Search Results (696)

Background/Objectives: Intimate partner abuse (IPA) is common among university students, including nursing students, and is linked to posttraumatic stress symptoms. Accessible online psychosocial interventions are needed to reduce trauma-related symptoms and support posttraumatic growth (PTG). This study examined the effects of an online psychosocial intervention grounded in social learning theory and cognitive behavioral therapy on posttraumatic stress symptoms and PTG among nursing students who experienced IPA in Türkiye. Methods: A randomized controlled trial was conducted among nursing students in Türkiye reporting IPA exposure. Participants were randomly assigned to an intervention group (n = 17) or a control group (n = 18). The intervention group received an eight-session online psychosocial program delivered individually. Assessments were conducted at pre-intervention, post-intervention, and at 1-, 3-, and 6-month follow-ups. Repeated-measures ANOVA was used, and partial eta-squared (ηp²) values were calculated. Results: The intervention group showed significant reductions in posttraumatic stress symptoms compared with the control group, with large effect sizes (p < 0.001; ηp² = 0.402–0.676). Furthermore, significant increases were observed in posttraumatic growth, indicating large and sustained effects over time (p < 0.001; ηp² = 0.515–0.773). Conclusions: The online psychosocial intervention effectively reduced posttraumatic stress symptoms and enhanced posttraumatic growth among nursing students who experienced IPA. However, results should be interpreted with caution due to the small sample size, and future studies with larger samples are warranted. Full article

Escalating environmental challenges have increased interest in understanding pro-sustainability behaviours (PSBs) within the built environment. Grounded in the Theory of Planned Behaviour (TPB) and the Value–Belief–Norm (VBN) Theory, this study maps the global PSB research landscape and examines how cognitive, normative, and moral behavioural determinants are conceptualised. Employing the PRISMA framework and scientometric analysis using VOSviewer, the study analysed 22 key publications sourced from multiple academic databases. The findings indicate a steady growth in PSB research since 2017, with substantial contributions from Asia, particularly Malaysia and China. However, the literature remains theoretically fragmented, with limited integration of established behavioural frameworks. Eight categories of PSBs were identified, demonstrating how TPB constructs (attitudes, subjective norms, perceived behavioural control) and VBN constructs (values, beliefs, personal norms) are reflected in stakeholder practices across the built environment lifecycle. The findings highlight conceptual gaps, notably the underutilisation of hybrid behavioural models, and emphasise the need for future research that enhances theoretical integration, interdisciplinarity, and geographical diversity. The study provides evidence-based insights to support policy, education, and industry efforts to strengthen PSBs in the built environment. Full article

Internet Gaming Disorder (IGD) has become a major behavioral health concern among adolescents, yet current assessment tools remain limited. These tools often fail to capture the disorder’s complex symptom variations and lack clinical interpretability. This study, taking an interdisciplinary approach that combines clinical psychology and psychometrics, summarizes recent progress in understanding adolescent IGD and the development of its assessment methods. We compare the diagnostic criteria of the DSM-5 TR and ICD-11 and argue that the nine DSM-5 TR criteria are particularly suited for transformation into distinct diagnostic attributes due to their detailed and actionable nature. We then review the strengths and weaknesses of Classical Test Theory (CTT), Item Response Theory (IRT), and Cognitive Diagnostic Models (CDMs) in assessing IGD. The review emphasizes the limitations of total-score and single latent-trait approaches in capturing the disorder’s multidimensional symptoms. Based on these insights, we propose a conceptual assessment framework, Cognitive Diagnosis Computerized Adaptive Testing (CD-CAT), that integrates CDMs with computerized adaptive testing. Rather than presenting an empirically validated system, this framework offers a theoretically grounded proposal that specifies the key components, logical relationships, and methodological pathways necessary for advancing precision assessment of adolescent IGD. CD-CAT uses a system of attributes and a Q-matrix based on the DSM-5 TR criteria to efficiently classify IGD symptoms in adolescents, reducing the number of items required while enhancing clinical relevance. Lastly, we discuss the theoretical contributions of the proposed framework, acknowledge its limitations as a conceptual proposal, and outline directions for future empirical research. Full article

