References - Advances in Personalized Web-Based Education - page 146

Carmona, C., Castillo, G., & Millán, E. (2008). Designing a dynamic bayesian network for mod-
eling students' learning styles. In Proceedings of the 8th IEEE International Conference on
Advanced Learning Technologies (ICALT 2008) , Santander, Cantabria, Spain (pp. 346-350).
Carver, R., & Nash, J. G. (2009). Doing data analysis with SPSS . United States: Cengage
Learning Inc.
Castillo, O., & Melin, P. (2008). Intelligent systems with interval type-2 fuzzy logic.
International Journal of Innovative Computing, Information and Control, 4 (4), 771-783.
Castillo, G., Gama, J., & Breda, A. M. (2009). An adaptive predictive model for student model-
ing. In Advances in Web-Based Education: Personalized Learning Environments (pp. 70-92).
USA: Information Science Publishing (Chapter IV).
Cetintas, S., Si, L., Xin, Y. P., & Hord C. (2010). Automatic detection of off-task behaviors
in intelligent tutoring systems with machine learning techniques. IEEE Transactions on
Learning Technologies , 3 (3), 228-236.
Chang, D. F., & Sun, C. M. (1993). Fuzzy assessment of learning performance of junior high
school students. In Proceedings of the 1993 first national symposium on fuzzy theory and
applications , Hsinchu, Taiwan, Republic of China (pp. 1-10).
Chen, S. M., & Lee, C. H. (1999). New methods for students' evaluating using fuzzy sets. Fuzzy
Sets and Systems, 104 (2), 209-218.
Cheung, R., Wan, C., & Cheng, C. (2010). An ontology-based framework for personalized adap-
tive learning. In Proceedings of the 9th International Conference on Web-based Learning
(ICWL 2010) (pp. 52-61). Shanghai, China.
Chieu, V. M., Luengo, V., Vadcard, L., & Tonetti, J. (2010). Student modeling in orthopedic sur-
gery training: Exploiting symbiosis between temporal bayesian networks and fine-grained
didactic analysis. Journal of Artificial Intelligence in Education, 20 (3), 269-301.
Chin, D. N. (2001). Empirical evaluation of the user models and user-adapted systems. User
Modelling and User-Adapted Interaction, 11 , 181-194.
Cho, M.-H., & Kin, B. J. (2013). Students' self-regulation for interaction with others in online
learning environments. Internet and Higher Education, 17 , 69-75.
Chrysafiadi, K., & Virvou, M. (2008). Personalized teaching of a programming language over the
web: Stereotypes and rule-based mechanisms. In Proceedings of the 8th Joint Conference on
Knowledge-Based Software Engineering , Piraeus, Greece (pp. 484-492).
Chrysafiadi, K., & Virvou, M. (2012). Evaluating the integration of fuzzy logic into the student
model of a web-based learning environment. Experts Systems with Applications, 39 (18),
13127-13134.
Chrysafiadi, K., & Virvou, M. (2013a). PeRSIVA: An empirical evaluation method of a student
model of an intelligent e-learning environment for computer programming. Computers and
Education, 68 , 322-333.
Chrysafiadi, K., & Virvou, M. (2013b). Student modeling approaches: A literature review for the
last decade. Expert Systems with Applications, 40 (11), 4715-4729.
Chrysafiadi, K., & Virvou, M. (2013c). Dynamically personalized e-training in computer pro-
gramming and the language C. IEEE Transactions on Education, 56 (4), 385-392.
Chrysafiadi, K., & Virvou, M. (2014). Fuzzy Logic for adaptive instruction in an e-learning envi-
ronment for computer programming. IEEE Transactions on Fuzzy Systems . d.o.i.: 10.1109/
TFU22.2014.2310242 .
Clancey, W. (1988). The role of qualitative models in instruction. In J. Self (eds.), Artificial
Intelligence and Human Learning, Chapman and Hall Computing.
Clemente, J., Ramírez, J., & de Antonio, A. (2011). A proposal for student modeling based on
ontologies and diagnosis rules. Expert Systems with Applications, 38 (7), 8066-8078.
Codara, L. (1998). Le mappe cognitive . Roma: Carrocci Editore.
Collins, A., & Michalski, R. (1989). The logic of plausible reasoning: A core theory. Cognitive
science (Vol. 13, pp. 1-49). The Netherlands: Elsevier Science.
Conati, C. (2009). Intelligent tutoring systems: New challenges and directions. In Proceedings
of the 21st International Joint Conference on Artificial Intelligence (pp. 2-7). San Francisco,
CA, USA.