Smart Education System Research Articles

Fog computing in classrooms: boosting efficiency, responsiveness, user experience

In the context of rapidly advancing smart education systems, the effective management and optimization of modern classroom remain critical challenges. This research presents a novel methodology leveraging cloud and fog computing-based simulations, with a specific focus on the implementation of iFogSim. Empirical findings validate the efficacy of fog computing in monitoring classrooms, demonstrating significant improvements in performance metrics compared to traditional cloud computing architectures. Specifically, fog computing ensures remarkably low latency, with a mere 7 milliseconds, even with scalable integration across multiple classrooms. In contrast, cloud computing infrastructures exhibit considerably higher initial latencies, starting at 210 milliseconds, which further escalate with the increasing number of monitored classrooms. Furthermore, our analysis reveals substantially lower network overhead associated with fog computing, measuring at 5,231.8 kilobytes, in sharp contrast to the significantly higher network usage of 80,808 kilobytes observed with cloud computing solutions. These findings underscore the potential of fog computing as a promising solution for efficient and real-time management classroom in smart education environments.

SIW MIMO antenna with high gain and isolation for fifth generation wireless communication systems

Abstract The possibilities for further development of mmWave fifth-generation wireless systems and their deployment in our lives are immense and will make human life simpler and faster. Fifth-generation wireless communication is opening up new possibilities for development in the near future. High data rates, low latency, and enormous bandwidth are the most essential factors. These factors are at the heart of making quality healthcare, smart education systems, and the fast and efficient distribution of energy. In account of the aforementioned benefits, 5G wireless systems are primarily made possible by high gain MIMO antenna with minimal coupling. The integration of traditional wave-guide components with planar circuits, on the other hand, is a difficult issue. SIW solves this problem by providing planar alternatives for waveguide-based devices like filters, antennas, and couplers. The SIW antenna has less interference, low radiation loss, excellent isolation, and outstanding shielding properties as compared with the conventional microstrip antenna. The proposed SISO SIW antenna has gain of 9.05 dBi with 87.54 % radiation efficiency. The −10 dB impedance bandwidth is 27.79–28.19 GHz. The SIW MIMO antenna has gain of 9.05 dBi with 81 % radiation efficiency. The antenna has an isolation 52 dB, and an ECC is 0.65 × 10−7 at 28 GHz. The proposed SIW MIMO antenna’s MEG lies below −3 to −5 dB for Gaussian and isotropic medium. The CCL for the proposed MIMO antenna is 0.29–0.36 bits/s/Hz in the operating band. This article presents a high-gain and high-isolation substrate integrated waveguide (SIW) MIMO antenna using orthogonal diversity for increasing isolation between the radiating elements. The designed MIMO antenna is operating in the 28 GHz band (27.42–28.79 GHz), which comes under the n261 (FR2 5G-New Radio frequency band) band used for 5G wireless communication.

Smart Education: An Emerging Teaching Pedagogy for Interactive and Adaptive Learning Methods

Smart education, which incorporates technology to enhance teaching and learning, offers several advantages that can benefit both students and educators. Personalization: Smart education can adapt to individual students' needs and learning styles. It offers personalized learning paths, allowing students to progress at their own pace and receive targeted support in areas where they need it most. Smart education can be accessed from virtually anywhere with an internet connection, making learning more accessible to students regardless of their location. This is especially valuable for remote or underserved communities. Technology-enhanced learning often includes interactive elements, multimedia, and gamification, which can increase student engagement and motivation. Interactive simulations and virtual labs can make complex concepts more approachable. Smart education platforms provide immediate feedback to students, helping them understand their mistakes and guiding them toward improvement. This immediate feedback loop can accelerate the learning process. Smart education allows for flexible learning schedules. Students can access materials and complete assignments at their convenience, making it easier to balance education with work or other responsibilities. Digital resources, including videos, animations, and interactive simulations, can provide a richer and more dynamic learning experience compared to traditional textbooks. Smart education systems collect and analyze data on student performance. Educators can use this data to identify struggling students, track progress, and adjust their teaching methods accordingly.

