Search Results (885)

The steady advancement of online education has not automatically translated into improved educational quality. Teacher training often continues to focus on the technical use of digital tools, while the pedagogical processes through which teachers report supporting students’ digital competence remain insufficiently understood. The objective of this study was to examine the sequential and predictive structure of teachers’ digital competence using the DigCompEdu framework as a reference. A quantitative cross-sectional study was conducted with a sample of 136 university teachers involved in online education. Data were collected through a self-reported questionnaire based on DigCompEdu and analyzed in two phases: Partial Least Squares Structural Equation Modeling (PLS-SEM) and Artificial Neural Networks (ANNs). The PLS-SEM results suggested a sequential pattern of associations among teacher-reported constructs: Professional Commitment (PC) was positively associated with Digital Resource Management (DR), which in turn was positively associated with Digital Pedagogy (DP) and Assessment and Feedback (AF). These dimensions were associated with Student Empowerment (SE), which showed the strongest positive relationship with teachers’ reported practices for Facilitating Students’ Digital Competence (FS). The ANN sensitivity analysis showed adequate predictive performance in the testing phase (RMSE = 0.155) and identified Student Empowerment as the predictor with the highest normalized importance within the specified model. These findings suggest that faculty development in online higher education may benefit from moving beyond basic digital literacy and platform management toward pedagogical design, formative assessment, inclusive participation, and learner agency. However, the results should be interpreted as evidence of teacher-reported facilitation practices within the analyzed sample, rather than as direct evidence of students’ actual digital competence development. Full article

This study presents a comprehensive review of more than 100 research papers on sign language recognition (SLR) published between 2020 and 2026. The analysis focuses on deep learning approaches applied to video-based SLR, including spatiotemporal feature extraction, temporal modeling, attention mechanisms, motion-based representations, hybrid frameworks, transfer learning methods and other methods. Particular attention is given to how these methods model spatiotemporal dynamics and capture subtle gesture characteristics in sign language communication. The review highlights several recent developments, such as the introduction of specialized datasets, the emergence of real-time recognition systems, and the integration of multimodal fusion strategies. At the same time, persistent challenges remain, including data scarcity in low-resource sign languages, limited linguistic standardization of datasets, and insufficient model interpretability. The findings underline the importance of developing scalable and generalizable models capable of handling diverse datasets and user variability. The distinct contributions of this review are fourfold: (1) a comprehensive synthesis of over 100 studies published between 2020 and 2026, covering the full spectrum of deep learning architectures for video-based SLR; (2) a structured six-category taxonomy enabling systematic cross-architectural comparison; (3) a comprehensive focus on low-resource sign languages, which remain underrepresented in the existing literature; and (4) a critical analysis of the current benchmark landscape for low-resource sign languages, identifying key gaps and outlining strategic directions for future dataset development. These contributions are intended to guide further research toward more robust, inclusive, and universally applicable SLR systems. Full article

Computational thinking (CT) is increasingly recognized as essential in education, yet teacher preparation programs struggle to develop both computational proficiency and pedagogical readiness in pre-service teachers (PSTs). This study examines an AI-mediated, game-making course grounded in the emerging “vibe coding” paradigm, where 24 novice PSTs iteratively constructed programs through natural language prompting. Adopting a mixed-methods design, the study drew on pre- and post-course attitude questionnaires, reflective accounts of prompting strategies, and open-ended responses. Results indicate that participants substantively engaged with core CT practices, particularly debugging, iterative refinement, and problem decomposition. Nonetheless, this downward recalibration in self-reported coding and teaching confidence represents a productive adjustment rather than a failure. Conversely, attitudes toward game-making improved significantly, with a statistically significant medium effect size for perceived instructional value (d = 0.51), the largest practical effect observed across dimensions. Most participants intended to integrate CT into future teaching. These findings suggest that prompt-driven learning environments support meaningful engagement with computational processes when carefully scaffolded, but do not inherently ensure pedagogical readiness, particularly for higher-order CT practices such as abstraction and pattern recognition. Unlike prior research that has examined game-making processes or PST attitudes toward CT in isolation, this study empirically integrates all three within a single scaffolded instructional design using vibe coding. This integration enables a process-level account of how CT is enacted—and how it develops—when code generation is partially delegated to AI systems. Beyond documenting attitude shifts, the study introduces an analytical rubric for identifying CT engagement in AI-mediated prompting and derives evidence-based design principles that specify the pedagogical conditions under which vibe coding supports, rather than bypasses, computational reasoning. Full article

