Search Results (251)

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

Background: Intermediate care units face growing complexity due to aging populations and chronic illnesses. Non-technical skills such as empathy and communication are crucial for quality care. We aimed to examine the relationship between communication skills, self-efficacy, and sense of coherence among intermediate care nurses. Methods: We conducted an observational, cross-sectional study with 60 intermediate care nurses from three units in a Catalan hospital, Spain. Participants engaged in high-fidelity simulation using geriatric end-of-life scenarios with an ultra-realistic manikin representing a geriatric patient at the end of life. NTSs were measured using validated tools: the Health Professionals Communication Skills Scale (HP-CSS), the General Self-Efficacy Scale, and the Sense of Coherence Questionnaire (OLQ-13). Sessions followed INACSL standards, including prebriefing, simulation, and debriefing phases. Results: Post-simulation outcomes revealed significant gains in interpersonal competencies, with men reporting higher assertiveness (p = 0.015) and greater satisfaction with both the simulation experience (p = 0.003) and the instructor (p = 0.008), underscoring gender-related perceptions in immersive training. Conclusions: Ultra-realistic clinical simulation is effective in enhancing NTS among intermediate care nurses, contributing to improved care quality and clearer professional profiles in geriatric nursing. Full article

As the demand for energy-efficient homes continues to rise, the importance of advanced mechanical ventilation systems in maintaining indoor air quality (IAQ) has become increasingly evident. However, challenges related to energy balance, IAQ, and occupant thermal comfort persist. This review examines the performance of mechanical ventilation systems in regulating indoor climate, improving air quality, and minimising energy consumption. The findings indicate that demand-controlled ventilation (DCV) can enhance energy efficiency by up to 88% while maintaining CO₂ concentrations below 1000 ppm during 76% of the occupancy period. Heat recovery systems achieve efficiencies of nearly 90%, leading to a reduction in heating energy consumption by approximately 19%. Studies also show that employing mechanical rather than natural ventilation in schools lowers CO₂ levels by 20–30%. Nevertheless, occupant misuse or poorly designed systems can result in CO₂ concentrations exceeding 1600 ppm in residential environments. Hybrid ventilation systems have demonstrated improved thermal comfort, with predicted mean vote (PMV) values ranging from –0.41 to 0.37 when radiant heating is utilized. Despite ongoing technological advancements, issues such as system durability, user acceptance, and adaptability across climate zones remain. Smart, personalized ventilation strategies supported by modern control algorithms and continuous monitoring are essential for the development of resilient and health-promoting buildings. Future research should prioritize the integration of renewable energy sources and adaptive ventilation controls to further optimise system performance. Full article

The growing prevalence of AI chatbots in everyday life has prompted educators to explore their potential applications in promoting student success, including support for classroom engagement and communication. This exploratory study emerged from semester-long observations of class participation apprehensions in an introductory educational psychology course, examining how chatbots might scaffold students toward active and independent classroom contribution. Four students experiencing situational participation anxiety voluntarily participated in a pilot intervention using AI chatbots as virtual peer partners. Following comprehensive training in AI use and prompt design given to the entire class, participants employed systematic consultation frameworks for managing classroom discourse trepidations. Data collection involved regular instructor meetings documenting student experiences, challenges, and developmental trajectories through qualitative analysis emphasizing contextual interpretation. While students reported general satisfaction with chatbot integration, implementation revealed three critical complexities: temporal misalignment between AI consultation and real-time discussion dynamics; feedback inflation creating disconnects between AI reassurance and classroom reception; and unintended progression from supportive scaffolding toward technological dependency. Individual outcomes varied, with some students developing independence while others increased reliance on external validation. AI-assisted participation interventions demonstrate both promise and limitations, requiring careful consideration of classroom dynamics. Effective implementation necessitates rehearsal-based rather than validation-focused applications, emphasizing human mentorship and community-centered approaches that preserve educational autonomy while leveraging technological scaffolding strategically. Full article