CYBER-PHYSICAL SYSTEMS FOR AUTOMATED EDUCATIONAL CONTROL

Cyber-physical systems (CPS) have become pervasive across various domains, shaping not only economic activities but also social organization. However, a unified definition and understanding of CPS remain elusive, leading to diverse interpretations across different disciplines. This interdisciplinary paper explores the need for comprehensive research in CPS and its implications for educational control. Drawing from discussions at the Allatra International Public Movement Conference, the paper delves into the benefits of CPS in education, emphasizing its potential to revolutionize learning experiences, enhance student engagement, and facilitate personalized education. By leveraging CPS, educational systems can adapt to individual learning needs while minimizing vulnerability to cyber-attacks. Despite the transformative potential, challenges such as privacy, security, and ethical implications need to be addressed. Looking ahead, the paper suggests future directions for advancing automated educational control through CPS, highlighting the promise of technology-mediated learning spaces and intelligent systems for academic improvement. Overall, this paper underscores the importance of interdisciplinary collaboration in harnessing CPS for innovative educational practices. Keywords: cyber-physical systems, education automation, smart education systems, adaptive learning, intelligent systems, digital learning environments.

Exploring factors influencing the acceptance of ChatGPT in higher education: A smart education perspective

AI-powered chatbots hold great promise for enhancing learning experiences and outcomes in today's rapidly evolving education system. However, despite the increasing demand for such technologies, there remains a significant research gap regarding the factors influencing users' acceptance and adoption of AI-powered chatbots in educational contexts. This study aims to address this gap by investigating the factors that shape users' attitudes, intentions, and behaviors towards adopting ChatGPT for smart education systems. This research employed a quantitative research approach, data were collected from 458 of participants through a structured questionnaire designed to measure various constructs related to technology acceptance, including perceived ease of use, perceived usefulness, feedback quality, assessment quality, subject norms, attitude towards use, and behavioral intention to use ChatGPT. Structural model analysis (SEM) Statistical techniques were then utilized to examine the relationships between these constructs. The findings of the study revealed that Perceived ease of use and perceived usefulness emerged as significant predictors of users' attitudes towards ChatGPT for smart education. Additionally, feedback quality, assessment quality, and subject norms were found to positively influence users' behavioral intentions to use ChatGPT for smart educational purposes. Moreover, users' attitudes towards use and behavioral intentions were significantly proved for the actual adoption of ChatGPT. However, a few hypotheses, such as the relationship between trust in ChatGPT and perceived usefulness, were not supported by the data. This study contributes to the existing body information systems applications for the determining factor of technology acceptance in smart education context.

Improving Performance of Smart Education Systems by Integrating Machine Learning on Edge Devices and Cloud in Educational Institutions

Improving Performance of Smart Education Systems by Integrating Machine Learning on Edge Devices and Cloud in Educational Institutions

Enhancing Collaborative Learning and E-Mentoring in a Smart Education System in Higher Education

The requirement to develop a smart education system is critical in the era of ubiquitous technology. In the smart education environment, intelligent pedagogies are constructed to take advantage of technological devices and foster learners’ competencies which undoubtedly assist learners in dealing with knowledge and handling issues in a dynamic society more effectively and productively. This research suggests two effective learning strategies: (1) collaborative learning, which helps learners improve their knowledge and skills by exchanging resources and experiences, and (2) e-mentoring, which connects learners to a wide range of professional communities. This research first proposes a model to show how these two learning methods help learners achieve their goals, along with a set of hypotheses that are explained in detail. Then, a smart education system is proposed which comprises the two learning strategies with the necessary features. Lastly, two questionnaires, one for facilitators and the other for learners, are used to evaluate the usefulness and the feasibility of the proposed model in a real-world educational environment. The great majority of respondents agreed with all the statements, demonstrating the efficiency of the research for educators and learners.

Building and Developing a Smart University Model Current Situation and Solutions in Vietnam

Digital transformation and building smart universities are current trends that Vietnamese universities are pursuing and promise to be a big step forward for the country's education. However, up to now, building and developing smart universities is still a new concept and has not been applied in many universities in Vietnam, but in developed countries, smart education systems This has become more popular and achieved great success in many fields. Based on the synthesis and development of domestic and foreign research and articles, analyzes on Smart Universities were selected such as studies or articles on Smart Universities in the US, UK, Vietnam, etc..., this article was written to focus on analyzing the current situation of smart education in general and in Vietnam in particular. The article aims to provide the most general overview of the smart university model and the current situation in the world and Vietnam, along with analyzing the impact of the transition process on related objects. Thereby, the article proposes some proposed solutions to help this smart university model develop and be more easily accessible in Vietnamese universities in the future.

