Online-Journals.org (International Association of Online Engineering)
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Curating an Offline Wikipedia for Schools in any Language: A Road Map
Get PDFAround the world, rural and remote communities face a myriad of challenges in providing quality education, especially in communities where internet and electricity access are unreliable or nonexistent. Many innovative projects and initiatives have attempted to provide technological solutions adapted to the infrastructural challenges in these unconnected areas. One such innovation, the offline digital library (ODL), has emerged as a promising and cost-effective solution for bringing information to offline locations without the need for massive infrastructure overhauls and costly ongoing maintenance. Despite the existence of numerous ODL initiatives and the critical importance of library content being relevant to its users, the process of curating collections for ODLs has not been sufficiently discussed in the scholarly literature. Using the SolarSPELL initiative as an ODL model, this article seeks to illuminate the process of curating an offline, customized encyclopedia. With the aim of enhancing the availability of digital content in offline environments, this article presents a roadmap detailing the practical insights gained from developing a tailored educational encyclopedia called Wikipedia for Schools, available in both English and Arabic. Finally, we offer best practices and lessons learned, including recommendations for future research in this field
A Data Mining-Based Approach to Managing Intercultural Teaching Activities in Online Classrooms
Get PDFDriven by globalization and technological advances, online education is coming into its own form, opening a window for students to learn across cultural boundaries. In such a context, the intercultural teaching activities in online classrooms are particularly important, as they provide students with a good opportunity to gain a deeper understanding of different cultures and merge into different backgrounds. However, most of the currently available methods of intercultural teaching activity management focus on conventional education modes or strategies, and there isn’t a deep enough analysis about the features of network environment. Aiming at these matters, this study gave an in-depth discussion on the current status of the management of intercultural teaching in online classrooms, and introduced the technology of data mining to propose a more comprehensive and systematic solution for educational issues caused by cultural differences
A Classification and Retrieval System for Learning Resources of MOOC
Get PDFIn this information age, massive open online courses (MOOCs) have become an integral component of modern education. These courses encompass a wide range of resources, such as videos, audios, texts, and other forms. An accompanying question is how to effectively organize, classify, and retrieve these resources. However, currently available classification and retrieval methods are mostly based on text retrieval technologies. As a result, multi-modal resources such as videos and audios are often ignored or incorrectly classified. Furthermore, more current methods exhibit low efficiency when processing the vast amount of data in MOOCs. To address and solve these issues, this study focuses on the extraction and fusion of multi-modal features of MOOC resources. It proposes an efficient classification and retrieval method based on 3D convolution, aiming to offer a more accurate and efficient approach for classifying and retrieving MOOC resources
Students' Perception of Mobile Applications in Calculus Learning
Get PDFMathematics is a crucial subject that students learn from primary school onward. However, some students have a negative perception of mathematics, considering it difficult and tedious, especially among Generation Z, who have grown up in a world where information is readily available at their fingertips. So, traditional teaching methods may not be very effective. This study aimed to determine students’ perceptions of mobile applications in calculus learning. The study involved 35 students from a private university in Kedah. This quantitative study utilizes a survey method and employs a questionnaire as the research instrument. The perception questionnaire is divided into four categories: attractiveness, effectiveness, relevance, and motivation. Based on the research findings, this study concludes that students had a positive perception of four aspects—attractiveness, effectiveness, relevance, and motivation— when using a mobile application for calculus learning
Data-Driven Insights in Higher Education: Exploring the Synergy of Big Data Analytics and Mobile Applications
Get PDFThis study explores the potential for transformation that may be achieved via the use of big data analytics and mobile apps in the context of higher education, specifically emphasizing the role of data-driven decision-making. Within the contemporary educational landscape, characterized by the increasing impact of digital technology and mobile devices, institutions of higher education are actively investigating innovative strategies. That enhances effectiveness, customizes learning experiences to suit individual students, and develops overall student accomplishments. The main objective of this research is to examine the effects of incorporating big data analytics and mobile applications into the decision-making capacities of higher education establishments. The PRISMA Statement was used to guide the selection and exclusion of records using the RStudio Biblioshiny approach for data analysis. A comprehensive review of the existing scholarly works, identification of groupings, and study of citation trends within the field. The results and findings illustrate the inherent importance of “big data,” “cloud computing,” “mobile computing,” and “higher education” in the field of research, underscoring their crucial role in data-driven decision-making. Furthermore, the study underscores the significant impact of contemporary technology on administrative processes, personalized learning, and scholastic attainment. This research provides a great addition to the academic field by presenting insightful findings on the substantial influence of big data analytics and mobile applications on the evolution of higher education. This study emphasizes the need to adopt data-driven insights to successfully navigate the ever-changing landscape of higher education
Brain Tumor Localization Using N-Cut
