Search Results (619)

Background/Objectives: KRAS mutations are among the most common oncogenic driver alterations in non-small cell lung cancer (NSCLC) and define a biologically heterogeneous disease. In the current era of molecular oncology, with targeted therapies increasingly incorporated into clinical practice, the prognostic relevance of individual KRAS mutation subtypes and their relationship with immune biomarkers such as programmed cell death ligand 1 (PD-L1) require further clarification. This study aimed to evaluate the prognostic impact of KRAS mutation subtypes and their association with PD-L1 expression in patients with NSCLC. Methods: In this retrospective analysis, 150 patients with KRAS-mutant NSCLC who underwent next-generation sequencing at Trakya University Faculty of Medicine between January 2015 and December 2023 were included. Clinicopathological features, KRAS mutation subtypes, PD-L1 expression, and survival outcomes were assessed. Overall survival (OS) and progression-free survival (PFS) were estimated using the Kaplan–Meier method, and prognostic factors were evaluated using Cox regression analyses. Results: KRAS G12C was the most frequent subtype (40.7%), followed by G12V (20.7%) and G12D (14.7%). OS differed significantly among KRAS mutation subtypes (log-rank p = 0.007), with median OS values of 18 months for G12D, 11 months for G12C, 11 months for other rare variants, 9 months for G12A and G12V, and 5 months for G13. PD-L1 positivity was significantly higher in KRAS G12C tumors compared with non-G12C subtypes and remained independently associated with improved OS in multivariate Cox regression analysis (HR = 0.622; 95% CI, 0.426–0.907; p = 0.014). In multivariate analysis, age, ECOG performance status, disease stage, and PD-L1 positivity were independent prognostic factors, whereas KRAS mutation subtype did not retain independent prognostic significance. Conclusions: These findings suggest that KRAS-mutant NSCLC represents a clinically and molecularly heterogeneous subgroup and that integrating KRAS mutation subtypes with immune biomarkers may support more refined prognostic stratification. Full article

The transition toward Industry 5.0 in sustainable construction demands a radical reconceptualisation of workforce development, moving beyond purely technical training to embrace human-centricity, digitalisation, green competencies, and socio-cognitive resilience. Traditional vocational and higher education systems have largely failed to bridge the gap between emerging construction industry demands and the competencies possessed by current and future professionals. This systematic review investigates how Learning Factories’ 5.0 immersive, experiential, and technology-rich educational environments can address these gaps in sustainable construction contexts. Drawing on a synthesis of 71 peer-reviewed publications spanning 2015–2026 and supplemented by targeted construction-domain literature, this study pursues three objectives: (1) identifying core competencies for Industry 5.0 readiness in sustainable construction, (2) examining how Learning Factories 5.0 support the development of these competencies, and (3) proposing a competency-driven framework for integrating Learning Factories 5.0 into sustainable construction education and training. Seven transdisciplinary competency clusters are identified—Attitude toward Digitalisation, Technical–Green Proficiency, Information and Data Literacy, Digital Security, Collaborative Systems Thinking, Adaptive Problem-Solving, and Reflective Sustainability Practice—and a theoretically derived, eight-phase Construction Learning Factory 5.0 (CLF5.0) Framework is proposed as a conceptual architecture for future empirical development and institutional adaptation. The framework is presented as a generative starting point rather than a prescriptive model, and its effectiveness in diverse construction education contexts requires empirical validation through future implementation studies. Findings reveal that while Learning Factories offer transformative potential, critical barriers remain in terms of economic feasibility, faculty development, industry–academia alignment, and empirical validation. This paper contributes a construction-specific competency architecture and implementation pathway to support the industry’s transition toward a sustainable, human-centric, and Industry 5.0-aligned future. Full article

