The Applied Research of Cloud Computing in the Construction of Collaborative Learning Platform Under E-Learning Environment

2010 ◽  
Author(s):  
Hui Ma ◽  
Zhongmei Zheng ◽  
Fei Ye ◽  
Sanhong Tong
Keyword(s):  
Cloud Computing ◽  
Collaborative Learning ◽  
Learning Environment ◽  
Applied Research ◽  
Learning Platform ◽  
E Learning

scholarly journals E-Learning Theories, Components, and Cloud Computing-Based Learning Platforms

2021 ◽  
Vol 16 (3) ◽  
pp. 1-16
Author(s):  
Vikas Kumar ◽  
Deepika Sharma
Keyword(s):  
Cloud Computing ◽  
Learning Environment ◽  
Learning Theories ◽  
Traditional Learning ◽  
Digital Learning ◽  
Learning Tools ◽  
Digital Platforms ◽  
Learning Platform ◽  
E Learning ◽  
Learning Platforms

The student habit of using the digital platforms can be used to compliment the traditional learning methods. Specifically, designed digital learning platform can support the learning with convenience of time, place, and pace. They can increase the engagement of students and produce higher learning outcomes with increased satisfaction and competence. With a number of embedded features, cloud computing technology-based platforms can deliver the convenience and flexibility in learning environment to compliment the traditional learning pedagogies. The present work identifies the essential learning components that comprise the e-learning environment and categorizes them according to the established learning theories. Further, the prominent cloud-based learning platforms have been mapped to these platforms to see their effectiveness through the existing theories. The work is targeted to form a basis for the development and implementation of successful e-learning tools using the cloud-based platforms.

Learning objects management and evaluatio in an e-learning environment

1970 ◽  
Vol 6 (2) ◽  
Author(s):  
Hugo Rego ◽  
Tiago Moreira ◽  
Francisco José García-Peñalvo
Keyword(s):  
Knowledge Management ◽  
Learning Environment ◽  
Learning Process ◽  
Group Learning ◽  
Learning Objects ◽  
Authoring Tools ◽  
Learning Platform ◽  
E Learning ◽  
Teaching Learning ◽  
Educational Contents

The main aim of the AHKME e-learning platform is to provide a system with adaptive and knowledge management abilities for students and teachers. This system is based on the IMS specifications representing information through metadata, granting semantics to all contents in the platform, giving them meaning. In this platform, metadata is used to satisfy requirements like reusability, interoperability and multipurpose. The system provides authoring tools to define learning methods with adaptive characteristics, and tools to create courses allowing users with different roles, promoting several types of collaborative and group learning. It is also endowed with tools to retrieve, import and evaluate learning objects based on metadata, where students can use quality educational contents fitting their characteristics, and teachers have the possibility of using quality educational contents to structure their courses. The learning objects management and evaluation play an important role in order to get the best results in the teaching/learning process.

Assessing the impact of cultural characteristics, economic situations, skills and knowledge on the development and success of cloud-based e-learning systems in the COVID-19 era

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Luyan Teng ◽  
Qinyi Tan ◽  
Ali Ehsani
Keyword(s):  
Cloud Computing ◽  
Learning System ◽  
Educational Institutions ◽  
Cultural Characteristics ◽  
Content Type ◽  
Learning Platform ◽  
E Learning ◽  
Training Courses ◽  
The Impact ◽  
The Relationship

