An attribute-based ant colony system for adaptive learning object recommendation

Expert Systems with Applications

Volumn 36, Issue 2 PART 2, 2009, Pages 3034-3047

  Yang, Yao Jung ^a,b   Wu, Chuni ^a  

^a HSING KUO UNIVERSITY OF MANAGEMENT   (Taiwan)

^b SOOCHOW UNIVERSITY   (China)

Author keywords

Adaptive learning; Ant colony optimization; Learning style

Indexed keywords

ANT COLONY OPTIMIZATION; ARTIFICIAL INTELLIGENCE; COMPUTER AIDED INSTRUCTION; E-LEARNING; LEARNING ALGORITHMS; TEACHING; WEBSITES;

ADAPTIVE LEARNING; ANT COLONY OPTIMIZATION ALGORITHMS; ANT COLONY SYSTEMS; LEARNING MATERIALS; LEARNING PACKAGES; LEARNING STYLE; PERSONALIZED LEARNING; WEB BASED LEARNING;

LEARNING SYSTEMS;

EID: 56349162947     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2008.01.066     Document Type: Article

References (44)

1. ADL (2004). Advance distributed learning initiative. Shareable content object reference model (SCORM) 204 (3rd ed.). Overview. ADL Web Site Accessed 18.06.07.
2. Berghel H. Cyberspace 2000: Dealing with information overload. Communications of the ACM 40 2 (1997)
3. Blum, C., & Dorigo, M. (2004). The hyper-cube framework for ant colony optimization. IEEE Transactions on Systems, Man, and Cybernetics-Part B, to appear.
4. Blum, C., Roil, A., & Dorigo, M. (2001). HC-ACO. The hyper-cube framework for ant colony optimization. In Proceedings of MIC'2001-Metaheuristics International Conference (vol. 2 pp. 399-403).
5. Brusilovsky, P. (2001). Adaptive hypermedia. In Alfread Kobas (Ed.), User modeling and user adapted interaction. Ten year anniversary issue (Vol. 11, pp. 87-110).
6. Brusilovsky P., and Vassileva J. Course sequencing techniques for large-scale web-based education. International Journal of Continental Engineering Education and Lifelong Learning 13 1-2 (2003) 75-94
7. Bullnheimer, B., Hartl, R. F., & Strauss, C. (1997). A new rank based version of the ant system - A computational study. Technical Report, University of Viena, Institute of Management Science.
8. Bullnheimer B., Hart1 R.F., and Strauss C. A new rank-nased version of the ant system: A computational study. Central European Journal for Operations Research and Economics 7 1 (1999) 25-38
9. Canales A., Peña A., Peredo R., Sossa H., and Gutiérrez A. Adaptive and intelligent web based education system: Towards an integral architecture and framework. Expert Systems with Applications 33 (2007) 1076-1089
10. Chen J.N., Huang Y.M., and Chu W.C. Applying dynamic fuzzy Petri net to web learning system. Interactive Learning 13 3 (2005) 159-178
11. Cordon, O., Herrera, F., Fernandez de Viana, I., & Moreno, L. (2000). A new ACO model integrating evolutionary computation concepts: The best-worst ant system. In Proceedings of the 2nd international workshop on ant algorithms, Brussels, Belgium (pp. 22-29).
12. Dorigo, M. (1992). Optimization, learning and natural algorithms. PhD thesis, Dipartimento di Elettronica, Politecnico di Milano, Milan.
13. Dorigo M., and Gambardella L.M. A study of some properties of Ant-Q. In: Voigt H., Ebeling W., Rechenberg I., and Schwefel H. (Eds). Proceeding of PPSN-IV, Forth International Conference on Parallel Problem Solving from Nature. Lecture Notes in Computer Science Vol. 1141 (1996), Springer-Verlag, Berlin 656-665
14. Dorigo M., and Gambardella L.M. Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Transaction on Evolutionary Computation 1 1 (1997) 53-66
15. Dorigo M., and Gambardella L.M. Ant colonies for the traveling salesman problem. BioSystems 43 2 (1997) 73-81
16. Dorigo M., and Gambardella L.M. Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation 1 1 (1997) 53-66
17. Dorigo, M., Maniezzo, V., & Colorni, A. (1991a). Positive feedback as a search strategy. Technical report 91-016 revised, Dipartmento di Elettonica, Politecnico di Milano, Milan.
18. Dorigo, M., Maniezzo, V., & Colorni, A. (1991b). The Ant System: An autocatalytic optimizing process. Technical report 91-016 revised, Dipartmento di Elettonica, Politecnico di Milano, Milan.
19. Dorigo M., Maniezzo V., and Colorni A. Ant System: Optimization by a colony of cooperating agents. IEEE Transactions on System, Man, and Cybernetics - Part B 26 1 (1996) 29-41
20. Dorigo M., and Stützle T. Ant colony optimization (2004), Bradford Books (Hardcover-Jul 1, 2004)
