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Interactive Mobile Technology in Education: A Systematic Mapping and Bibliometric Analysis
Get PDFInteractive mobile technology (IMT) has become an important element in the transformation of modern education, bringing a more open, dynamic, and responsive approach to learning for learners in the digital age. This study aims to explore the impact of using IMT in various educational settings, ranging from primary to tertiary levels. Through bibliometric analysis combined with a systematic review of a number of articles taken from the Scopus database and published over the last five years (2019–2023). This study identified key findings related to the purpose and focus of using IMT in education. The results indicate that these technologies aim to enhance student motivation, engagement, learning quality, critical thinking skills, and creativity, while also improving the accessibility and flexibility of learning. The findings imply that IMT has great potential to bring about positive changes in education, creating a learning environment that is more open, dynamic, and responsive to students’ individual needs. By utilizing this technology effectively, education can deliver learning experiences that are more engaging, relevant, and adaptive to learners’ development. Therefore, it needs to be continuously supported and developed to provide maximum benefits for the learning process and the advancement of education
AI-Powered Teaching: Literature Review of ChatGPT’s Impact on University Educators
Get PDFThe conversational artificial intelligence (AI) model ChatGPT has drawn significant interest from educators, as it opens up opportunities for innovations and provides substantial potential for use by university instructors. Since there is a lack of certainty about the effective application of ChatGPT in university teaching, the research reviews the available scientific studies to address these questions. The systematically analysed data cover two years (30.11.2022–30.11.2024). Based on the tasks set, the research identifies the spheres of university educators’ activities in which ChatGPT offers educationally valuable assistance. The ChatGPT-assisted teaching activities are examined and analysed with special attention to the benefits and concerns that the application of ChatGPT may bring to university teaching. ChatGPT’s integration into mobile-supported learning settings, highlighting its role in enabling ubiquitous learning, real-time feedback, and adaptive teaching support, is scrutinised. The recommendations for coping with the perceived negative aspects of ChatGPT in university educators’ teaching are revealed and highlighted. The distinct role of ChatGPT as a supportive means in university teaching is supported
Distributed Fuzzy Logic Algorithm for Cyberattack Detection and Energy Efficiency in Wireless Sensor Networks
Get PDFWireless sensor networks (WSNs) are critical for applications like environmental monitoring and industrial automation but face challenges balancing cybersecurity and energy efficiency. Existing approaches, such as centralized intrusion detection systems (IDS) and machine learning (ML) models, suffer from high computational overhead, scalability issues, and an inability to adapt to dynamic threats. This paper proposes a distributed fuzzy logic algorithm (DFLA) that integrates cyberattack detection and energy optimization through a decentralized architecture. By employing fuzzy logic to handle uncertainty, Dempster-Shafer theory for decision fusion, and the Reptile Search Algorithm for parameter adjustment, DFLA uses dual-objective rules to dynamically evaluate metrics such as packet drop rate, residual energy, and signal strength deviation. Nodes autonomously compute an attack risk level (ARL) and adjust transmission using localized fuzzy inference systems (FIS), minimizing reliance on cluster heads. Validated on real-world datasets (WSN-DS, CIC-IDS2017) and testbeds (TinyOS), DFLA achieves 99.87% detection accuracy for Blackhole and Flooding attacks, outperforming E-LEACH and RSA-IT2FLS while reducing energy consumption by 48%. The distributed design ensures scalability with lower communication overhead than centralized systems
Gamified Mobile Learning: EFL Students’ Attitudes Toward Quizizz for Grammar Instruction
