Automatic Recommendations for E-Learning Personalization Based on Web Usage Mining Techniques and Information Retrieval

The World Wide Web (WWW) is becoming one of the most preferred and widespread mediums of learning. Unfortunately, most of the current Web-based learning systems are still delivering the same educational resources in the same way to learners with different profiles. A number of past efforts have deal...

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Bibliographic Details
Main Authors: Khribi, M.K., Jemni, M., Nasraoui, O.
Format: Conference Proceeding
Language:English
Subjects:
Automatic Recommendation
Collaboration
Data mining
E-learning
Electronic learning
History
Information filtering
Information filters
Information retrieval
Knowledge engineering
Navigation
Personalization
Uniform resource locators
Web Mining
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Summary:The World Wide Web (WWW) is becoming one of the most preferred and widespread mediums of learning. Unfortunately, most of the current Web-based learning systems are still delivering the same educational resources in the same way to learners with different profiles. A number of past efforts have dealt with e-learning personalization, generally, relying on explicit information. In this paper, we aim to compute on-line automatic recommendations to an active learner based on his/her recent navigation history, as well as exploiting similarities and dissimilarities among user preferences and among the contents of the learning resources. First we start by mining learner profiles using Web usage 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 different recommendation strategies.
ISSN:2161-3761
2161-377X
DOI:10.1109/ICALT.2008.198