21. Felder R.M., and Silverman L.K. Learning and teaching styles in engineering education. Engineering Education 78 7 (1988) 674-681
22. Gambardella L.M., and Dorigo M. Ant-Q: A reinforcement learning approach to the traveling salesman problem. In: Prieditis A., and Russell S. (Eds). Proceedings of the Twelfth international Conference on Machine Learning (ML-95) (1995), Morgan Kaufmann, Palo Alto, CA 252-260
23. Grassé, P. P. (1959). La reconstruction du nid er les coordinations interindividulles chez Bellicositermesnatalensis er Cubitermes sp. La théorie de la stigmergie: Essai d'interprétation du comportement des termites constructeurs. Insectes Sociaux, 6.
24. Huang Y.M., Chen J.N., Huang T.C., Jeng Y.L., and Kuo Y.H. Standardized course generation process using dynamic fuzzy Petri nets. Expert Systems with Applications 34 1 (2007)
25. Huang Y.M., Chen J.N., Kuo Y.H., and Jeng Y.L. An intelligent human-expert forum system based on fuzzy information retrieval technique. Expert Systems with Applications 34 2 (2007)
26. IMS Global Learning Consortium, Inc. (2003). IMS learning design best practice and implementation guide. Version 1.0 final specification. Accessed 10.05.06.
27. Jamont, Y., Valigiani G., Collect, P., Lutton, E., Biojout, R., & Bourgeois Republique, C. (2005). Experimenting with a real-size man-hill to optimize pedagogical paths. In SAC'05 proceedings (pp. 13-17). Mars, Santa Fe, NM, USA.
28. Kettel, L., Thomson, J., & Greer, J., (2000). Generating indivisualized hypermedia applications. In P. Brusilovski (Ed.), Proceddings of ITS-2000 workshop on adaptive and intelligent web-based education systems (pp. 28-36), Montreal.
29. Kolb D.A. Learning style inventory technical (1974), Boston McBer & Co
30. Larkin-Hein T., and Bundy D.D. Research on learning style: Applications in the physics and engineering classroom. IEEE Transaction on Education 44 3 (2001) 276-281
31. Maniezzo V. Exact and approximate nondeterministic tree-search procedures fir the quadratic assignment problem. INFORMS Journal on Computing 11 4 (1999) 358-369
32. Manouselis N., and Sampson D. Agent-based e-learning course recommendation: Matching learner characteristics with content attributes. International Journal of Computers and Applications (IJCA) 25 1 (2003)
33. MASIE Center (2002). Making sense of learning specifications & standards: A decision marker's guide to their adoption. Industry Report. Accessed 18.06.07.
34. Melis E., Andres E., Büdenberder J., Frishauf A., Goguadse G., Libbrecht P., et al. ActiveMath: A generic and adaptive web-based learning environment. International Journal of Artificial Intelligence in Education 12 4 (2001) 385-407
35. Robert P.B., Lorne O., and Maung K.S. The importance of learning style in end-user training. MIS Quarterly 14 1 (1990) 101-119
36. Semet, Y., Jamont, Y., Biojout, R., Lutton, E., & Collet, P. (2003). Artificial ant colony and e-learning: An optimization of pedagogical paths. HCI.
37. Semet, Y., Lutton, E., & Collet, P. (2003). Ant colony optimization for e-learning: Observing the emergence of the pedagogic suggestions. IEEE Swarm Intelligence Symposium, April (pp. 46-52).
38. Stewart, C., Cristea, A., Brailsford, T., & Ashman, H. (2005). Authoring once, delivering many: Creating reusable adaptive courseware. In Web-based education conference WBE'05, Grindelwald, Switzerland.
39. Stützle, T. (1999). Local search algorithms for the combinatorial problems: Analysis, improvements, and new applications. DISKI (vol. 220). Sankt Augustin, Germany, Infix.
40. Stützle, T., & Hoos, H. H. (1996). Improving the ant system: A detailed report on the MAX -MIN Ant System. Technical report AIDA-96-12, FG Intellektik, FB Information, TU Darmstadt, Germany.
41. Stützle T., and Hoos H.H. MAX-MIN Ant System. Future Generation Computer System 16 8 (2000) 889-914
42. Wang, T.-I., Wang, K.-T., & Huang, Y.-M. (2007). Using a style-based ant colony system for adaptive learning. Expert Systems with Applications. Available online.
43. Weber, G. (1999). Adaptive learning systems in the world wide web. In: J. Kay (Ed.), Proceedings of the international conference on user modeling, UM99, Banff, Canada, June 20-24, 1999 (pp. 371-378). Wien, NewYork: Springer.
44. Yang, Y. J., & Song, K. (2006). FOAPA: A fuzzy ontology e-education adaptive portal architecture for e-learner. In ICS 2006 proceedings of the international computer symposium 2006 (pp. 949-957).