Online-Journals.org (International Association of Online Engineering)
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Application of Mobile Technology-Based Learning Analytics in Educational Assessment
Get PDFWith the rapid development of mobile technology, its application in the educational sector, particularly in learning analytics, has garnered increasing attention. The widespread adoption of modern mobile devices has introduced new opportunities for educational assessment. However, challenges remain in effectively utilizing these technologies for educational assessment. Current research predominantly focuses on data collection and analysis, yet the methods employed are limited in scope and application. This study aims to address these gaps by exploring data collection methods and teaching assessment approaches based on mobile technology. The objective is to contribute new perspectives and methodologies that modernize educational assessment. Through an in-depth analysis of the application of mobile technology in educational assessment, this study seeks to provide educators with scientifically grounded assessment tools and strategies, enabling more precise teaching management and personalized education
Leveraging Mobile Technology for Enhanced Mental Health Levels in University Students
Get PDFWith the advent of the digital era, the application of mobile technology in higher education has become increasingly prevalent, particularly in the management of students’ mental health. University students face multiple pressures, including academic, social, and career-related challenges, which have led to a rise in mental health issues. As a result, the utilization of mobile technology to collect and analyze students’ mental states and behavioral data in realtime has emerged as significant research focus. Current studies predominantly rely on traditional survey methods, which fail to capture students’ dynamic mental states in real-time and often lack an in-depth understanding of complex behavioral patterns. Moreover, few existing studies have examined the integration of multi-source data, thereby limiting comprehensive analyses of mental health risks. This study proposes a dynamic mental behavior inference and mental health risk assessment framework for university students based on multi-source data integration. The framework aims to analyze students’ mental health conditions comprehensively by integrating diverse data from mobile technology. Experimental results and analyses were presented to verify the framework’s effectiveness and practicality, providing new insights for mental health management in higher education and laying the foundation for future research
Adaptive Recommendation of Teaching Content in Higher Education Using Mobile Interaction Technology
Get PDFWith the rapid advancement of mobile interaction technology, teaching methodologies in higher education are increasingly moving toward personalization and intelligence. The use of mobile interaction technology for adaptive recommendation of teaching content has become a critical topic for enhancing educational effectiveness. Existing research in content recommendation, primarily based on collaborative filtering algorithms, often relies on single-dimensional data applications and lacks comprehensive consideration of both location information and temporal effects. Consequently, these approaches fall short in addressing the complex requirements of dynamic learning environments. This study proposes a multi-dimensional dynamic adaptive recommendation system for teaching content based on mobile interaction technology to address the limitations of existing methods. The research encompasses location-based collaborative filtering for teaching content, time-effect-based collaborative filtering, and an integrated multi-dimensional dynamic recommendation model that considers both location and temporal factors. This study is expected to provide a more precise and dynamically adaptive solution for personalized teaching in higher education
Exploring Digital Empathy in Vietnamese Tertiary EFL Education: Perspectives of Teachers across Career Stages
Get PDFThis qualitative study investigates the roles of learners’ digital empathy in English as a Foreign Language (EFL) education, focusing on the perspectives of Vietnamese tertiary EFL teachers. Amidst the increasing integration of digital tools in education, understanding the impact of digital empathy—empathetic engagement facilitated by digital platforms—becomes crucial, especially in language learning contexts. The study utilized semi-structured interviews with nine Vietnamese EFL teachers, who represented different career stages (novice, mid-career, and near-end), from two tertiary institutions. Thematic analysis revealed that digital empathy significantly enhances student engagement and the effectiveness of language learning. Teachers at all career stages emphasized the importance of understanding and addressing students’ emotional needs in digital environments. The study contributes to the existing literature by providing insights into the integration of digital empathy in EFL teaching, highlighting its importance across various teaching experiences
Changing Trends in Teaching Computer Vision at Ukrainian Universities in the Age of Artificial Intelligence
Get PDFIn recent years, professional education in computer vision and artificial intelligence (AI) has witnessed transformative changes, a process significantly accelerated by the COVID-19 pandemic, which catalyzed the shift towards online learning formats. In Ukraine, as in many other countries, universities have rapidly adapted their curricula to align with these new realities. The onset of martial law in Ukraine further entrenched online learning as the dominant mode of education. Additionally, there has been a noticeable shift in research activities towards projects tailored to the defense sector, unfolding against the backdrop of significant global advancements in AI for computer vision. This article explores the impact of these shifts and the “Artificial Intelligence Development Concept” introduced by the Ministry of Digital Transformation of Ukraine on the quality and methodologies of computer vision education. It scrutinizes specific training courses in this domain, along with the literature used in the educational process. The exploration includes how deep neural network technologies such as TensorFlow, PyTorch, OpenCV, and Python libraries are being integrated into computer vision curricula, reflecting the latest advancements in the field. It also examines the needs of Ukrainian startups and companies specializing in computer vision projects, analyzing how curricula are evolving to meet the specific requirements of modern software developers. Furthermore, the article proposes directions for the further development of online education in computer vision teaching in Ukraine
New Algorithm for Evaluation of Online Courses Based on Quality Matters Rubric Using Fuzzy Soft Expert Sets
