Search Results (1,495)

The rapid digitalization of higher education has intensified the need for reliable methods to assess higher-order cognitive skills, particularly critical thinking, in adaptive learning environments. However, most existing assessment approaches rely primarily on test outcomes and academic performance indicators, which do not adequately capture the multidimensional and process-based nature of critical thinking. This study proposes a multi-criteria hierarchical model for identifying and quantitatively assessing students’ critical thinking based on individual process indicators of learning activity in an intelligent educational environment. The model integrates cognitive, metacognitive, and behavioral indicators, including knowledge dynamics, task complexity, time characteristics, learning activity intensity, error rate, level of doubt, user interaction patterns, and system operating modes. These indicators are aggregated into a three-component structure representing metacognitive awareness, analytical depth, and strategic learning activity. The proposed model was empirically validated through a quasi-experimental longitudinal study involving 500 university students divided into control and experimental groups. The results demonstrate a statistically significant increase in all latent components of critical thinking and in the integral indicator within the experimental group. The model shows satisfactory internal consistency (Cronbach’s $\alpha \ge 0.77$) and acceptable construct validity confirmed by confirmatory factor analysis. The findings indicate that the proposed model can serve as a practical analytical tool for monitoring critical thinking development and supporting personalized learning trajectories in adaptive digital educational systems. Full article

Low-carbon technology innovation serves as the core driver for multiple countries striving to achieve their dual-carbon goals. Therefore, building efficient low-carbon technology innovation networks and accelerating low-carbon technological innovation have become key focuses of academic research. Leveraging patent and publication data, this study constructs dual networks for low-carbon basic and applied research. It employs Exponential Random Graph Models (ERGMs) and Multilevel Exponential Random Graph Models (MERGMs) to explain the different formation factors and dependency relationships within dual networks. Building on this, this study introduces the NK model to analyze the order of effects of these network formation factors and dependencies. The findings reveal the following: (1) The formation factors of dual low-carbon innovation networks differ significantly. For the basic research network (BRN), the key formation factors—in order of effect—are collaboration stability, transitive closure, partner addition, the Matthew effect, and knowledge siphoning. For the applied research network (ARN), the key formation factors—in order of effect—are historical collaboration, collaboration stability, partner addition, cognitive proximity, and knowledge siphoning. (2) The BRN and ARN exhibit an asymmetric dependency. The dependence of the BRN on the ARN is manifested as structural symbiosis, whereas the ARN, guided by the BRN, demonstrates the transmission of collaborative relationships. This study elucidates the complex formation mechanisms and dependency patterns of low-carbon technology innovation networks, providing a theoretical foundation and decision-making support for the differentiated governance of network structures and the optimized allocation of innovation resources. Full article

In order to achieve more accurate and efficient oil-paper insulation aging assessment, and to improve the operation and maintenance level of oil-paper insulated power equipment, this paper proposes an aging evaluation method of oil-paper insulation based on Raman spectrum and frequency-domain spectroscopy. First, oil-paper insulation samples with different aging degrees were prepared by an accelerated thermal aging test in this experiment. Then, Raman spectroscopy and frequency-domain dielectric spectroscopy were used to examine the samples and analyze the aging characteristics of the samples by LightGBM R2019b. Finally, the gray neural network is used to establish a prediction model for the degree of polymerization of insulating paper based on frequency-domain dielectric features and Raman spectral features. The results of this study showed that there is a certain correlation between the Raman characteristics of insulating oil and the FDS characteristics of insulating paper. The average absolute error of the prediction of the R-F-PGNN model developed in this paper is 20.4. The research in this paper provides a strong support for the development of Raman spectroscopy diagnosis technology for oil-paper insulation aging in the power industry, which has certain academic value and engineering application significance. Full article