Get PDFA brain tumor is an abnormal collection of tissue in the brain. When tumors form, they are classified as either malignant or benign. It is critical to notice and identify the existence of tumors in brain images since they can be life threatening. This paper illustrates a novel segmentation method in which threshold technique is combined with normalized cut (Ncut) for the segregation of the tumors from brain magnetic resonance (MR) images. Image segmentation is a technique for grouping images. It is a method of splitting an image into sections with comparable attributes such as intensity, texture, colour, and so on. In thresholding, an object is distinguished from the background, and for the proposed segmentation methodology, the threshold value is determined by normalized graph cut. A weighted graph is divided into disjointed sets (groups) in which the similarity within a group is high and the similarity across groups is low. A graph-cut is a grouping approach in which the total weight of edges eliminated between these two pieces is used to calculate the degree of dissimilarity between these two groups. The normalized cut criterion is used to calculate the total likeness within the groups as well as the dissimilarity between the different groups
Segmentation of Retinal Images Using Improved Segmentation Network, MesU-Net
Get PDFGiven the immense importance of medical image segmentation and the challenges associated with manual execution, a diverse range of automated medical image segmentation methods have been developed, primarily focusing on specific modalities of images. This paper introduces an innovative segmentation algorithm that effectively segments exudates, hemorrhages, microaneurysms, and blood vessels within retinal images using an enhanced MesNet (MesU-Net) model. By combining the MES-Net model with the U-Net model, this approach achieves accurate results in a shorter period. Consequently, it holds significant potential for clinical application in computer-aided diagnosis. The IDRID and DRIVE datasets are utilized to assess the efficacy of the proposed model for retinal segmentation. The presented method attains segmentation accuracy rates of 97.6%, 98.1%, 99.2%, and 83.7% for exudates, hemorrhages, microaneurysms, and blood vessels, respectively. This proposed model also holds promise for extension to address other medical image segmentation challenges in the future
Factors That Influence the Adoption of Digital Dental Technologies and Dental Informatics in Dental Practice
Get PDFThe factors affecting information systems and technology have become a growing topic in many disciplines. This study focuses on factors affecting the adoption of digital dental technologies and dental informatics in dental practice. There are limited studies in the literature on factors that affect the adoption of digital dental technologies (DDT) and dental informatics (DI). Understanding the factors is important for the success of the adoption of technologies. Therefore, this study aims to fill that gap. This paper reviews peer-reviewed literature to analyze factors that affect the adoption of digital dental technologies (DDT) and dental informatics (DI) and critically examines an array of technology acceptance models to unveil the underlying determinants of DDT and DI adoption. Usability and practical considerations, work efficiency factors, socioeconomic and organizational aspects, aspects of the learning curve, and system design are the most important factors influencing the adoption of digital dental technologies and dental informatics. The study results identified the conceptual framework for the factors affecting the adoption of digital dentistry
Exploring the Path of Biomedical Technology in Consumer Neuroscience Research: A Comprehensive Bibliometric Analysis
Get PDFThis study performs a comprehensive bibliometric analysis of biomedical (i.e., non-brain) technology such as eye-tracking (ET), electromyography (EMG), galvanic skin response (GSR), implicit association test (IAT), and electrocardiogram (ECG) tools in studying consumer’ behavior. To achieve this aim, we adopted the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol and bibliometric analysis (VOSviewer software) for extracting the relevant documents from the Web of Science (WOS) database between 2013 and June 2023. A total of 58 documents (fifty-one articles and seven review articles) were included in the analysis. The results showed an increasing trend in publications over the years—the top countries in terms of publication outcome were Spain (13 papers) and the USA (10 papers). The analysis also identified the most influential authors, such as Babiloni, F. and Cherubino, P. It was further analyzed for the most cited article, which is titled “Neurophysiological Tools to Investigate Consumer’s Gender Differences during the Observation of TV Commercials”, and keywords related to neuromarketing and non-brain tools. Additionally, Frontiers in Psychology was determined as the most-productive journal. This bibliometric analysis reveals insights into the current state of non-brain tools research. It also provides insights into future research directions in the consumer neuroscience field. This study will provide general insights and details about current trends in consumer neuroscience research using biomedical technology
Validation of an Instrument to Assess Deductive Reasoning in Solving Types of Problems
Get PDFThe accelerated pace of knowledge generation requires engineering students to develop different types of reasoning during their education. Deductive reasoning is essential to establishing self-regulated judgments based on reasoned argumentation. This paper aims to describe and illustrate the process by which a test of multiple-choice, open-response verbal mathematical problems was designed, applied, and validated to assess the deductive reasoning of second-semester students of two engineering degrees from a university in a region of southern Chile. The research used a non-experimental, cross-sectional approach focused on psychometric aspects. The evaluation instrument was developed on the basis of a typology of mathematical problems and a model of deductive reasoning. The resolutions of types of problems are classified according to their nature, routine and non-routine, and according to their context, real, realistic, fantasy, and purely mathematical, while the deductive argumentative model comprises the phases of data, claim, and warrant. The results guarantee sufficient content and construct validity, item discrimination, and reliability; therefore, they represent a useful tool for measuring the level of deductive reasoning among first-year engineering students during the process of solving a type of mathematical problem