A Collaborative E-learning System Based on Multi-agent

2005 ◽  
pp. 455-463 ◽  
Author(s):  
Jun Wang ◽  
Yong-Hong Sun ◽  
Zhi-Ping Fan ◽  
Yan Liu
Keyword(s):  
Learning System ◽  
E Learning ◽  
Multi Agent

Design and Development of a Multi-Agent E-Learning System

2016 ◽  
pp. 390-447
Author(s):  
Terje Kristensen ◽  
Marius Dyngeland
Keyword(s):  
Software Development ◽  
Service Oriented Architecture ◽  
Learning System ◽  
Agent Architecture ◽  
Quality Requirements ◽  
Client Server ◽  
Dynamic Content ◽  
Service Oriented ◽  
E Learning ◽  
Multi Agent

In this paper the authors present the design and software development of an E-learning system based on a multi-agent (MAS) architecture. The multi-agent architecture is established on the client-server model. The MAS architecture is combined with the Dynamic Content Manager (DCM) model of E-learning developed at Bergen University College, Norway. The authors first present the quality requirements of the system before they describe the architectural decisions taken. They then evaluate and discuss the benefits of using a multi-agent architecture. Finally, the MAS architecture is compared with a pure service-oriented architecture (SOA) to observe that a MAS architecture has a lot of the same qualities as this architecture, in addition to some new ones.

Design and Development of a Multi-Agent E-Learning System

2015 ◽  
Vol 7 (2) ◽  
pp. 19-74 ◽  
Author(s):  
Terje Kristensen ◽  
Marius Dyngeland
Keyword(s):  
Software Development ◽  
Service Oriented Architecture ◽  
Learning System ◽  
Agent Architecture ◽  
Quality Requirements ◽  
Client Server ◽  
Dynamic Content ◽  
Service Oriented ◽  
E Learning ◽  
Multi Agent

In this paper the authors present the design and software development of an E-learning system based on a multi-agent (MAS) architecture. The multi-agent architecture is established on the client-server model. The MAS architecture is combined with the Dynamic Content Manager (DCM) model of E-learning developed at Bergen University College, Norway. The authors first present the quality requirements of the system before they describe the architectural decisions taken. They then evaluate and discuss the benefits of using a multi-agent architecture. Finally, the MAS architecture is compared with a pure service-oriented architecture (SOA) to observe that a MAS architecture has a lot of the same qualities as this architecture, in addition to some new ones.

scholarly journals Multi-Agent Adaptive e-Learning System Based on Learning Styles

2021 ◽  
Vol 4 (1) ◽  
pp. 1-12
Author(s):  
Faith Ngami Kivuva ◽  
Elizaphan Maina ◽  
Rhoda Gitonga
Keyword(s):  
Learning Styles ◽  
Learning Process ◽  
Intelligent Agents ◽  
Personalized Learning ◽  
Learning Systems ◽  
Learning System ◽  
Personalized Recommendation ◽  
Information Agent ◽  
E Learning ◽  
Multi Agent

Most traditional e-learning system fails to provide the intelligence that a learner may require during their learning process. Different learners have different learning styles but the current e-learning systems are not able to provide personalized learning. In this paper, we discuss how intelligent agents can aid learners in their learning process. Three agents have been developed namely, learner agent, information agent, and tutor agents that will be integrated into a learning management system (Moodle). Learners are provided with a personalized recommendation based on the learning styles.

scholarly journals Design of an Adaptive e-Learning System based on Multi-Agent Approach and Reinforcement Learning

2021 ◽  
Vol 11 (1) ◽  
pp. 6637-6644
Author(s):  
H. El Fazazi ◽  
M. Elgarej ◽  
M. Qbadou ◽  
K. Mansouri
Keyword(s):  
Reinforcement Learning ◽  
Learning Style ◽  
Learning Process ◽  
Learning Experience ◽  
Personalized Learning ◽  
Learning Objects ◽  
Learning Systems ◽  
Learning System ◽  
E Learning ◽  
Multi Agent

Adaptive e-learning systems are created to facilitate the learning process. These systems are able to suggest the student the most suitable pedagogical strategy and to extract the information and characteristics of the learners. A multi-agent system is a collection of organized and independent agents that communicate with each other to resolve a problem or complete a well-defined objective. These agents are always in communication and they can be homogeneous or heterogeneous and may or may not have common objectives. The application of the multi-agent approach in adaptive e-learning systems can enhance the learning process quality by customizing the contents to students’ needs. The agents in these systems collaborate to provide a personalized learning experience. In this paper, a design of an adaptative e-learning system based on a multi-agent approach and reinforcement learning is presented. The main objective of this system is the recommendation to the students of a learning path that meets their characteristics and preferences using the Q-learning algorithm. The proposed system is focused on three principal characteristics, the learning style according to the Felder-Silverman learning style model, the knowledge level, and the student's possible disabilities. Three types of disabilities were taken into account, namely hearing impairments, visual impairments, and dyslexia. The system will be able to provide the students with a sequence of learning objects that matches their profiles for a personalized learning experience.

The Role of Multi-Agent Social Networking Systems in Ubiquitous Education

2010 ◽  
pp. 72-88 ◽  
Author(s):  
Jonathan Bishop
Keyword(s):  
Social Networking ◽  
Information Science ◽  
Learning Systems ◽  
Learning System ◽  
Future Research ◽  
System A ◽  
E Learning ◽  
Multi Agent ◽  
Social Networking Technology ◽  
Networks Of Practice

Knowledge it could be argued is constructed from the information actors pick up from the environments they are in. Assessing this knowledge can be problematic in ubiquitous e-learning systems, but a method of supporting the critical marking of e-learning exercises is the Circle of Friends social networking technology. Understanding the networks of practice in which these e-learning systems are part of requires a deeper understanding of information science frameworks. The Ecological Cognition Framework (ECF) provides a thorough understanding of how actors respond to and influence their environment. Forerunners to ecological cognition, such as activity theory have suggested that the computer is just a tool that mediates between the actor and the physical environment. Utilising the ECF it can be seen that for an e-learning system to be an effective teacher it needs to be able to create five effects in the actors that use it, with those being the belonging effect, the demonstration effect, the inspiration effect, the mobilisation effect, and the confirmation effect. In designing the system a developer would have to consider who the system is going to teach, what it is going to teach, why it is teaching, which techniques it is going to use to teach and finally whether it has been successful. This chapter proposes a multi-agent e-learning system called the Portable Assistant for Intelligently Guided Education (PAIGE), which is based around a 3D anthropomorphic avatar for educating actors ubiquitously. An investigation into the market for PAIGE was carried out. The data showed that those that thought their peers were the best form of support were less likely to spend more of their free time on homework. The chapter suggests that future research could investigate the usage of systems like PAIGE in educational settings and the effect they have on learning outcomes.

A Multi-agent Based Adaptive E-Learning System

2021 ◽  
pp. 693-707
Author(s):  
Birol Ciloglugil ◽  
Oylum Alatli ◽  
Mustafa Murat Inceoglu ◽  
Riza Cenk Erdur
Keyword(s):  
Learning System ◽  
Agent Based ◽  
E Learning ◽  
Multi Agent
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