Search Results (1,156)

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

The rapidly changing dynamics of the digital age reshape the addiction relationship that high school students establish with technology. While smartphones remove boundaries in terms of communication and access to information, their usage triggers a source of anxiety and nomophobia. The increase in students’ anxiety levels because of their over-reliance on mobile phone use leads to significant behavioral changes in their mental health, academic performance, social interactions and financial dependency. This study examined the nomophobia levels of high school students according to selected socio-demographic indicators. Using the relational screening model, the multistage sampling technique was used to select a sample of 884 participants: 388 from Science High School and 496 from Anatolian High School (459 female, 425 male, Mage = 16.45 ± 1.14 year). Independent sample test and One-way ANOVA were applied. Depending on the homogeneity assumption of the data, Welch values were considered, and Tukey tests were applied as a second-level test from post hoc analyses. Comprehensive analyses of nomophobia levels revealed that young individuals’ attitudes towards digital technology differ significantly according to their demographic and behavioral characteristics. Variables such as gender, physical activity participation, grade level and duration of smartphone use are among the main factors affecting nomophobia levels. Female individuals and students who do not participate in physical activity exhibit higher nomophobia scores. Full article

Based on our question, “Are reels/short-videos the real deal in video-based learning?” this study explores the effectiveness of short (around 2 min) video-based learning in engaging medical students from the second large medical Portuguese school. With the increasing integration of digital tools in education, video content has emerged as a dynamic method to enhance learning experiences. This cross-sectional survey was conducted by using anonymous self-administered questionnaires, prepared with reference to previous studies, and distributed to 264 informed students who voluntarily agreed to participate. This sample represented 75.5% of the students attending the classes. The questionnaires included topics related to the 65 short videos about practical classes, as well as the students’ learning preferences. The collected data were analyzed using descriptive and comparative statistics. The students considered that the content and format of the videos were adequate (99.6% and 100%, respectively). Specifically, the videos helped the students to better understand the practical classes, consolidate and retain the practical content, and simplify the study for the exams. Additionally, the videos were praised for their high-quality audiovisual content, being innovative, complete, concise, short and/or adequate, or better than other formats such as printed information. The combination of written and audiovisual support materials for teaching and studying is important and has been shown to improve students’ performance. This pedagogical methodology is well-suited for the current generation of students, aiding not only in study and exam preparation but also in remote learning. Full article

This study addresses fairness in machine learning for student academic performance prediction using the UCI Student Performance dataset. We comparatively evaluate logistic regression, Random Forest, and XGBoost, integrating the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance and 5-fold cross-validation for robust model training. A comprehensive fairness analysis is conducted, considering sensitive attributes such as gender, school type, and socioeconomic factors, including parental education (Medu and Fedu), cohabitation status (Pstatus), and family size (famsize). Using the AIF360 library, we compute the demographic parity difference (DP) and Equalized Odds Difference (EO) to assess model biases across diverse subgroups. Our results demonstrate that XGBoost achieves high predictive performance (accuracy: 0.789; F1 score: 0.803) while maintaining low bias for socioeconomic attributes, offering a balanced approach to fairness and performance. A sensitivity analysis of bias mitigation strategies further enhances the study, advancing equitable artificial intelligence in education by incorporating socially relevant factors. Full article

Background/Objectives: Cardiorespiratory fitness (CRF) is of great interest in children and adolescents. Due to the limited availability of cardiopulmonary exercise testing, simple and reliable alternatives are needed. A stair climbing test (SCT) for the assessment of CRF developed at the Department of Pediatric Cardiology of the LMU University Hospital in Munich showed a strong correlation with VO₂max. The aim of this study is to prove its feasibility in a non-clinical setting and to analyse its results in a larger study population. Methods: During the “Hand aufs Herz” study, a comprehensive cardiovascular examination was carried out on 922 pupils and siblings (13.2 ± 7.8 years) at a high school in Bavaria. The SCT was performed to evaluate CRF: participants had to run up and down a total of four floors (14.8 m) as quickly as possible without skipping steps or holding on to the banister. Absolute time has been normalized over the standard height of 12 m to allow comparisons with different settings. An SCT Index was calculated to adjust results to the different weights of participants and the exact height of the staircase. Results: The SCT proved to be easily feasible and safe in non-clinical contexts. Out of 922 participants, 13 (1.4%) were not able to perform the test, and 3 (0.3%) had to interrupt it following fatigue or stumbling. A total of 827 participants aged from 9 to 17 years (13.1 ± 2.1 years, 45.8% girls) had a mean absolute SCT time of 53.4 ± 6.2 s and 43.3 ± 5.1 s when normalized over 12 m. Conclusions: The SCT represents a simple, cost- and time-saving test that allows a rapid and solid assessment of cardiorespiratory fitness in children and adolescents. We could demonstrate that it is safe and feasible in non-clinical contexts. Its short duration and universal applicability are valuable advantages that could facilitate the establishment of a repetitive cardiovascular screening in the pediatric population, particularly in outpatient departments or settings with low-resource systems. Full article

