Search Results (6,290)

Game-based learning (GBL) has increasingly been recognized as a valuable approach for supporting student engagement, motivation, and skill development in educational settings. However, comparatively little research has examined analog gameplay within informal, co-curricular higher education environments. Grounded in Self-Determination Theory (SDT), this exploratory study examined students’ perceptions of their experiences at a university’s Academic Skills Centre’s (ASC) Game Café (not course-based), with particular attention to motivation, social engagement, and perceived academic skill development. Using a descriptive, cross-sectional survey design, data were collected from 44 student participants through a researcher-developed survey consisting of Likert-scale and open-ended questions. Quantitative data were analyzed using descriptive statistics, while qualitative responses were thematically coded. Internal consistency analyses demonstrated acceptable to strong reliability across survey scales (Q6 α = 0.70; Q7 α = 0.89). Findings indicated that students perceived a consistent association between the Game Café and positive emotional experiences, collaboration, peer interaction, and the perceived development of transferable skills such as communication, critical thinking, and problem-solving. Participants also described the café as a low-pressure and socially supportive environment that fostered motivation and informal learning beyond the classroom. Overall, the findings suggest that analog, co-curricular game-based environments may support students’ perceived engagement, belonging, and learning within higher education contexts. Full article

Accurate shelf-life prediction of perishable products remains challenging because quality deterioration involves multiple physicochemical changes that are not adequately captured by conventional univariate approaches. This study proposes a multivariate shelf-life prediction framework for sweet corn based on near-infrared (NIR) spectroscopy coupled with multivariate curve resolution–alternating least squares (MCR-ALS). NIR spectra were collected from sweet corn samples and analyzed using MCR-ALS to extract chemically interpretable concentration and spectral profiles. A total of 100 and 85 corn samples were used for model training and validation, respectively. The dominant MCR-ALS component showed strong correlations with total soluble solids, dry matter, and individual sugar contents (sucrose, glucose, and fructose), effectively describing the overall quality degradation process. Based on the zero-order kinetic model, the predicted shelf lives were 41.3, 11.0, and 8.9 days at 4, 13, and 25 °C, respectively. Arrhenius analysis of the MCR-ALS concentration profile yielded a temperature-dependent degradation rate with an activation energy of 54.05 kJ mol⁻¹ (R² = 0.8387). The practical applicability of the proposed framework was further examined using a separate harvest batch of sweet corn that underwent repeated non-destructive NIR measurements throughout storage. Overall, the proposed NIR–MCR-ALS framework provides a rapid, non-destructive, and chemically interpretable approach for shelf-life prediction and postharvest quality monitoring of perishable produce. Full article

Background/Objectives: Vitamin D deficiency (VDD) is a major global health concern. Although maternal VDD during pregnancy may influence metabolic health in offspring, previous studies have focused predominantly on male offspring, leaving its effects in females insufficiently characterized. This study aimed to comprehensively assess hepatic metabolic profiles in female offspring exposed to maternal VDD during gestation. Methods: Pregnant 129/Sv mice were fed either a control diet or a vitamin D-deficient diet throughout pregnancy. After weaning, female offspring were maintained on a normal diet and analyzed at 12 weeks of age following a 16 h fast. Hepatic metabolomic profiling was conducted using GC–MS/MS, followed by multivariate analysis. To evaluate the potential contribution of fasting, publicly available liver RNA-seq data from ad libitum-fed and 16 h-fasted mice (GSE130127) were also analyzed. Results: No overt metabolic abnormalities were detected between the groups. However, principal component analysis revealed differences in hepatic metabolic profiles between the Control and VDD groups. Levels of 4-aminobutyric acid and proline were significantly elevated in the VDD group. Pathway analysis revealed significant alterations in arginine and proline metabolism and purine metabolism, along with changes in pathways associated with amino acid and energy metabolism. Comparison with fasting-associated liver RNA-seq data revealed minimal overlap, although the potential influence of fasting cannot be completely excluded. Conclusions: Maternal VDD during pregnancy was associated with altered hepatic metabolic profiles in female offspring despite the absence of overt metabolic abnormalities. These findings suggest that maternal VDD may influence hepatic metabolism in female offspring and indicate that the potential long-term effects of maternal VDD on offspring metabolism warrant further investigation. Full article

