Search Results (4,456)

Traditional academic monitoring practices rely on retrospective data analysis, generally identifying at-risk students too late to take meaningful action. To address this, this paper proposes a real-time, rule-based decision support system designed to increase student achievement by early detection of disengagement, meeting the growing demand for prompt academic intervention in online and blended learning contexts. The study uses the Open University Learning Analytics Dataset (OULAD), comprising over 32,000 students and millions of virtual learning environment (VLE) interaction records, to simulate weekly assessments of engagement through clickstream activity. Students were flagged as “at risk” if their participation dropped below defined thresholds, and these flags were associated with assessment performance and final course results. The system demonstrated 72% precision and 86% recall in identifying failing and withdrawn students as major alert contributors. This lightweight, replicable framework requires minimal computing power and can be integrated into existing LMS platforms. Its visual and statistical validation supports its role as a scalable, real-time early warning tool. The paper recommends integrating real-time engagement dashboards into institutional LMS and suggests future research explore hybrid models combining rule-based and machine learning approaches to personalize interventions across diverse learner profiles and educational contexts. Full article

Background: The number of students with Specific Learning Disorders (SLDs) in universities has recently increased. Thus, it is important to analyze their difficulties throughout their academic studies and propose adequate interventions to prevent emotional problems and dropout. Previous research has reported higher levels of internalizing problems (anxiety and depression) in students with SLDs compared to those with typical development. Surprisingly, academic burnout among students with SLDs remains a largely overlooked and under-researched issue. The present work is one of the first studies that seeks to address this critical gap by examining the levels of academic burnout, and exploring its relationship with depression and anxiety in university students both with and without SLDs. Methods: The sample included 120 university students (M = 42, F = 78; mean age = 21.16, SD = 2.26). Of these, 60 students had SLDs and 60 had typical development (TD). Students were asked to complete three questionnaires assessing burnout (BAT-C), depression (BDI-II), and anxiety (STAI-Y). Results: The comparison between groups revealed that students with SLDs reported significantly higher levels of total burnout (mean difference = −3.98, t[118] = −2.59, p = 0.011, d = 0.47) and trait anxiety (mean difference = −2.87, t[118] = −2.73, p = 0.007, d = 0.50), with a moderate effect size for both differences. They also exhibited greater cognitive impairment related to burnout (U = 2333.50, p = 0.006, r = 0.25). No group differences were found in depression. Path analyses showed that while trait anxiety mediated the burnout–depression link in both groups, state anxiety was a significant mediator only for students with SLDs (β = 0.22, p = 0.025). Conclusions: The findings provide new evidence of the importance of monitoring academic burnout and anxiety in students with SLDs. The results show that anxiety plays a crucial mediating role between burnout and depression in students with SLDs, reinforcing the need for specific psychological support programs in universities. Full article

Background/Objectives: Cerebral palsy (CP) is the most common cause of motor disability in childhood and is frequently associated with cognitive impairments that limit autonomy and participation. While motor function is a known predictor of functional outcomes, the specific contribution of cognitive domains within the International Classification of Functioning, Disability and Health (ICF) framework remains unexplored. This systematic review examines the relationship between cognitive domains and the ICF components of Activities and Participation, and Environmental Factors in people with CP. Methods: Following PRISMA guidelines, a systematic search was conducted across six databases (PubMed, PsycINFO, CENTRAL, CINAHL, ERIC, and WOS) for studies published between 2002 and 2025. Eligible studies included participants with CP (n = 3056) and analyzed associations between cognitive functions and ICF domains using standardized tools and statistical methods. Risk of bias was evaluated using the Oxford Centre for Evidence-Based Medicine criteria. Results: Forty-four studies met inclusion criteria, involving mostly children and adolescents with spastic CP and mild to moderate motor impairment. General intellectual functioning, language, and visual perception were the most studied domains, showing consistent associations with ICF chapters such as Learning and applying knowledge, Communication, and Mobility. Although fewer studies examined Environmental Factors, relevant associations emerged with support systems, attitudes, and services. Heterogeneity in assessment methods and participant profiles was observed, and adult representation was limited. Conclusions: Cognitive functioning is significantly associated with multiple ICF domains in CP. Environmental Factors remain insufficiently addressed. Further research should consider CP heterogeneity and promote standardized assessments to support ICF-based intervention planning. Full article

