Online-Journals.org (International Association of Online Engineering)
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Driving the Integration of Mobile Learning and Blended Learning Models in Higher Education
Get PDFWith the rapid advancement of information technology, particularly the widespread adoption of mobile internet, the integration of mobile learning and blended learning models in higher education has emerged as a significant innovation in educational practice. Vocational education, as an educational model that emphasizes practical skills and applied abilities, faces challenges in effectively combining these two approaches. The pervasive use of mobile devices enables learners to engage in learning at any time and location, yet it raises critical issues related to the construction of efficient interactive learning networks and the enhancement of learning outcomes. Consequently, study on “mobile device-to-mobile device” offline blended learning interaction networks has gained prominence. These networks not only prioritize interaction and collaboration among learners but also aim to provide personalized and precise learning support in multidimensional and dynamic learning environments. While existing studies have yielded insights into the integration of mobile learning and blended learning, as well as the design of learning interaction networks, they often lack in-depth exploration of complex learning interaction models and dynamic data relationships. Additionally, traditional learning network models frequently suffer from limited adaptability and insufficient accuracy in practical applications. In particular, as for the construction and optimization of “mobile device-to-mobile device” offline learning interaction networks, robust theoretical frameworks and practical solutions are lacking. Therefore, this study focuses on the definition and link prediction challenges of such networks. Through scientific model design and algorithmic optimization, the study seeks to enhance interaction efficiency and personalized support within learning networks, thereby advancing innovation and development in educational models
Beyond Learner Reaction: Measuring the Impact of Leadership Development at The Ivey Academy
Get PDFThis paper provides an in-depth exploration of The Ivey Academy’s transition from traditional satisfaction-based evaluations to a more comprehensive impact evaluation approach in leadership development. Recognizing the limitations of relying solely on participant satisfaction, The Ivey Academy adopted a modified framework inspired by the Kirkpatrick Model, which evaluates satisfaction, learning, application, and longterm impact. This framework utilizes a range of data collection tools, including surveys, interviews, and action plans. The paper details the implementation process, from securing stakeholder engagement to designing effective surveys and overcoming the challenges of resistance and operational limitations. A key focus of the paper is on the impact survey results from the first term of open enrollment programs, which demonstrate significant improvements in workplace behavior and leadership strategies among participants. Additionally, it highlights the challenges in ensuring data comparability across diverse programs and audiences. Looking ahead, the paper discusses future directions for The Ivey Academy, emphasizing the refinement of the evaluation process, expanding impact measurement, and exploring standardization across various leadership development programs. This approach underscores The Ivey Academy’s commitment to driving realworld change through leadership education, offering valuable insights for other institutions aiming to adopt similar evaluation practices
The Role of Open-Source Software as a Technological Alternative for Blended Learning in LMS Systems
Get PDFBlended learning is a modality that combines the advantages of face-to-face teaching with those of e-learning. It requires the integration of information and communication technologies (ICT) in the teaching and learning process. For e-learning, it is necessary to configure a virtual learning environment (VLE). The virtualization task is carried out with the use of software that allows the digitalization of pedagogical interaction and learning management. This paper presents a literature review on the blended learning modality and the importance of software as a technological support. Subsequently, the role of open-source software (OSS) in technological development as a driver of innovation in the educational field is explored. This exploration was conducted by identifying the most widely used learning management systems (LMS) at the top universities listed in the 2024 QS World University Rankings, in the United States, Europe, and Mexico, and by describing the technological components of a VLE that has been fully and successfully implemented using OSS. The results of the literature search in the field of educational innovation reveal a scarce recognition of OSS in technological development and, therefore, in the processes of educational intervention. Therefore, works such as this one contributes to the recognition of software as a driver of technological progress
