Search Results (1,129)

This study investigates whether a university teaching complex equipped with CSA S600 continuous air purification and sanitation units can maintain indoor air quality (IAQ) within recommended thresholds under real occupancy conditions and evaluates the impact of renovation works on IAQ. The work provides the first real-world assessment of the CSA S600 integrated monitoring system in an academic environment. CO₂, PM_2.5, PM₁₀ and VOCs were continuously measured over three months; moreover, indoor PM₁₀ values were compared with outdoor data from the regional monitoring network. Indoor CO₂ generally remained below 800 ppm, with short peaks of 1000–1500 ppm during high occupancy. PM_2.5 and PM₁₀ consistently stayed below the latest WHO guidelines, showing uniform recurring temporal patterns overtime; furthermore, indoor PM₁₀ showed limited coupling with outdoor trends, indicating the predominance of internal sources and ventilation dynamics. After renovation of the main Lecture Hall, particulate levels remained low, while VOCs showed a modest increase attributable to new materials. Overall, the findings demonstrate that the CSA S600 system effectively supports healthy IAQ in educational settings and that continuous monitoring is essential for managing occupancy-driven fluctuations and assessing the effects of structural interventions. Full article

The rapid development of intelligent learning guidance systems has created a favorable environment for personalized learning. By accurately predicting students’ future performance, education can be tailored and teaching strategies optimized. However, traditional prediction algorithms seldom account for highly imbalanced datasets in basic education, overlook temporal factors, and lack further interpretability of the prediction results. To address these shortcomings, we propose Temporal Variational Autoencoder-Generative Adversarial Network (TVAE-GAN), a temporal variational autoencoder-generative adversarial network model aimed at providing early warnings for high-risk students in basic education, with in-depth interpretability analysis of the prediction results to suit the unique context of basic education. TVAE-GAN extracts features from real samples and introduces a Long Short-Term Memory (LSTM) network to capture dynamic features in time series, helping the model better understand temporal dependencies in the data, remember the sequential causal information of students’ online learning, and achieve better data generation performance. Using these features, the generative model generates new samples, and the discriminator model evaluates their quality, producing outputs that closely resemble real samples through training. The effectiveness of the TVAE-GAN model is validated on a collected online basic education dataset while also advancing the timing of interventions in predictions. The performance differences between the proposed method and classic resampling methods, as well as their impact in the educational field, are analyzed, highlighting that misclassification increases teacher workload and affects students’ emotions. Key influencing factors are identified using a decision-tree surrogate model, providing teachers with multidimensional references for academic assessment. Full article

Molecular docking is a foundational technique in computational drug discovery, widely used to generate binding hypotheses, prioritize compounds, and support target-selectivity studies. The continued growth of open-source docking resources, together with improvements in scoring functions, sampling strategies, and hardware acceleration, has substantially lowered barriers to teaching, early-stage hit identification, and reproducible research. Beyond standalone docking engines, the open-source ecosystem now encompasses browser-accessible tools, preparation and analysis utilities, integrative modeling platforms, and AI-augmented methods for pose prediction, rescoring, and virtual screening. These developments have made docking workflows more accessible, customizable, and transparent across diverse research settings. This review examines open-source docking from a workflow-centered perspective, spanning study design, structural-data acquisition, binding-site definition, receptor and ligand preparation, docking execution, and post-docking validation. It further evaluates how open AI methods are being incorporated into these stages to expand structural coverage, improve screening efficiency, and support contemporary structure-based drug design. Collectively, this review outlines a practical and evidence-based framework for the effective use of open-source docking and virtual-screening pipelines in modern drug discovery. Full article

