Search Results (3,601)

Open Educational Resources (OER) are widely promoted as mechanisms for expanding access to knowledge and supporting sustainability in higher education. Yet their long-term viability remains constrained by fragmented governance, unstable funding arrangements, weak faculty incentives, policy gaps, and uneven digital infrastructure. This article develops a conceptual and policy-oriented framework that reconceptualizes OER as sustainable knowledge commons embedded within higher education systems rather than merely repositories of open content. Using an integrative review and thematic synthesis of global scholarship on OER sustainability, commons governance, and higher education policy, the study identifies four interrelated governance dimensions: institutional embedding, participatory stewardship, equitable access and inclusion, and long-term resource sustainability. The analysis shows that sustainable OER ecosystems depend not only on open licensing and technological platforms but also on coherent policy design, institutional alignment, academic recognition structures, and collaborative governance arrangements. Each dimension is associated with indicative governance mechanisms and policy indicators such as institutional OER strategies, faculty incentive programs, and shared digital infrastructure. The framework also recognizes institutional diversity, emphasizing that governance models must be adapted to different policy environments, academic cultures, and stages of OER adoption across higher education systems. By conceptualizing OER as governable knowledge commons, the article clarifies how open knowledge initiatives can con-tribute to social equity, educational resilience, and sustainable transformation in higher education. Full article

In the last few years, universities have increasingly implemented online learning environments, allowing students to study at their own pace. These environments utilize technological tools and implement methods to support training, deliver content, and promote the acquisition of new knowledge and skills. As an example of these technologies, eye tracking has emerged as a powerful tool for studying visual attention, cognitive processes, and learning behaviors. The main aim of this study is to provide a scoping review of recent eye-tracking research across diverse learner populations, ranging from K-12 students to university-level learners and educators. The present study examined recent advances in eye-tracking technologies, focusing on their potential, especially when combined with artificial intelligence (AI) techniques such as machine learning. It analyzed 54 empirical studies in the last few years, highlighting their applicability, strengths, and limitations. The research findings highlight the promise of eye-tracking technology to transform educational practices by providing data-driven insights regarding student behavior and cognitive processes. Future research must address implementation and data-analysis challenges to maximize the educational benefits of eye tracking. Full article

Blue space, as an important natural and social composite feature system in cities, not only provides supporting, regulating, and provisioning services, but also plays a key role in human well-being, recreational experience, and urban sustainable development. The blue space cultural ecosystem service (CES) has gradually attracted the attention of academia in recent years, but there is a lack of systematic integration research in related fields. Therefore, it is necessary to conduct a comprehensive analysis of current studies to clarify how, and to what extent, blue spaces influence CESs. This study adopts a PRISMA-based systematic search combined with qualitative synthesis, aiming to review the research status of CES and its developmental trajectory within blue space studies, and to identify future research trends and critical gaps. A total of 52 studies meeting the inclusion criteria were finally selected through database screening. The research innovatively divides the evolution of blue space CES into three stages (2012–2017/2018–2022/2023–2025), revealing a shift in research focus from single value identification to complex policy support. Secondly, through the mapping of six typical blue space types (such as rivers and wetlands) and 10 CES indicators, combined with a Pearson correlation heatmap, it provides quantitative insights into the coupling mechanisms between indicators, such as the significant synergy between spiritual and educational values. Methodologically, it systematically discriminates between the application boundaries of monetary valuation based on the contingent valuation method and non-monetary valuation represented by social media big data and PPGIS, pointing out that technological progress is driving the evaluation toward high dynamics and refinement. Finally, the study points out current bottlenecks such as uneven geographical distribution and insufficient planning transformation, emphasizing that future research should use artificial intelligence to improve data processing accuracy and transform blue space CESs from “invisible welfare” into “explicit policy assets” to guide sustainable urban renewal and healthy space design. 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

Generative AI (GenAI) is transforming higher education assessments, yet empirical research on students’ lived experiences with GenAI during graded, time-constrained classroom assessments remains scarce. This study investigates how direct experience with GenAI in examinations shapes student perceptions of learning, metacognition, and engagement. Drawing on self-regulated learning research and cognitive load theory, we employed a retrospective pre–post design to analyze qualitative reflections and quantitative data from 90 undergraduate computer science and engineering students. Our qualitative analysis suggests a complex recalibration from idealized expectations of efficiency toward what may be described as a state of measured pragmatism. Interpretive analysis of Post-experience reflections indicates that direct practical engagement appeared to make students more conscious of the need for metacognitive engagement, with a focus on real-time output verification and the restrictive role of time pressure. Concerns regarding assessment authenticity and fairness emerged only after direct engagement. Quantitative results show that although 68.5% preferred the GenAI format, this preference did not correlate significantly with academic performance (r = 0.014, p = 0.89). Those findings suggest that student engagement is driven by pedagogical and professional relevance rather than grade improvement alone. Overall, the findings underscore the need for assessment designs that balance cognitive support with active student monitoring and responsibility. Full article