In this article, we describe an approach to teaching introductory quantum mechanics and machine learning techniques. This approach combines several key concepts from both fields. Specifically, it demonstrates solving the Schrödinger equation using the discrete-variable representation (DVR) technique, as well as the architecture and training of neural network models. To illustrate this approach, a Python-based Jupyter notebook is developed. This notebook can be used for self-learning or for learning with an instructor. Furthermore, it can serve as a toolbox for demonstrating individual concepts in quantum mechanics and machine learning and for conducting small research projects in these areas. Full article

This paper presents a human-in-the-loop interactive framework for skeleton-based posture recognition, designed to support model training and artistic education. A total of 4870 labeled images are used for training and validation, and 500 images are reserved for testing across five core posture categories: standing, sitting, jumping, crouching, and lying. From each image, comprehensive skeletal features are extracted, including joint coordinates, angles, limb lengths, and symmetry metrics. Multiple classification algorithms—traditional (KNN, SVM, Random Forest) and deep learning-based (LSTM, Transformer)—are compared to identify effective combinations of features and models. Experimental results show that deep learning models achieve superior accuracy on complex postures, while traditional models remain competitive with low-dimensional features. Beyond classification, the system integrates posture recognition with a visual recommendation module. Recognized poses are used to retrieve matched examples from a reference library, allowing instructors to browse and select posture suggestions for learners. This semi-automated feedback loop enhances teaching interactivity and efficiency. Among all evaluated methods, the Transformer model achieved the best accuracy of 92.7% on the dataset, demonstrating the effectiveness of our closed-loop framework in supporting pose classification and model training. The proposed framework contributes both algorithmic insights and a novel application design for posture-driven educational support systems. Full article

A persistent challenge in training future philology educators is engaging students in deep textual analysis across historical periods—especially in large classes where limited resources, feedback, and assessment tools hinder the teaching of complex linguistic and contextual features. These constraints often lead to superficial learning, decreased motivation, and inequitable outcomes, particularly when traditional methods lack interactive and scalable support. As digital technologies evolve, there is increasing interest in how Artificial Intelligence (AI) can address such instructional gaps. This study explores the potential of conversational AI chatbots to provide scalable, pedagogically grounded support in philology education. Using a mixed-methods case study, twenty-six (n = 26) undergraduate students completed structured tasks using one of three AI chatbots (ChatGPT, Gemini, or DeepSeek). Quantitative and qualitative data were collected via usability scales, AI literacy surveys, and semi-structured interviews. The results showed strong usability across all platforms, with DeepSeek rated highest in intuitiveness. Students reported confidence in using AI for efficiency and decision-making but desired greater support in evaluating multiple AI-generated outputs. The AI-enhanced environment promoted motivation, autonomy, and conceptual understanding, despite some onboarding and clarity challenges. Implications include reducing instructor workload, enhancing student-centered learning, and informing curriculum development in philology, particularly for instructional designers and educational technologists. Full article

Background: Whole-body electromyostimulation (WB-EMS) combines full-body electrical muscle stimulation with instructor-assigned exercise. Electrical impulses are transmitted to the peripheral muscles through electrodes applied to the body. This study compared two training methodologies, WB-EMS training and traditional resistance training, to determine which approach leads to greater strength improvement in terms of 1-repetition maximum (1-RM). Methods: Twenty sedentary women participated in a 10 weeks protocol with five evaluations conducted every two weeks. The WB-EMS group trained for 20 min per week, and the resistance training group (RT) performed an average of two training sessions per week, lasting 60 min each. Both groups were evaluated using three exercises: back squat and hammer curl (1-RM), and plank exercise (time to exhaustion). Results: Both groups increased their performance in squat (WB-EMS +36%, p = 0.0001; RT +34%, p = 0.0001), curl (WB-EMS +42%, p = 0.0001; RT +33%, p = 0.0001), and plank (WB-EMS +103%, p = 0.0001; RT +65%, p = 0.0001). No significant time × training interaction was found for any exercise, indicating that the two groups improved similarly. Conclusions: Although WB-EMS did not confer greater strength improvement than traditional resistance training, it offers a time-efficient alternative, achieving similar results with reduced time commitment. Full article