Background: Most individuals develop smoking habits in adolescence, highlighting the need for a smoking prevention program targeted at this age group. The use of “Implementation Intentions” (If-Then plans) about how to refuse a cigarette combined with anti-smoking messages has been shown to be effective in the UK. However, there is a scarcity of data regarding school-based smoking prevention interventions among adolescents available to countries with high tobacco consumption rates, like Greece. Objectives: To describe the cultural adaptation procedure and the evaluation protocol for the school-based “Implementation Intentions” program aimed at reducing tobacco use susceptibility among Greek adolescents aged 13–16 in school settings. Methods: The present study is part of the EU-funded FRESHAIR4Life Program. We will use a mixed-methods approach with a pre- and post-intervention design in six conveniently selected secondary schools in Heraklion, Crete, Greece, to measure the intervention’s Reach, Effectiveness, Adoption, Implementation, and Maintenance using the RE-AIM framework. The study plans to involve three Master Trainers (MTs), 20–25 school teachers (to be trained by the MTs), and approximately 480 students. Participating schools will receive the “Implementation Intentions” intervention, which is based on a goal-setting technique where individuals commit to perform a particular behavior when a specific context arises. The study will consist of five sequential phases: Phase I involves training three Master Trainers (MTs) using the International Primary Care Respiratory Group (IPCRG’s) Teach-the-Teacher (TtT) curriculum, specifically focused on the implementation of our intervention. In Phase II, workshops will be held to co-create and culturally adapt the intervention. Phase III will involve teachers trained by MTs on delivering the intervention. In Phase IV, teachers will deliver the intervention among students in their schools. Data will be collected pre- and post-intervention through surveys, session logs, fidelity observations, feedback forms, and follow-up interviews or focus groups (Phase V). Quantitative data will be analyzed descriptively and by using paired t-tests and multiple linear regression analyses, while qualitative data will undergo thematic analysis. Discussion: The study protocol’s potential benefits extend beyond educating Greek adolescents on the risks associated with smoking. Active participation will empower and motivate young people to make informed, healthy choices. We expect the results could help create more effective, context-specific interventions, support policy changes aimed at decreasing the prevalence of adolescent smoking in Crete, Greece, and potentially be used by other countries as well. Full article

A classroom is an educational environment where teaching and intellectual engagement take place. It is designed specifically to integrate technological advances in education and improve student outcomes. This study examines the impact of various visual design elements on students’ academic performance across different indoor classroom configurations in Erbil. Furthermore, it aims to determine the most effective classroom design elements for optimizing educational results. The research employed a mixed-methods approach, integrating qualitative content analysis with illustrations, pictures, and graphs. The quantitative component comprises two methodologies: a researcher-developed closed-ended questionnaire along with ergonomic and visibility evaluations utilizing DepthMapX 10 simulation software. Research shows that Modularity, Visual diversity display, Legibility, Sightline, and easy Accessibility enhance the quality of learning. The study suggests that spatial and visual design elements can establish a dependable model, reinforcing the notion that classroom space design is a crucial component of the learning environment. Full article

Digital maturity (DM) has emerged as a popular concept for explaining how higher education institutions (HEIs) develop digitally supported academic teaching and learning, research, administration, and community outreach (third mission). However, DM is often framed as a technical problem of adopting information and communication technologies (ICTs), infrastructure, and tool deployment. In this paper, we conceptualize DM as an organizational capability that enables HEIs to align digital tools with strategy, governance, and teaching. Building on entrepreneurial orientation (EO) research, we argue that EO is an antecedent of digital maturity, but that this relationship cannot be realized without a change-supportive organizational culture. We develop a conceptual model in which EO is positively associated with DM, both directly and indirectly through the change-ready organizational culture, and present propositions for future empirical research. We provide a preliminary qualitative evaluation of the model through in-depth interviews with stakeholders in a single, digitally advanced university case from the Southeast Europe (SEE) region. Based on the thematic analysis, we identify patterns suggesting that CRC links EO and DM in this case. We use the findings to refine construct boundaries and show possible mechanisms; assessing generalizable effects is left to future quantitative studies on larger national and regional samples. Full article

