Search Results (883)

This exploratory descriptive-correlational study analyses the perceptions of 257 adult students (doctoral, master’s, and teachers) at DPPD, USV Iași, during the 2025–2026 academic year regarding hybrid education, teaching quality, and environmental sustainability. Using a structured Likert-scale questionnaire, the analysis indicates good-to-excellent internal consistency, with Cronbach’s alpha values ranging between 0.886 and 0.901, and a high overall global average score of 4.64. The findings reveal strong support for the hybrid model. Perceived teaching quality received the highest subscale rating (M = 4.80), closely followed by the perceived ecological impact (M = 4.65). The analysis indicates strong Pearson correlations, specifically between the hybrid learning experience and perceived teaching quality (r = 0.818), as well as between the perceived ecological impact and pro-sustainability attitudes (r = 0.809). Regarding academic mobility, the estimate indicates 81,283 km of avoided commuting travel and approximately 12,295 kg of avoided commuting-related CO₂ emissions, based on self-reported distance, means of transport, and number of physical attendances replaced by online activities. These findings suggest that hybrid learning may represent a relevant managerial option for university sustainability policies. The model appears well suited to postgraduate programmes addressed to employed adults, although the ecological benefits should be read as partial and do not displace perceived teaching quality as the central factor. Full article

This study examines the role of generative artificial intelligence in higher education, focusing specifically on creative degree programs and students’ perceptions of its academic and creative value. Employing a mixed-methods design, data were collected from 555 university students enrolled in communication- and design-related degrees in Spain. By combining an online survey with focus groups, the research analyzed the frequency, purposes, and meanings of AI use in academic tasks. The results show that students primarily use generative AI to clarify concepts, develop ideas, review literature, and support academic production. Although they acknowledge its utility as a learning tool, participants also raised concerns regarding overreliance, reduced creative effort, unreliable outputs, and potential threats to originality and authorship. Consequently, the study concludes that while generative AI is already influencing learning practices in higher education, its educational potential relies heavily on clear pedagogical guidance, ethical implementation, and active teacher mediation. Based on these findings, the article proposes a ten-step teaching framework for AI-supported creative interactive content design, aimed at fostering pedagogical innovation while preserving critical thinking, creativity, and student authorship. Full article

Drawing on Self-Determination Theory, this study examines how perceived learning support, smartphone addiction, and user resistance jointly shape online learning outcomes among Chinese pre-service teachers. Data from 1134 participants enrolled in China’s government-funded targeted teacher education program-primarily oriented toward strengthening the supply of teachers for basic education, especially in central and western China, were analyzed using Mplus 8.3. The results showed that perceived learning support was positively associated with learning outcomes, whereas smartphone addiction was negatively associated with them. The role of user resistance was more complex in the full structural model and should therefore be interpreted with caution. Self-regulated learning (SRL) strategies served as a key mediator: perceived learning support improved learning outcomes partly by fostering SRL, while smartphone addiction hindered learning outcomes by weakening SRL. By examining a distinctive cohort of pre-service teachers, this study highlights the importance of supportive learning conditions and institutional context in online learning, and identifies SRL as a key learning process through which learning support and digital constraints are associated with learning outcomes. Full article

Studies on language-learning psychology demonstrate that perceived teacher confirmation may contribute to student motivation, grit and engagement. However, little attention has been paid to investigating the role of perceived teacher confirmation in language learners’ well-being, including those who learn English as a foreign language. Moreover, the potential working mechanism between perceived teacher confirmation and student well-being remains unsolved. In order to address these gaps, the current study aimed to investigate to what extent teacher confirmation may contribute to EFL learners’ sense of well-being via the mediating role of L2 grit. A total of 440 Chinese college-level EFL learners were recruited through convenience sampling to complete an online composite questionnaire. Pearson correlation and structural equation modeling were employed for statistical analyses. The results were as follows: (1) teacher confirmation was positively associated with Chinese EFL learners’ well-being; (2) L2 grit mediated the relationship between perceived teacher confirmation and EFL learners’ well-being. The research findings reveal that the employment of teacher confirmation behaviors in EFL settings may help to develop learners’ perseverance and sustain their interest in learning a language and, in turn, enhance their sense of well-being. Drawing on the findings, pedagogical implications were systematically discussed. Full article

