Enhancing E-Learning with Collaborative Filtering Recommender System at Amirkabir University
"Explore the integration of collaborative filtering techniques into e-learning systems at Amirkabir University of Technology in Tehran, Iran. Learn about learner analysis, resource evaluation, system architecture, and the benefits of personalized recommendations in reducing information overload for users."
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Presentation Transcript
Amirkabir University of Technology Tehran, Iran June2012
Index Introduction Contribution Basic Theory System Design Analysis of the Learners Analysis of the Resources System Architecture Proposed Method for Learner Classification Result Conclusion 2
Introduction Information Overload Recommender System Motivation rarely is being used in E-learning offering the right resources learner characteristics shortest possible time 3
Contribution Collaborative filtering Two groups Self-paced learning or recommending? 4
Target User Self-paced learning method Recommender system Collaborative Filtering Method User-based method Item-based method 5
architecture of recommender system Learners collaborative filtering unit learning resources two sub-systems 6
60 participants First group : self-paced learning Second group: recommender system 7
Analysis of the Resources 10 resources about hardware ergonomic abstract 5 suitable resources 8
System Architecture Subsystem1 Subsystem2 Learners Collaborative Technique Data Entry Data Entry Similar Users Finding Similar Users Finding Collaborative Filtering Method Data Entry Data Entry DB DB Similar User' Sources Select Similar User's s Sources Select Resources Selection Resources Selection Learning Resources Resources Score Resources Score Recommended Resources Recommended Resources Test Test Test Test 9
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
Group 1 Similar Users Group 2 CF 11
Comparison of Selected Resources for Group1 (left) and Received Resources for Group2 (right) 0% 76% 0% Excellent 40% Good Fary bad Awful 60% 0% 16% 8% First Group Second Group 13
80 70 60 of sources Reading 50 40 30 20 10 0 1 48 72 2 24 16 3 28 24 4 12 0 5 28 20 6 20 12 7 8 9 10 44 60 11 12 8 12 12 8 Select resources-1th group Select resources-2th group 32 64 20 12 20 4 Resources 14
100 90 80 Correct answers 70 60 50 40 30 20 10 0 Q1 84 88 Q2 40 92 Q3 48 72 Q4 44 80 Q5 56 68 Q6 56 68 Q7 32 80 Q8 56 72 Q9 64 80 Correct answers-1th group Correct answers-2th group Questions (Test) 15
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
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. Mortensen Magnus; Design and Evaluation of a Recommender System , INF-3981 Master's Thesis in Computer Science, University of Troms, 2009. 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 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. 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 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 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. 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. 1. 2. 3. 4. 5. 6. 7. 8. 17
