Search Results (146)

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

This study investigated the effects of “The Golden Triangle”, a blended-learning intervention program designed to promote academic language literacy among Arabic-speaking seventh-grade students in Israel. Receptive and productive academic vocabulary and academic reading comprehension were assessed before and after the intervention (Grade 7), and again five months later at the beginning of the eighth grade. Participants were 70 Arabic-speaking students from four middle schools, divided into a high/average reading comprehension (HRC) group and a low reading comprehension (LRC) group. Results showed that the LRC group improved its reading comprehension immediately after the intervention but declined at follow-up, whereas the HRC group improved during the program and maintained gains. Moreover, in the HRC group, baseline receptive academic vocabulary predicted reading comprehension at follow-up, whereas no similar predictive effect was found in the LRC group. These findings highlight the need to address persistent heterogeneity in reading abilities and to design differentiated interventions that support the long-term consolidation of academic language among struggling readers. Full article

The COVID-19 pandemic transformed healthcare education, increasing the shift to digital tools and establishing a hybrid curriculum blending online learning with traditional clinical practice. This study aims to understand how this shift impacts the educational growth and skill building of nursing students. A qualitative approach was conducted to understand the experience of Hong Kong nursing students adapting to online learning during the pandemic and beyond. Fifty nursing students were interviewed, and Colaizzi’s phenomenological method revealed key themes in their learning narratives. The analysis revealed four distinct themes characterizing the students’ experiences: (i) Learning on their terms: the mandated shift in healthcare reflecting a lack of agency during the educational transition; (ii) Knowledge without touch: the perceived incompetence of the COVID-19 nursing cohort, highlighting anxieties regarding a lack of hands-on clinical proficiency; (iii) Words left unsaid: The weight of insecurity, indicating a decline in interpersonal skills due to isolation; and (iv) Beyond the perfect algorithm: the unrehearsed art of care, describing the difficulty in translating digital simulations to complex, human-centric patient care. Findings show that while digital progress ensured continuity in education, it also contributed to reduced clinical confidence, weaker communication skills, and shifts in how nursing students approached their learning. Consequently, the post-COVID environment demands that training programs evolve to address these specific deficits. Advancing the existing pandemic-era nursing literature, this study emphasizes the need for diverse, targeted teaching methods to mitigate these gaps. By intentionally bridging theoretical knowledge with hands-on clinical practice, educators can better support student wellbeing and help restore the confidence and competence required of future graduates. Full article

Rational design of high-performance viscosifying polymers is critical for enhancing supercritical CO₂ flooding efficiency in enhanced oil recovery (EOR). Traditional experimental and simulation approaches are limited in exploring the vast design space of polymer architecture, flexibility, and intermolecular interactions. This work presents an integrated machine learning (ML) and mesoscopic simulation framework using Dissipative Particle Dynamics (DPD) to accelerate the development of tailored polymeric thickeners. We systematically investigate synergistic effects of linear and branched polymer blends on solvent viscosity under Poiseuille flow, representative of flow in micro-fractures and pore throats. Key molecular descriptors are varied to generate a comprehensive rheological database. This data trains a deep neural network (DNN) surrogate model linking molecular parameters to macroscopic viscosity. The DNN is coupled with gradient ascent optimization for inverse design, enabling rapid virtual screening of thousands of formulations. A focused case study demonstrates that the star-like architectures with associative cores and semi-flexible backbones outperform linear analogs for supercritical CO₂ viscosity enhancement. The optimal candidate—a four-arm star polymer with linear side chains—was validated by DPD simulation. This multiscale “simulation-to-surrogate” methodology bridges molecular design with continuum-scale flow behavior, offering a transformative tool for formulating cost-effective, efficient, and sustainable next-generation EOR chemicals. Full article

