Search Results (1,144)

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

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 investigates the effectiveness of contact and online biology teaching by assessing student performance and gathering perceptions from students, teachers, and parents. Conducted in autumn 2021 with 3035 students, 124 biology teachers, and 719 parents, this study combined post-instruction assessments of student performance in knowledge reproduction and conceptual understanding with questionnaires examining perceptions of contact and online biology teaching effectiveness across students, teachers, and parents. To investigate how various teaching-related factors influence perceived understanding of biological content, we applied a CHAID-based decision tree model to questionnaire responses from students, teachers, and parents. Results indicated that students value engaging, flexible instruction, sufficient time to complete tasks and support for independent thinking. Teachers emphasized their satisfaction with teaching and efforts to support student understanding. In contact lessons, students preferred problem-solving, teacher guidance, and a stimulating environment. In online learning, they preferred low-stress, interesting lessons with room for independent work. Parents emphasized satisfaction with their child’s learning and the importance of a focused, stimulating environment. This comparative analysis highlights the need for student-centered, research-based biology teaching in both formats, supported by teachers and delivered in a motivating environment. The results offer practical insights for improving biology instruction in different teaching modalities. Full article

Effective energy management in Low Earth Orbit satellites is critical, as inefficient energy management can significantly affect mission objectives. The dynamic and harsh space environment further complicates the development of effective energy management strategies. To address these challenges, we propose a Deep Reinforcement Learning approach using Deep-Q Network to develop an adaptive energy management framework for Low Earth Orbit satellites. Compared to traditional techniques, the proposed solution autonomously learns from environmental interaction, offering robustness to uncertainty and online adaptability. It adjusts to changing conditions without manual retraining, making it well-suited for handling modeling uncertainties and non-stationary dynamics typical of space operations. Training is conducted using a realistic satellite electric power system model with accurate component parameters and single-orbit power profiles derived from real space missions. Numerical simulations validate the controller performance across diverse scenarios, including multi-orbit settings, demonstrating superior adaptability and efficiency compared to conventional Maximum Power Point Tracking methods. Full article

Inspired by the human ability to learn continuously and adapt to changing environments, researchers have proposed Online Open-World Object Detection (OLOWOD). This emerging paradigm faces the challenges of detecting known categories, discovering unknown ones, continuously learning new categories, and mitigating catastrophic forgetting. To address these challenges, we propose Category Prototype-guided Streaming Multi-Level Perturbation, PSMP, a plug-and-play method for OLOWOD. PSMP, comprising semantic-level, enhanced data-level, and enhanced feature-level perturbations jointly guided by category prototypes, operates at different representational levels to collaboratively extract latent knowledge across tasks and improve adaptability. In addition, PSMP constructs the “contrastive tension” based on the relationships among category prototypes. This mechanism inherently leverages the symmetric structure formed by class prototypes in the latent space, where prototypes of semantically similar categories tend to align symmetrically or equidistantly. By guiding perturbations along these symmetric axes, the model can achieve more balanced generalization between known and unknown categories. PSMP requires no additional annotations, is lightweight in design, and can be seamlessly integrated into existing OWOD methods. Extensive experiments show that PSMP achieves an improvement of approximately 1.5% to 3% in mAP for known categories compared to conventional online training methods while significantly increasing the Unknown Recall (UR) by around 4.6%. Full article

This study examines how teacher–student relationships and school engagement change across online and in-school learning, based on the experiences of 105 cross-regional secondary vocational students in China. Using questionnaire surveys, the study explores students’ perceptions and learning needs in both settings. The findings confirm that teachers play a consistently positive role in promoting student engagement across both online and in-school learning modalities. While affective engagement was higher during online learning, driven by stronger teacher responsiveness and improved student–teacher relationships, students reported increased pride in their schools after returning home, reflecting a renewed appreciation. In-school learning was associated with higher behavioral engagement and greater motivation, despite tensions over intensified academic tasks. Online learning facilitated cognitive engagement through easier vocabulary searches; nevertheless, poor home environments reduced motivation. Enhancing engagement may require offering students autonomy, valuing their input, and clarifying the relevance of the learning content. Full article

Piano fingering estimation remains a complex problem due to the combinatorial nature of hand movements and no best solution for any situation. A recent model-free reinforcement learning framework for piano fingering modeled each monophonic piece as an environment and demonstrated that value-based methods outperform probability-based approaches. Building on their finding, this paper addresses the more complex polyphonic fingering problem by formulating it as an online model-free reinforcement learning task with a novel training strategy. Thus, we introduce a novel Elite Episode Replay (EER) method to improve learning efficiency by prioritizing high-quality episodes during training. This strategy accelerates early reward acquisition and improves convergence without sacrificing fingering quality. The proposed architecture produces multiple-action outputs for polyphonic settings and is trained using both elite-guided and uniform sampling. Experimental results show that the EER strategy reduces training time per step by 21% and speeds up convergence by 18% while preserving the difficulty level and result of the generated fingerings. An empirical study of elite memory size further highlights its impact on training performance in solving piano fingering estimation. Full article

