Adaptive neuro-fuzzy pedagogical recommender

Expert Systems with Applications

Volumn 39, Issue 10, 2012, Pages 9797-9806

  Sevarac, Zoran ^a   Devedzic, Vladan ^a   Jovanovic, Jelena ^a  

^a UNIVERSITY OF BELGRADE   (Serbia)

Author keywords

E learning; Fuzzy logic; Neural networks; Recommender systems

Indexed keywords

NEURO-FUZZY; SIMULATED DATA; SOFTWARE COMPONENT; SPECIALIZED TOOLS; TECHNOLOGY ENHANCED LEARNING;

COMPUTER SIMULATION; E-LEARNING; FUZZY INFERENCE; FUZZY LOGIC; NEURAL NETWORKS; RECOMMENDER SYSTEMS;

EDUCATION;

EID: 84859216025     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2012.02.174     Document Type: Article

References (36)

1. Al-Hammadi, A. S.; Milne, R. H.; 2004. A neuro-fuzzy classification approach to the assessment of student performance. In Proceedings of the IEEE international conference on fuzzy systems (pp. 837-841).
2. Amruth, N. K. (2006). Using enhanced concept map for student modeling in a model-based programming tutor. In Proceedings of 19th international FLAIRS conference on artificial intelligence (FLAIRS 2006) special track on intelligent tutoring systems (pp. 527-532). Melbourne Beach, Florida, USA.
3. Arnold, M. Lund, Measuring usability with the USE questionnaire, STC usability SIG newsletter, October 2001 issue (Vol. 8, No. 2). < http://www.stcsig.org/usability/newsletter/0110-measuring-with-use.html >.
4. C. Basu, H. Hirsh, W. Cohen, and C. Nevill-Manning Technical paper recommendations: A study in combining multiple information sources Journal of Artificial Intelligence Research 1 2001 231 252
5. R. Bell, Y. Koren, and C. Volinsky Matrix factorization techniques for recommender systems IEEE Computer 42 8 2009 30 37
6. J. Bobadilla, A. Hernando, F. Ortega, and J. Bernal A framework for collaborative filtering recommender systems Expert Systems with Applications 38 12 2011 14609 14623
7. R. Boukezzoula, S. Galichet, and L. Foulloy MIN and MAX operators for fuzzy intervals and their potential use in aggregation operators IEEE Transactions on Fuzzy Systems 15 6 2007 1135 1144 ISSN: 1063-6706
8. Breese, J.; S.; Heckerman, D.; Kadie, C.; 1998. Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (Vol. 461, No. 8, pp. 43-52). San Francisco, CA. ISBN: 155860555X.
9. Castellano, G.; Fanelli, A. M.; Torsello, M. A.; 2008. NEWER: A neuro fuzzy web recommendation system. In Applications of first international conference on the digital information and web technologies, 2008. ICADIWT 2008 (pp. 162-167). doi:10.1109/ICADIWT.2008.4664338. ISBN 978-1-4244-2623-2.
10. M. Deshpande, and G. Karypis Selective Markov models for predicting Web page accesses Transactions on Internet Technology 4 2 2004 163 184 [Association for Computing Machinery]
11. H. Drachsler, H.G.K. Hummel, and R. Koper Identifying the goal, user model and conditions of recommender systems for formal and informal learning Journal of Digital Information 10 2 2009 [Texas Digital Library. ISSN: 1368-7506]
12. R. Felder, and L. Silverman Learning and teaching styles in engineering education Journal of Engineering Education 78 7 1988 674 681
13. Gantner, Z.; Drumond, L.; Freudenthaler, C.; Rendle, S.; Schmidt-Thieme, L. (2010). Learning attribute-to-feature mappings for cold-start recommendations. In Proceedings of the 10th IEEE international conference on data mining (ICDM 2010). IEEE Computer Society.
14. K. Ghauth, and N. Abdullah Learning materials recommendation using good learners' ratings and content-based filtering Educational Technology Research and Development 2010 1042 1629
15. Hatala, M.; Gasevic, D.; Siadaty, M.; Jovanovic, J.; Torniai, C. (2009). Can educators develop ontologies using ontology extraction tools: An end user study. In Proceedings of the 4th European conference on technology enhanced learning (ECTEL09), LNCS 5794 (pp. 140-153). Springer-Verlag.
16. J.L. Herlocker, J.A. Konstan, L.G. Terveen, and J.T. Riedl Evaluating collaborative filtering recommender systems ACM Transactions on Information Systems 22 1 2004 5 53 [Association for Computing Machinery]
17. H.G.K. Hummel, B. van Den Berg, A.J. Berlanga, H. Drachsler, J. Janssen, and R. Nadolski Combining social-based and information-based approaches for personalized recommendations on sequencing learning activities International Journal of Learning Technology 2007 [Interscience]