Background and Objectives: Diabetes mellitus represents one of the most prevalent chronic metabolic disorders worldwide, necessitating precise insulin dose management to prevent both acute and long-term complications. The optimization of insulin dosing remains a significant clinical challenge, as inappropriate dosing can lead to hypoglycemia or hyperglycemia, each carrying substantial morbidity risks. Machine learning approaches have emerged as promising tools for developing clinical decision support systems; however, their practical implementation requires both high predictive accuracy and model interpretability. This study aimed to develop and evaluate an explainable machine learning framework for predicting insulin dose adjustments in diabetic patients. We sought to compare multiple ensemble learning approaches and identify the optimal model configuration that balances predictive performance with clinical interpretability through comprehensive SHAP and LIME analyses. Materials and Methods: A comprehensive dataset comprising 10,000 patient records with 12 clinical and demographic features was utilized. We implemented and compared nine machine learning models, including gradient boosting variants (XGBoost, LightGBM, CatBoost, GradientBoosting), AdaBoost, and four ensemble strategies (Voting, Stacking, Blending, and Meta-Learning). Model interpretability was achieved through SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) analyses. Performance was evaluated using accuracy, weighted F1-score, area under the receiver operating characteristic curve (AUC-ROC), precision-recall AUC (PR-AUC), sensitivity, specificity, and cross-entropy loss. Results: The Meta-Learning Ensemble achieved superior performance across all evaluation metrics, attaining an accuracy of 81.35%, weighted F1-score of 0.8121, macro-averaged AUC-ROC of 0.9637, and PR-AUC of 0.9317. The model demonstrated exceptional sensitivity (86.61%) and specificity (91.79%), with particularly high performance in detecting dose reduction requirements (100% sensitivity for the ‘down’ class). SHAP analysis revealed insulin sensitivity, previous medications, sleep hours, weight, and body mass index as the most influential predictors across different insulin adjustment categories. The meta-model feature importance analysis indicated that LightGBM probability estimates contributed most significantly to the ensemble predictions. Conclusions: The proposed explainable Meta-Learning Ensemble framework demonstrates robust predictive capability for insulin dose adjustment recommendations while maintaining clinical interpretability. The integration of SHAP-based explanations facilitates clinician understanding of model predictions, supporting transparent and informed decision-making in diabetes management. This approach represents a significant advancement toward the clinical implementation of artificial intelligence in personalized insulin therapy. Full article

The integration of artificial intelligence (AI) into early childhood education presents both opportunities and challenges to longstanding Froebelian pedagogies, particularly regarding child agency and nature-based play. This mixed-methods study explores this tension within the Chinese context. It examines how Chinese Froebelian practitioners perceive the alignment between AI tools and core principles and investigates the strategies they employ to navigate the integration of technology with humanistic educational values. The survey results, from 50 practitioners, revealed that AI can support autonomous and holistic learning, yet significant concerns persisted regarding the displacement of sensory and nature-based experiences. Follow-up interviews uncovered a practitioner-led “dual-track integration” approach, which strategically blends physical manipulation and nature engagement with AI-enabled personalization. Through an iterative dialogue between theory and data, this study develops and refines the “dual-track integration” framework as an empirically grounded, sensitizing model. This framework offers principled strategies for hybrid learning that uphold the developmental primacy of play. Situated within the discourse on Sustainable Development Goal 4 (quality education) and Goal 10 (reduced inequalities), the analysis highlights AI’s dual potential to advance or hinder equity. By examining China’s hybrid position, which combines advanced digital infrastructure with persistent equity gaps, this research highlights the critical role of educator agency and pedagogical design in leveraging AI to advance equitable, high-quality early childhood education. Full article

As the Industry 5.0 transition unfolds, engineering education must evolve to integrate Lean manufacturing with advanced digital tools and sustainable, human-centred practices. This study presents the design and implementation of a Lean Learning Factory (LLF) that addresses this challenge by combining traditional Lean methods with technologies such as simulation, robotics, and virtual reality in a modular educational environment. At the University Centre Pitești, six hands-on projects were implemented to guide students through key concepts, including production system layout, digital assistance, sustainability, and human–robot collaboration. Through experiential learning, students engage in iterative design, data analysis, and practical validation using real equipment and software platforms. The results indicate that the LLF effectively supports the development of technical, digital, transversal, and human-centred competencies aligned with EUR-ACE^® standards. Students acquire skills in process optimisation, ergonomics, and sustainable production, while also reflecting on the ethical and social implications of automation. The study concludes that the LLF model provides a scalable and adaptable framework for engineering education. It fosters competence-based learning and prepares students for the demands of Industry 5.0. This paper contributes a replicable educational approach that blends Lean efficiency, digital transformation, and human-centred values into a cohesive learning ecosystem. Full article