Get PDFThe field of instructional technology has experienced significant growth in recent times. Due to the rapid shift towards online courses, the technology-based learning system is facing challenges in ensuring quality and assurance. The aim of this study was to develop online course evaluation tools by proposing a new algorithm to assess the success of the provided online courses and address quality assurance issues. The proposed algorithm is based on quality matters (QM) attributes and the use of fuzzy soft expert sets (FSESs). One key advantage of the proposed algorithm is that it incorporates experts’ opinions, which significantly contributes to achieving the study objective. The proposed algorithm was successfully implemented using the ASP.NET programming language. It resulted in the development of an EOC-FSES prototype system. The experimental evaluation of the prototype system confirms that it requires low effort and achieves high levels of performance, satisfaction, and behavioral intention to use. This paper includes several recommendations and suggestions
Development of Board Game Media on Air Theme for Children Aged 5-6 Years
Get PDFBoard games are educational tools consisting of a board with boxes drawn on it. In this game, there are several rules that must be followed, which involve social interaction between children during playtime. This study aims to develop a board game on the theme of air to support children’s growth and development as well as enhance their knowledge related to the topic. Moreover, this R&D study employed 4D development with data analysis techniques for validation, practicality, and effectiveness. The data were collected through interviews and expert validation of the product. Moreover, this study has reached the fourth stage, which is dissemination. The population of this study consisted of 65 children aged 5–6 years at Aisyiyah Sicincin Kindergarten, Padang Pariaman Regency. Based on the results, it was found that board game media are effective in promoting children’s growth and development in terms of cognitive, linguistic, and motor skills
Biological Butterfly Characterization with Mobile System Using Convolutional Neural Network (CNN) Classify Image
Get PDFThis study presents the development of a mobile identification system that detects biological butterfly characteristics through deep learning by capturing images. The challenge identified is that butterfly identification and recognition are difficult tasks because there are too many species, and it is hard to classify the types of butterfly species. Butterflies are also difficult to differentiate from each other, and limited studies are done using computer database referrals for butterflies’ characterization. This study aims to develop an automated computer program to easily identify the species of butterflies. Deep learning in image processing is programmed, which can control the qualification, segmentation, and classification of images and automatically detect butterfly characterization. The design system consists of three stages: capture, feature extraction, and butterfly recognition. Then, multiple recognition clues such as shape, color, texture, and size are extracted and analyzed to analyze and recognize the butterfly. This approach is faster and less complex than the previous approach. The result successfully presents a convolutional neural network (CNN) to classify images after training and characterization. The graphics processing unit (GPU) that trains the image dataset presents 86% image accuracy in the allocated time. This research is significant in such a way that new butterfly species will be automatically collected and stored on the online server. The information could be treasured as a valuable butterfly database
Assessing Smartphone Addiction among Mexican Students: Insights, Implications, and Interventions in the Era of Mobile Learning and Virtual Environments
Get PDFThis study conducted a new adaptation of the short version of the Smartphone Addiction Scale (SAS-SV) for the Mexican population. The scale exhibited excellent internal consistency (α = .88), with one factor explaining 54.8% of the variance. 286 students aged 15 to 35 years of age from central and southeast Mexico revealed a prevalence of 30% excessive users (18% men, 12% women). They used smartphones primarily for communication, social media, entertainment, productivity, and browsing the Internet. No gender differences were found; however, a significant association with age emerged (rho = .221, p < .001). Withdrawal (72.7% women) and tolerance (80.8% men) were common symptoms of addiction, while ignorance of consequences was less frequent (33.3% women, 30.8% men). The prevalence of potential excessive use of smartphones was close to the average identified in the reviewed studies. The results revealed a substantial risk of addiction to smartphones, particularly among men. Finally, the successful adaptation of the scale offers a valuable instrument for future study and clinical evaluation. These findings can guide targeted interventions and educational programs tailored to different groups. Additionally, recognition of common symptoms of addiction allows individuals to identify and address potential problems at an early stage. However, more study is needed to validate the findings and explore additional contributing factors in the Mexican context
The Effects of Augmented Reality Geometry Learning Applications on Spatial Visualization Ability for Primary School Pupils
Get PDFGeometry is considered a fundamental component to be mastered in the elementary school mathematics curriculum. Lower primary school pupils frequently experience obstacles because of the abstract nature of geometric concepts and the requirement for spatial visualization skills, which are necessary for strengthening their conceptual understanding. Augmented reality (AR) technology offers a promising solution by providing visual representations of geometric shapes. Thus, this study aimed to investigate the effects of AR technology in primary school education on pupils’ spatial visualization abilities in geometry. Employing a quasiexperimental pre- and post-test research design, this study involved 61 second-grade pupils from two Northern Malaysian primary schools. The selection of schools followed cluster sampling techniques, while intact groups were utilized to select samples. Data were collected from two groups: an experimental group that utilized the LearnGeoAR applications and a control group that employed conventional teaching methods to learn geometric shapes. This study used the spatial visualization ability test (SVAT) instrument, grounded in Van Hiele’s theory of geometric thinking. The findings revealed that the experimental group, which utilized AR applications, exhibited a higher mean score in pupils’ spatial visualization ability in geometry compared to the control group. Additionally, the experiment demonstrated a moderate effect on both groups. This finding highlights the potential of AR technology in enhancing pupils’ spatial visualization skills in geometry instruction, providing valuable insights for instructional methodologies