Search Results (992)

Recent studies report that gradient guidance extracted from a single-reference model can improve Limited-Sample regression. However, one reference model may not capture all relevant characteristics of the target function, which can restrict the capacity of the learner. To address this issue, we introduce the Multi-Gradient Guided Network (MGGN), an extension of single-gradient guidance that combines gradients from several reference models. The gradients are merged through an adaptive weighting scheme, and an orthogonal-projection step is applied to reduce potential conflicts between them. Experiments on sine regression are used to evaluate the method. The results indicate that MGGN achieves higher predictive accuracy and improved stability than existing single-gradient guidance and meta-learning baselines, benefiting from the complementary information provided by multiple reference models. Full article

This study investigates the interplay between adaptive online learning, students’ digital intelligence, sustainable learning, and digital mental well-being among female university students in Saudi Arabia. In response to the growing reliance on digital platforms in higher education, a structured questionnaire was distributed via social media to capture student perceptions of their online learning experiences. Using Partial Least Squares Structural Equation Modelling (PLS-SEM), the analysis revealed that while adaptive online learning is a critical enabler, its influence is most effective when mediated by students’ digital intelligence. The findings highlighted that students with higher digital intelligence are more likely to engage in sustainable learning practices and maintain better mental well-being in digital environments. Furthermore, innovative teaching practices were shown to strengthen these relationships, underscoring the importance of interactive and adaptive pedagogies. This research contributes to the growing discourse on digital education by emphasizing the importance of indirect pathways and learner-centred dynamics in shaping positive educational and psychological outcomes. This study offers practical and theoretical implications for educators, institutions, and policymakers aiming to create inclusive, resilient, and psychologically supportive digital learning environments. Future research is encouraged to examine these relationships across different cultural and institutional contexts and explore the longitudinal impacts of digital learning strategies. Full article

This study introduces an educational framework that merges 3D modeling with AI-assisted narrative interaction to apply digital technology in cultural heritage education, exemplified by an ancient carriage culture. Through immersive tasks and contextual narratives, the course notably improved learners’ professional skills and cultural awareness. Experimental results revealed significant knowledge acquisition among participants post-engagement. Additionally, the user experience improved, with increased satisfaction in the narrative interaction design course. These enhancements led to heightened interest in cultural heritage and deeper knowledge acquisition. Utilizing Norman’s three-layer interaction model, Ryan’s contextual narrative theory, and Falk and Dierking’s museum learning experience model, the study developed a systematic course for multi-sensory design and contextual interaction, confirming the positive impact of multimodal interaction on learning outcomes. This research provides theoretical support for the digital transformation of cultural education and practical examples for educational practitioners and cultural institutions to implement in virtual presentations and online learning. Full article

Hip implant optimization is increasingly receiving attention due to the development of manufacturing technology and artificial intelligence interaction in the current research. This study investigates the development of hip implant stem design with the application of lattice structures, and the utilization of the MATLAB regression learner app in finding the best predictive regression model to calculate the mechanical behavior of the implant’s stem based on some of the design parameters. Many cases of latticed hip implants (using 3D lattice infill type) were designed in the ANSYS software, and then 3D printed to undergo simulations and lab experiments. A surrogate model of the implant was used in the finite element analysis (FEA) instead of the geometrically latticed model to save computation time. The model was then generalized and used to calculate the mechanical behavior of new variables of hip implant stem and a database was generated for surgeon so they can choose the lattice parameters for desirable mechanical behavior. This study shows that neural networks algorithms showed the highest accuracy with predicting the mechanical behavior reaching a percentage above 90%. Patients’ weight and shell thickness were proven to be the most affecting factors on the implant’s mechanical behavior. Full article

Ubiquitous blended learning, leveraging mobile devices, has democratized education by enabling autonomous and readily accessible knowledge acquisition. However, its reliance on traditional interfaces often limits learner immersion and meaningful interaction. The emergence of the wearable metaverse offers a compelling solution, promising enhanced multisensory experiences and adaptable learning environments that transcend the constraints of conventional ubiquitous learning. This research proposes a novel framework for ubiquitous blended learning in the wearable metaverse, aiming to address critical challenges, such as multi-source data fusion, effective human–computer collaboration, and efficient rendering on resource-constrained wearable devices, through the integration of embodied interaction and multi-agent collaboration. This framework leverages a real-time multi-modal data analysis architecture, powered by the MobileNetV4 and xLSTM neural networks, to facilitate the dynamic understanding of the learner’s context and environment. Furthermore, we introduced a multi-agent interaction model, utilizing CrewAI and spatio-temporal graph neural networks, to orchestrate collaborative learning experiences and provide personalized guidance. Finally, we incorporated lightweight SLAM algorithms, augmented using visual perception techniques, to enable accurate spatial awareness and seamless navigation within the metaverse environment. This innovative framework aims to create immersive, scalable, and cost-effective learning spaces within the wearable metaverse. Full article

