Agent-Based Approach for E-Learning

2011

In this paper an open agent-based modular framework for personalized and adaptive curriculum generation in e-learning environment is proposed. Agent-based approaches offer several potential advantages over alternative approaches. Agent-based systems exhibit high levels of flexibility and robustness in dynamic or unpredictable environments by virtue of their intrinsic autonomy. The presented framework enables integration of different types of expert agents, various kinds of learning objects and user modeling techniques. It creates possibilities for adaptive e-learning process. The KM e-learning system is in a process of implementation in Varna Free University and will be used for supporting the educational process at the University.

In e-learning systems, currently present, the adaptive feedback and facilitation to student learning is very absolute. The teacher as well as students involved in distance learning education face a critical lapse towards proper development of skills as the present technologies are not sufficiently A.I.capable.We ought to propose a paradigm in reference to addition of soft computing technologies and agent based simulation in this paper. The technologies currently under implementation don’t possess the centralized controlling via artificially enhanced agents which can favour the suggestion process. The algorithms and methodologies used in this paper create an agent which is efficient. This paper gives a general description of technologies, contrasting them and provides a proposed solution to it.

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.

IEEE Distributed Systems Online, 2000

Di stance learning is becoming increasingly popular for two main reasons: it lets institutions overcome problems of scale (not enough students in a single location or course) and rarity (a specialized subject isn't locally available). Unfortunately, most learning environments focus primarily on the infrastructure and neglect the learning services and personalized pedagogy aspects of eteaching. Designing Distributed Learning Environments with Intelligent Software Agents discusses a possible solution to this problem. Using agents as an abstraction tool or a metaphor for the design and construction of distance-learning systems provides novel possibilities in delivering user-focused elearning content. Furthermore, the book presents the agent-oriented approach as a way to address the complexity of distributed-learning environments. Each of the book's 10 chapters covers different issues arising in agent-based approaches to developing distributed e-learning systems. The first two chapters as well as the final chapter focus on implementation-related problems, while the others cover issues such as theory, legacy, and pedagogy.

This short paper illustrates our approach to e -learning. We first consider the current challenges presented by e -learning (and the limitations of the traditional e -learning systems in fulfilling these needs). In the second section we describe our vision on how elearning systems could be designed in the future (we identify the features that e-learning systems should support). Finally we analyse how K-InCA, an agent-based system that we have designed to support the acquisition of Knowledge Management concepts and practices, can address the e-learning challenges responding to our vision.

2017

In this paper, the advantages of using intelligent agents were investigated in order to facilitate and customize suitable electronic learning resources and reinforce collaboration in e-learning environment. Using intelligent agent, learning is personalized based on person’s prior knowledge. E-learning agents monitor electronic learning environment and improve learning and collaboration which is based on learners’ prior knowledge, social features and learning styles. E-learning agents should easily admit of exploration of new learning objects, allowing learners to customize materials presented so that learning and collaboration results can improve in an e-learning environment; it is a learning agent that should offer tangible benefits to organization if operated in online learning or learning environments.

International Journal of Agent Technologies and Systems, 2015

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.

The paper presents the basic requirements that must cover distance education processes ("e-learning") in universities. We show the concepts of instruction design, an adaptive learning model for evaluating necessities, accreditation, and quality proposal. The experience indicates that to obtain good results we should evaluate the differences between the criteria of the professor and the criteria of the student about: the educative aspects, the user reaction (in each perspective), the reading aspects (in the student) and complementary material. Thus, collaboration and quality are managed when the learning process is based on design, the designer, the professor, participation level, student profile and his characteristics, the motivations and evaluations. We will need continuous reflection and evaluation for the organizations and the structured learning programs, having looked for educative yield and productive development. The paper is our contribution to work with ethical issues and human collaboration for the information society

The notion of a learning society has long been promoted as the next stage of evolution of the educational system from the inculation of skills and knowledge to the development of capacities which enable people to learn continuously for the rest of their lives. This evolutionary step is vital to the continued health of human societies in a post-modern age of rapid and unceasing change where the certainties of the past have been replaced by the uncertainties of a future that requires continuous learning. The move to a learning society requires changes in personal attitudes and the educational infrastructure. It also requires major technological support to provide open access to a learning environment for all people, in all places at all times. This paper presents progress in the development of the Learning Web, an agent-oriented network supporting knowledge access, collaboration and simulation, in order to provide a widely accessible learning environment. The essence of the Learning Web is an open architecture supporting integration of heterogeneous subsystems in which their is a natural symbiosis between human and computer agents. Learning Web. There are already University courses being taught via Mosaic server over the Internet (Orsak, 1994). From the users' perspective, the function of the Learning Web is to provide an 'environment' for participatory learning. Learning in a basic sense is the acquiring of expertise. This definition, even if thought simplistic, allows a useful transference of knowledge about expertise in individuals, organizations and societies, to the learning sphere. Fundamental to developing expertise are the capabilities shown in Figure 1.

International Journal of Machine Learning and Applications, 2013

Many scholars are interested in improving e-learning in order to provide easy access to educational materials. There is, however, the need to incorporate the ability to classify learners into these learning systems. Learner classification is used adaptively to provide relevant information for the various categories of learners. There is also a need for learning to continue, whether learners are on- or off-line. In many parts of the world, especially in the developing world, most people do not have reliable continuous internet connections. We tested an Adaptive e-Learning Model prototype that implements an adaptive presentation of course content under conditions of intermittent Internet connections. This prototype was tested in February 2011 on undergraduate students studying a database systems course. This study found out that it is possible to have models that can adapt to characteristics such as the learner’s level of knowledge and that it is possible for learners to be able to st...