Blended emotion recognition introduces the challenge of identifying not only which emotions are present in an expressive display but also their relative salience. The proposed methodology builds upon the pre-extracted features provided with the dataset and enhances performance through a combination of temporal modeling and multimodal fusion strategies. Unimodal experiments revealed that visual encoders consistently outperformed audio ones, with the multimodal HiCMAE encoder achieving the strongest single-encoder results with 34% presence accuracy and 18.23% salience accuracy. Multimodal fusion further improved performance, with the best validation results obtained using a combination of simple concatenation and attention-based fusion, reaching 47.86% in presence accuracy and 27.92% in salience accuracy. Overall, the proposed methodology surpasses the chosen baseline introduced in the original paper across a k-fold experiment, confirming the effectiveness of multimodal attention-based fusion for the accurate prediction of both emotion presence and salience in blended affective behaviour. The experimental results further indicate that multimodal expression recognition consistently outperforms unimodal approaches, highlighting the complementary nature of cross-modal information. Full article

Digital-avatar systems still provide limited control over emotionally expressive behavior in human–computer interaction, especially in Large Language Model (LLM)-based chatbots and virtual assistants with personalized visual embodiments. To address this problem, we propose Multimodal Avatar Generation (MAVAGEN), a multimodal avatar generation framework for synthesizing upper-body digital avatars with personalized appearance and controllable emotional expression. The user specifies the desired gender and age, as well as provides a short text input from which the target emotional state is inferred. MAVAGEN then retrieves an identity image from the HaGRIDv2-1M corpus and generates an avatar clip with synchronized facial expressions, hand gestures, and expressive speech. The framework uses the following six feature streams: textual features, emotion-distribution features, landmark-based pose features, depth-geometry features, RGB-appearance features, and acoustic features. In a quantitative evaluation against recent human animation methods, MAVAGEN achieves the best overall avatar quality, with FID 48.20, FVD 592.00, SSIM 0.741, Sync-C 7.40, HKC 0.929, HKV 25.30, CSIM 0.563, and EmoAcc 0.88. Ablation results show that emotion and acoustic features contribute most to emotional agreement, while landmark-based pose and depth features improve geometric and motion stability. These results support the practical use of MAVAGEN in personalized LLM-based assistants and other emotion-sensitive interactive systems. Full article

Universal Accessibility in Astronomy requires a paradigm shift from visual-centric communication to multisensory data interaction. Because astronomy communication relies inherently on high-resolution imagery and visual metaphors, it creates significant accessibility barriers for blind and low-vision (BLV) audiences. To address this, multimodal encoding offers a feasible and meaningful solution by redistributing information across alternative sensory channels, ensuring that the absence of sight does not preclude the comprehension of spatial data. This article explores the development and evaluation of a low-cost, multimodal tool designed to represent complex astronomical concepts—specifically stellar magnitude and color—through tactile and auditory stimuli. Unlike traditional methods, our approach focuses on the haptic-cognitive link, allowing users to “feel” data through physical relief models. We present a structured impact study involving a heterogeneous group of blind, low-vision, and sighted participants. The methodology followed a mixed-methods approach, including a participatory workshop with 20 individuals and a detailed usability assessment with a core group (n= 6) of blind and low-vision participants. Preliminary results from this pilot phase demonstrate that multimodal integration effectively reduces the perceived mental effort for complex spatial data comprehension. Quantitative and qualitative feedback suggests that tactile-auditory sensory substitution not only improves accessibility but also enhances engagement and information retention across all user groups. These findings highlight the potential of multimodal models in transforming public scientific environments, such as museums and observatories, into inclusive, interactive spaces. Full article