The integration of sustainability competences into education presents significant challenges, particularly in embedding Education for Sustainable Development (ESD) into contextually relevant learning experiences. This study presents the design and validation of the Art Nouveau Path, a Mobile Augmented Reality Game (MARG) developed within the EduCITY ecosystem to foster competences, such as sustainability values, systems thinking, and future literacy. Grounded in the GreenComp framework and employing a Design-based Research (DBR) approach, the intervention was validated with 33 in-service teachers through a simulation-based workshop and a curricular review and complemented by a diagnostic questionnaire was administered to 221 students. This questionnaire (S1-PRE) provided the baseline data on sustainability awareness, digital readiness, and heritage-related learning interest. The teachers confirmed the MARG’s curricular adequacy value and interdisciplinary potential, while the students’ diagnostics revealed mixed conceptions; although 73.30% considered sustainability competences important, only 61.10% expressed interest in learning more about them. Also, 72.40% showed interest in learning about sustainability through local Art Nouveau heritage, and 79.60% considered the theme attractive, indicating potential for emotional and cognitive engagement. The Art Nouveau Path provides an exploratory and replicable model of curriculum-integrated ESD, connecting cultural heritage with competence-based learning for the operationalization of the GreenComp framework in support of SDG 4.7. Full article

Developmental dyscalculia is a neurobiologically based learning disorder that impairs numerical processing and calculation abilities. Numerous studies underscore the critical importance of early detection to enable effective intervention, highlighting the need for individualized, structured, and adaptive approaches. Digital tools, particularly those based on serious games, appear to offer a promising level of personalization. This systematic review aims to evaluate the relevance of serious game-based digital solutions as tools for the detection and remediation of developmental dyscalculia in children aged 5 to 12 years. To provide readers with a comprehensive understanding of this field, the selected solutions were analyzed and classified according to the technologies employed (including emerging ones), their thematic focus, the mathematical abilities targeted, the configuration of experimental trials, and the outcomes reported. A systematic search was conducted across Scopus, Web of Knowledge, PubMed, Eric, PsycInfo, and IEEEXplore for studies published between 2000 and March 2025, yielding 7799 records. Additional studies were identified through reference screening. A total of 21 studies met the eligibility criteria. All procedures were registered in PROSPERO and conducted in accordance with PRISMA guidelines for systematic reviews. The methodological analysis of the included studies emphasized the importance of employing both control and experimental groups with adequate sample sizes to ensure robust evaluation. In terms of remediation, the findings highlight the value of pre- and post-intervention assessments and the implementation of individualized training sessions, ideally not exceeding 20 min in duration. The review revealed a greater prevalence of remediation-focused serious games compared to screening tools, with a growing trend toward the use of mobile technologies. However, the substantial methodological limitations observed across studies must be addressed to enable the rigorous evaluation of the potential of SGs to detect and support the improvement of difficulties associated with developmental dyscalculia. Moreover, despite the recognized importance of personalization and adaptability in effective interventions, relatively few studies incorporated machine learning algorithms to enable the development of fully adaptive systems. Full article

This study analyzes the effects of an educational pill strategy in a nursing anatomy course on academic performance, grade redistribution versus a control group, and student satisfaction, acknowledging that digital teaching innovations in higher education may not benefit all students equally. A learning pill strategy was implemented in a first-year nursing anatomy course. A pre–post quasi-experimental design assessed academic performance, while video usage and student satisfaction were analyzed using an ad hoc questionnaire. In the control group, 44.1% and 40.8% of students failed the first and second exams, respectively. In the intervention group, these percentages were 42.9% and 28.9%. While mean scores showed no significant differences in the control group, the intervention group improved significantly on the second exam (p < 0.001). Grade distribution differed between groups (χ² = 8.635; p < 0.05), with fewer students scoring below 4 and more scoring between 6 and 8. Satisfaction analysis revealed three factors: usefulness/self-efficacy, motivation/learning, and structure/accessibility, with motivation (Factor 2) significantly associated with greater strategy use. Initial group heterogeneity influences how students use and benefit from teaching resources. These findings suggest that integrating educational pills into teaching practices may enhance conceptual understanding and increase student motivation. Full article