Sustainable education requires addressing the challenges posed by digital transformation, including technostress among university students. This study evaluates technostress levels in higher education through the validation of the TS4US scale and its implications for sustainable learning environments. A cross-sectional study was conducted with 328 university students from four districts in Lima, Peru, using an online survey to measure technostress. Confirmatory factor analysis (CFA) was performed to assess the psychometric properties of the TS4US scale, resulting in a refined model with two latent factors and thirteen validated items. Findings indicate that 28% of students experience high technostress levels, while 5% report very high levels, though no significant associations were found between technostress and sociodemographic variables such as campus location, employment status, gender, and academic level. The TS4US instrument had been previously validated in Chile; this study confirms its structure in a new sociocultural context, reinforcing its cross-cultural applicability. These results highlight the need for sustainable strategies to mitigate technostress in higher education, including institutional support, digital literacy programs, and policies fostering a balanced technological environment. Addressing technostress is essential for promoting sustainable education (SDG4) and enhancing student well-being (SDG3). This study directly contributes to the achievement of Sustainable Development Goals 3 (Good Health and Well-being) and 4 (Quality Education) by providing validated tools and evidence-based recommendations to promote mental health and equitable access to digital education in Latin America. Future research should explore cross-country comparisons and targeted interventions, including digital well-being initiatives and adaptive learning strategies, to ensure a resilient and sustainable academic ecosystem. Full article

Self-regulated learning (SRL) has been widely recognized as a critical skill for academic success in online and blended learning contexts. However, many students experience difficulty in effectively applying SRL strategies in the absence of structured instructional guidance. To address this challenge, this study developed and implemented a learning management system integrated with SRL support (SRL-LMS), specifically designed for the online component of a blended learning course. The SRL-LMS consisted of two sections: a conventional course content section and a SRL training section designed to support students in applying SRL strategies. A quasi-experimental design was adopted with 69 college students assigned to either an experimental group, with access to both course and SRL sections, or a control group, which accessed only the course section. Results indicated that students in the experimental group reported higher levels of self-regulation and showed more frequent and diverse application of SRL strategies compared to the control group. In terms of academic performance, the experimental group performed significantly better than the control group on the first exam, though no significant difference was observed on the second exam. These results highlight the effectiveness of structured SRL interventions within digital learning platforms for improving students’ self-regulatory behaviors. Future implementations should address cognitive load and incorporate strategic approaches to sustain student motivation. This study advances current SRL intervention designs and offers valuable insights for educators and instructional designers aiming to integrate targeted SRL supports in online and blended learning environments. Full article

This study examines how a visitor-centered approach enhances engagement, participation, and intangible heritage transmission to support cultural sustainability in ethnographic museums. We conducted online and on-site behavioral observations, questionnaire surveys, and in-depth interviews at the She Ethnic Minority Museum to identify gaps in current visitor experience design. We combined the Analytic Hierarchy Process (AHP) with the Contextual Model of Learning (POE) and Emotional Experience Theory (EET) to develop a hierarchical evaluation model. The model comprises one goal layer, three criterion layers (Experience, Participation, Transmission), and twelve sub-criteria, each evaluated across People, Object, and Environment dimensions. Quantitative weighting revealed that participation exerts the greatest influence, followed by transmission and experience. Findings indicate that targeted interventions promoting active participation most effectively foster emotional resonance and heritage transmission, while strategies supporting intergenerational engagement and immersive experiences also play a significant role. We recommend prioritizing small-scale, low-cost participatory initiatives and integrating online and offline community engagement to establish a participatory chain where engagement leads to meaningful experiences and sustained cultural transmission. These insights offer practical guidance for museum practitioners and policymakers seeking to enhance visitor experiences and ensure the long-term preservation and vibrancy of ethnic minority cultural heritage. Full article

While prior work has explored learner behavior using learning management systems (LMS) data, few studies provide week-level clustering validated against external engagement labels. To understand and assist students in online learning platforms and environments, this study presents a week-level engagement profiling framework for e-learning environments, utilizing K-means clustering and label-based validation. Leveraging log data from 127 students over a 13-week course, 44 activity-based features were engineered to classify student engagement into high, moderate, and low levels. The optimal number of clusters (k = 3) was identified using the elbow method and assessed through internal metrics, including a silhouette score of 0.493 and R² of 0.80. External validation confirmed strong alignment with pre-labeled engagement levels based on activity frequency and weighting. The clustering approach successfully revealed distinct behavioral patterns across engagement tiers, enabling a nuanced understanding of student interaction dynamics over time. Regression analysis further demonstrated a significant association between engagement levels and academic performance, underscoring the model’s potential as an early warning system for identifying at-risk learners. These findings suggest that clustering based on LMS behavior offers a scalable, data-driven strategy for improving learner support, personalizing instruction, and enhancing retention and academic outcomes in digital education settings such as MOOCs. Full article

