Search Results (7,720)

In the digital age, online teacher professional development (TPD) has become a key strategy for enhancing instructional quality and ensuring equitable access to continuous learning. This research compares and analyzes Chinese online teacher professional development (TPD) with the United States over a period of ten years, from 2014 to 2024. This study uses a mixed-methods approach based on policy documents, structured surveys, and interviews to investigate how governance regimes influence TPD outcomes for fair education. Both countries experienced a massive expansion of web-based TPD access and engagement, with participation rates over 75% and effectiveness scores over 4.3 by 2024. China focused on fast scaling by way of centralized mandates and investments in infrastructure, while the United States emphasized gradual expansion through decentralized, locally appropriate models. Most indicators had converged by the end of the period, even with these different approaches. Yet, qualitative evidence reveals persisting gaps in functional access and contextual appropriateness, especially in rural settings. Equality frameworks with attention to teacher agency, policy implementation, and digital usability must supplant weak access metrics. A hybrid paradigm presents itself as an attractive means toward building equitable and productive digital TPD environments through the symbiotic integration of China’s successful scalability and the United States’ professional autonomy. Full article

The increasing cultural and demographic diversity among higher education students highlights the challenges regarding accessibility and inclusion. The COVID-19 pandemic has accelerated the shift toward flexible, technology-based teaching practices. However, inclusive, and accessible pedagogical practices lack consistency, particularly when supporting students with disabilities or diverse learning needs. This study evaluates the effectiveness of the Learning Design for Flexible Education (FLeD) Tool—a web-based platform developed to support teachers in designing flexible and inclusive learning scenarios. The research adopts a qualitative approach, featuring semi-structured interviews with two Portuguese experts in accessibility and inclusion. The experts analyzed three learning scenarios designed using the FLeD Tool, through the lens of Universal Design for Learning standards. The collected dataset was analyzed using thematic analysis to identify common issues, strengths, and opportunities for improvement. The findings show a gap between institutional policies and their practical application, mainly due to inconsistent teacher training and technical limitations. While the FLeD Tool supports more flexible and inclusive pedagogical designs, experts have identified key shortcomings such as the lack of automated accessibility checks and limited support for specific disabilities. Despite the reduced number of participants (two experts) and dataset (three learning scenarios), which limits the study’s generalisability, the conclusions draw attention to the pivotal role of systematic teacher training, embedded accessibility features and solid institutional policies in bridging the gap between policy aspiration and effective inclusive practice. Full article

Autism Spectrum Disorder (ASD) remains significantly underdiagnosed in women, resulting in a persistent gender gap with important clinical, functional, and psychosocial implications. This narrative review explores the multifactorial barriers contributing to diagnostic disparities, including the male-oriented structure of current diagnostic criteria, the prevalence of co-occurring psychiatric conditions, and the phenomenon of social camouflaging shaped by culturally reinforced gender norms. These factors frequently lead to delayed identification, clinical misinterpretation, and suboptimal care. The review synthesizes evidence from clinical, psychological, and sociocultural research to demonstrate how the under-recognition of ASD in women impacts mental health outcomes, access to education, occupational stability, and overall quality of life. Special emphasis is placed on the consequences of missed or late diagnoses for healthcare delivery and the educational needs of clinicians involved in ASD assessment and care. This article concludes with actionable, evidence-based recommendations for enhancing diagnostic sensitivity, developing gender-responsive screening strategies, and integrating training on female autism presentation into medical and allied health education. Addressing these challenges is essential to reducing diagnostic inequities and ensuring timely, accurate, and person-centered care for autistic women throughout their lifespan. Full article

Generative AI tools such as ChatGPT are reshaping educational practice, yet first-year teacher-education students often lack the prompt-engineering skills and confidence required to use them responsibly. This pilot study examined whether a concise three-session clinic on prompt engineering could simultaneously boost AI literacy and reduce technology anxiety in prospective teachers. Forty-five freshmen in a Peruvian teacher-education program completed validated Spanish versions of a 12-item AI-literacy scale and a 12-item technology-anxiety scale one week before and after the intervention; normality-checked pre–post differences were analysed with paired-samples t-tests, Cohen’s d, and Pearson correlations. AI literacy rose by 0.70 ± 0.46 points (t (44) = −6.10, p < 0.001, d = 0.91), while technology anxiety fell by 0.58 ± 0.52 points (t (44) = −3.82, p = 0.001, d = 0.56); individual gains were inversely correlated (r = −0.46, p = 0.002). These findings suggest that integrating micro-level prompt-engineering clinics in the first semester can help future teachers engage critically and comfortably with generative AI and guide curriculum designers in updating teacher-training programs. Full article

