An Intelligent Content Recommendation System for E-Learning Using Social Network Analysis

摘要

Electronic learning encompasses all forms of learning that use recent technology or the internet as the medium for education. It allows students to learn anywhere at any time through web. E-learning is synonymous to online education, virtual learning, distance education, or web-based training. However, the selection of appropriate content for effective and fast learning is a challenging issue to the e-learners. E-learning content selection through group discussions using social media networks is a novel approach that aids the learners to select the suitable content from the web. For many group members, it takes a longer time to get the suitable content itself due to their level of understanding. Hence, it is necessary to have an intelligent content recommendation system that can provide simple contents to slow learners, standard contents to normal learners and complex high quality contents to fast learners and learners with experience. In this paper, we propose a new content recommendation system called Intelligent Content Recommendation System for E-Learning using Social Networks (ICRSESN) for selecting and retrieving the good quality e-content for teaching the subject "Software Engineering" based on group discussions through the social media. In this process, we analyze the various contents pertaining to the subject Software Engineering and select the suitable e-content for recommending to the academicians, students and software developers. For this purpose, we propose a preprocessing algorithm and a new ranking algorithm for ranking of e-contents. In addition, we use the existing Support Vector Machine (SVM) classifier and Fuzzy C-Means clustering algorithm to help the decision making system to recommend suitable contents using fuzzy rules. The main objective of the proposed system is that it provides different types of contents which are suitable to different types of learners accurately.