1. Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology 1 Automatic Recommendations for E-Learning Personalization Based on Web Usage Mining Techniques and Information Retrieval Presenter : Cheng-Han Tsai Authors : Mohamed Koutheair Khribi, Mohamed Jemni, Olfa Nasraoui ETS, 2009

2. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 2 Outlines Motivation Objectives Methodology Experiments Conclusions Comments

3. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 3 Motivation  Most e-learning platforms are still delivering the same educational resources to learners  Most e-learning platforms have not been personalized

4. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Objectives 4  To build an automatic recommendations in e- learning platforms

5. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 5 Methodology offline online Content models Learner model CF + KNN & CBF + TF-IDF CF & Cosine Similarity & Apriori algorithm & Association Rules & Confidence CBF & LOM & Inverted Index

6. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology 6 Learner model Confidence

7. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology  Using the open source search engine Nutch in content model- ing followed by CBF  Automatically generates invert- ed index 7 Content model

8. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology 8

9. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments 9

10. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments 10

11. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Conclusions  The proposed approaches can provide adaptive learning objects to different users  The recommendation system can compute against massive repository of educational resources in "real time". 11

12. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 12 Comments  Advantages ─ Integration of many approaches in this paper  Applications ─ IR