Artificial intelligence (AI) is widely viewed as a technological foundation for organizational resilience. Yet firms may strategically exaggerate their AI-related narratives without corresponding substantive investment. This study examines whether such AI washing is associated with lower organizational resilience. We conceptualize AI washing as a narrative–investment misalignment within organizational systems, in which symbolic AI claims move ahead of substantive AI investment and capability formation. Based on Chinese A-share listed firms during 2010–2024, we develop a firm-level AI washing index by comparing firms’ within-industry ranking in AI disclosure with their within-industry ranking in actual AI investment. AI disclosure is identified from annual reports using a large language model, while actual AI investment is measured through AI-related software and hardware investments. Using double-debiased machine learning, we estimate a significantly negative association between AI washing and organizational resilience. Economically, a one-standard-deviation increase in AI washing is associated with a decline in organizational resilience equivalent to approximately 3.276% of the average annual change in organizational resilience. This estimated pattern remains stable when we employ alternative variable constructions, replace the machine learning algorithms, adjust the cross-fitting folds, use propensity score matching, and further apply a deep instrumental variable strategy. Mechanism tests based on organizational legitimacy provide evidence consistent with legitimacy-related transmission channels, suggesting that AI washing is associated with lower resilience through weakened pragmatic, moral, and cognitive legitimacy under the maintained mediation assumptions. Further analysis reveals an asymmetric pattern: firms whose AI narratives exceed actual investment experience lower resilience, whereas firms whose actual investment exceeds external narratives exhibit higher resilience. The negative estimated association is particularly evident in high-tech industries, enterprises with established bank-firm ties, and enterprises with higher educational heterogeneity in their top management teams. This study advances research on AI disclosure and organizational resilience by showing that symbolic AI narratives can signal system-level fragility when technological claims are misaligned with substantive capability formation. Full article

This article describes the design, development, and user acceptance evaluation of the Monitor CRIS UCSM, a research monitoring system (RMS) built on the Elsevier Pure REST API for the Universidad Católica de Santa María (UCSM), Arequipa, Peru. The study addresses two research questions: (RQ1) can an API-based CRIS monitoring solution be implemented on a commercial Research Information Management System (RIMS) to meet institution-specific regulatory requirements, and (RQ2) to what extent do institutional stakeholders perceive such a system as usable, normatively relevant, and useful for strategic decision-making? The system integrates bibliometric data from Pure into dashboards covering research production metrics, RENACYT-qualified researcher profiles, SDG alignment, and regulatory compliance indicators aligned with Peruvian higher education legislation (Law No. 30220, SUNEDU, CONCYTEC), implemented on a three-tier architecture (Laravel 11/Vue.js 3/MySQL 8.0) with role-based access control. A User Acceptance Test (UAT) conducted with 134 institutional stakeholders, using an instrument adapted from the System Usability Scale (SUS) and structured into four dimensions, showed excellent internal consistency (α = 0.90), a composite SUS-adapted score of M = 81.12 (SD = 5.83), and very favorable ratings across all four dimensions (global M = 4.32; SD = 0.14 on a 1–5 Likert scale). These findings, bounded to the UCSM institutional case, support the technical feasibility of API-based CRIS monitoring within a single institution. The decision-making utility reported here reflects internal stakeholder perceptions gathered through a single UAT administration, not measured organizational outcomes, external adoption, or longitudinal use. Full article

This paper examines how the emergence of over-parameterized artificial intelligence models and the phenomenon of double descent challenge the classical assumption that simpler models generalize better. Traditional predictive analytics relied on parsimonious models grounded in the bias-variance trade-off, where increasing complexity was expected to produce overfitting. However, recent advances in deep learning demonstrate that highly over-parameterized models can achieve superior generalization after surpassing the interpolation threshold. This paradigm shift has enabled systems such as AlphaFold, Aurora, Delphi-2M, and recommenders to model complex, high-dimensional relationships through contextual attention rather than global feature selection. The paper argues that higher education analytics remains largely reductionist, relying on limited variables such as GPA, demographics, and course completion rates to identify “at-risk” students. While interpretable, these approaches often fail to capture the dynamic and multidimensional nature of student success. In response, this study proposes a transition toward over-parameterized personalization, where students’ academic and behavioral histories are modeled as longitudinal high-dimensional sequences. Drawing parallels to commercial recommendation systems such as Amazon, Netflix, and YouTube, the paper explores how higher education can move from generalized early-warning systems toward adaptive “n-of-1” interventions. Importantly, the paper is conceptual rather than empirical: it develops a research agenda and a set of testable propositions, and it identifies the evaluation designs—temporally valid prediction protocols and causal intervention studies—by which the promise of over-parameterized personalization in higher education should be assessed before any claim of superiority can be made. Full article

