Search Results (329)

The rapid expansion of private universities in the past a few decades has created a unique sector in Chinese higher education system. Unlike public research-oriented institutions, Chinese private universities are tuition-dependent, resource-constrained, and primarily vocation-oriented. Lacking the prestige of academics, the campus physical environment in these institutions becomes a key strategic asset for student recruitment, retention, and performance. However, academic research addressing these contexts remains scarce. This study aims to develop a reliable measurement tool—the University Campus Environment Satisfaction Scale (UCESS)—specifically tailored to assess student satisfaction with the physical environment in Chinese private universities. Based on 1050 valid questionnaires from 4 representative universities in Guangdong province, exploratory and confirmatory factor analyses revealed a hierarchical structure comprising 10 first-order factors and 3 second-order dimensions: (1) Safety and accessibility; (2) Core living and learning environment; and (3) Developmental and amenity resources. The findings reveal that students in Chinese private universities prioritize tangible living, teaching and safety conditions over higher-level developmental amenities, reflecting a layered satisfaction logic. Furthermore, this study demonstrates the differentially weighted relationships between campus elements and overall campus satisfaction, providing administrators with a scientific diagnostic tool to optimize resource allocation and implement student-centered planning strategies. Full article

This study explored dynamic assessment (DA) of writing in a linguistically and culturally diverse context. Drawing on conceptualizations of DA and ecological languaging competencies (ELC), an ELC-based Loop Pedagogy was designed and adapted in a primary English language teaching (ELT) classroom aiming to foster ongoing development of a dynamic, dialogic, and differentiated assessment approach. A mixed methods research design was adopted with data sources including questionnaires, lesson observations, interviews, and documents/artifacts of student works. Research findings indicated that with optimized choices of learning, timely scaffolding, personalized written feedback, as well as a caring and supportive environment, students with diverse learning needs improved their writing abilities, enhanced their language awareness, and increased their positive affect toward writing activities. Full article

At present, generative AI has problems of insufficient content generation accuracy, weak personalized response, and low reasoning efficiency in adaptive learning scenarios, which limit its in-depth application in intelligent teaching. To solve this problem, this paper proposed a Transformer fine-tuning method based on low-rank adaptation technology, which realized efficient parameter update of pre-trained models through low-rank matrix insertion, and combined the instruction fine-tuning strategy to perform domain adaptation training on the model for the constructed educational scenario dataset. At the same time, a dynamic prompt construction mechanism was introduced to enhance the model’s context perception ability of individual learners’ behaviors, thereby achieving precise alignment and personalized control of generated content. This paper embeds the “wrong question guidance” and “knowledge graph embedding” mechanisms in the model, provides intelligent feedback based on student errors, and promotes in-depth understanding of subject knowledge through knowledge graphs. Experimental results showed that this method scored higher than 0.9 in BLEU and ROUGE-L. The average response delay was low, which was significantly better than the traditional fine-tuning method. This method showed good adaptability and practicality in the fusion of generative AI and adaptive learning and provided a generalizable optimization path and application solution for intelligent education systems. Full article

The article presents an approach to synthesizing artificial intelligence agents (AI agents), in particular, control and decision support systems for process operators in various industries. Such a system contains an identifier in the feedback loop that generates digital predictive associative search models of the Just-in-Time Learning (JITL) type. It is demonstrated that the system can simultaneously solve (outside the control loop) two additional tasks: online operator pre-training and mutual adaptation of the operator and the system based on real-world production data. Solving the latter task is crucial for teaching the operator and the system collaborative handling of abnormal situations. AI agents improve control efficiency through self-learning, personalized operator support, and intelligent interface. Stabilization of process variables and minimization of deviations from optimal conditions make it possible to operate process plants close to constraints with sustainable product qualities. Along with higher yield of target product(s), this reduces equipment wear and tear, utilities consumption and associated harmful emissions. This is the key merit of Model Predictive Control (MPC) systems, which justify their application. JITL-type models proposed in the article are more precise than conventional ones used in MPC; therefore, they enable the operation even closer to process constraints. Altogether, this further improves the reliability of production systems and contributes to their sustainable development. Full article

