Online-Journals.org (International Association of Online Engineering)
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Impacts of Social Media Experiences on Academically Related Peer Influence and Fear of Missing Out of Secondary and High School Students
Get PDFAdolescents nowadays experience significant mental and behavioral changes, largely influenced by the pervasive use of social media, the rise in peer influence, and the fear of missing out (FoMO). However, many crucial questions regarding the relationships between social media, peer influence, and FoMO remain unanswered. Therefore, this study was conducted to explore the interconnections among social media processes, FoMO, and peer influence among secondary and high school students, with a specific focus on posts sharing academic achievements or experiences among peers. The research employed a quantitative approach, using a 5-point Likert scale questionnaire to collect data via an online survey. A total of 419 valid samples were collected and analyzed using factor analyses and structural equation modeling (SEM). The findings revealed that certain social media experience processes, namely online peer norms and novel peer experiences, significantly influenced both peer influence and FoMO. While online peer norms affected both phenomena, novel peer experiences only had an impact on peer influence. Moreover, FoMO played a mediating role in the relationship between social media experience and peer influence. Based on these findings, the study proposes solutions to optimize the impact of social media on adolescents. Gaining insights into the dynamics of social media processes, FoMO, and peer influence can enhance support for adolescents’ mental well-being and address challenges in the digital age
The Assessment of a Gradeless Residency Model for Software Engineering Education
Get PDFThe traditional practice of assessing academic performance through conventional letter and numerical grades has faced criticism for its limitations in promoting active engagement and curiosity among students. In response, the concept of gradeless education has gained traction, with the aim of fostering a more holistic learning experience. This work explores the implementation of the residency model, a form of gradeless education, in the context of engineering education. The model focuses on skill acquisition and competency demonstration while enhancing student wellness by minimizing assessment-related anxiety that students often face in graded assessments. This study evaluates the effectiveness of the residency model through a comprehensive survey conducted in a software engineering technology program at McMaster University. The survey investigates student perspectives on the model’s impact on motivation, learning experience, and attitudes towards learning. The results reveal a complex interplay of attitudes, with students acknowledging the importance of grades while appreciating the model’s rigorous assignments. The findings suggest that the residency model can encourage transformative learning experiences while warranting ongoing attention to optimize both learning outcomes and student well-being. Further research is recommended to assess the long-term impact and effectiveness of gradeless education models, emphasizing both their benefits and challenges
Enhancing Academic Performance through Blended Learning: A Study on the Relationship between Self-Efficacy and Student Success
Get PDFThis study compares the efficacy of traditional learning to blended learning, which combines in-person instruction with online learning, in terms of academic performance and selfefficacy. The two main goals of the study are to determine whether there are any significant differences in post-general self-efficacy (GSE) scores between the experimental group (EXP. G.), which participated in blended learning, and the control group (CON. G.), which only received traditional instruction, and to investigate the connection between self-efficacy scores and grade point average (GPA). The study measured self-efficacy with a pre- and post-GSE scale, evaluated academic performance using a GPA analysis, and used a quasi-experimental design with non-equivalent comparison groups. The findings showed that, in comparison to traditional learning, blended learning significantly increased students’ self-efficacy. Additionally, a positive correlation between self-efficacy levels and GPA was found, suggesting that greater self-assurance was linked to better academic performance. These results offer compelling proof of how blended learning can improve academic outcomes and foster self-efficacy. Because of its potential to enhance student learning experiences and promote academic achievement, blended learning is strongly advocated in educational settings, according to the study. Blended learning provides exceptional opportunities to engage and motivate students, ultimately resulting in improved learning outcomes. It does this by fusing conventional faceto- face instruction with online educational strategies
Enhanced Machine Learning Based Network Traffic Detection Model for IoT Network
Get PDFEnsuring the security of networks is a significant hurdle in the rollout of the Internet of Things (IoT). A widely used protocol in the IoT ecosystem is message queuing telemetry transport (MQTT), which is based on the published-subscribe model. IoT manufacturers are expected to expand their usage of the MQTT protocol, which is expected to increase the number of cyber security threats against the protocol. IoT settings are crucial to overcoming scalability and computing resource issues and minimizing the characteristics needed for categorization. Machine learning (ML) is extensively used in traffic categorization and intrusion detection. This study proposes a ML-based network traffic detection model (MLNTDM) to enhance IoT application layer attack detection. The proposed architecture for the MQTT protocol is evaluated based on its effectiveness in detecting malicious attacks and how these affect various MQTT brokers. This study focuses on low-power-consuming ML algorithms for detecting IoT botnet offenses and identifying typical attacks and their responses. With this framework, each network flow provides information that can help identify the source of generated traffic and network assaults. Results from our approach, as shown in the experiment, prove more accuracy
Designing Digital Modules in Project-Based Learning-Based Printing Graphic Design Subjects at SMK N 1 Koto Baru Dharmasraya
Get PDFLearning in the 21st century is directed towards activities that train students’ skills, emphasizing the learning process. This can be achieved by optimizing the use of technology as an educational tool, one of which is using digital learning media in the form of modules, utilizing a project-based learning model. In the development of project-based learning digital modules, the multimedia development life cycle (MDLC) development model is employed. In the process of creating this digital module application, Unity software is used as the main software to manage content layout and animation. Adobe Illustrator is used for creating UI and required assets, while Visual Studio code is utilized for coding the application. Overall, the digital module in the subject of graphic design printing has successfully passed the validity test with very positive results. Based on the practicality analysis of the digital module in the graphic design printing subject, it can be concluded that the module is highly practical for both teachers and students