Spatial qualities are central to architectural reasoning; yet, in studio-based education, they often remain implicit rather than structured as a shared analytical framework. This study examines how a multi-scalar taxonomy of spatial qualities can function as a collaborative analytical language in studio-based architectural education. Situated in Košanćićev venac and Dorćol, two historically layered areas of Belgrade’s old town, this study integrates expert spatial analysis with a student questionnaire administered across bachelor and master study levels. Empirical testing was conducted to evaluate structural coherence, conceptual differentiation and the distribution of spatial qualities across detail, architectural and urban drawing scales. The findings indicate consistent internal stability, clear differentiation among constructs and statistically significant cross-scale articulation. Form- and composition-related qualities showed high usability, while interpretative constructs were more variable. Master-level students demonstrated greater engagement with cognitive and interpretative constructs, indicating a shift toward more conceptually grounded design reasoning without affecting overall structural coherence. These results suggest that spatial qualities can operate as a level-independent analytical language, supporting inclusive participation, shared interpretation and structured dialogue within the design studio. By positioning spatial qualities as a collaborative pedagogical framework, this study contributes to interdisciplinary communication and more equitable engagement in architectural education. Full article

In Vietnamese primary mathematics education, geometry instruction often emphasizes rote calculation and formula memorization rather than meaningful contextualization, leaving students disconnected from abstract concepts and lacking opportunities to connect learning with cultural identity. This quasi-experimental study investigates how integrating generative AI tools (ChatGPT, DALL·E, Canva) with the culturally grounded Vietnamese folktale Bánh Chưng—Bánh Giầy can support Grade 5 students’ understanding of circle geometry. Employing a mixed-methods design with 30 students divided into experimental (AI + storytelling) and control (traditional instruction) groups, the study measured cognitive and affective learning outcomes through pre/post-tests, a validated 25-item questionnaire, interviews, and classroom observations. Quantitative results revealed significant improvements in the experimental group across all measured dimensions, learning interest, attentional focus, conceptual understanding, mathematics passion, and cultural preservation awareness, with large effect sizes. Qualitative findings confirmed enhanced engagement, multimodal conceptual clarity, and cultural affective resonance. The study demonstrates that low-cost, teacher-mediated generative AI can effectively support learning in resource-constrained primary settings when anchored in local narratives. Implications for ethical AI integration and teacher professional development in Vietnamese contexts are discussed. Full article

Achieving urban climate neutrality and interim mitigation targets requires rapid demand-side emission reductions, yet current user-centred interventions remain fragmented, are often concentrated on low-impact actions, and rarely provide a traceable basis for comparing outcomes, validity conditions, and equity implications across contexts. This paper reframes demand-side mitigation as a design problem of “use infrastructures”: integrated configurations of communication, product-technology, services, interaction, and governance that make low-carbon choices practicable within everyday routines. We introduce the Design Evidence Link (DEL) as a traceability device supporting ex ante configuration (selection and orchestration of levers) and ex post verification (monitoring, attribution of outcomes, and trade-off control). Through a design-led comparative analysis of nine international cases in high-impact sectors (household energy, ground mobility, food systems, and circular economy/materials), we derive and consolidate a shared extraction and coding protocol that links determinants (barriers and enablers) to design requirements and decision-grade metrics (carbon impact, adoption, continuity, and equity), explicitly qualifying uncertainty and evidence levels. Cross-case results show that effective interventions rely less on isolated information and more on coordinated action packages that reduce cognitive and economic frictions, enhance data credibility through standards and accountability, and embed follow-up mechanisms that support behavioural continuity. DEL also surfaces recurring validity conditions and failure modes (digital exclusion, trust erosion, rebound, and lock-in), translating them into operational criteria for policy and design. Compared with behaviour-change or theory-of-change framings, DEL focuses on the observable orchestration of integrated conditions of use and on the explicit grading of evidence. It should therefore be read as a structured analytical–operational framework for ex ante and ex post assessment, whose transferability remains conditional on source quality, contextual prerequisites, and the limits of the selected cases. Full article

