Search Results (272)

Research highlights a growing demand for active, experiential learning methods in higher education, especially in teacher education. While the benefits of parental involvement (PI) are well-documented, Hungary lacks tools to effectively prepare teacher trainees for fostering family–school cooperation. This study addresses this gap by introducing a custom-designed board game as an innovative teaching tool. The game simulates real-world challenges in PI through a cooperative, scenario-based framework. Exercises are grounded in international and national research, ensuring their relevance and evidence-based design. Tested with 110 students, the game’s educational value was assessed via post-gameplay questionnaires. Participants emphasized the strengths of its cooperative structure, realistic scenarios, and integration of humor. Many reported gaining new insights into parental roles and strategies for effective home–school partnerships. Practical applications include integrating the game into teacher education curricula and adapting it for other educational contexts. This study demonstrates how board games can bridge theory and practice, offering an engaging, effective medium to prepare future teachers for the challenges of PI. Full article

Road traffic accidents are a leading cause of injury and death among adolescents, making road safety education crucial. This study assesses the performance of and users’ opinions on the Route 2 School (R2S) traffic safety education program, designed for secondary school students (13–17 years) in Belgium. The program incorporates gamified e-learning modules containing, among others, podcasts, interactive 360° visuals, and virtual reality (VR), to enhance traffic knowledge, situation awareness, risk detection, and risk management. This study was conducted across several cities and municipalities within Belgium. More than 600 students from school years 3 to 6 completed the platform and of these more than 200 students filled in a comprehensive questionnaire providing detailed feedback on platform usability, preferences, and behavioral risk assessments. The results revealed shortcomings in traffic knowledge and skills, particularly among older students. Gender-based analysis indicated no significant performance differences overall, though females performed better in risk management and males in risk detection. Furthermore, students from cities outperformed those from municipalities. Feedback on the R2S platform indicated high usability and engagement, with VR-based simulations receiving the most positive reception. In addition, it was highlighted that secondary school students are high-risk groups for distraction and red-light violations as cyclists and pedestrians. This study demonstrates the importance of gamified, technology-enhanced road safety education while underscoring the need for module-specific improvements and regional customization. The findings support the broader application of e-learning methodologies for sustainable, behavior-oriented traffic safety education targeting adolescents. Full article

Background: Insomnia and excessive sleepiness are significant health problems with a complex etiology, increasingly affecting young people, especially students. This study aimed to assess the prevalence of sleep disturbances and patterns of psychoactive drug use among female Polish students. We also explored the potential impact of the COVID-19 pandemic on sleep behaviors. We hypothesized that sleep disorders are common in this group, that medical students are more likely to experience insomnia and excessive sleepiness, and that the pandemic has exacerbated both sleep disturbances and substance use. Methods: This cross-sectional study utilized a custom survey designed using standardized questionnaires—the Athens Insomnia Scale and Epworth Sleepiness Scale—that was distributed online using the Computer-Assisted Web Interviewing method. A total of 11,988 responses were collected from 31 January 2016 to 1 January 2021. Inclusion criteria were being female, having a college student status, and giving informed consent. Results: Among the 11,988 participants, alcohol use declined after the pandemic began (p = 0.001), while sedative use increased (p < 0.001). Insomnia (AIS) was associated with study year, university profile, and field of study (p < 0.001), with the highest rates in first-year and non-medical students. It was more common among users of sedatives, psychostimulants, and multiple substances. No significant change in insomnia was found before and after the pandemic. Excessive sleepiness (ESS) peaked in first-year and medical students. It decreased during the pandemic (p < 0.001) and was linked to the use of alcohol, psychostimulants, cannabinoids, and multiple substances. Conclusions: These findings highlight that female students are particularly vulnerable to sleep disorders. The influence of the COVID-19 pandemic on sleep disturbances remains inconclusive. Given the varied results in the existing literature, further research is needed. Full article

Anxiety is a significant mental health concern in universities worldwide. This study examines the structure of anxiety symptoms and their relationship with contextual stressors among pre-service English teachers. The sample included 269 students enrolled in a Teaching English as a Foreign Language program at a public university in Manabí, Ecuador. Data were collected using the Zung Self-Rating Anxiety Scale and a custom-designed questionnaire identifying anxiety triggers. Results showed that while most students reported normal or mild anxiety levels, a considerable portion exhibited moderate to severe symptoms. Cluster analysis revealed three emotional profiles, with the high-anxiety group strongly associated with stressors such as economic hardship and job insecurity. Academic pressure and financial instability emerged as the strongest predictors of anxiety. These findings highlight the urgent need to develop and evaluate targeted psycho-educational strategies to prevent and reduce anxiety within teacher training programs in higher education. Full article