18. Jameson, A.; Konstan, J.; Riedl, J. (2002). AI techniques for personalized recommendation. Tutorial notes, AAAI-02. Edmonton, Canada.
19. J. Jovanović, D. Gašević, and V. Devedžić TANGRAM for personalized learning using semantic web technologies Journal of Emerging Technologies in Web Intelligence 1 1 2009 6 21 ISSN: 1798-0461
20. R. Kass Student modelling in intelligent tutoring systems-implications for user modelling A. Kosba, W. Wahlster, User models in dialog systems 1989 Springer-Verlag Berlin 386 410
21. Kinshuk, A.; Nikov, A.; Patel (2001). Adaptive tutoring in business education using fuzzy backpropagation approach. In Proceedings of the ninth international conference on human-computer interaction 2001 (Vol. 1, pp. 465-468). < http://www2.sta.uwi.edu/~anikov/publ/adaptive-tutoring-HCII2001. pdf >.
22. N. Manouselis, H. Drachsler, R. Vuorikari, H. Hummel, and R. Koper Recommender systems in technology enhanced learning P.B. Kantor, F. Ricci, L. Rokach, B. Shapira, 1st Recommender systems handbook 2010 Springer Berlin 1 29
23. Melville, P.; Sindhwani, V. (2010). Encyclopedia of machine learning (1st ed.). Springer. ISBN 978-0-387-30164-8.
24. Mir Sadique, A.; Ghatol, A. A. (2004). A neuro fuzzy inference system for student modeling in web-based intelligent tutoring systems. < http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.90.9687&rep= rep1&type=pdf >.
25. Papagelis, M.; Plexousakis, D.; Kutsuras, T. (2005). Alleviating the sparsity problem of collaborative filtering using trust inferences. In Proceedings of the 3rd international conference on trust management. LNCS 3477 (pp. 224-239). Berlin, Heidelberg, New York: Springer.
26. Preisach, C.; Marinho, L. B.; Schmidt-Thieme, L. (2010). Semi-supervised tag recommendation-using untagged resources to mitigate cold-start problems. In 14th Pacific-Asia conference on knowledge discovery and data mining (PAKDD 2010), Lecture Notes in Computer Science (Vol. 6118, pp. 348-357). Springer.
27. Rosenblatt, F. (1962). Principles of neurodynamics: Perceptrons and the theory of brain mechanisms. Washington DC: Spartan Books [ASIN: B0006AXUII].
28. O.C. Santos, and J.G. Boticario Modeling recommendations for the educational domain Procedia Computer Science 1 2 2010 2793 2800 [Proceedings of the 1st Workshop on Recommender Systems for Technology Enhanced LearninRecSysTEL 2010]
29. Sevarac, Z. (2006). Neuro fuzzy reasoner for student modelling. In Proceedings of the sixth IEEE international conference on advanced learning technologies 2006 (pp. 740-744). < http://www.computer.org/portal/web/csdl/ doi/10.1109/ICALT.2006.245 >.
30. Stathacopoulou, R.; Magoulas, G. D.; Grigoriadou, M. (1999). In Neural network-based fuzzy modeling of the student in intelligent tutoring systems, international joint conference on neural networks (pp. 3517-3521). Washington DC, USA. doi:10.1109/IJCNN.1999.836233. ISBN 0-7803-5529-6.
31. R. Stathakopoulou, G. Magoulas, M. Grigoriadou, and M. Samarakou Neuro-fuzzy knowledge processing in intelligent learning environments for improved student diagnosis Information Sciences 170 2005 273 307
32. Tang, T. Y.; Mccalla, G. (2003). In Smart recommendation for an evolving e-learning system workshop on technologies for electronic documents for supporting learning, international conference on artificial intelligence in education (AIED).
33. Tang, T.; McCalla, G. (2004). Beyond learners interest: Personalized paper recommendation based on their pedagogical features for an e-learning system. In Pacific rim international conference on artificial intelligence - PRICAI 2004 (pp. 301-310).
34. Thai-Nghe, N.; Drumond, L.; Horváth, T.; Krohn-Grimberghe, A.; Nanopoulos, A.; Schmidt-Thieme, L. (2011). In O. C. Santos, J. G. Boticario, (Eds.), Factorization techniques for predicting student performance. Educational recommender systems and technologies: Practices and challenges (ERSAT 2011). IGI Global. doi:10.4018/978-1-61350-489-5. ISBN13: 9781613504895.
35. B. Widrow, and M.A. Lehr Perceptrons, adalines, and backpropagation M.A. Arbib, Handbook of brain theory and neural networks 1995 MIT Press 719 724 ISBN 978-0-262-01148-8
36. Xu, D.; Wang, H.; Su, K. (2002). Intelligent student profiling with fuzzy models. In Proceedings of the 35th Hawaii international conference on system sciences.