Search Results (373)

Driven by the digital transformation of territorial spatial governance, traditional urban planning is irreversibly shifting towards a data-driven empirical paradigm. However, constrained by mimetic isomorphism and path dependence, many geography-based regional universities remain trapped in an educational dilemma: they overemphasize morphological representation while marginalizing quantitative decision-making, fostering a structural mismatch between graduate competencies and industry demands. To explore a systematic pathway out of this dilemma, this study chronicles a three-year pedagogical intervention utilizing a mixed-methods design with a historical control cohort (N = 275) within the urban planning program of Gansu Agricultural University—a regional institution situated in a less-developed frontier where territorial renewal demands macro-spatial synthesis over aesthetic forms. The intervention strategically redefined the graduate competency profile as “spatial data analysts”, constructing a pedagogical model comprising foundational algorithmic training, cross-disciplinary faculty collaboration, and real-world Project-Based Learning (PBL), coupled with a restructured, evidence-based evaluation system. Longitudinal tracking and quantitative analyses indicate a structural alignment with elevated educational efficacy. At the macro level of employment trajectories, the proportion of graduates securing knowledge-intensive data positions experienced a structural shift, rising from a baseline of 14.5% to 42.5%, reflecting an enhanced capacity to capitalize on expanding societal demands. At the meso level of practical competence, the award rate in high-level professional competitions increased by 35.4%. At the micro cognitive level, the new evaluation mechanism is associated with a successful redirection of students’ cognitive resources toward algorithmic logic and policy translation (p < 0.001) while highly significantly enhancing their self-efficacy in tackling complex, wicked engineering problems (p < 0.001). Rather than isolating pure causal mechanics, this study interprets these systemic gains as a contextual realignment of academic supply. It provides a context-sensitive, reproducible methodological reference for cultivating professional distinctiveness and reshaping the spatial planning education system in the digital era. Full article

International accreditation has become an important reference point for quality assurance in higher education; however, its relevance depends on how global standards are interpreted and adapted to local institutional contexts. This study analyzes institutional stakeholders’ perceptions of an international accreditation process, with the aim of examining how global standardization interacts with local relevance in quality assurance. A quantitative, descriptive, cross-sectional study was conducted with 408 participants linked to a university degree program, including students, graduates, faculty members, administrative staff, and authorities. Data were collected using a 49-item questionnaire developed from the evaluation criteria of an international accreditation manual and adapted to the institutional context. Descriptive and nonparametric inferential statistics were applied. The results indicate: (a) an overall positive assessment of the quality model implemented; (b) comparatively higher ratings for management- and resource-oriented dimensions; (c) comparatively lower ratings for pedagogical dimensions; (d) no statistically significant differences across stakeholder profiles, suggesting a broadly shared interpretation of the accreditation process; and, (e) statistically significant but small gender differences, which should be interpretated cautiously. The findings suggest that international accreditation is perceived as contributing to transparency, comparability, and external recognition, although its value depends on the extent to which standardized frameworks remain sensitive to pedagogical and contextual realities. Full article

The Architecture, Engineering, and Construction (AEC) industry has significant potential to improve productivity, quality, and sustainability of its projects through emerging digital technologies. Advances in technology and the complexity of what new graduates need to learn have resulted in persistent training gaps and have highlighted new needs to be addressed in education. One of the new needs is the level of learners’ awareness of new technologies and their adoption practices. This research examines how current education and training practices in the selected sample of the Australian AEC sector support or hinder the development of digital capabilities. The set of technologies considered in this study focuses on Artificial Intelligence (AI), Building Information Modelling (BIM), Digital Twins (DTs), Virtual and Augmented Reality (VR/AR), and the Internet of Things (IoT). A mixed-method design integrates a structured survey of industry professionals and students, along with semi-structured interviews of industry and academic stakeholders, to evaluate exposure, self-rated capability, training participation, organisational support, and perceptions of graduate preparedness. Findings show comparatively higher maturity in BIM, but limited capability in other technologies, inconsistent formal training, and barriers linked to time, cost, organisational priorities, and rapid technological change. Qualitative findings and interpretation of preparedness-related survey responses indicate that stakeholders place greater value on transferable, interdisciplinary digital competencies than on narrow tool-specific proficiency. The research delivers statistically robust findings and actionable recommendations that address the identified barriers and promote the development of a skilled workforce in the AEC industry. Full article

