A personalized web content recommendation system for E-learners in E-learning environment

Abstract

Recommendation system has become an important application in the web that provides suggestions for the contents automatically based on individual user. The web has a vast, diverse and dynamic collection of data. Therefore, web mining is the application of data mining techniques used to get knowledge out from a massive volume of data in web. Nowadays electronic Learning (e-Learning) is a popular and interactive social aspect of the Web. Many studies have been done regarding web mining in e-Learning, and they mostly focus on e-learner's profiles and contents. This research tries to use web mining techniques in an e-learning environment to give recommendations to the e-Learners based on their navigation behaviors, web contents, performances and profiles. This means a personalized course contents that are delivered to e-Learners. The course instructors prepare the web contents in different formats and those contents are published through the web site and they can identify e-learner's navigation pattern and the site topology can be changed in an adaptive manner with relevant and useful contents. In this system, web content mining and web usage mining are used for searching resources and for discovering e-learner's navigation patterns. Then collaborative filtering and content filtering are used to make personalized recommendations.