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Metaverse for Education: Developments, Challenges, and Future Direction

computer Applications in engineering Education 2025年第3期33卷

作者： Chamola, Vinay Peelam, Mritunjay Shall Mittal, Uday Hassija, Vikas Singh, Anushka Pareek, Ritawari Mangal, Poorvi Sangwan, Devika de Albuquerque, Victor Hugo C. Mahmud, Mufti Brown, David J. Department of Electrical and Electronics Engineering Internet of Things Research Group APPCAIR BITS-Pilani Rajasthan Pilani India Department of Electrical and Electronics Engineering BITS-Pilani Rajasthan Pilani India School of Computer Engineering Kalinga Institute of Industrial Technology Odisha Bhubaneswar India Department of Computer Science and Engineering Jaypee Institute of Information Technology Uttar Pradesh Noida India Department of Humanities and Social Sciences BITS-Pilani Rajasthan Pilani India Fortaleza Brazil Department of Computer Science Computing and Informatics Research Centre Medical Technologies Innovation Facility at Nottingham Trent University Clifton Lane Nottingham United Kingdom Department of Computer Science Computing and Informatics Research Centre at Nottingham Trent University Clifton Lane Nottingham United Kingdom

The rapid advancements in digital technologies such as artificial intelligence (AI), virtual reality (VR), augmented reality (AR), mixed reality (MR), extended reality (XR), and the internet of things (IoT) have revolutionized various sectors, including education. Metaverse, a convergence of these transformative technologies, offers immersive, personalized, and interactive experiences, making it a powerful tool in modern education. This paper explores the Metaverse's role in enhancing education by examining its architecture, types, and components while addressing practical implementation challenges, and follows a structured review protocol to ensure a comprehensive analysis, including systematic research, paper selection, and a critical examination of relevant studies from reputable databases such as Google Scholar, IEEE Xplore, ACM, and Springer. The research objectives focus on evaluating the Metaverse's applications in education, ethical challenges, technological limitations, and potential strategies for sustainable integration. Key research questions address the need for Metaverse adoption in education, its benefits, challenges, and future directions. The Metaverse cultivates essential skills such as empathy, ethical reasoning, and effective communication by providing students with customized, immersive learning environments. However, ethical concerns, technical barriers, and infrastructural costs pose significant obstacles to its widespread adoption. It discusses strategies to solve these barriers, explores applications in distance learning, and proposes future research directions to create scalable and sustainable educational models in the Metaverse. Through this structured inquiry, the paper establishes the Metaverse as a transformative force in education, blending technological innovation with instructional advancement. © 2025 Wiley Periodicals LLC.

关键词： Contrastive Learning

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Multilingual Hate Speech Detection: Innovations in Optimized Deep Learning for English and Arabic Hate Speech Detection

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SN computer Science 2025年第3期6卷 1-16页

作者： AL-Sukhani, Hassan Bsoul, Qusay Elhawary, Abdelrahman H. Nasr, Ziad M. Mansour, Ahmed E. Batyha, Radwan M. Alqadi, Basma S. Alqurni, Jehad Saad Alfagham, Hayat Madbouly, Magda M. Cybersecurity Department Faculty of Science and Information Technology Jadara University Irbid Jordan Cybersecurity Department College of Computer Sciences and Informatics Amman Arab University Amman Jordan Faculty of Engineering Ain Shams University Cairo Egypt Faculty of Computer Science Misr International University Cairo Egypt Faculty of Information Technology Applied Science Private University Amman 11931 Jordan Computer Science Department College of Computer and Information Sciences Al Imam Mohammad Ibn Saud Islamic University Riyadh Saudi Arabia Department of Educational Technologies College of Education Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam Saudi Arabia Department of MIS College of Applied Studies and Community Service Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam Saudi Arabia Institute of Graduate Studies & Research Alexandria University Alexandria Egypt Faculty of Computer and Data Science Alexandria University Alexandria Egypt