Background/Objectives: This study aims to uncover the variables related to the success of the intervention. Methods: A retrospective study was conducted on patients who underwent joint replacement surgery utilizing a customized alloplastic system between 2018 and 2023, comprising subjects with complete records for both the planning and follow-up phases. The Student’s t-test was applied with a significance threshold of p < 0.05. Results: Forty-eight subjects were admitted for initial analysis, and 31 subjects were evaluated with a minimum follow-up of 1 year and a maximum of 7 years, with a mean age of 36.37 ± 15.53. The TMJ diagnosis was mainly with degenerative TMJ disease, followed by ankylosis and craniofacial syndromes, and an average of 2.1 ± 1.2 previous surgeries were noted. Degenerative joint disease correlated with increased pain (p < 0.0001) and a higher prevalence of prior joint surgery (p < 0.0001). Thirty-one subjects were followed up with 47 prostheses installed; 74.4% underwent complementary surgery with other facial osteotomies. Significant improvements (p < 0.0001) were observed when comparing pain levels pre- and postoperatively, with a decrease from 5.5 (±2.3) to 2.2 (±0.4). Concerning the interincisal opening, there was a significant increase (p < 0001) from 25.85 (±10.2) mm to 35.93 (±4.2) mm in mouth opening. TMJ replacement treatment is efficient and effective, demonstrating stability in follow-up assessments for up to 7 years. Conclusions: The indications for replacement are diverse and may benefit patients who have not yet progressed to end-stage TMJ disease. Full article

Teaching of embedded systems, including communication protocols such as SMBus, is commonly faced with difficulties providing the students with interactive and personalized, practical learning experiences. To overcome these shortcomings, this report presents a new conceptual framework that exploits generative artificial intelligence (GenAI) via customized DV-GPT. Coupled with prepromises techniques, DV-GPT offers timely targeted support to students and engineers who are studying SMBus protocol design and verification. In contrast to traditional learning, this AI-based tool dynamically adjusts feedback based on the users’ activities, providing greater insight into challenging concepts, including timing synchronization, multi-master arbitration, and error handling. The framework also incorporates the industry de facto standard UVM practices, which helps narrow the gap between education and the professional world. We quantitatively compare with a baseline GPT-4 and show significant improvement in accuracy, specificity, and user satisfaction. The effectiveness and feasibility of the proposed GenAI-enhanced educational approach have been empirically validated through the use of structured student feedback, expert judgment, and statistical analysis. The contribution of this research is a scalable, flexible, interactive model for enhancing embedded systems education that also illustrates how GenAI technologies could find applicability within specialized educational environments. Full article

The rapid expansion of virtual education has highlighted both its opportunities and limitations. Conventional virtual learning environments tend to lack flexibility, often applying standardized methods that do not account for individual learning differences. In contrast, Artificial Intelligence (AI) empowers the creation of customized educational experiences that address specific student needs. Such personalization is essential to mitigate educational inequalities, particularly in areas with limited infrastructure, scarce access to trained educators, and varying levels of digital literacy. This study explores the role of AI in advancing virtual education, with particular emphasis on supporting differentiated learning. It begins by selecting an appropriate pedagogical model to guide personalization strategies and proceeds to investigate the application of AI techniques across three key areas: the characterization of educational resources, the detection of learning styles, and the recommendation of tailored content. The primary contribution of this research is the development of a scalable framework that can be adapted to a variety of educational contexts, with the goal of enhancing the effectiveness and personalization of virtual learning environments through AI. Full article

This paper examines the relationship between investment in water education and economic performance, focusing on the context of widening countries (EU Member States and Associated Countries with lower research and innovation performance). Through time-series data and panel regression analysis, the study investigates whether increased spending on education correlates with Gross Domestic Product (GDP) growth. While the initial static model indicates a positive but statistically insignificant association, a dynamic model with lagged GDP significantly improves explanatory power, suggesting that educational investments may influence growth with a temporal delay. Complementing the macroeconomic data, the paper analyses how targeted investments in educational innovation, especially in digital technologies such as virtual reality (VR) applications, enhance teaching quality and student engagement. Examples from partner universities involved in the WATERLINE project (Horizon Europe, 101071306) show how custom-built VR modules, aligned with existing hydraulic labs, contribute to advanced water-related skills. The paper also presents a cost-benefit analysis of VR applications in water education, highlighting their economic efficiency compared to traditional laboratory equipment. Additionally, it explores how micro-level innovations in education can generate macroeconomic benefits through widespread adoption and systemic impact. Ultimately, the research highlights the long-term value of education and innovation in strengthening both economic and human capital across diverse regions. Full article

