Intelligent Agent Based Architectures for E-Learning System: Survey

Muhammad Arif, Mehdi Hussain
<span title="2015-06-30">2015</span> <i title="Science and Engineering Research Support Society"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/cjfkr6nrjbcjrhn6a6mvtme4ay" style="color: black;">International Journal of u- and e- Service, Science and Technology</a> </i> &nbsp;
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
more &raquo; ... ace, 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. The proposed architecture [12] has three levels: User Interface Layer, Learning Services Layer and Infrastructure Layer. User interface layer delivers interface to user to access all types of source and learning material. Jade is used for multivalent system implementation. The Learning Services layer contains all resources and services components. The Infrastructure Layer offers the underlying services that can support the data exchange and transmission. The ELMS is contains of three players learner, instructor and teacher. The learner aim is to learn by questioning, reading data, taking quiz and exercise. The instructor aim to teach online by managing and arranging seminar and he can guide student which subject he should choose. A virtual collaborative group is constructed of small containers and one main container which control all other containers. Each container contains many learners and instructor agents. When the user wants some information about course student agent sends request instructor agent that will guide the learner after checking its evaluation. Agents are used in the field of e-learning for the ease of user learning [15, 39] . Multi-Agent System for Collaborative E-learning (MASCE) is introduced which helps teaching and learning process and support collaborative learning among peers. Multi agent can intelligent interact with each other to support the educational processes. System has three agents: student agent and teacher agent with respect to its users. Student agent helps student in learning process. Instructor agent maintains course progress, avail course material and checks course progress. Assistant agent which is the basic advancement in architecture is placed on the system's server. It acts more effectively between end units. Educational is divided into sub-divided into categories till leave node. Student can check out their performance by taking their own quiz [15] . Agents are used in the field of e-learning for the ease of user learning. Multi-Agent System for Collaborative E-learning (MASCE) is introduced which helps teaching and learning process and support collaborative learning among peers. Multi agent can intelligent interact with each other to support the educational processes. System has three agents: student agent and teacher agent with respect to its users. Student agent helps student in learning process. Instructor agent maintains course progress, avail course material and checks course progress. Assistant agent which is the basic advancement in architecture is placed on the system's server. It acts more effectively between end units [33] . Educational is divided into sub-divided into categories till leave node. Student can check out their performance by taking their own quiz. Assistant agent helps them if material regarding to any topic is required. System contains static as well as dynamic parameters. Static parameter information is provided by the student while dynamic parameter information is gained from student colleague at end of each session [33] . The main consideration of this paper is complication of E-Learning system. Mutual learning grouping of multi-agent is controlled. Rough set theory is combined with mutual learning grouping for solving problem of information absence [34] . Full information theory for grouping model and algorithm is designed. The architecture is implemented into the frame of NJUT ELearning to fulfill the requirements. [36] The study has been conducted on six emotions of human defined by Ekman which are as: happiness, surprise, anger, fear, sadness, disgust. Interface agent provides learner information to other agents. Emotional embodied conversational agent contains three layers. First layer perceives
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