The reorganization of academic work following the COVID-19 pandemic has intensified debate regarding the effects of work modality on faculty well-being, particularly in Latin American contexts characterized by structural inequalities and digital divides. This study examines the influence of work modality on the integrated well-being of university faculty in Peru. A quantitative, non-experimental explanatory design was employed with a sample of 448 faculty members from public and private universities. Occupational well-being and quality of life were assessed using validated instruments and subsequently integrated into a higher-order construct due to the absence of discriminant validity. Structural relationships were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with bootstrapping (5000 resamples). The results indicate that work modality significantly predicts integrated well-being (β = 0.823; p < 0.001), although the model explains a limited proportion of variance (R² = 0.168). Comparative analysis revealed that faculty in in-person modality report significantly higher levels of well-being and quality of life than those in virtual modality. These findings suggest that work modality operates as a structural condition shaping faculty well-being and highlight the need for institutional policies that promote balanced and human-centered work designs in higher education. Full article

The construction industry accounts for approximately 13% of global GDP but suffers from chronic productivity stagnation. Although artificial intelligence (AI) offers transformative potential, its adoption is constrained by three key barriers: data integrity issues (H1), socio-technical challenges (H2), and system integration problems (H3). This study investigates whether academic research attention aligns with these practitioner-identified barriers through a bibliometric analysis of 4668 publications from OpenAlex (1990–2025), applying a five-pillar analytical framework synthesized into composite scores (0–100 scale) via min-max normalization, weighted summation, and bootstrap validation. H3 achieved a nominal 15.9% prevalence rate (adjusted to ~13.0% after correcting for an 18.2% false positive rate in keyword classification), robust growth (R² = 0.654), significant overrepresentation in top-cited works (risk ratio = 1.31, p = 0.003), and received a composite score of 62/100 (confirmed). H1 (2.7%, score: 17/100) and H2 (4.6%, score: 13/100) were both rejected. The rank ordering by prevalence (H3 > H2 > H1) remains robust under all adjustment scenarios. These findings contrast notably with the RICS Global Construction Monitor (2025, n = 2200+), where practitioners most frequently reported socio-technical barriers (46%), followed by system integration (37%) and data quality (30%), yielding practitioner-to-publication ratios of 4.7:1, 5.2:1, and 1.1:1, respectively. This apparent research–practice paradox appears primarily volume-driven rather than clearly quality-driven: H1/H2 publications receive citation attention broadly comparable to the baseline, though this comparison is limited by control group heterogeneity. We call for rebalanced research agendas addressing data governance frameworks, competency development, and organizational change management. Full article

The rise of generative artificial intelligence (GenAI) is creating new opportunities for assessment design in universities, particularly in subjects that emphasize analytical and creative skills. This paper introduces the Educator–GenAI Partnership Model, an iterative five-stage model that helps educators create assessments that foster higher-order thinking (HOT). The model is grounded in constructive alignment and Bloom’s taxonomy, with a central emphasis on preserving human oversight to ensure educators retain control over assessment validity, academic integrity, and the ethical use of AI. The model maps out the unique strengths and responsibilities of both educators and GenAI, showing how each plays a distinct role in the assessment design process. It illustrates how GenAI can support the rapid generation of assessment tasks and marking rubrics, while positioning educators as critical decision-makers who only review, adapt, and iteratively refine AI-generated outputs to ensure alignment with higher-order learning outcomes. Overall, this paper presents a structured and practical model for utilizing GenAI responsibly in assessment design, thereby strengthening academic rigor while enhancing efficiency for educators. Full article

Smart contracts raise persistent challenges regarding compliance with traditional contract formalities, including writing, signature, notarization, and in certain transactions, registration. These issues are particularly significant in high-value and public-facing transactions such as real estate, where formalities determine legal validity, evidentiary sufficiency and publicity effects. While existing scholarly work has examined these challenges from either doctrinal or technological perspectives, limited attention has been given to how the functional roles of formalities interact with blockchain architecture, practitioner perceptions and institutional legal frameworks. This study addresses this gap through a mixed-methods approach combining doctrinal legal analysis with qualitative socio-legal research based on 27 semi-structured interviews with legal professionals including attorneys, judges, and academic scholars. The analysis is grounded in a civil law framework, with particular reference to the Jordanian legal system, while references to the European Union’s eIDAS Regulation are used illustratively to demonstrate regulatory approaches to digital authentication. The findings demonstrate that blockchain-based systems can effectively support the evidentiary and attribution functions of contractual formalities through cryptographic verification, consensus mechanisms, and automated execution. However, they do not independently satisfy formalities that perform cautionary, constitutive, protective or public order function, namely notarization and registration, which remain dependent on institutional validation and legal recognition. The analysis further shows that practitioner concerns reflect not only doctrinal constraints but also institutional roles and varying levels of technical familiarity. To address these limitations, the study proposes a function-based analytical framework for evaluating smart contract formalities and identifies two complementary pathways for legal adaptation: (i) institutional integration, including registry-linkage systems and hybrid contracts; and (ii) technological adaptation, including digital authentication frameworks and legal oracles that connect on-chain execution to off-chain legal conditions. The study concludes that smart contract formalities’ challenges arise not solely from technological limitations, but from the interaction between legal doctrine, institutional structures, and system design. It advances a functional framework for aligning automation with the evidentiary, protective, and publicity functions of contractual formalities. Full article

