Online-Journals.org (International Association of Online Engineering)
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Development of an Advanced Biology Learning Website in the Fields of Biotechnology, Biochemistry, and Biomedicine with the STEAM Approach
Get PDFThe growing industry has given birth to various innovations, one of which is the emergence of three new branches of knowledge in biology: biotechnology, biochemistry, and biomedicine. However, this branch of science has not been explored in biology education for students. Technology, in the form of online learning platforms, can address current challenges. STEAM-based biology learning can increase student engagement, motivate creative exploration, and stimulate critical thinking. This study aims to develop an advanced biology learning platform and test the feasibility of the learning media. This study follows the analysis, design, development, implementation, and evaluation (ADDIE) model. The results of this study have led to the development of the “Advanced Biology” website, which contains various exciting features for students. This feature includes a homepage display, learning content display, case-based learning, and research project display. This platform provides engaging educational videos and comprehensive learning materials. The case studies on this site offer an overview of problem-solving and the current state of the industry. This media validates learning materials, media, and technology in the ideal category
Research on the Construction and Application of an Intelligent Education Learner Model Based on the UTAUT Theory
Get PDFWith the rapid development of computer technology, a new educational model, the innovative education learner model, has emerged as a product of the deep integration of technology and education. In this paper, we will begin by organizing the theories and models related to technology acceptance. We will select the UTAUT model, known for its high explanatory power, as the theoretical framework. Subsequently, we will comprehensively analyze the dataset and conduct in-depth habit mining. The effectiveness of applying K-means concepts to address the classification of clusters of learners’ learning habits is confirmed. The feasibility of the LSTM algorithm in predicting learners for exercise responses is also demonstrated. Next, a learning cluster construction method based on intelligent learner clustering is proposed. The methods of MDS+K-means and spectral clustering are selected for clustering. Learning clusters are constructed, and the performance of the two types of algorithms is compared and analyzed. Finally, the enhanced text feature extraction algorithm is utilized to design and implement the corresponding system for the practical application of the innovative educational learner model. The final experiment proves that the text features extracted by the model are effective, with an error rate of only about 2.8%, thus demonstrating that the intelligent educational learning model in this paper is reasonable
Design and Optimization of Human-Computer Interaction System for Education Management Based on Artificial Intelligence
Get PDFThe continuous improvement and refinement of artificial intelligence (AI) technology has facilitated the broader application of human-computer interaction in the field of education management. The construction of an educational management human-computer interaction system based on AI technology can optimize and improve key parameters of educational management human-computer interaction scenarios, thereby creating a more comprehensive mobile learning (m-learning) application system. This paper is based on AI technology, analyzing gesture semantics and speech semantics, and combining fusion algorithms to construct an education management human interaction system. The performance changes of the system were compared with real experimental operations and the NOBOOK platform analysis. The results show that the education management human-computer interaction system constructed in this article can enhance the m-learning experience of participants. It ensures high recognition accuracy, leading to higher scores in all dimensions of indicator evaluation. Therefore, as one of the crucial forms of m-learning, the human-computer interaction system for education management based on AI can establish a foundation for the further enhancement and development of education management
Mobile Technology and University Climate: Impact on Academic Well-Being
Get PDFThis study explores the complex relationship between mobile technology, perceived university climate quality (PUCQ), and their combined impact on predicting students’ academic subjective well-being (ASW) at King Khalid University (KKU). The research aimed to identify the nature of the correlation between the PUCQ and ASW and to verify the degree of the relative contribution of the PUCQ in predicting ASW among KKU students. To achieve the objectives of the study, a descriptive approach was employed, incorporating both correlational and comparative methods. The researchers prepared a measure of PUCQ and a measure of ASW for university students. The research sample consisted of 134 students, with an equal split of 67 males and 67 females, whose ages ranged from 18 to 45 years. They belonged to various programs at KKU. Pearson’s correlation coefficient, simple regression analysis using the Enter method, and multiple regression analysis using the stepwise method were employed for data processing. The results of the study revealed a statistically significant positive correlation between the PUCQ and the values of citizenship. In addition, the PUCQ in its three dimensions contributes to predicting ASW among the research sample. In an era dominated by mobile technology, understanding its role is pivotal for creating a positive and supportive academic environment at King Khalid University
Secured Computation Offloading in Multi-Access Mobile Edge Computing Networks through Deep Reinforcement Learning
Get PDFMobile edge computing (MEC) has emerged as a pivotal technology to address the computational demands of resource-constrained mobile devices by offloading tasks to nearby edge servers. However, ensuring the security and efficiency of computation offloading in multiaccess MEC networks remains a critical challenge. This paper proposes a novel approach that leverages deep reinforcement learning (DRL) for secure computation offloading in multi-access MEC networks. The proposed framework utilizes DRL agents to dynamically make offloading decisions based on the current network conditions, resource availability, and security requirements. The agents learn optimal offloading policies through interactions with the environment, aiming to maximize task completion efficiency while minimizing security risks. To enhance security, the framework integrates encryption techniques and access control mechanisms to protect sensitive data during offloading. The proposed approach undergoes comprehensive simulations to assess its performance in terms of security, efficiency, and scalability. The results demonstrate that the DRL-based approach effectively balances the tradeoffs between security and efficiency, achieving robust and adaptive computation offloading in multi-access MEC networks. This study contributes to advancing the state-of-the-art in secure and efficient mobile edge computing systems, fostering the development of intelligent and resilient MEC solutions for future mobile networks