The knowledge tracing method based on deep learning is used to assess learners’ cognitive states, laying the foundation for personalized education. However, deep learning methods are inefficient when processing long-term series data and are prone to overfitting. To improve the accuracy of cognitive state prediction, we design a Multi-layer Attention Self-supervised Knowledge Tracing Method (MASKT) using self-supervised learning and the Transformer method. In the pre-training stage, MASKT uses a random forest method to filter out positive and negative correlation feature embeddings; then, it reuses noise-processed restoration tasks to extract more learnable features and enhance the learning ability of the model. The Transformer in MASKT not only solves the problem of long-term dependencies between input and output using an attention mechanism, but also has parallel computing capabilities that can effectively improve the learning efficiency of the prediction model. Finally, a multidimensional attention mechanism is integrated into cross-attention to further optimize prediction performance. The experimental results show that, compared with various knowledge tracing models on multiple datasets, MASKT’s prediction performance remains 2 percentage points higher. Compared with the multidimensional attention mechanism of graph neural networks, MASKT’s time efficiency is shortened by nearly 30%. Due to the improvement in prediction accuracy and performance, this method has broad application prospects in the field of cognitive diagnosis in intelligent education. Full article

This study develops and validates an artificial intelligence (AI)-assisted internship learning platform for automotive electronics based on the Llama 3 large language model, aiming to enhance pedagogical effectiveness within vocational training contexts. Addressing critical issues such as the persistent theory–practice gap and limited innovation capability prevalent in existing curricula, we leverage the natural language processing (NLP) capabilities of Llama 3 through fine-tuning based on transfer learning to establish a specialized knowledge base encompassing fundamental circuit principles and fault diagnosis protocols. The implementation employs the Hugging Face Transformers library with optimized hyperparameters, including a learning rate of 5 × 10⁻⁵ across five training epochs. Post-training evaluations revealed an accuracy of 89.7% on validation tasks (representing a 12.4% improvement over the baseline model), a semantic comprehension precision of 92.3% in technical question-and-answer assessments, a mathematical computation accuracy of 78.4% (highlighting this as a current limitation), and a latency of 6.3 s under peak operational workloads (indicating a system bottleneck). Although direct trials involving students were deliberately avoided, the platform’s technical feasibility was validated through multidimensional benchmarking against established models (BERT-base and GPT-2), confirming superior domain adaptability (F1 = 0.87) and enhanced error tolerance (σ² = 1.2). Notable limitations emerged in numerical reasoning tasks (Cohen’s d = 1.15 compared to human experts) and in real-time responsiveness deterioration when exceeding 50 concurrent users. The study concludes that Llama 3 demonstrates considerable promise for automotive electronics skills development. Proposed future enhancements include integrating symbolic AI modules to improve computational reliability, implementing Kubernetes-based load balancing to ensure latency below 2 s at scale, and conducting longitudinal pedagogical validation studies with trainees. This research provides a robust technical foundation for AI-driven vocational education, especially suited to mechatronics fields that require close integration between theoretical knowledge and practical troubleshooting skills. Full article