As social robots are increasingly deployed in domains such as healthcare, education, and entertainment, there is growing demand for affective agents that can interpret users’ affective states and respond in contextually appropriate ways. Existing work has established strong foundations for emotion generation and appraisal, but the step that connects generated emotion to behavioral execution still relies heavily on model-specific rules or implicit links. We frame this issue as a Mechanism Gap and propose an Emotional BDI framework that introduces Frijda’s action tendency as an intermediate representation layer between the Affective Core and the Belief–Desire–Intention (BDI) Executor. Rather than mapping emotion directly to concrete behavior, the framework first transforms affective state into a directional action tendency and then lets BDI reasoning realize that tendency according to role and context. This creates an explicit emotion-to-behavior mediation structure through which the same emotion can be expressed differently across situations and roles. In an exploratory user evaluation with 26 participants, the proposed model received more favorable ratings than an Emotion-Driven Agent in satisfaction ($p=0.010$) and appropriateness ($p=0.002$). Compared with a Cooperative Agent, the proposed model showed a significant advantage only in satisfaction ($p=0.030$). These findings suggest that the proposed framework offers a useful architectural direction for affective decision making beyond direct mapping or unconditional compliance. Full article

This study falls within the field of lifelong education and investigates the role of aesthetic-sensory experiences—particularly those mediated by digital visual arts—in the development of critical thinking, a key competence identified in the European LifeComp Framework. In light of contemporary social transformations characterised by complexity, uncertainty, and change, critical thinking emerges as a transversal skill of fundamental importance for the education of conscious and autonomous citizens. The objective of this research is to analyse how digital sensory experiences, integrated with innovative teaching methodologies—such as laboratory-based teaching and collaborative learning—can foster the activation and enhancement of critical thinking in educational contexts. The work is based on a narrative analysis of the literature, conducted through the consultation of academic databases (e.g., Scopus, ERIC, and Google Scholar), and is developed within a theoretical framework encompassing digital education, laboratory didactics, and collaborative learning strategies. Studies published in recent years were selected according to their relevance to digital aesthetic experiences and critical thinking in educational contexts and were analysed through a thematic synthesis of the main conceptual contributions. The knowledge activities include the selection, categorisation, and discussion of recent studies that relate aesthetic experiences to the development of soft skills, in line with the principles of visual education understood as aesthetic and critical literacy in visual languages. The results of the review indicate that the intentional use of aesthetic digital environments, in combination with active and reflective teaching approaches, can stimulate complex cognitive processes and significantly contribute to the formation of critical thinking. The contribution implements an interdisciplinary approach among visual education, digital education, and experiential aesthetics, emphasising the need for further empirical research to consolidate the evidence and guide the implementation of innovative educational practices based on this approach. Full article

Technical and vocational education and training (TVET) colleges in South Africa continue to experience challenges related to staff commitment, organisational performance, and institutional effectiveness. These challenges highlight the need to better understand leadership approaches that sustain academic engagement and stability. This study examines the relationship between transformational leadership and organisational commitment among academic staff at a selected TVET college in Gauteng, South Africa. Grounded in the transformational leadership theory of Bass and Avolio, the study adopted a quantitative, cross-sectional survey design. Data were collected from 203 academic staff across six campuses using a structured self-administered questionnaire. Descriptive statistics and multiple regression analysis were performed using SPSS. The findings revealed low levels of organisational commitment among academic staff. While transformational leadership, as a composite construct, did not significantly predict organisational commitment, specific components—namely intellectual stimulation, inspirational motivation, and individualised consideration—showed significant positive relationships with organisational commitment. Theoretically, the study refines the application of transformational leadership theory within the TVET context by demonstrating that its components may operate differentially rather than as a unified construct in predicting organisational commitment. These findings challenge assumptions regarding the holistic predictive power of transformational leadership and extend leadership scholarship within under-researched TVET settings in developing-country contexts. Practically, the results provide evidence-based guidance for TVET management to design targeted leadership development interventions that emphasise specific transformational leadership behaviours to enhance academic staff commitment. Full article