Empowering Education: Exploring the Impact of IoT in Smart Learning Environments

This research study examines the profound impact of IoT (Internet of Things) technology within smart education systems. It delves into how IoT can improve accessibility, engagement, and individualized learning experiences for students, regardless of their backgrounds. Additionally, the research emphasizes the necessity for educational institutions to embrace IoT solutions to maintain competitiveness in our interconnected world. While also addressing security and privacy concerns, it underscores the evident benefits of implementing IoT in educational settings. This study also discusses and shows a few real-life case studies across the globe wherein many universities have already implemented the IoT technology and are benefitting from the same. Through effective utilization of IoT, we can establish a more dynamic and efficient educational ecosystem, empowering students, supporting educators, and equipping future generations for the challenges and opportunities of the digital era.

Smart Education Systems Supported by ICT and AI

Contemporary society, the society of the future (Industry 4 [...]

BK-SAD: A Large Scale Dataset for Student Activity Recognition

Skeleton-based human action recognition has emerged as a prominent research topic in the field of artificial intelligence due to its broad applicability in a wide range of domains, including but not limited to healthcare, security and surveillance, entertainment, and intelligent environments. In this paper, we propose a novel data collection methodology and present BK-Student Activity Dataset (BK-SAD), a new 2D dataset for student activity recognition in smart classrooms that outperforms the existing NTU RGB+D 120 dataset, SBU Kinect Interaction dataset. Our dataset contains three classes: hand raising, doze-off, and normal activities. The dataset was collected using cameras placed in real classroom environments and consisted of video data from multiple viewpoints. The dataset contains over 2700 videos of students raising their hands, over 1700 videos of students dozing off during class, and over 8500 videos of normal activities. In addition, to evaluate the effectiveness of the proposed dataset, we give some baseline performance figures for neural network architectures trained and tested for student activity recognition on BK-SAD dataset. These ConvNet architectures demonstrate significant performance improvement on the proposed dataset. The effectiveness of the proposed novel data collection methodology and BK-SAD dataset in this paper will enable further research and development of activity recognition models for classroom environments, with potential applications in smart education and intelligent classroom management systems.

Smart education system to improve the learning system with CBR based recommendation system using IoT

Over the last few years, the research fields of intelligent learning systems have been improving the process of learning systems. Smart Tutoring System-(STS) applications have been used in e-learning. The results signify the importance of the learner's engagement in customizing a model. The design outcomes of this IoT-based personalized learning system purely work on the audience's learning requirements, their keyword search, their learning experience levels, proficiency level of subjects, and the type of the course being taught. Students spent an average of 25.67 h accessing textual materials weekly, 27.4 h accessing video assets weekly, and similarly 6.5 h accessing visual learning materials. The research analysis part concludes participants' percentage of learning as per evaluations assessment, which increases whenever the outcome analysis comes with Case-Based Reasoning classifiers-CBR based search model. The findings displayed significant differences before and after learning case by case for every learner as per chosen topic and quiz assessments: 42.57% of the students responded before learning the first question assessments whereas 74.82%, of the students responded, after completion of learning from online resources based on their choice with CBR. Recommendation Model Analysis discussed root means square error-RMSE lies from 10% to 20% for 550 students group size. The RMSE result is 24% for a size of 1600, which is low performance compared to other group sizes. This study focuses on the STS recommendation model for the slow learner group to identify required learning from various online resources.

Smart Education System Enhancing Collaborative Learning with Virtual Reality and Cloud-Edge Computing Task Scheduling Algorithm

Smart Education System Enhancing Collaborative Learning with Virtual Reality and Cloud-Edge Computing Task Scheduling Algorithm

RETRACTED ARTICLE: Machine learning-based AI approaches for personalized smart education systems using entropy and TOPSIS approach

RETRACTED ARTICLE: Machine learning-based AI approaches for personalized smart education systems using entropy and TOPSIS approach

Understanding smart education continuance intention in a delayed benefit context: An integration of sensory stimuli, UTAUT, and flow theory