Detecting unnatural pauses is a critical component of automated quality assessment (AQA) in interpreter training, as pause patterns directly reflect an interpreter’s cognitive load and fluency. Traditional pause detection methods rely on static temporal thresholds (e.g., 1.0 s), which often fail to account for segment-specific speech rate variability and individual speaking styles. This study proposes a context-adaptive pause detection framework that integrates unsupervised anomaly detection using Isolation Forest (iForest) with a sliding window technique. To enhance pedagogical validity, we specifically focused on intra-sentential pauses by delineating sentence boundaries using a specialized segmentation model. The proposed model was evaluated against ground-truth labels annotated by professional interpreting experts. Our results demonstrate that the sliding window–based contextual anomaly detection model significantly outperforms the conventional static baseline, particularly in terms of recall and Cohen’s kappa. Furthermore, by applying a weighted F3-score and the “Recognition-over-Recall” principle, we confirmed that the proposed model substantially reduces the instructor’s total operational burden by shifting the workload from de novo annotation creation to more efficient corrective pruning. These findings suggest that speech-adaptive modeling provides a more reliable and labor-saving framework for automated interpreting assessment and feedback. Specifically, this study makes three main contributions: (1) the proposal of a context-adaptive pause detection framework using anomaly detection, (2) the integration of sliding window–based local contextual modeling for speech-rate–aware analysis, and (3) the introduction of an evaluation strategy based on the Recognition-over-Recall principle to reduce instructor workload in interpreter training. Full article

The rapid development of Generative Artificial Intelligence (GenAI) in graduate education has changed human–AI interaction within knowledge-intensive environments, leading to important questions about user-side cognitive adaptation in probabilistic AI systems. While many studies focus on ethical implications, limited attention has been paid to the cognitive mechanisms underlying graduate students’ adoption of GenAI. Drawing on the Technology Acceptance Model (TAM), this study explores the cognitive and interactional mechanisms shaping graduate students’ adoption and usage of GenAI. Using thematic analysis of in-depth interviews with 20 graduate students from diverse academic backgrounds, the study identifies seven interrelated constructs: perceived usefulness, perceived ease of use, external environment, risk perception, attitude, behavioral intention, and interaction subjectivity. This study demonstrates that the adoption of GenAI is not merely a result of perceived efficiency but is shaped by cognitive calibration between trust and risk evaluation. Moreover, interaction subjectivity emerges as a metacognitive factor that determines whether engagement results in human–AI collaboration or passive automation. By integrating external environment, risk perception, and interaction subjectivity, this study provides a cognitively grounded framework for understanding human–AI adoption and interaction dynamics. Practically, the findings provide design-relevant insights for developing GenAI systems that support calibrated trust, uncertainty awareness, and adaptive cognitive participation. Full article

Multimedia learning requires optimising information presentation in line with learners’ cognitive processes. Active processing depends on selecting relevant information, organising it into coherent mental representations, and integrating it with prior knowledge. When grounded in instructional design principles (cognitive load management, active learning, and alignment with learning objectives), video animations can be highly effective. In teaching complex content, visual strategies such as examples, analogies, images, infographics, and videos help make abstract or invisible concepts accessible. This review examines research on animations in formal education, noting its rapid growth (partly driven by AI), STEM predominance, and mixed findings on learning gains and persistent biases. Full article