Generative AI and Mobile Learning in Higher Education: Comparing Student and Faculty Perspectives on Employability Impact
Get PDFGlobally, the production of moral citizens and professionals depends heavily on higher education. The advent of ChatGPT and other generative artificial intelligence (GenAI) tools has presented both opportunities and problems for the established educational model. The future of higher education, however, is a topic of little inquiry; instead, discussions these days are mostly focused on policy creation and evaluation. The purpose of this paper is to examine how AI is affecting international students’ education. It uses a research approach, as opposed to a standard literature review, to look at possible applications of AI and talk about related issues. The paper examines several AI applications, including chatbots for research and learning, adaptive testing, personalized learning, and statistical analysis. This study clarifies how AI may enhance learning effectiveness and offer tailored educational support by examining its role in education for international students. It also points out important dangers and constraints that must be properly addressed, such as privacy issues, cultural differences, language barriers, and ethical considerations. Predicting future advancements in AI, however, will be exceedingly challenging
Innovative Applications and Teaching Effectiveness Analysis of Interactive Mobile Technology in Music Education
Get PDFWith the rapid advancement of mobile Internet technology, the application of interactive mobile technology in education has emerged as a significant area of research, particularly in music education. Mobile technology has introduced transformative changes to both learning styles and teaching methods. Through the utilization of smart devices and mobile platforms, students are provided with personalized and flexible learning environments for music education, while teachers can leverage advanced technological tools to enhance teaching effectiveness. Although previous studies have examined the role of mobile technology in music education, most have focused on its isolated effects, neglecting a systematic analysis of the evolution of teaching effectiveness over time. Furthermore, existing methodologies often fail to account for temporal characteristics and dynamic changes, resulting in an incomplete evaluation of long-term educational outcomes. This study aims to analyze the network characteristics of interactive mobile technology applied in music education and to explore the evolution of teaching effectiveness with consideration of temporal characteristics. This study is expected to offer novel theoretical perspectives for the digital transformation of music education along with practical guidance for its implementation
Integration of Mobile Interaction Technologies in Supply Chain Management for S2B2C E-Commerce Platforms
Get PDFWith the rapid development of e-commerce, the supplier to business to consumer (S2B2C) model, as an emerging business model, has become an essential component of modern supply chain management. However, traditional supply chain management models are increasingly inadequate to meet the demands of the fast-changing market and complex supply chain collaboration, particularly in areas such as information sharing, real-time data updates, and demand forecasting. Existing research primarily focuses on the optimization of individual supply chain components, such as inventory management, order tracking, or logistics scheduling, with limited attention given to the collaboration between parties and the overall management of the supply chain under the S2B2C model. Additionally, while some studies have proposed information-sharing mechanisms and demand forecasting models based on mobile platforms, the practicality and accuracy of existing methods are still limited in practical applications due to factors such as data processing capabilities, algorithm accuracy, and the dynamic nature of consumer behavior. Therefore, this study proposes an integrated solution for supply chain management based on mobile interaction technology within the S2B2C e-commerce platform. The aim is to enhance the intelligence, flexibility, and transparency of the supply chain through technological innovation. The core research focuses on the implementation technologies for mobile terminals in supply chain management on the S2B2C e-commerce platform, along with the design and implementation of a demand forecasting model and algorithm based on mobile applications
Enhancing Cybersecurity in Wireless Sensor Networks: Innovative Framework for Optimized Data Aggregation