Generative artificial intelligence (AI) is increasingly used in higher education as an interactive tutoring partner rather than a passive information tool. While AI offers opportunities to support learning, concerns remain regarding cognitive offloading, reduced engagement, and unreflective use. Although instructional scaffolding is a well-established design principle for supporting complex learning, its role in shaping cognitive and metacognitive processes in AI-supported settings remains underexplored. This quasi-experimental pre–post study examined how varying levels of scaffolding influence learning outcomes and motivational, cognitive and metacognitive processes during AI-tutored learning. A total of 175 first-semester students from two faculties and diverse academic backgrounds completed the same academic task within a four-hour university session under one of three conditions: (1) full scaffolding, including a structured prompting template based on the Goal–Context–Constraints (GCC) strategy, iterative refinement, and reflective guidance; (2) light scaffolding, including the GCC prompting template; or (3) no scaffolding template as the control condition. Measures included knowledge gain, motivation, cognitive load, critical thinking, and reflective use. Data were analysed using ANOVAs, ANCOVAs, regression models, and PROCESS moderation and mediation analyses. Across the conditions, students showed significant gains in knowledge, critical thinking, and reflective use, while motivation remained stable and intrinsic and extraneous cognitive load decreased; no significant differences between scaffolding conditions were observed. The scaffolding conditions did not produce significant interaction effects, although descriptive trends suggested higher gains in higher-order knowledge under scaffolded conditions. Overall, the findings suggest that short-term learning gains in AI-supported settings may not depend on scaffolding intensity alone, but rather on how learners engage with AI during the learning process. Full article

Artificial intelligence has been considered as a transformative element capable of reshaping STEM education into equitable, resource-efficient, and scalable learning environments. However, realizing this potential requires striking a careful balance between technological innovation, pedagogical considerations, and ethical concerns. This study sought to examine the implementation of artificial intelligence (AI) tools by STEM university faculty members in Saudi Arabia to promote Sustainable Development Goal 4 (quality education). While doing so, the study attempted to explore how Saudi STEM university faculty members integrated AI tools in their instructional practices and analyze their perceptions towards these tools. To achieve these goals, the study employed an explanatory sequential mixed-methods design. In the first phase of data collection, a close-ended questionnaire was applied to a random sample of (324) STEM university faculty members. The second phase involved gathering qualitative data using a semi-structured interview administered to 12 purposively selected experts. Key quantitative findings revealed an overall AI integration at a medium level with a mean of (2.71) and standard deviation of (0.36) across three instructional practices, namely planning, implementation, and assessment. The highest integration level was in assessment (M = 2.93, medium) while the lowest was in planning (M = 2.61, medium). The results also revealed that the participants’ perceptions towards integrating AI tools were highly positive (M = 4.00, high), albeit with some concerns regarding the effect of excessive and unguided use of AI tools on students’ higher-order thinking skills, particularly the risk of AI functioning merely as an information delivery mechanism rather than serving its more pedagogically valuable role as a brainstorming scaffold. Furthermore, the study unveiled a number of barriers to integrating AI tools, including the weakness of digital infrastructure, lack of professional development, the limited credibility of AI-generated content, and ethical concerns related to academic integrity and copyrights. The research suggests the establishment of a sustainable digital environment by improving the infrastructure, providing specific training in accordance with the principles of sustainability, and implementing policies that promote equitable, transparent, and responsible integration of AI. These strategies can coordinate the growth of technology with the larger needs of the quality of education, inclusion, and sustainability of STEM education in the long term. Full article

