Search Results (664)

Artificial intelligence technologies, particularly machine learning and computer vision, are being increasingly utilized to preserve, restore, and create immersive virtual experiences with cultural artifacts and sites, thus aiding in conserving cultural heritage and making it accessible to a global audience. This paper examines the performance of Generative Adversarial Networks (GAN), especially Style-Based Generator Architecture (StyleGAN), as a deep learning approach for producing realistic images of Egyptian monuments. We used Sigmoid loss for Language–Image Pre-training (SigLIP) as a unique image–text alignment system to guide monument generation through semantic elements. We also studied truncation methods to regulate the generated image noise and identify the most effective parameter settings based on architectural representation versus diverse output creation. An improved discriminator design that combined noise addition with squeeze-and-excitation blocks and a modified MinibatchStdLayer produced 27.5% better Fréchet Inception Distance performance than the original discriminator models. Moreover, differential evolution for latent-space optimization reduced alignment mistakes during specific monument construction tasks by about 15%. We checked a wide range of truncation values from 0.1 to 1.0 and found that somewhere between 0.4 and 0.7 was the best range because it allowed for good accuracy while retaining many different architectural elements. Our findings indicate that specific model optimization strategies produce superior outcomes by creating better-quality and historically correct representations of diverse Egyptian monuments. Thus, the developed technology may be instrumental in generating educational and archaeological visualization assets while adding virtual tourism capabilities. Full article

Adaptive learning tailors learning to the specific needs and preferences of the learner. Although studies focusing on adaptive learning systems became popular decades ago, there is still a need for empirical evidence on the usability of adaptive learning in various educational environments. This study uses LMS log data to elucidate an adaptive course design explicitly developed for formal educational environments in higher education institutions. The framework utilizes learning analytics and machine learning techniques. Based on learners’ online engagement and tutors’ assessment of course activities, adaptive learning paths are presented to learners. To determine whether our system can increase learner engagement and prevent failures, learner success and engagement are measured during the learning process. The results show that the proposed adaptive course framework can increase course engagement and success. However, this potential depends on several factors, such as course organization, feedback, time constraints for activities, and the use of incentives. Full article

Autonomous drone navigation presents challenges for users unfamiliar with manual flight controls, increasing the risk of collisions. This research addresses this issue by developing a multifunctional drone control system that integrates hand gesture recognition, obstacle avoidance, and 3D mapping to improve accessibility and safety. The system utilizes Google’s MediaPipe Hands software library, which employs machine learning to track 21 key landmarks of the user’s hand, enabling gesture-based control of the drone. Each recognized gesture is mapped to a flight command, eliminating the need for a traditional controller. The obstacle avoidance system, utilizing the Flow Deck V2 and Multi-Ranger Deck, detects objects within a safety threshold and autonomously moves the drone by a predefined avoidance distance away to prevent collisions. A mapping system continuously logs the drone’s flight path and detects obstacles, enabling 3D visualization of drone’s trajectory after the drone landing. Also, an AI-Deck streams live video, enabling navigation beyond the user’s direct line of sight. Experimental validation with the Crazyflie drone demonstrates seamless integration of these systems, providing a beginner-friendly experience where users can fly drones safely without prior expertise. This research enhances human–drone interaction, making drone technology more accessible for education, training, and intuitive navigation. Full article

Data visualization has empowered analyzing, exploring, and communicating data effectively. It has been widely adopted across diverse disciplines. However, research indicates that data visualization in education is mainly favored in distance learning environments, leaving traditional classroom settings largely unexplored. Moreover, despite the growing emphasis on data-driven decision-making in education, a notable gap exists in using visualized assessment data to develop curriculum planning in language classrooms. Therefore, we developed a model for syllabus design and material development in an EFL classroom in Taiwan based on diagnostic test results. An online adaptive diagnostic test was used to gather visualized assessment data, which was analyzed with an AI tool to identify language learning needs and to develop the syllabus design and materials. By incorporating visualized diagnostic assessment data into the decision-making process, educators can design responsive and individualized syllabi that meet the needs of students. This approach enhances the effectiveness of language teaching and makes curriculum development more accessible and manageable for educators. Full article

The digital age has transformed education, making distance learning essential. With rapid knowledge evolution, flexible and personalized learning is crucial. This article examines how ontology and semantic web technologies enhance e-learning. Ontology structures knowledge in specific domains, while the semantic web enables data automation and integration. Their adoption revolutionizes content organization and personalization. This study explores key concepts, applications, benefits, challenges, and future implications. By analyzing innovations and obstacles, it provides recommendations for educators. Ultimately, it highlights the need for a collaborative approach to leverage these technologies for a more inclusive and adaptive educational environment. Full article

This qualitative study investigates how critical thinking is cultivated in postgraduate distance learning, focusing on two time points, 2016 and 2025, in the context of the Greek higher education system. It draws on semi-structured interviews with 30 participants (15 tutors and 15 students or graduates) from two online postgraduate programmes: Education Sciences and Education and Technologies in Distance Teaching and Learning Systems. Thematic analysis was used to explore participants’ perceptions of critical thinking development. The two-phase comparison captures how understandings and practices have evolved, particularly in light of the emergence of generative AI between 2016 and 2025. In Phase B, this research specifically examines AI’s potential role in supporting critical thinking and the pedagogical adaptations required by tutors. Nine key themes were identified. One of the most pressing concerns raised was that educators are perceived as largely ineffective in fostering critical thinking through online teaching. This study contributes empirical insight and practical recommendations to improve critical thinking cultivation in digital learning environments, especially in the age of AI. Full article

