Search Results (5,890)

This study examines the transformative role of educational technologies in higher education, with a focus on their impact on student engagement and collaboration in online and hybrid learning environments. It draws on data from 11 educators at an Australian university across Education, Health Sciences, and Humanities disciplines. Utilising the online tool Padlet, these educators facilitated interactive activities that enhanced teaching and learning. This article analyses Padlet’s unique features and how they were employed to optimise student engagement and learning outcomes. Semi-structured interviews reveal how Padlet supported multimedia presentations, group work, and discussions. The findings underscore the versatility of Padlet in promoting critical thinking and knowledge sharing, ultimately enhancing the student experience in both online and hybrid learning settings. This study encourages educators to adopt innovative strategies that incorporate Padlet and similar technologies to enhance their teaching practices. Full article

This work presents the development of a web system using deep learning (DL) neural networks to solve the inverse kinematics problem of the Quetzal robotic arm, designed for academic and research purposes. Two architectures, LSTM and CNN, were designed, trained, and evaluated using data generated through the Denavit–Hartenberg (D-H) model, considering the robot’s workspace. The evaluation employed the mean squared error (MSE) as the loss metric and mean absolute error (MAE) and accuracy as performance metrics. The CNN model, featuring four convolutional layers and an input of 4 timesteps, achieved the best overall performance (95.9% accuracy, MSE of 0.003, and MAE of 0.040), significantly outperforming the LSTM model in training time. A hybrid web application was implemented, allowing offline training and real-time online inference under one second via an interactive interface developed with Streamlit 1.16. The solution integrates tools such as TensorFlow™ 2.15, Python 3.10, and Anaconda Distribution 2023.03-1, ensuring portability to fog or cloud computing environments. The proposed system stands out for its fast response times (1 s), low computational cost, and high scalability to collaborative robotics environments. It is a viable alternative for applications in educational or research settings, particularly in projects focused on industrial automation. Full article

This study investigates how different mobile advertising cues (WOM, product, and price cues) affect consumer responses in terms of advertisement clicks and purchases. A large-scale field experiment was conducted on a mobile online learning platform with 45,000 users representing different customer life cycle stages, in which users were randomly assigned to one of three mobile advertisement types. Behavioral data on clicks and purchases were collected, and the dual-system processing model was used to analyze mediating effects. Consumers were more likely to click on adverts featuring WOM and price cues than product cues, but less likely to purchase. Purchasing experience moderated this effect: experienced consumers showed higher purchase probabilities for WOM and price cues. Affective processing mediated click behavior, while cognitive processing mediated purchases. This study advances cue theory in the mobile context by identifying distinct psychological and behavioral mechanisms driving consumer engagement and conversion. It highlights the importance of tailoring mobile advert strategies based on cue type and user experience. Full article

Virtual Reality (VR) simulations provide immersive, realistic educational experiences that are increasingly used to enhance teaching and learning in nursing and medicine; however, use in dietetics lags. To fill this gap, four Nutrition Counselor VR simulations were developed collaboratively with the goal of building confidence in dietetic students’ nutrition counseling skills. After formative testing, pilot testing, and refinements, simulations were field tested with 34 dietetic students (91% women; age 25.67 ± 3.79 SD years; 68% White) from four supervised practice programs using a standard protocol administered by trained researchers (N = 5). Students completed a pre-survey, one VR simulation (≥2 times w/varying outcomes), and a post-survey. Online pre- and post-surveys examined changes in nutrition counseling skills, knowledge and self-efficacy, and comfort in using nutrition counseling skills. Paired t-tests revealed significant (p < 0.05) mean differences in nutrition counseling skill self-efficacy (medium effect size, d = 0.46) and comfort in using nutrition counseling skills (large effect size, d = 0.96) between the pre- and post-survey. At post-survey, >75% agreed the simulations helped build their nutrition assessment skills (79%) and counseling skills (88%) and prepared them to work with real patients (97%). Findings suggest the Nutrition Counselor VR simulations provided a realistic and safe learning environment that may be a valuable learning tool for dietetic students. Full article

