Online-Journals.org (International Association of Online Engineering)
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Mobile Adaptive Routing Algorithm for Road-Aware Infrastructure-Assisted Communication in Cognitive Internet of Vehicles
Get PDFThe Internet of Things (IoT) is expanding the capabilities of traditional vehicular ad-hoc networks (VANET) into the Internet of Vehicles (IoV). However, there are a few challenges that need to be addressed in order to enhance the intelligence of IoV, leading to the development of a new evolving technology called Cognitive Internet of Vehicles (CIoV). In this study, we propose a road-aware infrastructure-assisted and vehicle-to-vehicle (V2V) adaptive routing system to select the most efficient route for delivering data from the source to the destination vehicle, with the aim of reducing data delivery delay. The performance is measured and evaluated based on packet delivery ratio (PDR), average end-to-end (E2E) delay, and normalised routing overhead using MATLAB. By comparing the proposed mobile adaptive routing algorithm (MARA) with existing protocols, it has been examined and found to outperform the existing ones in terms of performance
Tech-Enhanced Learning: Assessing the Impact of an Innovative Microlearning Module on Postgraduate Students' Perceptions and Academic Progress
Get PDFThe current study uses the lens of Robert Gagne’s nine events of instruction and the flipped classroom approach to construct a proposed educational module to teach the instructional technology course for preservice teachers at Al Ain University in UAE. The content was broken down into small parts and presented to students through videos and interactive learning exercises. A quasi-experiment was conducted to study the designed module’s effectiveness. The sample involved two groups of 50 female postgraduates from different fields of specialization. The experimental group students were taught through the implementation of the flipped microlearning module, while the control group was taught the same way, except that the educational content was conveyed without chunking. The researcher collected data by administering an achievement test and using the UTAUT questionnaire to determine student acceptance of the module. The result of the ANCOVA test revealed the existence of a substantial difference between the two groups, as f reached 4.427 in favor of the experimental group, with a significance level of 0.038. The questionnaire findings indicated high student acceptance of the module. The findings are of great importance for educational policy and curriculum designers and highlight the advantages of this module in enhancing student performance in different specialist fields
Optimizing Attendance Management in Educational Institutions Through Mobile Technologies: A Machine Learning and Cloud Computing Approach
Get PDFThe primary goal of the study is to optimize and streamline the attendance recording and monitoring process for learning sessions by leveraging advanced technologies such as machine learning and cloud computing. The methodology employed is based on the extreme programming (XP) project management approach. Throughout its phases, the entire implementation process of the application, from conception to launch, is described in detail. Firebase is used as the database manager to ensure the efficiency and security of student information and attendance records. Additionally, the Firebase machine learning kit is used to verify attendance registration through QR codes. The application was tested with fifth-year high school students from an educational institution. The user interface has been designed to be attractive, intuitive, and easy to use for both teachers and students. The study results demonstrate that the use of this application significantly reduces the time spent on attendance recording compared to traditional methods. There has been a high level of satisfaction and acceptance of the “ASYS” application among teachers and students. In conclusion, this study has successfully implemented a mobile application that revolutionizes attendance recording and monitoring in educational institutions. It harnesses the power of machine learning and cloud computing to enhance efficiency and the user experience
The Influence of Utilizing Inquiry-Based Learning Strategy on Science Accomplishment of Primary Students’ Stage
Get PDFThis study examines the impact of inquiry-based learning (IBL) on the academic performance of third-grade students in the science unit “Materials” from the third-grade science textbook in schools under the Directorate of Education in Irbid, Jordan. A sample of 71 students was selected and separated into two groups: an experimental group of 36 students and a control group of 35 students. The empirical group used IBL, while the reference group was taught using conventional teaching techniques. An achievement test of multiple-choice questions was prepared for the study, comprising 24 items. The test’s content validity was confirmed through expert judgment, and its reliability was assessed using Cronbach’s alpha coefficient, which was found to be 0.83. The test’s content validity was confirmed through expert judgment, and its reliability was assessed using Cronbach’s alpha coefficient, which was found to be 0.83. Data were collected and processed using SPSS software. The findings showed statistically significant differences in the average scores of the study participants on the post-test based on the teaching technique variable, indicating a preference for survey-based learning. Based on these findings, the investigation recommends providing in-service and pre-service training on IBL for science supervisors and teachers at the primary education level