This paper presents the implementation of a modern approach for automatic measurement, data acquisition, and processing using custom-developed software based on the ARDUINO version: 2.3.6 platform. State-of-the-art sensing elements are employed for enhanced precision and reliability. The obtained results are graphically visualized for comprehensive analysis. The ARDUINO microcontroller and its associated open-source programming environment are primarily designed for general-purpose users rather than specialized industrial applications. The study focuses on the experimental investigation of the characteristics of a Peltier element. The interdependence between current, voltage, internal resistance, and temperature differential is examined in detail. The findings concerning efficiency analysis are intended to support students across various engineering disciplines during their educational process. Full article

Background/Objectives: The objective of this study was to compare muscular strength and neuromuscular activation characteristics between male gymnasts and physical education (PE) students during isometric shoulder extension and flexion tasks. Methods: Thirteen competitive male gymnasts (age: 19.59 ± 1.90 years; body mass: 66.54 ± 6.10 kg; height: 169.38 ± 6.28 cm; mean ± SD) and thirteen male physical education (PE) students (age: 20.96 ± 2.30 years; body mass: 74.00 ± 8.69 kg; height: 174.96 ± 4.93 cm) voluntarily participated in the study. Peak torque (PT), rate of torque development (RTD), RTD normalized to body mass (RTD/BM), and muscle activation assessed via surface electromyography (EMG), normalized to maximal EMG activity (EMG/EMGmax), were evaluated during bilateral isometric shoulder extension and flexion at a joint angle of 45°. Measurements were analyzed across the following time intervals: −50 to 0 ms (pre-tension), 0–30 ms, 0–50 ms, 0–100 ms, and 0–200 ms relative to contraction onset. Custom MATLAB R2024b scripts were used for data processing and visualization. One-way and two-way multivariate analyses of variance (MANOVAs) were conducted to test for group differences. Results: Gymnasts exhibit higher values of PT, PT/BM, RTD, and RTD/BM particularly within the early contraction phases (i.e., 0–50 ms and 0–100 ms) compared to PE students (p < 0.05 to <0.001; η² = 0.04–0.66). Additionally, EMG activity normalized to maximal activation (EMG/EMGmax) was significantly greater in gymnasts during both early and mid-to-late contraction phases (0–100 ms and 0–200 ms), (p < 0.05 to <0.001; η² = 0.04–0.48). Conclusions: These findings highlight gymnasts’ superior explosive neuromuscular capacity. Metrics like RTD, RTD/BM, and EMG offer valuable insights into rapid force production and neural activation, supporting performance monitoring, training optimization, and injury prevention across both athletic and general populations. Full article

The increasing prevalence of sedentary lifestyles poses significant global health challenges, including obesity, diabetes, musculoskeletal disorders, and cardiovascular issues. This paper presents the design and development of a universal smart chair system aimed at mitigating the adverse effects of prolonged sitting. The proposed solution integrates a pressure sensor, vibration motors, an LED strip, and Bluetooth Low-Energy (BLE) communication into a modular and adaptable design. Powered by an STM32WB55CGU6 microcontroller and a rechargeable lithium-ion battery system, the smart chair monitors sitting duration and the user’s posture, and provides alerts through tactile, visual, and auditory notifications. A complementary mobile application allows users to customize sitting time thresholds, monitor activity, and assess battery status. Designed for universal compatibility, the system can be adapted to various chair types. Technical and functional testing demonstrated reliable performance, with the chair operating for over eight workdays on a single charge. The smart chair offers an innovative, cost-effective approach to improving workplace ergonomics and health outcomes, with potential for further enhancements such as posture monitoring. A pilot study with 83 students at VILNIUS TECH showed that the smart chair detected correct posture with 94.78% accuracy, and 97.59% of users responded to alerts by adjusting their posture within an average of 3.27 s. Full article

