Search Results (367)

The use of augmented reality (AR) tools and innovative learning environments in education have increased over the last few years due to the rapid advancement of technology. In this study, an AR mathematics learning intervention has been proposed which consisted of the creation of 3D multibase blocks to perform AR arithmetic calculations conducted through active methodologies in the future classroom lab (FCL). The aim of this study was to analyze pre-service teachers’ (PSTs) affective domain (emotion, self-efficacy, and attitude), engagement, motivation, and confidence. The sample consisted of 97 PSTs enrolled on the second year of the Primary Education degree, who were attending the “Mathematics and its Didactics” subject. The findings revealed a significant increase in PSTs’ satisfaction, fun, confidence, and pride, and a decrease in uncertainty, nervousness, and concern. Regarding PSTs’ self-efficacy, a significant improvement was observed in knowing the necessary steps to teach mathematical concepts and work in the FCL. No significant differences were found in attitude, engagement, and motivation; however, the PSTs showed a high disposition in all of them before starting the intervention. Additionally, the PSTs reported to be more confident, and it enhanced their knowledge in the use of 3D design and AR applications to create multibase blocks to support the teaching–learning content of arithmetic operations. Full article

This study investigates the creators, effects, and inhibitors of technostress among STEM teachers, addressing a critical yet underexplored issue in the digitalization of education. Grounded in the technostress model and the job demands–resources (JD-R) model, the study examines the relationships among technostress creators, burnout, organizational effects (job satisfaction, organizational commitment, and work performance), and technostress inhibitors. A cross-sectional survey was conducted with 378 STEM teachers from Zhejiang Province, China. Structural equation modeling (SEM) was employed to test the hypothesized paths. The results revealed that technostress creators significantly increased teacher burnout and negatively affected organizational commitment and work performance. Burnout mediated the impact of technostress creators on job satisfaction and organizational commitment. Technostress inhibitors were found to alleviate burnout, mitigate technostress creators, and enhance STEM teachers’ commitment. These findings validate the applicability of the technostress model in the context of K–12 STEM education in China and highlight the importance of organizational mechanisms for supporting teacher well-being and performance. The study contributes to both theory and practice by proposing an integrative model of technostress and offering actionable recommendations for school leadership to effectively manage technostress. Full article

Teachers’ 21st century technological pedagogical content knowledge affects their performance self-evaluations, and it is considered that their attitudes towards sustainable education disposition and their digital competencies may also have an impact on their performance self-evaluations and thus may significantly affect these relationships. In this study, it was aimed to examine the effect of teachers’ 21st century technological pedagogical content knowledge on their performance self-evaluations, and the moderating role of digital competencies mediated by sustainable educational disposition in the model established for this purpose was examined. The research sample consisted of 478 teachers (305 female (63.8) and 173 (36.2) male teachers) working in various fields in schools in Türkiye. Within the scope of the research, data analyses were carried out in SPSS 21 and PROCESS Macro package programs using Model 4 and Model 58 developed by Hayes (2022). As a result of the analyses, it was seen that sustainable education tendencies had a mediating role in the relationship between teachers’ 21st-century technological pedagogical content knowledge and their performance self-evaluations. In addition to this, it was seen that 21st-century technological pedagogical content knowledge, sustainable educational dispositions and performance self-evaluations depend on the level of digital competencies. Full article

Underwater acoustic target recognition (UATR) is challenged by complex marine noise, scarce labeled data, and inadequate multi-scale feature extraction in conventional methods. This study proposes DART-MT, a semi-supervised framework that integrates a Dual Attention Parallel Residual Network Transformer with a mean teacher paradigm, enhanced by domain-specific prior knowledge. The architecture employs a Convolutional Block Attention Module (CBAM) for localized feature refinement, a lightweight New Transformer Encoder for global context modeling, and a novel TriFusion Block to synergize spectral–temporal–spatial features through parallel multi-branch fusion, addressing the limitations of single-modality extraction. Leveraging the mean teacher framework, DART-MT optimizes consistency regularization to exploit unlabeled data, effectively mitigating class imbalance and annotation scarcity. Evaluations on the DeepShip and ShipsEar datasets demonstrate state-of-the-art accuracy: with 10% labeled data, DART-MT achieves 96.20% (DeepShip) and 94.86% (ShipsEar), surpassing baseline models by 7.2–9.8% in low-data regimes, while reaching 98.80% (DeepShip) and 98.85% (ShipsEar) with 90% labeled data. Under varying noise conditions (−20 dB to 20 dB), the model maintained a robust performance (F1-score: 92.4–97.1%) with 40% lower variance than its competitors, and ablation studies validated each module’s contribution (TriFusion Block alone improved accuracy by 6.9%). This research advances UATR by (1) resolving multi-scale feature fusion bottlenecks, (2) demonstrating the efficacy of semi-supervised learning in marine acoustics, and (3) providing an open-source implementation for reproducibility. In future work, we will extend cross-domain adaptation to diverse oceanic environments. Full article

