Search Results (1,341)

(1) Background: In today’s digital society, challenges like cyberbullying, harmful social media use, and unhealthy digital habits demand innovative and inclusive educational responses. This study investigates the potential of service-learning (SL) and electronic service-learning (e-SL) as experiential approaches to enhance digital well-being among youth. By actively engaging students, educators, and community stakeholders in co-designed projects, SL/e-SL promotes critical awareness, digital citizenship, and prosocial values while addressing digital risks. (2) Methods: This review offers a literature-based analysis of existing programs and good practices that apply experiential education to encourage responsible digital engagement. It explores SL and e-SL experiences across various educational settings. (3) Results: The findings show that SL and e-SL can be effective educational tools, creating meaningful opportunities for youth to participate in tackling digital issues and building inclusive spaces where students, faculty, and communities collaborate to foster digital literacy and well-being. The analysis also led to the development of quality standards for SL and e-SL practices that promote digital well-being. (4) Conclusions: This study highlights key implications for teaching, underscoring the value of integrative pedagogies that connect experiential learning to digital challenges, promoting a more inclusive and responsible digital culture. Full article

This qualitative study examines how teacher candidates in one mathematics methods course negotiated curriculum integration of mathematics with social justice through the use of multicultural children’s literature. Drawing on multiple sources of data including teacher candidate selection process of the literature, lesson plans artifacts, and reflection essays, this study explores how teacher candidates balanced competing learning goals when developing an integrated unit. The findings from this study reveal that while this process of planning was challenging for many teacher candidates, the results show that when mathematics is grounded in a culturally relevant context, students are more engaged and are able to connect mathematical learning to real-world and useful meaningful applications in their lived experiences. Additionally, teacher candidates were able to develop a broader conception of mathematics teaching, underscoring the value that a focus on social justice can have not just on student learning but on teacher professional development. Full article

Chronic kidney disease (CKD) affects over 850 million individuals worldwide, yet conventional risk stratification approaches fail to capture complex disease progression patterns. Current machine learning approaches suffer from inefficient parameter optimization and limited clinical interpretability. We developed an integrated framework combining advanced Bayesian optimization with explainable artificial intelligence for enhanced CKD risk assessment. Our approach employs XGBoost ensemble learning with intelligent parameter optimization through Optuna (a Bayesian optimization framework) and comprehensive interpretability analysis using SHAP (SHapley Additive exPlanations) to explain model predictions. To address algorithmic “black-box” limitations and enhance clinical trustworthiness, we implemented four-tier risk stratification using stratified cross-validation and balanced evaluation metrics that ensure equitable performance across all patient risk categories, preventing bias toward common cases while maintaining sensitivity for high-risk patients. The optimized model achieved exceptional performance with 92.4% accuracy, 91.9% F1-score, and 97.7% ROC-AUC, significantly outperforming 16 baseline algorithms by 7.9–18.9%. Bayesian optimization reduced computational time by 74% compared to traditional grid search while maintaining robust generalization. Model interpretability analysis identified CKD stage, albumin-creatinine ratio, and estimated glomerular filtration rate as primary predictors, fully aligning with established clinical guidelines. This framework delivers superior predictive accuracy while providing transparent, clinically-meaningful explanations for CKD risk stratification, addressing critical challenges in medical AI deployment: computational efficiency, algorithmic transparency, and equitable performance across diverse patient populations. Full article

Community-based ecotourism in community forests, such as Suan Pa Ket Nom Klao, Thailand, offers a promising avenue for promoting sustainable development through meaningful tourist experiences. This study develops and validates the Community Forest Intention Model (CFIM) to examine the relationships among Tourist Experience (TE), Tourist Satisfaction (SAT), and Sustainable Intention (SI) using Partial Least Squares Structural Equation Modeling (PLS-SEM). Data were collected from 128 participants who engaged in six structured activities at Suan Pa Ket Nom Klao, with responses measured via a 5-point Likert scale questionnaire. The results indicate that TE, comprising Service Quality, Accessibility, and Learning Engagement, significantly influences SAT (R² = 0.562), with Learning Engagement exerting the strongest effect (β = 0.413; p < 0.001). SAT, in turn, positively predicts SI (β = 0.502; p < 0.001; R² = 0.252). All hypothesized paths were statistically significant, confirming the model’s validity. These findings highlight the critical role of educational and service-related experiences in fostering tourist satisfaction and sustainable behaviors. This study provides actionable insights for enhancing ecotourism programs to support conservation and community engagement. Full article

