Online-Journals.org (International Association of Online Engineering)
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Intelligent Education Based on Mobile Learning: Transitioning from Traditional Classrooms to Adaptive Learning Environments
Get PDFWith the rapid advancement of information technology and the widespread adoption of mobile devices, a paradigm shift has been observed in education—from traditional classroom- based instruction to more flexible and personalized mobile learning environments. Mobile learning has not only eliminated spatial and temporal constraints but has also enabled new application scenarios for intelligent education. However, existing learning content recommendation systems exhibit notable limitations in addressing the dynamically evolving nature of learning environments and individualized learning needs. A predominant focus on students’ static characteristics and historical learning behaviors has resulted in the neglect of the dynamic changes in the learning environment and students’ time management. Consequently, optimizing the timing of content delivery to accommodate individual learning requirements and contextual variability has emerged as a critical research challenge. To address this issue, a learning content recommendation model tailored for mobile learning environments was proposed in this study. The model comprises three main components: an encoding layer, a Transformer layer, and a prediction layer. The encoding layer combines the graph structure of the mobile learning network with the learning content recommendation problem by encoding student and interactive learning content nodes and corresponding time information. The Transformer layer adjusts the temporal influence of each node and aggregates the embeddings of both nodes and time. The prediction layer leverages the output embeddings from the Transformer layer—infused with temporal features—to perform learning content delivery prediction. Through the construction and optimization of this model, the objective is to enhance the precision and efficiency of content delivery, thereby improving educational quality and learning outcomes in mobile learning environments
Learning with Mixed Reality: From the Conference Room to the Classroom
Get PDFMixed reality technology marries human and artificial intelligence and provides an authentic and responsive learning experience. It has been used to increase learning in a variety of educational settings. Recently, custom mixed reality scenarios have been created that redefine learning by providing opportunities to develop and assess skills that were otherwise not available. Two areas of contemporary interest are the development of interpersonal skills in business education and the development of professionalism in preservice teachers
Immersive Educational Assessments with Augmented Reality as a Tool for Teacher Training Using Synthetic Data
Get PDFThis study explores using immersive assessments with Augmented Reality (AR) and synthetic data to enhance teacher training. By combining AR technology with artificial data, the study aims to create dynamic, realistic scenarios that simulate classroom environments, allowing teachersin- training to apply their pedagogical knowledge in real time. The research employed a Delphi method, involving three experts from different fields—pedagogy, technology, and literature—to evaluate the effectiveness of these assessments using a rubric. The experts provided feedback on key areas such as cognitive skills assessment, AR integration, and instructional clarity, identifying strengths and improvement areas. The results show that AR-based assessments improve engagement, foster higher-order thinking, and offer personalized learning experiences. However, challenges such as the cost of AR technology, the need for teacher training, and ensuring the integrity of assessments were also noted. The findings highlight the potential of AR and synthetic data to transform teacher education by providing more interactive, practical, and adaptive assessments. Future research is needed to fully understand the long-term impact of these technologies in real-world educational settings and to explore the combination of natural and synthetic data to enhance the effectiveness of immersive assessments further
Integrating English and Digital Literacy for Older Adults
Get PDFThis paper investigates how best to integrate English and digital literacy education for older adults to promote their digital social inclusion. The research of the ERASMUS+ project Coll-Oya focuses on the learning needs of adults aged 55 and older from Tarragona-Spain, Silesia-Poland, and Adana-Türkiye. These participants make up part of a divergent group that, though still active in life, are not digital natives. Most of them have insufficient knowledge of digital tools and the English language to be able to handle new technologies. This project thus responds to these gaps since it seeks to equip participants with both English language skills and digital literacy through content-based instruction combined with practical applications in reallife activities such as online shopping, ticket booking, and engaging in digital communication. The study explores participants’ digital awareness, language proficiency, and collaborative learning experiences with younger generations. A mixed-methods approach involving 204 participants completing an online survey and 45 interviews explores their preferences and challenges in acquiring language and digital skills. The main findings show that tailored learning opportunities increase engagement and motivation among older learners and empower them, instilling a sense of hope and inspiration. Intergenerational collaboration creates a supportive learning environment. Participants reported feeling more confident using technology and improving their English language skills through the joint teaching initiatives. The paper suggests that integrating English and digital literacy training could offer an opportunity to empower the active contribution and social inclusion of those over age 55 in the increasingly digital world. These findings provide pragmatic insights into how the digital competencies of older adults and their language proficiency can be supported in intergenerational learning, thus practically presenting strategies to educators, policymakers, and other stakeholders involved in adult education and digital literacy
Proposed Architecture for Hospital 4.0: Integrating IoT, Edge AI, and Blockchain for Secure and Efficient Healthcare Systems
Get PDFThe rapid development of various healthcare technologies results in an innovative context known as Hospital 4.0, which integrates Internet of Things (IoT), AI, and blockchain to enhance patient care, optimize operational efficiency, and guarantee robust data security. This paper proposes an innovative layered architecture that can solve some critical problems of healthcare systems: patient identification, real-time data processing, security, scalability, and interoperability. The architecture ensures that it is highly adaptable and accurate by incorporating three IoT-enabled methods of identification, including facial recognition, fingerprint scanning, and NFC/RFID. Advanced AI algorithms process biometric data, enabling real-time insight with privacy guarantees. Blockchain technology enhances security, offering an immutable, decentralized ledger for managing interactions with electronic health records (EHRs). The interoperability of the proposed framework also rests on standards like HL7 FHIR and protocols like MQTT that ensure seamless exchange of data amongst diverse systems. It further explores scalability and regulatory compliance through practical applications, including patient-centric engagement tools and predictive analytics. The architecture puts together dispersed technological innovations in an effort to provide a robust foundation for safe, efficient, patient-centered healthcare delivery and sets the stage for further research into its widespread adoption