Background: Unlike medical training, science teacher training in Brazil does not include PBL as a curricular methodology. However, there is a Pedagogical Residency Program (PRP) that allows teaching experiences that are different from those provided in the undergraduate course. Thus, in this research, we propose a formative intervention in PBL for scholarship holders in the Pedagogical Residency Program (hereinafter Residents), aiming to answer the following question: “What are the perceptions of pre-service teachers about the planning, implementation, and evaluation of a PBL intervention in physics teaching?”. Methods: Five Residents taught an elective course specially designed for the application of PBL to teach secondary school physics. The training of the Residents in PBL occurred almost simultaneously with the offering of the elective subject. To reveal their perceptions, we collected Residents’ teaching plans, problem scenarios, and reflective analyses. Results: The results demonstrate that the Residents encountered several difficulties in developing and implementing the PBL methodology when teaching physics. Regarding development, the difficulties lie in coherently aligning the learning objectives with the highly complex active methodology of PBL. In addition, another clear difficulty is developing a problem situation appropriate to the knowledge that one wishes to develop. During the intervention, the Residents realized how difficult it is to implement PBL to allow students to develop skills and knowledge in a reflective way. Conclusions: The results indicate that PRP is necessary to develop methodologies such as PBL, as it allows supervision and reflection on practice. However, we also observed that the results point to the urgent need to introduce PBL in the initial training of science teachers; this process can be established in three stages: strategically studying lesson planning for the implementation of PBL, developing problem situations that align with the knowledge that one wishes to develop, and developing metacognitive regulation and argumentation skills to conduct interventions based on PBL. Full article

This study investigates how Iyengar yoga postures influence autonomic nervous system (ANS) activity by analyzing multimodal physiological signals collected via wearable sensors. The goal was to explore whether subtle postural variations elicit measurable autonomic responses and to identify which sensor features most effectively capture these changes. Participants performed a sequence of yoga poses while wearing synchronized sensors measuring electrodermal activity (EDA), heart rate variability, skin temperature, and motion. Interpretable machine learning models, including linear classifiers, were trained to distinguish physiological states and rank feature relevance. The results revealed that even minor postural adjustments led to significant shifts in ANS markers, with phasic EDA and RR interval features showing heightened sensitivity. Surprisingly, micro-movements captured via accelerometry and transient electrodermal reactivity, specifically EDA peak-to-RMS ratios, emerged as dominant contributors to classification performance. These findings suggest that small-scale kinematic and autonomic shifts, which are often overlooked, play a central role in the physiological effects of yoga. The study demonstrates that wearable sensor analytics can decode a more nuanced and granular physiological profile of mind–body practices than traditionally appreciated, offering a foundation for precision-tailored biofeedback systems and advancing objective approaches to yoga-based interventions. Full article