Inclusive music education refers to the use of musical experiences and practices as tools for promoting participation, equity, and meaningful engagement among all learners, including those with Special Educational Needs (SEN). Music education has long been recognized not only for its value in emotional expression and cultural transmission but also for its cognitive and relational benefits. This entry examines the inclusive and transformative potential of music, highlighting how it can foster equitable, accessible, and culturally relevant learning environments. Drawing from pedagogy, neuroscience, and educational technology, the entry explores how music contributes to cognitive, emotional, and social development, with a focus on learners with SEN. It emphasizes the importance of early exposure to music, the strong connections between music and language acquisition, and the need to challenge persistent misconceptions about innate musical talent. The findings demonstrate that when supported by digital tools and educational games, music education becomes a powerful driver of inclusion, enhancing participation, relational dynamics, and cognitive engagement. The entry concludes by advocating for a reimagining of music not as a secondary subject, but as a foundational component of holistic and inclusive education, capable of building more empathetic, connected, and equitable societies. Full article

The integration of artificial intelligence (AI) into educational environments is fundamentally transforming the learning process, raising new questions regarding student engagement and motivation. This empirical study investigates the relationship between AI-based learning support and the experience of flow, defined as the optimal state of deep attention and intrinsic motivation, among university students. Building on Csíkszentmihályi’s flow theory and current models of technology-enhanced learning, we applied a validated, purposefully developed AI questionnaire (AIFLQ) to 142 students from two Hungarian universities: the Ludovika University of Public Service and Eszterházy Károly Catholic University. The participants used generative AI tools (e.g., ChatGPT 4, SUNO) during their academic tasks. Based on the results of the Mann–Whitney U test, significant differences were found between students from the two universities in the immersion and balance factors, as well as in the overall flow score, while the AI-related factor showed no statistically significant differences. The sustainability of the flow experience appears to be linked more to pedagogical methodological factors than to institutional ones, highlighting the importance of instructional support in fostering optimal learning experiences. Demographic variables also influenced the flow experience. In gender comparisons, female students showed significantly higher values for the immersion factor. According to the Kruskal–Wallis test, educational attainment also affected the flow experience, with students holding higher education degrees achieving higher flow scores. Our findings suggest that through the conscious design of AI tools and learning environments, taking into account instructional support and learner characteristics, it is possible to promote the development of optimal learning states. This research provides empirical evidence at the intersection of AI and motivational psychology, contributing to both domestic and international discourse in educational psychology and digital pedagogy. Full article

Interest in language-learning motivation has been growing recently, particularly in multilingual contexts where individuals acquire additional languages beyond English. Despite increasing the focus on multilingualism within second-language acquisition (SLA) research, less research focuses on the motivational dynamics of multilingual learners in learning languages other than English (LOTE). Addressing this gap, the present study investigates the complex motivational factors influencing multilingual university students in learning French as an additional language and LOTE within the Belgian context. The participants consisted of 121 multilingual university students who were learning French as an additional language and LOTE. Data were collected through questionnaire and semi-structured interviews, and analyzed using a combination of quantitative and qualitative methods to provide a comprehensive understanding of learners’ motivational profile. Findings revealed that multilingual learners’ motivation is multifaceted and dynamic, shaped by a combination of intrinsic interests (e.g., cultural appreciation and personal growth), extrinsic goals (e.g., academic and career aspirations), integrative motives, and prior language-learning experiences. The study also sheds light on the overlapping and evolving nature of motivational patterns and provides nuanced insights into LOTE learning motivation within multilingual settings. Full article

Gamification has the potential to significantly enhance student engagement and motivation in educational contexts. However, there is a lack of empirical research that compares different guiding strategies between AI-driven gamified and non-gamified modes in virtual learning environments to scaffold language learning. This paper presents an empirical study that examines the impact of AI-driven gamification and learning strategies on the learning experience and outcomes in virtual environments for English-language learners. A gamified English learning prototype was designed and developed. A between-group experiment was established to compare different gamified scaffolding groups: a traditional linear group (storytelling), an AI-driven gamified linear group (task-based learning), and a gamified exploration group (self-regulated learning). One hundred students learning English as a second language participated in this study, and their learning conditions were evaluated across three dimensions: engagement, performance, and experience. The results suggest that traditional learning methods may not be as effective as the other two approaches; there may be other factors beyond in-game interaction and engagement time that influence learning and engagement. Moreover, the results show that different gamified learning modes are not the key factor affecting language learning. The research presents guidelines that can be applied when gamification and AI are utilised in virtual learning environments. Full article