Background/Objectives: Artificial intelligence (AI) is rapidly transforming healthcare. Its integration into community health nursing—a discipline centered on population-level prevention, health promotion, and primary care in community settings—remains insufficiently explored. This narrative review examines the opportunities, ethical challenges, and future directions for integrating AI into community health nursing education and practice. Methods: A literature search was conducted across PubMed, CINAHL, Scopus, Web of Science, and IEEE Xplore for publications between January 2017 and March 2026. The initial search yielded 612 records; after the removal of duplicates and screening of titles, abstracts, and full texts against predefined criteria, 58 sources were retained for thematic synthesis, comprising empirical studies, systematic and umbrella reviews, scoping reviews, meta-analyses, and authoritative policy documents. Screening and data extraction were performed by two reviewers, with disagreements resolved by discussion. Results: AI offers opportunities for community health nursing across four interconnected domains: clinical decision support for community-based assessments, predictive analytics for population health management, enhanced disease surveillance and outbreak detection, and personalized health education delivery. Significant challenges persist, including algorithmic bias, data privacy concerns, threats to the therapeutic nurse–client relationship, inadequate AI literacy among nursing faculty, and regulatory gaps. Most empirical evidence originates from hospital or general nursing settings; transferability to community contexts is therefore inferred rather than directly demonstrated. Conclusions: Responsible integration of AI into community health nursing requires curriculum reform, ethical governance frameworks, faculty development, equitable access, and interdisciplinary collaboration. AI should augment, not replace, the relational and culturally sensitive care that defines this discipline. Given the narrative nature of the review and the limited community-specific evidence, conclusions are framed as a vision of the AI–community health nursing interface rather than a definitive synthesis. Full article

Background: The flipped classroom is an innovative student-centered teaching approach frequently applied in nursing education. The success of the approach relies on students coming prepared to class. Faculties play a critical role in facilitating students’ pre-class preparation. Objective: The objective of this study was to elicit nursing students’ preferred designs for pre-class preparation in large-group, flipped-classroom teaching in evidence-based practice, and to use these insights to inform practical, faculty-driven changes to course design and delivery. Methods: An action research study was conducted among bachelor’s students in nursing at a Norwegian university college. Data were collected through questionnaires with closed and open-ended questions, focus group interviews, and class meetings. Descriptive statistics and thematic analysis were applied to analyze data. Data were analyzed sequentially, and findings provided guidance for further actions. Results: The action was carried out and evaluated in two cohorts. The thematic analysis revealed one main theme—students need motivation for pre-class preparation—and three associated sub-themes: (1) Information: Communicate relevance and provide timely reminders, (2) Organization: Learning platform and workload, and (3) Engage learners: Diverse, interactive and aligned learning activities. Conclusions: Faculties can support motivation through clear communication of relevance, a well-organized learning platform, activating pre-class activities, and timely reminders. Successfully accommodating pre-class preparation for large-group teaching also appears to require coordinated faculty engagement and a shared commitment to student-centered approaches. Further evaluation is needed to determine which specific configurations work best in different contexts. Full article

The evolving landscape of higher education necessitates a re-evaluation of professional development (PD) frameworks to support instructors in enhancing their teaching practices. This paper introduces the Professional Development in Teaching and Learning Recommender (PDTLR), developed to identify instructors’ needs based on salient categories of PD in teaching and learning common across higher education. Utilising Rasch Measurement Theory (RMT) and a sample of 245 university instructors from a state-funded university in Singapore, the PDTLR was found to provide a reliable and valid measure of PD needs in teaching and learning and addresses inadequacies in existing instruments for identifying higher education instructors’ PD needs. The PDTLR represents a significant advancement in identifying PD needs within higher education, offering a valuable resource for institutions aiming to foster continuous improvement and professional growth among their faculty. Full article

Learning management systems (LMS) are essential for sustainable teaching and learning procedures due to the growing integration of digital technologies in higher education. Despite the widespread adoption of platforms such as Moodle, limited research has examined the students’ behavioral engagement and their subjective learning experiences. Addressing this gap, this study investigates the relationship between learning analytics indicators and academic performance, and how students’ experiences influence their participation in online learning environments. It adopted a convergent parallel design. Quantitative data were collected from the Moodle-based BirDeHa platform, drawing on learning analytics logs of 137 pre-service teachers enrolled in various programs within a faculty of education. Key indicators included frequency of material downloads, system usage, video engagement, and quiz performance. Qualitative data were collected via focus group interviews with nine participants. The results revealed a clear relationship between students’ interaction patterns within the LMS and their academic performance. Indicators of active engagement, particularly time spent on the platform and frequency of interaction with course materials, emerged as strong predictors of academic success. Qualitative findings further indicate that students perceive the LMS as flexible, inclusive, and supportive of their learning needs. Overall, this study underlines the importance of integrating data-driven insights with student-centered perspectives to achieve a comprehensive understanding of online learning environments and to inform effective design. The findings contribute to the sustainability of digital learning environments by providing behavioral indicators that can inform data-driven instructional design. Full article