This paper examines how exclusionary epistemic practices embedded in science as a discipline are reproduced in classroom settings and how these practices can be disrupted when teachers adapt existing materials toward goals of epistemic inclusion. The inequities in access, participation, and representation in science that are reproduced at a societal level are developed in the classroom. Students learn how to do science—they develop the epistemic and social practices of the scientific discipline—in science education classes. When exclusion of some students from classroom knowledge building is normalized, marginalization becomes part of how students learn to engage in science and engineering practices. Drawing on a curricular redesign project conducted during the COVID-19 transition to virtual learning, the study highlights how the repositioning of teachers as active agents and experts in curriculum adaptation was central to the work. Using qualitative data-driven coding with productive disciplinary engagement and epistemic inclusion to guide the analysis, critical cases of curricular adaptations were developed with 10 elementary science teachers during a year-long project-based learning online science curriculum enactment project. Five themes associated with disruption of entrenched exclusionary practices emerged that can be codified as adaptation principles: 1. adapt features of project-based learning to: (1) bring in and leverage students’ identities and intellectual resources from home; (2) provide student-centered choice; (3) elicit diverse knowledge, expertise and ideas; (4) support students to make use of many different modalities; (5) dismantle the usual (traditional) structures between the teacher and students. Full article

Electric motors consume over 45% of global electricity and are a primary source of unplanned industrial downtime. Real-time fault detection at scale faces severe constraints, including distributed topologies, intermittent connectivity, and strict energy budgets on battery-powered edge nodes. Existing hierarchical federated learning approaches address resource disparities across tiers but lack downward feature transfer. This prevents resource-constrained edge sensors from utilizing cloud-learned representations when local fault data is sparse. This paper proposes a hierarchical federated cyber-physical architecture featuring three online cross-layer transfer mechanisms: warm-start Convolutional Neural Network (CNN) weight extraction, Long Short-Term Memory (LSTM) embedding alignment, and adaptive teacher–student distillation. This work is best characterized as a hierarchical federated-learning protocol and edge-hardware feasibility study: it validates the communication protocol, cross-layer transfer mechanisms, and sensor-tier hardware budget end-to-end, using the Gym-Electric-Motor (GEM) simulator as a controlled, reproducible, and openly available substitute for physically instrumented motor faults, rather than as a validated physical motor-fault diagnosis system. The framework is evaluated on a ten-node low-power System-on-Chip (SoC) microcontroller, low-power single-board computer, and cloud computing platform test bench, using GEM-simulated operating trajectories as a controlled, reproducible proxy for non-IID motor fault conditions in five-class motor fault detection. Under this test bench, the framework achieves an over 3-fold convergence speedup and reduces the sensor–cloud accuracy gap by nearly 74% (from 10.1% down to 2.7%) at a 200× lower compute budget. It improves minority-class diagnostic reliability, with F1 scores improving by 45% on average, while achieving over 95% sensor accuracy on this test bench, and the proposed mechanism also limits Macro-F1 degradation under injected sensor noise (SNR = 10 dB) to 8.0%, versus up to 22.5% for independent per-tier training. From an embedded electronics implementation perspective, the system operates under a 3.3 ms latency and consumes only 2.1 mJ per inference on the low-power SoC hardware—outperforming prior edge PdM deployments that report 4–6 mJ per inference—indicating a feasible architecture for energy-constrained edge intelligence, pending validation on physically measured fault data. Full article

Higher education is undergoing a profound transformation driven by online and generative learning environment expansion, rapidly evolving labour market demands, and the emergence of new educational providers offering flexible, industry-aligned learning activities. Universities are responding by redesigning and expanding their degree programmes, even though institutional rigidity sometimes limits their ability to change. This study investigates how higher education institutions can shift from reactive adaptation to proactive leadership in shaping the future of skills and jobs. We propose a skills mapping approach, applying text mining to syllabi using the European Skills, Competences, Qualifications and Occupations (ESCO) classification. The resulting course–skill–occupation map can support communication among stakeholders and update learning objectives, enabling modular curriculum design and micro-credential development. The educational offer of one of the leading technical universities in Europe is presented as a case study. Findings demonstrate how systematic skills extraction can enhance career guidance for students, curriculum development for teachers, and provide actionable insights for upskilling and reskilling. Full article

