Search Results (392)

This study empirically analyzed changes in the representation of older adults in Korean digital media from 2020 to 2024. As Korea enters a super-aged society, social perceptions of aging and older adults are rapidly evolving through digital platforms. This study aimed to identify how public discourse about older adults has shifted in emotional tone and thematic structure within online media environments. Approximately 200,000 text data points were collected from news and YouTube comments containing keywords related to older adults. Text mining techniques—including Latent Dirichlet Allocation (LDA) topic modeling, sentiment analysis, and time-series analysis—were applied to examine annual trends and emotional transitions. The findings revealed a clear shift in thematic emphasis from “health,” “care,” and “vulnerability” toward “participation,” “self-management,” and “digital activity.” Negative sentiments decreased (from 58.3% in 2020 to 37.8% in 2024), while positive sentiments increased (from 22.5% to 42.7%). These results indicate that the image of older adults in digital discourse has transformed from that of passive care recipients to active and independent participants in society. The study supports the ongoing policy debate in Korea on redefining the age threshold for “older adults” from 65 to 70 years, emphasizing capability over chronological age. Digital media play a critical role in shaping these changing perceptions, highlighting the need for intergenerational media literacy education and policy interventions that promote inclusive and age-positive communication. Full article

This study examines the impact of a Science–Technology–Society–Environment (STSE) educational intervention on the teaching of acid–base reactions to 11th-grade students (n = 17). The didactic sequence combined laboratory experiments, real-data analysis, and an interdisciplinary role-play debate, designed to connect chemical concepts with pressing socio-environmental challenges such as ocean acidification, acid rain, and acid mine drainage. Data collection included a pre- and post-test on environmental awareness and semi-structured interviews, enabling the assessment of both conceptual learning and attitudinal change. Significant conceptual gains were observed, with five of eleven test items reaching a normalized Hake gain ≥ 0.70, alongside increased environmental awareness. Qualitative findings further revealed that students valued the real-world context and interdisciplinary integration, reporting enhanced motivation, civic responsibility, and a more meaningful engagement with science. Overall, the results suggest that STSE-based chemistry instruction not only strengthens students’ understanding of acid–base equilibria but also fosters sustainability competencies essential for responsible and informed citizenship in the 21st century. Full article

Early identification of students at risk for low academic confidence, poor problem-solving skills, or poor academic performance is crucial to achieving equitable and sustainable learning outcomes. This research presents a hybrid artificial intelligence (AI) framework that combines feature selection using a Random Forest (RF) algorithm with data classification via an Artificial Neural Network (ANN) to predict risks related to Academic Confidence and Problem-Solving Skills (ACPS) among higher education students. Three real-world datasets from Saudi universities were used: MSAP, EAAAM, and MES. Data preprocessing included Min–Max normalisation, class balancing using SMOTE (Synthetic Minority Oversampling Technique), and recursive feature elimination. Model performance was evaluated using five-fold cross-validation and a paired t-test. The proposed model (RF-ANN) achieved an average accuracy of 98.02%, outperforming benchmark models such as XGBoost, TabNet, and an Autoencoder–ANN. Statistical tests confirmed the significant performance improvement (p < 0.05; Cohen’s d = 1.1–2.7). Feature importance and explainability analysis using a Random Forest and Shapley Additive Explanations (SHAP) showed that psychological and behavioural factors—particularly study hours, academic engagement, and stress indicators—were the most influential drivers of ACPS risk. Hence, the findings demonstrate that the proposed framework combines high predictive accuracy with interpretability, computational efficiency, and scalability. Practically, the model supports Sustainable Development Goal 4 (Quality Education) by enabling early, transparent identification of at-risk students, thereby empowering educators and academic advisors to deliver timely, targeted, and data-driven interventions. Full article

Student dropout rates continue to present major difficulties for educational institutions, leading to academic, operational, and financial impacts. Educational Data Mining (EDM) methods, particularly those combining clustering techniques with predictive models, have demonstrated potential in identifying at-risk students early and accurately. This systematic review explores how cluster-based prediction models have been applied in educational contexts to enhance student performance prediction. A total of sixty-one relevant studies published between 2010 and 2025 were selected and analysed using PRISMA guidelines. The review focuses on the clustering techniques used, how these are integrated with predictive models, and what types of student data are involved. Key findings show that cluster-based models help capture behavioural and academic differences among students, which enables educational institutions to provide more adaptable support. The review also highlights challenges related to generalisability, scalability, and ethical concerns, especially when applying models across different institutions or datasets. The main contribution of this study is the identification of how clustering can be used not only to segment student populations but also to improve prediction accuracy by tailoring models to each subgroup. This review contributes to the literature by emphasising the practical benefits of cluster-based predictive modelling and providing clear directions for further studies aimed at reducing student dropout through targeted support. Full article