International graduate students studying Science, Technology, Engineering, and Mathematics (STEM) in the United States (U.S.) diversify universities and contribute to research and innovation. They are critical to the U.S. STEM pipeline, workforce and economy; therefore, it is important to understand their experiences. This systematic literature review investigated international graduate STEM students’ characteristics and the supports and barriers they experience while studying in the U.S., following PRISMA guidelines. Thirty-nine peer-reviewed articles were systematically selected from 552 articles for inclusion in this review. Ecological systems theory situated the study within the broader system of graduate education. Findings revealed great diversity, such as country of origin and cultural identity, gender, STEM fields, and prior experiences. Students expressed differences in their reasons to pursue U.S. education and their post-graduation intentions to remain in the U.S. or leave. Support came from institutions, faculty members/academic advisors, and peers. Reported barriers included unfamiliarity with norms and institutional resources, limited English proficiency and writing skills, issues with advisor and being a teaching assistant, underrepresentation, and family responsibilities. Themes were placed within the levels of the ecological framework; most were in the macrosystem, reflecting the strong influence of society, institutions, culture, and norms on students’ experiences. Full article

The rapid integration of artificial intelligence (AI) into higher education is reshaping teaching, learning, and assessment, particularly in programming education. While AI coding assistants can enhance feedback, scaffolding, and student engagement, their educational value depends on pedagogical alignment, institutional readiness, and faculty practice, not merely technical capability. Existing adoption frameworks, however, inadequately address these pedagogical and institutional dimensions in domain-specific contexts. This study proposes the AI Coding Assistant Adoption Framework (AICAAF), a theoretically grounded model integrating the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), and Self-Determination Theory (SDT). The framework was developed iteratively from prior literature and refined through faculty perspectives. It conceptualises adoption across four interrelated dimensions: usability, pedagogical adequacy, institutional readiness, and faculty engagement. Using PyChatAI as an instrumental case study, this qualitative research draws on semi-structured interviews with 15 faculty members teaching programming courses at Jouf University, a public institution in Saudi Arabia operating in a low- to mid-resource context. Data were analysed using reflexive thematic analysis. Findings indicate that PyChatAI is intuitive and beneficial for novice learners, particularly through instant feedback and automated error correction. However, its pedagogical value is limited in advanced and industry-aligned contexts. Institutional barriers, such as inadequate infrastructure, limited technical support, and the absence of policy frameworks, significantly constrain effective integration. Despite this, faculty expressed strong commitment to adopting AI tools, proposing strategies including curriculum redesign, professional development, and gamified instruction. The study reconceptualises AI adoption as a pedagogical and institutional transformation rather than a purely technological shift. The AICAAF provides a robust foundation to guide curriculum design, teaching practice, and policy development for responsible AI integration in programming education. Full article

Universities are increasingly expected to contribute to societal and environmental challenges, yet the concept of the fourth mission remains fragmented and weakly operationalized in the literature. This study aims to develop a measurement framework for the fourth mission of universities. The research adopts a theory-driven qualitative design based on a systematic literature review, synthesizing contributions on community engagement, social innovation, sustainability and institutional embeddedness. Building on this synthesis, the study develops a three-level conceptual model that integrates an institutional framework, core fourth mission processes and transformative impact domains. The core processes are structured around community engagement, co-creation and social innovation, and knowledge diffusion and local application, while the model explicitly links these processes to societal challenges and environmental sustainability outcomes. Furthermore, the model is operationalized through a structured indicator system applicable in document-based analysis, supported by a four-point scoring scale capturing the depth of institutionalization. An illustrative pilot test was also conducted using the example of one university faculty. The study contributes by clarifying the boundaries of the fourth mission and offering an operational framework that shifts attention from output-based measurement to universities’ transformative societal roles. Full article