Background: Gamification may enhance engagement and higher-order learning in health-care profession education, but evidence from undergraduate nursing programs—particularly in the Middle East—is limited. We evaluated whether integrating structured gamified activities into an anatomy and physiology course improves class engagement and knowledge-based critical thinking. Methods: In this pragmatic, nonrandomized, section-allocated quasi-experimental study at a single Saudi institution, 121 first-year female nursing students were assigned by existing cohorts to traditional instruction (control; n = 61) or instruction enhanced with gamified elements (intervention; n = 60) groups. The intervention (introduced mid-semester) comprised time-limited competitive quizzing with immediate feedback and aligned puzzle tasks. Outcomes were measured at baseline, mid-semester, and end-semester using a four-item Class Engagement Rubric (CER; scale 1–5) and a 40-item high-cognitive multiple-choice (MCQ) assessment mapped to course objectives. Analyses used paired and independent t-tests with effect sizes and 95% confidence intervals. Results: No attrition occurred. From baseline to end-semester, the intervention group had a mean CER increase of 0.59 points (95% CI, 0.42 to 0.76; p < 0.001)—approximately a 15% relative gain—and a mean MCQ increase of 0.30 points (95% CI, 0.18 to 0.42; p < 0.001), an ~8% relative gain. The control group showed no material change over the same interval. Between-group differences in change favored the intervention across CER items and for the MCQ outcome. Semester grade-point average did not differ significantly between groups (p = 0.055). Conclusions: Embedding a brief, structured gamification package within an undergraduate nursing anatomy and physiology course was associated with measurable improvements in classroom engagement and modest gains in knowledge-based critical thinking, with no detectable effect on overall semester GPA. Given the nonrandomized, single-site design, causal inference is limited. Multi-site randomized trials using validated critical-thinking instruments are warranted to confirm effectiveness and define dose, durability, and generalizability. Full article

Risk prediction models are increasingly used in the management of coronary artery disease (CAD), with applications ranging from diagnostic stratification to prognostic assessment and therapeutic guidance. In the context of CAD and percutaneous coronary intervention, clinical decision-making often relies on risk scores to estimate the likelihood of ischemic and bleeding events and to tailor antithrombotic strategies accordingly. Traditional scores are derived from clinical, anatomical, procedural, and laboratory variables, and their performance is evaluated based on discrimination and calibration metrics. While many established models are simple, interpretable, and externally validated, their predictive ability is often moderate and may be limited by outdated derivation cohorts, overfitting, or lack of generalizability. Recent advances have introduced artificial intelligence and machine learning models that can process large, high-dimensional datasets and identify patterns not apparent through conventional methods, with the aim to incorporate complex data; however, they are not exempt from limitations and struggle with integration into clinical practice. Notably, ethical issues, such as equity in model application, over-stratification, and real-world implementation, are of critical importance. The ideal predictive model should be accurate, generalizable, and clinically actionable. This review aims at providing an overview of the main predictive models used in the field of CAD and to discuss methodological challenges, with a focus on strengths, limitations and areas of applicability of predictive models. Full article

Distributed denial-of-service (DDoS) attacks are a prevalent threat to resource-constrained IoT deployments. We present an edge-based detection and mitigation system integrated with the oneM2M architecture. By using a Raspberry Pi 4 client and five Raspberry Pi 3 attack nodes in a smart-home testbed, we collected 200,000 packets with 19 features across four traffic states (normal, SYN/UDP/ICMP floods), trained Decision Tree, 2D-CNN, and LSTM models, and deployed the best model on an edge computer for real-time inference. The edge node classifies traffic and triggers per-attack defenses on the device (SYN cookies, UDP/ICMP iptables rules). On a held-out test set, the 2D-CNN achieved 98.45% accuracy, outperforming the LSTM (96.14%) and Decision Tree (93.77%). In end-to-end trials, the system sustained service during SYN floods (time to capture 200 packets increased from 5.05 s to 5.51 s after enabling SYN cookies), mitigated ICMP floods via rate limiting, and flagged UDP floods for administrator intervention due to residual performance degradation. These results show that lightweight, edge-deployed learning with targeted controls can harden oneM2M-based IoT systems against common DDoS vectors. Full article

Integration of machine learning (ML) and artificial intelligence (AI) into point-of-care (POC) sensor systems represents a transformative advancement in healthcare. This integration enables sophisticated data analysis and real-time decision-making in emergency and intensive care settings. AI and ML algorithms can process complex biomedical data, improve diagnostic accuracy, and enable early disease detection for better patient outcomes. Predictive analytics in POC devices supports proactive healthcare by analyzing data to forecast health issues and facilitating early intervention and personalized treatment. This review covers the key areas of ML and AI integration in POC devices, including data analysis, pattern recognition, real-time decision support, predictive analytics, personalization, automation, and workflow optimization. Examples of current POC devices that use ML and AI include AI-powered blood glucose monitors, portable imaging devices, wearable cardiac monitors, AI-enhanced infectious disease detection, and smart wound care sensors are also discussed. The review further explores new directions for POC sensors and ML integration, including mental health monitoring, nutritional monitoring, metabolic health tracking, and decentralized clinical trials (DCTs). We also examined the impact of integrating ML and AI into POC devices on healthcare accessibility, efficiency, and patient outcomes. Full article

