Cloud Computing-based Higher Education Platforms during the COVID-19 Pandemic

• URL: http://arxiv.org/abs/2203.03714v1
• Date: Fri, 18 Feb 2022 11:20:22 GMT
• Title: Cloud Computing-based Higher Education Platforms during the COVID-19 Pandemic
• Authors: Hui Han and Silvana Trimi
• Abstract summary: Cloud computing-based education platforms have been widely applied to assist online teaching during the COVID-19 pandemic. This paper examines the impact and importance of cloud computing in remote learning and education.
• Score: 2.952783755616848
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Cloud computing has become the infrastructure that supports people's daily activities, business operations, and education delivery around the world. Cloud computing-based education platforms have been widely applied to assist online teaching during the COVID-19 pandemic. This paper examines the impact and importance of cloud computing in remote learning and education. This study conducted multiple-case analyses of 22 online platforms of higher education in Chinese universities during the epidemic. A comparative analysis of the 22 platforms revealed that they applied different cloud computing models and tools based on their unique requirements and needs. The study results provide strategic insights to higher education institutions regarding effective approaches to applying cloud computing-based platforms for remote education, especially during crisis situations.

Related papers

• Driving Intelligent IoT Monitoring and Control through Cloud Computing and Machine Learning [3.134387323162717]
  This article explores how to drive intelligent iot monitoring and control through cloud computing and machine learning. The paper also introduces the development of iot monitoring and control technology, the application of edge computing in iot monitoring and control, and the role of machine learning in data analysis and fault detection.
  arXiv  Detail & Related papers  (2024-03-26T20:59:48Z)
• Application of Machine Learning Optimization in Cloud Computing Resource Scheduling and Management [18.462300407761873]
  The scale of cloud computing in China has reached 209.1 billion yuan. This paper proposes an innovative approach to solve complex problems in cloud computing resource scheduling and management.
  arXiv  Detail & Related papers  (2024-02-27T05:14:27Z)
• Scalable, Distributed AI Frameworks: Leveraging Cloud Computing for Enhanced Deep Learning Performance and Efficiency [0.0]
  In recent years, the integration of artificial intelligence (AI) and cloud computing has emerged as a promising avenue for addressing the growing computational demands of AI applications. This paper presents a comprehensive study of scalable, distributed AI frameworks leveraging cloud computing for enhanced deep learning performance and efficiency.
  arXiv  Detail & Related papers  (2023-04-26T15:38:00Z)
• Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review [62.997667081978825]
  This review aims at providing a comprehensive vision of the main state-of-the-art libraries and frameworks for machine learning and data analytics available today. The main simulation, emulation, deployment systems, and testbeds for experimental research on the Edge-to-Cloud Continuum available today are also surveyed.
  arXiv  Detail & Related papers  (2022-04-29T08:06:05Z)
• Unsupervised Point Cloud Representation Learning with Deep Neural Networks: A Survey [104.71816962689296]
  Unsupervised point cloud representation learning has attracted increasing attention due to the constraint in large-scale point cloud labelling. This paper provides a comprehensive review of unsupervised point cloud representation learning using deep neural networks.
  arXiv  Detail & Related papers  (2022-02-28T07:46:05Z)
• Molecular Dynamics Simulations on Cloud Computing and Machine Learning Platforms [0.8093262393618671]
  We see a paradigm shift in the computational structure, design, and requirements of scientific computing applications. Data-driven and machine learning approaches are being used to support, speed-up, and enhance scientific computing applications. Cloud computing platforms are increasingly appealing for scientific computing.
  arXiv  Detail & Related papers  (2021-11-11T21:20:26Z)
• Edge-Cloud Polarization and Collaboration: A Comprehensive Survey [61.05059817550049]
  We conduct a systematic review for both cloud and edge AI. We are the first to set up the collaborative learning mechanism for cloud and edge modeling. We discuss potentials and practical experiences of some on-going advanced edge AI topics.
  arXiv  Detail & Related papers  (2021-11-11T05:58:23Z)
• Comparative Study of Learning Outcomes for Online Learning Platforms [47.5164159412965]
  Personalization and active learning are key aspects to successful learning. We run a comparative head-to-head study of learning outcomes for two popular online learning platforms.
  arXiv  Detail & Related papers  (2021-04-15T20:40:24Z)
• Peer-inspired Student Performance Prediction in Interactive Online Question Pools with Graph Neural Network [56.62345811216183]
  We propose a novel approach using Graph Neural Networks (GNNs) to achieve better student performance prediction in interactive online question pools. Specifically, we model the relationship between students and questions using student interactions to construct the student-interaction-question network. We evaluate the effectiveness of our approach on a real-world dataset consisting of 104,113 mouse trajectories generated in the problem-solving process of over 4000 students on 1631 questions.
  arXiv  Detail & Related papers  (2020-08-04T14:55:32Z)
• SARS-CoV-2 Impact on Online Teaching Methodologies and the Ed-Tech Sector: Smile and Learn Platform Case Study [50.591267188664666]
  The study analyzes the importance of online methodologies and usage tendency of an educational resource example: The Smile and Learn platform. Thereby, the study presents the different models implemented to support education and its impact in the use of the platform.
  arXiv  Detail & Related papers  (2020-07-15T10:06:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.