Lifelong learning, particularly in adult education, has gained considerable attention due to rapid lifestyle changes, including pandemic-induced lockdowns. This research targets adult learners returning to higher education after gap years, emphasizing their preference for technology with clear, practical benefits. However, many still need help operating digital media. This research aims to identify best practices for sustainably providing digital scientific materials to students by examining respondents’ tendencies in viewing journal article pages and scientific posters, with a focus on layout designs that include both textual and schematic elements. The research questions focus on (1) identifying the characteristics of Areas of Interest (AoI) that effectively attract learners’ attention and (2) determining the preferred characteristics for each learner group. Around 110 respondents were selected during the experiments using web tracking technology. Utilizing this web-based eye-tracking tool, we propose eight activities to detect learners’ perceptions of text-based learning object materials. The fact that first language significantly shapes learners’ attention was confirmed by time-leap analysis and AoI distances showing they focus more on familiar elements. While adult learners exhibit deeper engagement with scientific content and sustained concentration during reading, their unique preferences toward digital learning materials result in varied focus patterns, particularly in initial interest and time spent on tasks. Thus, it is recommended that lecturers deliver digital content for adult learners in a textual format or by placing the important parts of posters in the center. Full article

Distance learning offers enhanced flexibility and reduced access restrictions, making it increasingly popular among non-traditional students and those juggling academic studies with professional and family obligations. This study explored the associations between study demands and resources (decision latitude and social support from lecturers and peers) and different study outcomes by applying the Job Demands-Resources Model in a distance learning context. Based on the model’s assumptions, we hypothesized that academic demands negatively predict study success in distance learning, while decision latitude and social support from lecturers and peers positively affect it. These associations were expected to be mediated by emotional exhaustion and different dimensions of engagement. The cross-sectional online study involved 286 psychology students from a German distance university. The multivariate path model revealed an association of demands and decision latitude with perceptions of competence and study satisfaction. While demands were significantly correlated with the grade point average, decision latitude was not. Consistent with the model’s assumptions, these effects were partially mediated by exhaustion and engagement. We did not find significant incremental associations of social support with the outcomes. The findings concerning measures to support students in distance education were discussed. Full article

Machine learning makes significant contributions in many areas of the applied sciences. One of these is the field of education, in the form of predicting students’ academic success and developing educational policies. In this study, two distance and kernel-based methods and eight tree-based and ensemble learning models were used to predict students’ academic success. The data set used in the study includes various variables, such as demographic information, academic information, course participation rates, and activity participation status, for 2392 students. Hyperparameter optimization was performed using genetic algorithm and grid search methods and model accuracy was tested with 10-fold cross-validation. In addition, the performances of all machine learning models were compared, using seventeen metric results for three cases, including results without hyperparameter optimization and determinations after hyperparameter optimization. Subsequent to the analyses performed, it was concluded that the SVR, GBM, and XGBoost methods have both high explanatory power and low error rates in regression problems requiring high accuracy, such as analyses aimed at predicting student success. Full article

Technological development in artificial intelligence (AI) has significantly transformed the learning context, and university-level students are now required to possess AI literacy. Effective research, however, has not been conducted to study factors influencing AI literacy. Grounded in engagement theory, self-efficacy theory, and transactional distance theory, this research investigates how anxiety, self-efficacy, and AI literacy are associated among Northern Cyprus University students. A cross-sectional survey was conducted, gathering data from 222 participating students from different universities in the region. Findings indicate that for university students in Northern Cyprus, student engagement significantly influences AI literacy. Also, the relationship between student engagement and AI literacy is mediated by anxiety reduction, which denotes that higher engagement decreases anxiety, enhancing AI literacy. Moreover, it is found that self-efficacy mediates the relationship between student engagement and AI literacy, which indicates that higher levels of engagement result in higher levels of self-efficacy, resulting in higher levels of AI literacy outcomes. Smart PLS 4 structural equation modeling (SEM) was used in data analysis and gaining meaningful insight into these relationships. The study contributes to Sustainable Development Goals (SDGs) 3 and 4 through the facilitation of mental well-being and inclusive quality education via improved AI competencies, proposing evidence-based perceptions into how engagement, anxiety reduction, and self-efficacy boost well-being and education. The findings of the study will enable educators, policymakers, and curriculum developers to design curricula and educational strategies that reduce anxiety, strengthen the self-efficacy of learners, and thereby strengthen their AI literacy level. Full article