Floods are among the most devastating natural disasters; predicting their depth and extent remains a global challenge. Machine Learning (ML) models have demonstrated improved accuracy over traditional probabilistic flood mapping approaches. While previous studies have developed ML-based models for specific local regions, this study aims to establish a methodology for estimating flood depth on a global scale using ML algorithms and freely available datasets—a challenging yet critical task. To support model generalization, 45 catchments from diverse geographic regions were selected based on elevation, land use, land cover, and soil type variations. The datasets were meticulously preprocessed, ensuring normality, eliminating outliers, and scaling. These preprocessed data were then split into subgroups: 75% for training and 25% for testing, with six additional unseen catchments from the USA reserved for validation. A sensitivity analysis was performed across several ML models (ANN, CNN, RNN, LSTM, Random Forest, XGBoost), leading to the selection of the Random Forest (RF) algorithm for both flood inundation classification and flood depth regression models. Three regression models were assessed for flood depth prediction. The pixel-based regression model achieved an R² of 91% for training and 69% for testing. Introducing a pixel clustering regression model improved the testing R² to 75%, with an overall validation (for unseen catchments) R² of 64%. The catchment-based clustering regression model yielded the most robust performance, with an R² of 83% for testing and 82% for validation. The developed ML model demonstrates breakthrough computational efficiency, generating complete flood depth predictions in just 6 min—a 225× speed improvement (90–95% time reduction) over conventional HEC-RAS 6.3 simulations. This rapid processing enables the practical implementation of flood early warning systems. Despite the dramatic speed gains, the solution maintains high predictive accuracy, evidenced by statistically robust 95% confidence intervals and strong spatial agreement with HEC-RAS benchmark maps. These findings highlight the critical role of the spatial variability of dependencies in enhancing model accuracy, representing a meaningful approach forward in scalable modeling frameworks with potential for global generalization of flood depth. Full article

Background/Objectives: This study assessed how to best prepare pediatric and adolescent cancer patients for amputation and support them afterward. Methods: This pilot qualitative study explored pre- and post-amputation experiences from the perspectives of nine pediatric and adolescent survivors who underwent amputation. Hour-long audio-recorded semi-structured interviews were transcribed and analyzed using the Sort and Sift, Think and Shift qualitative approach. Results: Participants described the informational supports they received before surgery, including guidance on what to expect, contact with amputation-related organizations, and exposure to tangible tools, such as a physical model of a knee joint. Emotional support from fellow amputees and healthcare providers, particularly surgeons, was also found to be meaningful. Individuals also identified unmet needs and gaps in emotional care. These included clearer guidance on post-surgical adaptations (e.g., basic self-care and navigating physical limitations) and the need for information tailored to their learning styles. Many emphasized the importance of improved pain management resources, expanded access to mental health services for both them and their families, and support in adjusting to changes in body image and social relationships. Participants also shared advice for future patients, recommending strategies such as personalizing hospital rooms, connecting with other amputees through social media, and using art to process their experience and say goodbye to the lost limb. Conclusions: Interviews with nine cancer survivors provide guidance for improving holistic, patient-centered care throughout the amputation process. Informational and emotional support should be tailored to an individual’s learning style and specific needs, in addition to their age at the time of surgery. Full article

Domain adaptation of pre-trained language models remains challenging, especially for specialized text collections that include distinct vocabularies and unique semantic structures. Existing contrastive learning methods frequently rely on generic masking techniques and coarse-grained similarity measures, which limit their ability to capture fine-grained, domain-specific linguistic nuances. This paper proposes an enhanced domain adaptation framework by integrating weighted contrastive learning guided by large language model (LLM) feedback and a novel topic-aware masking strategy. Specifically, topic modeling is utilized to systematically identify semantically crucial domain-specific terms, enabling the creation of meaningful contrastive pairs through three targeted masking strategies: single-keyword, multiple-keyword, and partial-keyword masking. Each masked sentence undergoes LLM-guided reconstruction, accompanied by graduated similarity assessments that serve as continuous, fine-grained supervision signals. Experiments conducted on an early 20th-century science fiction corpus demonstrate that the proposed approach consistently outperforms existing baselines, such as SimCSE and DiffCSE, across multiple linguistic probing tasks within the newly introduced SF-ProbeEval benchmark. Furthermore, the proposed method achieves these performance improvements with significantly reduced computational requirements, highlighting its practical applicability for efficient and interpretable adaptation of language models to specialized domains. Full article

SCALENEo (Smart ClinicAL rEasoning iN physiothErapy) is an innovative online serious game designed to improve clinical reasoning in musculoskeletal physiotherapy education. Adapted from the “Happy Families” card game, it provides an interactive, structured approach to developing students/learners’ ability to categorize clinical information into families of hypotheses. This digital platform supports both self-directed and collaborative learning, eliminating the need for continuous instructor supervision while ensuring meaningful engagement. SCALENEo features a unique feedback and scoring system that not only assesses students/learners’ decision-making processes but also promotes cautious and reflective reasoning over random guessing. By aligning with evidence-based pedagogical strategies, such as serious games and formative assessment, SCALENEo offers educators a powerful tool to reinforce critical thinking, improve student/learner engagement, and facilitate deeper learning in clinical reasoning education. Full article