Online asynchronous learning offers considerable flexibility but frequently faces challenges in sustaining engagement, interactivity, and inclusivity across diverse learner populations. This study introduces the OPTIMAL framework—an Online, Pedagogy- and Technology-Integrated, Microcurricula Approach for interactive and inclusive Learning—synthesising universal design for learning, active learning, and constructive alignment with technology integration frameworks (TPACK and PICRAT), operationalised through a microcurricula-as-a-service architecture. A three-year longitudinal case study (2022/23 to 2024/25) examined the application of the framework to a data literacy and analytics module serving over 5000 students across more than 15 programs and five faculties at Dublin City University. The module design constructively aligned learning outcomes, content, and technology at three levels to support multiple learning pathways, formative assessment, and transdisciplinary engagement, deliberately fostering transformative uses of technology in a fully asynchronous environment. Mixed-methods evaluation—combining learning analytics, surveys (n = 1743), and qualitative feedback—demonstrated sustained positive outcomes across all three years, including 95–99% completion rates, consistently high satisfaction, and longitudinal gains in engagement and pass rates. These findings demonstrate how the deliberate integration of pedagogical theory, technological frameworks, and modular curriculum architecture can deliver scalable, inclusive, and high-engagement online education, offering both a transferable, evidence-based model for educators and curriculum designers and longitudinal empirical validation for researchers. Full article

Driven by global sustainable development and intelligent technological innovation, the geotechnical engineering industry is transforming toward the direction of “Intelligent technology + Low-carbon circulation + Ecological friendliness”, creating an urgent demand for interdisciplinary talents with corresponding professional capabilities and sustainable awareness. To address the deficiencies in traditional postgraduate education (e.g., disjointed knowledge systems, inadequate practice oriented to Sustainable Geotechnical Engineering (SGE), and superficial integration of Education for Sustainable Development (ESD), this study constructs a “Four-Dimensional and Four-Stage” integrated talent training model based on the Sustainable Development Goals (SDGs) of the United Nations. Taking “Intelligent Technology—Geotechnical Theory—SGE Scenarios—ESD Literacy” as its core framework and adopting a progressive path of “Basic Cognition—Collaborative Application—Innovative Development—Sustainable Transformation”, this model was piloted among 23 postgraduate students through the course titled “Intelligent Design and Construction of Geotechnical Engineering”. The results show that all the students obtained officially granted software copyrights, their core professional capabilities were significantly improved, 100% of them applied their research achievements to SGE-related practices, and their ESD literacy was notably enhanced. Breaking through the traditional “knowledge-practice” dualistic framework of engineering education, this model achieves the in-depth integration of professional training and sustainable awareness cultivation and thus provides a replicable paradigm for the ESD education of interdisciplinary postgraduate students in the intelligent age. Full article

Despite recent expansions in K-12 computer science (CS) education, entrenched equity gaps persist, and elementary students are notably underserved. Early experiences shape identities and interests, yet best practices for elementary CS remain understudied. Over four years, this study explored elementary teacher perceptions of both the learning experiences offered in CS education and the reported impact of those experiences on teacher perceptions of their students. Results indicate that high-quality, integrated CS experiences have the potential to transform both the student learning experiences as well as teacher perceptions of what students can accomplish. The results have significant bearing for broadening participation in CS education and creating a more equitable CS landscape. Full article

Road traffic safety is central to socially resilient and sustainable cities, yet many middle-income countries lack harmonized subnational data on exposure, infrastructure, and enforcement. This study examines whether routinely available demographic composition can serve as a practical structural baseline for provincial traffic accident rates and as a diagnostic layer for richer safety models. Using official province–year data from Türkiye (2008–2019 and 2022–2024; n = 1215), demographic shares by sex, education, and age were treated as compositional inputs and transformed using isometric log-ratio (ILR) methods, with GDP per person included as a scalar covariate. A Tabular Residual Network (ResNet) was trained on the historical panel and evaluated on a post-period calibration/evaluation window (2022–2024), which was used for checkpoint selection and seed screening rather than as an independent held-out test set. Among the evaluated specifications, the ResNet seed-ensemble achieved the strongest performance on the 2022–2024 calibration/evaluation period (R² = 0.5717), outperforming the best single-seed model (R² = 0.5539), a province-specific last-value-carried-forward temporal heuristic based on 2019 values (R² = 0.4779), tree-based tabular benchmarks (Random Forest: R² = 0.1328; XGBoost: R² = 0.0706), and pooled statistical reference models (linear: R² = 0.1375; negative binomial: R² = 0.0686; Poisson: R² = −0.0634). Year-wise diagnostics indicated gradual temporal drift, suggesting that periodic recalibration or the inclusion of additional policy-relevant covariates is needed to preserve calibration. Overall, ILR-based compositional geodemography provides a scalable and interpretable baseline for traffic safety monitoring and prioritization in data-constrained settings. Full article