Social networks generate vast amounts of data that can reveal patterns of human behaviour, social attachment, and mental states. This paper explores advanced machine learning techniques to detect and model such patterns, focusing on community structures, influential users, and information diffusion pathways. To address the scale, noise, and heterogeneity of social data, we leverage recent advances in graph theory, natural language processing, and anomaly detection. Our framework combines clustering for community detection, sentiment analysis for emotional state inference, and centrality metrics for influence estimation, while integrating multimodal data—including textual and visual content—for richer behavioural insights. Experimental results demonstrate that the proposed approach effectively extracts actionable knowledge, supporting mental well-being and strengthening digital social ties. Furthermore, we emphasise the role of privacy-preserving methods, such as federated learning, to ensure ethical analysis. These findings lay the groundwork for responsible and effective applications of machine learning in social network analysis. Full article

This study explored the Erasmus+ project ’H2OMap: Innovative Learning by Hydraulic Heritage Mapping’, integrating environmental awareness and cultural heritage into secondary education through interdisciplinary, ICT, and STEM-based approaches. Focused on water-related heritage in the Mediterranean, the study pursued three aims: integrate ICT-supported participatory mapping bridging history/geography subjects with digital innovation; identify learning benefits and implementation conditions; and generate transferable outputs and datasets for classroom reuse. Intellectual outputs include a methodological guide, an e-learning course, and an educational multiplatform comprising a mobile mapping app for in situ geocataloguing, an online database, and a geoportal with interactive StoryMaps. Evidence came from classroom testing across age groups, teacher feedback from the e-learning course, student mobilities in Spain, Italy, and Portugal, and platform usage records. More than 390 students and teachers participated, documenting over 100 hydraulic heritage elements. Additionally, dissemination through nine multiplier events and conferences reached over 550 external attendees. Findings show increased student engagement and ICT/GIS skills, clearer cross-curricular integration, and a replicable open workflow supported by structured coordination that strengthens school–university partnerships. Learner experience emphasised hands-on, place-based exploration and collaborative documentation of water heritage. Recommendations include using open geospatial standards, providing teacher training, and maintaining geoportals for classroom reuse. Full article

Educator agency in the form of choice over learning experiences is widely thought to enhance educator engagement and instructional improvement, yet causal evidence is scarce. We conducted a preregistered randomized controlled trial in an online computer science course with volunteer instructors who teach students worldwide. All instructors (N = 583) received automated feedback on their instruction, with half randomly assigned to have choice over the feedback topic. Choice alone did not increase feedback engagement or yield observable changes in practice, but it raised student attendance—an effect that was strongest for instructors who voluntarily engaged with additional training resources, including training modules and teaching simulations. For this subset of instructors, having choice over feedback had significant positive impacts on their instruction and student outcomes compared to the control group. This suggests that agency in choosing feedback topics may be most effective when combined with instructors’ intrinsic motivation to pursue self-directed improvement. Our study also demonstrates a scalable method for testing design principles in educator training and underscores the need to examine when, how and for whom agency might drive improvement. Full article

Unmanned aerial vehicles (UAVs) often face significant challenges in trajectory tracking within complex dynamic environments, where uncertainties, external disturbances, and nonlinear dynamics hinder accurate and stable control. To address this issue, a bio-inspired deep reinforcement learning (DRL) algorithm is proposed, integrating behavior cloning (BC) and long short-term memory (LSTM) networks. This method can achieve autonomous learning of high-precision control policy without establishing an accurate system dynamics model. Motivated by the memory and prediction functions of biological neural systems, an LSTM module is embedded into the policy network of the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. This structure captures temporal state patterns more effectively, enhancing adaptability to trajectory variations and resilience to delays or disturbances. Compared to memoryless networks, the LSTM-based design better replicates biological time-series processing, improving tracking stability and accuracy. In addition, behavior cloning is employed to pre-train the DRL policy using expert demonstrations, mimicking the way animals learn from observation. This biomimetic plausible initialization accelerates convergence by reducing inefficient early-stage exploration. By combining offline imitation with online learning, the TD3-LSTM-BC framework balances expert guidance and adaptive optimization, analogous to innate and experience-based learning in nature. Simulation experimental results confirm the superior robustness and tracking accuracy of the proposed method, demonstrating its potential as a control solution for autonomous UAVs. Full article