This paper presents the development of a multilingual hate speech detection model that effectively processes and classifies content in both Arabic and English. The study leverages both traditional machine learning models, such as K-Nearest Neighbors (KNN), Naive Bayes, and Support Vector Machines (SVM), as well as advanced deep learning models, specifically Bi-directional Long Short-Term Memory (Bi-LSTM) networks. A key challenge addressed is the classification of mixed-language content, which is common on social media platforms in the MENA region. To enhance detection performance, preprocessing techniques were applied to the text data, and the Synthetic Minority Over-sampling Technique (SMOTE) was used to balance the dataset. The results show that the Bi-LSTM model outperformed traditional machine learning approaches, particularly in identifying hate speech across multiple languages. The proposed model demonstrates superior accuracy and robustness in handling mixed-language input, providing a more effective solution for real-world hate speech detection tasks. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Class imbalance Deep learning Hate speech detection Machine learning Mixed Arabic and English training Multilingual NLP Natural language processing (NLP) Sentiment analysis

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A Review on Leveraging Language Agnostic Automated Text Data Augmentation with Explainable Semantic Integrity 5

A Review on Leveraging Language Agnostic Automated Text Data...

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5th International Conference on Advanced research in Computing, ICARC 2025

作者： Wijesundera, Sarith Peiris, Suresh Michael University of Westminster Department of Computer Science and Engineering London United Kingdom Informatics Institute of Technology School of Computing Colombo Sri Lanka

ISBN: (纸本)9798331530983

Data Augmentation (DA) is an effective strategy to increase model generalisation. In Natural Language Processing (NLP), DA remains in its early stages, primarily due to the inherent sensitivity of textual data, which complicates the augmentation process. These complexities are further increased in multilingual contexts, as DA techniques effective in one language may not yield comparable results in others. As a result, manually identifying optimal DA techniques that preserve semantic integrity and are language agnostic remains a significant challenge. It emphasises the necessity for automating the text DA process with Explainable Artificial Intelligence (XAI), which offers a promising approach by calculating word importance scores to effectively guide the augmentation. In this paper, a comprehensive review of existing approaches in auto-text DA is presented, beginning with an exploration of the concept of auto-text DA, followed by a discussion on its cross-lingual applications, and concluding with the integration of XAI for semantic preservation. The paper highlights the critical need for further research to enhance the effectiveness and applicability of XAI in auto-text DA, enabling its use across diverse languages. © 2025 IEEE.

关键词： Transfer learning

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A Systematic Literature Review on Faculty Learning in Service-Learning 14

A Systematic Literature Review on Faculty Learning in Servic...

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14th IEEE Global engineering Education Conference, EDUCON 2023

作者： Bandi, Surendra Reddy Joshi, Gopalkrishna Shettar, Ashok Kandakatla, Rohit Hyderabad Institute of Technology and Management Department of Computer Science & Engineering Hyderabad India KLE Technological University Centre for Engineering Education Research Hubballi India KLE Technological University Hubballi India KG Reddy College of Engineering and Technology Department of Electronics and Communication Hyderabad India

ISBN: (纸本)9798350399431

Service-learning is a pedagogical approach which makes learning more meaningful. researchers have reported this so far taking the cases of student learning experiences and impact studies. There are also studies which analyzed the role of community and academic institutions in the success of Service-learning. Faculty being an important stakeholder, less research took place on the role of faculty. When it is targeted to undergraduate students, Service-learning courses require to be designed and delivered in a distinctive style. Undergraduate students think and learn independently compared to children under 18 years of age. There are learning theories which are applicable for faculty and students. Faculty learning is equally important to make learning more effective. Faculty becoming co-learners while teaching increases the confidence of students in the learning process. As per the literature available, faculty learning as a specific area is not given focus in the process as compared to the other areas of service-learning like models of integrating service-learning into curriculum, benefits of service-learning, complementing professional skills and improved ethics etc. Hence this study is an attempt to explore the concept of faculty learning in service-learning and report the significance of faculty learning in implementing service-learning courses to undergraduate engineering students. © 2023 IEEE.