Blended and virtual learning have become an integral part in international higher education, especially in the wake of the COVID-19 pandemic and the European Union’s Digital Education Action Plan. These modalities have enabled more inclusive, flexible, and sustainable forms of international collaboration, such as Collaborative Online International Learning (COIL) and Blended Intensive Programs (BIPs), reshaping the landscape of global academic mobility. This paper introduces the INVITE Learning Design Framework (LDF), developed to support higher education instructors in designing high-quality, internationalized blended and virtual learning experiences. The framework addresses the growing need for structured, theory-informed approaches to course design that foster student engagement, intercultural competence, and motivation in non-face-to-face settings. The INVITE LDF was developed through a rigorous scoping review of existing models and frameworks, complemented by needs-identification analysis and desk research. It integrates Self-Determination Theory, Active Learning principles, and the ADDIE instructional design model to provide a comprehensive, adaptable structure for course development. The framework was successfully implemented in a large-scale online training module for over 1000 educators across Europe. Results indicate that the INVITE LDF enhances educators’ ability to create engaging, inclusive, and pedagogically sound international learning environments. Its application supports institutional goals of internationalization by making global learning experiences more accessible and scalable. The findings suggest that the INVITE LDF can serve as a valuable tool for higher education institutions worldwide, offering a replicable model for fostering intercultural collaboration and innovation in digital education. This contributes to the broader transformation of international higher education, promoting equity, sustainability, and global citizenship through digital pedagogies. Full article

Computational Fluid Dynamics (CFD) is regarded as an important tool for analyzing the flow field, thermal environment, and air quality around the built environment. However, for built environment applications, the high computational cost of CFD hinders large-scale scenario simulation and efficient design optimization. In the field of built environment research, surrogate modeling has become a key technology to connect the needs of high-fidelity CFD simulation and rapid prediction, whereas the low-dimensional nature of traditional surrogate models is unable to match the physical complexity and prediction needs of built flow fields. Therefore, combining machine learning (ML) with CFD to predict flow fields in built environments offers a promising way to increase simulation speed while maintaining reasonable accuracy. This review briefly reviews traditional surrogate models and focuses on ML-based surrogate models, especially the specific application of neural network architectures in rapidly predicting flow fields in the built environment. The review indicates that ML accelerates the three core aspects of CFD, namely mesh preprocessing, numerical solving, and post-processing visualization, in order to achieve efficient coupled CFD simulation. Although ML surrogate models still face challenges such as data availability, multi-physics field coupling, and generalization capability, the emergence of physical information-driven data enhancement techniques effectively alleviates the above problems. Meanwhile, the integration of traditional methods with ML can further enhance the comprehensive performance of surrogate models. Notably, the online ministry of trained ML models using transfer learning strategies deserves further research. These advances will provide an important basis for advancing efficient and accurate operational solutions in sustainable building design and operation. Full article

The contemporary digital era has fundamentally reshaped pupil education. It has transformed learning into a dynamic environment with enhanced access to information. The focus shifts to the educator, who must employ teaching strategies, practices, and methods to engage and motivate the pupils. New possibilities are emerging for adopting active pedagogical approaches. One example is the use of educational online escape games. In the theoretical part of this paper, we present online escape games as a tool that broadens pedagogical opportunities for schools in primary school chemistry education. These activities are known to foster pupils’ transversal or soft skills. We investigate the practical dimension of implementing escape games in education. This pilot study aims to analyse primary school teachers’ perceptions of online escape games. We collected data using Q methodology and conducted the Q-sort through digital technology. Data analysis utilised both the PQMethod programme and ChatGPT 4-o, with a subsequent comparison of their respective outputs. Although some numerical differences appeared between the ChatGPT and PQMethod analyses, both methods yielded the same factor saturation and overall results. Full article

Healthcare abuse is a critical human rights and public health issue, particularly impacting medical interns and trainees who are vulnerable to mistreatment during their formative professional years. This cross-sectional study, conducted from February to June 2024, evaluated the prevalence and psychological impact of harassment and discrimination among 463 healthcare interns in Saudi Arabia from various specialties, including medicine, nursing, pharmacy, and dentistry. Using a self-administered online questionnaire, we found that mistreatment was widely reported, with female interns experiencing significantly higher rates of sexual harassment and gender-based discrimination. Common perpetrators included residents, lecturers, professors, nurses, and patients, with incidents most frequently occurring in surgical and internal medicine departments. Despite high prevalence, only 9% of interns reported the abuse due to mistrust in reporting systems or failure to recognize the behavior as abuse. These experiences were associated with significant psychological distress, including frustration, reduced motivation to learn, and higher DASS scores, particularly among female interns. The study underscores the need for institutional reforms, including policy development, cultural change, and effective reporting systems to ensure a safe and supportive learning environment for future healthcare professionals. Addressing abuse in medical training is essential for individual well-being and the sustainability and integrity of healthcare systems. Full article