On Modelling Cognitive Styles of Users in Adaptive Interactive Systems using Artificial Neural Networks

Efi Papatheocharous, Marios Belk, Panagiotis Germanakos, George Samaras

2012

Abstract

User modelling in interactive Web systems is an essential quality to optimally filter, personalise and adapt their content and functionality to serve the intrinsic needs of individual users. The mechanism for obtaining the user model needs to be intelligent, adaptive and transparent to the user, in the sense that user experience should not be disrupted or compromised. Human factors are extensively employed lately for enriching user models by capturing more intrinsic perceptual characteristics of the users. Accordingly, this paper proposes the use of Artificial Neural Networks (ANNs) for attaining cognitive styles of users in adaptive interactive systems. One of the main benefits is the automatic prediction of cognitive typologies of users by avoiding psychometric tests, which are among the typical ways of constructing user profiles and are particularly time-consuming. Furthermore, ANNs can efficiently model the relationship between cognitive styles and user interaction. The experimental setup and the results obtained show that ANNs are suitable for predicting the cognitive styles ratio of users in respect to their actual cognitive style ratio value.

References

  1. Antoniou, A., Lepouras, G., 2010. Modelling Visitors' Profiles: A Study to Investigate Adaptation Aspects for Museum Learning Technologies. J. Computing Cultural Heritage. 3(2), 1-19.
  2. Belk, M., Papatheocharous, E., Germanakos, P., Samaras, G., 2012. Investigating User Models through Cognitive Styles and Navigation Behavior in Web 2.0 Environments using Clustering Techniques. J. Web 2.0 engineering: new practices and emerging challenges, Elsevier (submitted).
  3. Brusilovsky, P., Kobsa, A., Nejdl, W., 2007. The Adaptive Web: Methods and Strategies of Web Personalization. Springer, Heidelberg, Berlin.
  4. Chou, P., Li, P., Chen, K., Wu, M., 2010. Integrating web mining and neural network for personalized ecommerce automatic service. Expert Systems with Applications, Elsevier. 37(4), 2898-2910.
  5. Elson, J., Douceur, J., Howell, J., Saul, J., 2007. Asirra: A CAPTCHA that Exploits Interest-Aligned Manual Image Categorization. In CCS'07, International Conference on Computer and Communications Security, ACM Press. 366-374.
  6. Felder, R., Silverman, L., 1988. Learning and Teaching Styles in Engineering Education. Engineering Education 78(7), 674-681.
  7. Frias-Martinez, E., Chen, S. Y., Macredie R. D., Liu, X., 2007. The Role of Human Factors in Stereotyping Behavior and Perception of Digital Library Users: A Robust Clustering Approach. J. User Modelling and User-Adapted Interaction, Springer, 17(3), 305-337.
  8. Frias-Martinez, E., Magoulas, G., Chen, S., Macredie, R., 2005. Modelling Human Behavior in User-Adaptive Systems: Recent Advances Using Soft Computing Technique. Expert Systems with Applications, 29(2), 320-329.
  9. Golle, P., 2008. Machine Learning Attacks Against the Asirra CAPTCHA. In CCS'08, International Conference on Computer and Communications Security, ACM Press. 535-542.
  10. Haykin, S., 1999. Neural Networks: A Comprehensive Foundation, Prentice Hall. Upper Saddle River, NJ. 2nd edition.
  11. Kim, M., Kim, E., Ryu J., 2004. A Collaborative Recommendation Based on Neural Networks. In Lee, Y., Li, J., Whang, K., Lee, D. (Eds.), Database Systems for Advanced Applications. LNCS, Springer, 2973, 425-430.
  12. Magoulas, G. D., Papanikolaou, K. A., Grigoriadou, M., 2001. Neuro-fuzzy synergism for planning the content in a web-based course. Informatica, 25(1), 39-48.
  13. Perkowitz, M., Etzioni, O., 2000. Adaptive Web Sites. Communications of the ACM, ACM Press. 43(8), 152- 158.
  14. Riding, R., 2001. Cognitive Style Analysis - Research Administration. Learning and Training Technology.
  15. Riding, R., Cheema, I., 1991. Cognitive styles - An Overview and Integration. Educational Psychology. 11, 3/4, pp. 193-215.
  16. Rumelhart, D. E., Hinton, G. E., Williams, R. J., 1986. Learning Internal Representations by Error Propagation. In Parallel Distributed Processing: Explorations in the Microstructure of Cognition, MIT Press. 1, 318-362.
  17. Securimage v.3.0., 2012. http://www.phpcaptcha.org.
  18. Von Ahn, L., Blum, M., Langford, J., 2004. Telling Humans and Computers Apart Automatically. Communications ACM, ACM Press. 47(2), 56-60.
  19. Weiss, S., Kulikowski, C. A., 1991. Computer Systems that Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning, and Expert Systems, Machine Learning Series, Morgan Kaufmann Publishers, Inc., San Mateo, CA.
  20. Witkin, H. A., Moore, C. A., Goodenough, D. R., Cox, P. W., 1977. Field-dependent and Field-independent Cognitive Styles and their Educational Implications. Review of Educational Research. 47(1), 1-64.
  21. Wu, D., Yang, Z., Liang, L., 2006. Using DEA-neural network approach to evaluate branch efficiency of a large Canadian bank. Expert Systems with Applications, Elsevier. 31, 108-115.
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Paper Citation


in Harvard Style

Papatheocharous E., Belk M., Germanakos P. and Samaras G. (2012). On Modelling Cognitive Styles of Users in Adaptive Interactive Systems using Artificial Neural Networks . In Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2012) ISBN 978-989-8565-33-4, pages 563-569. DOI: 10.5220/0004158905630569


in Bibtex Style

@conference{ncta12,
author={Efi Papatheocharous and Marios Belk and Panagiotis Germanakos and George Samaras},
title={On Modelling Cognitive Styles of Users in Adaptive Interactive Systems using Artificial Neural Networks},
booktitle={Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2012)},
year={2012},
pages={563-569},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004158905630569},
isbn={978-989-8565-33-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2012)
TI - On Modelling Cognitive Styles of Users in Adaptive Interactive Systems using Artificial Neural Networks
SN - 978-989-8565-33-4
AU - Papatheocharous E.
AU - Belk M.
AU - Germanakos P.
AU - Samaras G.
PY - 2012
SP - 563
EP - 569
DO - 10.5220/0004158905630569