As software systems continue to increase in complexity and scale, the number of reported bugs also grows. Bug reports are essential artifacts in software maintenance, supporting critical tasks such as detecting duplicate reports, predicting bug severity, and assigning priority levels. Although stop word removal is a common text preprocessing step in natural language processing, its effectiveness in deep learning-based bug report analysis has not been thoroughly evaluated. This study investigates the impact of stop word removal on three core bug report classification tasks. The analysis uses a dataset containing over 1.9 million bug reports from eight large-scale open-source projects, including Eclipse, FreeBSD, GCC, Gentoo, Kernel, RedHat, Sourceware, and WebKit. Five deep learning models are applied: convolutional neural networks, long short-term memory networks, gated recurrent units, Transformers, and BERT. Each model is evaluated on its performance with and without stop word removal during preprocessing. The results show that the F1 score difference was less than 0.01 in over 85% of comparisons, so stop word removal has little to no effect on predictive performance in eight open-source projects. Average F1-scores remain consistent across all tasks and models, with 0.36 for duplicate detection, 0.33 for severity prediction, and 0.33 for priority prediction. Statistical significance tests confirm that the observed differences are not meaningful across datasets or model types. The findings suggest that stop word removal is not necessary in deep learning-based bug report analysis. Removing this step may simplify preprocessing pipelines without reducing accuracy, particularly in large-scale and real-world software engineering applications. Full article

This work draws on the principles of Environmental Education as a framework for designing meaningful teaching interventions that foster a critical understanding of socio-environmental issues. The proposal focuses on the specific case of plastic pollution and its impact on marine ecosystems, adopting an integrative perspective that connects animal, environmental, and human health. To this end, the One Health approach is incorporated, highlighting the close interdependence between the health of ecosystems, animals, and people, which allows the issue to be analyzed from a systemic and global perspective. The intervention is grounded in the principles of Transformative Environmental Education—a pedagogical orientation that seeks to promote deep changes in how students understand their environment and engage with the challenges of today’s world. This approach encourages ethical reflection, critical thinking, and the ability to imagine sustainable futures, as well as the development of competencies for action and civic engagement. The teaching proposal takes the form of a learning experience designed and implemented in three 7th-grade classrooms (1º ESO) in Cádiz, Spain, through a mixed-methods approach with 79 students (12–13 years old), structured around an escape box activity. This is a variation of the escape room format in which students, working in teams, must open a series of boxes by solving a sequence of puzzles. In this case, the escape box is set in a marine context. Through a gamified narrative, students receive a suitcase containing objects, clues, and materials that require the application of scientific knowledge about ocean acidification, biodiversity loss, and types of plastics. Data were collected through field notes, student artifacts, and a final questionnaire. The proposal is designed to foster critical environmental literacy, a holistic vision of environmental challenges, and the capacity to propose collective solutions from a One Health perspective. The results revealed high levels of motivation, engagement with the storyline, and a solid understanding of the link between marine plastic pollution and its effects on animal and human health, aligned with the One Health perspective. Full article

Background: Spinal metastasis is the third most common site for metastatic localization, following the lung and liver. Manual detection through imaging modalities such as CT, MRI, PET, and bone scintigraphy can be costly and inefficient. Preliminary artificial intelligence (AI) techniques and computer-aided detection (CAD) systems have attempted to improve lesion detection, segmentation, and treatment response in oncological imaging. The objective of this review is to evaluate the current applications of AI across multimodal imaging techniques in the diagnosis of spinal metastasis. Methods: Databases like PubMed, Scopus, Web of Science Advance, Cochrane, and Embase (Ovid) were searched using specific keywords like ‘spine metastases’, ‘artificial intelligence’, ‘machine learning’, ‘deep learning’, and ‘diagnosis’. The screening of studies adhered to the PRISMA guidelines. Relevant variables were extracted from each of the included articles such as the primary tumor type, cohort size, and prediction model performance metrics: area under the receiver operating curve (AUC), accuracy, sensitivity, specificity, internal validation and external validation. A random-effects meta-analysis model was used to account for variability between the studies. Quality assessment was performed using the PROBAST tool. Results: This review included 39 studies published between 2007 and 2024, encompassing a total of 6267 patients. The three most common primary tumors were lung cancer (56.4%), breast cancer (51.3%), and prostate cancer (41.0%). Four studies reported AUC values for model training, 16 for internal validation, and five for external validation. The weighted average AUCs were 0.971 (training), 0.947 (internal validation), and 0.819 (external validation). The risk of bias was the highest in the analysis domain, with 22 studies (56%) rated high risk, primarily due to inadequate external validation and overfitting. Conclusions: AI-based approaches show promise for enhancing the detection, segmentation, and characterization of spinal metastatic lesions across multiple imaging modalities. Future research should focus on developing more generalizable models through larger and more diverse training datasets, integrating clinical and imaging data, and conducting prospective validation studies to demonstrate meaningful clinical impact. Full article