Objectives: The aim of the present study was to analyze the relationships among physical activity, academic stress, and motivation in Physical Education, as well as to determine differences according to sex and grade level. Methods: A total of 310 secondary education students participated in the study. Data were collected using the PAQ-C, the Questionnaire on Academic Stress in Secondary Education, and the Motivation in Physical Education Questionnaire. Spearman’s correlation test and a MANOVA were used for the statistical analysis. Results: The findings revealed that physical activity was positively correlated with more self-determined forms of motivation and negatively correlated with amotivation. The dimensions of academic stress were positively associated with amotivation. Regarding sex differences, statistically significant differences were found in physical activity, with higher levels among males, and in academic overload, with higher values among females. According to grade level, differences were identified in academic overload, intrinsic motivation, and identified regulation. Furthermore, the sex × grade interaction was also statistically significant, showing that females reported higher levels of stress as they progressed through educational stages, whereas in males, levels of academic stress decreased and both physical activity and intrinsic motivation showed a differential increase in the final year of secondary education. Conclusions: In this regard, it is concluded that sex and grade level influence the variables analyzed. However, further research is needed to better understand these dimensions, their impact, and their interrelationships, considering the potential benefits of promoting self-determined motivation in Physical Education and encouraging regular physical activity practice, particularly in reducing and coping with academic stress, with the aim of comprehensively improving the physical and mental well-being of the adolescent population and contributing to their healthy and balanced development. Full article

The Australian construction industry is currently navigating a productivity challenge in which workforce skills shortages represent a contributing factor. Historically, attempts to quantify this deficit have relied upon subjective surveys or ethically complex data collection methodologies, lacking the objective scale required for policy reform. To bridge this methodological gap and contribute to the literature with a current skills alignment analysis, this research introduces a natural language processing (utilising SBERT)-driven computational approach to explore the alignment between industry demand and educational supply. A sample of 3468 industry job advertisements from 2016 to 2018 and in 2026 were mapped against a weighted national educational supply vector (72.5% Vocational Education and Training, 27.5% Higher Education) using the standardised Australian Skills Classification taxonomy. The analysis indicates that curriculum–industry misalignment is a potential structural contributor to the skills gap in the sampled construction skills gap. Cosine similarity and Spearman’s rank analyses exposed a misalignment of skills priority by academia (ρ_top20 = 0.32 and ρ_global = 0.43). While the education sector dedicates nearly 90% of its combined curriculum to specialist tasks skills, these skills showed a misalignment (60%) with rapidly shifting industry demands. Furthermore, while explicit demand for technology tools skills remained stagnant, a misalignment (54%) quantitatively reflects the documented industry concerns regarding digital readiness caused by a lag in digital-skills training. Ultimately, this study contributes a data-driven methodology and a current skills gap analysis that enables educational policymakers to conduct agile, computation-driven curriculum audits. This supports more frequent and evidence-informed curriculum review in response to the evolving Australian construction industry skill demands. Full article

This study examines whether higher education development (HED) in China remains geographically locked in a core–periphery structure over 2002–2021 despite long-standing policies promoting balanced development. Using a composite assessment of HED and multiple spatial-inequality diagnostics, we identify strong persistence in the national spatial hierarchy and limited restructuring over time. Results indicate: (1) a stable top tier concentrated in a small set of leading cities, with the top-ranked cities exhibiting a stability index above 0.90, alongside a persistent lagging group; (2) an education-quality “center of gravity” anchored along the North China–East China axis, with a reduced migration distance from 76.58 km to 16.63 km, indicating only modest cross-regional movement; (3) pronounced clustering in major eastern coastal urban agglomerations, contrasted with a broader and weaker distribution in the west; and (4) regionally differentiated constraints—HED in eastern China is jointly driven by economic scale, market demand, and innovation investment, while the central region exhibits no stable dominant driver, the western region remains primarily constrained by economic scale and consumption conditions, and the northeastern region is increasingly shaped by demographic structure and consumption dynamics. These findings indicate that the spatial differentiation of HED is jointly shaped by economic, demographic, industrial, and innovation-related factors. For sustainability-oriented governance, the findings suggest that narrowing gaps requires moving beyond scale expansion toward targeted capability building, cross-regional collaboration, and institutional reforms that improve peripheral regions’ capacity to convert inputs into sustained quality and outcomes. Data granularity remains a limitation; future research should integrate micro-level data and comparative evidence. Full article

