Online-Journals.org (International Association of Online Engineering)
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Correlation Between Cognitive Levels of Teachers’ Questions and Response Enthusiasm of Students
Get PDFTeachers’ questions and students’ responses are important interactive links in the educational process. In recent years, with the deepening of research on educational psychology, more and more studies have begun to pay attention to the influence of cognitive levels of teachers’ questions on students’ responses. However, although some studies have shown that the way teachers ask questions has a significant impact on the learning outcomes of students, there is no clear answer to the precise relationship between cognitive levels of teachers’ questions and response enthusiasm of students. In addition, existing research methods often rely too much on descriptive statistics and lack a deep understanding and exploration of correlation. This study aimed to explore the correlation between cognitive levels of teachers’ questions and response enthusiasm of students. A new method was first introduced to measure the response enthusiasm of students. Taking English teaching as an example, an evaluation model was constructed through factor analysis, and the evaluation results were analyzed through Kaiser-Meyer-Olkin (KMO) test and Bartlett’s Test of Sphericity. Then grey relational analysis was used to measure the correlation between cognitive levels of teachers’ questions and response enthusiasm of students. The results revealed that cognitive levels had a significant impact on the response enthusiasm. The findings of this study not only provide teachers with an effective strategy to enhance the learning participation of students, but also bring new theoretical knowledge and practical experience to the field of educational psychology
Augmented Reality Technology in Learning Arabic Vocabulary from the Perception of University Students
Get PDFAugmented reality technology has been widely adopted in various fields, particularly in the realm of education. In the west, this technology has been applied to various foreign languages such as Malay, English, Chinese, Spanish, French, and Ukrainian. However, in the context of learning Arabic in Malaysia, the use of this technology has been less explored, particularly from the perspective of students at the public university level. Hence, this study aims to examine the level of perception among students at University Sains Islam Malaysia (USIM) regarding the use of augmented reality as a new technology in Arabic language learning today. A questionnaire was used in this study to collect data from a sample of 30 respondents. Analytical findings on frequency distribution, percentage, mean, and standard deviation were obtained through descriptive statistical analysis. Cronbach’s alpha analysis showed that all eight items of the questionnaire had good internal consistency. The overall mean score of the items was 4.48, indicating a high level of data interpretation. Based on the analysis of the findings, the study revealed that the majority of students responded positively and expressed satisfaction with their readiness to use augmented reality in language learning. In conclusion, augmented reality is a valuable learning aid that can greatly benefit students in understanding Arabic vocabulary more easily
The Impact of Social Media on Women's Body Image Perception: A Meta-Analysis of Well-being Outcomes
Get PDFThis study examines the correlation between female body image and social media usage, focusing on the impact it has on overall well-being. The popularity of social networking sites (SNSs) has significantly impacted our digital culture, providing options for self-expression, engagement, and communication. However, SNSs also bring certain drawbacks, such as the potential for body dissatisfaction and negative psychological effects. Gap: While previous research has examined the relationship between social media use and body image, there is a need for a comprehensive analysis that specifically focuses on the effects on female well-being. This study aims to fill the gap by providing a comprehensive exploration of the effects of social media use on female body image and its influence on overall well-being. The objective of this study is to examine the correlation between social media usage and female body image, specifically focusing on the detrimental effects on well-being and mental health. By analyzing academic papers and research findings, this study aims to contribute to the understanding of the intricate relationship between social media, body image, and well-being. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework was used to compile relevant literature. Data from Scopus sources was analyzed using the RStudio 4.2.1-win program. Various factors, such as the publishing year, author details, keywords, and document categories, were considered to identify important patterns and insights. It is recommended that awareness and educational campaigns be developed to promote a healthy body image and address the potential negative consequences of social media use. The analysis significantly contributes to the body of knowledge by offering a thorough examination of the connection between female body image on social media and usage, highlighting the detrimental effects on well-being and mental health
A Review of Material, Design, and Techniques in 3D Printing for Medical Applications
Get PDFThis review research assesses the numerous 3D printing methods utilized in medical applications and the materials and design methods that are associated with the current and existing technology. The article thoroughly examines the advantages and disadvantages of various techniques and materials and the difficulties of applying 3D printing technology to the medical sector. Further research and development are required to overcome current challenges since the review highlights the importance of design strategies in achieving positive medical outcomes. Overall, the article provides a thorough overview of the state of 3D printing in medical applications today and its potential to revolutionize the industry
Optimizing Machine Learning Algorithms for Heart Disease Classification and Prediction