Artificial Intelligence and Mobile Apps Support Intelligent Healthcare Systems for Mental Health Services
Get PDFPatients and healthcare practitioners have commended the systems’ effectiveness, ease, and user-friendliness in several situations. Utilizing cutting-edge ideas and methods from the multidisciplinary domains of electricity, computing, medical engineering, and medicine, mobile healthcare (m-health) technology advances these professions’ contributions to healthcare systems. The monitoring and delivery of healthcare interventions are becoming increasingly dependent on mobile phones. Because of their sophisticated processing functions, improved preferences, and wide range of capabilities, they are frequently referred to as pocket computers. Their advanced sensors and intricate software programmers increase the viability and innovation of m-health solutions. To design the m-health application, the design science research methodology (DSRM) framework was used. Additionally, the architecture for connecting the hospital information system and mobile device network together was established. Additionally, a few exemplary intelligent healthcare applications are examined to demonstrate how data analytics and mobile computing can be used to improve the quality of healthcare services
District Heating System: Interactive Website Project for Weilheim, Germany
Get PDFDistrict heating systems play a crucial role in the European Union for sustainable energy practices by contributing to reducing carbon emissions and thus counteracting climate change. In most European countries, the energy consumption for heating is a topic that needs to be considered. Germany has recognized the need for more awareness among its citizens about district heating systems. Therefore, an interactive website was developed in close cooperation with the city of Weilheim in Bavaria, Germany. The goal is to inform citizens about the district heating system under construction. The website aims to improve user engagement through interactive features, including a quiz, an interactive calculator, and various animations and images. The application focuses on the primary energy sources biomethane, solar energy, wood chips, and the use of environmental heat through heat pumps. The website was evaluated at a local secondary school, which yielded positive feedback
Multi-Approach Learning with Embedded Sensors Application in Gesture Recognition
Get PDFThe increased attention to human daily activities in academic circles has proven highly valuable, serving various specific needs and producing desired outcomes across different fields. Evaluating human activity data opens up numerous possibilities for researchers, facilitating personalized support options such as timely stress interventions, real-time feedback mechanisms, and applications for assisting individuals with disabilities or monitoring mental health. This paper presents a comprehensive approach integrating multiple sensors to recognize human body movements, applicable to real-life scenarios such as classrooms, driving, and kitchen-related activities. Our focus is to enhance the precision of motion classification and improve motion classification rates by merging acceleration and rotation signals and analyzing an enhanced array of features using various high-caliber machine-learning models. This methodology achieves exceptional performance and flexibility, with accuracy rates ranging between 96% and 98%, substantiating activity recognition within diverse contexts. It aims to reduce system recognition errors, improve the classification process, and promote the advanced utilization of artificial intelligence algorithms in signal processing and in controlling and enhancing bionic hands
Enhancing the Efficiency of Internship Management through the Implementation of Progressive Web App
Get PDFThis study aims to 1) develop and evaluate Progressive Web App (PWA) for student internship management, 2) assess the effectiveness of these PWA, and 3) evaluate user satisfaction with the PWA. The research sample consists of 102 students and teachers selected through purposive sampling, along with five experts in system development. Data collection tools used in this study include a system performance evaluation questionnaire and a user satisfaction questionnaire. Data analysis was conducted using mean and standard deviation. The results indicate that the PWA for Student Internship Management, developed using the ADDIE Model, supports cross-platform functionality and can be used immediately without installation. The system efficiently streamlines the management of student internships, reducing workflow steps, and allowing teachers to manage student internship data and evaluate performance effectively. Teachers can also provide continuous supervision and consultation throughout the internship period. According to the satisfaction evaluation, the PWA effectively meets user experience needs, with an average User Experience score of 4.85. The overall system performance is rated as very high with a score of 4.76, and user satisfaction is also rated very high with a score of 4.80
An Adaptive Framework for Classification and Detection of Android Malware
Get PDFThe hardware and software of a computer are controlled by its operating system (OS), which performs essential tasks such as input and output processing, file and memory management, and the management of peripheral devices such as disk drives and printers. Application software refers to programs designed for specific purposes, these applications, often freely available and open source, contribute to the rising number of downloads. In the third quarter of 2022, combined downloads from the Apple App Store and Google Play Reached an estimated 35.3 billion. However, the prevalence of insecurity in these applications and technologies heightens the potential for cybercrimes. Protection against unauthorized intruders is crucial in identifying malicious applications. Machine learning (ML) serves as a promising avenue for detecting malware attacks, offering potential solutions to bolster cybersecurity measures. We propose a novel approach utilizing ML to enhance malware detection accuracy by segmenting datasets into distinct groups. Our research employs supervised ML techniques on the CICMaldroid2020 dataset, which includes comprehensive information such as intent actions, permissions, and sensitive APIs. The dataset was partitioned into four groups, each containing 150 features, and analyzed across four experiments to distinguish between attack and benign classes. Our proposed model demonstrated exceptional performance, with the random forest algorithm achieving an accuracy of 98.6% and a precision of 98.75%. These results highlight the effectiveness of our segmentation approach and its significant contribution to advancing malware detection in Android applications, offering a promising direction for future cybersecurity solutions