Machine learning models and web-based tools have been developed for predicting key properties of imidazolium-based ionic liquids. Two high-quality datasets containing experimental density and viscosity values at 298 K were curated from the ILThermo database: one containing 434 systems for density and another with 293 systems for viscosity. Molecular structures were optimized using the GOAT procedure at the GFN-FF level to ensure chemically realistic geometries, and a diverse set of molecular descriptors, including electronic, topological, geometric, and thermodynamic properties, was calculated. Three support vector regression models were built: two for density (IonIL-IM-D1 and IonIL-IM-D2) and one for viscosity (IonIL-IM-V). IonIL-IM-D1 uses three simple descriptors, IonIL-IM-D2 improves accuracy with seven, and IonIL-IM-V employs nine descriptors, including DFT-based features. These models, designed to predict the mentioned properties at room temperature (298 K), are implemented as interactive applications on the atomistica.online platform, enabling property prediction without coding or retraining. The platform also includes a structure generator and searchable databases of optimized structures and descriptors. All tools and datasets are freely available for academic use via the official web site of the atomistica.online platform, supporting open science and data-driven research in molecular design. Full article

Background: Recent research emphasizes the need to synthesize empirical studies on “K–12” math teachers’ professional development (PD) programs and their impact on student learning. Objective: This meta-analysis examines how teachers’ participation in PD programs affects students’ math achievement, analyzing the influence of program characteristics, such as duration, PD teaching approach, modality, grade level, type of math content, PD category, and study design. Design: Using online databases, 30 randomized or quasi-experimental studies from the U.S. and Canada (2003–2021) were selected, yielding 164 independent effect sizes, as some studies reported multiple interventions. Results: Only 1% of publications met the inclusion criteria. Most were excluded due to duplication, geographic location, lack of K–12 focus, missing data, or non-empirical content. PD was most effective when programs were under a year, focused on geometry, combined content and pedagogy, targeted grades 6–8, used online video, were reform-initiated, and employed randomized designs. Modality did not significantly impact outcomes. Conclusions: While extensive research exists on PD best practices, few studies empirically link program features to student achievement. This study offers evidence that well-designed math PD can significantly improve student outcomes, providing actionable insights for educators and policymakers. Full article

This paper presents the study of integrating a deep motion model into simple online and real-time tracking for video multi-object tracking. The tracking-by-detection paradigm faces significant challenges in handling nonlinear motion and occlusions. Although conventional Kalman-filter-based methods such as the SORT are efficient, they suffer from error accumulation because of their linear motion assumption. We propose KalmanFormer, a novel framework that enhances Kalman-filter-based tracking through adaptive motion modeling for video sequences. KalmanFormer consists of three key components. First, the inner-trajectory motion corrector, built upon the transformer architecture, refines Kalman filter predictions by learning nonlinear residuals from historical trajectories, thereby improving adaptability to complex motion patterns in videos. Second, the cross-trajectory attention module captures interobject motion correlations, significantly boosting object association under occlusions. Third, a pseudo-observation generator is integrated to provide neural-based predictions when detections are missing, stabilizing the Kalman filter update process. To validate our approach, we conduct comprehensive evaluations on the video benchmarks DanceTrack, MOT17, and MOT20 to demonstrate its effectiveness in handling complex motion and occlusion. The experimental results on the DanceTrack, MOT17, and MOT20 benchmarks demonstrate that KalmanFormer achieves competitive performance, with HOTA scores of 66.6 on MOT17 and 63.2 on MOT20, and strong identity preservation, IDF1: 82.0% and 80.1%, respectively. Full article

As a perceptual representation of ecosystem structure and function, the soundscape has become an important indicator for evaluating ecological health and assessing the impacts of human disturbances. Understanding the spatiotemporal heterogeneity of soundscapes is essential for revealing ecological processes and human impacts in protected areas. This study investigates such heterogeneity in Zhangjiajie National Forest Park using deep learning approaches. To this end, we constructed a dataset comprising eight representative sound source categories by integrating field recordings with online audio (BBC Sound Effects Archive and Freesound), and trained a classification model to accurately identify biophony, geophony, and anthrophony, which enabled the subsequent analysis of spatiotemporal distribution patterns. Our results indicate that temporal variations in the soundscape are closely associated with circadian rhythms and tourist activities, while spatial patterns are strongly shaped by topography, vegetation, and human interference. Biophony is primarily concentrated in areas with minimal ecological disturbance, geophony is regulated by landforms and microclimatic conditions, and anthrophony tends to mask natural sound sources. Overall, the study highlights how deep learning-based soundscape classification can reveal the mechanisms by which natural and anthropogenic factors structure acoustic environments, offering methodological references and practical insights for ecological management and soundscape conservation. Full article

