Online-Journals.org (International Association of Online Engineering)
Not a member yet
9805 research outputs found
Sort by
IoT-Based Smart Walking Assistant for Fall Detection in the Elderly
Get PDFThis study introduces the design, development, and evaluation of a smart walking assistant tailored to enhance the mobility, safety, and independence of elderly individuals. The system integrates an ESP32 microcontroller to interface with multiple sensors, including the MAX30102 for monitoring heart rate and blood oxygen saturation, the GY-906 infrared sensor for body temperature measurements, and the MPU6050 accelerometer and gyroscope for precise motion tracking and fall detection. A compact and modular control unit, seamlessly integrated into the walker, enables real-time data collection and wireless transmission using LoRa and Wi-Fi technologies. This connectivity facilitates the delivery of alerts to caregivers through a user-friendly mobile application. Rigorous testing, including simulated fall scenarios and physiological parameter measurements, validated the system’s accuracy, reliability, and responsiveness. The results demonstrated high precision in detecting obstacles, falls, and physiological anomalies, while the system’s integration with IoT-based communication platforms ensures timely intervention. The smart walking assistant offers a comprehensive and effective solution, promoting safety and quality of life for elderly users
Optimizing Cheating Detection in Online Exams with K-Shingling, MinHashing, and LSH: A Comparative Analysis with TF-IDF and BoW
Get PDFDetecting cheating in online exams is a major challenge, not least to guarantee the originality and independence of answers. This paper presents a comparative analysis of three feature extraction methods for cheating detection based on similarity detection: Term frequencyinverse document frequency (TF-IDF), Bag of Words (BoW), and a new approach combining K-Shingling, MinHashing, and Locality Sensitive Hashing (LSH). We evaluate these methods in terms of their ability to accurately and efficiently identify similarities between student responses. Experimental results show that the K-Shingling, MinHashing, and LSH pipelines consistently outperform or match traditional approaches. Logistic regression and random forest classifiers with MinHashing + LSH achieve perfect scores of 1.00 in terms of precision, recall, F1 score, and accuracy, demonstrating the robustness and effectiveness of the method. In comparison, TF-IDF and BoW show mixed performance between classifiers, with notable limitations in terms of scalability and sensitivity to text variations. This study highlights the scalability and computational efficiency of the K-shingling, MinHashing, and LSH approaches, making them particularly suitable for large-scale online examination environments. By offering a detailed performance comparison, we demonstrate that K-shingling, MinHashing, and LSH provide a more reliable and efficient solution for detecting cheating in online exams, paving the way for greater academic integrity in digital education
Rethinking Youth Mental Health: Challenges, Interventions, and Cultural Perspectives
Get PDFYouth mental health disorders, particularly anxiety and depression, are rising globally, exacerbated by factors such as the COVID-19 pandemic, technology overuse, and climate anxiety. This paper explores key challenges, cultural influences, and potential interventions through qualitative interviews with three psychologists from different cultural backgrounds who are experts in youth mental health, supported by a comprehensive literature review. Thematic analysis identified four major themes: 1) cultural influences on mental health, 2) the role of community support, 3) digital psychotherapy, and 4) mental health literacy (MHL). Findings highlight the necessity of culturally responsive interventions, the benefits of integrating digital solutions with traditional therapy, and the importance of education and policy reforms in reducing stigma and improving accessibility to mental healthcare. While experts agree on the need for systemic changes, differences in perspectives on the feasibility of solving the youth mental health crisis emerged. Addressing this crisis requires a holistic, multidisciplinary approach that balances medical, social, and educational strategies while promoting personal responsibility for mental well-being
Emerging Technologies in Learning: A Bibliometric Analysis of Technology Integration and Applications