This study explores the digital competence of foreign language teachers in Bulgarian secondary education by focusing on the institutional context of which they are a part, the strengths and gaps of their competence, and their levels of competence. It draws upon empirical data that were collected and analyzed within an integrated, dual-instrument framework, combining the SELFIE (Self-reflection on Effective Learning by Fostering the Use of Innovative Educational Technologies) and SELFIE for TEACHERS (Self-reflection on Effective Learning by Fostering the Use of Innovative Educational Technologies for Teachers) EU-aligned assessment tools. The results from the questionnaire data show that the foreign language teachers state that they work in a relatively good technological environment and evaluate the usage of digital technologies for teaching and communication purposes within the school context as a salient aspect of their digital competence. The results also reveal three areas in the study participants’ digital competence that are in need of improvement: (1) empowering learners/personalizing the educational process, (2) assessment and (3) facilitating learners’ digital competence. In addition, the findings indicate that the foreign language educators rate their digital competence at a low to medium level. By blending institutional and teacher-oriented perspectives into a single integrated study of Bulgarian secondary school foreign language teachers, this investigation extends the existing research and makes evidence-based recommendations for institutional capacity building, teacher education policy and targeted professional development aimed at improving the educators’ digital competence. Full article

Artificial Intelligence (AI) literacy and competency in pre-service teacher education refer to a programme-level implementation that enables teachers to work with AI systems effectively, critically, and ethically across university coursework, school placements, and early-career practice. This includes not only capability, but also professional enactment, where teachers apply AI-related knowledge in context-sensitive and pedagogically grounded ways. AI literacy refers to a shared knowledge base for understanding how AI systems generate outputs, how to evaluate and verify AI-supported information, and how to reason about task–tool fit in relation to fairness, privacy, transparency, accountability, academic integrity, equity, and environmental sustainability. AI competency refers to the application of this literacy in routine professional tasks, such as designing and justifying AI-informed teaching, learning, and assessment, protecting students’ and school data, documenting decisions, and revising AI-supported materials after checking for reliability, transparency, accountability, and equity. Together, literacy and competency extend beyond personal use of AI by preparing future teachers to support students’ creative, critical, and ethical engagement with AI, while keeping classroom practice aligned with educational goals, objectives, and values. Full article

Generative Artificial Intelligence (GAI) is rapidly transforming higher education by introducing new mechanisms for supporting the development of advanced cognitive processes and research-related capabilities. This study examines how postgraduate students employ GAI to develop their cognitive and research talent, conceptualized here as higher-order academic skills such as analysis, synthesis, and critical reasoning, across six domains: literature review, theoretical development, research design, data analysis, academic writing, ethical use, and challenges encountered—signaled explicitly rather than listed line by line. We administered a validated multidimensional scale to 214 postgraduate students, and the results indicate a moderate overall use of GAI, with notably high involvement in practices that emphasize ethics and responsibility. Students reported clear cognitive benefits in tasks involving information processing, linguistic refinement, and conceptual clarification while showing caution toward delegating higher-order analytical or theoretical reasoning to AI systems. Key challenges included limited institutional training, concerns about data privacy and academic integrity, and difficulties evaluating the originality and reliability of AI-generated content. Inferential analyses indicated significant differences based on gender, academic level, and general technology proficiency, whereas no differences emerged across age groups, departments, or specializations. Overall, this study demonstrates how GAI can contribute to the development of higher-level cognitive skills and research competencies, with “moderate use” operationalized as consistent but selective engagement across domains, while underscoring the need for structured training, clear guidelines, and teaching approaches that foster the responsible and effective incorporation of AI within postgraduate research. The results highlight practical implications for higher education, including the importance of institutional training programs, governance frameworks for responsible AI use, and pedagogical models that foster critical engagement with GAI. Full article