Urban areas are responsible for most of Europe’s energy demand and emissions and urgently require building retrofits to meet climate neutrality goals. This study evaluates the energy efficiency potential of three public school buildings in western Macedonia, Greece—a cold-climate region with high heating needs. The buildings, constructed between 1986 and 2003, exhibited poor insulation, outdated electromechanical systems, and inefficient lighting, resulting in high oil consumption and low energy ratings. A robust methodology is applied, combining detailed on-site energy audits, thermophysical diagnostics based on U-value calculations, and a techno-economic assessment utilizing Net Present Value (NPV), Internal Rate of Return (IRR), and SWOT analysis. The study evaluates a series of retrofit measures, including ceiling insulation, high-efficiency lighting replacements, and boiler modernization, against both technical performance criteria and financial viability. Results indicate that ceiling insulation and lighting system upgrades yield positive economic returns, while wall and floor insulation measures remain financially unattractive without external subsidies. The findings are further validated through sensitivity analysis and policy scenario modeling, revealing how targeted investments, especially when supported by public funding schemes, can maximize energy savings and emissions reductions. The study concludes that selective implementation of cost-effective measures, supported by public grants, can achieve energy targets, improve indoor environments, and serve as a replicable model of targeted retrofits across the region, though reliance on external funding and high upfront costs pose challenges. Full article

Academic performance has become a consolidated indicator of a nation’s educational and social equity. Consequently, increasing attention has been paid to determining the factors associated with school performance, particularly in the case of students with extreme academic outcomes. The aim of this study is to identify and compare the factors related to the level of mathematical competence of Spanish students with low and high levels of achievement, based on data from the Spanish sample of PISA 2022 (n = 30,800). The results of the multilevel quantile regression analysis reveal that the social, economic, and cultural status of the students have a significant and positive effect on both groups. Other variables, such as gender, grade repetition, and length of pre-primary education, show differentiated effects depending on the level of competence. Moreover, school-related factors, such as school location and competition among centres, exhibit opposite effects. Finally, aspects such as school ownership, average class size, and the degree of curricular autonomy only have a significant impact on the mathematical competence of low-achieving students. These findings highlight the need for differentiated educational policies that address the specific needs of each group of students. Full article

LEDs are readily controllable and demonstrate rapid switching capabilities. These attributes facilitate their efficient integration across a broad spectrum of applications. Indeed, their inherent versatility renders them ideally suited for diverse sectors, including consumer electronics, traffic signage, automotive technology, and architectural illumination. Furthermore, LEDs serve as effective light sources for applications in spectroscopy, agriculture, pest control, and wireless optical transmission. The capability to choice high-efficiency LED devices with a specified dominant wavelength renders them particularly well-suited for integration into underwater optical communication systems. In this paper, we present the state-of-the-art of Light-Emitting Diodes (LEDs) for use in underwater wireless optical communications (UOWC). In particular, we focus on the challenges posed by water turbidity and evaluate the optimal wavelengths for communication in coastal environments, especially in the presence of chlorophyll or suspended particulate matter. Given the growing development and applications of underwater optical communication, it is crucial that the topic becomes not only a subject of research but also part of the curricula in technical school and universities. To this end, we introduce a simple and cost-effective UOWC system designed for educational purposes. Some tests have been conducted to evaluate the system’s performance, and the results have been reported. Full article

