scholarly journals Building an e-Learning Application Using Multi-agents and Fuzzy Rules

2021 ◽  
Vol 19 (3) ◽  
pp. pp199-208
Author(s):  
Magdi Amer ◽  
Hossam Aldesoky
Keyword(s):  
Classroom Environment ◽  
Learning System ◽  
Teaching Mathematics ◽  
Advanced Mathematics ◽  
Agent Architecture ◽  
New Concepts ◽  
E Learning ◽  
Multi Agent ◽  
Conventional Classroom ◽  
Multi Agents

One of the biggest challenges in education is teaching mathematics, especially to children. It has been proven that difficulties students face when learning basic mathematics are often the result of previously acquired misconceptions. These misconceptions prevent the student from understanding new concepts and will eventually create a psychological barrier that prevents the student from learning more advanced mathematics. The conventional classroom environment does not provide the teacher with the most efficient means to detect and correct such misconceptions. The goal of our research is to develop an e-learning system for basic mathematics that is capable of providing each student with personalized content to overcome these misconceptions. The system uses a multi-agent architecture to monitor the activity of the student while simultaneously observing and modeling the student’s knowledge and misconceptions. Lessons and exam questions are chosen dynamically by the multi-agent system to cover the prerequisites of new lessons depending on the profile of the user.

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 INCA: A cognitive multi-agents architecture for designing intelligent & adaptive learning systems

2005 ◽  
Vol 2 (2) ◽  
pp. 99-114 ◽  
Author(s):  
Thierry Nabeth ◽  
Liana Razmerita ◽  
Albert Angehrn ◽  
Claudia Roda
Keyword(s):  
Knowledge Sharing ◽  
Adaptive Learning ◽  
Management Practices ◽  
Learning Systems ◽  
Learning System ◽  
Cognitive Agents ◽  
E Learning ◽  
Personal Agent ◽  
Adaptive Learning Systems ◽  
Multi Agents

This paper presents a cognitive multi-agents architecture called Intelligent Cognitive Agents (InCA) that was elaborated for the design of Intelligent Adaptive Learning Systems. The InCA architecture relies on a personal agent that is aware of the user's characteristics, and that coordinates the intervention of a set of expert cognitive agents (such as story telling agents, assessment agents, stimulation agents or help agents). This InCA architecture has been applied for the design of K"InCA, an e-learning system aimed at helping people to learn and adopt knowledge-sharing management practices.

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

Incremental Ontology Population and Enrichment through Semantic-based Text Mining

2015 ◽  
Vol 11 (3) ◽  
pp. 44-66 ◽  
Author(s):  
Saira Gillani ◽  
Andrea Ko
Keyword(s):  
Text Mining ◽  
Domain Knowledge ◽  
Learning System ◽  
Domain Specific ◽  
Automatic Categorization ◽  
New Concepts ◽  
Domain Specific Knowledge ◽  
E Learning ◽  
Ontology Population ◽  
It Audit

Higher education and professional trainings often apply innovative e-learning systems, where ontologies are used for structuring domain knowledge. To provide up-to-date knowledge for the students, ontology has to be maintained regularly. It is especially true for IT audit and security domain, because technology is changing fast. However manual ontology population and enrichment is a complex task that require professional experience involving a lot of efforts. The authors' paper deals with the challenges and possible solutions for semi-automatic ontology enrichment and population. ProMine has two main contributions; one is the semantic-based text mining approach for automatically identifying domain-specific knowledge elements; the other is the automatic categorization of these extracted knowledge elements by using Wiktionary. ProMine ontology enrichment solution was applied in IT audit domain of an e-learning system. After ten cycles of the application ProMine, the number of automatically identified new concepts are tripled and ProMine categorized new concepts with high precision and recall.

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 Influence of Constructivist E-Learning System on Student Learning Outcomes

2011 ◽  
pp. 45-57
Author(s):  
Thanakorn Wangpipatwong
Keyword(s):  
Learning Environment ◽  
Learning Outcomes ◽  
Classroom Environment ◽  
Test Method ◽  
Learning System ◽  
Knowledge Development ◽  
Traditional Learning ◽  
Two Phase ◽  
E Learning ◽  
High Level

In this article, the study of how a constructivist e-learning system affects students’ learning outcomes was explored and a two-phase study was designed. The first study sought to create a constructivist e-learning environment (CEE) and discover how students expected their learning outcomes under CEE. CEE is composed of three constructs, which are exploration, collaboration, and construction. The statistical results showed the high level of student expectation on every construct. Consequently, constructivist e-learning system (CES) was developed. In the second study, CES was used in the actual classroom environment. The purpose was to compare the learning outcomes and knowledge development of students who studied the course using CES with those of students who learned it under a traditional learning environment. A T-test method was used to analyze the learning outcomes. The results showed that students who used CES had better learning outcomes and knowledge development than students who did not use CES.

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