This study aims to develop and validate a user experience scale to construct an Actual System Use model for online courses based on the Technology Acceptance Model, allowing for a comprehensive assessment of the multidimensional factors affecting Learning Outcomes and Actual System Use in the context of online courses. The scale includes six core dimensions: Interactive Experience, Content Quality, Learning Outcomes, Teaching Quality, Technical Support, and Learning Motivation. Through a literature review, pre-survey, exploratory factor analysis, and confirmatory factor analysis, the reliability and validity of the developed scale were verified. A second-order complex Structural Equation Model was used to measure users’ Actual System Use with respect to online courses. The results demonstrate that the Interactive Experience and Learning Motivation dimensions play crucial roles in enhancing learners’ engagement and learning satisfaction, while Perceived Usefulness and Perceived Ease of Use significantly influence system usage behaviors. This study provides a systematic theoretical basis and empirical data for the design of online courses, offering valuable insights for optimizing course design and enhancing user experiences. Full article

This study explores the usability and interaction experience of preschool-aged children with the Artsteps platform, a web-based tool for creating and navigating virtual exhibitions. The goal is to assess the platform’s suitability for early childhood learners and to reflect on its potential educational value. Following a child-centered design methodology, the research involves the participation of 35 children aged 4–6 in classroom-based activities. Observational methods and an adapted version of the System Usability Scale (SUS) for young learners were used to collect quantitative and qualitative data. The analysis focuses on interaction patterns, the comprehension of basic functions, and emotional responses during the use of the platform. Findings offer exploratory insights into the affordances and limitations of the platform in preschool settings, offering insights into how such digital tools may support digital literacy and experiential learning in early childhood education. Full article

The Language, Culture, and Knowledge-building through Science (LaCuKnoS) project tests and refines a model of science teaching and learning that brings together current research on the role of language in science communication, the role of cultural and community connections in science engagement, and the ways people apply science knowledge to their daily decision making. One key component of the model brings families together as co-learners and co-teachers through family learning experiences. We describe our work to promote more robust family conversations about science in our lives within an existing research practice partnership, using a two-tiered qualitative conversational analysis to compare the family conversations that result from three family engagement models: (a) family science festivals; (b) family science workshops; and (c) family science home learning. More specifically, this paper addresses the question: How do families describe and evaluate science in their lives and communities during family conversations that occur during each of these three engagement models? Discourse analysis using the appraisal dimension of systemic functional linguistics highlights the affective components of families evaluating science in their lives, as well as how each model provided unique affordances for different communicative goals. These findings are used to propose a set of design principles to guide the continued exploration of community-sustaining and family-centric models of family engagement as a key strategy for broadening science participation. Full article

Digital maps have become important teaching and learning tools in education. However, limited research has examined the factors influencing learners’ acceptance of map-based online learning systems. This study proposes and validates an extended Technology Acceptance Model (TAM) that integrates two psychological constructs—habit and self-efficacy—into the original TAM framework to better explain students’ behavioural intention to use a map-based online learning system (Map-OLS). Structural equation modelling (SEM) was employed to analyse data from 812 participants with prior online learning experience. The results indicated that perceived ease of use (PEoU) and perceived usefulness (PU) had direct positive effects on the behavioural intention to use Map-OLS. PEoU positively affected PU and indirectly influenced behavioural intention to use Map-OLS via PU. Both habit and self-efficacy had significantly positive influences on PEoU and PU. Self-efficacy also directly influenced the behavioural intention to use Map-OLS. This study makes a theoretical contribution by extending and empirically validating TAM in the context of map-based learning environments, while also offering practical insights for designing more engaging and effective online learning systems. Full article

This work explores the integration of Generative AI (GenAI) tools into web development educational settings, with a focus on enhancing the user experience (UX) design process and supporting students with limited technical backgrounds. The democratization of GenAI has allowed non-technical users to engage in the creation of computing applications. However, its adoption among UX-focused learners remains limited. In this study, we propose an innovative instructional approach designed to facilitate the effective use of GenAI tools in web development courses for UX designers. This approach is based on incorporating interaction mechanisms that allow students to prompt GenAI tools using an incremental strategy. Moreover, this scaffolding process enables the definition of benchmarks that can be used as a reference for the development tasks proposed and their assessment by comparing the students’ outcomes with the benchmarks provided. The results obtained from applying the proposed approach in a web development course using GitHub Copilot environments show its potential to address the challenges UX design students face in these educational settings. Full article

Immersive learning has been recognized as a promising paradigm for enhancing educational experiences through the integration of VR. We propose an architecture for intelligent tutoring in immersive VR environments that employs LLM-based non-playable characters. Key system capabilities are identified, including natural language understanding, real-time adaptive dialogue, and multimodal interaction through hand tracking, gaze detection, and haptic feedback. The system synchronizes speech output with NPC animations, enhancing both interactional realism and cognitive immersion. This design demonstrates that AI-driven VR interactions can significantly improve learner engagement. System performance was generally stable; however, minor latency was observed during speech processing, indicating areas for technical refinement. Overall, this research highlights the transformative potential of VR in education and emphasizes the importance of ongoing optimization to maximize its effectiveness in immersive learning contexts. Full article