Get PDFSmart learning, a field marked by rapid evolution and innovation, leverages emerging technologies to address modern educational challenges, transforming teaching methodologies and driving significant progress. This study analyzes the impact of key technologies, including artificial intelligence (AI), the Internet of Things (IoT), big data, and generative AI, between 2010 and 2024 using bibliometric and content analysis methods. Drawing from Scopus and Web of Science (WoS) databases, it highlights how these innovations foster personalized learning environments and dynamic educational content. The findings reveal exponential growth in research on AI and the IoT in education since 2015, with major contributions from Chinese and American researchers. The study profiles influential researchers, leading institutions, and pioneering countries, offering insights into the evolving landscape of smart education. By examining trends and the interplay between technology and educational reform, the paper underscores the importance of data-driven strategies for designing and implementing adaptive learning systems. It also anticipates future challenges and opportunities, proposing a framework to guide ethical integration of these technologies into education, ensuring they enhance global learning outcomes in an increasingly digital world
Challenge-Based Learning in Biomedical Engineering: Developing Skills for the Future
Get PDFOne of the primary objectives of higher education (HE) is to produce specialized human resources with the necessary competencies for the challenges encountered in our professional lives and our complex environment. One form of experiential education for biomedical engineering students is to expose them to real situations so that, based on acquired knowledge, they develop highlevel disciplinary competencies that prepare them for a future job with greater expectations. This report analyzes the use of teaching strategies such as challenge-based learning (CBL) for the development of technical skills through the design and manufacture of a walking aid device, the implementation of effective methods for the development of medical devices, and the identification of sustainability in engineering. A tournament skills event evaluated the results by highlighting specific solution proposal points. This study is an illustrative case that provides significant evidence of the effectiveness of CBL and can serve as a model for pedagogically sensitive evaluation of the engineering classroom by integrating blended learning schemes using gamification techniques
Development of an Ultrasound Image Extraction Method for Detection and Classification of Kidney Abnormalities Using a Convolutional Neural Network
Get PDFThe Kidney is an important organ that filters waste, toxins, and excess fluids from the blood and removes the waste in the urine. Kidney disease is a condition that occurs when it becomes damaged or impaired. In this study, a model was proposed using a convolutional neural network (CNN) and ML technology, which were trained on a dataset of 306 kidney ultrasound images. This development included Canny extraction methods, grey level co-occurrence matrix (GLCM), and principal component analysis (PCA) to obtain kidney abnormalities detection features. Additionally, the new method created was called Canny Grey Principal Component Analysis Pattern (CGPCAP). This CGPCAP method was developed to achieve better results in feature extraction and classification multi detection kidney used scale invariant feature transform (SIFT) to detect extreme, low-contrast, and edge areas in kidney images. Following this discussion, CGPCAP was tested on an image retrieved from the database, with a set of 16 features extracted. CGPCAP achieved high classification accuracy through the experimental use of a CNN classifier. Other objectives included performing feature extraction and classification between normal kidneys and kidney abnormalities. In this study, multi detection kidney was used to detect extreme, low-contrast, and edge areas in kidney images. CNN was used to classify kidney images based on the feature extraction results. Relating to this discussion, the results of the proposed method were compared with automatic feature extraction using Canny, GLCM, SIFT, and PCA. The extracted features were inputted to the CNN classifier, which achieved the highest accuracy of 97.5% compared to other abnormality detection methods. This suggests that the CGPCAP algorithm not only improves the model’s ability to make a more accurate prediction but is also more efficient at handling more complex data. Overall, the use of CGPCAP with the CNN test algorithm provides better results in the context of renal abnormality detection
Enhancing Electronics Courses Education: Active Learning Strategies for Undergraduate Engineering Students
Get PDFThis study provides insights into the effectiveness of active learning strategies in enhancing student outcomes in undergraduate electronics engineering education. The findings demonstrate the significant impact of these strategies on student engagement, practical skills, and overall course performance. While traditional lecture-based teaching methods have long been used, evidence suggests they may not effectively engage students or foster deep learning. This study integrates active learning strategies, such as project-based learning (PBL), simulation software, and flipped classroom techniques, to address these challenges. The study’s relevance is highlighted by the rapid advancements in electronics engineering and the demand for engineers proficient in collaboration and problem-solving. It aims to answer: (1) How do active learning strategies impact student engagement and learning outcomes? (2) How do traditional teaching methods compare to active learning in student performance? A quasi-experimental design compared traditional teaching (control group) with active learning strategies (experimental group) over one semester. Results showed a 15% increase in overall course performance, a 20% enhancement in practical skills, a 14% rise in student engagement, and an 8% improvement in exam scores in the experimental group, supporting the broader adoption of active learning in electronics education