关键词： Students

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Multidetector CT in the Analysis of Pulmonary Thromboembolism 5

Multidetector CT in the Analysis of Pulmonary Thromboembolis...

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5th International Conference on Mobile Computing and Sustainable informatics, ICMCSI 2024

作者： Rohini, M.S. Balaji, V. Vijai, R. Rajesh, V. Chandramohan, D. Yadav, Ajay Singh Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu 600062 India Sri Sairam College of Engineering Department of Mechanical Engineering Karnataka 562 106 India St. Peter's Institute of Higher Education and Research Department of Mechanical Engineering Chennai600054 India SRM Institute of Science and Technology Department of Mathematics Delhi-NCR Campus Uttar Pradesh 201204 India

ISBN: (纸本)9798350395235

This study aims to investigate the utility of Multidetector computed tomography (MDCT) with a focus on the application of reformats in maximum intensity projection (MIP) in enhancing the diagnostic accuracy of PTE. A retrospective analysis was conducted on a cohort of [insert number] patients who underwent MDCT for suspected PTE. The MDCT images were reconstructed using MIP, allowing for comprehensive visualization of vascular structures. The diagnostic performance of MDCT with MIP reformats was compared to standard MDCT images. The inclusion of MIP reformats significantly improved the detection of PTE, providing enhanced visualization of thrombi within the pulmonary vasculature. The sensitivity and specificity of MDCT with MIP were [insert percentage] and [insert percentage], respectively, compared to [insert percentage] and [insert percentage] for standard MDCT. The findings highlight the added diagnostic value of MDCT with reformats in MIP for the evaluation of PTE. The improved visualization of thromboemboli within the pulmonary arteries (PA) enhances diagnostic accuracy, aiding clinicians in making timely and informed decisions. MDCT with MIP reformats stands as a valuable technique in the comprehensive assessment of pulmonary thromboembolism, contributing to better patient outcomes. © 2024 IEEE.

关键词： Multidetector computed tomography

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Scale-Aware Super-Resolution Network With Dual Affinity Learning for Lesion Segmentation From Medical Images

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IEEE Transactions on Neural Networks and Learning Systems 2024年第6期36卷 10530-10543页

作者： Luyang Luo Yanwen Li Zhizhong Chai Huangjing Lin Pheng-Ann Heng Hao Chen Department of Computer Science and Engineering The Hong Kong University of Science and Technology Sai Kung Hong Kong Department of Biomedical Informatics Harvard University Cambridge MA USA Imsight AI Research Lab Shenzhen China Department of Computer Science and Engineering The Chinese University of Hong Kong Shatin Hong Kong Department of Computer Science and Engineering Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong China HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute Futian Shenzhen China

Convolutional neural networks (CNNs) have shown remarkable progress in medical image segmentation. However, the lesion segmentation remains a challenge to state-of-the-art CNN-based algorithms due to the variance in scales and shapes. On the one hand, tiny lesions are hard to delineate precisely from the medical images which are often of low resolutions. On the other hand, segmenting large-size lesions requires large receptive fields, which exacerbates the first challenge. In this article, we present a scale-aware super-resolution (SR) network to adaptively segment lesions of various sizes from low-resolution (LR) medical images. Our proposed network contains dual branches to simultaneously conduct lesion mask SR (LMSR) and lesion image SR (LISR). Meanwhile, we introduce scale-aware dilated convolution (SDC) blocks into the multitask decoders to adaptively adjust the receptive fields of the convolutional kernels according to the lesion sizes. To guide the segmentation branch to learn from richer high-resolution (HR) features, we propose a feature affinity (FA) module and a scale affinity (SA) module to enhance the multitask learning of the dual branches. On multiple challenging lesion segmentation datasets, our proposed network achieved consistent improvements compared with other state-of-the-art methods. Code will be available at: https://***/poiuohke/SASR_Net .