Academic research laboratories often exhibit gaps between formal safety policies and everyday practices, driven by variability in training, leadership engagement, and safety practices. Effective safety therefore depends not only on formal compliance programs but also on operational factors such as training quality, SOP use, audit practices, and reporting culture. This study examines how operational factors within a large research-intensive university, including laboratory role, access to and adequacy of safety training, use of standard operating procedures (SOPs), experience with audits, near-miss reporting practices, and laboratory workers’ perceptions of risk and safety culture, are related to one another. A cross-sectional anonymous survey was administered to 1340 individuals, of whom 245 self-identified as currently working in research laboratories. Categorical data were analyzed using likelihood ratio chi-square tests with false discovery rate adjustments. Respondents reported high overall use of SOP use (85%), but staff indicated significantly lower SOP use than graduate students (69% vs. 91%, p = 0.004), and staff were more likely than faculty to view audits as helpful (97% vs. 85%, p = 0.050). Only 68% of laboratories reported documenting near misses, and 25% of respondents reported difficulty locating required training, despite 88% of training users rating it as sufficient once accessed. Although 52% of respondents classified their laboratory as moderate or high risk, 96% nonetheless described their laboratory as safe, suggesting normalization of risk based on self-reported perceptions. No significant associations were observed between perceived laboratory safety and years of experience, hours worked in the laboratory, or extent of training completed. Overall, the findings highlight the importance of not only accessible training and consistent procedures but also institutional conditions that support reporting, learning, and shared responsibility for hazard mitigation in academic research laboratories. Full article

This paper investigates the relationship between green vocational education and training (VET), structural economic features, and green employment in Central and Eastern European (CEE) economies. For the purpose of the research, an initial database covering the post-2010 period was assembled from Eurostat and related statistical sources. Due to data availability and cross-country comparability constraints, the final empirical analysis employs a balanced panel of six EU Member States covering the period 2018–2022. The empirical analysis employs pooled OLS and fixed-effects estimators over the period 2018–2022, following a stepwise modeling strategy to assess baseline relationships and robustness. The results show that VET enrollment alone is not a reliable predictor of green employment growth, while VET graduation rates exhibit a more consistent—yet not robust—association once country-specific heterogeneity is controlled for. By contrast, structural reliance on industrial sectors is consistently linked to lower green employment shares, while environmental tax revenues demonstrate modest positive effects. Overall, the findings suggest that green employment dynamics are driven primarily by structural and macroeconomic conditions rather than by skill formation alone. The study contributes to the literature on the green transition by providing an integrated perspective on the interaction between skills, structural transformation, and policy incentives in shaping sustainable labor market outcomes. Full article

Background: Entrustable Professional Activities (EPAs) that transform competencies into distinct, assessable clinical tasks have not yet been developed for US nephrology fellowships. We created and achieved consensus on a set of nephrology-specific EPAs and aligned them with Accreditation Council for Graduate Medical Education (ACGME) competency standards. Methods: This study was conducted within the University of New Mexico nephrology fellowship program. An initial EPA list was generated by the study team using program objectives, a literature review, and clinician insight. Study participants included eight faculty nephrologists and one nephrology fellow, who completed an online-based three-round modified Delphi consensus-building processes. Each EPA was rated on a five-point Likert scale with consensus requiring strict criteria. Finalized EPAs were independently mapped to ACGME nephrology program requirements. Results: Nine study participants (100% response rate) completed all survey rounds. Through iterative consensus, utilizing strict criteria, a final list of 22 distinct EPAs was created, covering 10 core domains of practice including dialysis management, acute kidney injury, chronic kidney disease, electrolyte abnormalities, hypertension, kidney stones, glomerular disease, pregnancy, transplant care, and education. Finalized EPAs were mapped to 38 different ACGME-required sub-competencies, showcasing diversity and applicability to national expectations. Conclusions: We developed the first consensus-based set of EPAs geared for US nephrology fellowship programs, providing a foundation for standardized assessment and curriculum development that could be implemented across nephrology fellowship programs nationally. Full article