Artificial intelligence (AI) is changing how we teach and learn, generating excitement and concern about its potential to transform education. To contribute to the debate, this systematic literature review examines current research trends (publication year, country of study, publication journal, education level, education field, and AI type), as well as the benefits and challenges of integrating AI into education. This review analyzed 155 peer-reviewed empirical studies published between 2015 and 2025. The review reveals a significant increase in research activity since 2022, reflecting the impact of generative AI tools, such as ChatGPT. Studies highlight a range of benefits, including enhanced learning outcomes, personalized instruction, and increased student motivation. However, there are challenges to overcome, such as students’ ethical use of AI, teachers’ resistance to using AI systems, and the digital dependency these systems can generate. These findings show AI’s potential to enhance education; however, its success depends on careful implementation and collaboration among educators, researchers, and policymakers to ensure meaningful and equitable outcomes. Full article

Mastering client-side Web programming is essential for the development of responsive and interactive Web applications. To support novice students’ self-study, in this paper, we propose a novel exercise format called the phrase fill-in-blank problem (PFP) in the Web Programming Learning Assistant System (WPLAS). A PFP instance presents a source code with blanked phrases (a set of elements) and corresponding Web page screenshots. Then, it requests the user to fill in the blanks, and the answers are automatically evaluated through string matching with predefined correct answers. By increasing blanks, PFP can come close to writing a code from scratch. To facilitate scalable and context-aware question creation, we implemented the PFP instance generation algorithm in Python using regular expressions. This approach targets meaningful code segments in HTML, CSS, and JavaScript that reflect the interactive behavior of front-end development. For evaluations, we generated 10 PFP instances for basic Web programming topics and 5 instances for video games and assigned them to students at Okayama University, Japan, and the State Polytechnic of Malang, Indonesia. Their solution results show that most students could solve them correctly, indicating the effectiveness and accessibility of the generated instances. In addition, we investigated the ability of generative AI, specifically ChatGPT, to solve the PFP instances. The results show 86.7% accuracy for basic-topic PFP instances. Although it still cannot fully find answers, we must monitor progress carefully. In future work, we will enhance PFP in WPLAS to handle non-unique answers by improving answer validation for flexible recognition of equivalent responses. Full article

The global emphasis on interdisciplinary teaching continues to shape educational discourse, promoting meaningful and valuable learning experiences. This study examines the interdisciplinary curricular process led by a group of second-career teacher trainees and explores its role in shaping their emerging professional identities. The research focuses on eight high-achieving individuals transitioning to teaching as a second career through a STEM-focused (Science, Technology, Engineering, Mathematics) teacher preparation program. Employing a qualitative case study methodology, the study reveals a curricular process characterized by confusion and conflict as second-career teacher trainees navigate interdisciplinary integration. The findings highlight a planning process driven by conceptual and epistemic deliberations at both inter- and intra-disciplinary levels, with a predominant focus on disciplinary considerations over pedagogical aspects. The study further identifies key tensions that challenged participants’ perceptions, emotional responses, and instructional practices, offering a nuanced perspective on the complexities of interdisciplinary teaching. These insights contribute to a deeper understanding of professional identity formation among second-career teachers in STEM education. Full article

Summer Youth Employment Programs (SYEPs) operate in most major U.S. cities and are known to build social–emotional and job skills in youth while reducing crime. Integrating STEM learning and summer employment offers a promising way to increase youth engagement in STEM—and allow leaders to access funding not typically used for education. Using a connected learning framework, we examined how STEM-focused SYEPs support STEM pathways, the practices they implement, and their connections with schools. Our study explored 10 diverse STEM programs (e.g., robotics, renewable energy, coding) within a citywide employment initiative in summer 2015. Through 22 staff interviews and focus groups with 59 youth, we found that these programs provided meaningful and engaging STEM experiences. They combined interest-driven exploration with hands-on, real-world learning in supportive environments. Many included mentors from groups underrepresented in STEM fields. While collaboration with schools was generally limited to recruitment and shared facilities, opportunities for deeper partnerships were evident. Our findings led to a list of ten promising practices for STEM-focused SYEPs. This study underscores the importance of lifelong, lifewide, and connected approaches to STEM learning through summer employment initiatives. Full article