Under the combined pressures of urbanization and tourism development, terraced agricultural heritage sites are increasingly threatened by the degradation of traditional landscapes, the weakening of living cultural practices, and mismatches between the supply and demand of cultural ecosystem services (CESs). As a representative type of Globally Important Agricultural Heritage Systems (GIAHSs), the rice terrace landscapes of southern China have formed an integrated system of forests, villages, terraces, and water networks, embodying multiple values related to production, ecology, landscape, and culture. To support the coordination of heritage conservation, tourism development, and the transformation of cultural value, this study takes the core area of the Longji Terraced Fields as a case study and develops an improved SolVES–IPA collaborative assessment framework from the perspective of tourist perception. Four CES categories are examined: recreational value, aesthetic value, historical and cultural value, and educational value. The results show that (1) the four CES categories exhibit significant spatial differentiation. Recreational and aesthetic values are mainly concentrated in high-altitude viewing spaces, whereas historical, cultural, and educational values depend more heavily on traditional architectural spaces and interpretive nodes. (2) Clear supply–demand mismatches exist across CES categories. Recreational value is constrained by limited activity diversity; aesthetic value is limited by inadequate architectural harmony; historical and cultural value is primarily restricted by insufficient continuity of living traditions; and educational value is constrained by incomplete interpretive content and single presentation formats. (3) CES optimization in the Longji Terraced Fields should adopt both type-specific and hierarchical intervention strategies, including priority optimization for high-value units with critical shortcomings, near-term improvement for high-value units with general shortcomings, functional enhancement for medium-value units with critical shortcomings, progressive optimization for medium-value units with general shortcomings, and potential cultivation of low-value units. Based on these findings, this study proposes several optimization directions, including strengthening participatory experiences, promoting the coordinated renewal of the architectural landscape, creating multisensory cultural display spaces, and establishing a multidimensional interpretation network. The improved SolVES–IPA collaborative assessment framework developed in this study integrates CES spatial identification, supply–demand diagnosis, and optimization priority setting, providing a methodological reference and practical support for enhancing cultural services and promoting the coordinated development of heritage conservation and cultural tourism in the Longji Terraced Fields and similar agricultural heritage sites. Full article

Fine-grained energy management in smart-campus buildings requires accurate load forecasting together with reliable and interpretable anomaly diagnosis. This study presents an integrated forecasting–diagnosis framework for building energy systems. Hourly energy demand is modeled using a Transformer-based sequence-to-sequence architecture, in which a domain-aware attention mechanism is introduced to separately represent historical consumption dynamics, environmental influences, and temporal regularities commonly observed in building energy use. Anomaly diagnosis is conducted through a dual-scale strategy that supports both the timely detection of abrupt abnormal events and the identification of gradual performance degradation. Short-term anomalies are detected from forecasting residuals using adaptive thresholds, while long-term anomalies are identified by comparing current residual patterns with same-season historical baselines and validating multi-window trends over a 48 h horizon. The two detection streams are jointly used to distinguish point, pattern, and composite anomalies. To support practical operation and maintenance, SHAP-based explanations are provided to interpret both energy predictions and detected anomalies. Case studies on two educational buildings from the Building Data Genome Project 2 demonstrate that the proposed framework achieves the best overall forecasting performance against both conventional baselines and stronger recent Transformer-based models, with mean absolute percentage errors of approximately 3%. The results indicate that the proposed framework provides a practical solution for data-driven energy monitoring and decision support in smart buildings. Full article

Transformational leadership and internal communication are widely studied variables in organizational management; however, their joint effect as simultaneous predictors of multidimensional job performance remains underexplored, particularly in Latin American higher education contexts. This study examines whether transformational leadership and internal communication jointly predict task performance, contextual performance, and counterproductive job performance among Peruvian university workers. A quantitative, non-experimental, cross-sectional, and predictive correlational design was applied to a sample of 385 workers from a private Peruvian university, analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM). Results confirm that both variables significantly predict job performance across its dimensions, with internal communication showing stronger predictive effects on task and contextual performance than transformational leadership. These findings contribute to organizational and management theory by proposing and validating a joint predictive model that addresses existing conceptual and empirical gaps in the literature, while providing evidence-based recommendations for leadership development and communication management in university institutions operating in emerging economy contexts. Full article