The Influence of Mobile Information Systems Implementation on Enhancing Human Resource Performance Skills: An Applied Study in a Small Organization
Get PDFThe human resources mobile information system (HRMoIS) provides users with direct access to information via mobile devices, enhancing efficiency, productivity, and the user experience. It includes mobile applications, websites, SMS services, and other communication mediums. This system enhances information mobility, facilitates operational processes, and supports decision-making in the ever-evolving technological landscape, ultimately improving users’ efficiency and productivity. Al-Anbar Province is undergoing significant urban and economic development. Still, more research is needed on the impact of HRMoIS on employee performance in Anbar to enhance their skills. This study aims to provide insights for top management and decision-makers on implementing similar methods to enhance employee performance. The study employs a questionnaire to survey managers and regular employees in 20 small organizations in Al Anbar, Iraq. The data is analyzed using percentages, frequencies, Pearson correlation, and Cronbach’s alpha coefficient. This allows for a comprehensive examination of all the essential aspects of human resource dynamics. The questionnaire upholds integrity and precision by aligning with roles and responsibilities. The study found that organizations implementing HRMoIS had higher mean values despite barriers such as organizational resistance, limited resources, and inadequate training. The results underscore the importance of organizations addressing these issues and enhancing their HRMoIS adoption. The study investigates the impact of HRMoIS on HR performance in small organizations in Iraq. The results show a positive correlation between mobile information systems and HR management efficiency. The study recommends implementing mobile information systems in small organizations in Al Anbar to enhance their performance. It emphasizes the need for providing necessary hardware and software, hiring experienced consultants, ensuring employee training and development, supplying HRMoIS with relevant data, and appointing experienced employees to keep up with technological advancements
A Study of the Factors Influencing Teachers' Willingness to Use Generative Artificial Intelligence Based on the UTAUT Model
Get PDFThe advancement of wireless communication and mobile computing technologies has paved the way for the extensive application of Artificial Intelligence across diverse sectors, including electronics, automotive, medical, industrial, and educational fields. Employing the Unified Theory of Acceptance and Use of Technology, this study investigates the key determinants influencing preschool teachers’ willingness to utilize Generative Artificial Intelligence (GAI) in Jiangsu Province, China. The research, involving 154 participants, analyzes their inclination towards GAI adoption through four dimensions: performance expectancy, effort expectancy, social influence, and facilitating conditions. Findings reveal that performance expectancy, social influence, and facilitating conditions significantly enhance their willingness to adopt GAI. Additionally, teaching experience and IT proficiency were found to moderate the effects of certain variables on this willingness. This aligns with the broader goal of integrating digital technology into education, which is a vital element of the nexus between digital technology development and innovation
Development of STEM-Based Learning Media FDS (Fire Detector System) Integrated with Blynk IoT to Improve Students' Creativity on Temperature Material
Get PDFThe Society 5.0 era emphasizes the use of technology and innovation to address social challenges, such as the utilization of Internet of Things (IoT) technology. Creative thinking plays a vital role in generating new ideas and approaches to problem-solving. Science, technology, engineering, and mathematics (STEM)-based education can help individuals develop creative thinking skills by engaging in problem-solving and creating innovative solutions. This study aims to enhance the creative thinking abilities of junior high school students on the topic of temperature through the development of STEM-based instructional media. This is achieved by creating the fire detector system (FDS) educational kit and testing its validity and practicality in the learning process. The study model used in this study is the analysis, design, development, implementation, and evaluation (ADDIE) model. The ADDIE model consists of five stages: analysis, design, development, implementation, and evaluation. The research instruments utilized include structured questionnaires for expert validation and application testing. The participants involved in this study include several seventh-grade students and one science teacher from a junior high school. The collected data is then analyzed using qualitative and quantitative descriptive data analysis techniques. By utilizing the FDS educational kit, it is hoped that it can effectively foster creative thinking skills among junior high school students