This study presents an enhanced hybrid TLBO–ANN model for daily photovoltaic (PV) power generation prediction. By combining the strong nonlinear modeling capacity of Artificial Neural Networks (ANN) with the robust optimization capability of the Teaching–Learning-Based Optimization (TLBO) algorithm, the proposed framework effectively improves prediction accuracy and generalization performance. The model was trained using real meteorological and power generation data and validated on a grid-connected PV power plant in Türkiye. Results indicate that the hybrid TLBO–ANN approach outperforms the conventional ANN by achieving 39.97% and 37.46% improvements on the test subset and overall dataset, respectively. The improved convergence behavior and avoidance of local minima by TLBO contribute to this enhanced accuracy. Overall, the proposed hybrid model provides a powerful and practical tool for reliable PV power forecasting, which can facilitate better grid integration, operational planning, and energy management in renewable energy systems. Full article

Given the pressing need to strengthen operational reliability in electrical distribution networks, this study proposes an optimization methodology based on the Teaching–Learning-Based Optimization (TLBO) algorithm for the strategic location of redundant lines. The model is validated on the “MV Distribution Network—Base Model” test system, considering the combination of the MTBF (Mean Time Between Failures) and MTTR (Mean Time To Repair) indicators as the objective function. After 500 independent runs, it is determined that the configuration with three redundant lines identified as LN_1011, LN_1058, and LN_0871 offers the most stable solution. Specifically, this topology increases the MTBF from 403.64 h to 409.42 h and reduces the MTTR from 2.351 h to 2.306 h. In addition, significant improvements are observed in the voltage profile and angle, along with a more balanced redistribution of active and reactive power, more efficient use of existing lines, and an overall reduction in energy losses. Full article

Background/Objectives: Evidence-Based Practice (EBP) positively impacts health safety and quality while also empowering nursing as a discipline. A useful strategy for promoting EBP is to build learning paths adapted to the individuality of nurses. These elements establish the framework for effective learning, determining the availability of specific content at certain times and influencing the design of learning objects to ensure optimal efficacy in the teaching-learning process. It is essential to identify effective strategies in evidence-based nursing education to advance EBP and thereby enhance the quality and safety of nursing care. This review aims to summarize the evidence on the effectiveness of learning paths in the acquisition of EBP competencies by nurses. Methods: A systematic review of the literature will be carried out in accordance with the Joanna Briggs Institute (JBI) methodology for systematic reviews of effectiveness. The results of the review will be reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocols (PRISMA-P). The protocol is registered in the PROSPERO database (CRD4202453155). The search will be performed using the EBSCOhost search engine in the following databases: CINAHL Plus, MedicLatina, MEDLINE, Psychology and Behavioral Sciences Collection, Academic Search Complete, eBook Collection, and Education Resources Information Center. The inclusion of studies, data extraction, and analysis will be carried out by two reviewers independently. Disagreements will be resolved by a third reviewer. All studies involving nurses, learning paths, EBP competencies, regardless of geographical area and context, with no time limit or language constraints, will be included. Results: Not applicable; this is a protocol. Findings will be synthesized as specified in the Methods. Conclusions: This review will provide a better understanding of the effectiveness of a learning path in the acquisition of EBP competencies by nurses. It will also assist in the identification of knowledge gaps in the literature and potential areas for future research and development. Full article

Modeling complex non-physical systems is essential for understanding the interdependent dynamics of human-centered adaptive environments. This study extends the Goal Tree–Success Tree and Master Logic Diagram (GTST-MLD) framework to represent and analyze these systems beyond their traditional engineering applications. A mixed-methods approach, combining a systematic literature review, expert interviews, and survey-based validation, was employed to test the framework using the teaching–learning process in Higher Education (HE) as an illustrative case study. The results show how function-centered modeling within the GTST-MLD structure decomposes the complexity of the system and reveals pedagogical bottlenecks, providing a structured basis for designing adaptive strategies. Rather than measuring learning gains directly, the model offers a structured representation of the conceptual and methodological pathways that influence learner engagement, conceptual integration, and adaptability. Within this bounded context, this study demonstrates a reproducible GTST-MLD modeling procedure for non-physical systems, an auditable dependency structure, based on explicitly defined nodes and edges, and a coherent alignment between Threshold Concepts (TCs), Learning Outcomes (LOs), and methodological strategies. Together, these contributions offer a basis for diagnosing and optimizing complex non-physical systems and form a foundation for future empirical evaluation. Full article

