Online-Journals.org (International Association of Online Engineering)
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A STEM Teachers’ Perspective: Mobile Application Consumption and Their Impact on Students' Creative Academic Performance
Get PDFThis study examines the effect of professional development (PD) programs on STEM teachers’ capacity to integrate STEM content into K-12 classrooms in China, employing a rigorous mixed-methods design. The study combines a systematic literature review (SLR) of empirical studies (2016–2023) from major databases (Web of Science, Scopus, ERIC, and CNKI) with quantitative data collected through stratified random sampling of 550 participants (150 teachers, 200 students, and 200 parents) across rural Chinese high schools. The methodology features: (1) a PRISMA-guided systematic review with strict inclusion criteria focusing on STEM PD outcomes and (2) crosssectional surveys analyzed via SPSS regression to examine relationships between PD participation and teaching efficacy/student outcomes. Thematic findings revealed that mobile apps enhance interactive learning but face adoption challenges due to infrastructural gaps and inadequate teacher training. Quantitative results aligned with the technology acceptance model (TAM), indicating that perceived usefulness (PU) and perceived ease of use are primary drivers of adoption, while perceived risk (PR) negatively impacts behavioral intention. The study concludes that mobile learning holds significant potential for STEM education, but it requires targeted professional development, equitable resource allocation, and risk-mitigation strategies to achieve a scalable impact. Practical implications include policy recommendations for integrating mobile technologies into teacher training programs and addressing socioeconomic disparities in digital access
Applied Informatics in the Sphere of Medical Informatics Innovation: A Review Article
Get PDFThis systematic review critically examines the pivotal role of applied informatics in advancing medical innovation, with a particular focus on the integration of artificial intelligence (AI) and machine learning (ML) technologies. By bringing together recent research, the study demonstrates how these computer tools can transform healthcare, particularly by enhancing the accuracy of illness diagnosis using advanced medical imaging and enabling real-time patient monitoring. New trends in the field indicate that deep learning (DL), the Internet of Things (IoT), and intelligent computer systems are being increasingly utilized, all contributing to enhanced patient care and the development of more effective healthcare systems based on data. The review also examines foundational enablers for sustainable innovation, including the standardization of medical data formats, interoperability across health information systems, and the implementation of robust cybersecurity protocols to safeguard patient privacy and ensure data integrity. While the integration of AI and ML is primarily perceived as beneficial within the healthcare domain, the review identifies several persistent challenges. These include issues of clinician trust in algorithmic decision-making, the need for ethically sound implementation practices, and the development of evolving regulatory frameworks to accommodate rapid technological change. Additionally, the application of ML and data mining in predicting outcomes and aiding clinical decisions shows enormous potential, which could transform how we approach preventive and personalized medicine
Open, Small, Rigmarole – Evaluating Llama 3.2 3B’s Feedback for Programming Exercises
Get PDFLarge Language Models (LLMs) have been subject to extensive research in the past few years. This is particularly true for the potential of LLMs to generate formative programming feedback for novice learners at university. In contrast to Generative AI (GenAI) tools based on LLMs, such as GPT, smaller and open models have received much less attention. Yet, they offer several benefits, as educators can let them run on a virtual machine or personal computer. This can help circumvent some major concerns applicable to other GenAI tools and LLMs (e.g., data protection, lack of control over changes, privacy). Therefore, this study explores the feedback characteristics of the open, lightweight LLM Llama 3.2 (3B). In particular, we investigate the models’ responses to authentic student solutions to introductory programming exercises written in Java. The generated output is qualitatively analyzed to help evaluate the feedback’s quality, content, structure, and other features. The results provide a comprehensive overview of the feedback capabilities and serious shortcomings of this open, small LLM. We further discuss the findings in the context of previous research on LLMs and contribute to benchmarking recently available GenAI tools and their feedback for novice learners of programming. Thereby, this work has implications for educators, learners, and tool developers attempting to utilize all variants of LLMs (including open and small models) to generate formative feedback and support learning
How to Motivate Engineering Education Faculty for Instructional Innovation: The Digital Leadership Effectiveness Test
Get PDFFaculty instructional innovation is the driving force behind engineering education reform and an important guarantee to promote the modernization of engineering education. With the advent of the digital era, digital leadership has become a key factor in encouraging faculty instructional innovation. Therefore, based on resource conservation theory and social cognition theory, this study explores the relationship between digital leadership, digital self-efficacy, and engineering education faculty instructional innovation and analyzes the mediating role of digital self-efficacy. Through the analysis of 316 valid samples, the results show that digital leadership and digital self-efficacy have a significant positive impact on engineering education faculty instructional innovation. Digital leadership also has a significant positive impact on digital self-efficacy, and digital self-efficacy plays a mediating role in the effect of digital leadership on engineering education faculty instructional innovation. This study reveals the impact and mechanism of digital leadership on engineering education faculty instructional innovation and provides empirical support for improving faculty instructional innovation in engineering education
NLP-Enhanced Techniques for Cheating Detection in Virtual Exams: A Comparative Study of String and Semantic Similarity Measures with K-Shingling, Minhashing, LSH, and K-Means
Get PDFAs online learning gains popularity, the issue of cheating becomes a topic of interest in discussions and scientific papers about virtual and distance education. The shift from in-person to online exams has raised many concerns about its potential to make cheating easier. The best way to detect cheating in online exams is to compute the closeness between answers. In this paper, we introduce a new approach based on similarity measure techniques from text mining to analyze answers in order to identify common patterns among academic responses. For this purpose, we have improved the K-Shingling-Minhashing-locality sensitive hashing (LSH) technique by incorporating the K-means clustering step, and then we applied our approach to a use case to efficiently find similar answers in a large dataset. Comparing our results with related works underscores the efficiency and applicability of our method in educational contexts, offering a novel contribution to the field of text similarity detection by highlighting the reliability and consistency of mathematical methods