Student dropout prediction remains critical in higher education, where timely identification enables effective interventions. Learning Management Systems (LMSs) capture rich temporal data reflecting student behavioral evolution, yet existing approaches underutilize this sequential information. Traditional machine learning methods aggregate behavioral data into static features, discarding dynamic patterns that distinguish successful from at-risk students. While Long Short-Term Memory (LSTM) networks model sequences, they assume discrete time steps and struggle with irregular LMS observation intervals. To address these limitations, we introduce Completion-aware Risk Neural Ordinary Differential Equations (CR-NODE), integrating continuous-time dynamics with completion-focused features for early dropout prediction. CR-NODE employs Neural ODEs to model student behavioral evolution through continuous differential equations, naturally accommodating irregular observation patterns. Additionally, we engineer three completion-focused features: completion rate, early warning score, and engagement variability, derived from root cause analysis. Evaluated on Canvas LMS data from 100,878 enrollments across 89,734 temporal sequences, CR-NODE achieves Macro F1 of 0.8747, significantly outperforming LSTM (0.8123), Extreme Gradient Boosting (XGBoost) (0.8300), and basic Neural ODE (0.8682). McNemar’s test confirms statistical significance ($p<0.0001$). Cross-dataset validation on the Open University Learning Analytics Dataset (OULAD) demonstrates generalizability, achieving 84.44% accuracy versus state-of-the-art LSTM (83.41%). To support transparent decision-making, SHapley Additive exPlanations (SHAP) analysis reveals completion patterns as the primary prediction drivers. Full article

The article identifies clusters of higher education institutions that are most oriented towards sustainable development (SD). We analysed the types of educational activities in which these institutions engaged and the institutional mechanisms affecting their implementation. The study addresses questions about the types of educational activities that are pursued today and how higher education institutions adapt to global norms and expectations concerning SD. The study employs a mixed approach. The first stage involved a cluster analysis based on QS World University Rankings: Sustainability 2025 data, which identified higher education institutions most engaged in SD. Next, we analysed 53 ESG reports from these institutions with qualitative content analysis and text mining. Sustainable development-oriented higher education institutions tend to cluster in Europe, North America, East Asia, and Australia. We identified four main educational activity areas: academic curricula and courses, teaching methods that support SD, practical student engagement, and cooperation with third parties. The results demonstrate an increase in professionalisation and institutionalisation of education for SD, which can suggest effects of institutional isomorphism. With its structured approach to university activities and selection of quantitative indicators that could be employed in ESG reports, the article contributes to the literature on education for SD. The proposed classification could be of practical value to institutions intent on intensifying their SD educational efforts. Full article

Background: Teledentistry constitutes a key component of digital health, enabling remote oral healthcare delivery through information and communication technologies (ICT). The COVID-19 pandemic accelerated its global adoption; however, data regarding its implementation within Romanian dental practice remain limited. Understanding usage patterns, perceived benefits, and implementation barriers is essential for effective integration. Objectives: This study examined the adoption of teledentistry among dental practitioners in Sibiu County, Romania, identified its main applications, assessed professional perceptions, and explored barriers and their interrelations using association rule mining (ARM). Methods: A cross-sectional online survey was distributed in 2025 to all 630 registered dentists in Sibiu County. The questionnaire collected demographic data, usage patterns, perceived benefits, and barriers. A total of 197 valid responses were obtained (response rate: 31.2%). Descriptive statistics, Chi-square tests, and ARM were used to identify associations between usage contexts and recorded obstacles. Results: Overall, 44.6% of respondents reported using teledentistry tools, primarily for interdisciplinary consultations (29.4%), postoperative counseling (26.4%), and treatment monitoring (25.3%). The most frequently cited barriers were the inability to perform direct clinical examinations (71.5%), practitioner reluctance (37.1%), insufficient infrastructure (29.9%), and the lack of a clear legislative framework (27.4%). ARM revealed frequent co-occurrence patterns among these barriers. Practitioners with prior experience in teledentistry reported significantly higher perceived utility (58% vs. 22.1%) and greater interest in training (58% vs. 38.5%, p < 0.05). Conclusions: Teledentistry shows moderate but increasing adoption among Romanian dentists. Addressing current barriers, through legislative clarification, infrastructure development, targeted professional training, and public education, is essential for achieving sustainable integration into modern dental practice. Full article