Smart education is a combination of AI and education, and its benefits are delayed. Existing research lacks an understanding of smart education continuance intention in the context of this delayed benefit. Thus, this study aimed to examine users' continuance intention in a delayed benefit context by integrating sensory stimulus, the extended UTAUT, and flow theory. The study focused on a mobile-based smart education (MBSE) system (Liulishuo) and collected data on 494 MBSE users. The PLS-SEM analysis suggested that the model is largely supported. Identifying MBSE's technical attributes as sensory stimuli, the extended UTAUT as primary information processing, flow as secondary information processing, and continuance intention as a decision, this study verifies the relationships between various constructs and their influence on MBSE continuance intention. The results also indicate that a user's flow state plays a crucial role in strengthening continuance intention and realizing the delayed benefits of smart education. Thus, this study provides a deeper understanding of continuous usage mechanisms as they relate to smart education.

Evolutionary machine learning builds smart education big data platform: Data-driven higher education

The development of machine learning has promoted the construction of smart education platforms. It is of great significance to deeply investigate the usage of machine learning techniques in smart education. In this paper, we explore how to utilize evolutionary algorithms and machine learning algorithms to build a smart education big data platform to promote the intelligent development of higher education and better assist the establishment of the smart education system. We combine evolutionary algorithms and machine learning models to build a personalized course recommendation model for the smart education big data platform, which uses deep belief networks and swarm intelligence evolutionary algorithms to recommend relevant content based on the interests of learners. We take advantage of the feature extraction of deep belief networks and combine supervised learning and unsupervised learning to design an intelligent recommendation model for teaching content. At the same time, we utilize the advantages of evolutionary algorithms to tune the model parameters to obtain the best parametric model. We compare our method with other methods on public dataset to show the model performance.

Retracted: The Construction of a Smart Education System in Colleges and Universities Based on Cloud‐Side Collaborative Computing Task Scheduling Algorithm

Retracted: The Construction of a Smart Education System in Colleges and Universities Based on Cloud‐Side Collaborative Computing Task Scheduling Algorithm

A DEEP LEARNING MODEL FOR EDUCATION ANALYTICS – A SHORT REVIEW

Integrating deep learning with learning management systems can result in intelligent course material and high accuracy without any manual intervention. This paper reviews factors that influence deep learning in education, and hence this article aims to achieve deep learning on a large scale in the smart education system with a deep learning model to predict. The proposed model can reduce development and maintenance costs, reduce risks, and facilitate communication between stakeholders. HIGHLIGHTS: The current review focus on deep learning as an important tool for Indian teachers.

Social Networks to Rescue COVID-19 Education: Learners' Awareness of Social Networks in Aiding Education

Governments around the world have temporarily closed educational institutions in an attempt to contain the spread of the novel coronavirus (COVID-19). This has culminated in the largest school disruption in recent history. This study explored learners’ awareness of using smart educational systems (social networks) to support remote learning (e-learning). The study examined three constructs: Motivation, Active role, and Interaction, based on the theory of Connectivism. These constructs contributed to the development of a survey used to gather perspectives on social networks in education from a group of Business Studies learners in a South African high school. Data was collected from a sample of Business Studies learners (n=133) just before the COVID-19 pandemic. The results show there was an awareness of the use of social networks in education among learners. Therefore, social networks had the potential to support distance education during the pandemic.

Multimedia Teaching for Applied Linguistic Smart Education System

The smart education system has attracted more popularity and explains learning in the modern age. Smart education system uses intelligent devices to access digital services via the wireless network and immerse students in personalized and seamless learning. Challenges related to the transition of separate environments into integration, the needs of both students and impaired students in regular classrooms, equality, technology hurdles, and instruction in classrooms. Hence in this article, Integrated Multimedia Teaching Model (IMTM) for technical support to develop student learning in a smart education system. The proposed IMTM applies linguistic-based teaching for enhancing students’ comprehensive capacity and uses smart educational resources, particularly listening and oral expression ability. Simultaneously, IMTM provides enhanced self-studying abilities and thorough learning skills to address the student’s need for development. The experimental results show that the IMTM based on smart education system fulfils the student needs in learning and applies linguistic-based personalized learning to improve student performance ratio of 92.3%, assumption ratio of 90%, and flexibility ratio of 91.2%, compared to other methods.