PurposeOne of the most significant threats of COVID-19 in the world is the closure of universities, schools, training courses and even companies and organizations. In such a situation and with the free time that has arisen, this threat of education closure can become a golden opportunity for learning and progress in virtual education. E-learning uses information technology (IT) to distribute knowledge and information for training and education. Also, cloud computing is a technology utilized in the IT domain. It can be employed in performing e-learning. Therefore, the main goal of this study is to assess the impact of cultural characteristics, economic situations, skills and knowledge on the development and success of CELS in the COVID-19 era.Design/methodology/approachCloud-based e-learning system (CELS) provides all e-learning requirements like software and hardware resources to promote conventional e-learning technologies. The CELS stands on several factors of diverse aspects that have been of high significance in CELS success. So, these systems must be checked to analyze their significance rate and successfully carry out their effectiveness. On the other hand, these days, the 2019 coronavirus disease (COVID-19) changes our daily lifestyles. Therefore, the present investigation provides a new model investigating the development and success of CELS in the COVID-19 era. Also, an online questionnaire was used to gather the data. The content validity of the questionnaire was obtained by applying the opinions of ten experts from e-learning specialists. The collected data are analyzed using LISREL and Smart PLS software.FindingsThe results from the path coefficient and the sample t-test have implied that skills and knowledge positively influence CELS in the COVID-19 era. In addition, the relationship between cultural characteristics and CELS in the COVID-19 era has been positive and significant. The relationship between the economic situations and the CELS in the COVID-19 era is positive and significant.Practical implicationsThe proposed model helps managers get a big picture of CELS necessities and more effectively in the COVID-19 era. This research has a unique impact on universities to develop an e-learning platform to facilitate the education process in the COVID-19 era. It provides guidelines for educational institutions to effectively implement the learning management system to facilitate students' education.Originality/valueCELS are getting increasingly essential to offer training courses more efficiently in educational institutions. Although the intersection between cloud computing and e-learning has increasingly grown in both practical and academic contexts, few studies on the impact of cultural characteristics, economic situations, skills and knowledge on the development and success of CELS in the COVID-19 era. This paper explores the ignored but critically important subject of CELS. This paper's main contribution is to present a new and integrated model containing the essential aspects of the development and success of CELS in the COVID-19 era. The proposed framework comprises cultural characteristics, economic situations, skills and knowledge aspects simultaneously, as well as sub-criteria denoting each element.

The Use of Cloud-Computing to Promote Collaborative Learning in Higher Education

2019 ◽  
pp. 32-45
Author(s):  
Melissa S. Martin ◽  
Rachel E. Hugues ◽  
Alison Puliatte
Keyword(s):  
Higher Education ◽  
Cloud Computing ◽  
Collaborative Learning ◽  
Learning Styles ◽  
Instructional Strategy ◽  
Collaborative Work ◽  
Learning Preferences ◽  
Generation Z ◽  
E Learning ◽  
Learning Platforms

Generation Z students are inherently different than previous generations. These students may need adapted forms of instruction in order to match their learning styles. Collaborative learning can be adapted using cloud-computing, which helps students work together online and manage their interactions. These students may benefit from a technological twist to a common instructional strategy and are inclined to use online means of communication to complete coursework. Technology has dominated the educational experiences of these students and they are no strangers to collaborative work through e-learning platforms. Higher education institutions and instructors must develop the format of courses in order to meet the technological learning preferences of Generation Z.

A Multi-Agent Question-Answering System for E-Learning and Collaborative Learning Environment

2014 ◽  
pp. 134-149
Author(s):  
Tannaz Alinaghi ◽  
Ardeshir Bahreininejad
Keyword(s):  
Collaborative Learning ◽  
Learning Environment ◽  
Question Answering ◽  
Question Answering System ◽  
Course Materials ◽  
Life Styles ◽  
History Of ◽  
E Learning ◽  
Multi Agent ◽  
Collaborative Learning Environment

The increasing advances of new Internet technologies in all application domains have changed life styles and interactions. E-learning and collaborative learning environment systems are originated through such changes and aim at providing facilities for people in different times and geographical locations to cooperate, collaborate, learn and work together by using various educational services. One of the most important requirements of learners in online and virtual environments is the ability to ask questions and receive appropriate answers. The nature of such environments and the lack of physical existence of teachers make such issues critical and challenging problems. This paper presents a multi-agent system for building a question-answering system in learning management systems and collaborative learning environments. In the proposed system, after validating the content of questions, all available resources including course materials, frequently asked questions and responses from other learners will be gathered and finally using a recommender system, the most appropriate answer(s) with respect to several criteria such as learner’s knowledge, research background, history of previous questions, and the candidate answers relevant to the question will be suggested. A simplified version of the system has been implemented and integrated to a well known open source collaborative learning environment system in order to simulate and evaluate the applicability and appropriateness of the proposed system. The result shows that the proposed question-answering system may be used efficiently and expanded to accommodate further advanced capabilities.