With the rapid proliferation of Generative Artificial Intelligence (AIGC) technologies in higher education, identifying effective integration pathways into design curricula has become a pressing issue in the field of educational technology. This study employs a mixed-methods approach grounded in the Technology Acceptance Model (TAM) and the Technological Pedagogical Content Knowledge (TPACK) framework, incorporating course analysis, questionnaire surveys, structural path modeling, and interview analysis. Focusing on both instructors and students, this research systematically investigates the acceptance, integration mechanisms, and sustainable development potential of AIGC in university-level design education. The findings indicate that students generally acknowledge the value of AIGC in enhancing creativity and improving efficiency, although gaps persist in their understanding of ethical considerations and original expression. On the teachers’ side, technological knowledge exerts a significant positive influence on the integration of content knowledge, while the impact of pedagogical knowledge remains underutilized. Interview data further reveal a structural tension within current teaching practices, characterized by the rapid adoption of technological tools contrasted with the slower evolution of pedagogical systems. Based on these insights, this study offers five strategic recommendations for sustainable teaching, including the development of teacher training systems, curriculum module design, student media literacy enhancement, and pedagogical reconstruction aligned with the Sustainable Development Goals (SDGs). These findings provide both theoretical and practical contributions to the effective and sustainable integration of AIGC into higher design education. Full article

This study compares the motivation and teaching practices of STEM and non-STEM instructors with the aim of discovering factors that may contribute to and can be applied to improve the retention and persistence of STEM students in higher education. Instructors completed measures of mindset, motivation, and teaching practices. We found that non-STEM instructors reported being more motivated by belief in student efficacy than STEM instructors, and they reported a greater use of evidence-based teaching practices. Subsequent analyses revealed that mastery goals predicted the use of evidence-based teaching practices for both sets of instructors; growth mindset was also a significant predictor of evidence-based teaching practices for non-STEM instructors. We propose that a pathway to STEM student success may be training and incentivizing instructors, especially those who may question students’ abilities to learn difficult STEM material. Full article

The paper explores virtual worlds as an innovative training platform for upskilling and reskilling smart city professionals, comprising technicians and engineers. Focusing on developing soft skills, the study presents findings from the pilot of a virtual training which was part of a comprehensive tech skills program that also included transversal skills, namely soft, entrepreneurial and green skills. Moreover, the paper describes the methodological approach adapted for the design and the use of the soft skills’ virtual world during the online multi-user sessions, and depicts the technical infrastructure used for its implementation. The virtual world was assessed with a mixed-methods approach, combining a specially designed evaluation questionnaire completed by 27 trainees with semi-structured interviews conducted with instructors. Quantitative data were analyzed to assess satisfaction, perceived effectiveness, and the relationship between curriculum design, support, and instructional quality. Qualitative feedback provided complementary insights into learner experiences and implementation challenges. Findings indicate high levels of learner satisfaction, particularly regarding instructor expertise, curriculum organization, and overall engagement. Statistical analysis revealed strong correlations between course structure and perceived training quality, while prior familiarity with virtual environments showed no significant impact on outcomes. Participants appreciated the flexibility, interactivity, and team-based nature of the training, despite minor technical issues. This research demonstrates the viability of VWs for soft skills development in technical professions, highlighting their value as an inclusive, scalable, and experiential training solution. Its novelty lies in applying immersive technology specifically to smart city training, a field where such applications remain underexplored. The findings support the integration of virtual environments into professional development strategies and inform best practices for future implementations. Full article