Generative AI (GenAI) tools are increasingly used by students in higher education, including in technically demanding engineering courses. However, fluent AI-generated responses may still contain incorrect or incomplete information, creating a risk that students overestimate their reliability. This exploratory study investigates the relationship between students’ perceived usefulness of GenAI and an instructor-benchmarked reference evaluation of model outputs in two digital systems design courses. The study involved voluntary survey responses from 32 students in an undergraduate course at MIUN and 20 students in a graduate-level course at UNISA. Student perception data were combined with teacher-side benchmarking of selected GenAI models on tasks categorized by cognitive depth. Findings indicate that prior GenAI familiarity was associated with interaction frequency and average perceived usefulness, whereas self-assessed subject knowledge showed limited association. A perception–performance gap emerged, with students often rating GenAI outputs as useful even when the instructor-side evaluation identified limitations in correctness or required substantial human scaffolding. The proposed framework should be interpreted as an exploratory guideline for studying and guiding GenAI use in engineering education, rather than as a definitive benchmark of model performance. Full article

Understanding student artificial intelligence (AI) literacy in the context of higher education is crucial as technology advances and AI use increases. The purpose of this study is to better understand how university students perceive, define, and apply AI literacy within their own educational experiences and from their own disciplinary lens. Collecting electronic survey responses from 130 graduate and undergraduate students across several disciplines including First-Year Writing, Communication Studies, and Education, this study attempts to elucidate how students articulate and perceive their own degree of AI literacy—Access, Understanding, Critical Thinking, Application, and Ethics—in the educational context. Overall, students reported infrequent use, using ChatGPT most often. Education students reported a lower understanding of AI than non-education students. Undergraduates reported higher rates within ethics than graduate students. No significant differences in AI literacy were found between students who were or were not first-generation students, students who did or did not receive financial aid, or by gender. Students reporting higher rates of use also reported higher rates of AI literacy. Crucially, this study provides key qualitative and quantitative insights exploring how students perceive their own AI literacy. Understanding the current state of students’ AI literacy is important to facilitating holistic student success in academic environments and career readiness as institutions of higher education adapt and prepare curricula, programs, and interventions addressing AI literacy across disciplines. Full article

Achieving carbon neutrality, enhancing energy efficiency, securing energy supply, and accurately forecasting energy demand are among the most urgent global energy priorities. In this study, Turkey’s geothermal, wind, and solar electricity consumption was forecasted for the 2025–2030 period using five years of historical data through eight different regression-based models. The forecast models included ARIMA, Linear Regression, Polynomial Regression, Exponential Smoothing, Ridge, Lasso, SVR, and XGBoost. Forecast accuracy was validated using 2023–2024 data. A hybrid model, integrating the Lasso and Random Forest approaches via weighted averaging, was developed to enhance forecast robustness. Pearson correlation was applied to quantify the impact of key socioeconomic variables—such as population, GDP, and university graduates—on energy consumption patterns. Forecast comparisons revealed that Random Forest and XGBoost produced results closest to the Hybrid model, with deviation rates of 1.84–7.27% and 0.03–1.08%, respectively. In contrast, Polynomial Regression and Exponential Smoothing showed significant biases, with deviations reaching up to 61.58% and 54.48% in 2030. ARIMA remained relatively consistent but exhibited increasing deviation over time. The Exponential and Polynomial models consistently overestimated demand, while SVR underestimated it throughout the forecast horizon. Ridge Regression provided stable but systematically higher forecasts. The findings indicate that the hybrid model provides a balanced forecasting structure and mitigates the under- or overestimation tendencies observed in singular models. This research supports strategic, data-driven energy planning in alignment with long-term sustainability goals. Full article