Classroom humor is an important instructional strategy that enhances teaching effectiveness and improves student engagement. However, its automatic detection remains challenging due to the strong contextual dependency and implicit semantic shifts that characterize humorous expressions in teaching discourse. Conventional pretrained language models capture global semantics but often fail to focus on the subtle humor anchors that trigger incongruity. To address this issue, we propose T-HumorAGSA, a cognitive-inspired classroom teacher humor language detection model. The model employs BERT for contextualized semantic encoding, followed by a Gated Anchor-Guided Self-Attention (AGSA) mechanism that adaptively amplifies anchor-related features responsible for humor generation. A bidirectional gated recurrent unit (BiGRU) layer is further integrated to model long-range temporal dependencies within teaching utterances. T-HumorAGSA is evaluated on five datasets, including SemEval 2021 Task 7-1a, ColBERT, CCL2018, CCL2019 and the self-constructed teacher humor language dataset (T-Humor), demonstrating consistently strong performance. For instance, it achieves 0.9874 F1 on ColBERT and 0.9508 F1 on SemEval 2021 Task 7-1a, both outperforming the best baseline models. On the T-Humor dataset, the model attains a high F1 score of 0.9895, validating its capacity to detect subtle humorous cues in instructional discourse. The results demonstrate that the proposed model delivers excellent performance in classroom humor detection. Full article

Live case studies are widely used in higher education to support active learning; however, their pedagogical potential is often limited by weak integration with learning theories and assessments. This research-to-practice study examines the systematic design of live case studies by integrating Kolb’s experiential learning cycle (ELC) and authentic assessment (AA) principles. This paper presents a framework that conceptualises live cases as the learning context, ELC as the learning process, and AA as evaluative logic. The framework is illustrated through a case study of an undergraduate Quality Management module in industrial engineering at a Mexican university, involving 31 final-year students. The study is design-oriented and illustrative, aiming to demonstrate framework enactment rather than evaluating causal effectiveness. Using a case study methodology, the instructional design and enactment were documented using the ADDIE model. Data were obtained from educational artefacts, assessment results, and student feedback surveys. The findings suggest that aligning teaching and assessment activities with the ELC stages and the AA principles effectively supports learning trajectories. This support covers experience, reflection, conceptualisation, and application. Live case studies enabled the integration of multiple assessment methods around shared organisational problems and supported personalised learning through students’ case selection. This study contributes a design logic and operational framework for distributing authentic assessment across Kolb’s experiential learning stages within live case pedagogy. Rather than offering statistical generalisation, the framework serves as a foundation for adaptation and research, emphasising transferability across disciplines, educational levels, and delivery modes. Limitations are acknowledged regarding the conceptual scope, methodological design, and empirical context. Full article

The traditional craft of Buddha statue carving represents an important form of cultural heritage in many Asian societies, yet the transmission of this knowledge is increasingly threatened by modernization and the declining number of skilled artisans. This study explores the use of Virtual Reality (VR) as an innovative tool for preserving and teaching the cultural knowledge associated with Salah Lanna stone Buddha carving. A VR-based learning environment was developed to simulate traditional carving techniques, tools, and cultural narratives related to Lanna Buddhist art. The system was designed using Unity 3D and integrated hand-tracking interaction to enable immersive practice of carving procedures. The prototype was evaluated through expert review involving ten specialists in Buddha carving, art education, and VR technology. The evaluation assessed five dimensions: usability, authenticity, cultural relevance, immersion, and perceived learning potential. Results indicate high levels of expert evaluation results regarding the effectiveness of the system, with average scores of 4.6 for usability, 4.8 for authenticity, 4.7 for cultural relevance, 4.5 for immersion, and 4.9 for perceived learning potential on a five-point scale. The findings suggest that VR technology can provide a promising platform for preserving traditional craftsmanship and supporting immersive cultural learning. By integrating technical training with cultural narratives, the system demonstrates potential for enhancing access to traditional craft education while contributing to the digital preservation of Salah Lanna cultural heritage. Full article