Adverse childhood experiences (ACEs) are associated with profound long-term health and developmental consequences. However, current identification strategies are largely reactive, often missing opportunities for early intervention. Therefore, the potential of machine learning to proactively identify children at risk of ACE exposure needs to be explored. Using nationally representative data from 63,239 children in the 2018–2020 National Survey of Children’s Health (NSCH) after listwise deletion, we trained and validated multiple machine learning models to predict ACE exposure categorized as none, one, or two or more ACEs. Model performance was assessed using accuracy, precision, recall, F1 scores, and area under the curve (AUC) metrics with 5-fold cross-validation. The Random Forest model achieved the highest predictive accuracy (82%) and demonstrated strong performance across ACE categories. Key predictive features included child sex (female), food insufficiency, school absenteeism, quality of parent–child communication, and experiences of bullying. The model yielded high performance in identifying children with no ACEs (F1 = 0.89) and moderate performance for those with multiple ACEs (F1 = 0.64). However, performance for the single ACE category was notably lower (F1 = 0.55), indicating challenges in predicting this intermediate group. These findings suggest that family environment factors can be leveraged to predict ACE exposure with clinically meaningful accuracy, offering a foundation for proactive screening protocols. However, implementation must carefully address systematic selection bias, clinical utility limitations, and ethical considerations regarding predictive modeling of vulnerable children. Full article

As Industry 5.0 emerges as a human-centric evolution of industrial systems, this study investigates the effectiveness of training interventions in companies aimed at supporting the transition to Industry 5.0, emphasizing human-centric and resilient skill development. Drawing from multiple case studies involving engineers and operators, the research applies both meta-analysis and meta-regression to assess the added value of experiential learning approaches such as Teaching and Learning Factories. In addition, a novel methodology combining quantitative analyses with qualitative interpretation of emerging competences is presented. Principal Component Analysis and classification frameworks are employed to identify and organize key competence clusters along technological, organizational, and social dimensions. Special attention is given to the emergence of human-centered competences such as decision empowerment, which are shown to complement traditional operational capabilities. The findings confirm that experiential training interventions enhance both self-efficacy and adaptive operational readiness, while the use of fusion techniques enables the generalization of results across heterogeneous corporate settings. This work contributes to ongoing discourse on Industry 5.0 readiness by linking training design to strategic company incentives and highlights the role of structured evaluation in informing future policy and implementation pathways. Full article

Introduction: Pandemic communication faces significant challenges due to the dynamic nature of disease outbreaks, societal influences, and evolving communication platforms. Effective non-pharmaceutical interventions (NPIs) depend on robust health communication strategies. This study aims to develop a conceptual model to guide NPIs communication during pandemics, grounded in widely applied risk communication theories. Methods: Using Jabareen’s conceptual framework analysis method, this study synthesized interdisciplinary literature from public health, psychology, and risk communication. The method involves mapping data sources and concept categorization and integration. We examined Crisis and Emergency Risk Communication (CERC), the Social Amplification of Risk Framework (SARF), and Social Cognitive Theory (SCT) to develop a comprehensive NPIs communication framework. Results: The Pandemic Behavioral Prevention Framework delineates pandemic communication into five phases: pre-crisis, initial event, maintenance, resolution, and evaluation. It emphasizes targeting vulnerable populations, addressing trust deficits, and leveraging effective communication channels. Key concepts such as self-efficacy, vicarious learning, and social risk amplification are integrated to enhance public adherence to NPIs. Conclusion: The framework bridges gaps in pandemic communication by integrating risk and health communication principles, fostering trust, and addressing social determinants of health. It highlights the importance of pre-crisis education and the utilization of social media for targeted messaging. Full article