This article highlights the nature and extent of challenges faced by students from refugee backgrounds in Australian higher education, and suggests potential cultural, institutional and policy reforms to meet these challenges. People from refugee backgrounds are less likely than other Australians to access higher education and often face barriers across and beyond the student life cycle. These issues include highly unequal graduate outcomes, resulting from factors such as unconscious (and conscious) employer bias and limited social networks. However, while national census data confirm relatively poor access rates and graduate outcomes, most people from refugee backgrounds have historically been subsumed under a broader non-English speaking background (NESB) category within higher education statistics. This approach has served to mask inequities and create a largely invisibilized group of under-represented domestic students. Improving access and outcomes will require a greater focus on collection and publication of equity data, more targeted institutional policies across the life cycle, and effective advocacy. Cultural change is also required for universities to better identify, recognize, and reward diverse forms of capital possessed by students from refugee backgrounds. Equally, effective advocacy could include allyship with the original displaced people in Australia, Aboriginal and Torres Strait Islander people, whose own voices are increasingly centered and central to reform of Australian higher education. Full article

Bridging the gap between theoretical heat exchanger analysis and physical intuition remains a persistent challenge in engineering education, particularly when students are confronted with real-system effects such as pressure losses, measurement uncertainty, and deviations from simplified models. This work addresses this challenge through the coupled development of a pedagogical framework and an experimental platform. A modular heat exchanger test bench was conceived, designed, and constructed by graduate students within a structured project-based learning environment, in which competitive and cooperative phases were combined to emulate real engineering practice. This approach positions the test bench not only as a laboratory tool, but as the outcome of an active learning process that integrates system design, instrumentation, and modeling. The resulting platform enables the comparative study of multiple heat exchanger technologies—including three water-to-water heat exchangers (plate, shell-and-tube, and double-pipe) and one air-to-water fin-and-tube heat exchanger—under parallel, counterflow, and crossflow arrangements across a wide range of operating conditions. Comprehensive instrumentation (temperature, flow rate, and pressure measurements) supports rigorous energy balance analysis, effectiveness evaluation, and hydraulic performance assessment. Beyond undergraduate experimentation, the test bench provides a framework for advanced learning objectives, including uncertainty propagation, $\epsilon$-NTU analysis, model development, and experimental validation. The confrontation between model predictions and experimental data, including observed discrepancies, is shown to play a central role in developing critical engineering judgment. The proposed approach demonstrates how the integration of project-based learning with a reconfigurable experimental platform can create a sustainable and scalable environment for heat transfer education. Full article

Engineering graphics and technical drawing represent the primary formal communication medium of manufacturing systems, conveying dimensional specifications, tolerances, surface conditions, and geometric intent across the full production chain. Despite their central role in industrial communication, persistent deficiencies in graduate CAD literacy and standards compliance are associated with reported production consequences, yet empirical, multi-stakeholder evidence documenting these associations remains scarce. This study investigates the engineering graphics competency gaps observed in recent engineering graduates and documents their perceived consequences for industrial performance across three national manufacturing contexts. A dual-phase methodology was employed: first, a structured review synthesizing peer-reviewed sources from Scopus, Web of Science, and ERIC; and second, a cross-national mixed-methods case study conducted in Türkiye, Austria, and Hungary, engaging 632 participants, 444 students, 100 educators, and 88 manufacturing and design professionals through validated multi-group questionnaires analyzed via descriptive statistics, chi-square tests, and thematic content analysis (Cohen’s κ = 0.82). Among the 88 industry respondents surveyed, 79.55% reported having experienced production delays attributable to drawing-related errors, 73.86% reported scrap generation, 78.41% reported rework costs, and 72.73% reported material wastage as perceived consequences of graduates’ technical drawing deficiencies. Graduate proficiency in dimensional tolerancing was rated adequate by only 26.14% of industry respondents, and standards/symbol compliance by merely 14.77%. Spatial visualization, ISO/GD&T literacy, and production-ready documentation were consistently identified as the most critically underdeveloped competencies. These findings establish a multi-stakeholder, cross-national evidence base documenting reported associations between engineering graphics instruction quality and manufacturing performance and provide actionable criteria for standards-aligned curriculum co-design between academia and industry. Full article