Background/Objectives: The Bologna Process and the European Higher Education Area consolidated the transition of nursing education in Spain from diploma-level training to a university degree framework. Although previous research has examined newly graduated nurses’ transition to practice, less attention has been paid to how key informants involved in educational reform interpret its long-term professional implications, including their potential relevance for nurses’ well-being. This study explored key informants’ perspectives on the implications of the Bologna Process and the Nursing White Paper on nursing education and professional development in Spain. Methods: An exploratory qualitative study was conducted using in-depth semi-structured interviews with six key informants involved in nursing educational reform in Spain. Interviews were conducted between June and November 2025, audio-recorded, transcribed verbatim, and analyzed using reflexive thematic analysis. Results: Three main themes were identified: (1) academic professionalization and the generation of professional expectations; (2) structural misalignment between university-based professionalization and clinical practice; and (3) Reality Shock, professional identity and nurses’ well-being. Conclusions: From the perspective of key informants, Reality Shock may be understood not only as an individual transition difficulty, but also as a possible expression of structural misalignment between academic professionalization and healthcare organizations. These findings suggest that strengthening nurses’ well-being may require organizational strategies that translate educational reform into professional recognition, autonomy, leadership opportunities, and coherent career development. Full article

Higher education plays a crucial role in equipping citizens to tackle contemporary socio-ecological challenges. However, little research has examined how different generations of university students understand socioecological transitions, or the implications of these differences for sustainability education. This study compares the perceptions of older learners (aged 55–70) enrolled in the ‘Classrooms of Experience’ programme with those of undergraduate students (aged 18–28) from the Faculty of Economics and Business at the University of the Basque Country (UPV/EHU). Qualitative data were collected from approximately 250 participants during the 2023/24 and 2024/25 academic years. The data were analysed using the Grid Elaboration Method and the IRaMuTeQ (Version 0.8 Alpha 7) text analysis tool to identify semantic structures, symbolic associations and patterns of meaning across the two age groups. The findings reveal significant generational differences in understanding socioecological transitions. Older learners tend to frame transitions as regulated processes linked to institutional action, public policy, welfare, and quality of life. In contrast, younger students interpret socioecological transitions as responses to interconnected ecological and social crises, emphasising socioecological justice, responsibility, sustainability, and technological innovation as key drivers of transformation. These results suggest the coexistence of complementary yet distinct socioecological imaginaries within the university context. The study highlights the pedagogical value of intergenerational dialogue and learning in higher education. By bringing together diverse perspectives on sustainability, universities can promote more critical, reflective, and transformative educational processes that are capable of addressing the complex challenges of contemporary socioecological transitions. Full article

Background: Family caregivers of children with autism spectrum disorder (ASD) increasingly utilize large language models (LLMs) for health information. This study presents a systematic comparative evaluation of three widely used LLMs as localized ASD health information tools in Saudi Arabia. Methods: Twenty-four clinically validated, caregiver-oriented questions were posed to Google Gemini 1.5 Pro, OpenAI ChatGPT (GPT-4o), and DeepSeek-V3 using a standardized prompt. Three expert raters independently evaluated responses across four dimensions: scientific accuracy, PEMAT-P understandability, PEMAT-P actionability, and neurodiversity (ND)-affirming language. Readability was assessed via Flesch–Kincaid Grade Level (FKGL) and SMOG indices. Non-parametric Kruskal–Wallis tests with post hoc Mann–Whitney U comparisons and one-sample t-tests were applied. Results: Gemini achieved the highest mean accuracy (2.96/3.00), significantly outperforming DeepSeek (p = 0.003, r = −0.37). Accuracy failures across all LLMs clustered on regional epidemiological, genetic risk, and financial inquiries. ChatGPT achieved significantly higher understandability than Gemini (p < 0.001, r = 0.55), while DeepSeek achieved significantly higher actionability than Gemini (p < 0.001, r = 0.59). However, all three LLM scores fell short of the Agency for Healthcare Research and Quality (AHRQ) 80% actionability benchmark (all p < 0.001). All LLMs exceeded patient education readability benchmarks (FKGL ≤ 6, SMOG ≤ 8; all p < 0.001); ChatGPT was the most readable (FKGL = 7.86; SMOG = 9.97) and Gemini the most complex. No model differed significantly on ND-affirming language, defaulting to a mixed medical-affirming register. Conclusions: Evaluated LLMs demonstrated distinct, specialized strengths: Gemini was the most accurate, ChatGPT the most readable, and DeepSeek the most actionable. Importantly, all models failed to meet established consumer education standards for readability and actionability. LLMs require extensive plain-language adaptation and cultural customization. Clinicians must guide families on navigating LLM outputs, particularly concerning country-specific epidemiological, economic, and healthcare service queries. Full article