This study investigates the impact of transformational leadership and the work environment on teacher performance, with a particular focus on how the work environment moderates this relationship within crisis-affected educational settings. A quantitative, survey-based approach was adopted, utilizing a random sample of 509 teachers from various schools across Lebanon. Data analysis was conducted using Smart PLS 4 to assess direct and moderating relationships. The results reveal that while the work environment has a strong and significant positive effect on teacher performance, transformational leadership does not exhibit a statistically significant direct impact. This contrasts with prior studies conducted in stable educational settings, where transformational leadership has consistently been linked to improved teacher performance. The findings suggest that prolonged socio-economic and political crises shift teachers’ reliance more toward a supportive work environment, as basic stability and resource availability become more critical determinants of performance. In such conditions, leadership effectiveness appears contingent on the presence of a positive work environment. This study contributes to the literature by highlighting the fundamental role of the work environment in shaping leadership effectiveness and teacher performance in crisis contexts. It offers empirical insights to inform leadership practices and policy interventions in fragile educational systems. Full article

Leaders are critical players in determining how their employees behave in the workplace. Particularly in higher education, teachers are required to utilize psychological, social, and physical resources to perform their tasks. This, along with institutional limitations, renders the role of ethical leaders more critical for driving positive performance outcomes. In this context, the current study investigates the role of ethical leadership on innovative work behavior of university teachers in Turkey. To provide a better understanding, mediating effect of work engagement and the moderating impact of perceived organizational support are also analyzed. With a total of 211 surveys gathered in a cross-sectional manner and using partial least squares—structural equation modeling with Smart-PLS software—the hypotheses were tested. By embedding social exchange, self-determination, and organizational support theories, the current study highlights the importance of the unique characteristics of ethical leaders in academia as antecedents of innovation for teachers, implementing long-term positive changes in the faculty. When institutional support systems exist, faculty deans can trigger engagement by leveraging the facilities and initiatives of the university, ultimately enhancing the learning environment of students while tending to the wellbeing of academic staff. Full article

Financial fraud detection is a critical application area within the broader domains of cybersecurity and intelligent financial analytics. With the growing volume and complexity of digital transactions, the traditional rule-based and shallow learning models often fall short in detecting sophisticated fraud patterns. This study addresses the challenge of accurately identifying fraudulent financial activities, especially in highly imbalanced datasets where fraud instances are rare and often masked by legitimate behavior. The existing models also lack interpretability, limiting their utility in regulated financial environments. Experiments were conducted on three benchmark datasets: IEEE-CIS Fraud Detection, European Credit Card Transactions, and PaySim Mobile Money Simulation, each representing diverse transaction behaviors and data distributions. The proposed methodology integrates a transformer-based encoder, multi-teacher knowledge distillation, and a symbolic belief–desire–intention (BDI) reasoning layer to combine deep feature extraction with interpretable decision making. The novelty of this work lies in the incorporation of cognitive symbolic reasoning into a high-performance learning architecture for fraud detection. The performance was assessed using key metrics, including the F1-score, AUC, precision, recall, inference time, and model size. Results show that the proposed transformer–BDI model outperformed traditional and state-of-the-art baselines across all datasets, achieving improved fraud detection accuracy and interpretability while remaining computationally efficient for real-time deployment. Full article

Teachers with mental illness are vulnerable to work-related triggers that can lead to relapse, affecting their mental health and ability to recover, stay employed, and deliver quality education. This empirical study explored such triggers among public school teachers in Limpopo Province, South Africa. Using Bronfenbrenner’s Ecological Systems Theory, a qualitative phenomenological design was adopted. Semi-structured face-to-face interviews were conducted with 14 participants that were purposively selected across four hospitals. Data were audio-recorded, transcribed verbatim, and analyzed using Tesch’s eight-step open-coding method. Findings revealed being gossiped about by colleagues, excessive workload, limited leadership and parental support, classroom management challenges, high performance expectations without support, and inadequate teacher mental health policies in schools. These triggers can lead to frequent absenteeism and poor teaching outcomes. They will further increase the risk of medication resistance and long-term cognitive decline due to progressive structural brain damage as a result of multiple relapses. The study highlights the urgent need for multi-stakeholder collaboration, including clinicians, academic institutions, union representatives, and the Department of Basic Education, to co-develop effective, context-sensitive strategies to mitigate work-related triggers of mental illness relapse. These strategies are not only essential for enabling long-term workforce participation but also advancing sustainable mental health and well-being. Full article