Augmented Reality (AR) has been found to produce significant effects on individual learning outcomes but its impact on collaborative applications remains moderate. Existing AR frameworks emphasize individual instructional design, whereas frameworks for collaborative learning rarely engage with the spatial and device-mediated affordances of mobile AR. In response to this inadequacy in the literature, we introduce the Mobile Augmented-Reality Storytelling for Vocational Education and Training (MARS-VET) framework, a four-dimensional conceptual architecture that integrates Computer-Supported Collaborative Learning (CSCL) scripting principles with mobile AR affordances for collaborative English as a Foreign Language (EFL) writing in Vocational Education and Training (VET) settings. MARS-VET synthesizes theoretical perspectives across four dimensions: contextual anchoring, which embeds activities within authentic workplace scenarios; collaborative orchestration, which structures group interaction through macro- and micro-level scripts; competency cultivation, which sequences writing progression from model-based reproduction toward autonomous professional text production; and capacity building, which addresses the professional-development requirements of implementing educators. Content validity was established through expert panel evaluation involving international specialists (N = 11) who rated the framework against 36 items using a four-point relevance scale and provided additional qualitative feedback. The Scale-level Content Validity Index (S-CVI/Ave = 0.91) exceeded established thresholds, with all four dimensions achieving satisfactory item-level indices. Experts reached unanimous agreement on items addressing workplace scenario identification and co-located access to linguistic resources. Qualitative feedback led to terminology refinements and clarification of orchestration mechanisms. The framework offers VET institutions and educators a reference for the design and evaluation of collaborative AR experiences in an area where integrative frameworks have so far been lacking. Full article

In recent years, artificial intelligence has been fully involved in design practice and educational activities, and its impact on practice and education has received widespread attention from the academic community. This study aimed to preliminarily explore, through a controlled experiment, the differences in the impact of generative artificial intelligence (AI) tools and traditional web/literature tools on the sustainable design learning outcomes of interior design students in a specific teaching context at a university in China. A study was conducted on 58 third-year college students who were divided into an AI tool group (Class B) and a traditional tool group (Class A). Three semi-structured questionnaire surveys were conducted over two months to collect data on their understanding, attitudes, and practical applications of sustainable design. Quantitative statistics and text analysis methods were used for the comparison. The results showed that under specific experimental conditions, students who used AI tools showed a more significant improvement in their self-evaluation of knowledge mastery, but their sense of recognition of the importance of knowledge and subsequent learning willingness also decreased. In subsequent design practice, students in the traditional tool group showed higher initiative in applying concepts and diversity in strategies. Text analysis further suggests that AI-assisted learning may be more conducive to the rapid structured acquisition of knowledge, while traditional learning methods exhibit different characteristics in promoting deep semantic associations. The conclusions of this study are based on short-term experimental observations of specific samples and toolsets, revealing the tension between efficiency and depth that may be faced when integrating AI tools into interior design education, providing a reference and discussion basis for broader and longer-term teaching research in the future. Full article

Trajectory planning algorithms are essential in human–robot collaboration (HRC), as they must generate efficient trajectories for seamless interaction. Given the risks and complexity of testing in real-world scenarios, a virtual environment was developed in Unity 3D, integrating a virtual model of the UR3 robot that delivers workpieces to a user equipped with a Meta Quest device. The RRT, RRT-Star (RRTS), and RRT-Connect (RRTC) algorithms were evaluated using ANOVA and Tukey post hoc tests, considering the following response variables: safety, feasibility, smoothness, and computation time across three experimental scenarios characterized by (i) low, (ii) medium, and (iii) high levels of movement of the participant’s left hand. The statistical results indicate that RRTC exhibited the best performance in terms of smoothness and computation time. Based on these findings, a multicriteria decision-making analysis was conducted using the Analytic Hierarchy Process (AHP), combining quantitative evidence derived from the statistical analysis with expert judgments supported by bibliographic references. This multicriteria analysis enabled the coherent integration of the different evaluation criteria and concluded that RRTC is the most suitable alternative for collaborative assembly tasks in HRC environments. Full article