This study investigates the educational potential of Large Language Models (LLMs) for automating rule-checking tasks in Building Information Modeling (BIM) instruction. A quasi-experimental classroom implementation was conducted over two consecutive semesters with 55 graduate students in a Construction Management program. In Fall 2024, students were taught manual rule-checking techniques, whereas in Spring 2025, students received additional instruction in LLM-based prompting and Python code generation for automated compliance checking. A mixed-methods evaluation was conducted using surveys, NASA Task Load Index ratings, assignment-based learning outcomes, and structured interviews. Compared with the manual-only cohort, the LLM-assisted cohort reported significantly lower mental, temporal, and frustration demands, as well as higher perceived time efficiency and overall effectiveness. The LLM-assisted group also achieved significantly higher performance in violation detection and method accuracy, although no significant differences were observed in code interpretation or reflective analysis. Qualitative findings further revealed both the efficiency benefits of AI-assisted automation and persistent challenges related to prompt refinement, debugging, and output validation. These findings suggest that LLMs can enhance BIM instruction when paired with structured pedagogical scaffolding to support critical oversight and novice learners. Full article

This study examines how hybrid AEC education can foster BIM-enabled construction workflow competencies in Sub-Saharan Africa, where digital limitations impact graduate readiness. A quantitative survey of 120 students and graduates yielded 81 valid responses (68% response rate). Data were analysed using descriptive statistics and Principal Component Analysis (KMO = 0.943; Bartlett’s test p < 0.001), revealing components that explained 77.65% of the variance. Respondents strongly supported hybrid learning, emphasising the importance of digital collaboration, curriculum reform, and equitable access to technology. However, institutional barriers such as limited digital readiness and inadequate BIM workflow training were also identified. These findings informed a competency-based framework linking hybrid delivery, digital infrastructure, curriculum modernisation, and learner capabilities to improved workflow proficiency. The study suggests that hybrid AEC education can help close digital skills gaps if supported by staff upskilling, accessible software, and workflow-focused pedagogy. Future work should validate the framework across contexts and track long-term learning outcomes. Full article

This study addresses national shortages in school-based mental health (SBMH) providers, particularly in rural and high-needs areas, by examining two innovative training models designed to expand the school psychology workforce. The Grow Your Own (GYO) program respecializes practicing educators in rural communities to become school psychologists, while the Dual-Credentialing Clinical Training (DCT) model integrates school psychology training with supervised clinical experiences, leading toward educational certification and state mental health licensure. Program evaluation data were used to assess early implementation, feasibility, and success of both programs. In the GYO program, nine educators completed training, with eight employed in rural schools one to two years post-graduation, and average supervisor ratings meeting or exceeding the program’s competency expectations across all ten domains. In the DCT program, five trainees completed internship, four earned provisional mental health licenses, two progressed to independent licensure, and four became certified school psychologists. Together, findings indicate that place-based respecialization can strengthen rural retention, while dual-credentialing can expand clinical capacity and funding flexibility, creating complementary training models to help grow the SBMH workforce. Continued scaling and evaluation may enhance access to comprehensive SBMH services for students in under-resourced settings. Full article