The main objective of the paper is to verify whether the integration of attention mechanisms could improve the effectiveness of online fake news detection models. The models were training using selected deep learning methods, which were suitable for text processing, such as CNN (Convolutional Neural Network), LSTM (Lon-short Term Memory), BiLSTM (Bidirectional LSTM), GRU (Gated Recurrent Unit), and transformer. The novelty of the paper lies in the addition of attention mechanisms to each of those models, and comparison of their performance across both datasets, LIAR and WELFake. Afterwards, an analysis of resulting changes in terms of the detection performance was carried out. The paper also describes the issue of toxicity in the online space and how it affects society, the toxicity sources, and methods to tackle it. Furthermore, the article provides a description of individual deep learning methods and the principles of attention mechanism. Finally, it was shown that the attention mechanism can increase the accuracy of basic models for fake news detection; however, the differences are insignificant in the case of the LIAR dataset. The reason for this can be found in the dataset itself. In contrast, the addition of attention mechanism to models on the WELFake dataset showed a significant improvement of results, where the average accuracy was 0.967 and average F1-rate was 0.968. These results were better than the results of experiments with the simple transformer. Comparison of the results showed that it makes sense to enrich the basic neural network models with the attention mechanisms, especially with the multi-head attention mechanism. The key finding is that attention mechanisms can enhance fake news detection performance when applied to high-quality, well-balanced datasets. Full article

Artificial intelligence is transforming higher education, particularly in flipped classroom settings, in which students learn independently prior to class and collaborate during in-person sessions. This study examines the role of ChatGPT as a virtual peer in a veterinary anatomy course centered on cardiovascular and respiratory systems. Over two academic years (2023–2025), 297 first-year veterinary students worked in small groups to explore anatomy through structured prompts in English and Spanish using ChatGPT versions 3.5 and 4. Activities involved analyzing AI output, evaluating anatomical accuracy, and suggesting alternative names for vascular variations. Learning outcomes were assessed using Bloom’s Taxonomy-based questions, and student perceptions were captured via online surveys. Progressive performance improvement was noted across three instructional phases, particularly in higher-level cognitive tasks (Bloom level 4). Responses to English prompts were more accurate than those to Spanish prompts. While students appreciated ChatGPT’s role in reinforcing knowledge and sparking discussion, they also flagged inaccuracies and emphasized the need for critical evaluation. Peer collaboration was found to be more influential than chatbot input. Conclusions: ChatGPT can enrich flipped anatomy instruction when paired with structured guidance. It supports content review, fosters group learning and promotes reflective thinking. However, developing digital literacy and ensuring expert oversight are essential to maximizing the educational value of AI. Full article

Architectural education programs are rapidly expanding the use of immersive technologies worldwide. An increasing number of architecture schools have incorporated 360-degree videos as one of the accessible and cost-effective immersive tools. Despite their availability and ease of use, research on their effectiveness as a learning tool in architectural pedagogy remains limited and mostly focused on architectural design education. Few studies have discussed their application in theoretical courses and their potential to support cognitive understanding of architecture. Learning cultural heritage is considered a foundation of architectural theory. This study examines how the utilization of 360-degree videos, compared to conventional 2D videos, supports spatial cognition, learning performance and experience in cultural heritage education among undergraduate architecture students. An educational experiment was conducted with 89 students in their second year of the architecture degree at the Applied Science Private University, Jordan. Both 360-degree videos and conventional 2D videos were inserted as learning tools within the curriculum of History of Architecture 1 and 2 courses. A mixed-research-method framework, including observation and a post-test survey, was carried out. Using SPSS and Excel programs, the data were analyzed through a set of statistical analyses such as paired-sample t-tests, AHP, and basic descriptive analysis. The findings demonstrate that students were highly immersed and motivated when using 360-degree videos. Compared to conventional 2D videos, 360-degree videos enhanced students’ spatial cognition, performance, engagement, and participation levels in both face-to-face and online courses. These results suggest that 360-degree videos can serve as a sufficient, low-cost, and equipment-free learning tool, responding to the urgent need to utilize technologies in both theoretical and practical architectural pedagogy. Full article