Search Results (2,512)

Digital inclusion has become an essential component in ensuring the autonomy, social participation, and well-being of older adults. However, their learning of digital skills depends to a large extent on the quality of support provided by the facilitator, whose age, training, and experience directly influence teaching processes and how older adults relate to technology. This study compares the digital competences, and ICT skills of 107 facilitators of digital literacy programs, classified into three groups: peer educators (PEERS), young students without gerontological training (YOS), and young gerontology specialists (YGS). A quantitative design was used. Statistical analyses included non-parametric tests (Kruskal–Wallis, Mann–Whitney, Kendall’s Tau) and parametric tests (ANOVA, t-tests), to examine associations between socio-demographic variables, the level of digital competence, and ICT skills for teachers (technological and pedagogical). The results show clear differences between profiles. YOS achieved the highest scores in digital competence, especially in problem-solving and tool handling. The YGS achieved a balanced profile, combining competent levels of digital skills with pedagogical strengths linked to their gerontological training. In contrast, PEERS recorded the lowest levels of digital competence, particularly in security and information management; nevertheless, their role remains relevant for fostering trust and closeness in training processes among people of the same age. It was also found that educational level is positively associated with digital competence in all three profiles, while age showed a negative relationship only among PEERS. The findings highlight the importance of creating targeted training courses focusing on digital, technological, and pedagogical skills to ensure effective, tailored teaching methods for older adults. Full article

Infrared thermal field (ITF) prediction for meter boxes is crucial for the early warning of power system faults, yet this method faces three major challenges: data sparsity, complex geometry, and resource constraints in edge computing. Existing physics-informed neural network-graph neural network (PINN-GNN) approaches suffer from redundant physics residual calculations (over 70% of flat regions contain little information) and poor model generalization (requiring retraining for new box types), making them inefficient for deployment on edge devices. This paper proposes the PhysGraphIR framework, which employs an Adaptive Residual Sampling (ARS) mechanism to dynamically identify hotspot region nodes through a physics-aware gating network, calculating physics residuals only at critical nodes to reduce computational overhead by over 80%. In this study, a `hotspot region’ is explicitly defined as a localized area exhibiting significant temperature elevation relative to the background—typically concentrated around electrical connection terminals or wire entrances—which is critical for identifying potential thermal faults under sparse data conditions. Additionally, it utilizes a Physics Knowledge Distillation Graph Neural Network (Physics-KD GNN) to decouple physics learning from geometric learning, transferring universal heat conduction knowledge to specific meter box geometries through a teacher–student architecture. Experimental results demonstrate that on both synthetic and real-world meter box datasets, PhysGraphIR achieves a hotspot region mean absolute error (MAE) of 11.8 °C under 60% infrared data missing conditions, representing a 22% improvement over traditional PINN-GNN. The training speed is accelerated by 3.1 times, requiring only five infrared samples to adapt to new box types. The experiments prove that this method significantly enhances prediction accuracy and computational efficiency under sparse infrared data while maintaining physical consistency, providing a feasible solution for edge intelligence in power systems. Full article

The success of STEM education in early childhood education is reliant on the pedagogical practices of teachers. Effective teaching of STEM requires specific knowledge of the four disciplines of STEM, appropriate teaching and learning methods and relevant experiences. In Saudi Arabia the teaching of STEM is a relatively new field, and this paper outlines a research project to promote the teaching and learning of STEM through professional development workshops. The research is informed by Vygotsky’s cultural-historical/socio-cultural theory, acknowledging the crucial role of social interaction and cultural context in a collaborative learning environment. To evaluate the project, a mixed methods approach was used involving the collecting, analyzing, and interpreting of quantitative and qualitative data. Surveys were conducted before and after professional development as well as semi-structured interviews. The findings indicate positive shifts in attitudes and enthusiasm among early childhood educators to teach STEM following the professional development program. However, the practical implementation remains a challenge due to the perceived lack of suitable resources, support from school leadership and the need for ongoing coaching and mentoring. Full article