This study aimed to develop an intellectual and analytical platform for assessing the psychophysiological load on flight instructors in a flight school (general aviation). As part of this study, an information model for evaluating the working environment’s load based on noise levels was developed, a model to predict individual psychophysiological load was created, an expert model to assess mental health was established, and a hybrid model was devised to determine the overall psychophysiological load on an instructor while performing their duties. Noise load was measured during flights with two aircraft (Zlín Z43 and Diamond DA-40 TDI), resulting in the acquisition of 4,361,300 data points. This dataset was collected during two data acquisition sessions for each aircraft, encompassing three phases of flight: takeoff, in-flight, and landing. During the flight, noise measurements were conducted based on five indicators: sound pressure, fluctuation strength, roughness, sharpness, and tonality. Based on the measured data, the platform was verified and configured, and example evaluations were demonstrated. This study employed modern methods of intelligent data analysis, utilizing both univariate and multivariate membership functions. The developed platform incorporates quantitative dynamic data obtained from devices measuring psychophysiological load, integrating professional mental health assessments and predicting dynamic work environment indicators for modeling load trends. Early detection of critical load levels helps protect the health of flight instructors, thus creating a safe working environment for training new pilots. Full article

To improve the quality of flight training in instrument failure scenarios, eye movement data were collected from flight instructors during climbing, descending, and turning flights when the primary attitude direction indicator failed. The performance data of the excellent instructors was selected to produce eye movement tutorials. These tutorials were used to conduct eye movement training for the experimental group of flight trainees. In contrast, the control group received traditional training. The performance and eye movement data of the two groups of flight trainees were then compared and analyzed. The results showed that flight trainees who received eye movement training performed better when facing instrument failure. Specifically, the deviations in the rate of descent, heading during the descent, airspeed during the turn, and slope during the turn were significantly different from those of the control group. Compared to the control group, the experimental group had a significantly lower fixation frequency on the failed instrument during the turn. Additionally, the average glance duration on the failed instrument during the climb and turn was significantly reduced. The study demonstrated the effectiveness of eye movement training in improving the quality of flight training in instrument failure scenarios. Full article

Background: Resilience is a cornerstone attribute for nursing students, enabling them to adapt to stressful situations encountered during their educational journey and subsequent healthcare career. Objective: This qualitative study aimed to explore nursing instructors’ experiences promoting resilience among nursing students during clinical education. Methods: Focus groups were conducted with 27 instructors from four nursing colleges in Thailand. Data were analyzed using Braun and Clarke’s inductive thematic analysis approach, guided by the Unitary Caring Science Resilience-Building Model. Results: Two main themes emerged: (1) Challenges to Nursing Students’ Resilience and (2) Support Strategies for Enhancing Resilience. Challenges included bridging theory and practice, upholding confidence in clinical skills, adapting to new clinical environments, and managing expectations. Support strategies encompassed providing comprehensive preparation, fostering open communication, implementing peer support systems, and utilizing reflective practice. Conclusions: The findings highlight the complex interplay of factors affecting nursing students’ resilience and the multifaceted approaches instructors use to support it. This study underscores the need for a holistic approach to nursing education that addresses clinical competence and psychological well-being. Implications include curriculum redesign to bridge the theory–practice gap, enhanced instructor training in mentorship and resilience-building, implementation of comprehensive student support systems, and technology integration to support learning and resilience. Full article

The purpose of this study is to explore how university instructors and students in five European countries (The Netherlands, Türkiye, Greece, Czechia, and Italy) perceive the incorporation of European Union (EU) values into higher education. To achieve this, a phenomenological research design was employed, utilizing semi-structured group interviews with 42 participants, comprising 20 instructors and 22 undergraduate students from various disciplines. While the findings revealed a shared understanding of fundamental EU values including democracy, human rights, equality, freedom, human dignity, and rule of law, they also demonstrated considerable challenges regarding their effective integration into educational practices. The main barriers identified include insufficient educational materials and training opportunities, a lack of coordination in exchange programs, a lack of time, and financial limitations. This study highlights that more structured and systematic strategies are needed to successfully integrate these values in university curricula. The implications of this research underscore the need for universities to enhance their support mechanisms, foster inclusivity, and develop innovative pedagogical approaches to overcoming these challenges and fully integrating EU values into higher education. Full article