Despite significant advances, multimodal sentiment analysis still faces critical challenges in modeling complex cross-modal interactions and extracting discriminative sentiment features. To address these limitations, this paper proposes a hierarchical multimodal sentiment analysis framework. Specifically, a cross-modal feature enhancement module is first introduced to capture deep correlations among textual, visual, and acoustic modalities via cross-attention mechanisms, thereby obtaining context-aware fused representations. Subsequently, an attention-gated feature disentanglement approach is employed to effectively separate sentiment-relevant information from content-specific features within the fused representations; an independence loss is further imposed to enforce orthogonality between these two feature subsets, thereby mitigating noise induced by repetitive visual frames and textual stop words. Finally, all disentangled features are integrated to facilitate high-level sentiment reasoning through a multimodal logical inference module, where supervised contrastive loss is incorporated to enhance the discriminability of sentiment expressions. Extensive experiments conducted on two public benchmarks, CMU-MOSI and CMU-MOSEI, demonstrate that the proposed framework achieves improvements of 2–6% across multiple evaluation metrics compared with state-of-the-art methods. Full article

Fitts’ law is a foundational model for predicting pointing performance and has been increasingly explored in immersive virtual reality (VR) environments. This paper presents a controlled experimental framework for deriving modality-specific Fitts’ law models in VR and evaluating their predictive transfer to applied interaction tasks. The framework comprises two scenarios. The first replicates a standardized ISO 9241 pointing task in a 3D virtual environment to derive predictive movement time models by systematically varying target distance (20–50 cm), target size (2.5–5 cm), and spatial configuration ($0^{\circ }$, ${45}^{\circ }$, ${90}^{\circ }$, ${135}^{\circ }$). The second simulates an applied warehouse-inspired task involving tool sorting and structured placement actions to evaluate the generalizability of the derived models in more ecologically valid VR interactions. Thirty-two participants completed all tasks using the Meta Quest 3 headset and two interaction modalities: a handheld controller and hand tracking with gesture recognition. Results show that Fitts’ law remains a strong predictor of movement time for 3D pointing in VR, with high linear fits for both the controller ($R^2=0.9615$) and hand tracking ($R^2=0.9668$). However, models derived from standardized pointing tasks showed limited transferability to applied object-manipulation scenarios, producing prediction errors of approximately 27–35% and systematically underestimating movement times. Additionally, both objective metrics and subjective evaluations indicated that controller-based interaction outperformed hand tracking in efficiency, accuracy, perceived workload, and usability. These findings highlight both the robustness and limitations of Fitts-based performance modeling in realistic VR interaction contexts. Full article

Non-alcoholic fatty liver disease affects approximately thirty percent of the global population, yet public awareness remains dangerously low among young adults facing occupational risk factors. This study introduces the Fatty Liver Awareness Game (FLAG), an educational serious game designed to improve liver health literacy among software engineering students at the University of Guayaquil. While evaluated with this specific sample, FLAG is intended for the broader target population of young adults in developing nations who face occupational sedentary risk and limited access to preventive health education. Through a controlled experiment with fifty participants randomly assigned to game-based or traditional lecture instruction, the game demonstrated superior effectiveness, with a twenty-percentage-point advantage in post-test scores and a seventy-two percent reduction in incorrect responses compared to fifty percent in the lecture group. The large effect size (Cohen’s d = 1.43) and reduced performance variability among game participants indicate that interactive, feedback-rich learning environments can outperform passive instruction for this population and content domain. While the present design does not isolate the contribution of individual game elements—such as narrative framing, explanatory feedback, or mini-game interleaving—the results establish FLAG as a replicable model for digital health interventions targeting underserved populations at critical developmental junctures. Future component analyses are needed to determine which specific design features drive the observed advantages. Full article