Background/Objectives: Smartphone addiction (SA) and gaming addiction (GA) have become risk factors for individuals of all ages in recent years. Especially during adolescence, it has become very difficult for parents to control this situation. Physical activity and the effective use of free time are the most important factors in eliminating such addictions. This study aimed to test a new machine learning method by combining routine regression analysis with the gradient-boosting machine (GBM) and random forest (RF) methods to analyze the relationship between SA and GA with leisure time management (LTM) and the enjoyment of physical activity (EPA) among adolescents. Methods: This study presents the results obtained using our developed GBM + RF hybrid model, which incorporates LTM and EPA scores as inputs for predicting SA and GA, following the preprocessing of data collected from 1107 high school students aged 15–19 years. The results were compared with those obtained using routine regression results and the lasso, ElasticNet, RF, GBM, AdaBoost, bagging, support vector regression (SVR), K-nearest neighbors (KNN), multi-layer perceptron (MLP), and light gradient-boosting machine (LightGBM) models. In the GBM + RF model, probability scores obtained from GBM were used as input to RF to produce final predictions. The performance of the models was evaluated using the R², mean absolute error (MAE), and mean squared error (MSE) metrics. Results: Classical regression analyses revealed a significant negative relationship between SA scores and both LTM and EPA scores. Specifically, as LTM and EPA scores increased, SA scores decreased significantly. In contrast, GA scores showed a significant negative relationship only with LTM scores, whereas EPA was not a significant determinant of GA. In contrast to the relatively low explanatory power of classical regression models, ML algorithms have demonstrated significantly higher prediction accuracy. The best performance for SA prediction was achieved using the Hybrid GBM + RF model (MAE = 0.095, MSE = 0.010, R² = 0.9299), whereas the SVR model showed the weakest performance (MAE = 0.310, MSE = 0.096, R² = 0.8615). Similarly, the Hybrid GBM + RF model also showed the highest performance for GA prediction (MAE = 0.090, MSE = 0.014, R² = 0.9699). Conclusions: These findings demonstrate that classical regression analyses have limited explanatory power in capturing complex relationships between variables, whereas ML algorithms, particularly our GBM + RF hybrid model, offer more robust and accurate modeling capabilities for multifactorial cognitive and performance-related predictions. Full article

This study assessed the baseline indoor environmental quality (IEQ) of secondary school classrooms in Tanzania by measuring temperature, relative humidity, noise, lighting, and indoor air quality. Objective measurements were conducted using calibrated sensors in 14 classrooms across five schools, with data collected during occupied school hours and additional noise measurements during unoccupied periods. All classrooms are naturally ventilated through operable windows and doors. The findings reveal a pattern of cumulative IEQ deficiencies: classroom temperatures frequently exceeded the recommended 20–24 °C range, reaching means as high as 30.4 °C, while relative humidity varied widely, with levels occasionally surpassing 65%. Noise levels consistently exceeded the World Health Organization (WHO)’s recommended 35 dBA threshold, with significant differences observed between occupied and unoccupied periods (p = 0.02). Light distribution was uneven, with significantly higher lux levels near windows than at classroom centers (p < 0.001), and artificial lighting was generally insufficient due to infrastructure limitations. Although CO₂ concentrations remained below the 1000 ppm threshold, indicating adequate ventilation, particulate matter levels were often elevated, with PM2.5 reaching up to 58.80 µg/m³ and PM10 up to 96.90 µg/m³, exceeding health-based guidelines. Together, these findings suggest that students are exposed to multiple environmental stressors that may impair health, comfort, and academic performance. This study provides a critical baseline for future research and context-specific interventions aimed at improving learning environments in Tanzanian schools and similar settings in East Africa. Full article

This research investigates topic-based sentiment classification in Greek educational-related data using transformer-based language models. A comparative evaluation is conducted on GreekBERT, XLM-r-Greek, mBERT, and Palobert using three original sentiment-annotated datasets representing parents of students with functional diversity, school directors, and teachers, each capturing diverse educational perspectives. The analysis examines both overall sentiment performance and topic-specific evaluations across four thematic classes: (i) Material and Technical Conditions, (ii) Educational Dimension, (iii) Psychological/Emotional Dimension, and (iv) Learning Difficulties and Emergency Remote Teaching. Results indicate that GreekBERT consistently outperforms other models, achieving the highest overall F1 score (0.91), particularly excelling in negative sentiment detection (F1 = 0.95) and showing robust performance for positive sentiment classification. The Psychological/Emotional Dimension emerged as the most reliably classified category, with GreekBERT and mBERT demonstrating notably high accuracy and F1 scores. Conversely, Learning Difficulties and Emergency Remote Teaching presented significant classification challenges, especially for Palobert. This study contributes significantly to the field of sentiment analysis with Greek-language data by introducing original annotated datasets, pioneering the application of topic-based sentiment analysis within the Greek educational context, and offering a comparative evaluation of transformer models. Additionally, it highlights the superior performance of Greek-pretrained models in capturing emotional detail, and provides empirical evidence of the negative emotional responses toward Emergency Remote Teaching. Full article