Objective: Against the backdrop of high-intensity academic evaluations, this study examines the longitudinal associations linking leisure-time physical activity (PA) with context-specific anxiety symptoms among university faculty. It further examines the cross-lagged indirect role of emotional exhaustion (EE) and the contrasting moderating effects of challenge and hindrance academic stressors. Methods: A three-wave cross-lagged panel design spanning six months was employed to track 356 faculty members from five universities in China. Validated scales were utilized to measure leisure-time PA, challenge/hindrance academic stressors, EE, and context-specific anxiety symptoms across three critical academic periods (T1, T2, and T3). Data were analyzed using Mplus 8.3 for longitudinal measurement invariance, cross-lagged path modeling, and Bootstrap mediation and moderation tests. Results: After controlling for baseline autoregressive effects, T1 leisure-time PA significantly and negatively predicted T2 EE (β = −0.16, p < 0.01), which in turn positively predicted T3 context-specific anxiety symptoms (β = 0.31, p < 0.001). The longitudinal indirect pathway linking T1 PA to T3 context-specific anxiety symptoms through T2 EE was statistically significant (95% CI [−0.088, −0.017]). Furthermore, the two types of stressors exhibited contrasting moderating effects on the PA-EE relationship. High challenge stressors strengthened the negative association between PA and subsequent EE (Simple Slope = −0.32, p < 0.001). Conversely, high hindrance stressors were associated with a reversal of the slope, wherein higher PA involvement was associated with higher subsequent EE (Simple Slope = 0.12, p < 0.05), a pattern consistent with what has been termed an “exercise paradox.” Conclusions: In this three-wave observational study, regular leisure-time PA was longitudinally associated with, but did not demonstrably cause, lower subsequent context-specific anxiety symptoms among university faculty, with this association potentially operating through reduced emotional exhaustion. The strength of this association was contingent on the type of academic stress: challenge stressors appeared to strengthen the negative association between leisure-time PA and subsequent EE, whereas high-intensity hindrance stressors were associated with a reversal of the predictive pattern. These findings, given the observational design, are best interpreted as longitudinal predictive patterns rather than causal effects. They suggest that interventions targeting faculty well-being may benefit from combining administrative burden reduction with individual recovery support, though direct experimental verification is needed. Full article

Teacher empathy in classroom promotes student-centered instruction, enhances students’ motivation and improves overall students’ performance but how is empathy incorporated in a classroom? In this study, a systematic literature review was conducted that analyzed thirty-three (33) articles between 2016 to 2024 to explore empathetic teaching practices that engineering faculty have utilized for undergraduate engineering students in the United States. For this purpose, two main research questions were drafted, a review protocol was established, search was conducted, results were screened, the quality of articles was analyzed, synthesis of articles took place and findings were collated. The results of the review were categorized into three sections. These sections include specific empathetic practices utilized by engineering faculty, ways in which the teaching practices were incorporated, and challenges that were faced by faculty while implementing those practices. The findings of the review reveal evidence-based insights into the integration of these teaching practices in the field of engineering. It also reflects the need to support engineering faculty in their professional development leading to effective pedagogical practices and fostering positive learning environments. Full article

University cafeterias generate solid waste as a result of high user turnover and routine food service operations. While waste characterization studies are common in higher education institutions, data-driven predictive modeling remains limited, particularly in Latin American contexts. This study addresses this gap by integrating physical waste generation with behavioral surveys to develop predictive tools for operational decision-making. The findings should be interpreted as a single-site operational demonstration; broader generalization requires replication and local recalibration in cafeterias with different operational and social characteristics. Waste generation was characterized in a Panamanian university cafeteria by shift over 20 consecutive working days, separating organic and inorganic fractions, and collecting 705 user surveys on consumption habits. Two complementary predictive approaches were developed: a rule-based classification model and a Monte Carlo simulation framework. Organic waste exhibited a stable pattern throughout the study period, with clear concentration during lunch hours and a strong dependence on user volume. In contrast, inorganic waste showed higher day-to-day variability and increased during evening service, reflecting changes in service practices rather than attendance alone. Statistical analysis indicated that waste generation was more closely associated with food type purchased and faculty affiliation than with self-reported environmental awareness. Overall, the results demonstrate that straightforward predictive approaches can support shift-level planning and operational waste management decisions in university cafeterias. Full article