Hand Gesture Recognition for Virtual Mouse Control
Get PDFOur work delved into the complexities of real-time hand motion interpretation and fingertip recognition to simulate the functionality of a traditional mouse. We developed a python-based technique that seamlessly translates hand movements into mouse commands by analyzing the angles between fingers and calculating the ratio of the hand’s silhouette to its convex hull. Our methodology was refined to ensure an intuitive and accurate user experience. However, challenges remained in achieving robustness and accuracy in gesture recognition systems in various scenarios, including variations in lighting, hand orientation, and individual human characteristics. These factors had a significant impact on system performance and reliability. To address these challenges, our approach incorporated the algorithms and machine learning models designed to adapt to different conditions. Despite these advances, further research and development were essential to improve the reliability and comprehensiveness of gesture recognition technologies
The Role of Mobile Technologies in Tracking Cyberbullying Trends and Social Adaptation among Teenagers
Get PDFThis study aimed to investigate the role of mobile technologies in tracking cyberbullying trends and promoting social adaptation among teenagers through a comprehensive bibliometric analysis. The study sought to identify key trends, influential factors, and potential intervention strategies in this rapidly evolving field. A systematic review following PRISMA guidelines was conducted, analyzing 64 documents from the Scopus database published between 2014 and 2024. The methodology employed bibliometric analysis techniques, including co-occurrence networks, bibliographic coupling, and keyword analysis. The results revealed a significant increase in research on cyberbullying and mobile technologies over the past decade. The analysis identified several prominent clusters, including the prevalence of cyberbullying, the role of social media platforms, and the effectiveness of intervention strategies. Gender and age emerged as influential factors in cyberbullying experiences and behaviors. The accessibility and ubiquity of mobile devices were found to play a crucial role in cyberbullying trends. The findings emphasize the importance of developing evidence-based approaches that leverage mobile technologies for intervention. Comprehensive educational programs and innovative approaches, such as chatbot-based coaching interventions, showed promise in reducing cyberbullying and promoting positive social adaptation. The study underscores the complex and multifaceted nature of cyberbullying, necessitating an interdisciplinary approach integrating insights from psychology, sociology, and computer science. The development of specialized assessment tools and the use of machine learning algorithms for detecting cyberbullying incidents highlight the increasing sophistication of research methodologies in this field. Future research should focus on longitudinal studies and the development of innovative, evidence-based intervention strategies
A Comprehensive Study on the Relationship between User Experience of Mobile Learning Platforms and Academic Anxiety among College Students
Get PDFWith the rapid advancement of information technology, mobile learning platforms have become essential tools for college students to acquire knowledge and enhance their academic performance. However, the widespread use of these platforms has also introduced potential issues such as academic anxiety, adversely affecting students’ learning outcomes and mental health. Existing research indicates a close correlation between the user experience of mobile learning platforms and students’ academic performance and psychological states. A thorough exploration of this relationship can aid in optimizing platform design, enhancing student learning efficiency, and alleviating academic anxiety. Current research methods primarily employ descriptive statistics and correlation analysis, lacking systematic quantitative analysis and the application of modern data mining techniques. This paper utilizes advanced clustering analysis methods to systematically investigate the relationship between college students’ user experiences on mobile learning platforms and their academic anxiety. The study includes predictive modeling based on clustering analysis and a detailed examination and validation of experimental results. The aim of this study is to unveil the intrinsic link between mobile learning platform user experience and academic anxiety, providing a scientific basis for improving platform design and optimizing user experience, thereby promoting the holistic development of college students and enhancing educational quality
Exploring the Impact of Interactive Technologies on Student Engagement in Blended Learning Environments at Higher Education Institutions
Get PDFAdopting new technologies in higher education is essential for enhancing students’ skills and knowledge. Blended learning, an innovative approach integrating traditional classroom instruction with online components, has emerged as an effective educational solution. Incorporating online elements into blended learning models has shown great promise in enriching the learning experience. This study employs a structural equation model to investigate the key factors influencing students’ adoption of blended learning in higher educational institutions. The study identifies significant relationships among various factors, highlighting their roles in shaping student experiences and intentions. These findings provide practical recommendations for educators and institutions aiming to optimize blended learning environments, ultimately fostering active student participation and satisfaction. This study contributes to the continuous improvement of educational practices and student outcomes within blended learning models, particularly with the integration of massive open online courses (MOOCs)
Artificial Intelligence in Education: Perspectives and Challenges
Get PDFUnderstanding the integration of artificial intelligence (AI) in academic institutions is crucial given Kuwait’s commitment to innovation and educational excellence. This study explores the integration of AI into higher education in Kuwait, revealing both positive perceptions and critical concerns. Qualitative insights from faculty and students highlight AI’s potential to improve learning across multiple academic disciplines. Quantitative data, collected from 310 students, shows that a majority of students hold optimistic views on AI’s effectiveness in enhancing educational processes and project-based activities. However, concerns were raised about the ethical implications, high costs, data privacy, and the complexity of AI tools. Importantly, no statistically significant difference was found between male and female students’ views on AI’s role in education. These findings are significant for policymakers and educators, guiding how to address practical and ethical challenges while facilitating the effective incorporation of AI into the educational system