Although reflective practice is recognised as a driver of instructional change, technology-focused professional development—particularly one-shot tool workshops—often lacks systematic analysis of student evidence, prioritising technical skills over evidence-based reflection. This study aimed to empirically develop and refine a technology integration competency framework for mathematics teachers by investigating how structured reflective practice serves as a mechanism for longitudinal development. Adopting a design-based research (DBR) approach, the study was conducted over 18 months with 21 in-service mathematics teachers in northern Thailand through two iterative cycles of design, enactment, analysis, and redesign. The intervention utilised structured tools, including guided reflective journals, classroom video reflection, and reflective dialogue, enabling teachers to connect pedagogical intentions with evidence of student response. Thematic analysis indicated that the initial framework required reconfiguration into a dynamic model characterised by three structural shifts: the merger of technological knowledge and tool proficiency into a single fundamental technology competency; the reclassification of teacher confidence as a transversal element; and the central positioning of flexible learning design for blended orchestration. These thematic findings were validated through data triangulation of journals, video reflections, and interviews. The study contributes an empirically warranted framework with actionable implications for designing professional development that fosters sustained instructional improvement in mathematics. Full article

Under the dual imperatives of environmental protection and sustainable socio-economic development, the high energy consumption of the manufacturing sector has become a significant barrier to its transition toward industrial ecological transformation. Integrating machine tool energy-saving technologies into engineering education serves as a pivotal strategy for enhancing energy efficiency and reducing operational consumption while fostering professionals who possess both sustainability awareness and technical expertise. Building on the core principles and future applications of energy-efficient machining, this study introduces a targeted curriculum reform. In response to challenges including cross-disciplinary content, heavy theoretical demands, and insufficient experiential learning, a blended teaching framework is designed, integrating virtual instruction with practical factory-based training. Implementation outcomes demonstrate that this reform significantly enhances students’ comprehension of energy-saving mechanisms and their capacity to apply sustainable concepts in practical engineering contexts. This study provides a reference for integrating energy-saving education into higher education under the framework of sustainable development. It also offers educational support for the manufacturing industry’s pursuit of low-carbon transition and industrial ecological development, demonstrating the important role of engineering education in advancing ecological civilization and contributing to the broader environmental governance system. Full article

This paper proposes a hybrid collaborative framework to optimize technology selection in Small and Medium-sized Enterprises (SMEs) by integrating machine learning (ML) predictions with Planning Poker, consensus-based estimation technique used in agile software development. Addressing known challenges such as cognitive bias, resource constraints, and the need for inclusive decision-making, the proposed model combines data-driven suitability analysis with stakeholder-driven consensus. ML generates quantitative, criterion-wise suitability scores based on historical SME data, providing transparent baselines for evaluation. Stakeholders independently assess candidate technologies using Planning Poker, and their consensus is blended with ML predictions through a flexible weighting mechanism. An illustrative case study on CRM tool selection illustrates the framework’s practical advantages: improved decision accuracy, transparency, and greater stakeholder engagement. The methodology is iterative, allowing for continuous learning and adaptation as new data emerges. This dual approach ensures that technology adoption decisions in SMEs are both empirically validated and contextually robust, offering a significant improvement over traditional, siloed methods. Full article

This paper presents Greenopoli, an innovative framework for sustainability and waste management education that has engaged over 600 schools and 90,000 students since 2014. Greenopoli is founded on the idea that children and youth can grasp environmental issues as well as adults and act as agents of change within their families and communities. The Greenopoli approach combines scientific accuracy with playful, creative pedagogy to simplify complex topics and stimulate peer-to-peer learning. It includes storytelling, games, field visits, and “green raps” (original environmental songs co-created with students). The framework is adaptive, with content and activities tailored to education stages from kindergarten through university. Educators adopt the role of moderators or facilitators, encouraging students to discuss and discover concepts collaboratively. Greenopoli’s participatory method has been implemented across all age groups, yielding enthusiastic engagement and tangible outcomes in waste sorting and recycling behaviors. The program’s reach has extended beyond schools through collaborations with national recycling consortia, NGOs, municipalities, and media (TV programs, social media, TEDx talks). Numerous awards and recognitions (2017–2025) have highlighted its impact. A comparative analysis shows that Greenopoli’s use of peer-led learning, gamification, and creative communication aligns with global best practices while offering a unique blend of tools. Greenopoli is a novel best-practice model in environmental education, bridging theory and practice and contributing to the goals of Education for Sustainable Development and a circular economy. It demonstrates the effectiveness of engaging youth as change-makers through interactive and creative learning, and it can inspire similar initiatives globally. Full article