The pandemic forced educators to shift abruptly to distance learning, also referred to as e-learning education. Educational institutions integrated new educational tools and online platforms. Several schools, colleges, and universities began incorporating online laboratories in different fields of education, such as engineering, information technology, physics, and chemistry. Online laboratories may take the form of virtual laboratories, software-based simulations available via the Internet, or remote labs, which involve accessing physical equipment online. Adopting remote laboratories as a substitute for conventional hands-on labs has raised concerns regarding the safety and security of both the remote lab stations and the Online Laboratory Management Systems (OLMSs). Design patterns and architectures need to be developed to attain security by design in remote laboratories. Before these can be developed, software architects and developers must understand the domain and existing and proposed solutions. This paper presents an extensive literature review of safety and security concerns related to remote laboratories and an overview of the industry, national and multinational standards, and legal requirements and regulations that need to be considered in building secure and safe Online Laboratory Management Systems. This analysis provides a taxonomy and classification of published standards as well as security and safety problems and possible solutions that can facilitate the documentation of best practices, and implemented solutions to produce security by design for remote laboratories and OLMSs. Full article

Background/Objectives: Although medical radiation practice is essential for current medical care, many nursing students and nurses lack sufficient basic knowledge about radiation, and they are unfamiliar with learning about it. This study aimed to evaluate the usefulness of self-made video teaching materials for radiation education for nursing students and nurses after clarifying their basic knowledge of radiation. Methods: Educational video materials were developed to provide basic radiation knowledge. The video materials included scenes of radiation measurement, such as the attenuation of scattered X-rays with distance, and illustrations drawn by nursing students to make them familiar to nursing staff. This study included 29 nursing students and 16 nurses. The participants were instructed to answer 20 questions regarding the characteristics of radiation and its influence and protection measures. The same questions were asked again after watching the video materials. Results: Nursing students and nurses correctly recognized the classification of medical or occupational exposure and the three principles for reducing external exposure; however, it became clear that dose limits do not apply to medical exposure and that radiation units and their effects on the human body were not correctly recognized. Furthermore, the educational materials were effective because the scores and the percentage of correct answers increased after viewing the video materials. Furthermore, positive comments were expressed regarding the scenes of the experiments and the illustrations drawn by the students about the teaching materials. Conclusions: The contents that should be addressed more intensively were clarified, and the effectiveness of using video teaching materials in radiation nursing education was demonstrated. Full article

As programming education becomes increasingly complex, grading student code has become a challenging task. Traditional methods, such as dynamic and static analysis, offer foundational approaches but often fail to provide granular insights, leading to inconsistencies in grading and feedback. This study addresses the limitations of these methods by integrating contrastive learning with explainable AI techniques to assess SQL code submissions. We employed contrastive learning to differentiate between student and correct SQL solutions, projecting them into a high-dimensional latent space, and used the Frobenius norm to measure the distance between these representations. This distance was used to predict the percentage of points deducted from each student’s solution. To enhance interpretability, we implemented feature ablation and integrated gradients, which provide insights into the specific tokens in student code that impact the grading outcomes. Our findings indicate that this approach improves the accuracy, consistency, and transparency of automated grading, aligning more closely with human grading standards. The results suggest that this framework could be a valuable tool for automated programming assessment systems, offering clear, actionable feedback and making machine learning models in educational contexts more interpretable and effective. Full article

The 20 min city concept ensures that essential services—such as work, education, healthcare, and recreation—are accessible within a 20 min walk or transit ride. This study evaluates urban accessibility in Ulaanbaatar by analyzing Points of Interest (POIs) and public bus transit networks using spatial analytics and deep learning techniques. Our finding highlights that geographical area characterization is a good proxy for predicting ridership in transit networks. For instance, healthcare and medical areas show a strong correlation with similar ridership behaviors. However, some areas lack nearby bus stations, leading to poorly placed transit stops with low walking scores. To address this, we propose the use of a Quad-Bus approach to identify optimal bus station locations in urban and suburban areas, considering amenity density and deep learning ridership models to diagnose and remedy accessibility gaps. This approach is evaluated using walking and transit scores for distances ranging from 5 to 20 min in the case of Ulaanbaatar city. Results show a moderate overall link between amenity density and ridership (r = 0.44), rising to 0.53 around healthcare clusters. However, >500 high-activity partitions contain no bus stop, and 40% of the city scores below 50 on a 0–100 walking index. Half of urban areas lack a stop within 300 m, leaving 60% of residents beyond a 10 min walk. Quad-Bus reallocations close many of these gaps, boosting walk and transit scores simultaneously. This research offers valuable insights for enhancing mobility, reducing car dependency, and optimizing urban planning to create equitable and sustainable 20 min city models. Full article

In this article, the authors examine the history, development, and current state of K-12 online learning, challenging the assertions that COVID-19-era distance education was unprecedented. Drawing on historical examples, the authors demonstrate how educational systems have repeatedly leveraged various technologies for remote instruction during disruptions, from correspondence courses to radio broadcasts to modern digital platforms. The analysis reveals persistent challenges in implementing effective online learning, including inadequate teacher preparation, inconsistent terminology, and limited theoretical frameworks. While K-12 online learning has shown promise for expanding educational access and flexibility, adoption remains relatively low globally. The article concludes that realizing the potential of K-12 online learning requires addressing fundamental issues in research, practice, and policy while learning from past experiences rather than treating each implementation as novel. Full article