This study investigates the factors influencing Chinese K-12 teachers’ adoption of generative artificial intelligence (GenAI) for instructional purposes by extending the Technology Acceptance Model (TAM) with pedagogical beliefs, perceived intelligence, perceived ethical risks, GenAI anxiety, and demographic moderators. Drawing on a theory-driven framework, survey data were collected from 218 in-service teachers across K-12 schools in China. The respondents were predominantly from urban schools and most had prior GenAI use experience. Eight latent constructs and fourteen hypotheses were tested using structural equation modeling and multi-group analysis. Results show that perceived usefulness and perceived ease of use are the strongest predictors of teachers’ intention to adopt GenAI. Constructivist pedagogical beliefs positively predict both perceived usefulness and intention, whereas transmissive beliefs negatively predict intention. Perceived intelligence exerts strong positive effects on perceived usefulness and ease of use but has no direct effect on intention. Perceived ethical risks significantly heighten GenAI anxiety, yet neither directly reduces adoption intention. Gender, teaching stage, and educational background further moderate key relationships, revealing heterogeneous adoption mechanisms across teacher subgroups. The study extends TAM for the GenAI era and highlights the need for professional development and policy initiatives that simultaneously strengthen perceived usefulness and ease of use, engage with pedagogical beliefs, and address ethical and emotional concerns in context-sensitive ways. Full article

The present exploratory study investigates how pre-primary and primary student teachers (STs) at the Universidad Autónoma de Madrid (UAM) perceive the impact of bilingual education programs (BEPs) on children’s learning experience. Specifically, it examines student teachers’ views on the benefits and challenges of implementing Content and Language Integrated Learning (CLIL) in pre-primary and primary education and explores whether there are differences between the opinions of the two groups. The analysis is based on data from six items of a structured questionnaire, validated in previous studies and completed by 170 prospective pre-primary and primary teachers at the UAM. The results suggest a shared perception among STs that BEPs enrich the learning experience of students in both pre-primary and primary education. Most STs recognize that CLIL enhances language proficiency and supports cognitive development, although they also point to insufficient teacher training and the low motivation of children with learning difficulties as major challenges. While no major differences emerged between the views of pre-primary and primary STs, subtle variations point to the existence of two distinct trainee profiles that determine their views on BEPs and that would require further mid-term investigation. The findings highlight areas for targeted support in teacher training programs. Full article

In the context of the increasing digitalisation of education, teachers are facing growing technological demands that may affect their professional well-being. The aim of this study was to examine the relationship between digital competencies, technostress, and teachers’ professional well-being. The research was conducted on a sample of primary school teachers using validated questionnaires. The data were analysed using descriptive statistics, Pearson correlation analysis, multiple regression analysis, and one-way analysis of variance. The results showed a statistically significant negative relationship between digital competencies and technostress, as well as a positive relationship between digital competencies and professional well-being. Digital competencies proved to be a significant positive predictor of professional well-being, while technostress did not make a significant independent contribution. Differences in the level of technostress were also found with regard to teachers’ years of work experience. In conclusion, the results highlight the importance of strengthening digital competencies as a key resource for maintaining teachers’ professional well-being in a digital environment. Full article

Semantic segmentation of laparoscopic images requires costly pixel-level annotations, which are often unavailable for real surgical data. This gives rise to an unsupervised domain adaptation scenario, where labeled synthetic images serve as the source domain and unlabeled real images as the target. We propose a frequency-aware unsupervised domain adaptation framework to mitigate the domain gap between simulated and real laparoscopic images. Specifically, we introduce a Radial Frequency Masking module that selectively masks frequency components of real images, and employ a Mean Teacher framework to enforce consistency between high- and low-frequency representations. In addition, we propose a module called Fourier Domain Adaptation-Blend, a style transfer strategy based on low-frequency blending, and apply entropy minimization to enhance prediction confidence on the target domain. Experiments are conducted on public datasets by jointly training on simulated and real laparoscopic images. Our method consistently outperforms representative baselines. These results demonstrate the effectiveness of frequency-aware adaptation in surgical image segmentation without relying on manual annotations from the target domain. Full article

Teachers’ and leaders’ gendered beliefs and practices shape students’ learning experiences, yet Estonian schools lack systematic approaches to address these dynamics. This study explored teachers’ and school leadership team members’ gender beliefs and gender-related practices. The framework was developed based on gender belief system theory, which was adapted to suit the educational context and specific aims of this research. Focus group interviews with four teachers and four leadership team members from one school were conducted using a qualitative abductive research strategy and thematic analysis. The findings revealed themes on gender stereotypes, roles, transgender and gender-diverse students, sexual orientation, students’ interests, and gender-related practices in schools. Teachers and leaders held varying and sometimes contradictory gender beliefs, exhibiting both stereotypical views and awareness of biases. Moreover, they were open to dialogue, with some willing to adjust their views. While perceiving their schools as gender-supportive, they acknowledged broader gender inequality issues and practices. Full article

