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Toward a Hybrid Recommender System for E-Learning Personalization Based on Web Usage Mining Techniques and Information Retrieval
PROCEEDINGS

, , Research Unit of Technologies of Information and Communication, Tunisia ; , Speed School of Engineering and Computer Science, United States

E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, in Quebec City, Canada ISBN 978-1-880094-63-1 Publisher: Association for the Advancement of Computing in Education (AACE), San Diego, CA

Abstract

The last decade has witnessed a great interest in e-learning and Web based education areas. Unfortunately, most of the e-learning environments used in the educational field today are still delivering the same educational resources and services in the same way to different learners. Hence, observing the increasing need for personalization in e-learning systems, we aim to make these systems deliver the most appropriate content to learners according to their interests and needs. This paper outlines the use of on-line automatic recommendations in e-learning systems based on learners' access history. First we start by mining learner profiles using usage Web mining techniques and content-based profiles using information retrieval techniques. Then, we use these profiles to compute relevant links to recommend for an active learner by applying a number of recommendation strategies.

Citation

Khribi, M.K., Jemni, M. & Nasraoui, O. (2007). Toward a Hybrid Recommender System for E-Learning Personalization Based on Web Usage Mining Techniques and Information Retrieval. In T. Bastiaens & S. Carliner (Eds.), Proceedings of E-Learn 2007--World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 6136-6145). Quebec City, Canada: Association for the Advancement of Computing in Education (AACE). Retrieved October 2, 2022 from .

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