关键词： Lesions Image segmentation Feature extraction Medical diagnostic imaging Superresolution Diseases Decoding Computed tomography Technological innovation Skin

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Development of Mutal Recognition of Education Approach in Advanced computer-Oriented engineering Technologies in the Baltic Region 64

Development of Mutal Recognition of Education Approach in Ad...

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64th IEEE Annual International Scientific Conference on Power and Electrical engineering of Riga Technical University, RTUCON 2023

作者： Kunicina, Nadezhda Rassolkin, Anton Bruzgiene, Rasa Plonis, Darius Caiko, Jelena Riga Technical University Institute of Electrical Engineering and Electronics Feee Riga Latvia Tallinn University of Technology Department of Electrical Power Engineering and Mechatronics Tallinn Estonia Kaunas University of Technology Faculty of Informatics Department of Computer Sciences Kaunas Lithuania Vilnius Gediminas Technical University Department of Electronic Systems Vilnius Lithuania

ISBN: (纸本)9798350317732

The continued improvement and development of study programmes content for mutual recognition of study programmes among the largest technical universities in the Baltic region provides a very good opportunity for regional development as it brings a good advancement in academics and research at the involved universities, allows for more flexible and reliable collaboration with industrial partners, and provides an opportunity to develop new technologies and implement them for regional support. The goal of the development is to promote the involvement of students in the design of adaptive systems by providing them with the necessary set of skills that will allow them to immediately engage in the design of innovative adaptive systems and successfully solve technological challenges that have been identified through the cooperation of educational and research institutions. The adoption of an innovative educational strategy will aid in the creation of new technology and creative solutions even before students begin their studies. Collaboration between academic personnel and industry will help students build their abilities. © 2023 IEEE.

关键词： Students

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Deep Learning Approaches for Solving Challenges in computer Vision Applications and Exploring Evolving Patterns 7

Deep Learning Approaches for Solving Challenges in Computer ...

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7th International Conference on Contemporary Computing and informatics, IC3I 2024

作者： Gejji, Sujay Supreeth, B.R. Khan, Pathan Firoze Venkat, S. Pradeepa, K. Kaliappan, S. S. G. Balekundri Institute of Technology Department of Electronics & Communication Engineering Karnataka Belagavi India Oxford Degree and Pg College Department of Computer Science Sir. M. Visveshwaraiah Layout Bangalore India Madanapalle institute of Technology & Science Department of Computer Science & Technology Andhra Pradesh Madanapalle India S. A. Engineering College Department of Electronics and communication Engineering Tamil Nadu Chennai India Kpr College of Arts Science and Research Department of Artificial Intelligence and Data Science Tamil Nadu Coimbatore India Lovely Professional University Division of Research and Development Punjab Phagwara India

ISBN: (纸本)9798350350067

Deep learning has brought substantial advancements to computer vision in recent years by providing answers to challenging visual interpretation problems. It investigates several DL methodologies, such as self-supervised learning strategies, hybrid CNN-RNN models, enhanced CNNs, generative adversarial networks (GANs), and vision transformers (ViTs). Novel CNN architectures are designed, GANs are used for image synthesis, hybrid models are used to integrate spatial and temporal information, self-attention is used in ViTs, and self-supervised learning is used for feature extraction. The results of the study show significant improvements: the accuracy of Enhanced CNNs was 81.6%, the accuracy of GANs was 14.8%, the accuracy of Hybrid CNN-RNN models was 86.2%, the accuracy of ViTs was 79.2%, and the accuracy of transfer for self-supervised learning techniques was 76.8%. These findings demonstrate the improved effectiveness and performance of the suggested techniques, highlighting their potential to resolve challenging problems in feature extraction, video analysis, object recognition, picture enhancement, and image classification. It concludes that advances in computer vision will be facilitated by the ongoing development of DL methods. These advancements will lead to more precise and adaptable applications in a range of fields, including autonomous driving, medical imaging, surveillance, and augmented reality. © 2024 IEEE.

