Search Results (967)

The transition towards sustainable manufacturing necessitates complex optimization that integrates economic goals with environmental factors, such as energy consumption and greenhouse gas emissions. This research addresses the critical challenge of optimizing the Incoming Quality Control (IQC) policy for raw material batches. The primary objective is formulated as a multi-criteria control problem that jointly minimizes the weekly final product cost, carbon footprint, and energy consumption. To handle sequential decision making under uncertainty, we adopt a scalarized reinforcement learning (RL) reward that combines these objectives into a single value function and explores different trade-offs through alternative weight configurations. To effectively handle the uncertainty in incoming quality and the sequential decision making required for dynamic control, the optimization problem is modeled as a Bayesian Adaptive Markov Decision Process (BAMDP). To maintain computational tractability despite the continuous belief space inherent in the BAMDP formulation, we employ a Deep Q-Network (DQN) architecture acting as an approximate dynamic programming solver. The Bayesian framework represents model uncertainty explicitly, updates beliefs as new inspection evidence becomes available, and allows prior domain knowledge on supplier quality to be incorporated into the learning process. The BAMDP formulation is used to learn a set of adaptive inspection policies that adjust the IQC strategy over time to achieve conflicting goals: reducing inspection costs while maintaining standard quality, minimizing energy consumption, and lowering CO₂-equivalent emissions. The goal is to find robust policies that balance these trade-offs under different quality and demand conditions. This methodology aligns with the principles of Industry 5.0 by leveraging advanced artificial intelligence (AI) methods, such as reinforcement learning (RL), coupled with a stochastic simulation of the production system, based on a geometric/physical model of the component’s tolerance chains, to support decision-makers in designing and assessing sustainable IQC strategies. Comparative simulations on the case study, including a benchmark against ISO 2859-1 sampling plans, confirm that this dynamic and risk-aware optimization paradigm can reduce overall cost, energy use, and environmental impact across various quality conditions, while preserving outgoing quality. Full article

Modern fulfillment centers share floor space with human workers, making warehouse multi-robot task allocation a safety-critical problem. We propose SHARP (Safe Heterogeneous Allocation with Risk Prediction), a Transformer-based constrained reinforcement-learning framework with a closed-form deployment-time safety mask. Under a Gaussian pedestrian belief and fixed closest-approach directions, the mask uses Bonferroni-allocated per-pair CVaR scores; a nonnegative mask score implies a conservative trajectory-level chance constraint under the stated assumptions. We also present an idealized primal–dual surrogate analysis, without claiming global convergence for the nonconvex Transformer/PPO implementation. Expanded experiments use ten training seeds per learned method and deterministic final-checkpoint evaluation on twenty independently generated held-out instances. No statistically significant difference between SHARP and Lagrangian-PPO was detected in any of the four scenarios. The held-out analysis further reveals late-training instability and severe over-conservatism in the dense S40_high scenario. These findings position SHARP as an auditable geometric filtering mechanism, while identifying conservatism and training stability as important limitations for deployment. Full article

Reading has long been recognised as a central mechanism for second language development, particularly in English as a Foreign Language (EFL) contexts where exposure to the target language is limited. However, learners do not seem to benefit equally from comparable reading demands, suggesting that factors beyond linguistic competence influence developmental outcomes. This study examines the relationship between reading self-efficacy and English language proficiency among undergraduate students enrolled in a Degree in English Studies at a Spanish university. A cross-sectional quantitative design was employed with a sample of 141 participants and data were collected using the Reader Self-Perception Scale 2 (RSPS2) and a standardised multilevel English placement test aligned with the Common European Framework of Reference for Languages (CEFR). The results revealed a statistically significant positive relationship between reading self-efficacy and language proficiency, whereas literary epistemological beliefs did not show a comparable association. Among the RSPS2 dimensions, perceived Progress and Physiological States emerged as the strongest correlates of proficiency, indicating that learners’ sense of development and emotional comfort while reading is particularly relevant to language achievement. The study argues that reading self-efficacy is related to textual exposure and language development, shaping whether learners engage with texts in sustained and productive ways. By linking learner self-perception to measurable proficiency outcomes, the study contributes empirical evidence to current discussions on affective variables in language learning and offers pedagogical implications for fostering engagement in higher education EFL contexts. Full article

