Enhancement of online education system by using a multi-agent approach

Abstract

Multi-Agent System (MAS) is popular in the fields where cooperative effort is required to fulfil the purpose of the end product. This study creates a Multi-Agent System as a potential solution for the implementation of an online education system in order to bring about swift and accurate responses from the system and thus a meaningful conversation between the learners and the system. The existing work on online education system employing MAS does not depict the means of flow of information from the browser to the internal MAS and also lacks the adaptivity element with respect to the changing demands of the learners. The study attempts to bridge this gap by employing a system with Event-Condition-Action model and intelligent agents which include a message passing agent to depict the means of message flow from the browser to the MAS and an adaptive course organiser agent which organises adaptive educational content with respect to the changing needs of the learner. The outcome of the study is the design and simulation of the said system as a standalone entity that can be attached to a virtual learning environment (VLE), with the addition of pedagogical agents performing different functionalities to form an adaptive system. The system is evaluated by validating the results generated for various case studies using a validation tool. The expected behaviour is that the resulting course agenda agrees with the learning mode preference of the user provided initially and the results are expected to change according to the changing user preferences through a self-assessment questionnaire. • A Multi-Agent system (MAS) is created as a potential solution for the implementation of an online education system. • Solutions for virtual learning concerns are presented. • System is standalone and can be attached to an existing virtual learning environment (VLE) to make the resulting system more effective in providing appropriate educational resources. • Resulting course agenda agrees with the learning style preference of the user. • Results change according to the changing user preferences through a self-assessment questionnaire.