Teacher adoption of virtual reality (VR) in education appears constrained despite the technology’s potential as an immersive multimodal learning environment. This cross-sectional online survey, based on convenience and snowball sampling, examined adoption perceptions among 408 Romanian teachers from primary, secondary, and tertiary levels using an integrated Technology Acceptance Model and Theory of Planned Behavior framework. Seven constructs were measured on five-point Likert scales and analyzed through internal consistency indices, confirmatory factor analysis, HTMT discriminant validity assessment, structural equation modeling, Spearman correlations, nonparametric group comparisons, supplementary manifest-score regression, and descriptive thematic coding of open-ended responses. The seven-factor CFA model showed acceptable fit (CFI = 0.945, TLI = 0.936, RMSEA = 0.068), and composite reliability and AVE supported convergent validity across all constructs. However, HTMT indicated limited discriminant validity between attitude toward using VR and attitude toward the behavior of adopting VR (HTMT = 0.928). In the SEM model, perceived usefulness showed the largest standardized association with attitude toward using VR, while behavioral intention was mainly associated with attitudinal evaluations and subjective norm; perceived behavioral control showed a weaker standardized path. All scales showed acceptable internal consistency (Cronbach’s $\alpha =0.81$–0.95), and construct means exceeded the scale midpoint (range: 3.45–4.03), indicating generally positive but differentiated perceptions. Supplementary manifest-score regression was consistent with the SEM results: the unified attitude factor showed the strongest statistical association with behavioral intention, followed by subjective norm and perceived behavioral control. Descriptive thematic coding of open-ended responses identified training, infrastructure, equipment access, curriculum-aligned content, cost, technical support, and time as recurrent perceived conditions associated with self-reported VR adoption intentions. The findings suggest that educational VR adoption should be interpreted through self-reported human factors and perceived implementation conditions, including perceived control, access to immersive equipment, practical training, and institutional support. Full article

As generative AI technologies become increasingly embedded in educational cloud platforms, understanding their impact on teacher professional development is essential. Grounded in the stimulus–organism–response (S–O–R) framework, this study investigates how AI-driven stimuli—hedonicity, interactivity, and immersion—influence teachers’ self-regulation and resilience. Using structural equation modeling, data were collected from in-service teachers in Taiwan who actively utilize educational cloud platforms. The results reveal that all three AI-driven stimuli significantly enhance teachers’ self-regulation and resilience, which in turn are significantly associated with perceived value co-creation intentions. Specifically, self-regulation enables teachers to manage goals effectively, while resilience supports their recovery from technical setbacks. The findings indicate that self-regulation positively influences resilience, and both appear to mediate the relationship between perceived AI-driven stimuli and teachers’ value co-creation intentions. This study highlights the potential role of teachers’ psychological adaptability in AI-enhanced environments. Practical implications suggest that platform developers and administrators should prioritize AI features that foster self-directed learning and emotional engagement to promote collaborative willingness and professional alignment within modern educational ecosystems rather than implying proven macro-level transformation. Full article

Although digital transformation has expanded classroom technology access, the integration of ethical development within digital learning environments remains limited. This study investigates the relationship between executive functions and ethical digital citizenship among primary school students aged 6–12 years. Key contributing factors examined include smart education, digital literacy, ethical competency, teacher agency, institutional support, and executive function. Empirical data were collected from 387 primary school teachers and administrators within the Office of the Basic Education Commission network via online and printed questionnaires, capturing educators’ professional observations and proxy-reports of their students’ behavior. Using partial least squares structural equation modeling (PLS-SEM), the study tested seven hypotheses. Results indicate that smart education directly influences digital literacy, a relationship significantly moderated by teacher agency. Furthermore, digital literacy directly impacts ethical competency, with institutional support serving as a critical moderator. Finally, executive functions demonstrated a significant, direct impact on students’ ethical competency. By highlighting the interconnected roles of these six factors, this study provides a novel, evidence-based framework that policymakers can utilize to effectively foster ethical digital citizenship among young Thai learners. Full article

The System for Teaching and Assessment using a Computer Algebra Kernel (STACK) is an open source, computer algebra-based online assessment system for teaching and learning mathematics at university. Although the popularity is increasing around the world, its problem generation needs a complex procedure such as algebraic scripting, dynamic randomization, and grading logic, which poses a substantial workload. In this paper, we propose a mathematics problem generation tool using Generative AI for STACK. It adopts a Retrieval-Augmented Generation (RAG) framework to guide the AI to produce pedagogically aligned problems across Depth of Knowledge (DoK) levels, while a Computer Algebra System (CAS) validates mathematical precision. The output is rendered into an XML template and is imported into the STACK system. For evaluation, we measured the success rate of generating 90 problem files for STACK by the proposal and compared the completion time with their manual generation. Learning Object Review Instrument (LORI) was also evaluated for user satisfactions. The results showed that the success rate was 79% while the time was reduced by 35.71%. Furthermore, the LORI evaluations demonstrated a feasibility score of 82.1%, confirming the potential to mitigate teacher workload. Full article

To address the residual lateral coil misalignment after an unmanned aerial vehicle (UAV) lands on a fixed wireless-charging platform, this study proposes a graph-attention-based knowledge distillation method for embedded self-alignment localization. Four detection-coil voltages form an induced-voltage fingerprint database organized as a multi-scale spatial graph. A graph attention network (GAT) teacher model is trained offline to learn neighborhood correlations in the voltage–position mapping, and its spatial knowledge is distilled into a lightweight Tiny-MLP student model for microcontroller unit (MCU)-based online inference. Experimental results show that the GAT teacher achieves a mean absolute error (MAE) of 0.589 cm, while the distilled Tiny-MLP reduces the MAE of the directly trained Tiny-MLP from 1.548 cm to 1.148 cm (a 25.8% reduction under a fixed seed). In 2000 closed-loop alignment trials with random initial positions, the system achieves an 85.5% success rate under a 0.5 cm threshold, indicating that the method supports low-complexity closed-loop self-alignment for UAV wireless charging. Full article