Early detection of suicidal risk in adolescents requires valid tools adapted to the clinical and educational context. However, there are currently no observational scales developed specifically for use by significant people in the adolescent’s environment. Therefore, the aim of the present study was to design, validate and apply to a pilot sample an observational scale to identify behavioural and emotional signs of suicidal risk in adolescents, from the perspective of adolescents, parents and teachers. Validation study of an Observational Adolescent Suicide Risk Scale (EORSA) based on a theoretical review and expert consensus. Content validity was evaluated through expert judgement by professionals with recognised experience in mental health, psychometrics, and suicide prevention. The scale was subsequently applied to a sample of adolescents, parents and teachers, analysing the mean scores per item in each group. The final scale included 19 items with a high level of agreement among experts (content validity index > 0.80). When applied to the pilot sample, significant differences were observed in the items considered most frequent by each group. The EORSA is a valid and potentially useful tool for identifying signs of suicidal risk in adolescents from an observational perspective. Its design and application allow for a contextualised and multidimensional assessment, favouring preventive interventions adapted to each setting. Full article

Better outcomes in English language learning through improving the quality of teaching is a policy objective in Thailand’s school system. Mandating continuing professional development aims to support the interaction of teachers’ practice, learning, and unique individual strengths and needs in development and refinement of their identities as professionals capable of optimising learning outcomes. The study reported in this paper used data obtained in interviews with five Thai English teachers to investigate how the teachers’ understanding of participation in PD influences their conceptions of professional identity in the Thai context. We found teachers view continuing learning and development as integral to their identities as effective teachers, yet they approach PD offerings/experiences with ideas about development as a teacher that are associated with linear relations between learning and practice and with teaching as technical activity. We conclude that policy prescribing mandated PD and association of PD participation with professional status and career structures shifts understanding of PD away from the needs of teachers and learners as unique individuals. Achieving quality English teaching and learning in Thailand through PD needs to focus on the complexity of negotiating identities in local contexts and engaging teachers in identification of their professional needs. Full article

With the acceleration of digital transformation, open-source software has become a fundamental component of modern smart grids and other critical infrastructures. However, the complex dependency structures of open-source ecosystems and the continuous emergence of vulnerabilities pose substantial challenges to software supply chain security. In power information networks and cyber–physical control systems, vulnerabilities in open-source components integrated into Supervisory Control and Data Acquisition (SCADA), Energy Management System (EMS), and Distribution Management System (DMS) platforms and distributed energy controllers may propagate along the supply chain, threatening system security and operational stability. In such application scenarios, large language models (LLMs) often suffer from limited semantic accuracy when handling domain-specific security terminology, as well as deployment inefficiencies that hinder their practical adoption in critical infrastructure environments. To address these issues, this paper proposes KD-SecBERT, a domain-specific semantic bidirectional encoder optimized through multi-level knowledge distillation for open-source software supply chain security in smart grid applications. The proposed framework constructs a hierarchical multi-teacher ensemble that integrates general language understanding, cybersecurity-domain knowledge, and code semantic analysis, together with a lightweight student architecture based on depthwise separable convolutions and multi-head self-attention. In addition, a dynamic, multi-dimensional distillation strategy is introduced to jointly perform layer-wise representation alignment, ensemble knowledge fusion, and task-oriented optimization under a progressive curriculum learning scheme. Extensive experiments conducted on a multi-source dataset comprising National Vulnerability Database (NVD) and Common Vulnerabilities and Exposures (CVE) entries, security-related GitHub code, and Open Web Application Security Project (OWASP) test cases show that KD-SecBERT achieves an accuracy of 91.3%, a recall of 90.6%, and an F1-score of 89.2% on vulnerability classification tasks, indicating strong robustness in recognizing both common and low-frequency security semantics. These results demonstrate that KD-SecBERT provides an effective and practical solution for semantic analysis and software supply chain risk assessment in smart grids and other critical-infrastructure environments. Full article