关键词： Generative adversarial networks

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KartalOl: a new deep neural network framework based on transfer learning for iris segmentation and localization task—new dataset for iris segmentation

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Iran Journal of computer Science 2023年第4期6卷 307-319页

作者： Khiarak, Jalil Nourmohammadi Oskouei, Amin Golzari Nasab, Samaneh Salehi Jaryani, Farhang Moafinejad, Seyed Naeim Pourmohamad, Rana Amini, Yasin Noshad, Morteza Institute of Control and Computation Engineering Warsaw University of Technology Ul. Nowowiejska 15/19 Warsaw 00-665 Poland Department of Computer Engineering Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran School of Engineering Lorestan University Aleshtar Campus Aleshtar Iran Faculty of Engineering Arak University Arak Iran Department of Physics Shahid Beheshti University Tehran Iran School of Computing Informatics and Decision Systems Engineering Arizona State University AZ United States Faculty of Electrical and Computer Engineering University of Kharazmi Tehran Iran Stanford University Stanford United States

Iris segmentation and localization in unconstrained environments are challenging due to long distances, illumination variations, limited user cooperation, and moving subjects. Some existing methods in the literature have somehow mitigated some of the above-mentioned issues. In this paper, motivated by these weaknesses, we propose a framework that employs a deep neural network-based approach to iris segmentation and localization. The proposed framework is based on a U-Net architecture initialized with a pre-trained MobileNetV2 model. In addition, to better study the detectors in iris recognition scenarios, we have collected 1000 images. The provided dataset (KartalOl) is made publicly available for the research community. In the proposed framework, to have better generalization, we fine-tuned the MobileNetV2 model on the provided data for NIR-ISL 2021 from the CASIA-Iris-Asia, CASIA-Iris-M1, and CASIA-Iris-Africa and our dataset. Likewise, data augmentation techniques are applied on images. We chose the binarization threshold for the binary masks by iterating over the images in the provided dataset. The proposed framework is trained and tested in CASIA-Iris-Asia, CASIA-Iris-M1, and CASIA-Iris-Africa, along with the KartalOl dataset. The experimental results highlight that our method surpasses state-of-the-art methods on mobile-based benchmarks. The implementation source code of KartalOl is made publicly available at https://***/Jalilnkh/KartalOl-NIR-ISL2021031301. © The Author(s) 2023.

关键词： Iris segmentation Localization MobileNetV2 model Unconstrained environment

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Machine Learning based Classification Algorithm for Classification of Dengue (Dengue Fever -DF, Dengue Harmonic Fever -DHF & Serve Dengue -SD) 1

Machine Learning based Classification Algorithm for Classifi...

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1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Kapoor, Rajeev Ahuja, Sachin Kadyan, Virender Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Informatics School of Computer Science University of Petroleum and Energy Studies Dehradun India

ISBN: (纸本)9781607685395

Dengue is becoming a burden for society worldwide and become challenge for the world. The main objective of this research paper is to classify dengue at an early stage. The author has adopted a methodology that is divided into five phases namely Data Collection, Transmission of Raw Data in Structural data format, Data Preprocessing, Model building with Train & Test Datasets and Prediction. Further, the proposed prediction model has been trained with the help of 250 data samples from primary sources namely clinical labs and 1000 data sample in the form of synthetic datasets adopted from 20 research papers. The proposed prediction Model has been tested and validated with the help of comparative study of different machine learning algorithms namely Decision Tree, Logistical Regression, Support Vector Machine (SVM), Gaussian Naïve Bayes etc. The proposed model has achieved 97.39% accuracy, 0.98 Precision, 0.97 Recall, 0.97 F1 Score, 0.02 MS and 0.14 RMSE. © The Electrochemical Society

关键词： Dengue Prediction Model Machine Learning Prediction Model Vector Borne Disease

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