This systematic review examined empirical studies on in-service professional development (PD) aimed at fostering inclusive pedagogy-related practices in mainstream primary and secondary schools, focusing specifically on research employing quantitative pre–post teacher-level outcome designs. The review addressed three areas: (1) the core content and design features of such PD programmes; (2) their reported effects on teacher-, student-, and classroom-related outcomes; (3) factors associated with broader or more sustained forms of change. Searches of ERIC, Web of Science Core Collection, and Scopus identified 1915 records, of which 16 studies met the inclusion criteria. Most programmes reported short-term improvements in teacher knowledge, beliefs, self-efficacy, and, in some cases, instructional practice. To interpret programme heterogeneity, this review applied an alignment framework derived from inclusive pedagogy theory to examine the pedagogical assumptions reflected in PD programmes. Programmes varied substantially in their degree of alignment, and those showing stronger alignment more often reported broader and, in some cases, more sustained teacher-, classroom-, or participation-related changes. However, these patterns should be interpreted cautiously given the methodological limitations of the evidence base. More uniformly positive findings were concentrated in weaker single-group studies relying largely on self-report, whereas studies with comparison group designs reported more mixed, modest, or less sustained effects. Overall, the review suggests that inclusion-oriented PD may support meaningful forms of teacher learning, although evidence regarding sustained classroom transformation and longer-term student-level impact remains limited. Full article

This paper describes the development and design of Nutrition for Athletes: A Focus on Dietary Supplements, an online educational intervention created for varsity athletes at the University of Guelph. Guided by the Theory of Planned Behaviour (TPB), the program aimed to improve athletes’ nutrition and dietary supplement-related knowledge while modifying attitudes, subjective norms, perceived behavioral control, intentions, and behaviors associated with dietary supplement use. Formative research with the target population revealed widespread misconceptions, strong social influences, and high perceived benefits of supplement use, which highlighted the need for an intervention focused on reshaping underlying beliefs. The program covered topics related to sports nutrition, hydration, dietary supplements, and risks of supplement use, and was integrated into the university’s learning management system. Each unit was mapped onto relevant TPB constructs, with an emphasis on correcting inaccurate beliefs and promoting a food-first approach. The program’s effectiveness was evaluated through pre- and post-intervention questionnaires assessing knowledge and TPB constructs. This paper outlines the theoretical framework, development process, and content structure of the intervention, and presents a model that can be replicated in future educational programs. Full article

This study examines how learning input quality shapes students’ trust, perceived learning value, and post-exposure behavioural intentions, and whether AI-supported instructional content contributes to conceptual learning. Grounded in the Technology Acceptance Model, trust theory, and the Cognitive Theory of Multimedia Learning, the study proposes the Learning Input Quality, Belief, User Learning Experience, and Continuance (LBUC) model. Data were collected from 320 university students in Türkiye via an online survey. To evaluate the proposed framework in an authentic instructional setting, participants watched NotebookLM-generated instructional videos in Turkish and completed pre-test and post-test knowledge measures together with Likert-type scales assessing Audio and Narration Quality, Perceived Visual Design Quality, AI Trust and Persuasion, Instructional Design Effectiveness, Perceived Learning Value, Using Intention, and Recommendation Intention. Learning gains were assessed using paired-samples t-tests, and the proposed LBUC model was evaluated using Partial Least Squares Structural Equation Modelling. The findings showed a significant within-group increase in post-test scores, suggesting short-term conceptual gains after exposure to the videos. In the structural model, Audio and Narration Quality strongly predicted AI Trust and Persuasion, whereas Perceived Visual Design Quality significantly predicted Instructional Design Effectiveness but did not directly predict trust. Both AI Trust and Persuasion and Instructional Design Effectiveness positively influenced Perceived Learning Value, which in turn strongly predicted Using Intention and Recommendation Intention. The results suggest that students’ immediate post-exposure Using Intention and Recommendation Intention are associated less with visual appeal alone than with pedagogically coherent narration, AI Trust and Persuasion, and Perceived Learning Value in the context of NotebookLM-generated instructional videos. Full article

