In this paper, we propose a new content recommendation system called Intelligent Content Recommendation System for E-Learning for selecting and retrievingthe exact 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 Engineeringand select the suitable e-content for recommending to the students and industrial people such as software developers. For this purpose, we use an intelligent preprocessing technique and also propose a new classification algorithm called Intelligent Ranked Document Classification algorithm for classifying and ranking the e-contents. In addition, we use the existing New Weighted Fuzzy C-Means clustering algorithm to help the decision making system to recommend suitable contents using fuzzy rules. The main advantage of the proposed system is that it provides different types of contents which are suitable to different types of learners accurately.
N. Partheeban, Dr. N. Sankar Ram
Feature Selection, Enhanced MSVM, Weighted Fuzzy C-Means, Ranking, Clustering, Classification, Semantic Analysis.
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|Published in :
||Volume 2 | Issue 4 | July-August - 2016
|Date of Publication
Cite This Article
N. Partheeban, Dr. N. Sankar Ram, "An Intelligent Content Classification Algorithm for Effective E-Learning
", International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 4, pp.285-289, July-August-2016.
URL : http://ijsrset.com/IJSRSET162469.php