While blended learning facilitates digital literacy development, the specific design models and student factors contributing to this process remain underexplored. This study examined the relationship between various blended learning design models and digital literacy skill acquisition among 106 upper-secondary Vocational Education and Training (VET) students. Relationships among student activities, digital competencies, and prior blended learning experience were analyzed. Engagement in collaborative, task-based instructional designs—specifically collaborative projects and regular quizzing supported by digital tools—was positively associated with digital competence. Conversely, passive participation in live sessions or viewing pre-recorded videos exhibited a comparatively weaker association with competence development. While the use of virtual/augmented reality and interactive video correlated positively with digital tool usage, it did not significantly predict perceptions of online safety or content creation skills. Students with prior blended learning experience reported higher proficiency in developmental competencies, such as content creation and research, compared to their inexperienced peers. Cluster analysis identified three distinct student profiles based on technical specialization and blended learning experience. Overall, these findings suggest that blended learning implementation should prioritize structured collaboration and formative assessment. Full article

In the evolving landscape of digital education, there is an increasing need to enhance traditional Learning Management Systems (LMSs) by integrating innovative pedagogical practices that promote active participation and learner autonomy. This study presents the transformation of a Greek LMS platform into an open learning ecosystem, incorporating three key educational innovations: collaborative hackathons, microcredentials, and blended learning support. The primary goal was to modernize the LMS in a way that encourages deeper engagement, social learning, collaboration, and mixed learning. To accomplish this objective, the system integrated advanced innovative tools designed to facilitate structured collaborative processes including hackathons, microcredentials aligned with specific learning objectives, and blended learning through flexible content delivery and student learning tracking tools. The use of these tools in the educational process contributes to the creation of a more dynamic and participatory learning environment, where knowledge is co-shaped and learning acquires a social character. In addition, the tools promote differentiated learning, allowing students to engage at their own pace and in their own way. Full article

The widespread adoption of virtual classrooms during the COVID-19 pandemic accelerated the integration of Learning Management Systems (LMSs) into Higher Education, positioning them as essential tools in blended learning environments. LMSs provide teachers with a wide range of tools and functionalities, generating heterogeneous teaching strategies and providing many useful indicators for analysis. However, the complexity of log data combined with the intricacies of hybrid environments presents a significant challenge. This paper presents a quantitative approach to analysing LMS log data in Higher Education, with a specific focus on identifying and characterising teaching strategies implemented in the post-pandemic context. It seeks to examine the extent to which virtual classrooms have been effectively integrated into teaching practices and to assess how different techno-pedagogical approaches influence students’ academic performance. Moreover, we try to develop and define a comprehensive methodology for data treatment, including selection of analytical variables, the identification and clustering of usage profiles based on LMS interactions, and a comparative interpretative analysis of the findings. Our results suggest that the techno-pedagogical strategies are not uniformly effective across all areas of knowledge. This highlights the need for a more nuanced understanding of how these strategies interact with disciplinary traditions, pedagogical practices, and student profiles. Full article

Teaching in a globalised world offers opportunities to reduce barriers, enhance understanding, and expand access for diverse learners. Blended approaches, combining in-person and online delivery, can encourage learning communities across geographical boundaries. However, disparities in access to technology, internet reliability, and conducive study environments highlight inequalities and varied learner experiences. While digital networks may support identity and belonging, some students report feeling distracted or disengaged in online settings. This study explores the experiences of first-year doctoral candidates completing the final taught module of their Doctorate in Business Administration (DBA) at the University of Gloucestershire, UK. Participants, mostly international students now based in the UK, are engaged in both in-person classes and online staff-led webinars. Data was gathered through four in-person focus groups. Engeström’s Cultural-Historical Activity Theory was applied as an analytical framework, conceptualising teaching and learning as an activity system mediated by Tools, Rules, Community, and Division of Labour. This enabled a comparison of students’ experiences in online and in-person contexts. The findings revealed contradictions within the system, identifying barriers to engagement and adaptation, and offering insights into the evolving pedagogical demands of blended doctoral education. Full article