digital era, integrating technology into education is essential to meet contemporary educational demands. This systematic review examines the competencies and skills in educational technology required from in-service teachers serving in elementary, middle, and high schools, alongside the training models and methods implemented over the past decade. Following PRISMA guidelines, a systematic search was conducted in the Scopus, WOS, and ERIC databases, focusing on studies published between 2014 and 2025. A total of 82 studies were selected based on predefined inclusion criteria. The review analyzed competencies, skills, training models, and methods, identifying prevailing trends in teacher training for educational technology. The review identified seven key competencies, emphasizing skills such as using software, educational applications, and platforms, as well as virtual collaboration. The TPACK model emerged as the predominant framework for teacher training, encompassing various methods, including professional learning communities and Problem-Based Learning. A progressive and structured approach is necessary to develop teachers’ competencies, encompassing both basic technical skills and the adoption of emerging technologies. Continuous and context-specific teacher training in educational technology is critical for sustainable integration and pedagogical transformation. Barriers such as limited infrastructure and resistance to change highlight the need for strong institutional support and mentorship. Future research should aim to expand to diverse educational settings to validate and extend these findings. Full article

Structural damage identification provides a theoretical foundation for the operational safety and preventive maintenance of in-service bridges. However, practical bridge health monitoring faces challenges in poor signal quality, difficulties in feature extraction, and insufficient damage classification accuracy. This study presents a bridge damage identification framework integrating time-varying filtering-based empirical mode decomposition (TVFEMD) with pre-trained convolutional neural networks (CNNs). The proposed method enhances the key frequency-domain features of signals and suppresses the interference of non-stationary noise on model training through adaptive denoising and time–frequency reconstruction. TVFEMD was demonstrated in numerical simulation experiments to have a better performance than the traditional EMD in terms of frequency separation and modal purity. Furthermore, the performances of three pre-trained CNN models were compared in damage classification tasks. The results indicate that ResNet-50 has the best optimal performance compared with the other networks, particularly exhibiting better adaptability and recognition accuracy when processing TVFEMD-denoised signals. In addition, the principal component analysis visualization results demonstrate that TVFEMD significantly improves the clustering and separability of feature data, providing clearer class boundaries and reducing feature overlap. Full article

Composite laminates are widely used in aerospace, automotive, construction, and luxury industries, owing to their superior mechanical properties and design flexibility. However, detecting manufacturing defects and in-service damage remains a vital challenge for structural safety. While traditional unsupervised machine learning methods have been used in structural health monitoring (SHM), their high false positive rates limit their reliability in real-world applications. This issue is mostly inherited from their limited ability to capture small temporal variations in Lamb wave signals and their dependence on shallow architectures that suffer with complex signal distributions, causing the misclassification of damaged signals as healthy data. To address this, we suggested an unsupervised anomaly detection framework that integrates a self-attention autoencoder with a Gaussian mixture model (SAE-GMM). The model is solely trained on healthy Lamb wave signals, including high-quality synthetic data generated via a generative adversarial network (GAN). Damages are detected through reconstruction errors and probabilistic clustering in the latent space. The self-attention mechanism enhances feature representation by capturing subtle temporal dependencies, while the GMM enables a solid separation among signals. Experimental results demonstrated that the proposed model (SAE-GMM) achieves high detection accuracy, a low false positive rate, and strong generalization under varying noise conditions, outperforming traditional and deep learning baselines. Full article

This paper presents a qualitative case study on the design, implementation, and validation of the Art Nouveau Path, a mobile augmented reality game developed to foster sustainability competences through engagement with Aveiro’s Art Nouveau built heritage. Grounded in the GreenComp framework and developed through a Design-Based Research approach, the game integrates location-based interaction, narrative storytelling, and multimodal augmented reality and multimedia content to activate key competences such as systems thinking, futures literacy, and sustainability-oriented action. The game was validated with 33 in-service schoolteachers, both through a simulation-based training workshop and a curricular review of the game. A mixed-methods strategy was used, combining structured questionnaires, open-ended reflections, and curricular review. The findings revealed strong emotional and motivational engagement, interdisciplinary relevance, and alignment with formal education goals. Teachers emphasized the game’s capacity to connect local identity with global sustainability challenges through immersive and reflective experiences. Limitations pointed to the need for enhanced pedagogical scaffolding, clearer integration into STEAM subjects, and broader accessibility across technological contexts. This study demonstrates that these games, when grounded in competence-based frameworks and inclusive design, can meaningfully support multimodal, situated learning for sustainability and offer valuable contributions to pedagogical innovation in Education for Sustainable Development. Full article