Real-world systems frequently exhibit hierarchical multipartite graph structures, yet existing graph neural network (GNN) approaches lack systematic frameworks for hyperparameter optimization in heterogeneous multi-level architectures, limiting their practical applicability. This study proposes a Bayesian optimization framework specifically designed for heterogeneous GNNs operating on three-level graph structures, addressing the computational challenges of configuring partition-aware architecture. Four GNN architectures—Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), Graph Isomorphism Networks (GINs), and GraphSAGE—were systematically evaluated using Gaussian Process-based Bayesian hyperparameter optimization with inter-partition message-passing mechanisms. The framework was validated on the TIMSS 2023 dataset (10,000 students, 789 schools, 25 countries), demonstrating that Bayesian-optimized GraphSAGE achieved the highest explained variance (R² = 0.6187, RMSE = 71.73, MAE = 64.32) compared to seven baseline methods. Bayesian optimization substantially improved model performance, revealing that two-layer architectures optimally capture cross-partition dependencies in three-level structures. GNNExplainer was used to identify the most influential student-level features learned by the model, providing explanatory insight into how the model represents individual characteristics. The optimization framework is broadly applicable to other heterogeneous and multi-level graph settings; however, the empirical findings, such as the optimal architecture depth, are specific to hierarchical graphs with structural properties like the TIMSS topology. Full article

Massive Open Online Courses (MOOCs) have gained increasing popularity in recent years, highlighting the growing importance of effective course recommendation systems (CRS). However, the performance of existing CRS methods is often limited by data sparsity and suffers under cold-start scenarios. One promising solution is to leverage course-level conceptual information as side information to enhance recommendation performance. We propose a general framework for integrating LLM-generated concepts as side information into various classic recommendation algorithms. Our framework supports multiple integration strategies and is evaluated on two real-world MOOC datasets, with particular focus on the cold-start setting. The results show that incorporating LLM-generated concepts consistently improves recommendation quality across diverse models and datasets, demonstrating that automatically generated semantic information can serve as an effective, reusable, and scalable source of side knowledge for educational recommendations. This finding suggests that LLMs can function not merely as content generators but as practical data augmenters, offering a new direction for enhancing robustness and generalizability in course recommendation. Full article

Academic dropout is a persistent challenge in higher education, particularly in contexts with socio-economic disparities and diverse learning conditions. Traditional predictive models often fail to capture the complex, non-linear interactions underlying student trajectories due to their reliance on low-dimensional and linear representations. This study introduces a visual representation–based computational approach for a student dropout analysis, applied to a real institutional dataset from Colombia. The methodology transforms structured student records into enriched visual encodings that map variable magnitudes, correlations, and latent relationships into spatial and textural patterns. These image-based representations allow convolutional neural networks (CNNs) to exploit hierarchical feature extraction, uncovering hidden dependencies not accessible through conventional classifiers. Experimental results demonstrate that a Convolutional Neural Network (CNN) trained from scratch outperforms both baseline machine learning models and transfer learning architectures across all evaluation metrics. Beyond predictive accuracy, the approach enhances data expressiveness, interpretability, and generalization, offering a visual-analytical perspective for understanding dropout dynamics. The Colombian case study confirms the feasibility and potential of this strategy in real educational settings, supporting early identification of at-risk students and contributing to the development of robust, explainable models in educational data mining and learning analytics. Full article

The project-based learning model in journalism and communication faces challenges of sparse multimodal behavior data and delayed teaching interventions, making it difficult to perceive student states and optimize decisions in real-time. This study aims to construct an intelligent decision-support framework integrating educational data mining (EDM) and deep reinforcement learning (DRL) to address these issues. A bidirectional long short-term memory (Bi-LSTM) network models behavioral sequences, while a conditional generative adversarial network (cGAN) with Wasserstein optimization enhances low-activity student data. The extracted and augmented features are then fed into a Double Deep Q-Network (DQN) to generate adaptive teaching intervention strategies. Experimental results from a 26-week study show that the proposed framework improved personalized learning-path matching from 0.42 to 0.68, increased knowledge mastery from 40.46% to 77.13%, and reduced intervention latency from 210.5 min to 144.6 min. The results demonstrate that the fusion of EDM and DRL can achieve efficient and adaptive decision-making, providing a viable approach for intelligent teaching support in journalism and communication education. Full article

