Automatic cognitive style identification of digital library users for personalization

Journal of the American Society for Information Science and Technology

Volumn 58, Issue 2, 2007, Pages 237-251

  Frias Martinez, Enrique ^a   Chen, Sherry Y ^a   Liu, Xiaohui ^a  

^a BRUNEL UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); DIGITAL LIBRARIES; ELECTRONIC DOCUMENT IDENTIFICATION SYSTEMS; INFORMATION RETRIEVAL; PARAMETER ESTIMATION; TELECOMMUNICATION SERVICES; WORLD WIDE WEB;

CLASSIFICATION SYSTEMS; COGNITIVE-STYLE POINTS; PERSONALIZATION; WEB PERSONALIZED SERVICES;

COGNITIVE SYSTEMS;

EID: 33846345002     PISSN: 15322882     EISSN: 15322890     Source Type: Journal    
DOI: 10.1002/asi.20477     Document Type: Article

References (66)

1. Bajraktarevic, N., Hall, W., & Fullick, P. (2003, August). Incorporating learning styles in hypermedia environment: Empirical evaluation. Paper presented at the Workshop on Adaptive Hypermedia and Adaptive Web-based Systems, Budapest, Hungary.
2. Beck, J., Jia, P, Sison. J., & Mostow. J. (2003). Predicting student help-request behavior in an intelligent tutor for reading. In P. Brusilovsky, A. T. Corbett, & F. de Rosis (Eds.), Proceedings of the Ninth International Conference on User Modeling (pp. 303-312). Berlin: Springer-Verlag.
3. Bidel, S., Lemoine, L,. & Piat. F. (2003). Statistical machine learning for tracking hypermedia user behavior. In P. Brusilovsky, A. Corbett, & F. de Rosis (Eds.), Proceedings of the Ninth International Conference on User Modeling: Workshop on Machine Learning, Information Retrieval and User Modeling (pp. 56-65). Berlin: Springer-Verlag.
4. Blandford, A., Stelmaszewska, H., & Bryan-Kinns, N. (2001). Use of multiple digital libraries: A case study. In E. Fox & C. Borgman (Eds.). Proceedings of the JCDL'Ol (pp. 179-188). New York: ACM Press.
5. Bonissone, P., Badami, C., & Chiang, X. (1995). Industrial applications of fuzzy logic at General Electric. In Proceedings of the IEEE, 83(3), 450-465.
6. Breiman, L., Friedman, J.H., Olshen, R.A., & Stone, C.J. (1984). Classification and regression trees. Belmont, CA: Wadsworth International Group/Probability Series.
7. Brusilovsky, P., & Schwarz. E. (1997). User as student: Towards an adaptive interface for advanced Web-based applications. In A. Jamesson, C. Paris, & C. Tasso (Eds.), User modeling: Proceedings of the Sixth International Conference, UM97 (pp. 177-188). New York: Springer-Verlag.
8. Bryan-Kinns, N., Blandford, A., & Thimbleby, H. (2000, September). Interaction modelling for digital libraries. In Workshop on Evaluation of Information Management Systems, London.
9. Callan, J., Smeaton, A., Beaulieu, M., Borlund, P., Brusilovsky, P., Chalmers, M., et al. (2003). Personalization and recommender systems in digital libraries (Joint NSF-EU DELOS Working Group Report). Retrieved August 12, 2004, from http://www.ercim.org/publication/ws-proceedings/ Delos-NSF/Personalisation. pdf
10. ChanLin, L. (1998). Students' cognitive styles and the need for visual control in animation. Journal of Educational Computing Research, 19(4). 351-363.
11. Chen, S.Y., & Macredie, R. (2002), Cognitive styles and hypermedia navigation: Development of a learning model. Journal of the American Society for Information Science and Technology, 53(1), 3-15.
12. Chuang, Y-R. (1999). Teaching in a multimedia computer environment: A study of effects of learning style, gender, and math achievement. Interactive Multimedia Electronic Journal of Computer-Enhanced Learning. 1(1), 1999. Retrieved August 12, 2004, from http://imej.wfu.edu/ articles/1999/1/10/
13. Demuth, H., & Beale, M. (1998). Neural network toolbox user's guide. Ver. 3.0. Cambridge, UK: The Mathworks, Inc.
14. Drigas, A., Kouremenos, S., Vrettos, S., Vrettaros, J., & Kouremenos, D. (2004). An expert system for job matching of the unemployed. Expert Systems with Applications, 26, 217-224.
15. Dufresne, A., & Turcotte, S. (1997). Cognitive style and its implications for navigation strategies. In B. Boulay & R. Mizoguchi (Eds.), Artificial intelligence in education knowledge and media learning system (pp. 287-293). Amsterdam: IOS Press.
16. Fausett L., (1994). Fundamentals of neural networks. Englewood Cliffs, NJ: Prentice-Hall.