The successful deployment of sustainable building construction (SBC) is connected to sound knowledge sharing. Concerning SBC, knowledge sharing has been identified to directly and indirectly increase innovation, environmental performance, cost saving, regulatory compliance awareness and so on. The necessity of enhancing SBC practice globally has been emphasised by earlier research. Consequently, this study aims to investigate knowledge-sharing elements to enhance SBC in South Africa (SA). Utilising a questionnaire survey, this study elicited data from 281 professionals in the built environment. Data analysis was performed with “descriptive statistics”, the “Kruskal–Wallis H-test”, and “principal component analysis” to determine the principal knowledge-sharing features (KSFs). This study found that “creating public awareness of sustainable practices”, the “content of SBC training, raising awareness of green building products”, “SBC integration in professional certifications”, an “information hub or repository for sustainable construction”, and “mentoring younger professionals in sustainable practices” are the most critical KSFs for SBC deployment. These formed a central cluster, the Green Education Initiative and Eco-Awareness Alliance. The results achieved a reliability test value of 0.956. It was concluded that to embrace the full adoption of SBC, corporate involvement is critical, and all stakeholders must embrace the sustainability paradigm. It is recommended that the principal knowledge-sharing features revealed in this study should be carefully considered to help construction stakeholders in fostering knowledge sharing for a sustainable built environment. Full article

Background/Objectives: Health education plays a vital role in training health professionals and caregivers, supporting both prevention and the promotion of self-care. In this context, technology serves as a valuable ally by enabling continuous and flexible learning. Among the various domains of health education, nutrition stands out as a key element in the management of Amyotrophic Lateral Sclerosis (ALS), helping to prevent malnutrition and enhance patient well-being. Accordingly, this study aimed to evaluate the effectiveness of the teaching and learning processes within a learning pathway focused on food and nutrition in the context of ALS. Methods: This study adopted a longitudinal, quantitative design. The learning pathway, titled “Food and Nutrition in ALS,” consisted of four self-paced and self-instructional Massive Open Online Courses (MOOCs), offered through the Virtual Learning Environment of the Brazilian Health System (AVASUS). Participants included health professionals, caregivers, and patients from all five regions of Brazil. Participants had the autonomy to complete the courses in any order, with no prerequisites for enrollment. Results: Out of 14,263 participants enrolled nationwide, 182 were included in this study after signing the Informed Consent Form. Of these, 142 (78%) completed at least one course and participated in the educational intervention. A significant increase in knowledge was observed, with mean pre-test scores rising from 7.3 (SD = 1.8) to 9.6 (SD = 0.9) on the post-test across all courses (p < 0.001). Conclusions: The self-instructional, technology-mediated continuing education model proved effective in improving participants’ knowledge about nutrition in ALS. Future studies should explore knowledge retention, behavior change, and the impact of such interventions on clinical outcomes, especially in multidisciplinary care settings. Full article

This study examines the factors influencing the adoption of enterprise resource planning (ERP) systems within Romanian organizations. The objective is to develop a comprehensive framework for ERP adoption decisions, thereby advancing the field of knowledge and offering managerial insights. To accomplish this research goal, a questionnaire is envisioned, employing various research hypotheses, and distributed to a representative sample. Quantitative econometric regression analysis is employed, considering potential factors such as user training and education, competitive pressures, user involvement and participation, decentralized ERP features, top management support, data quality, the quality of the ERP system, cost and budget considerations, and business process reengineering. Of the 12 factors analyzed, 9 were found to be relevant in terms of influence on the decision to adopt ERP systems, in the context of the Romanian market. The other three factors were found to be irrelevant, thus obtaining results partially different from other areas of the world. By validating the hypotheses and answering the research questions, this work addresses a research gap regarding the lack of a comprehensive understanding of the influencing factors that shape the adoption process of ERP systems in Romania. Full article

This study explores the perceptions of future educators, specifically Early Childhood Education students at the Universities of Cádiz and Gdansk, regarding the interconnections between critical thinking and sustainability. The work aims to provide valuable insights into general teacher training, examining how these students’ experiences are contextualized within their respective educational systems and cultural contexts. To achieve this, eleven group interviews (three in Cádiz, eight in Gdansk) were conducted using a structured and expert-validated script. The transcribed data were qualitatively analyzed using QDA MINER v.6 software. Key findings reveal divergent perceptions of critical thinking among pre-service teachers: while Spanish students leaned towards a subjective understanding, Polish students emphasized an objective, data-driven approach. This distinction has significant implications for the conceptualization and teaching of critical thinking in educator training. Despite these differences, both groups of participants highlighted the necessity of implementing active methodologies in higher education (such as cooperative learning, problem-solving, and debates) to foster critical thinking, both for their own development and for preparing for their future practice with young children. This study also identified an excessive emphasis on theoretical aspects of sustainability in these future teachers’ training and a limited understanding of their practical application in the classroom. Furthermore, explicit connections between critical thinking and sustainability were scarce in student responses, highlighting a gap in current educator training in these areas. Collectively, the results suggest significant weaknesses in current teacher training efforts regarding the development of critical thinking and its effective integration with sustainability competencies. Full article