This article presents a sustainable, system-level approach to personalized practical learning in digital education environments based on tightly integrating formal models of practical tasks and artificial intelligence technologies. The authors resolve the limitations of current methods in e-learning personalization—such as lack of scalability, insufficient adaptability, and unreliable automation—by introducing an improved application which uses Belief–Desire–Intention (BDI) multi-agent system with adaptive orchestration and domain-specific language of formal practical task specification in the framework of an AI assistant, based on service-oriented architecture (SOA). The proposed approach provides automation for the entire lifecycle of practical tasks, encompassing generation, parameterization, and deployment of a virtual run-time environment and result verification for correctness, reproducibility, and academic integrity. Experimental tests demonstrate that combining a large language model (LLM) with dynamic verification significantly outperforms traditional purely generative approaches in terms of reliability, scalability, and reduction in instructor workload, as well as contributing to more effective task performance by students in practice-oriented learning scenarios. The study concludes that the synergistic integration of formal control mechanisms and AI-driven adaptivity offers a robust foundation for building sustainable smart environments for digital learning ecosystems. Full article

Parental emotional expressivity toward their child is an integral component of creating a family emotional climate, which is the primary context in which children develop social–emotional skills. The current study sought to empirically test Darling and Steinberg’s model that parent attitudes that make up parenting style effect parental emotional expressivity during parent–child interaction. Using longitudinal data from the NICHD Study of Early Child Care and Youth Development (SECCYD), the authors examined the compounding effects of maternal authoritarian attitudes measured soon after birth on maternal emotional expressivity toward their infant across three time points (child at 6, 15, and 24 months old). Hierarchical linear modeling analyses (HLMs) demonstrated that a mother’s (n = 1165, M_age = 28.2 years) authoritarian attitudes were associated with both decreased positive expressivity and increased negative expressivity toward their child at 6 months of age. Mothers who held more authoritarian attitudes at baseline demonstrated an increased rate of growth in negative expressivity toward their child over time. Maternal race and income were also significantly associated with the linear rate of growth of negative expressivity over time but not in positive expressivity. This suggests that authoritarian attitudes measured when the child is 1 month old continue to impact parent behavior up to 23 months later. This pattern suggests a potential window for effective universal prevention efforts in promoting nurturing parent behavior and promoting positive parent–child relationships. A possible target of prevention intervention could be providing parents with components of a modularized emotion regulation curriculum. The content could help parents to regulate their negative expressivity toward the child and focus on the message they want to convey to the child related to the child’s specific behavior. Full article

Pre-service teachers’ (PTs) beliefs about young children’s cognitive abilities shape both their instructional practices and their developing understandings of teaching and learning. This study examined PTs’ beliefs about preschool children’s cognitive abilities, focusing on cognitive operations, conceptual change, and learning processes, in relation to emerging professional identity development. A cross-sectional comparative design was employed with a convenient sample of 241 students from Early Childhood Education Departments who completed the Childhood and Cognitive Abilities Questionnaire. The findings revealed statistically significant differences between participants with and without practicum experience, with the former reporting more sophisticated beliefs, aligned with constructivist learning approaches. However, many participants simultaneously endorsed child-centered perspectives and traditional transmission-based conceptions of teaching, indicating the coexistence of contradictory beliefs. Correlation and cluster analyses further suggested that participants’ beliefs formed broader but only partially coherent belief systems rather than consistent conceptual profiles. These findings may reflect tensions within PTs’ emerging professional identities and suggest that practicum-related experience may coincide with opportunities for reflection on and restructuring of prior beliefs, processes associated with a coherent professional identity. Overall, this study highlights the importance of teacher education programs systematically addressing misconceptions about children’s cognitive abilities, while fostering coherent, research-informed professional identities and evidence-based instructional practices in early childhood education. Full article

The growing centrality of data in contemporary society has intensified calls to expand data literacy across K–12 education, positioning teachers as key agents in this effort. This article traces the emergence of data literacy as a domain of educational research and reports findings from a systematic review of empirical studies on K–12 STEM teacher data literacy published between 2015 and 2025. Guided by the PRISMA framework, searches of Academic Search Complete, APA PsycINFO, and supplementary sources yielded a final sample of 26 studies. The review examines (1) what has been prioritized in research on teaching data literacy and (2) the conceptual models used to study data literacy in educational contexts. Findings indicate that existing research primarily emphasizes teachers’ knowledge, beliefs, and use of technological tools, with comparatively limited attention to classroom enactment and student learning. Conceptually, the field is characterized by the use of diverse and often disconnected frameworks, including competency-based, statistical reasoning, and pedagogical models, resulting in a fragmented knowledge base. We argue that this fragmentation stems from underlying epistemological, methodological, and contextual tensions that have yet to be theoretically reconciled. In response, we propose an integrative perspective that conceptualizes data literacy as a situated, practice-based, and socio-epistemic phenomenon. This framing highlights the dynamic interplay among interpretive reasoning, instructional design, mediational tools, and contextual conditions. Advancing the field requires moving beyond isolated lines of inquiry toward theoretically coherent approaches that connect teacher cognition, instructional practice, and student learning in order to support meaningful and equitable participation in a data-driven world. Full article

