An Agent based Personalized Intelligent elearning

Abstract

is foundation of any modern society. Self Discovery is a key to an individual's education success. Self Discovery learning is the ability of an individual to learn through personal experience and natural exploration. Online Discovery learning tends to be learner-driven and learner-initiated. Digital reposito- ry on web is supportive for online discovery learning. In recent era, availability of infrastructure, flexibility of time, learning resources and their means of sharing has increased adaptability of World Wide Web to learn and attain knowledge to a great extent. However, most of the e-learners find difficulty to select location specific, relevant and significant tutorial on the web. Search engines results pages are not based on the interest, know- ledge level and literacy rate of learner. More amount of time spent on searching and filtering and comparatively less time is devoted in actual learning. This paper proposes an agent based personalized intelligent e-learning model. The essence of perso- nalized search engine is to save learner's time to re-rank future result list. This Model uses multiple agents which delivers per- sonalized SERP (Search Engine Results Page) and is more suited for personalization of web pages based on learner's query expanded to manifold queries with novel concept of keyword research and discover knowledge using browser's behaviour. The proposed model uses web usage mining techniques to dis- cover the knowledge of learner.