The integration of artificial intelligence (AI) into higher education assessment has prompted growing interest in how students perceive and prefer AI involvement in scoring and feedback. While prior research has largely focused on technical performance and accuracy, this study aims to fill a gap in the literature by examining students’ preferences and perceptions regarding AI scoring and feedback, with particular attention to context, assignment stakes, and post-evaluation reflections. Ninety-three undergraduate students completed a survey consisting of Likert-type items, scenario-based questions, and an activity in which they generated AI-based scoring and feedback using ChatGPT. Results showed that students preferred structured, moderately detailed AI feedback, particularly for grammar and organization, but generally favoured human evaluation, especially for subjective tasks. While AI was seen as useful in lower-stakes contexts, concerns remained about its ability to assess more complex aspects of writing. Participants expressed a strong preference for hybrid approaches in which AI augments rather than replaces human judgment, along with a need for transparency and opportunities for human review. Collectively, these findings highlight that undergraduate students’ preferences are highly context-sensitive and role-specific, underscoring the importance of student-centred implementation strategies of AI in higher education. Full article

The contemporary educational system places complex demands on educators, emphasizing the importance of pedagogical-didactical competencies and teacher self-efficacy alongside subject-matter expertise. The purpose of this study was to examine how future STEM educators assess their own perceived pedagogical-didactical capabilities for direct teaching and classroom management through the construct of teacher self-efficacy, examining variations by gender, student cohort, and academic major. The research was conducted in autumn 2025 using a near-census sample of graduate teaching track students at the Faculty of Science in Split, utilizing the Norwegian Teacher Self-Efficacy Scale. The descriptive and non-parametric results indicate that future teachers express higher baseline scores in the domains of instruction and coping with changes, whereas the lowest self-assessment was recorded regarding the adaptation of teaching to individual student needs. While gender and student cohort demonstrated no statistically significant impact on overall self-efficacy, a significant difference was determined in the domain of maintaining discipline with respect to the study major, with Informatics and Technology students exhibiting the greatest sense of confidence. In conclusion, the findings highlight that while students are well-prepared for content delivery, there remains a critical need to enhance initial teacher education programs by introducing practical tools for managing challenging student behaviors and working within heterogeneous classrooms. Full article

The use of AI-assisted feedback systems has steadily increased in higher education, yet there has been limited research as to which components of expert feedback can be supported by AI and which require continued human judgment. This issue is particularly important in professional training domains where feedback must rely upon contextual interpretation, source–output comparison, and domain expertise. Accordingly, this study analyzes instructor-generated feedback in TalkTrack, an automatic speech recognition (ASR)-supported digital platform for interpreter training, as a basis for human-in-the-loop AI feedback design. The dataset consists of 44 Korean–Japanese, Japanese–Korean, and Chinese–Korean interpreting performances produced by 12 graduate students and includes 931 feedback tags and 756 substantive qualitative comments manually assigned by 10 instructors. The categories most frequently assigned were expression and translation-error feedback, while corrective feedback was the dominant instructional function. Further, feedback behavior varied substantially by instructor and feedback category, indicating that different types of feedback require different levels of human intervention and system support. Accordingly, this study proposes a human-in-the-loop feedback architecture employing ASR and large language models to support detection, organization, retrieval, and suggestion generation, while instructors retain responsibility for source-text fidelity, discourse coherence, pedagogical judgment, and final feedback validation, from which this study proposes empirical design requirements for scalable AI-supported feedback systems in professional e-learning environments. Full article