This study investigates how immersive virtual reality (VR) simulations support international postgraduate preservice teachers (PSTs) in preparing for their initial professional experiences in Australian early childhood settings. Positioned within growing concerns about PST readiness, confidence, and cultural adjustment, the study examined the use of VR as a preparatory pedagogical tool. Sixty-six PSTs participated in human-in-the-loop mixed-reality teaching simulations in which they interacted with avatar children aged 3 to 5 in a realistic classroom environment prior to their professional placement. Guided by Bandura’s four sources of self-efficacy, video analysis examined how these simulated experiences contributed to PSTs’ self-efficacy-related practices, decision-making, and cultural readiness. Three themes emerged: (1) a movement from anxiety to relational regulation through social–emotional learning, (2) the use of simulation to bridge the theory–practice gap in classroom management and inclusion, and (3) the role of technology-supported reflection in enabling pedagogical revision through repeatable practice. The findings indicate that the simulation design compressed mastery experiences, modelling, credible feedback, and emotional regulation within a structured learning context. Participants demonstrated opportunities for reflective engagement with classroom dynamics in a low-risk environment before placement, including moments of hesitation, revision, and growing interactional control, highlighting the value of a programme-wide, technology-infused approach to immersive simulation in early childhood initial teacher education. Rather than directly measuring changes in efficacy beliefs, this study shows how mixed-reality rehearsal can operationalise the conditions that support efficacy formation within a repeatable pedagogical environment. In doing so, the paper contributes to current debates on technology-infused teaching by positioning mixed-reality simulation as a coherent model for future-oriented ITE design. Full article

School physical education (PE) has often relied on linear progressions in which teachers demonstrate, pupils practise prescribed techniques, and achievement is judged through visible reproduction of preferred movement forms. Non-linear pedagogy (NLP) and the constraints-led approach (CLA) offer an alternative ecological-dynamics rationale for supporting pupils’ integral development, including motor competence, adaptable movement capability, and dispositions for lifelong physical activity and physical literacy. However, existing review work has not sufficiently explained why principled NLP/CLA designs remain unevenly enacted across ordinary school PE systems. We conducted a theory-informed interpretive synthesis drawing on critical interpretive synthesis and thematic synthesis. A structured English-language search of ERIC, SPORTDiscus, Scopus, and Google Scholar (2010–2025) was combined with title-and-abstract screening, full-text assessment, backward and forward citation chaining, and purposive retention of foundational or Singapore-context records, and reporting was strengthened through PRISMA-like transparency aids adapted to interpretive synthesis. The final coded corpus comprised 36 included sources: 9 empirical studies, 3 reviews, 9 conceptual or practitioner texts, 6 theoretical or critical sources, 4 review-method papers, and 5 Singapore policy, context, or professional-learning documents used as an illustrative policy lens. Through iterative coding, descriptive theme development, and analytical integration, we identified six coherence domains shaping enactment: teacher beliefs and knowledge; curriculum and lesson structure; assessment and accountability; systemic and resource constraints; professional development ecosystems; and stakeholder and cultural factors. These domains informed a Systemic Coherence Framework spanning micro, meso, and macro levels. The synthesis suggests that assessment coherence may be a high-leverage condition because it links curriculum legitimacy, reporting, and teacher defensibility, but its comparative influence across domains remains a hypothesis for future empirical testing. The framework is offered as an analytic heuristic rather than a prescriptive model and is intended to help researchers, teacher educators, school leaders, and policy actors diagnose where curriculum intent, assessment language, professional learning, and organisational routines support or inhibit ecologically informed practice. Full article

