Information Technology Reference
In-Depth Information
Rich, E. (1979). User modelling via stereotypes. Cognitive Science, 3 (4), 329-354.
Rivers, R. (1989). Embedded user models—where next? Interacting with Computers, 1 , 14-30.
Rodrigo, M., Baker, R., Maria, L., Sheryl, L., Alexis, M., Sheila, P., Jerry, S., Leima, S., Jessica, S., &
Sinath, T. (2007). Affect and usage choices in simulation problem solving environments. In
Proceedings of the 13th International Conference on Artificial Intelligence in Education , Marina
Del Ray, CA, USA (pp.145-152).
Rodriguez-Repiso, L., Setchi, R., & Salmeron, J. L. (2007). Modelling IT projects success with
fuzzy cognitive maps. Expert Systems with Applications, 32 (2), 543-559.
Sabourin, J., Mott, B., & Lester, J. C. (2011). Modeling learner affect with theoretically
grounded dynamic bayesian networks. In Proceedings of the 4th international conference on
Affective computing and intelligent interaction , Memphis, Tennessee (pp. 286-295).
Salim, N., & Haron, N. (2006). The construction of fuzzy set and fuzzy rulef or mixed approach
in adaptive hypermedia learning system. In Proceedings of the 1st International Conference
on Technologies for E-Learning and Digital Entertainment (Edutainment 2006) , Hangzhou,
China (pp. 183-187).
Salmeron, J. L. (2009). Augment fuzzy cognitive maps for modelling LMS critical success fac-
tors. Knowledge-Based Systems, 22 (4), 275-278.
Salmeron, J. L., Vidal, R., & Mena, A. (2012). Ranking fuzzy cognitive map based scenarios
with TOPSIS. Expert Systems with Applications, 39 (3), 2443-2450.
Salomon, G. (1990). Studying the flute and the orchestra: Controlled vs. classroom research on
computers. International Journal of Educational Research, 14 , 521-532.
Sison, R., & Shimura, M. (1998). Student modeling and machine learning. International Journal
of Artificial Intelligence in Education, 9 , 128-158.
Schiaffino, S., Garcia, P., & Amandi, A. (2008). eTeacher: Providing personalized assistance to
e-learning students. Computers and Education, 51 (4), 1744-1754.
Shakouri, H. G., & Menhaj, M. (2008). A systematic fuzzy decision-making process to
choose the best model among a set of competing models. IEEE Trans on Systems Man and
Cybernetics Part A: Systems and Humans, 38 (5), 1118-1128.
Shapiro, J. A. (2005). An algebra subsystem for diagnosing students' input in a physics tutorin
system. International Journal of Artificial Intelligence in Education, 15 (3), 205-228.
Siddapa, M., & Manjunath, A. S. (2007). Knowledge representation using multilevel hierarchical
model in intelligent tutoring system. In Proceedings of the Third International Conference on
Advances in Computer Science and Technology. Thailand.
Siddappa, M., Manjunath, A. S., & Kurian, M. Z. (2009). Design, implementation and evaluation
of intelligent tutoring system for numerical methods (ITNM). International Conference on
Computational Intelligence and Software Engineering , Wuhan, China (pp. 1-7).
Smith, S. (1998). Tutorial on. Retrieved March 15, 2002, from http://www.cs.mdx.ac.
uk/staffpages/serengul/table.of.contents.htm .
Song, H., Miao, C., Roel, W., Shen, Z., & D'Hondt, M. (2011). An extension to fuzzy cognitive
maps for classification and prediction. IEEE Transactions on Fuzzy Systems, 19 (1), 116-135.
Spada, H. (1993). How the role of cognitive modeling for computerized instruction is chang-
ing. In Proceedings of AI-ED'93, World Conference on Artificial Intelligence in Education ,
Edinburgh, Scotland (pp. 21-25).
Staff, C. (2001). HyperContext: A framework for adaptive and adaptable hypertext. Ph.D. Thesis.
University of Sussex .
Stansfield, J. C., Carr, B., & Goldstein, I. P. (1976). Wumpus advisor I: A first implementa-
tion of a program that tutors logical and probabilistic reasoning skills. At Lab Memo 381.
Cambridge, Massachusetts: Massachusetts Institute of Technology.
Stash, N., Cristea, A., & De Bra, P. (2006). Adaptation to Learning Styles in E-Learning:
Approach Evaluation. In Proc. of World Conference on E-Learning in Corporate,
Government, Healthcare, and Higher Education , Orlando, Florida (pp. 284-291).
Stathacopoulou, R., Magoulas, G. D., Grigoriadou, M., & Samarakou, M. (2005). Neuro-fuzzy
knowledge processing in intelligent learning environments for improved student diagnosis.
Information Sciences, 170 (2-4), 273-307.
Search WWH ::




Custom Search