This study focuses on an experimental device for the adaptive adjustment of the preload of high-speed motorized spindles. Firstly, based on Hirano’s criterion, the optimal preload for bearings at different rotational speeds was determined, and an adaptive preload adjustment mechanism was developed, with its accuracy experimentally validated. Secondly, the optimal lubrication conditions were obtained by a single-factor experiment. Then, the vibration characteristics under different preload conditions were explored, and the axial displacement variations were analyzed across a range of rotational speeds. Finally, the temperature rise in the bearings with the speed at the constant preload force and the optimal preload force were compared. The results demonstrated that the adaptive preload adjustment device outperformed the constant preload application. In teaching practice, this study enhanced students’ systematic understanding of the adaptive preload adjustment process in motorized spindles, promoted the integration of theoretical knowledge with practical application, and strengthened their learning interest. In addition, this device can provide experimental equipment for studying the performance of high-speed motorized spindles and bearings in scientific research. Full article

As AI systems increasingly structure educational processes, they shape not only what is learned, but also how epistemic authority is distributed and whose knowledge is recognized. This article explores the normative and technopolitical implications of this development by examining two prominent paradigms in AI ethics: Ethical AI and Responsible AI. Although often treated as synonymous, these frameworks reflect distinct tensions between formal universalism and contextual responsiveness, between rule-based evaluation and governance-oriented design. Drawing on deontology, utilitarianism, responsibility ethics, contract theory, and the capability approach, the article analyzes the frictions that emerge when these frameworks are applied to algorithmically mediated education. The argument situates these tensions within broader philosophical debates on technological mediation, normative infrastructures, and the ethics of sociotechnical design. Through empirical examples such as algorithmic grading and AI-mediated admissions, the article shows how predictive systems embed values into optimization routines, thereby reshaping educational space and interpretive agency. In response, it develops the concept of situated ethics, emphasizing epistemic justice, learner autonomy, and democratic judgment as central criteria for evaluating educational AI. To clarify what is at stake, the article distinguishes adaptive learning optimization from education as a process of subject formation and democratic teaching objectives. Rather than viewing AI as an external tool, the article conceptualizes it as a co-constitutive actor within pedagogical practice. Ethical reflection must therefore be integrated into design, implementation, and institutional contexts from the outset. Accordingly, the article offers (1) a conceptual map of ethical paradigms, (2) a criteria-based evaluative lens, and (3) a practice-oriented diagnostic framework to guide situated ethics in educational AI. The paper ultimately argues for an approach that attends to the relational, political, and epistemic dimensions of AI systems in education. Full article

The project-based learning model in journalism and communication faces challenges of sparse multimodal behavior data and delayed teaching interventions, making it difficult to perceive student states and optimize decisions in real-time. This study aims to construct an intelligent decision-support framework integrating educational data mining (EDM) and deep reinforcement learning (DRL) to address these issues. A bidirectional long short-term memory (Bi-LSTM) network models behavioral sequences, while a conditional generative adversarial network (cGAN) with Wasserstein optimization enhances low-activity student data. The extracted and augmented features are then fed into a Double Deep Q-Network (DQN) to generate adaptive teaching intervention strategies. Experimental results from a 26-week study show that the proposed framework improved personalized learning-path matching from 0.42 to 0.68, increased knowledge mastery from 40.46% to 77.13%, and reduced intervention latency from 210.5 min to 144.6 min. The results demonstrate that the fusion of EDM and DRL can achieve efficient and adaptive decision-making, providing a viable approach for intelligent teaching support in journalism and communication education. Full article

Artificial Intelligence (AI) has brought a revolution in many areas, including the education sector. It has the potential to improve learning practices, innovate teaching, and accelerate the path towards personalized learning. This work introduces Reinforcement Learning (RL) methods to develop a personalized examination scheduling system at a university level. We use two widely established RL algorithms, Q-Learning and Proximal Policy Optimization (PPO), for the task of personalized exam scheduling. We consider several key points, including learning efficiency, the quality of the personalized educational path, adaptability to changes in student performance, scalability with increasing numbers of students and courses, and implementation complexity. Experimental results, based on case studies conducted within a single degree program at a university, demonstrate that, while Q-Learning offers simplicity and greater interpretability, PPO offers superior performance in handling the complex and stochastic nature of students’ learning trajectories. Experimental results, conducted on a dataset of 391 students and 5700 exam records from a single degree program, demonstrate that PPO achieved a 42.0% success rate in improving student scheduling compared to Q-Learning’s 26.3%, with particularly strong performance on problematic students (41.3% vs 18.0% improvement rate). The average delay reduction was 5.5 months per student with PPO versus 3.0 months with Q-Learning, highlighting the critical role of algorithmic design in shaping educational outcomes. This work contributes to the growing field of AI-based instructional support systems and offers practical guidance for the implementation of intelligent tutoring systems. Full article