The educational process of developing web design competence remains a persistent challenge for many students and educators, particularly in developing countries where conventional teaching methodologies and assessment models often fall short in promoting higher-order thinking and problem-solving. In this study, we respond to the call for innovative assessment approaches by examining the impacts of assessment models on a web design and development course and students’ cognitive load when adopting the AI-assisted assessment model (AAAM) compared to the traditional assessment model (TAM). We employed a mixed-methods research approach, incorporating a quasi-experimental, non-equivalent pretest–posttest control group design and a qualitative component, involving 63 undergraduate students enrolled in CRE 625. The intervention lasted approximately 10 weeks and focused on web design and development across two universities in a developing country. Consistent with quasi-experimental principles, students were assigned to treatment groups based on pre-existing institutional class structures, thereby controlling allocation using criteria rather than randomization. Two validated instruments were used to assess students’ web design and development competence (WDDC) and cognitive load (CL), and the data were analyzed using ANCOVA to evaluate performance gains and the interaction effect with gender. Full article

The modeling and control of photovoltaic and other engineering systems highly depend on the accuracy of parameter identification. However, parameter extraction for photovoltaic equivalent models typically presents a high-dimensional, strongly nonlinear, and multimodal global optimization problem. Traditional analytical or gradient-based methods are sensitive to initial values and easily fall into local optima. To address this issue, this paper proposes a multi-strategy improvement teaching–learning-based optimization algorithm (SNTLBO). A social learning network structure with symmetric interaction topology is introduced into the classical TLBO framework to characterize the knowledge propagation relationships among individuals. Through this symmetric and balanced information exchange mechanism, learners can be guided not only by the teacher but also by multiple neighbors within the network, enabling more diverse and symmetric exploration of the search space and enhancing population diversity and global search capability. Furthermore, a teacher reputation mechanism is constructed, where historical performance is used to weight teacher influence, strengthening the guidance of high-quality solutions and accelerating convergence. Meanwhile, an adaptive teaching factor is designed to dynamically adjust the teaching intensity based on the distance between the teacher and students in the solution space, maintaining a dynamic balance (symmetry) between exploration and exploitation. To evaluate the performance of the proposed algorithm, SNTLBO is systematically compared with 11 advanced optimization algorithms on two benchmark test suites, CEC2017 (30D, 50D) and CEC2022 (10D, 20D). Non-parametric statistical tests are conducted to assess significance. The results demonstrate that SNTLBO shows competitive advantages in terms of convergence speed, solution accuracy, and stability. Finally, SNTLBO is applied to the parameter estimation of single-diode, double-diode, triple-diode, quadruple-diode, and photovoltaic module models. Experimental results show that the proposed algorithm achieves higher identification accuracy and robustness in terms of RMSE, IAE, and I–V/P–V curve fitting, verifying its effectiveness and practical value for complex global optimization and practical engineering applications. Full article

The integration of immersive educational technologies into medical education has attracted growing attention owing to their potential to improve the learning of complex anatomical structures and specialized terminology. This study investigates the use of zSpace technology as an interactive, learner-centered instructional tool for teaching the human respiratory system to undergraduate students in Nursing, Midwifery, and Physician Assistant programs. A structured pedagogical framework combined prior theoretical instruction in anatomy and Latin medical terminology with a zSpace-based practical learning activity was used. After the workshop, the students completed a survey evaluating perceived learning effectiveness, student engagement, and the quality of three-dimensional (3D) visualization. Data from 34 participants were analyzed using descriptive statistics and reliability analysis. The results indicated high levels of student satisfaction regarding the clarity, anatomical detail, and educational value of the immersive 3D models, along with higher levels of engagement compared with traditional methods. Despite challenges related to technical infrastructure, lecturer readiness, and students’ digital competencies, the findings support the pedagogical relevance of immersive 3D technologies in medical education. Overall, the findings suggest that students perceive zSpace technology as supporting anatomical understanding and enhancing engagement within the studied context. Full article