This study presents a multimodal deep learning framework for automatic proficiency and style classification of parallel Bilingual Tamil–Hindi learner data. The proposed system employs a dual-headed neural architecture to simultaneously predict proficiency levels (Basic, Advanced) and stylistic categories (Formal, Literary) using shared feature representations. A curated dataset of bilingual text samples is utilized, along with synthetic speech generated through text-to-speech (TTS) to enable controlled multimodal experimentation. Five deep learning architectures are evaluated under text-only, audio-only, and learnable fusion settings. Experimental findings indicate that text-based models consistently achieve strong performance in both proficiency and style classification tasks. In contrast, the audio-only model demonstrates limited effectiveness, highlighting the constraints of synthetic acoustic features in capturing meaningful linguistic information. The fusion models provide only marginal improvements over text-based approaches, suggesting that textual representations play a dominant role in proficiency and stylistic classification within controlled datasets. These results emphasize the importance of linguistic features over acoustic signals for automated language assessment in low-resource settings. The proposed framework provides a scalable and reproducible approach and offers a foundation for future work incorporating real speech data and more diverse linguistic inputs. Full article

This study examines how AI-enhanced motion capture (AI-MoCap) mediates the preservation, transmission, and re-creation of Chinese shadow puppetry as performative intangible cultural heritage. Through a state-of-the-art review and comparative analysis of three representative application models—technology-driven, culturally integrated, and entertainment-oriented—the paper explores how AI-MoCap supports the digitization of performative techniques while reshaping modes of cultural presentation and interaction. Cross-case comparison highlights recurring tensions between technical standardization and cultural authenticity while also indicating possibilities for symbolic reconstruction, contextual continuity, and ethically grounded design. Based on this comparison, the paper develops a dual-channel inheritance framework—“perception–symbol” and “design–performance”—and treats cultural resolution and digital ethics as analytical and normative principles for resisting algorithmic homogenization. Rather than functioning only as a digitization tool, AI-MoCap can be understood as a mediating mechanism whose cultural value depends on how it remains embedded in community-based performative logics, symbolic systems, and ethical boundaries. The resulting framework offers transferable guidance for future research, curation, training, and policy discussion in the digital safeguarding of performance-based heritage. Full article

The impact of oncological diseases extends far beyond the clinical patient, profoundly affecting the mental health of caregivers, family members, and volunteers who navigate complex emotional landscapes of grief, anxiety, and trauma. While the domain of digital health has seen a proliferation of serious games aimed at pediatric patient education and treatment adherence, the specific perspective of the “second-order patient”, the caregiver or survivor, remains significantly under-explored. The primary objective of this study is to systematically review the current state of interactive narratives in oncology, palliative care, and grief support, identifying research gaps to inform the broader design space of empathy-driven serious games. Following the PRISMA guidelines, 31 articles were selected from an initial query of 116 records. Interventions were categorized into Serious Games, Games, and Gamification. The analysis reveals a critical thematic transition: early interventions relied heavily on biological “battle” metaphors to empower patients, whereas the current literature advocates for “thanatosensitive” designs that foster empathy. However, a distinct research gap persists regarding narratives that explore post-loss meaning reconstruction and the hospital volunteer experience. Synthesizing these findings, this paper establishes an evidence-based theoretical framework demonstrating a significant opportunity for games that prioritize dialogue and emotional processing over traditional winning conditions. As a practical application of these findings, we also briefly outline the conceptualization of a prototype simulating a widower’s experience volunteering in a palliative ward, shifting the ludic focus from defeating a disease to navigating loss. Full article