Augmented Reality Mobile Application for Promoting Culture and Heritage in Thailand and Malaysia: The Prototype Development and Heuristic Evaluation
Get PDFThe global coronavirus pandemic sparked widespread lockdowns and travel restrictions, leading to a significant economic downturn. However, the subsequent development of vaccines prompted governments to ease restrictions and explore technology-driven strategies for tourism revival, including the use of mobile applications and augmented reality (AR). In light of the progress of AR, this study addresses the lack of AR applications for promoting tourism in Southeast Asia, particularly in Thailand and Malaysia, where language barriers hinder cross-cultural communication. Therefore, this study aims to develop a content-specific AR mobile application to enhance tourists’ exploration of cultural heritage and facilitate communication in these multicultural and Islamic-influenced regions. This study adopts the design thinking approach for a solution-based methodology. The developed AR mobile application aims to enhance the tourist experience by providing immersive and informative content specific to the culture and heritage. The prototype undergoes a heuristic evaluation by ten experts in human-computer interaction (HCI). The results show a positive consensus on many aspects, highlighting the potential of the AR mobile application to promote culture and heritage effectively. Qualitative exploration through expert feedback is recommended in future studies to refine specific elements for a universally satisfactory user experience
Advancements in Satellite Remote Sensing for Predicting Large-Scale Wildfire Risks: An Image Processing Algorithmic Framework
Get PDFWith the escalation of global warming and human activities, large-scale wildfires have become increasingly frequent, posing significant threats to both ecological environments and human societal safety. Satellite remote sensing technology plays a pivotal role in wildfire monitoring and risk assessment, providing extensive geographical coverage and continuous monitoring capabilities. Traditional methods for predicating wildfire risk, however, face limitations in processing large-scale remote sensing data, especially in cloud detection and temporal information analysis. In response to this challenge, a novel set of image processing algorithms has been developed to enhance the efficiency and accuracy of wildfire risk prediction. Initially, a cloud detection and removal algorithm based on deep learning is introduced. This algorithm effectively identifies and eliminates cloud interference in remote sensing images, thereby significantly improving the quality and usability of image data. Subsequently, a temporal information capturing technique is proposed, capable of processing vast amounts of remote sensing data and extracting time series features. This technique provides robust data support for wildfire risk prediction. The application of these technologies not only improves the efficiency of the data processing workflow but also enhances the timeliness and accuracy of the prediction model, holding significant practical importance for guiding actual wildfire prevention and response measures
Machine Learning Models to Classify and Predict Depression in College Students
Get PDFDepression is an increasingly common mental health condition worldwide and is influenced by various factors such as anxiety, frustration, obesity, medical issues, etc. In severe cases, it can even result in suicide. This study aimed to utilize machine learning (ML) models to categorize and forecast student depression. The research involved analyzing a dataset of 787 college students through a series of steps, including cleansing, model training, and testing using techniques to classify and predict student depression. Three ML models were employed: logistic regression (LR), K-nearest neighbor (KNN), and decision tree (DT). The findings revealed that the LR model achieved the highest accuracy in prediction, with a rate of 77%, 70% recall, and 72% F1 score. Moreover, the study highlighted that two out of five students experience mild depression, around 90% of depressed students do not seek treatment, obese students are 2.5 times more prone to depression, male students are twice as likely to be obese, and male students generally have a higher body mass index (BMI) compared to female students. The study concludes that integrating ML models into the triggers that lead to depression among students