Introduction: Knowledge of Iberian freshwater fish fauna among the general public is scarce and generally limited to a handful of species. Moreover, this knowledge gap increases as time goes by, particularly in younger generations, due to the lack of content on native fish fauna in school programmes. Nevertheless, schools across the country are proving increasingly receptive to innovative approaches that engage students in meaningful, real-world learning. Objectives: The LIFE PREDATOR programme leverages this opportunity to educate young people about freshwater fish communities. It aims to prevent the spread of the largest invasive fish in Portugal, the European catfish (Silurus glanis), by engaging students as active conservation ambassadors. Methodology: In inland Portugal, fishing is a cultural practice, and children frequently participate in angling activities alongside friends and family members. By reaching children, the programme simultaneously targets future anglers, potential decision-makers, and a channel for intergenerational knowledge transfer. Results: Thus far, the programme has reached over 5000 students and almost 60 schools, mostly throughout the Tagus basin. Preliminary assessments revealed improvements in students’ ability to name emblematic native fish species like the Iberian nase (Pseudochondrostoma spp.), European eel (Anguilla anguilla) and Iberian barbel (Luciobarbus spp.), and recognise the threats posed by invasives like the European catfish. To ensure national scalability, we have developed learning materials designed for use by teachers across Portugal, which are to be made available for free online. Beyond the dissemination directed to adult fishermen, which is often more demanding, Conclusions: LIFE PREDATOR ensures that knowledge about native river fauna, invasive species, and responsible fishing practices is conveyed through trusted, familiar voices. This intergenerational transmission model has the potential to embed long-lasting behavioural change within future fishing communities. Full article

In partially observable adversarial environments, heterogeneous unmanned aerial vehicle (UAV) swarms must generate tasks online from noisy observations while respecting platform capabilities, consumable resources, and structural dependencies among tasks. This paper proposes a task graph generation method that converts local observations, target beliefs, and UAV resource states into executable task graphs with explicit resource semantics and inter-task relations. The method first constructs a sufficiently expressive candidate task graph in the belief and resource spaces. An offline search teacher then evaluates future trajectory particles, resource feasibility, and structural interaction values to produce supervision for node selection, marginal task value, and relation prediction. A relation-biased graph attention network learns to generate task graphs online, and a task manager further performs task filtering, dependency repair, conflict completion, and resource checking. Simulation results under complex observation pressure and unseen adversarial strategies show that the proposed method consistently improves structural generation quality and execution feasibility. Compared with Graphormer, it improves the task-graph utility, task-edge F1-score, and executable-graph ratio by 5.83%, 5.41%, and 2.68%, respectively, while reducing the infeasible-task ratio by 35.14%. These results indicate that combining an offline search teacher with resource-constrained graph modeling provides an effective front-end task organization mechanism for heterogeneous UAV swarm planning. Full article

Teacher professional development (TPD) is increasingly mediated by online platforms, yet emotion analysis in this context remains underdeveloped because teachers’ professional discourse is often reflective, evaluative, and shaped by professional norms. To address this challenge, this study proposes a fine-grained, low-intrusion affective-appraisal analysis framework for TPD-oriented online professional learning that integrates textual evidence with platform interaction logs. The framework retains pleasure, arousal, and dominance from the pleasure–arousal–dominance (PAD) model and introduces utility as an appraisal-related dimension, capturing teachers’ perceived usefulness, value judgment, and professional learning gain. Methodologically, it combines textual representations based on Bidirectional Encoder Representations from Transformers (BERT), intra-week long short-term memory (LSTM) aggregation, interpretable behavioral-log features, and feature-level fusion. Data were collected from an authentic TPD-oriented online course involving 107 pre-service teachers, yielding 1276 teacher-week samples from 4300 texts and 264,028 interaction records. Results show that intra-week sequential modeling improves the macro-averaged F1 score (Macro-F1) over both the term frequency–inverse document frequency plus support vector machine (TF-IDF+SVM) baseline and BERT-based weekly text concatenation, with statistically significant gains over the non-sequential BERT-concat model across all four dimensions. Adding interaction logs improves accuracy across all dimensions and provides complementary process-based evidence, especially for arousal and utility. By linking a four-dimensional affective-appraisal framework with text-log fusion, this study offers a scalable and context-sensitive approach to affective-appraisal analytics in pre-service teacher professional learning. Full article