1. Amirkabir University of Technology Tehran, IranJune2012

2.  Introduction  Contribution  Basic Theory  System Design  Analysis of the Learners  Analysis of the Resources  System Architecture  Proposed Method for Learner Classification  Result  Conclusion Index 2

3. Introduction Information Overload Recommender System Motivation rarely is being used in E-learning offering the right resources learner characteristics shortest possible time 3

4. Contribution  Collaborative filtering  Two groups  Self-paced learning or recommending? 4

5.  Target User  Self-paced learning method  Recommender system  Collaborative Filtering Method  User-based method  Item-based method 5

6. architecture of recommender system  Learners  collaborative filtering unit  learning resources two sub-systems two sub-systems 6

7.  60 participants  First group : self-paced learning  Second group: recommender system 7

8.  10 resources about “hardware ergonomic”  abstract  5 suitable resources Analysis of the Resources 8

9. Data Entry Resources Selection Resources Selection Resources Score Resources Score Test Data Entry Similar User's Sources Select Similar User's Sources Select Similar Users Finding Recommended Resources Recommended Resources Test Subsystem1Subsystem2 Collaborative Technique Learners Learning Resources Collaborative Filtering Method DB 9

10.  5 questions in the registration section  Compare answers more similar answers = more scores Score user (i) = 2Q1 + 2Q2 + 4Q3 + 6Q4 + 6Q5 Q = {0, 1} 10

11. Finding the Similar Users Group 1Similar UsersGroup 2 CF 11

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13. Second GroupFirst Group Comparison of Selected Resources for Group1 (left) and Received Resources for Group2 (right) 13

14. 14 Reading of sources Resources

15. Correct answers Questions (Test) 15

16. information overload recommender system speed and quality score for each activity Recommendations for both groups Limitations of this Study  few learners  interest for studying  educational environment 16

17. 1. Adomavicius Gediminas; Tuzhilin Alexander; “Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions”, IEEE, pp.1-16, 2008. 2. Mortensen Magnus; “Design and Evaluation of a Recommender System”, INF-3981 Master's Thesis in Computer Science, University of Troms, 2009. 3. John O’Donovan, Barry Smyth,"Trust in Recommender Systems", Adaptive Information Cluster Department of Computer Science, University College Dublin, Belfield, Dublin 4 Ireland, {john.odonovan, barry.smyth}@ucd.ie 4. E. Reategui, E. Boff, "Personalization in an interactive learning environment through a virtual character", Department of Computer Science, Universidad de Caxias do Sul, 95070-560 Caxias do Sul, RS, Brazil, J.A. Campbell, a b Department of Computer Science, University College London, Gower, St., London WC1E 6BT, UK, Received 21 February 2007; received in revised form 29 May 2009. 5. Huiyi Tan1, Junfei Guo3, Yong Li2,"E-Learning Recommendation System", International School of Software, Wuhan University, Wuhan, China, Information School, Estar University, Qingdao, China, tan6043@gmail.com 6. Mohammed Almulla, "School e-Guide: a Personalized Recommender System for E-learning Environments", Kuwait University, P.O.Box 5969 Safat,First Kuwait Conf. on E-Services and E-Systems, Nov 17-19, 2009 7. Vinod Krishnan, Pradeep Kumar Narayanashetty, Mukesh Nathan, Richard T. Davies, and Joseph A. Konstan, "Who Predicts Better? – Results from an Online Study Comparing Humans and an Online Recommender System", Department of Computer Science and Engineering, University of Minnesota- Twin Cities, RecSys’08, October 23–25, 2008, Lausanne, Switzerland. 8. Ricci, F., Venturini, A,.Cavada, D., Mirzadeh, N., Blaas, D., Nones, M. "Product recommendation with interactive query management and twofold similarity". In Proceedings of the 5th International Conference on Case-Based Reasoning, ICCBR'03, pages 479-493, 2009. 17

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