The modern system of higher education and research is undergoing deep institutional transformations, accompanied by changes in funding mechanisms, increased competition, the growing importance of project forms of scientific activity organization, and more complex requirements for performance. In the conditions of digital transformation and institutional instability, higher education faces the need to form sustainable smart management systems. The modern understanding of smart education goes beyond e-learning and includes the intellectualization of all levels of organization of educational and scientific activities. This requires the creation of new models capable of integrating the behavior of teachers and researchers in the context of digital, project, and institutional logics. Thus, the task of building intelligent models capable of reflecting the complex, multi-layered structure of interactions between researchers, organizations, forms of support, and the system of evaluation of scientific work becomes relevant. This article proposes an agent-based approach to modeling the process of formation of scientific knowledge, considered as a key element of the sustainable development of scientific and educational environment. The model reflects the interaction of agents—researchers with different characteristics: age, qualification level, scientific productivity, affiliation, and trajectory of professional development. The modeling results allow us to draw conclusions about the regularities of the reproduction of scientific potential, the factors of academic environment sustainability, and the effectiveness of institutional support mechanisms. The obtained results have both theoretical and applied significance. The model can be used to forecast the effectiveness of science policy, assess the risks and prospects of scientific teams, and justify incentive systems and the long-term design of the development of scientific organizations. The presented approach allows us to form a comprehensive view of the dynamics of scientific knowledge in the context of sustainable management in higher education. Full article

Nonlinear characteristics of solar photovoltaic (PV) and nonuniform surrounding conditions, including partial shading conditions (PSCs), are the major factors responsible for lower conversion efficiency in solar panels. One major condition is the cause of the multiple peaks and oscillation around the peak point leading to power losses. Therefore, this study proposes a novel hybrid framework based on an artificial neural network (ANN) and fractional order PID (FOPID) controller, where new algorithms are employed to train the ANN model and to tune the FOPID controller. The primary aim is to maintain the computed power close to its true peak power while mitigating persistent oscillations in the face of continuously varying surrounding conditions. Firstly, a modified shuffled frog leap algorithm (MSFLA) was employed to train the feed-forward ANN model using real-world solar PV data with the aim of generating a reference solar PV peak voltage. Subsequently, the parameters of the FOPID controller were tuned through the application of the Sanitized Teacher–Learning-Based Optimization (s-TLBO) algorithm, with a specific focus on achieving maximum power point tracking (MPPT). The robustness of the proposed hybrid framework was assessed using two different types (monocrystalline and polycrystalline) of solar panels exposed to varying levels of irradiance. Additionally, the framework’s performance was rigorously tested under cloudy conditions and in the presence of various partial shading scenarios. Furthermore, the adaptability of the proposed framework to different solar panel array configurations was evaluated. This work’s findings reveal that the proposed hybrid framework consistently achieves maximum power point with minimal oscillation, surpassing the performance of recently published works across various critical performance metrics, including the $MP{P}_{efficiency}$, relative error (RE), mean squared error (MSE), and tracking speed. Full article

This study presents a systematic mapping of inclusive pedagogical models in STEM education, focusing on integrating emotional intelligence, resilience, and motivation from a gender perspective. The research aims to identify strategies that promote inclusive learning environments and reduce gender gaps in STEM disciplines. A total of 753 studies were initially identified, with 51 articles meeting the inclusion criteria and being analyzed in depth. The results reveal that active methodologies, emotional intelligence training, mentoring programs, and the presence of female role models are key strategies for fostering women’s participation and retention in STEM fields. Additionally, the findings highlight the growing importance of integrating socio-emotional skills in STEM education to improve academic performance and strengthen resilience and motivation, particularly in under-represented groups. The study discusses challenges such as teacher resistance, lack of training, and contextual barriers that affect the implementation of inclusive models. It also reflects on the influence of cultural and linguistic factors, especially in Latin American contexts. This work expands the understanding of inclusive pedagogical practices in STEM and provides relevant recommendations for educators, curriculum designers, and policymakers aiming to foster equity and sustainability in education. Full article