Problem-based learning (PBL) relies on the problem as the generative trigger for inquiry, collaboration, and assessment, yet school-based reports often provide limited guidance on how problems are actually designed. This PRISMA-informed review analyzed 24 PBL cases published in Korea Citation Index (KCI) journals (2020–2025) to characterize enacted problem design in Korean K–12 settings. Using a literature-grounded framework covering authenticity, cognitive demand, collaboration, and pedagogical alignment, five expert coders conducted calibration and consensus coding of 12 elements on a 0–3 rubric. The findings showed that Perspective Integration, Analysis Requirements, and Assessment Opportunities were most strongly represented, whereas Curriculum Integration and Cognitive Conflict were less frequently advanced. Correlation and triad analyses further indicated recurring design patterns centered on collaboration and authenticity, while cognitively destabilizing configurations were rare. These results suggest that Korean K–12 PBL tends to emphasize socially supported, contextually meaningful inquiry. By contrast, the rarely observed cognitive-demand triad suggests a tendency to avoid problems requiring strong conceptual destabilization and deep analysis. These findings identify context-bounded design patterns and offer practical guidance for designing PBL problems that better balance socio-cognitive support, conceptual challenge, and curricular coherence. Full article

Global urbanization has long been hampered by the “metrocentric priority” paradigm, with small towns—core hubs for urban–rural integration—severely undervalued in practical value. Amid China’s transition to high-quality urban–rural integration, unbalanced small town development has become a critical bottleneck for county-level factor flows, demanding systematic research to unlock their strategic value and resolve urban–rural dual predicaments. Existing studies suffer from scientific gaps including unidirectional linear cognition, insufficient complex system thinking, and weak interpretation of regional heterogeneity, remaining at the stage of static correlation description and failing to reveal the two-way reciprocal feedback logic between small towns and urban–rural integration. Meanwhile, the application of complex system theory in urban–rural research is still confined to theoretical narratives, which hinders the advancement of research from descriptive analysis to mechanism interpretation. Taking Henan Province (a typical agricultural and populous province reflecting China’s urban–rural development) as a case, this study builds a “local emergence–global synergy” framework based on complex system theory, establishes a dual indicator system for small towns’ multidimensional performance and county-level urban–rural integration, and integrates spatial statistical analysis, bidirectional regression and coupling coordination models to explore their cross-scale spatiotemporal evolution and reciprocal feedback during 2019–2023. Findings show the following: (1) The multidimensional performance of small towns presents a pattern characterized by polarized expansion of high-value regions and overall improvement of low-value regions, while county-level urban–rural integration evolves into a polycentric structure featured by central dominance and southern growth. (2) There is a significant two-way asymmetric relationship between small towns’ multidimensional performance and county-level urban–rural integration: the positive effect is significantly stronger than the reverse effect, and both direct impacts are significantly weakened after introducing economic variables, indicating that economic development serves as a key transmission channel. (3) The coupling mechanism presents three evolutionary paths with pronounced core–periphery spatial heterogeneity. Grounded in complex system theory, this study constructs a systemic analytical framework of “local emergence of small-town subsystems and global synergy of county-level systems”, verifies the core proposition of two-way interactions between subsystems and the overall system in the urban–rural complex giant system, and enriches the localized application of complex system theory and the urban–rural continuum theory in traditional agricultural regions of China. This study provides a foundational empirical paradigm for the in-depth exploration of nonlinear characteristics and threshold effects in future research. It offers theoretical support for policy formulation of county-level urban–rural integration in traditional agricultural regions of China, and it provides Chinese experiences for the Global South with similar contexts to explore inclusive urbanization pathways, promoting cross-cultural dialogue and practical transformation of urban–rural integration theory. Full article

Large language models (LLMs) are increasingly used by retail traders to interpret information and design complex strategies, yet existing adoption constructs do not capture the decision-time experience of being cognitively scaffolded by an LLM. We define Perceived Cognitive Assistance (PCA) as the trader’s felt expansion of cognitive capability at the moment of a trading decision when an LLM is available, and we report initial content validation of a PCA item pool. Study 1 specified the PCA content domain using a two-tier qualitative corpus (eight interviews and 44 YouTube narratives on LLM-assisted trading, plus 24 qualitative and mixed-method studies on robo-advice and social trading). Reflexive thematic analysis yielded five facilitative assistance facets and one adjacent risk facet (over-reliance), and these were translated into a 16-item PCA pool. Study 2 used a naïve-judge sort-and-rate task with 48 retail traders to test whether items show definitional correspondence to PCA and definitional distinctiveness from similar constructs: perceived usefulness, perceived ease of use, trust in the LLM, and trading self-efficacy. The resulting nine-item set is ready for subsequent factor-analytic and predictive validation. This study advances our understanding of how large language models shape retail trading behaviour by identifying and empirically grounding Perceived Cognitive Assistance as the decision-time psychological experience through which LLMs cognitively scaffold traders, clarifying how LLM use differs from generic technology adoption, trust, or self-efficacy effects. Full article