The integration of Lesson Study (LS) and Didactic Suitability Criteria (DSC) presents an innovative framework for enhancing mathematics teacher training in higher education. This study examines how LS-DSC fosters instructional refinement, professional growth, and pedagogical transformation among in-service teachers. Using a quasi-experimental mixed-methods approach, the study analyzed data from 520 mathematics educators participating in a six-month training program incorporating collaborative lesson planning, structured pedagogical assessment, and reflective teaching practices. Findings indicate significant improvements in instructional design, mathematical discourse facilitation, and adaptive teaching strategies, with post-training evaluations demonstrating a strong positive correlation (r = 0.78) between initial competency levels and learning gains. Participants reported increased confidence in implementing student-centered methodologies and sustained engagement in peer collaboration beyond the training period. The results align with prior research emphasizing the effectiveness of lesson study models and structured evaluation frameworks in teacher professionalization. This study contributes to higher education policy and practice by advocating for the institutional adoption of LS-DSC methodologies to promote evidence-based professional development. Future research should explore the long-term scalability of LS-DSC in diverse educational contexts and its impact on student learning outcomes. Full article

This study explores teachers’ understanding and implementation of inclusive education in a rural mainstream secondary school in Limpopo Province, South Africa. Grounded in the inclusive pedagogy framework, the research employed a qualitative approach, combining classroom observations and semi-structured interviews with three purposively selected teachers. Findings revealed a significant disconnect between teachers’ conceptual support for inclusion and their classroom practices, which remained largely traditional and undifferentiated. Teachers expressed narrow or fragmented understandings of inclusion, often equating it solely with disability integration, and cited systemic barriers such as overcrowding, rigid curricula, and inadequate training as key challenges. Despite emotional discomfort and pedagogical insecurity, participants demonstrated a willingness to adopt inclusive strategies if provided with contextualised professional development and systemic support. The study underscores the need for strengthened pre-service and in-service teacher training, curriculum flexibility, and resource provision to bridge the policy-practice gap in rural inclusive education. Recommendations include collaborative learning communities, stakeholder engagement, and further research to advance equitable implementation. Full article

Police officers work in high-stress environments that demand emotional resilience, interpersonal skills, and effective communication. Occupational stress can negatively impact their motivation, conflict resolution abilities, and professional effectiveness. Loving-Kindness Meditation (LKM), a mindfulness-based intervention focused on cultivating compassion and empathy, has shown promise in enhancing prosocial attitudes and emotional regulation. With the rise of short video platforms, digital interventions like video-guided LKM may offer accessible mental health support for law enforcement. This study examines the effects of short video app-guided LKM on police officers’ mindfulness, public service motivation (PSM), conflict resolution skills (CRSs), and communication skills (CSSs). It aims to determine whether LKM can enhance these psychological and professional competencies. A randomized controlled trial (RCT) was conducted with 110 active-duty police officers from a metropolitan police department in China, with 92 completing the study. Participants were randomly assigned to either the LKM group (n = 46) or the waitlist control group (n = 46). The intervention consisted of a 6-week short video app-guided LKM program with daily 10 min meditation sessions. Pre- and post-intervention assessments were conducted using several validated scales: the Mindfulness Attention Awareness Scale (MAAS), the Public Service Motivation Scale (PSM), the Conflict Resolution Styles Inventory (CRSI), and the Communication Competence Scale (CCS). A 2 (Group: LKM vs. Control) × 2 (Time: Pre vs. Post) mixed-design MANOVA was conducted to analyze the effects. Statistical analyses revealed significant group-by-time interaction effects for PSM (F(4,177) = 21.793, p < 0.001, η² = 0.108), CRS (F(4,177) = 20.920, p < 0.001, η² = 0.104), and CSS (F(4,177) = 49.095, p < 0.001, η² = 0.214), indicating improvements in these areas for LKM participants. However, no significant improvement was observed for mindfulness (F(4,177) = 2.850, p = 0.930, η² = 0.016). Short video app-guided LKM improves public service motivation, conflict resolution skills, and communication skills among police officers but does not significantly enhance mindfulness. These findings suggest that brief, digitally delivered compassion-focused programs can be seamlessly incorporated into routine in-service training to strengthen officers’ prosocial motivation, de-escalation competence, and public-facing communication, thereby fostering more constructive police–community interactions. Full article

