Agent-Based Design for E-learning Environment

International Journal of Emerging Technologies in Learning (iJET), 2009

The current life knows a remarkable development which changes all aspects of our life such as: our working environment, educational system, traveling patterns, sport activitiesâ?¦etc. The busy lifestyle faced by individuals today and the fast pattern of life may lead to their inability to be involved in a process of education. There is also a considerable technological development, especially the development of computing and communication technology. All these factors led to the idea of the need to reduce the time and to benefit from this technology, particularly in the field of education. The way of the classical education is very slow, and the student is obliged to be present at specific times which may be inappropriate for the majority of learners. Moreover, the substantial funds allocated for the success of classical learning process. Thus, it emerged the so-called E-learning via the Internet; the aims of this new mode of learning across the network (web) are to reduce the time ...

Journal of Emerging Trends in Computing and Information Sciences, 2012

The article presents a structure for using intelligent multi-agents architecture for an educational system and internet teaching. In this architecture, a model of multi-agents learning environments consists of: the domain model, the communication model and the user model. They contain all the domain knowledge of the target application and the user's profiles. Reconceptualizing the computer as a constructionist medium increases the computer's educational value by allowing the development and support of communities of users. The communication model facilitates communication via more conventional media, but also enables the communication of ideas through the creation and sharing of computational objects (e.g. agents and analysis tools). An agent appears to be appropriate for the implementation of the major functions of intelligent training, support and teaching environments by providing an environment for the definition and sharing of computational components, through the World Wide Web.

… OF THE 8TH EUROPEAN CONFERENCE ON E …, 2009

An intelligent collaborative learning environment (iCLE) provides an online learning community with an interactive and multi-functional work area with intelligent support for the whole cycle of collaborative education, including organizing teams, advising on group work and communication, tutoring, and testing individual contributions. An agent-based approach lends itself to developing iCLE systems since many of the desired properties and requirements of iCLE systems coincide with those provided by the use of agents, such as autonomy, reactiveness and proactiveness (goal-oriented). Existing agent-based designs for online collaborative learning identify the agent types and the system topology, but lack certain design specifications. In particular, there is a lack of precision with respect to areas such including: the key roles that intelligent agents can play in online collaborative learning management; the computational resources consumed and generated by a role for performing a pedagogical task; the protocols adopted for the interactions between different roles; the agent types with mapped roles and the number of instances of each type in an actual system; and the services that the agents provide. Fully specifying these aspects will enable the system to fully exploit the strengths of agents (including pro-activeness, autonomy and flexibility). In this paper, we propose a new design, GAOOLE (Gaia Design of Agent-based Online Collaborative Learning Environment), which includes a detailed analysis and design specification. It consists of five sub-models: the environment model (describing the computational resources in a collaborative learning environment that are needed by the identified roles and their relationships with them), the roles model (describing the attributes of the roles for managing online collaborative learningresponsibilities, permissions, activities and protocols), the interaction model (defining the protocols for each type of inter-role interaction), the agent model (defining the types of agents and the number of instances of each agent type in actual system) and the services model (describing the services associated with each agent type). We specify these models in this paper by following the Gaia methodology. By applying an established agent-oriented methodology we develop a detailed system design that makes full use of the agent-based approach both in terms of system development, for example by facilitating use of existing components and systems, and in system use, providing characteristics such as flexibility and pro-activeness. In this paper we give an overview of the design, and focus in particular on how the collaborative aspects of the learning environment are supported.

E-learning refers to learning that is delivered or enabled via electronic technology. It is the effective learning process created by combining digitally delivered content with support and services. E -learning is classified as synchronous or asynchronous. Agent -based E-learning system retrieves relevant learning materials, process and analyzes data to enable meaningful E -learning recommendations to instructors and learners. E -learning intelligent agents are sets of independent software tools that are linked with other applications and database software running within a computer environment. Personalized agent-based E-learning system provide a set of personalization functionalities such as personalizing learning plans, learning materials, test and necessary instant messages etc., to online learners. In recent years, numerous efforts have been made in the direction of automating the content acquisition and distribution, but personalized content delivery, access and interaction rem...