Background: Reverse total shoulder arthroplasty (RTSA) is increasingly used for managing cuff tear arthropathy, osteoarthritis, complex fractures, and revision procedures. As the demand for surgical precision and reproducibility grows, immersive technologies such as virtual reality (VR), augmented reality (AR), and metaverse-based platforms are being explored for surgical training, intraoperative guidance, and rehabilitation. While early data suggest potential benefits, a focused synthesis specific to RTSA is lacking. Methods: This systematic review was conducted in accordance with PRISMA 2020 guidelines. A comprehensive search of PubMed, Scopus, and Cochrane Library databases was performed through 30 May 2025. Eligible studies included those evaluating immersive technologies in the context of RTSA for skill acquisition or intraoperative guidance. Only peer-reviewed articles published in English were included. Data were synthesized narratively due to heterogeneity in study design and outcome metrics. Results: Out of 628 records screened, 21 studies met the inclusion criteria. Five studies evaluated immersive VR for surgical training: four randomized controlled trials and one retrospective case series. VR training improved procedural efficiency and showed non-inferiority to cadaveric training. Sixteen studies investigated intraoperative navigation or AR guidance. Clinical and cadaveric studies consistently reported improved accuracy in glenoid baseplate positioning with reduced angular and linear deviations in postoperative controls as compared to preoperative planning. Conclusions: Immersive technologies show promise in enhancing training, intraoperative accuracy, and procedural consistency in RTSA. VR and AR platforms may support standardized surgical education and precision-based practice, but their broad clinical impact remains limited by small sample sizes, heterogeneous methodologies, and limited long-term outcomes. Further multicenter trials with standardized endpoints and cost-effectiveness analyses are warranted. Postoperative rehabilitation using immersive technologies in RTSA remains underexplored and presents an opportunity for future research. Full article

If we do not urgently educate current and future medical professionals to critically evaluate and distinguish credible AI-assisted diagnostic tools from those whose performance is artificially inflated by data leakage or improper validation, we risk undermining clinician trust in all AI diagnostics and jeopardizing future advances in patient care. For instance, machine learning models have shown high accuracy in diagnosing Parkinson’s Disease when trained on clinical features that are themselves diagnostic, such as tremor and rigidity. This study systematically investigates the impact of data leakage and feature selection on the true clinical utility of machine learning models for early Parkinson’s Disease detection. We constructed two experimental pipelines: one excluding all overt motor symptoms to simulate a subclinical scenario and a control including these features. Nine machine learning algorithms were evaluated using a robust three-way data split and comprehensive metric analysis. Results reveal that, without overt features, all models exhibited superficially acceptable F1 scores but failed catastrophically in specificity, misclassifying most healthy controls as Parkinson’s Disease. The inclusion of overt features dramatically improved performance, confirming that high accuracy was due to data leakage rather than genuine predictive power. These findings underscore the necessity of rigorous experimental design, transparent reporting, and critical evaluation of machine learning models in clinically realistic settings. Our work highlights the risks of overestimating model utility due to data leakage and provides guidance for developing robust, clinically meaningful machine learning tools for early disease detection. Full article