17. Fink, J., Kobsa, A., & Nill, A. (1997). Adaptable and adaptive information access for all users, including the disabled and the elderly. In A, Jamesson, C. Paris, & C. Tasso (Eds.), User modeling: Proceedings of the Sixth International Conference, UM97 (pp. 171-173). New York: Springer-Verlag.
18. Ford, N., & Chen, S.Y. (2000). Individual differences, hypermedia navigation and learning: An empirical study. Journal of Educational Multimedia and Hypermedia, 9(4), 281-312.
19. Goodenough, D. (1976). The role of individual differences in field dependence as a factor in learning and memory. Psychological Bulletin, 83, 675-694
20. Graff, M. (2003). Cognitive style and attitudes towards using online learning and assesment methods. Electronic Journal of e-learning. 1(1), 1-28.
21. Harrison, A.W., & Rainer, R.K. (1992). The influence of individual differences on skill in end-user computing. Journal of Management Information Systems, 9(2), 93-111.
22. Haykin S., (1999). Neural networks (2nd ed.). Englewood Cliffs. NJ: Prentice-Hall.
23. Hong, J., Heer, J., Waterson, S., & Landay, J.A. (2001 ). WebQuilt: A proxy based approach to remote Web usability testing. ACM Transactions on Information Systems, 19(3), 263-385.
24. Jang, J.S. (1993). ANFIS: Adaptive-Network-Based Fuzzy Inference Systems. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665-685.
25. Jang, J.S.R., & Sun, C.T. (1995). Neurofuzzy modelling and control. Proceedings of IEEE, 83(3), 378-406.
26. Jang, J.S.R., Sun, C.T., & Mizutani E. (1997). Neurofuzzy and soft computing: A computational approach to learning and machine intelligence. Englewood Cliffs, NJ: Prentice-Hall.
27. Kass, G.V. (1980). An exploratory technique for investigating large quantities of categorical data. Applied Statistics, 29, 119-127.
28. Klir, J., & Yuan, B. (1995). Fuzzy sets and fuzzy logic. Theory and applications. Englewood Cliffs, NJ: Prentice-Hall.
29. Kobsa, A. (2001). Generic user modeling systems. User Modeling and User-Adapted Interaction, 11, 49-63.
30. Lee, R.S.T. (2002). iJADE IWShopper: A new age of intelligent Web shopping system based on fuzzy-neuro agent technology. Web intelligence: Research and development. In N. Zhong, Y. Yao, J. Liu, & S. Ohsuga (Eds.), Lecture Notes in Artificial Intelligence, 2198 (pp. 403-412). New York: Springer-Verlag.
31. Liaw, S., & Huang, H. (2003, November). An investigation of user attitude toward search engines as an information retrieval tool. Computers in Human Behavior, 19(6), 751-765.
32. Lippmann, R.P. (1987, April). An introduction to computing with neural nets. IEEE ASSP Magazine, 4-28.
33. Looney, C.G. (1997). Pattern recognition using neural networks: Theory and algorithms for engineers and scientists. New York: Oxford University Press.
34. Magoulas, G.D., Papanikolau, K.A., & Grigoriadou, M. (2001). Neurofuzzy synergism for planning the content in a Web-based course. Informatica, 25(1), 39-48.
35. Marchionini, G., Plaisant, C., & Komlodi, A. (1998). Interfaces and tools for the Library of Congress National Digital Library Program. Information Processing & Management, 34(5), 535-555.
36. Marrison, D.L., & Frick, M.J. (1994). The effect of agricultural students' learning styles on academic achievement and their perceptions of two methods of instruction. Journal of Agricultural Education, 35(1), 26-30.
37. Martinez, L.W., & Martinez, A.R. (2001). Computational statistics handbook with MATLAB. Boca Raton, PL: Chapman & Hall/CRC.
38. MathWorks. (2002). Car mileage prediction with ANFIS. The MathWorks, Inc. Revision: 1.9. Retrieved August 12, 2004, from http://www.
39. mathworks.com/products/fuzzylogic/demos.html
40. Messick S. (1976). Individuality in learning. San Francisco: Jossey-Bass.
41. Mitchell, T. (1997). Decision tree learning. In Machine learning (pp. 52-78). Columbus, OH: McGraw-Hill, Inc.
42. Moss, N, & Hale, G. (1999). Cognitive style and its effect on Internet searching: A quantitative ingestigation. Proceedings of the European Conference on Educational Research (pp. 22-25).
43. Paliouras, G., Karkaletsis, V., Papathedorou, C., & Spyropoulos, C. (1999). Exploiting learning techniques for the acquisition of user stereotypes and communities. In J. Greer (Ed.), Proceedings of the International Conference on User Modelling (pp. 169-178).
44. Palmquist, R.A., & Kim, K.-S. (2000). Cognitive style and on-line database search experience as predictors of Web search performance. Journal of the American Society for Information Science, 51(6), 558-66.