DeepSkinNet: A Deep Learning Induced Skin Lesion Extraction System from Dermoscopic Images
Get PDFIn this work, a DeepSkinNet model was developed based on an encoder-decoder type framework. The designed encoder incorporates three blocks where each block sandwiches convolution, rectified linear unit (ReLU), and maxpooling layers to retain the prominent details. The developed DIL (dilated convolution + instance normalization + Leaky ReLU) module comprises three branches, where each branch consists of an atrous convolution layer with a sampling rate of two, followed by instance normalization and a Leaky ReLU activation function to retain the subtle details accurately. Further, the proposed decoder network with a feature fusion mechanism stacks convolution, transposed convolution, and ReLU activation functions to precisely extract the lesion regions from the dermoscopic images. The efficacy of the designed DeepSkinNet is validated through subjective as well as objective analysis and found to be suitable for medical diagnosis against various SOTA methods. The Dice coefficients (DC) found using ISIC 2016, ISIC 2017, ISIC 2018, and PH2 datasets are found to be 93.33%, 89.00%, 92.05%, and 91.24%, respectively
An Assessment of Organizational Competency, Organizational Readiness and Perceived Usefulness in Light of Behavioral Intention to Use Blockchain Technology
Get PDFThis study examines the connections between organizational competency (OGC), organizational readiness (OGR), perceived usefulness (PU), perceived ease of use (PEOU), and behavioral intention to use (BIU) blockchain technology (BCT), with a specific focus on how technostress (TCS) influences these relationships as a moderator. Using structural equation modelling (SEM) using the SMART PLS 4.0 software, we found that OGC and OGR positively impact PU and PEOU, significantly improving BIU. However, TCS substantially negatively impacts the correlations between PU and BIU, as well as between PEOU and BIU. When TCS (technostress) levels are higher, the positive impact of PU and PEOU on BIU (behavioral intentions to use) decreases. This indicates that higher levels of TCS decrease the perceived advantages of BCT, which subsequently diminishes the desire to use it. These findings highlight the importance of effectively managing TCS to optimize the positive impact that OGC and preparedness have on users’ perspectives and intentions. The study underscores the importance of conducting research across multiple cultures in technology acceptance studies and suggests incorporating TCS into future models for more accurate technology adoption outcomes
Impact and Opportunities of Generative Artificial Intelligence in Education: A Study of Academic Perceptions
Get PDFThis study evaluates the impact, adoption and perceptions of “Generative Artificial Intelligence” among professors in a higher education environment. The aim is to understand how these tools can enrich teaching and learning. Through a descriptive analysis of a questionnaire distributed to a sample of 71 professors focused on higher education, both the advantages and disadvantages of this technology were examined by formulating two key hypotheses: (1) the use of generative artificial intelligence (GAI) increases understanding of its benefits over potential barriers, and (2) the experience with the use of GAI increases willingness towards its integration into educational practice. The analysis, based on “R” software, supports the first hypothesis by observing that professors consistently perceive more advantages than disadvantages when using the tool. However, the second hypothesis is rejected since a decrease in the intention to use GAI over time was detected. This result suggests that, despite recognising the advantages, some professors are still not fully convinced or prepared to adopt this technology in their daily teaching activities
Advanced Anomaly Detection in Mobile Networks: A Hybrid Approach Based on Statistical and Machine Learning Techniques
Get PDFNetwork traffic analysis (NTA) is a technique used by network administrators to monitor network activity, ensure availability, and detect unusual patterns to identify potential anomalies. However, traditional traffic monitoring systems often struggle to detect these anomalies accurately because they rely on rigid models and a limited pool of data. Additionally, anomaly detection is particularly challenging, as anomalies exhibit patterns that differ from most network activities, making their identification based on prior knowledge difficult. This underscores the necessity for an automated and unsupervised approach capable of detecting various types of anomalies despite these limitations. In this paper, we propose an unsupervised framework that combines three statistical machine learning (ML) methods for mobile network failure detection, such as the lower control limit, the cumulative sum algorithm (CUSUM), and the robust stat detector model. Compared to previous studies, models often struggle with large datasets and generate high rates of false positives. However, our approach has proven to be better suited to the demands of traffic monitoring in large telecommunications infrastructures. It offers improved data handling and significantly reduces the rate of false positives while achieving an impressive 98% detection rate for anomalies on telecommunications sites
Design and Implementation of a Web GIS Application for Heritage Documentation Using Drones, Lidar, and Laser Scanning: The Case of Lubonja, Korçë, Albania
Get PDFCultural legacy is an integral part of a country’s identity and its historical and social development. This heritage must be preserved and protected through modern documentation, monitoring, and management technologies. In this context, information and communication technologies (ICT), specifically geographic information systems (GIS), have emerged as transformative tools. Recently, Web GIS applications have expanded opportunities for collaboration and real-time information sharing by offering accessible and interactive platforms for geospatial data integration. Web GIS empowers heritage experts to create precise maps, 3D models, and real-time heritage experts by allowing them to create precise maps, 3D models, and real-time representations of cultural assets. Despite its potential, a significant challenge lies in effectively disseminating information to attract tourists and promote heritage sites. This study explores the application of Web GIS in addressing these challenges in Lubonja, a prehistoric village in Korçë, Albania. It discusses the benefits and limitations of this technology in preserving and promoting cultural heritage and how advanced geospatial technologies can enhance accessibility, public participation, and heritage management