Artificial Intelligence (AI), particularly Generative AI (GenAI) and Large Language Models (LLMs), is rapidly reshaping higher education by transforming teaching, learning, assessment, research, and institutional management. This entry provides a state-of-the-art, comprehensive, evidence-based synthesis of established AI applications and their implications within the higher education landscape, emphasizing mature knowledge aimed at educators, researchers, and policymakers. AI technologies now support personalized learning pathways, enhance instructional efficiency, and improve academic productivity by facilitating tasks such as automated grading, adaptive feedback, and academic writing assistance. The widespread adoption of AI tools among students and faculty members has created a critical need for AI literacy—encompassing not only technical proficiency but also critical evaluation, ethical awareness, and metacognitive engagement with AI-generated content. Key opportunities include the deployment of adaptive tutoring and real-time feedback mechanisms that tailor instruction to individual learning trajectories; automated content generation, grading assistance, and administrative workflow optimization that reduce faculty workload; and AI-driven analytics that inform curriculum design and early intervention to improve student outcomes. At the same time, AI poses challenges related to academic integrity (e.g., plagiarism and misuse of generative content), algorithmic bias and data privacy, digital divides that exacerbate inequities, and risks of “cognitive debt” whereby over-reliance on AI tools may degrade working memory, creativity, and executive function. The lack of standardized AI policies and fragmented institutional governance highlight the urgent necessity for transparent frameworks that balance technological adoption with academic values. Anchored in several foundational pillars (such as a brief description of AI higher education, AI literacy, AI tools for educators and teaching staff, ethical use of AI, and institutional integration of AI in higher education), this entry emphasizes that AI is neither a panacea nor an intrinsic threat but a “technology of selection” whose impact depends on the deliberate choices of educators, institutions, and learners. When embraced with ethical discernment and educational accountability, AI holds the potential to foster a more inclusive, efficient, and democratic future for higher education; however, its success depends on purposeful integration, balancing innovation with academic values such as integrity, creativity, and inclusivity. Full article

The integration of Soft Open Points (SOPs) into distribution networks has been an essential method for enhancing operational flexibility and efficiency. But simultaneous optimization of network reconfiguration and SOP scheduling constitutes a difficult mixed-integer nonlinear programming (MINLP) problem that is likely to suffer from premature convergence with standard metaheuristic solvers, particularly in large power networks. This paper proposes a novel hybrid algorithm, hTLBO–CO, which synergistically integrates the exploitative capability of Teaching–Learning-Based Optimization (TLBO) with the explorative capability of the Cheetah Optimizer (CO). One of the notable contributions of our framework is an in-depth problem formulation that enables SOP locations on both tie and sectionalizing switches with an efficient constraint-handling scheme, preserving topo-logical feasibility through a minimum spanning tree repair scheme. The evolved hTLBO–CO algorithm is systematically validated across IEEE 33-, 69-, and 119-bus test feeders with differential operational scenarios. Results indicate consistent dominance over established metaheuristics (TLBO, CO, PSO, JAYA), showing significant efficiency improvement in power loss minimization, voltage profile enhancement, and convergence rate. Remarkably, in a situation with a large-scale 119-bus power grid, hTLBO–CO registered a significant 50.30% loss reduction in the single-objective reconfiguration-only scheme, beating existing state-of-the-art approaches by over 15 percentage points. These findings, further substantiated by comprehensive statistical and multi-objective analyses, confirm the proposed framework’s superiority, robustness, and scalability, establishing hTLBO–CO as a robust computational tool for the advanced optimization of future distribution networks. Full article

This paper proposes a stepwise expert-teaching reinforcement learning framework for intelligent frequency control in hydro–thermal–wind–solar–compressed air energy storage (CAES) integrated systems under high renewable energy penetration. The proposed method addresses the frequency stability challenge in low-inertia, high-volatility power systems, particularly in Southwest China, where large-scale renewable-energy-based energy bases are rapidly emerging. A load frequency control (LFC) model is constructed to serve as the training and validation environment, reflecting the dynamic characteristics of the hybrid system. The stepwise expert-teaching PPO (SETP) framework introduces a stepwise training mechanism in which expert knowledge is embedded to guide the policy learning process and training parameters are dynamically adjusted based on observed performance. Comparative simulations under multiple disturbance scenarios are conducted on benchmark systems. Results show that the proposed method outperforms standard proximal policy optimization (PPO) and traditional PI control in both transient response and coordination performance. Full article