45. Papanikolau, K.A., Grigoradiou, M., Kornilakis, H., & Magoulas, G.D, (2003). Personalizing the interaction in a Web-based educational hypermedia system: The case of INSPIRE. User Modelling and User-Adapted Interaction, 13, 213-267.
46. Peterson, E.R., Deary, I.J., & Austin, E.J. (2003). The reliability of Riding's cognitive style analysis test. Personality and Individual Differences, 34(5), 881-891.
47. Quintan, J.R. (1986). Induction of decision trees. Machine Learning, 1, 81-106.
48. Quinlan, J.R. (1993). C4.5: Programs for machine learning. San Francisco: Morgan Kaufmann.
49. Riding, R., & Rayner, S.G. (1998). Cognitive styles and learning strategies. London: David Fulton Publisher.
50. Riding, R.J. (1991). Cognitive styles analysis. Birmingham, UK: Learning and Training Technology.
51. Riding R.J., & Grimley, M. (1999). Cognitive style, gender, and learning from multimedia materials in 11 year-old children. British Journal of Education Technology, 30(1), 43-56.
52. Sas, C., Reilly, R., & O'Hare, G. (2003). A connectionist model of spatial knowledge acquisition in a virtual environment. Proceedings of the Ninth International Conference on User Modeling: Workshop on Machine Learning, Information Retrieval and User Modeling.
53. Sheperd, A., Watters, C., & Marath, A.T. (2002). Adaptive user modeling for filtering electronic news. In S.W. Smollar (Ed.), Proceedings of the 35th Annual Hawaii International Conference on System Sciences (HICSS-02) (p. 102.2). Washington, DC: IEEE Computer Society.
54. Stathacopoulou, R., Grigoriadou, M., & Magoulas, G.D. (2003). A neurofuzzy approach in student modeling. In P. Brusilovsky, A.T. Corbett, & F. de Rosis (Eds.), Proceeding of the Ninth International Conference on User Modeling, UM2003, Lecture Notes in Artificial Intelligence, 2702 (pp. 337-342). Berlin: Springer-Verlag.
55. Theng, Y.L., Duncker, E., & Mohd-Nasir, N. (1999). Design guidelines and user-centred digital libraries. In S. Abiteboul & A. Vecoustre (Eds.), Proceedings of the Third European Conference on Research and Advanced Technology for Digital Libraries, LNAI 1696 (pp. 167-183). London: Springer-Verlag.
56. Triantafillou, E., Pomportsis, A., & Georgiadou, E. (2003). AEC-CS: Adaptive educational system based on cognitive styles. In Proceedings of WASWBE'02: Workshop on Adaptive Systems for Web-based Education, Second International Conference on Adaptive Hypermedia and Adaptive Web-based Systems.
57. Tsukada, M., Washio, T., & Motoda, H. (2001). Automatic Webpage classification by using machine learning methods. In N. Zhong, J, Yao, Y.Y. Liu, & S. Ohsuga (Eds.), Web Intelligence: Research and Development, LNAI 2198 (pp. 303-313). London: Springer-Verlag.
58. Webb, G.I., Chiu, B.C., & Kuzmycz, M. (1997). Comparative evaluation of alternative induction engines for feature based modelling. International Journal of Artificial Intelligence in Education, 8, 97-115.
59. Weller, H.G., Repman, J., & Rooze, G.E. (1994). The relationship of learning, behavior, and cognitive styles in hypermedia-based instruction: Implications for design of HBI. Computers in the Schools, 10(3/4), 401-420.
60. Winston, P. (1992). Learning by building identification trees. In Artificial intelligence (pp. 423-442). Boston: Addison-Wesley.
61. Witkin, H.A. & Goodenough, D.R. (1981). Cognitive styles: Essence and origins: Field dependence and field independence. New York: International Universities Press.
62. Witkin, H.A., Moore, C.A., Goodenough, D.R., & Cox, P. (1977). Fielddependent and field independent cognitive styles and their educational implications. Review of Educational Research, 47(1), 1-64.
63. Witten, I.H., & Frank E. (1999). Data mining: Practical machine learning tools and techniques with JAVA implementations. San Francisco: Morgan Kaufman Publishers.
64. Yan, J., Ryan, M., & Power, J. (1994).Using fuzzy logic. Englewood Cliffs, NJ: Prentice-Hall.
65. Zhu, T, Greiner, R., & Haubl, G. (2003). Learning a model of a Web user's interests. In P. Brusilovsky, A.T, Corbett, & F. de Rosis (Eds.), Proceedings of the Ninth International Conference on User Modeling, LNAI 2702 (pp. 11-21). Berlin: Springer-Verlag.
66. Zukerman, I., Albrecht, D.W., & Nicholson, A.E. (1999). Predicting users request on the WWW. In J. Greer (Ed.), Proceedings of the 7th International Conference on User Modeling, UM99 (pp. 275-284). articles/1999/1/10/