The physical inactivity of adolescents and their sedentary lifestyle with profuse usage of screens has been a growing issue for the last few years. In contrast, there is some evidence that videogame-based exercising improves cognitive abilities and psychological well-being during growth and maturation. Therefore, there is a need for the wider exploration of innovation tools in physical education (PE) and extracurricular activities for schoolchildren. The aim of this study was to determine the change in psychological well-being and cognitive skills of adolescents when exercising is supplemented with videogame-based activity. The short-term physical activity (PA) program, initiated by in-service PE teachers (n = 3), involved 13–15-year-old adolescents (n = 63, of them 20 were boys) from one of biggest cities in Lithuania. The research subjects were participants of extracurricular exercise groups on a regular basis, attending their respective three-times-a-week sessions for 1 month. The first intervention group engaged in a 60 min functional training program (FT group, n = 31). The second group had 30 min of FT followed by 30 min of video-based dance class (FT + Just Dance group, n = 32). The Trail-Making test (part A and B), the Visual Digit Span test, and the Stroop test were performed to investigate students’ cognitive abilities. In addition, the WHO-5 questionnaire was used to analyse the respondents’ psychological well-being. When comparing pre- and post-intervention results, no changes were observed in the psychological state, visual–executive skills, and short-term visual memory in both groups. Reaction time improved significantly in both groups (p < 0.05). The working memory significantly improved in the FT + Just Dance group (p < 0.05). The implementation of videogame-based training, Just Dance, improved adolescents’ working memory, but had no effect on subjectively perceived psychological well-being. Full article

This study combines a structural and functional perspective on distributed leadership to disentangle its beneficial and detrimental effects on job satisfaction and work performance. Specifically, we explore the interaction between structural (SDL) and functional distributed leadership (FDL) on leadership support, organizational identification, and empowerment. This study also tests the mediating role of leadership support, organizational identification and empowerment as mechanisms that explain the association between distributed leadership and work-related outcomes in teachers. We used a multilevel mediation analysis to test the overall model in a sample of 2632 teachers embedded in 203 Romanian schools. The results replicate previous findings regarding the negative association between SDL and empowerment and identification and show that FDL has an overall positive association with leadership support, identification, and empowerment, as well as with job satisfaction and work performance reported by teachers. SDL had a negative indirect association with job satisfaction mediated by leadership support and with work performance mediated by organizational identification. The association between FDL and job satisfaction was significantly mediated by leadership support, identification, and empowerment within schools. Finally, the association between FDL and work performance was significantly mediated by organizational identification within as well as between schools. Full article

Despite the limited academic focus on the context of Official Language Schools (OLSs), these institutions play a crucial role in the formal education system, which are exclusively dedicated to foreign language teaching across various modalities in Andalusia, where ten different languages are taught. The main aim of this study is to perform an analysis the following two specific aspects: first, a descriptive analysis of the digital competence of 105 OLS teachers, and, second, a contrastive analysis examining potential differences in digital competence based on the language and teaching modalities (e.g., face-to-face vs. blended learning). This study uses the DigCompEdu framework to evaluate the digital skills of the teachers, revealing that, while they receive some training in digital competence, the overall level is only moderate, indicating a significant need for further professional development. Notably, the study highlights that the teachers’ ability to convey the importance of digital tools for educational purposes is a crucial area, particularly in an environment where digital natives and immigrants coexist, presenting an intergenerational digital divide. The contrastive analysis shows no significant differences in digital competence based on language or modality, pointing to the lack of specialized training for blended learning teachers, who must rely heavily on technology in their work. This study suggests future research should focus on the digital competence of students, considering age as a potential influential factor in language learning, and recommends designing a tailored digital competence training plan for OLS teachers based on the DigCompEdu framework, which could benefit foreign language educators broadly. Full article

Stripe-like space target detection (SSTD) plays a crucial role in advancing space situational awareness, enabling missions like satellite navigation and debris monitoring. Existing unsupervised methods often falter in low signal-to-noise ratio (SNR) conditions, while fully supervised approaches require extensive and labor-intensive pixel-level annotations. To address these limitations, this paper introduces MRSA-Net, a novel encoder-decoder network specifically designed for SSTD. MRSA-Net incorporates multi-receptive field processing and multi-level feature fusion to effectively extract features of variable and low-SNR stripe-like targets. Building upon this, we propose the Collaborative Static-Dynamic Teaching (CSDT) architecture, a semi-supervised learning architecture that reduces reliance on labeled data by leveraging both static and dynamic teacher models. The framework uses the straight-line prior of stripe-like targets to customize linearity and presents an innovative Adaptive Pseudo-Labeling (APL) strategy, dynamically selecting high-quality pseudo-labels to enhance the student model’s learning process. Extensive experiments on AstroStripeSet and other real-world datasets demonstrate that the CSDT framework achieves state-of-the-art performance in SSTD. Using just 1/16 of the labeled data, CSDT outperforms the second-best Interactive Self-Training Mean Teacher (ISMT) method by 2.64% in mean Intersection over Union (mIoU) and 4.5% in detection rate ($P_d$), while exhibiting strong generalization in unseen scenarios. This work marks the first application of semi-supervised learning techniques to SSTD, offering a flexible and scalable solution for challenging space imaging tasks. Full article