This article examines how algorithmic mediation reshapes social trust and solidarity in the post-secular age. Historically grounded in shared moral horizons shaped by religion, tradition, and communal practices, trust has increasingly been displaced by technocratic governance, market rationality, and algorithmic systems that mediate work, cognition, communication, and political life. Through a critical analysis of contemporary developments—including algorithmic labour management, neurotechnology, large language models, digital public spheres, technological sovereignty, and global AI governance—the article argues that algorithmic mediation intensifies the fragility of trust by instrumentalizing human agency, fragmenting public reason, and concentrating power within opaque technological infrastructures. Against technological determinism and purely procedural approaches to ethics, the article advances a normative framework rooted in solidarity and the common good. Drawing on post-secular perspectives, a retrieval of natural law normativity, and the resources of Catholic Social Teaching, it contends that trust cannot be sustained through efficiency, prediction, or regulation alone. Instead, social trust depends upon relational goods—dignity, responsibility, participation, and truth—that resist reduction to data-driven optimization. Reclaiming solidarity therefore requires re-embedding AI within moral horizons capable of guiding technological development toward integral human flourishing. In this sense, the governance of AI emerges not merely as a technical challenge but as a decisive moral and political task for post-secular societies. Full article

Research on academic achievement highlights the combined role of cognitive abilities and motivational beliefs. Grounded in the CHC framework, this study examined how three broad cognitive abilities—verbal, numeric, and spatial—and academic self-concept jointly predict achievement in Portuguese and mathematics. A sample of 3034 students from the third cycle (grades 7–9) and secondary education (grades 10–12) completed the BAC-AB cognitive battery and a validated academic self-concept scale. Using multigroup structural equation modelling, we tested whether the predictive patterns differed across educational stages. Academic self-concept emerged as the most consistent predictor across subjects and levels. Cognitive contributions displayed clear developmental differentiation: verbal ability was more strongly associated with Portuguese (and increasingly with Mathematics) in secondary education, whereas numeric and spatial abilities were comparatively more relevant for Mathematics in the third cycle. These patterns support the view that linguistic, quantitative, and visuospatial processes contribute to achievement in distinct and developmentally sensitive ways. Overall, the findings underscore the importance of instructional approaches that build on quantitative and spatial strengths in earlier grades while progressively supporting advanced verbal comprehension and reasoning in later schooling. Full article

(1) Background: While anticipatory anxiety is well-established in the psychological literature, the specific phenomenon of distress experienced during waiting for positive, rationally certain outcomes remains under-theorized and clinically under-recognized. (2) Methods: This paper presents a conceptual analysis and theoretical proposal introducing ‘Waiting Anxiety,’ defined as a hypothesized pattern of anticipatory distress characterized by heightened cognitive rumination, physiological arousal, and emotion regulation failure during periods of delayed resolution, specifically when the awaited outcome is positive and rationally certain (e.g., an approaching wedding, confirmed promotion, or approved visa). (3) Results: Distinct from traditional anticipatory anxiety tied to threat perception, waiting anxiety is proposed as a paradoxical form of distress that emerges despite primary outcome certainty. The construct is theoretically grounded in emotion regulation failures, temporal perception distortions, and impatience mechanisms, and is illustrated through five clinical cases. (4) Conclusions: This paper argues for waiting anxiety as a hypothesized psychological pattern warranting empirical investigation. Future psychometric, epidemiological, and neurobiological research is needed to establish its validity, prevalence, and clinical utility. If validated, integration into clinical frameworks could improve understanding of affective experience during positive life transitions. Full article