Grounded in the circumplex model, this study examined how need-thwarting teaching styles, control and chaos, are combined by Physical Education teachers (as perceived by students) and how these combinations differ in relation to students’ basic psychological needs and motivational outcomes within (experiences and perceived learning) and outside (intention to be physically active) the Physical Education context. A total of 431 Spanish secondary school students (M_age = 14.92; 53% girls) participated. Latent profile analysis identified three profiles: (1) high control—moderate chaos (35%), (2) moderate control—high chaos (9%), and (3) high demanding—low chaos (56%). Mean comparisons revealed that students in the “high demanding—low chaos” profile reported the most adaptive outcomes, including greater autonomy and competence satisfaction, more positive Physical Education experiences, higher perceived learning, and stronger intentions to be physically active. Conversely, the “moderate control—high chaos” profile was linked to the most maladaptive outcomes, characterized by greater basic psychological needs frustration and poorer experiences, learning, and physical activity intentions. The “high control—moderate chaos” profile yielded intermediate results. Overall, findings indicate that chaotic teaching, especially in its abandoning form, was associated with the worst quality of students’ motivation, while a demanding approach may be comparatively less harmful but still detrimental. Teacher training should therefore reduce controlling and chaotic practices and foster autonomy support and structure. Full article

Active music and movement engagement has been widely integrated in human socialization across history and cultures, and is particularly prevalent in early childhood play and learning. For clinical populations, music therapy is known to support social skills and wellbeing for young children. However, there is less evidence for the value of active music engagement for non-clinical populations in terms of supporting social and behavioral wellbeing in the early years. This study reports results from the Rhythm and Movement for Self-Regulation (RAMSR) program delivered by generalist kindergarten teachers in low socioeconomic communities. This randomized control trial involved 213 children across eight preschools in disadvantaged communities in Queensland, Australia. The intervention group received 16 to 20 sessions of RAMSR over eight weeks, while the control group undertook usual preschool programs. Data was collected through teacher report at pre and post intervention, and again six months later once children had transitioned into their first year of school. Robust mixed models accounting for repeated measures and clustering of children within kindergartens (random effects), evidenced significant intervention effects across the three time points for improved prosocial skills (p = 0.04, n_p² = 0.02), and reduced externalizing (p < 0.01, n_p² = 0.03) and internalizing behavior problems (p = 0.04; n_p² = 0.02), with small to moderate effect sizes. These findings highlight the valuable role that intentional active music engagement in universal settings such as preschool can play in terms of social and behavioral wellbeing. The importance of these results lies in the fact that children from lower socioeconomic backgrounds are more likely to experience risks to social and behavioral development, requiring additional supports, yet experience inequities in access to high-quality music and movement programs. Full article

The success of large-scale deep learning models in remote sensing tasks has been transformative, enabling significant advances in image classification, object detection, and image–text retrieval. However, their computational and memory demands pose challenges for deployment in resource-constrained environments. Knowledge distillation (KD) alleviates these issues by transferring knowledge from a strong teacher to a student model, which can be compact for efficient deployment or architecturally matched to improve accuracy under the same inference budget. In this paper, we introduce Hierarchical Multi-Segment Knowledge Distillation (HIMS_KD), a multi-stage framework that sequentially distills knowledge from a teacher into multiple assistant models specialized in low-, mid-, and high-level representations, and then aggregates their knowledge into the final student. We integrate feature-level alignment, auxiliary similarity-logit alignment, and supervised loss during distillation. Experiments on benchmark remote sensing datasets (RSITMD and RSICD) show that HIMS_KD improves retrieval performance and enhances zero-shot classification; and when a compact student is used, it reduces deployment cost while retaining strong accuracy. Full article

Within the global sustainable development agenda, Sustainable Development Goal 4 (SDG 4) highlights improving the accessibility, quality, and learning experience of technical and vocational education and training (TVET). In China, students in vocational colleges often face greater disparities in academic preparation and access to educational resources than their peers in general higher education. Although artificial intelligence (AI) can provide additional learning support and help mitigate such inequalities, there is little empirical evidence on whether and how Gen-AI usage is associated with vocational students’ learning experiences and emotional outcomes, particularly academic anxiety. This study examines how Gen-AI usage is related to academic anxiety among Chinese vocational college students and explores the roles of class engagement and teacher support in this relationship. Drawing on Conservation of Resources (COR) theory, we analyse survey data from 511 students using structural equation modelling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA). The SEM results indicate that Gen-AI usage is associated with lower academic anxiety, with class engagement mediating this relationship. Teacher support for Gen-AI usage positively moderates the association between Gen-AI usage and class engagement. The fsQCA results further identify several configurations of conditions leading to low academic anxiety. These findings underscore AI’s potential to enhance learning quality and experiences in TVET and provide empirical support for advancing SDG 4 in vocational education contexts. Full article