The identification of gifted students with learning disabilities (GLD) remains theoretically and methodologically contested. The present study examined cognitive and reading profiles of 150 Greek students in Grades 4–6, classified as gifted with learning disabilities (GLD) (n = 36), gifted (n = 31), or dyslexic of average intellectual ability (n = 83). Gifted classification was based on National Association for Gifted Children guidelines issued in 2018, using reasoning-based WISC-VIndices (FSIQ, GAI, EGAI, NVI, VECI ≥ 120), while learning disability was determined through formal multidisciplinary diagnosis. Cognitive performance was assessed with the WISC-V and reading with the standardized DADA battery (decoding, fluency, and comprehension). One-way ANOVAs and ROC analyses were conducted. GLD students demonstrated reasoning abilities and processing speed abilities comparable to gifted peers, but working memory deficits compared to gifted peers. In reading, GLD students showed decoding deficits like dyslexic peers and fluency impairments indistinguishable from them, yet significantly stronger comprehension. These findings reveal a differentiated literacy profile in which higher-order reasoning appears to support meaning construction despite persistent efficiency-based constraints in decoding and fluency. Overall, the results indicate that twice-exceptionality reflects a structurally uneven cognitive–academic configuration, underscoring the importance of multidimensional assessment approaches that simultaneously evaluate reasoning strengths and reading-specific vulnerabilities. Full article

Mixed-signal simulation is indispensable for verifying modern integrated circuits that tightly couple analog and digital subsystems, yet the field lacks a unified framework for systematically comparing its diverse methodologies. This paper addresses that gap by proposing a novel three-axis taxonomy that classifies simulation methods along abstraction level, solver methodology, and analysis type, together with a comparative evaluation framework based on five quantitative metrics: accuracy, throughput, capacity, convergence reliability, and scalability. Applying this framework, we systematically compare thirteen classical method categories—spanning SPICE, FastSPICE, RF/periodic steady-state, behavioral modeling, co-simulation, and model order reduction—and eight AI/ML approaches including Gaussian process surrogates, graph neural networks, physics-informed neural networks, Bayesian optimization, and reinforcement learning. Our analysis reveals a clear maturity stratification: classical methods remain the only signoff-accurate approaches, Bayesian optimization represents the most industrially validated AI contribution with integration across all three major EDA platforms, while Neural ODE solvers and LLM-based design tools remain at the research stage. We identify a persistent academic-to-industry gap driven by foundry model complexity, limited benchmark diversity, and topology-specific overfitting. The proposed taxonomy and comparative framework provide practitioners with structured guidance for simulation method selection and highlight specific research directions needed to bridge the gap between AI promise and industrial deployment. Full article

Educational escape rooms have been widely used in education in recent years. On the other hand, capture the flag (CTF) competitions have been spreading from the cybersecurity area into other domains. In this paper, a fusion of both concepts is proposed in the form of a CTF-like escape room in order to carry out the assessment of a STEM course. Each puzzle within the escape room consisted of designing a short code snippet as part of a protocol engineering course, with up to three hidden hints available. Each hint incurred a 25% penalty, deducted from the score awarded for solving the puzzle, as is common in CTF environments. This approach can be applied to small problem-solving tasks across many disciplines and educational contexts. Data collection consisted of gathering the marks of all students enrolled in the courses on an anonymized basis, after they had provided informed consent, and data analysis was then performed using descriptive and inferential statistics. The results suggest a statistically significant increase in average academic performance compared to the previous academic year, during which assessment was conducted through written exams. In addition, engagement was measured using the ISA Engagement Scale, showing a high level of student involvement. Nevertheless, further research with a larger sample of students is needed in order to confirm these findings. In summary, a novel approach to STEM assessment is presented, as the solution of each puzzle requires not only recall but also critical thinking, technical proficiency, and application. Full article