Background/Objectives: Hypoglycemia remains the most frequent acute complication of diabetes, particularly among insulin-treated patients, with significant implications for morbidity, length of hospital stay, and healthcare costs. Nurses play a critical frontline role in its recognition and management, yet their competence varies widely. This study aimed to assess the knowledge and attitudes of Croatian nurses regarding hypoglycemia management and to identify key demographic and professional predictors. Methods: We conducted a cross-sectional online survey following CHERRIES guidelines of 317 nurses across Croatia using a validated 26-item knowledge test and a 6-item attitude scale. Descriptive statistics, Mann–Whitney U tests, and standardized effect sizes were used to assess group differences. Multivariable logistic and linear regression models examined the independent effects of education, sex, experience, and workplace setting. Results: The mean knowledge score was 66.9% (SD = 17.8), and the mean attitude score was 3.42 (SD = 0.70) on a 5-point scale. Nurses with tertiary education had significantly higher odds of achieving adequate knowledge (OR = 68.3, 95% CI: 19.9–234.2) and more favorable attitudes (β = +1.02, p < 0.001). Female sex had a small independent effect on knowledge (OR = 2.59, 95% CI: 1.02–6.62), while experience and workplace setting were not significant predictors. Conclusions: Although overall knowledge and attitudes were moderately positive, substantial disparities persist, particularly across educational levels. Clinical Practice Implications: These findings support integrating structured hypoglycemia training into nursing curricula and in-service programs to improve patient safety. Full article

Background/Objectives: This study assessed teacher self-efficacy in school-based asthma management in two southern states in the United States. Current literature focuses primarily on supporting school-based asthma management, but few studies have focused on teacher self-efficacy in the asthma management process. Methods: With data collected from a two-state survey of a randomly selected group of teachers in grades kindergarten to grade eight (n = 379), teachers’ demographic variables, general opinions about asthma management practices, and their self-perceptions on the Teacher Asthma Management and Information Seeking Scale, which assesses self-efficacy, were examined. Results: Teachers’ self-efficacy in managing asthma and seeking information was significantly higher among teachers who had completed in-service professional learning sessions and those who had access to community resources or links to community agencies. Additionally, teachers with personal experience of chronic illness, asthma, or allergies and those who had students with chronic illnesses in their classrooms reported higher self-efficacy scores. Conclusions: Findings suggest that providing professional learning about asthma for teachers, offering access to asthma action plans and community resources, and increasing awareness of chronic conditions and training for handling medical emergencies can enhance teachers’ self-efficacy and improve outcomes for students with chronic illnesses. Full article

This study investigated community engagement in developing the 21st-century skills of pre-service and in-service teachers in the context of four skills: communication, creative thinking, collaboration, and critical thinking. It focused specifically on the effectiveness of community engagement in promoting the 4Cs for pre- and in-service teachers and whether such effectiveness differs between pre-service and in-service teachers. This study used a sequential mixed-methods design. A quantitative survey of 160 pre-service and 80 in-service teachers in Abu Dhabi was conducted followed by purposeful qualitative interviews with 20 pre-service teachers. The instrument was adapted from an existing 21st century skills measures. Quantitative data were described using descriptive statistics and analyzed using inferential statistics. The interview transcripts were analyzed. The findings showed that in-service teachers’ performance was better than that of pre-service teachers in all four elements of 21st-century skills, with significant disparities recorded in critical thinking and collaboration, possibly because of field experience. These qualitative results show that community engagement promotes the 4Cs by allowing teachers to apply theoretical knowledge in field contexts and sharpen problem-solving, communication, and teamwork skills. Nevertheless, challenges such as limited resources and time must be compensated for with better initiatives that organizations can employ to promote community engagement activities. This study suggests that using social engagement activities in teacher training is a fruitful way to address this skill gap. This provides implications for teacher preparation and the infusion of community engagement into teachers’ training to foster the 21st-century development of competencies in teachers-to-be. Full article