Emerging Engineering Education in China: A Systematic Literature Review
Get PDFThis systematic literature review (SLR) delves into current research trends and challenges in articles related to China’s Emerging Engineering Education (EEE), as indexed by the Web of Science (WOS). This study focuses on three significant research questions: 1) What are the current research trends based on existing literature? 2) What is the current development status of China’s emerging engineering disciplines? and 3) What are the future research directions? In examining the current development status of China’s EEE, this study reviewed topics such as the development of EEE policy and practice teaching, as well as related challenges and issues. The future research directions suggested in this study emphasize the significance of the institutionalization of Emerging Engineering Research (EER), fostering collaborative networks and global dialogues, bridging the gap between theoretical research and practical applications, embracing innovative models and practices, integrating new principles into curricula, evaluating educational interventions and programs, and exploring interdisciplinary approaches. This review provides insights into the current state of engineering education in China and highlights future research directions to address new challenges and opportunities in this field
Systematic Insights and Trends in AI-Based Student Engagement Detection: A Systematic Review and Bibliometric Analysis
Get PDFThis systematic review critically examines the growing field of artificial intelligence (AI) applications in tracking student engagement and disengagement in educational settings. We synthesize current literature, employing bibliometric analysis to understand the complexities of technology-integrated teaching methods and their effectiveness in creating engaging learning environments. This study employs a rigorous methodological framework, incorporating the preferred reporting items for systematic reviews and meta-analyses (PRISMA) model and the population, intervention, comparison, outcomes, and study design (PICOS) criteria to ensure a structured and comprehensive review. A systematic search strategy was implemented to identify relevant studies from authoritative academic databases. The research findings indicate a significant use of new datasets and virtual learning environments, particularly emphasizing higher education. Despite the promising advancements in AI-driven engagement detection, our analysis reveals critical research gaps, such as the lack of detailed demographic information, especially the age factor that greatly influences engagement behaviors. This absence highlights the need for more specific engagement detection tools suitable for different educational levels. Another key observation is the limited research on early education, a critical area where engagement is crucial yet subtly indicated. Considering these points, we offer recommendations for future research, calling for a comprehensive approach that includes detailed demographics, integration of various learning settings, ensuring broad technology access, improving multimodal techniques, and maintaining privacy and ethical standards. The study’s practical implications underscore the need for more adaptable, inclusive, and ethically responsible technological contributions to education, benefiting educators, policymakers, and AI developers
Navigating a 360-Degree Cued Virtual Classroom: A Case Study of Learner Behaviors and Experiences
Get PDFThis study explores the navigation behaviors and learning experiences of six undergraduate and graduate students interacting with a 360-degree cued virtual classroom embedded within the learning management system canvas. Grounded in the cognitive theory of multimedia learning and signaling principles, the study employed a case study design. Data were collected through screen recordings, post-navigation surveys, and interviews. Findings revealed that learners predominantly engaged with front-facing cues, often missing those located in peripheral directions due to a lack of spatial navigation awareness. Prior experience with virtual reality (VR) and the type of instructional guidance influenced navigation effectiveness. While participants responded positively to the immersive format, several required additional training to fully explore the virtual environment. The study contributes to research on desktopbased VR integration in authentic online classroom contexts and offers practical insights for instructional designers developing 360-degree learning tools