Teaching-learning by means of a fuzzy-causal user model

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5845 LNAI, Issue , 2009, Pages 521-532

  Peña Ayala, Alejandro ^a,b  

^a DEPARTAMENTO DE QUÍMICA   (Mexico)

^b INSTITUTO POLITÉCNICO NACIONAL   (Mexico)

Author keywords

[No Author keywords available]

Indexed keywords

ADDED VALUES; CAUSAL RELATIONSHIPS; EMPIRICAL EVIDENCE; HIGHER LEARNING; LINGUISTIC VARIABLE; TEACHING-LEARNING; USER MODELS; WEB-BASED EDUCATION SYSTEMS;

ARTIFICIAL INTELLIGENCE; COMPUTATIONAL LINGUISTICS; E-LEARNING; INTERNET; MULTIMEDIA SYSTEMS; TEACHING;

FUZZY INFERENCE;

EID: 70549108082     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-05258-3_46     Document Type: Conference Paper

References (17)

1. Brusilovsky, P., Peylo, Ch.: Adaptive and Intelligent Web-based Educational Systems. Int. J. Artificial Intelligence in Education 13, 156-169 (2003)
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)
3. Zukerman, I., Albrecht, D.W.: Predictive Statistical Models for User Modeling. Int. J. User Modeling and User-Adapted Interaction 11, 5-18 (2001)
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)
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)
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)
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)
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)
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)
10. Liegler, R.M.: Predicting Student Satisfaction in Baccalaureate Nursing Programs: Testing a Causal Model. J. of Nursing Education 36(8), 357-364 (1997)
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)
12. Nykänen, O.: Inducing Fuzzy Models for Student Classification. J. Educational Technology and Sociaty 9(2), 223-234 (2006)
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)
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)
15. Self, J.: Formal Approaches to Student Modeling. Tech-Report AI-59, Lancaster (1991)
16. Peña, A.: Student Model based on Cognitive Maps. PhD Thesis, National Polytechnic Institute, Mexico (January 2008)
17. Carvalho, J.P.: Rule Base-based Cognitive Maps: Qualitative Dynamic Systems Modeling and Simulation. PhD Thesis, Lisboa Technical University, Portugal (October 2001)