The evaluation of open-ended questions is typically performed by human instructors using predefined criteria to uphold academic standards. However, manual grading presents challenges, including high costs, rater fatigue, and potential bias, prompting interest in automated essay scoring systems. While automated essay scoring tools can assess content, coherence, and grammar, discrepancies between human and automated scoring have raised concerns about their reliability as standalone evaluators. Large language models like ChatGPT offer new possibilities, but their consistency and fairness in feedback remain underexplored. This study investigates whether ChatGPT can provide stable and fair essay scoring—specifically, whether identical student responses receive consistent evaluations across multiple AI interactions using the same criteria. The study was conducted in two marketing courses at an engineering school in Chile, involving 40 students. Results showed that ChatGPT, when unprompted or using minimal guidance, produced volatile grades and shifting criteria. Incorporating the instructor’s rubric reduced this variability but did not eliminate it. Only after providing an example-rich rubric, a standardized output format, low temperature settings, and a normalization process based on decision tables did ChatGPT-4o demonstrate consistent and fair grading. Based on these findings, we developed a scalable algorithm that automatically generates effective grading rubrics and decision tables with minimal human input. The added value of this work lies in the development of a scalable algorithm capable of automatically generating normalized rubrics and decision tables for new questions, thereby extending the accessibility and reliability of automated assessment. Full article

The school climate is defined as the conditions present within a learning environment that influence the development of educational practices in the context of the school. A positive school climate is crucial for improving learning opportunities, students’ academic performance, and the overall quality of daily schoolwork, thereby promoting educational sustainability. The aim of this research was to analyse and compare the relationships among teachers and among students to understand the school climate in high-efficacy schools and low-efficacy schools. A content analysis was conducted on 50 semistructured interviews with members of the leadership teams from both types of schools. The findings showed that in highly effective schools, there are more favourable perceptions of the school climate, better relationships among teachers, and higher expectations for students than in schools with low efficacy. Improving the school climate can optimise educational performance and should be considered a key strategy to improve both the effectiveness of schools and their long-term sustainability. Full article

Background/Objectives: Children with above-average cognitive functioning often present complex developmental profiles, combining high cognitive potential with heterogeneous socio-emotional and learning trajectories. Although the cognitive and behavioral characteristics of giftedness have been widely studied in Anglophone countries, evidence remains limited in Southern Europe. This study aimed to investigate the cognitive, academic, and emotional–behavioral profiles of Italian children and adolescents with above-average cognitive functioning, using an inclusive, dimensional approach (IQ > 114). Methods: We analyzed a cross-sectional sample of 331 children and adolescents (ages 2.11–16.5 years), referred for clinical cognitive or behavioral evaluations. Participants were assessed using the WPPSI-III or WISC-IV for cognitive functioning, the MT battery for academic achievement, and the Child Behavior Checklist (CBCL) for emotional and behavioral symptoms. Comparative and correlational analyses were performed across age, gender, and functional domains. A correction for multiple testing was applied using the Benjamini–Hochberg procedure. Results: Gifted participants showed strong verbal comprehension (mean VCI: preschoolers = 118; school-aged = 121) and relative weaknesses in working memory (WM = 106) and processing speed (PS = 109). Males outperformed females in perceptual reasoning (PR = 121 vs. 118; p = 0.032), while females scored higher in processing speed (112 vs. 106; p = 0.021). Difficulties in writing and arithmetic were observed in 47.3% and 41.8% of school-aged participants, respectively. Subclinical internalizing problems were common in preschool and school-aged groups (mean CBCL T = 56.2–56.7). Working memory negatively correlated with total behavioral problems (r = −0.13, p = 0.046). Conclusions: These findings confirm the heterogeneity of gifted profiles and underscore the need for personalized educational and psychological interventions to support both strengths and vulnerabilities in gifted children. Caution is warranted when interpreting these associations, given their modest effect sizes and the exploratory nature of the study. Full article

Background: Boredom is a common but insufficiently explored experience in adolescence, which has been increasingly linked to the development of risky behaviours. This study explores the extent to which boredom predicts engagement in various risky behaviours among adolescents aged 15 to 17. Methods: A cross-sectional study on a sample of 281 high-school students in Croatia was performed in 2024. The participants completed a structured online questionnaire, including validated scales measuring their proneness to boredom, substance use, disordered eating, theft, and cyberbullying. Data were analysed using non-parametric tests, correlation coefficients, and linear regression. Results: Boredom showed a significant association with all forms of risky behaviour, with the strongest association found for disordered eating. Moderate predictive ability was observed for alcohol, marijuana, and drug use, while weaker but significant associations with cyberbullying and theft were also found. Male adolescents reported higher involvement in certain risky behaviours, although no significant gender differences were observed in boredom levels. Conclusions: Boredom is a developmental risk factor, notably when leisure time lacks structure and meaning. While the cross-sectional design of this study limits causal conclusions, the findings highlight the importance of taking boredom into consideration regarding pedagogical prevention efforts. Pedagogical activities targeting quality leisure time, especially within schools and families, may reduce adolescents’ susceptibility to harmful behaviours. Full article