This study explores the sustainability of pedagogical innovation in higher education by examining a faculty-collaborative, ADDIE-based selective flipped learning framework in an engineering education context. It addresses a persistent challenge in engineering classrooms, namely the mismatch between traditional teaching approaches and the diverse learning needs of students, while also considering whether such innovations can be realistically sustained over time. A mixed-methods sequential explanatory design was implemented in a foundational Circuits I course at Princess Sumaya University for Technology (PSUT), involving 110 undergraduate students and eight faculty members. The ADDIE model guided the design and implementation of selectively flipped instructional materials. Quantitative data were analyzed using independent samples t-tests, while qualitative data from surveys, interviews, and focus groups were examined through thematic analysis to better understand faculty experiences and implementation processes. The findings show statistically significant improvements in student performance. Independent samples t-tests indicated significant differences in overall scores and final exam performance (p < 0.001), while additional analyses of formative assessment components also demonstrated statistically significant improvements. At the same time, the results reveal important implementation challenges. Although the course was collaboratively designed, implementation varied across instructors, and faculty were divided in their willingness to continue using the approach. This highlights a clear gap between instructional design and classroom practice, shaped by both human and institutional factors. Overall, the study suggests that well-structured instructional design models such as ADDIE can support improved learning outcomes. However, the findings do not provide conclusive evidence of long-term sustainability; rather, they highlight the conditions under which pedagogical innovations may be sustained, including institutional support, faculty engagement, and alignment with teaching realities. Full article

The rapid proliferation of generative artificial intelligence (GenAI) tools has created an urgent need for instruments to evaluate their educational value as teachers, faculty, administrators, and instructional designers consider adopting them. While rubrics exist to assess mobile applications and virtual reality tools, no comparable instrument has been developed specifically for large language models (LLMs) and AI media generators. The authors reviewed existing evaluation rubrics for edtech and GenAI tools, with edtech meaning digital tools that support ethical teaching to improve student learning and GenAI referring to neural networks that simulate human interactions by contextualizing relevant content based on learning needs. Grounded in Waks’ framework, the resulting Edu-GenAI Rubric comprises multiple dimensions organized into five domains: the Instrumental, Technical, Hedonic, Use, and Beneficial values. Dimensions include accuracy, productivity, personalization, citation, user interface, user experience, sharing, storage, and ethical dimensions encompassing data privacy, data transparency, guardrails, fair use, and algorithmic discrimination. The Edu-GenAI Rubric offers decision-makers with a preliminary, theory-informed instrument for evaluating GenAI tools in educational contexts that can be applied to institutional adoption decisions, developer benchmarking, and future research. Full article

Background: Regulatory restrictions and mandates typically offer short-term behaviour guidance, whereas interventions to improve knowledge and attitudes could result in more sustainable behavioural changes. Health authorities implemented awareness campaigns to enhance public knowledge and attitudes regarding COVID-19. This study explored the interplay between knowledge, attitudes and behaviours related to COVID-19 among university undergraduate students in Jordan, aiming to inform public health initiatives and educational programmes. Methods: A cross-sectional survey targeting undergraduate students enrolled at Yarmouk University in Jordan was conducted between January and May 2021. Participants consented to complete an anonymised validated self-administered questionnaire to evaluate their understanding of COVID-19 symptoms, treatment and transmission and attitudes and behaviours towards preventive measures. Data were analysed using descriptive and inferential statistics and structural equation modelling to investigate the associations between knowledge, attitudes and behaviours. Results: A total of 1375 undergraduate students participated in the survey. Knowledge of COVID-19 was low among most participants, with only 1.3% demonstrating high knowledge. Conversely, 58.5% exhibited good behaviour, and 31.4% reported full compliance with recommended behaviours. Significant differences were found in knowledge, attitudes and behaviours across different faculty clusters, with health faculties showing superior knowledge and more positive attitudes. Female participants (66.3%) were more likely to engage in positive behaviours than males (p-value = 0.02). Structural equation model (SEM) analysis showed that knowledge significantly influenced attitudes, which affected behaviours, confirming the model’s validity. Conclusions: The study highlights the critical role of knowledge and attitudes in shaping COVID-19-related behaviours among university students. Significant variations in knowledge and attitudes across different academic disciplines highlight the need for tailored educational interventions. The analysis supports the theoretical model linking knowledge, attitudes and behaviours, emphasising the importance of improving knowledge and attitudes to drive behaviour change. The findings suggest that comprehensive health education programmes targeting cognitive and affective aspects are essential for effective public health responses during pandemics. Full article