A multi-agent architecture for adaptive E-learning systems using a blackboard agent

Hammami, Salah; Mathkour, Hassan; Al-Mosallam, Entesar A.

doi:10.1109/iccsit.2009.5234741

Cited by 18 publications

(9 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the present work, the work of Brusilovsky [18] has been taken as reference, where an architecture for distributed learning is defined, called Knowledge Tree; and the use of a blackboard agent for the implementation of adaptive e-learning systems, proposed by Hammani [19].…”

Section: Methodologies For the Development Of Intelligent Multiagementioning

confidence: 99%

Fuzzy neural System Model for Online Learning Styles Identification, as an Adaptive Hybrid ELearning System Architecture Component

Alfaro¹,

Rivera²,

Luna-Urquizo³

et al. 2018

EasyChair Preprints

View full text Add to dashboard Cite

Abstract-In the present work, we present a Fuzzy Neural System Model for online identification of Learning Styles which gives support for contents personalization. The model was developed to serve as a component for an Adaptive Hybrid ELearning System Architecture, which focus on a high degree of customization and content adaptation. We proposal a Hybrid System model, in which techniques of Neural Networks, Fuzzy Logic and Case Based Reasoning are incorporated into the multiagent system. Finally, the authors present the architecture of the Fuzzy Neural System model, the results of the analysis of the model validation tests establishing conclusions and recommendations.

show abstract

Section: Methodologies For the Development Of Intelligent Multiagementioning

confidence: 99%

Fuzzy neural System Model for Online Learning Styles Identification, as an Adaptive Hybrid ELearning System Architecture Component

Alfaro¹,

Rivera²,

Luna-Urquizo³

et al. 2018

EasyChair Preprints

View full text Add to dashboard Cite

show abstract

“…Therefore, the main contribution of this work is to provide formal definitions based on object Petri Net [4] formalisms to model the internal behaviors of agents and intelligent blackboards and to better understand their collaboration. Adaptive and intelligent e-Learning systems appear as a superior alternative to the traditional approach for the development of educational systems [5]. An intelligent e-Learning system provides help to the student during problem solving and aids the student in determining the best sequence through the learning material.…”

Section: Introductionmentioning

confidence: 99%

“…We adopt such technologies to enhance e-learning systems in an intelligent manner. The multi-agent methodology using web-based technologies is a trend in the modeling and development of learning environments [5]. Using intelligent and reactive agents in an elearning architecture enables researchers to obtain a personalized elearning system that adapts to the goals and characteristics of each learner [9].…”

Section: Introductionmentioning

confidence: 99%

Adaptive e‐learning system based on agents and object petri nets (AELS‐A/OPN)

Hammami

Mathkour

2013

Comp Applic In Engineering

Self Cite

View full text Add to dashboard Cite

Agent technology plays an important role in today's software development at the industrial level and brings advantages to the development of educational applications. The main focus of the multi‐agent systems community has been on the development of concepts, architectures, interaction techniques, and general approaches to the analysis and specification of multi‐agent systems. We introduce herein a novel concept involving the development of an e‐Learning system architecture that explores several recent technologies, including multi‐agent technology and adaptive e‐Learning. The main concern of our research is twofold: to build a multi‐agent architecture that provides the ability to adapt to the learner's preferences in the e‐Learning environment and to analyze and control the communication and interactions among the different agents of the proposed system. Toward this aim, we present a distributed intelligent blackboard agent that ensures the communication among the participating agents. Furthermore, we describe a formal model that can be used to specify the message exchanges and their synchronization using Object Petri Nets. An exploratory implementation has been developed and conducted in practice. © 2013 Wiley Periodicals, Inc. Comput Appl Eng Educ 23:170–190, 2015; View this article online at http://wileyonlinelibrary.com/journal/cae; DOI

show abstract

“…Another proposal is presented in [14]. The author is introduced a multi-agent approach for design architecture of e-learning.…”

Section: Related Workmentioning

confidence: 99%

Architecture of e-Learning

Alharbi¹,

Jemmali²

2016

IJKE

View full text Add to dashboard Cite

Abstract-Recent years, traditional learning has moved to electronic learning (e-learning). Many institutions and university has forced on e-learning in. In the form of e-learning, instructors and learners live in different places and they do not engage in a classroom environment, but within virtual space. There are small number of e-learning architecture have proposed in the literature. However, the proposed architecture has lack of embedding intelligent system in the architecture of e-learning. In this paper, we propose an architecture and algorithm for the problem of e-learning. This research has proposed architecture for e-learning. Thus there is intelligence built into the architecture and thus the e-learning system is automatically responsive to the user requirements.

show abstract

A multi-agent architecture for adaptive E-learning systems using a blackboard agent

Cited by 18 publications

References 15 publications

Fuzzy neural System Model for Online Learning Styles Identification, as an Adaptive Hybrid ELearning System Architecture Component

Fuzzy neural System Model for Online Learning Styles Identification, as an Adaptive Hybrid ELearning System Architecture Component

Adaptive e‐learning system based on agents and object petri nets (AELS‐A/OPN)

Architecture of e-Learning

Contact Info

Product

Resources

About