Confluence 2013: The Next Generation Information Technology Summit (4th International Conference), 2013

The multi -agent system is one of the emerging trends being used today and the application of multi-agent systems is spreading in almost every field. It can provide better solutions to very complex and restrictive system. Here in this paper, we propose a new Multi agent based Architecture named I-MBLS (Interactive Multi-Agent Based Learning System) that we have designed for distance learning on the web. This architecture makes use of six different agents named Student Interface Agent (SIA)-responsible for monitoring student learning activity, Tutor Interface Agent (TIA)-responsible for providing an interactive interface to the tutor, Decision support Intelligent Agent (DSIA)responsible for important decision making, Collaborate agent (CA)-responsible for establishing collaboration among students, Test Agent( TA) -responsible for conducting tests, evaluating marks scored and lesson Planning agent (LPA)responsible for helping tutor for preparing a course content. These agents collaborate with each other and provide an elearning environment that is interactive, secure and efficient

2008

Collaborative learning involves students working together to aid their learning and is considered an effective method to implement learning goal. However, it involves complicated processes such as have inconvenient assistance to manage the increasing demand for information and support extension of interaction and how to activate collaboration interaction and communication between different types of interactions. A collaborative learning model based on agent can improve learning effectiveness and give good impact to learning process. This paper discusses the definition, classification and roles of agent to support collaborative learning interaction in E-learning. It overviews the rapidly use of agents to support collaborative learning and describes the types of interaction and communication tools to enhance interaction between all participants. We identify three types of major agents used in collaborative learning interaction; interface agent, information and internet agent, and collaborative agent.

Quality & Quantity, 2014

E-learning is the synthesis of multimedia and social media platforms powered by Internet and mobile technologies. Great popularity of e-learning is encouraging governments and educational institutions to adopt and develop e-learning cultures in societies in general and universities in particular. In traditional e-learning systems, all components (agents and services) are tightly coupled into a single system. In this study, we propose a new architecture for e-learning with two subsystems, namely, e-learning and e-testing. The motivation of the research is to improve the effectiveness of the learning process by extracting relevant features for elastic learning and testing process. We employ a multi-agent system because it contains five-layer architecture, including agents at various levels. We also propose a novel method for updating content through question and answer between e-learners and intelligent agents. To achieve optimization, we design a system that applies various technologies, which guarantee various dynamic features for e-learning systems, such as intelligence, distributed nature, adaptive attitude, interaction, accessibility, and security. Agent assisted e-learning enable the users to collect the quantifiable and sensible material; examine, and distribute customized knowledge from multiple e-learning sources. Intelligent agents, being program helper or assistants, are deployed at different levels of abstraction in this architecture to manage information overload and create environment for learners. Moreover, this proposed system is designed by keeping in view several characteristics specific to e-learning system such as interaction, personalization, adaptation, intelligence, interoperability, accessibility and security. The architecture is designed to support instructional design, to retrieve relevant learning materials, to process and analyses data to enable meaningful e-learning recommendations for instructors and learners by considering all issues that existing e-learning architectures don't address. Most of the existing e-learning architectures don't consider all the features in a single system so there is a need for a generic architecture that should support all the features to make the e-learning system more efficient. The outcome of this approach is to provide flexible and lightweight systems for e-learning environments.

2007

This paper presents a framework to describe the crossover domain of e-learning and agent technology. Furthermore it is used to classify existing work and possible starting points for the future development of agent techniques and technologies order to enhance the performance and the effectiveness of several aspects of e-learning systems. Agents are not a new concept but their use in the field of e-learning constitutes a basis for consequential advances.

International Journal of u- and e-Service, Science and Technology, 2015

E-learning is the internet enabled learning. Internet has ongoing to restructuring education. Intelligent agent based e-learning provide a common infrastructure to assimilate varied software components. There are two sorts of e-learning synchronous and asynchronous. This paper described the detail of well-known agent based architecture for e-learning. E-learning is going to be gigantic. There are multiple benefits of e-learning; it is convenient, self-service mix, match, on demand any time anyplace, private learning, Self-paced and elastic. E-learning provide cost effective and virtual environment. E-learning gives the ability to user to collect the quantifiable and sensible material, examine, and distribute and custom e-learning knowledge from multiple e learning sources.

… and Network-Based …, 2003

In this paper the problem of educational resource management in a cooperative learning environment is discussed. A task model was elaborated to determine both functional and level-ofservice requirements. Among the former we salient: (1) the remote control of classroom hardware devices, and (2) the accessibility to course materials. Among the later, we emphasize scalability, extensibility and reliability. A multi-tiered agent-based software architecture is proposed and a distributed deployment is presented in order to satisfy all the requirements.