Teaching-Learning by Means of a Fuzzy-Causal User Model

  • Alejandro Peña Ayala
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5845)


In this research the teaching-learning phenomenon that occurs during an E-learning experience is tackled from a fuzzy-causal perspective. The approach is suitable for dealing with intangible objects of a domain, such as personality, that are stated as linguistic variables. In addition, the bias that teaching content exerts on the user’s mind is sketched through causal relationships. Moreover, by means of fuzzy-causal inference, the user’s apprenticeship is estimated prior to delivering a lecture. This supposition is taken into account to adapt the behavior of a Web-based education system (WBES). As a result of an experimental trial, volunteers that took options of lectures chosen by this user model (UM) achieved higher learning than participants who received lectures’ options that were randomly selected. Such empirical evidence contributes to encourage researchers of the added value that a UM offers to adapt a WBES.


User Model Linguistic Variable Membership Degree Linguistic Term Rule Fire 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Brusilovsky, P., Peylo, Ch.: Adaptive and Intelligent Web-based Educational Systems. Int. J. Artificial Intelligence in Education 13, 156–169 (2003)Google Scholar
  2. 2.
    Kay, J.: Life-long Learning, Learner Models and Augmented Cognition. In: Woolf, B.P., Aïmeur, E., Nkambou, R., Lajoie, S. (eds.) ITS 2008. LNCS, vol. 5091, pp. 3–5. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  3. 3.
    Zukerman, I., Albrecht, D.W.: Predictive Statistical Models for User Modeling. Int. J. User Modeling and User-Adapted Interaction 11, 5–18 (2001)zbMATHCrossRefGoogle Scholar
  4. 4.
    Kapoor, A., Horvitz, E.: Principles of Lifelong Learning for Predictive User Modeling. In: Conati, C., McCoy, K., Paliouras, G. (eds.) UM 2007. LNCS (LNAI), vol. 4511, pp. 37–46. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  5. 5.
    Noguez, J., Sucar, L.E., Espinoza, E.: A Probabilistic Relational Student Model for Virtual Laboratories. In: Conati, C., McCoy, K., Paliouras, G. (eds.) UM 2007. LNCS (LNAI), vol. 4511, pp. 303–308. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. 6.
    Kapoor, A., Horvitz, E.: Experience Sampling for Building Predictive User Models: A Comparative Study. In: Czerwinski, M., Lund, A., Tan, D. (eds.) Proc. 26th SIGCHI C. on Human Factors in Computing Systems, Florence, Italy, April 05–10, pp. 657–666. ACM, New York (2008)Google Scholar
  7. 7.
    Legaspi, R., Sison, R., Fuki, K., Numao, M.: Cluster-based Predictive Modeling to Improve Pedagogic Reasoning. Int. J. Computers in Human Behavior 24(2), 153–172 (2008)CrossRefGoogle Scholar
  8. 8.
    Chen-S., L.: Diagnostic, Predictive and Compositional Modeling with Data Mining in Integrated Learning Environments. Int. J. Computers and Education 49(3), 562–580 (2005)Google Scholar
  9. 9.
    Valle, A., Gonzalez, R., Nuñez, J.C., Rodriguez, S., Pineiro, I.: A Causal Model of the Cogntive-Affective Factors of the Academic, Achievement. J. of General and Applicated Psychology 52(4), 499–519 (1999) (in Spanish)Google Scholar
  10. 10.
    Liegler, R.M.: Predicting Student Satisfaction in Baccalaureate Nursing Programs: Testing a Causal Model. J. of Nursing Education 36(8), 357–364 (1997)Google Scholar
  11. 11.
    Kavcic, A.: Fuzzy Student Model in InterMediActor Platform. In: Proc. 26th Int. C. on Information Technology Interfaces, Cavtat, Croatia, June 7-10, vol. 10, pp. 297–302 (2004)Google Scholar
  12. 12.
    Nykänen, O.: Inducing Fuzzy Models for Student Classification. J. Educational Technology and Sociaty 9(2), 223–234 (2006)Google Scholar
  13. 13.
    Xu, D., Wang, H., Su, K.: Intelligent Student Profiling with Fuzzy Models. In: Proc. 35th Int. C. on System Sciences, HICSS 2002, Hawaii, USA, January 07-10, p. 81 (2002)Google Scholar
  14. 14.
    Zadeh, L.: Towards a Theory of Fuzzy Information Granulation and its Centrality in Human Reasoning and Fuzzy Logic. Int. J. Fuzzy Sets and Systems 19 (1997)Google Scholar
  15. 15.
    Self, J.: Formal Approaches to Student Modeling. Tech-Report AI-59, Lancaster (1991)Google Scholar
  16. 16.
    Peña, A.: Student Model based on Cognitive Maps. PhD Thesis, National Polytechnic Institute, Mexico (January 2008)Google Scholar
  17. 17.
    Carvalho, J.P.: Rule Base-based Cognitive Maps: Qualitative Dynamic Systems Modeling and Simulation. PhD Thesis, Lisboa Technical University, Portugal (October 2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Alejandro Peña Ayala
    • 1
    • 2
  1. 1.WOLNMIztapalapaMexico
  2. 2.ESIME-Z and CIC – Instituto Politécnico NacionalIztapalapaMexico

Personalised recommendations