This work presents a Computer Vision (CV) platform for Food Waste (FW) detection in canteen plates exploring a research gap in automated FW detection using CV models. A machine learning methodology was followed, starting with the creation of a custom dataset of canteen plates images before and after lunch or dinner, and data augmentation techniques were applied to enhance the model’s robustness. Subsequently, a CV model was developed using YOLOv11 to classify the percentage of FW on a plate, distinguishing between edible food items and non-edible discarded material. To evaluate the performance of the model, we used a real dataset as well as three benchmarking datasets with food plates, in which it could be detected waste. For the real dataset, the system achieved a mean average precision (mAP) of 0.343, a precision of 0.62, and a recall of 0.322 on the test set as well as demonstrating high accuracy in classifying waste considering the traditional evaluation metrics on the benchmarking datasets. Given these promising results and the provision of open-source code on a GitHub repository, the platform can be readily utilized by the research community and educational institutions to monitor FW in student meals and proactively implement reduction strategies. Full article

This paper explores the application of large language models (LLMs) to automate the evaluation of programming assignments in an undergraduate “Introduction to Programming” course. This study addresses the challenges of manual grading, including time constraints and potential inconsistencies, by proposing a system that integrates several LLMs to streamline the assessment process. The system utilizes a graphic interface to process student submissions, allowing instructors to select an LLM and customize the grading rubric. A comparative analysis, using LLMs from OpenAI, Google, DeepSeek and ALIBABA to evaluate student code submissions, revealed a strong correlation between LLM-generated grades and those assigned by human instructors. Specifically, the reduced model using statistically significant variables demonstrates a high explanatory power, with an adjusted R² of 0.9156 and a Mean Absolute Error of 0.4579, indicating that LLMs can effectively replicate human grading. The findings suggest that LLMs can automate grading when paired with human oversight, drastically reducing the instructor workload, transforming a task estimated to take more than 300 h of manual work into less than 15 min of automated processing and improving the efficiency and consistency of assessment in computer science education. Full article

Background/Objectives: Despite the growing awareness of nutrition and the popularity of healthy lifestyles among adolescents, disordered eating behaviors—such as anorexia nervosa, bulimia nervosa, binge eating disorder (BED), and avoidant/restrictive food intake disorder (ARFID)—remain significant public health concerns. ARFID, officially recognized only in 2013, is still poorly understood among youth. This study aimed to assess the relationship between adolescents’ nutritional knowledge, emotional regulation, media influence, and eating behaviors. Methods: A cross-sectional study was conducted in 2024 among 120 students aged 15–17 attending W. Reymont Secondary School No. II in Ostrów Wielkopolski, Poland. Participants completed a custom-designed, paper-based questionnaire consisting of 30 single-choice questions and demographic items. The instrument assessed knowledge of eating disorders, body satisfaction, social media impact, and the emotional determinants of food choices. The tool was developed with expert input but has not undergone formal psychometric validation. While many adolescents demonstrated basic nutritional knowledge—such as awareness of BMI norms and food group distribution—they often failed to apply this knowledge to their dietary behaviors. Results: Significant gender differences were observed: girls were more likely to restrict food intake, report emotional eating, and engage in slimming behaviors, while boys were less emotionally reactive and less influenced by social media. Most participants reported eating one meal daily with family but rarely discussed nutrition at home. Emotional involvement in eating, particularly among girls, emerged as a key factor, more influential than social media in shaping dietary behaviors. Conclusions: The findings highlight a clear gap between nutritional knowledge and actual behavior among adolescents, driven in part by emotional dysregulation and body image concerns. School-based interventions should incorporate not only nutritional education but also emotional regulation strategies and media literacy to effectively support healthy eating behaviors in youth. Full article

Virtual reality (VR) technology is becoming increasingly relevant as a modern educational tool. However, its application in teaching and learning computational thinking remains relatively underexplored. This paper presents the implementation of selected tasks from the international Bebras Challenge in a VR environment called ThinkLand. A comparative study was conducted to evaluate the usability of the developed game across two interface types: mobile devices and desktop computers. A total of 100 participants, including high school and university students, took part in the study. The overall usability rating was classified as “good”, suggesting that ThinkLand holds promise as a platform for supporting computational thinking education. To assess specific aspects of interface usability, a custom Virtual Environment Usability Questionnaire (VEUQ) was developed. Regression analysis was performed to examine the relationship between participants’ age, gender, and interface type with both learning performance and perceived usability, as measured by the VEUQ. The analysis revealed statistically significant differences in interaction patterns between device types, providing practical insights for improving interface design. Validated in this study, the VEUQ proved to be an effective instrument for informing interaction design and guiding the development of educational VR applications for both mobile and desktop platforms. Full article