One of the main focuses of social innovation is the fight for social inclusion for all. The prevalence of violence against women with disabilities shows how the lack of recognition of diversity hinders real equality for these individuals. This violence is a structural problem that is caused by a combination of gender and disability dynamics, the perpetuating inequalities and social exclusion. Researchers have analyzed specific vulnerabilities in the areas of work, education and health, and point out that only by addressing diversity can we overcome the consequences of such discrimination. Using a qualitative methodological approach, this study utilized critical documentary analysis and a thematic review of recent scientific, academic, and institutional literature, alongside empirical data, to identify the most prevalent forms of violence faced by women with disabilities: sexual (affecting up to 80% of women with disabilities), economic (with wage gaps exceeding 24%), and institutional (a lack of accessibility to basic services). The barriers hindering these women’s access to resources and services are also highlighted. The results point to the urgent need to develop innovative public policies and social strategies that value social diversity as a driver of change. This study concludes with specific recommendations for implementing inclusive approaches that promote equality, universal accessibility, and comprehensive protection in order to move toward more just, cohesive, and innovative societies. Full article

Air pollution is one of the major environmental challenges threatening global sustainable development and human health. The World Health Organization identifies it as a critical factor contributing to non-communicable diseases and inequality, especially in vulnerable populations such as children. The findings highlight the negative effects of environmental degradation on physical health and underline the urgent need to incorporate health metrics, such as children’s fitness, into sustainability monitoring frameworks and public policies aiming at cleaner and healthier urban environments. The aim of this study was to examine the association between ambient particulate pollution and cardiorespiratory fitness in school-aged children from two rural villages in southern Spain characterised by relatively higher and lower levels of particulate matter. A total of 938 children (primary and secondary school levels) participated in a naturalistic pre–post study design. Cardiorespiratory fitness was assessed using the 6 min walk test, where maximal oxygen uptake (VO₂max) was estimated. Assessments were conducted before and after a period characterised by unfavourable air-quality conditions in the higher-pollution village. The students were assigned by convenience into an experimental [n = 476 (EG)] and a control group [n = 462 (CG)]. The t-test, repeated measures analysis and MANOVA test were used in order to report differences within and between groups, as well as time-points and academic levels. The significance level was set at p < 0.05. Significant differences between groups were reported within the pre-test period, showing elevated pre-test values in the CG compared to the EG. The EG showed a higher pre–post difference in estimated VO₂max compared to the CG for primary education level (16.19%, ES(d) = 0.91 vs. 3.07%, ES(d) = 0.26; p < 0.001, respectively); secondary education (EG: 12.29%, ES = 0.91 vs. CG: 1.69, ES(d) = 0.16); and the whole population (EG: 14.72%, ES = 0.91 vs. CG: 2.84, ES = 0.25). It seems that the environmental context, and specifically the air pollution in the area of residence, may be an important factor to consider in relation to the assessment of physical fitness in the school-aged youth population. Full article

Background/Objectives: Dental Practice-Based Research Networks (DPBRNs) bridge the gap between academic research and private dental practice, addressing questions relevant to everyday medical care. Despite their growing scientific output, little research has explored the experiences of practitioners engaged in these networks. Our study therefore aims to investigate these practitioners’ perspectives in order to identify strategies for improving investigator recruitment, training and data quality in future DPBRN studies. Methods: The qualitative methodology was chosen, and our study adhered to the Standards for Reporting Qualitative Research (SRQR) guidelines. Semi-structured interviews were conducted with dentists who had participated in a DPBRNs study and transcribed before being thematically analysed using Braun and Clarke’s framework. MaxQDA 2022 software was used to facilitate coding of the verbatim quotes. Results: Three major themes emerged: (1) obstacles to participation, including time constraints, difficulties in patient recruitment, and a perceived disconnect between academia and private practice; (2) facilitators of engagement, such as strong leadership, logistical support, and a collaborative research environment; and (3) personal benefits, such as skill development, breaking professional routines, and counteracting stereotypes about private practitioners’ involvement in research. Conclusions: The findings align with existing literature on medical Practice-Based Research Networks (PBRNs), highlighting logistical and motivational barriers while also emphasizing the importance of social and professional benefits. Notably, although financial compensation or credits for continuing professional development are frequently cited as motivators for research participation, these were not significant concerns for our participants. This study sheds light on the experiences of health practitioners in PBRNs, offering recommendations to overcome challenges through strategies such as accessible training, practical incentives and collaboration opportunities. Full article