Get PDFThe various cyberattacks in wireless sensor networks (WSNs) have made confidentiality and data integrity as crucial principles in data aggregation. Therefore, several applications are presented to control the sharing of data and information as well as the associated cybersecurity aspects that must be preserved during data transfer. Most cybersecurity breaches that occur these days are categorized as cyberattacks. The WSN’s resource-constrained architecture makes cybersecurity lapses and insider attacks possible. This study proposes a novel technique named multi-objective pigeon-inspired optimal long short-term memory (MPI-OLSTM) networks to develop the data aggregation in cybersecurity model. Initially, the WSN-detection systems (WSN-DS) dataset is collected and pre-processed using min-max normalization. For extracting features, the principal component analysis (PCA) is employed. The model’s predictive power is assessed using the following metrics: accuracy (96.5%), precision (92.3%), and recall (90.4%). The findings demonstrate that, in comparison to existing techniques, our approach yielded more accurate results
Trends and Insights in Financial Inclusion and Mobile Money: A Decade of Bibliometric Analysis (2014–2023)
Get PDFThis study conducts a comprehensive bibliometric analysis to explore research trends in financial inclusion and mobile money over the last decade (2014–2023), using data from the Scopus database. Analyzing 439 publications, this study identifies key trends, influential authors, and leading countries in the field. The analysis, supported by tools such as VOS Viewer, reveals dominant themes such as “financial inclusion,” “mobile money,” and “financial system,” with a particular focus on technological advancements and their regional impacts, especially in developing countries. The findings highlight the growing academic interest in mobile money’s role in promoting economic development and poverty alleviation. Moreover, this study underscores the significant geographical disparities in research output, with developed economies producing the most research. Insights from this analysis provide guidance for policymakers and researchers to support future financial inclusion initiatives. The study concludes by recommending further exploration of under-researched regions and the qualitative impact of mobile money on socio-economic outcomes
Smart Diagnosis: Leveraging Machine Learning for Early Detection of Hepatitis in Healthcare
Get PDFThere is high variance related to the detection and prevention of human diseases. This is a concerning factor considering hepatitis. It is a disease that affects the functioning of the liver, which causes the deaths of millions of people in a year, around the world. Conventional methods are slow and inaccurate to a large extent. Machine learning (ML) is a process where a machine is trained using large amounts of data for it to predict about the disease. This paper aims at developing a hybrid machine-learning model using a stacking-based classifier. The hepatitis dataset is available in the UCI ML repository used in this work. A few data pre-processing steps were implemented on the dataset to create an optimum database. This includes imputing missing values, balancing the dataset using the synthetic minority over-sampling technique (SMOTE) and scaling the dataset using robust scaler. For selecting the optimum features, Chi-Square and Pearson Correlation tests were performed. The proposed model has reported a classification accuracy of 98.7%
AI-Driven Innovations in Adult EFL Learning: Exploring Potentials and Practicalities
Get PDFIn a worldwide world, adults must master numerous languages, which is difficult. Recent brain research reveals that high proficiency can be attained with minimal practice, while artificial intelligence (AI) advances offer targeted and efficient language education. This study analyzes how AI techniques such as active learning, intelligent tutoring, and natural language processing can improve adult EFL acquisition. AI can improve learning, feedback, engagement, motivation, and outcomes compared to traditional techniques. However, interpersonal communication is crucial. The study uses instructor-learner questionnaires to emphasize the cautious acceptance of AI technologies and the need for ethical frameworks and balanced integration with human educators. This study evaluates and compares attitudes, defines the best balance between human-human and human-AI, identifies best practices for ethical risk, age matters, and adult self-determination, and guides AI integration and framework development. Adapting these technologies to decrease risk and maximize autonomy is crucial. For the following few steps, stakeholders and joint research to develop credible AI guidelines for adult EFL teaching are essential. This study examines the views of 30 EFL teachers and 35 adult learners regarding using AI to teach English. The goals are to assess expected benefits, compare attitudes, and define ethical difficulties. Questionnaires examined AI impacts such as technology use, evaluation, and instigating variables. 100% of teachers expected engagement improvements. Student attitudes toward such assessment adoption were neutral, with 57% neutral on its validity. Statistical analysis using analysis of variance (ANOVA) revealed significant differences in attitudes regarding independence restriction and constraint (p < 0.05). AI augments instruction, not replaces it. Including adult learners in AI development, merging AI with traditional approaches, and using AI-augmented pedagogy to promote linguistic competency while addressing contextual and social restrictions are key recommendations