Rural Tourism Management Cloud Service Platform Based on Interactive Mobile Embedded Systems
Get PDFAiming to address the issues of low data execution efficiency and slow storage speed on traditional tourism cloud service platforms, a rural tourism management cloud service platform based on an embedded system is proposed. The embedded device components, such as the ARM processor, NOR Flash memory, and RTL network chip, are utilized to optimize the hardware structure of the cloud service platform. This optimization aims to enhance data processing and storage efficiency, as well as improve the reliability and durability of system operation. The unstructured feature grasping algorithm is employed to gather rural tourism resource data for target demand characterization. Associated algorithms are then utilized to develop data analysis, service recommendation, and tourism management modules, creating a comprehensive tourism cloud service platform. Additionally, the shortest path algorithm is used to enhance database storage speed, improve business docking efficiency, establish a big data monitoring center, comprehensively monitor changes in the scenic area and related industries, and enhance platform service efficiency. It has been proven through experiments that the latency of the tourism management cloud service platform, based on the embedded system, is reduced by almost 46.7% compared to the traditional approach, and the throughput is increased to 0.69 b/s. The practical application effect is positive, as it contributes to the development of tourism resources and the economic growth of rural areas. It also brings about economic and social benefits
Implementation of Project-Based Learning Computational Thinking Models in Mobile Programming Courses
Get PDFThis study focuses on investigating the integration of project-based learning with computational thinking (PjBL-CT) skills to enhance students’ proficiency in learning mobile programming. The findings of the model provide an evaluation of cognitive aspects, affective aspects in the form of 4C soft skills, and psychomotor aspects. To measure the effectiveness of the model, we will test the differences in the quality of student learning outcomes between two groups: the control group and the experimental group. The cognitive test results showed a value of 80.85 for the experimental class and 73.21 for the control class. For the assessment of soft skill 4C, student creativity aspects are superior to other aspects. Whereas for aspects of enhancing the project’s value, an important finding from the results obtained is the significant improvement in the quality of learning by incorporating computational thinking into the problem-based learning (PBL) model
A Conceptual Approach of an Integrated Multi Criteria Decision Making Techniques and Deep Learning for Construction Project Managers Selection Problem
Get PDFThe success of a construction project depends on several critical success factors in such a hazardous scenario characterized by COVID-19 and its consequent stress. One important factor is supervision by a competent project manager with higher emotional intelligence (EI) skills especially in these pandemic times of uncertainty. The selection of this kind of project manager is, by nature, one of the most important and, at the same time, most complicated decisions to be made due to a multi-criteria decision-making (MCDM) problem. Based on previous studies, the human emotion element is often overlooked in the decision-making process. Modern evaluation would require a multimodal dataset to evaluate a competent candidate for the position. In addition, it is identified that classical MCDM is static and unable to quantify real-time human emotion. Hence, in this study, our approach uses an integrated techniques for MCDM and deep learning to address the managers’ selection problem. Accordingly, a number of techniques, such as convolutional neural networks and other variations of algorithms, will be tested and compared. The emotion in our facial emotion recognition intensities value will be forwarded to MCDM as part of the input and eventually yield a non-bias and quality decision. It is anticipated that this study will enable employers to simplify and implement an effective decision-making process by embedding EI into the decision-making process to improve the quality of their hires and source the perfect candidate for construction project managers. Therefore, this study is aligned with the national construction agenda under the Construction 4.0 Strategic Plan (2021–2025), which requires changes to be made within the construction industry in tandem with the rapid development of technology and smarter systems. It emphasizes the utilization of digital technology as well as skills and knowledge enhancement
Impact of Mobile Learning on Self-Regulated Learning Abilities of Higher Education Students
Get PDFThis study examines the influence of mobile learning (m-learning) on the self-regulated learning skills of college students using the Z-number AROMAN decision-making approach. The rapid integration of mobile technologies into educational practices has transformed traditional learning environments, offering unprecedented opportunities to enhance student autonomy and engagement. This study introduces a novel method for evaluating complex and uncertain educational scenarios through the Z-number AROMAN decision-making process. This approach provides a robust framework for assessing various intricate aspects of self-regulated learning, including goal setting (GS), self-monitoring, and reflective practices. The study underscores the transformative potential of m-learning in education, underscoring the importance of leveraging such technology to foster student-centered learning environments. This study contributes to the body of literature on educational technology, offering valuable insights for educators, policymakers, and scholars seeking to effectively implement m-learning strategies to enhance student learning outcomes
Machine Learning-Based Personalized Learning Path Decision-Making Method on Intelligent Education Platforms
Get PDFWith the rapid development of information technology, the application of intelligent education platforms has become increasingly widespread. Traditional teaching methods struggle to meet the demands for personalized learning. Personalized learning path decision-making methods, which analyze learners’ behavioral data and mastery of knowledge points, tailor learning paths for each individual. Current research indicates that these methods can significantly improve learning efficiency and effectiveness. However, existing personalized learning path decision-making methods still have shortcomings in predicting the difficulty of knowledge points and dynamically adjusting path recommendations. This paper proposes a machine learning-based personalized learning path decision-making method, focusing on two main aspects: predicting the difficulty of knowledge points for personalized learning and integrating knowledge point localization for personalized learning path decisions. Through accurate prediction of knowledge point difficulty and dynamically optimized learning path recommendations, this method provides more refined and personalized learning support on intelligent education platforms, aiming to enhance learners’ learning experience and outcomes