Background/Objectives: The increasing incidence rates of keratinocyte carcinoma (KC), particularly in fair-skinned populations, call for efforts to intensify health education of the general population in addressing this prevalent skin cancer type. As a preparatory step, this systematic review summarizes the published research on the knowledge and risk perception regarding KC among individuals without medical training. Methods: The review was registered in PROSPERO (CRD42024618851) and adheres to PRISMA guidelines. The databases PubMed, Scopus, Web of Science, PsycArticles, and PsycINFO were searched on 30 July 2024. Studies were eligible if knowledge and/or risk perception was assessed in lay people. Risk of bias (ROB) was assessed with the Joanna Briggs Institute checklist for prevalence studies. Comparable outcomes (e.g., awareness of terms for KC) were meta-analyzed. Results: Included reports (n = 17) were published between 1991 and 2024 with 16,728 individuals assessed. Awareness for the most common type of KC, basal cell carcinoma (BCC), was low (20.75% of respondents (95% confidence interval (CI): 15.24–27.61)), while more respondents were familiar with colloquial terms (60.9–72.8%). Meta-analysis indicated an underestimation of the frequency of KC, with only 7.21% (CI: 4.03–12.58) identifying BCC as the most common type of skin cancer. Furthermore, concern about developing KC as assessed in only two overlapping studies was reported by only 25–30% of respondents, indicating a significant gap in risk awareness and a lack of research on risk perception regarding KC. Conclusions: This review highlights the need for targeted health education interventions to improve knowledge and preventive behaviors regarding KC. Given the limitations of the included studies, characterized by high ROB, heterogeneity of results, and a lack of standardized assessment tools, further research is essential to enhance the understanding and awareness of KC in diverse populations. Full article

In the context of increasing Artificial Intelligence integration in higher education, understanding the factors influencing university teachers’ adoption of AI tools is critical for effective implementation. This study adopts a perception–intention–behavior framework to explores the roles of perceived usefulness, perceived ease of use, perceived trust, perceived substitution crisis, and perceived risk in shaping teachers’ behavioral intention and actual usage of AI tools. It also investigates the moderating effects of peer influence and organizational support on these relationships. Using a comprehensive survey instrument, data was collected from 487 university teachers across four major regions in China. The results reveal that perceived usefulness and perceived ease of use are strong predictors of behavioral intention, with perceived ease of use also significantly influencing perceived usefulness. Perceived trust serves as a key mediator, enhancing the relationship between perceived usefulness, perceived ease of use, and behavioral intention. While perceived substitution crisis negatively influenced perceived trust, it showed no significant direct effect on behavioral intention, suggesting a complex relationship between job displacement concerns and AI adoption. In contrast, perceived risk was found to negatively impact behavioral intention, though it was mitigated by perceived ease of use. Peer influence significantly moderated the relationship between perceived trust and behavioral intention, highlighting the importance of peer influence in AI adoption, while organizational support amplified the effect of perceived ease of use on behavioral intention. These findings inform practical strategies such as co-developing user-centered AI tools, enhancing institutional trust through transparent governance, leveraging peer support, providing structured training and technical assistance, and advancing policy-level initiatives to guide digital transformation in universities. Full article

Background/Objectives: Despite growing interest in oral frailty as a public health issue, no nationwide study has assessed regional differences in oral frailty awareness, and the factors associated with such differences remain unclear. This study investigated regional differences in oral frailty awareness among older adults in Japan and identified the associated individual- and municipal-level factors, focusing on local policy measures and community-based oral health programs. Methods: A cross-sectional analysis was conducted using data from the 2022 wave of the Japan Gerontological Evaluation Study. The analytical sample comprised 20,330 community-dwelling adults aged ≥65 years from 66 municipalities. Awareness of oral frailty was assessed via self-administered questionnaires. Individual- and municipal-level variables were analyzed using multilevel Poisson regression models to calculate prevalence ratios (PRs). Results: Awareness of oral frailty varied widely across municipalities, ranging from 15.3% to 47.1%. Multilevel analysis showed that being male (PR: 1.10), having ≤9 years (PR: 1.10) or 10 to 12 years of education (PR: 1.04), having oral frailty (PR: 1.04), and lacking civic participation (PR: 1.06) were significantly associated with lack of awareness. No significant associations were found with municipal-level variables such as dental health ordinances, volunteer training programs, or population density. Conclusions: The study found substantial regional variation in oral frailty awareness. However, this variation was explained primarily by individual-level characteristics. Public health strategies should focus on enhancing awareness among socially vulnerable groups—especially men, individuals with low educational attainment, and those not engaged in civic activities—through targeted interventions and community-based initiatives. Full article