Knowledge Discovery from E-Learning Activities Scott, D.W., & Sain, S.R. (2004). Multi-dimensional density estimation. In C. R. Rao, E. J. Wegman & J. L. Solka (Eds.), Handbook of Statistics, Data Mining and Computational Statistics, Vol. 24, (pp. 229-261). Elsevier. Selim, H. (2007). Critical success factors for e- learning acceptance: Confirmatory factor models. Computers & Education, 49(2), 396-413. Shee, D., & Wang, Y. (in press). Multi-criteria evaluation of the Web-based e-learning system: A methodology based on learner satisfaction and its applications. Computers & Education. Shin, N., & Kim, J. (1999). An exploration of learner progress and dropout in Korea National Open University. Distance Education an International Journal, 20, 81-97. Silvescu, A., Reinoso-Castillo, J., & Honavar, V. (2001). Ontology-driven information extraction and knowledge acquisition from heterogeneous, distributed, autonomous biological data sources. In International Joint Conferences on Artificial Intelligence (IJCAI) (pp. 1-10). Sowa, J.F. (2000). Knowledge representation: Logical, philosophical and computational foundations. Pacific Grove, CA: Brooks Cole. Srivastava, J., Cooley, R., Deshpande, M., & Tan, P. (2000). Web usage mining: Discovery and applications of usage patterns from web data. In SIGKDD Explorations (pp. 12-23). Stephenson, J.E., Brown, C., & Griffin, D.K. (in press). Electronic delivery of lectures in the university environment: An empirical comparasion of three delivery styles. Computers & Education. Sun, P., Tsai, R., Finger, G., Chen, Y., & Yeh, D. (in press). What drives a successful e-learning? An empirical investigation of the critical factors influencing learner satisfaction. Computers & Education. Uschold, M., King, M., Morales, S., & Zorgios, Y. (1998). The enterprise ontology. Knowledge Engineering Review, 13, 32-89. Weigand, H. (1997). Multilingual ontology-based lexicon for news filtering. In IJCAI Workshop on Multilingual Ontologies (pp. 138-159). Xenos, M. (2004). Prediction and assessment of student behaviour in open and distance education in computers using Bayesian networks. Computers & Education, 43(4), 345-359. Zang, W., & Lin, F. (2003). Investigation of Web-based teaching and learning by boosting algorithms. In IEEE International Conference on Information Technology: Research and Education (pp. 445-449). Ziehe, A., & Müller, K.R. (1998). TDSEP-an efficient algorithm for blind separation using time structure. In 8th International Conference on Artificial Neural Networks (pp. 675-680). AddItIonAL reAdIng data mining and knowledge Discovery Chi, X., & Spedding, T.A. (2006). A Web-based intelligent virtual learning environment for industrial continous improvement. In IEEE 4 th International Conference on Industrial Informatics (pp. 1102-1107). Hammouda, K., & Kamel, M. (2006). Data mining in e-learning. In S. Pierre (Ed.), E-learning networked environments and architectures: A knowledge processing perspective. Springer Book Series: Advanced Information and Knowledge Processing. Han, J., & Kamber, M. (2001). Data mining concepts and techniques. Academic Press.
Knowledge Discovery from E-Learning Activities Markou, M., & Singh, S. (2003). Novelty detection: A review. Part 1: Statistical approaches. Signal Processing, 83(12), 2481-2497. Yang, Y., Wu, X., & Zhu, X. (2006). Mining in anticipation for concept change: Proactive-reactive prediction in data streams. Data Mining and Knowledge Discovery, 13(3), 261-289. ontologies Berners-Lee, T., Hendler, J., & Lassila, O. (2001). The semantic Web: A new form of Web content that is meaningful to computers will unleash a revolution of new possibilities. Scientific American Magazine, 284(5), 34-43. Cardoso, J., & Sheth, A. (2002). Semantic e- workflow composition. Journal of Intelligent Information Systems, 21(3), 191-225. Ceccaroni, L., & Ribiere, M. (2002). Experiencesin modeling agentcities utility-ontologies with a collaborative approach. Paper presented at the Ontologies in Agent Systems Workshop, Autonomous Agents and Multi-Agent Systems Conference. Davies, J., Studer, R., & Warren, P. (2006). Semantic Web technologies: Trends and research in ontology-based systems. Wiley. Sowa, J.F. (2006). Categorization in cognitive computer science. In H. Cohen & C. Lefebvre (Eds.), Handbook of categorization in cognitive science (pp. 141-163). Elsevier. Sowa, J.F. (2006). A dynamic theory of ontology. In B. Bennett & C. Fellbaum (Eds.), Formal ontology ininformation systems (pp. 204-213). IOS Press. Pattern recognition Langseth, H., & Nielsen T.D. (2005). Latent classification models. Machine Learning, 59(3), 237-265. Lee T.W., Lewicki, M.S., & Sejnowski, T.J. (2000). ICA mixture models for unsupervised classification of non-gaussian classes and automatic context switching in blind signal separation. IEEE Transactions on Pattern Analysis & Machine Intelligence, 22(10), 1078-1089. Salazar, A., Igual, J., Vergara, L., & Serrano, A. (in press). Learning hierarchies from ICA mixtures. Paper presented at the International Joint Conference on Neural Networks. Vergara, L., Salazar, A., Igual, J., & Serrano, A. (2006). Data clustering methods based on mixture of independent component analyzers. Paper presented at the ICA Research Network International Workshop, ICArn (pp. 127-130). Webb, A.R. (2002). Statistical pattern recognition. John Wiley and Sons. education Butler, K.A. (1990). Learning and teaching style: In theory and practice (2 nd ed.). Gregorc Associates, Incorporated. Entwistle, N. (1990). Styles of learning and teaching - an integrated outline of educational psychology for students, teachers, and lecturers. Fulton, David Publishers. Entwistle, N., & Peterson, E. (2004). Conceptions of learning and knowledge in higher education: Relationships with study behaviour andinfluences of learning environments. International Journal of Educational Research, 41(6), 407-428.