Get PDFThis study explores Vietnamese English as a Foreign Language (EFL) students’ attitudes toward the application of Quizizz as a mobile-assisted language learning (MALL) tool for grammar instruction. With the increasing integration of gamified platforms in language education, understanding students’ perceptions and engagement with such tools is essential. The study employs a quantitative research design, collecting data from 102 students enrolled in a general English course at a university in northern Vietnam. Using a questionnaire, students’ attitudes were examined across affective, behavioral, and cognitive dimensions, along with their level of satisfaction with Quizizz. The findings indicate that students generally hold highly positive attitudes toward Quizizz, with high mean scores in affective (M = 4.4828, SD = 0.70383), behavioral (M = 4.3652, SD = 0.60594), and cognitive (M = 4.5025, SD = 0.61690) dimensions, highlighting strong emotional engagement, motivation, and perceived usefulness. A significant positive correlation between attitudes and satisfaction (r = .861, p < .01) underscores the effectiveness of Quizizz in fostering an engaging learning experience. However, an analysis of gender differences revealed no statistically significant variations in attitudes, suggesting that both male and female students perceive the platform similarly. The study concludes that Quizizz is a valuable tool for grammar learning, reinforcing motivation, engagement, and satisfaction. Findings contribute to theoretical discussions on MALL and gamification, offering practical implications for EFL educators seeking to integrate technology effectively into grammar instruction
Reinventing Trust: Traditional Media Credibility and Audience Engagement in the Mobile-First Era
Get PDFThe media landscape has undergone significant transformation due to the rapid development of digital technology. This has seriously threatened the legitimacy of traditional media. Given the emergence of new media platforms, this study investigated the variables affecting public confidence in traditional media. Using the uses and gratifications theory, the study examined how media audiences perceived news credibility, content quality, and technological adaptation. In-depth interviews with media professionals and regular news audiences revealed important themes pertaining to trust, bias, content depth, and audience engagement. The study provides insights into the necessity of digital transformation tactics to preserve audience trust, as well as the importance of fact-checking, investigative journalism, and balanced reporting in preserving credibility. The study also suggests ways to strengthen the position of traditional media in the digital information ecosystem
Uncovering the Paradox: Digital Shifts in Human Resource Management through Mobile Technology and Wireless Communication – A Content Co-occurrence Analysis Using Citespace
Get PDFThis study explores digital shifts in human resource management (HRM) through content co-occurrence analysis, examining trends and the role of mobile technologies. A bibliometric content co-occurrence analysis was conducted on 28 papers from 2014 to 2024, sourced from the Scopus database. Cite space software visualized the scientific landscape and themes. The findings reveal that advanced technologies—including artificial intelligence (AI), big data analytics, cloud computing, Internet of Things (IoT), blockchain, and mobile computing—are increasingly integrated into HRM practices. These advancements have reinforced HRM’s centrality in digital transformation, focusing on data-driven decision-making, employee engagement, and strategic alignment with business goals. However, challenges remain, such as data privacy concerns, skill gaps, and balancing technology with human-centric approaches. Mobile technology is growing, particularly in M-learning applications, mobile web and video conferencing, remote laboratories, and wireless networks, in reshaping HRM processes. Integrating mobile computing and smart agent technologies enhances adaptive environments for HR professionals and employees. This study emphasizes the need for research to navigate these complexities and improve HRM outcomes. Future research should focus on mobile architectures integration, cost-effectiveness, applications, and the social impact of next-generation mobile technologies on human resource management
Guest Editorial
Get PDFThis special focus issue belongs to the annual conference on “Innovating for Impact: Digital Technologies for Public Administration, Engineering Application, Environmental Protection, Sustainable Business Operations, and Healthcare.” This conference was organized by Confab 360 Degree in collaboration with Manipal Academy of Higher Education, Dubai Campus; Rushford Business School, Switzerland; and European Global Institute of Innovation and Technology, Malta, on 19th and 20th February 2025. The official venue of the conference was the Manipal Academy of Higher Education, Dubai Campus. The chief patron(s) of the conference were Prof. (Dr.) S. Sudhindra, MAHE, Dubai; Prof. (Dr.) Tufail Syed; Prof. (Dr.) Padmakali Banerjee; and Prof. (Dr.) Nishu Ayedee. The patron(s) of the conference were Prof. (Dr.) Anuj Kumar, Prof. (Dr.) S.K. Pandey, Prof. (Dr.) Sunitha Prabhuram, Prof. (Dr.) Alok Satsangi, and Prof. (Dr.) Kanika Gupta. Prof. (Dr.) Anuj Kumar was also heading the editorial board of the conference. In this conference, more than 400 papers have been received, and 160 papers have been shortlisted for the conference presentation. More than 400 authors have participated in the conference as authors and co-authors of the papers. Out of 160 papers, 10 extended papers have been shortlisted for the special issue of the International Journal of Interactive Mobile Technologies. The authors have reviewed and revised these papers before shortlisting them