Class distribution asymmetry (imbalanced data) is a prevalent problem in the field of Industrial Internet cybersecurity, where normal data far outnumber abnormal data. This causes traditional machine learning classifiers to be biased towards the majority class, severely degrading their attack detection capability. To address this issue while meeting the requirement for traceability of the decision-making process in industrial scenarios, this paper proposes an imbalanced data classification method based on the Belief Rule Base (BRB). First, the Cluster-Based Oversampling (CBO) algorithm is employed to restore the symmetry of class distribution at the data level. Then, the Evidential Reasoning (ER) iterative algorithm is used to perform attribute fusion, which reduces the number of antecedent attributes of BRB while maintaining the information, effectively alleviating the rule explosion problem. Finally, interpretable classification is realized based on BRB, and the Circle chaotic mapping Gray Wolf Optimizer (Circle-GWO) algorithm is introduced to complete model construction, parameter optimization and fine-tuning. Experimental results on the UNSW-NB15 and TON_IoT datasets demonstrate that the proposed method can effectively handle imbalanced data classification tasks in this field, providing a practical technical solution to improve the accuracy and efficiency of cybersecurity decision-making in the Industrial Internet. Full article

Understanding academic achievement requires moving beyond the identification of influential factors toward explaining how these factors are organized into functional learning and cognitive mechanisms. Although prior research has extensively documented the roles of socioeconomic status, student attitudes, and learning behaviors, less attention has been paid to how these elements interact within structured pathways that reflect underlying learning intelligence across educational systems. This study adopts a mechanism-oriented perspective to examine mathematics achievement using data from PISA 2018. Focusing on high-performing regions in East Asia and Western countries, it integrates interpretable artificial intelligence methods with structural modeling to investigate how contextual, psychological, and learning-process factors jointly shape achievement outcomes. The findings show that high achievement is not governed by a single set of dominant predictors, but by distinct organizational mechanisms of learning intelligence. In East Asian systems, achievement follows a chain-like convergent structure, in which socioeconomic background is systematically translated into academic outcomes through sequential psychological and self-regulatory processes. Psychological factors, particularly educational expectations and self-beliefs, function as key mediating mechanisms that organize learning engagement and strategy use. By contrast, high-performing systems in Europe and North America exhibit a parallel configuration, in which multiple cognitive and behavioral factors independently contribute to achievement through more decentralized pathways, reflecting a distributed structure of learning intelligence. Across regions, learning processes such as reading engagement and digital literacy show consistently positive associations with achievement. However, their roles vary depending on how they are embedded within broader system-level structures. These results suggest that self-regulation operates not merely as an associated factor, but as an organizing mechanism of learning intelligence that structures the translation of background resources into performance. By reconceptualizing prediction as a means of revealing the organization of learning intelligence, this study proposes a unified analytical framework that links interpretable artificial intelligence with theory-driven explanation. The findings contribute to a deeper understanding of how achievement systems function and highlight that high performance can emerge through multiple, structurally distinct pathways, with important implications for educational research, cognitive theory, and policy design. Full article

Background/Objectives: This study explored parents’ perspectives regarding digital media use in children and adolescents with neurodevelopmental disorders (NDs) and examined how these views vary according to family and clinical characteristics. Methods: Data were collected from an Italian survey involving 352 families. Items assessed the perceived effects of digital devices on child development and parenting, awareness of screen time guidelines, and use of time- and content-limiting tools. Quantitative analyses were complemented by a reflexive thematic analysis of open-ended responses describing how digital media influenced parenting. Results: Parents expressed divergent attitudes towards digital media, with broadly similar proportions reporting positive, neutral, and negative views regarding both child development and parenting. More favourable views were associated with greater perceived benefits for children and were more frequent among parents of children with more severe functional disabilities. About half had discussed screen use with health professionals, and most were aware of existing guidelines. Thematic analysis identified six themes related to digital parenting: educational means (digital devices as tools for communication, learning, and socialisation), entertainment (screens as a source of leisure or behavioural management), reward (digital media used as reinforcement), screen time as a “necessity” (technology as an integral and sometimes rehabilitative part of daily life), negative effects on the child (concerns about detachment, reduced social interaction, and mood dysregulation), and parental behaviour and attitudes (reflecting the emotional burden of regulation and broader beliefs about digital media). Conclusions: Parents of children with NDs navigate digital media use through a complex balance of perceived risks and benefits. Findings highlight the need for family-centred guidance and assistive technology approaches that promote digital inclusion while addressing parental stress and regulatory challenges. Full article