Professional skills training and academic success are key challenges for contemporary educational systems, particularly within higher education. The labour market increasingly demands well-prepared graduates with specific competencies that are still insufficiently embedded in university curricula. In this context, acquiring new professional skills becomes a decisive factor for students’ employability and competitiveness. At the same time, academic success remains a crucial indicator of educational quality, and its improvement is an urgent priority for universities. In response to these demands, our study evaluates cognitive-emotional competencies—emotional intelligence, creativity, and subjective well-being—in a sample of 300 university students from the Community of Madrid (Spain), analysing their influence on academic success with the aim of enhancing it. A non-experimental, cross-sectional research design was employed, using standardised self-report measures (TMMS-24, CREA, SHS, OHI, SLS, and OLS), innovative data mining algorithms (Random Forest and decision trees), and binary logistic regression techniques. The results highlight the importance of creativity, life satisfaction, and emotional attention in predicting academic success, with creativity showing the strongest discriminative power among the variables studied. These findings reinforce the need to integrate emotional and creative development into university curricula, promoting competency-based educational models that enhance training quality and students’ academic outcomes. Full article

This study presents a fuzzy-Apriori model that analyses student background data, along with end-of-lesson student-generated questions, to identify interpretable rules. After linguistic and semantic preprocessing, questions are represented in a fuzzy form and combined with background and performance variables to generate association rules, including support, confidence, and lift. The dataset includes 202 students, parent reports from 174 families, 5832 student-generated questions, and 510 teacher-generated questions collected in regular lessons in grades 7–8. The model also incorporates a topic-level dynamic updating step that refreshes the rule set over time. The findings indicate descriptive associations between background characteristics, question complexity and alignment, and classroom performance. It is essential to note that this phase explores possibilities rather than providing a validated instructional method. Question coding inevitably involves subjective elements, and while we conducted the study in real classroom settings, we did not perform causal analyses at this stage. The next step will be developing reliability metrics through longitudinal studies across multiple classroom environments. Future work will test whether using these patterns can inform instructional adjustments and support student learning. Full article

Cognitive diagnosis is an important component of adaptive learning, as it infers learners’ latent knowledge states and enables tailored feedback. However, existing approaches often emphasize sequential modeling or latent factorization, while insufficiently incorporating curriculum structures that embody prerequisite relations. This gap constrains both predictive accuracy and pedagogical interpretability. To address this limitation, we propose a Curriculum-Aware Graph Neural Cognitive Diagnosis (CA-GNCD) framework that integrates curriculum priors into graph-based neural modeling. The framework combines graph representation learning, knowledge-prior fusion, and interpretability constraints to jointly capture relational dependencies among concepts and individual learner trajectories. Experiments on three widely used benchmark datasets, ASSISTments2017, EdNet-KT1, and Eedi, show that CA-GNCD achieves consistent improvements over classical probabilistic, psychometric, and recent neural baselines. On average, it improves AUC by more than 4.5 percentage points and exhibits relatively faster convergence, greater robustness to noisy conditions, and stronger cross-domain generalization. These results suggest that aligning diagnostic predictions with curriculum structures can enhance interpretability and reliability, offering implications for personalized learning support. While promising, further validation in diverse educational contexts is required to establish the generalizability and practical deployment of the proposed framework. Full article

Accurate and interpretable prediction of student academic performance is a cornerstone of data-driven educational support systems, enabling timely interventions, personalized learning pathways, and equitable resource allocation. While ensemble machine learning models such as Random Forest, Extra Trees, and CatBoost have shown promise in educational data mining, their predictive power and generalizability are often limited by suboptimal weighting schemes and sensitivity to hyperparameter configurations. To address this, we propose a Grey Wolf Optimizer (GWO)-guided ensemble framework that dynamically optimizes each base regressor’s contribution to minimize prediction error while preserving model transparency. Evaluated on a real-world student performance dataset, the proposed approach achieves a coefficient of determination (R²) of 0.93, significantly outperforming individual and conventional ensemble baselines. Furthermore, we integrate SHAP (SHapley Additive exPlanations) to provide educator-friendly interpretability, revealing that daily study hours, study effectiveness, lifestyle score, and screen time are the most influential predictors of exam outcomes. By bridging an optimized machine learning model with educational analytics, this work delivers a robust, transparent, and high-performing AI solution tailored for intelligent tutoring systems, early-warning platforms, and adaptive learning environments. The methodology exemplifies how nature-inspired optimization can enhance not only accuracy but also actionable insight for stakeholders in smart education ecosystems. Full article