Get PDFAccording to the World Health Organization (WHO), cardiovascular disease is one of the leading causes of death worldwide. Thus, the prevention of this kind of illness is considered as a huge human health challenge. Additionally, the diagnostic process often involves a combination of clinical examination, laboratory tests, and other diagnostic procedures, which can be complex and time-consuming. However, advances in medical technology and research have led to improved methods for diagnosing heart disease, which can help to improve patient outcomes. Furthermore, Machine Learning (ML) methods have shown promise in helping to improve the diagnosis of heart disease. Each method requires specific parameters to produce good results. In this paper, we propose a diagnosis support system based on optimized Machine Learning algorithms, which is Artificial Neural Network (ANN), Support Vector Machine (SVM), K_Nearest Neighbour (KNN), Naive Bayes (NB), and Decision Tree (DT) to analyze the major cardiovascular risk factors, such as age, gender, high blood pressure, etc. To train and validate the ML models, a medical dataset of 558 patients with atherosclerosis is used. In this work, we achieved a 96.67% as promising accuracy level for the atherosclerosis prediction with ANN
Towards an Adaptive Learning Model using Optimal Learning Paths to Prevent MOOC Dropout
Get PDFCurrently, massive open online courses (MOOCs) are experiencing major developments and are becoming increasingly popular in distance learning programs. The goal is to break down inequalities and disseminate knowledge to everyone by creating a space for exchange and interaction. Despite the improvements to this educational model, MOOCs still have low retention rates, which can be attributed to a variety of factors, including learners’ heterogeneity. The paper aims to address the issue of low retention rates in MOOCs by introducing an innovative prediction model that provides the best (optimal) learning path for at-risk learners. For this purpose, learners at risk of dropping out are identified, and their courses are adapted to meet their needs and skills. A case study is presented to validate the effectiveness of our approach using classification algorithms for prediction and the ant colony optimization (ACO) algorithm to optimize learners’ paths
A Review of Joint Applications of IoT and Deep Learning
Get PDFIn recent years, graph convolutional networks (GCNs) have been widely used in image classification tasks. The combination of GCNs and the Internet of Things (IoT) has led to the development of some branches of the latter. This paper explores cases where convolutional neural networks and GCNs are combined with IoT to achieve better results. This paper also focuses on discussing the semi-supervised classification task of GCNs. The innovative approach explored for innovative GCNs dealing with semi-supervised classification tasks lies in optimizing the GCN topology and using graph convolutional operations in the topological space for better training of the model
Investigating Student and Teacher Perceptions in e-Learning with Learning Analytics and Ontologies
Get PDFThis work is an approach that brings together Learning Analytics and Ontologies for a data classification that promotes improvements and behavioral changes for students and teachers on e-Learning platforms. Combining training courses, dashboards, user's evaluations, and based on Design Science Research (DSR) methodology, artifacts were created. One of the most important artifacts of our work is the Sapes tool that aims to improve students’ perceptions of their learning path and to promote a better teacher overview to follow their students' progress. The results showed high approval by the participating students and teachers, who perceived the Sapes tool as a good facilitator of the teaching-learning process, with possibilities for self-monitoring, dynamization of the learning sequence and better interactivity with colleagues, highlighted as absent in standard e-Learning courses. In addition, the application changed the behavior of users towards the content provided by the teacher, with students performing self-management and self-regulation that were not commonly performed previously
A Systematic Review of Voice-based Intelligent Virtual Agents in EFL Education
Get PDFSince its debut in the field of education nearly three decades ago, Artificial Intelligence (AI) has been considered as a powerful tool to facilitate new paradigms for instructional design and innovative educational practice in the form of intelligent tutoring systems, chatbots, teaching robots and adaptive learning systems among others. Recent technological advances in the adjacent areas of natural language processing, machine learning and computer graphics focusing primarily on design features that can improve their human-like qualities of naturalness and believability as human interlocutors have also amplified new application opportunities for Intelligent Virtual Agents (IVAs) or Animated Pedagogical Agents (APAs) within the area of Intelligent Computer-Assisted Language Learning (ICALL). Although AI-powered IVAs hold the potential to enhance the learning process in nearly any knowledge domain and personalize automation in teaching by embodying different roles in the learning environment, strikingly few studies have empirically attempted to assess IVAs impact on L2 learners’ academic achievement when learning English as a Foreign (EFL) so far. This study addresses this issue via a systematic review of relevant interventionist IVA studies that were conducted in EFL settings and published within the 2015-2020 timeframe examining IVAs key affordances, major barriers in their adoption for language learning purposes, and the CALL research trends currently prevalent on the topic. Pedagogical implications for the effective implementation of IVA technology in L2 contexts are discussed and future research avenues in the area are highlighted
“Kaleidoscope Eyes”: The Exploration of a Sense of Place Through Art Strolling in Mozilla Hubs
Get PDFBased on my workshop in Cambridge, the UK, I discuss Mozilla Hubs as an intervention to
enact in-person strolling experiences through an online photovoice exhibition. I explore this
virtual space with stakeholders from relevant backgrounds to open dynamic understandings of a
sense of place in a virtual world. I argue that this online space intervenes in the power-centered
system of curation. Additionally, I find that there is a nonlinear narration of the past and present in
the Hubs, since it unfolds the understanding of time, art and memory. And I notice that not only
optimistic possibilities happen in this online space but also there are several challenges, like
ethical problems, waiting to be explored