Bringing AI to E-Learning

2012 ◽  
pp. 126-138
Author(s):  
K. Giotopoulos ◽  
C. Alexakos ◽  
G. Beligiannis ◽  
A. Stefani
Keyword(s):  
Learning Environment ◽  
Computational Intelligence ◽  
Intelligent Agents ◽  
Student Model ◽  
Basic Part ◽  
Learning Platform ◽  
Front End ◽  
E Learning ◽  
Autonomous Component

This paper presents a newly developed student model agent, which is the basic part of an e-learning environment that incorporates Intelligent Agents and Computational Intelligence Techniques. The e-learning environment consists of three parts, the E-learning platform Front-End, the Student Questioner Reasoning and the Student Model Agent. The basic aim of this contribution is to describe in detail the agent’s architecture and the innovative features it provides to the e-learning environment through its utilization as an autonomous component. Several basic processes and techniques are facilitated through the agent in order to provide intelligence to the e-learning environment.

Authentic and Situated Learning with the Use of an Adaptive Search Engine and a QR-Code in Mobile Mode

2013 ◽  
Vol 4 (3) ◽  
pp. 19-30
Author(s):  
Orlando De Pietro
Keyword(s):  
Learning Environment ◽  
Knowledge Base ◽  
Search Engine ◽  
Situated Learning ◽  
Authentic Learning ◽  
Virtual Learning Environment ◽  
Qr Code ◽  
Adaptive Search ◽  
Learning Platform ◽  
E Learning

The paper presents a technique of interrogation, in mobile technology, of a knowledge base contained in an e-Learning platform. The query is done through the integrated use of an adaptive search engine (ASE) and QR codes. The QR-Code are used to label objects in real environments of cultural matter (eg: museums, art galleries, archaeological sites, etc.). These objects can then be analyzed in more detail during a real learning activity (situated learning and authentic learning). The knowledge base of the virtual learning environment (VLE), is interrogated through a mobile device (smartphone or tablet) which, through an appropriate decoder software, interprets the instructions contained in a QR label previously positioned on the object actually observed by the learner (eg, during a visit to a museum). ASE combines to the data contained in the QR-Code those relating to the user who performs the query (previously recognized via the log-in on the e-Learning platform), therefore, extracts information about the observed object, adapting to the profile of the user-learner. With the help of these tools the learner can expand his real learning experience, while interacting with a virtual learning environment. In this way, situated and authentic learning activities can be upgraded and virtualized, maturing a significative educational experience with the use of intuitive and user friendly digital tools.

Cloud Computing Enabled Virtual Learning Environment

2013 ◽  
Vol 4 (3) ◽  
pp. 52-65
Author(s):  
Rabindra Ku Jena
Keyword(s):  
Cloud Computing ◽  
Learning Environment ◽  
Teaching And Learning ◽  
Cost Effective ◽  
Virtual Learning ◽  
Virtual Learning Environment ◽  
Technological Advancement ◽  
Learning And Teaching ◽  
Information And Communication ◽  
E Learning

Recent advances in Information and Communication Technology (ICT) provide an opportunity to build a self growing and sharing virtual environment for teaching and learning. Cloud computing is one of the latest technological advancement in ICT domain. Cloud computing technologies have changed the way applications are developed and accessed. A Virtual Learning Environment (VLE) is a system for delivering learning materials to students via the web. Cloud computing is provides one of the most emerging cost effective solution for virtual learning and teaching environment. This paper discusses how cloud computing has been contributing to virtual learning environment and an overview of the current state of the structure of Cloud Computing based e-learning is discussed. The readers will also find a brief overview of cloud computing and the different efficient cloud based virtual learning models. Towards the end different offers from different cloud vendors are discussed.

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