Development of Innovative Mobile QR-EFI Simulator in Problem-Based Teaching Factory (PBTF) Model to Enhance Students' 4C Skills
Get PDFThis study addresses the urgent need to bridge the competency gap between vocational education graduates and the rapidly evolving demands of the modern automotive industry, particularly in the increasingly complex electronic fuel injection (EFI) systems. Conventional learning approaches have proven insufficient in equipping students with the necessary technical and non-technical skills to adapt to current technologies. Therefore, this study aims to develop and validate a mobile-based EFI simulator integrated with QR code technology, enabling interactive visualization of the components, data flow, operational processes, and diagnostic procedures of the EFI system in an easily understandable manner. The simulator’s effectiveness is further evaluated through its integration within the Problem-Based Teaching Factory (PBTF) model to enhance students’ 4C skills: collaboration, communication, creativity, and critical thinking. Employing a Research and Development (R&D) approach with the 4-D model (Define, Design, Develop, Disseminate), the study involved 50 Automotive Engineering students at FT-UNP, divided into experimental and control groups. Validity analysis using SEM-PLS confirmed high validity of the simulator, especially in visualization aspects, while effectiveness testing showed that the experimental group using the simulator scored significantly higher in 4C skills than the control group. These results confirm that integrating the Mobile QR-EFI Simulator within the PBTF model significantly improves students’ critical and creative skills, bridges the gap between theory and practice in vocational automotive education, and meets the demand for interactive learning tools aligned with modern industry developments
Hybrid Deep and Machine Learning Framework for Predicting Alzheimer’s Disease
Get PDFDementia is term related to many symptoms regarding brain abilities for old people. These symptoms include losing memory and thinking abilities. There are many causes leading to dementia, such as vascular dementia, Parkinson’s disease, and also severe head injury. But one of the biggest reasons is Alzheimer’s disease. Diagnostic of Alzheimer’s is challenging for the psychiatrists. There are many ways to diagnostic Alzheimer’s from conducting tests for memory to thinking skills to being evaluated by a healthcare professional. Brain-imaging as MRI, can be used to diagnose Alzheimer’s dementia earlier. This paper proposes a hybrid model to predict Alzheimer’s early by combining different machine learning (ML) models with deep learning models. Many models in this hybrid are used to get the powerful from each model and increasing the accuracy and to overcome the shortage of other models if it exist. We use two datasets of MRI for the brain from Kaggle. The result shows some hybrid models achieved outstanding results, as MobileNet with KNN scores the highest accuracy of 0.96, precision of 0.96, recall of 0.96, and F1-score of 0.96. This suggests that KNN is highly effective in leveraging the MobileNet. These top classifiers from the hybrid models indicate that combining robust feature extractors such as MobileNet, InceptionV3, and VGG16 with effective ML algorithms such as KNN, MLP, and random forest (RF) provides the best results for Alzheimer’s disease prediction
A Hybrid Model for Alzheimer’s Disease Classification Based on Neural Network Architectures Enhanced by GAN Model
Get PDFAlzheimer’s disease (AD) is a neurodegenerative disorder marked by progressive cognitive decline, making early and accurate diagnosis vital for timely intervention. This study explores the efficacy of combining generative adversarial networks (GANs), convolutional neural networks (CNNs), and vision transformers (ViTs) for AD classification using magnetic resonance imaging (MRI) data. GANs were employed to generate synthetic brain images, addressing data scarcity by augmenting the dataset. CNNs were then used for feature extraction, accelerating model training, and mitigating overfitting. These extracted features were subsequently fed into ViTs, known for their ability to capture spatial dependencies in image data. Experimental results demonstrated that the proposed GAN-CNN-ViT fusion model achieved high accuracy (96%) and robustness, outperforming traditional machine learning (ML) and deep learning approaches. GAN-generated synthetic images enhanced dataset generalization, improving ViT performance in distinguishing AD patients from healthy controls. Comparative analyses validated the superiority of this approach over recent methods in AD classification. This framework underscores the potential of deep learning techniques in advancing neuroimaging-based disease diagnosis. It holds significant promise for early AD detection, ultimately contributing to improved patient outcomes and quality of life through the integration of cutting-edge computer vision and ML methodologies in medical applications