| Reference: | Abramowitz, M. and Stegun, I. A. (Eds.), Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables, ninth printing, New York: Dover, 824−825, 1972.
Aurenhammer, F., Voronoi diagrams - a survey of a fundamental geometric data structure, ACM Computing Surveys, 23, 345−405, 1991.
Birenbaum, M., Kelly, A. E., Tatsuoka, K. K., and Gutvirtz, Y., Attribute mastery patterns from rule space as the basis for student models in Algebra, International Journal of Human-Computer Studies, 40(3), 497–508, 1994.
Brian, T. L., K-Means Clustering, http://fconyx.ncifcrf.gov/~lukeb/kmeans.html, 2005.
Brusilovsky, P., Schwarz, E., and Weber, G., ELM-ART: An intelligent tutoring system on world wide web, Proceedings of the Third International Conference on Intelligent Tutoring Systems, 261−269, 1996.
Bunt, A. and Conati, C., Assessing effective exploration in open learning environments using Bayesian networks, Proceedings of the Sixth International Conference on Intelligent Tutoring Systems, 698−707, 2002.
Bunt, A. and Conati, C., Probabilistic student modeling to improve exploratory behavior, User Modeling and User-Adapted Interaction, 13(3), 269−309, 2003.
Burns, H. L. and Capps, C. G., Foundations of Intelligent Tutoring Systems: An Introduction, Lawrence Erlbaum Associates, Hillsdale, NJ, 1988.
Chen, C.-M., Lee, H.-M., and Chen, Y.-H., Personalized e-leaning system using item response theory, Computer & Education, 44(3), 237−255, 2005.
Collins, J. A., Greer, J. E., and Huang, S. X., Adaptive assessment using granularity hierarchies and Bayesian nets, Proceedings of the Third International Conference on Intelligent Tutoring Systems, 569–577, 1996.
Conati, C., Gertner, A. S., VanLehn, K., and Druzdzel, M. J., On-line student modeling for coached problem solving using Bayesian networks, Proceedings of the Sixth International Conference on User Modeling, 231−242, 1997.
Conati, C., Gertner, A. S., and VanLehn, K., Using Bayesian networks to manage uncertainty in student modeling, User Modeling and User-Adapted Interaction, 12, 371–417, 2002.
Cover, T. and Hart, P., Nearest neighbor pattern classification, Institute of Electrical and Electronics Engineers, Transactions on Information Theory, 13, 21−27, 1967.
Cover, T. M. and Thomas, J. A., Elements of Information Theory, John-Wiley and Sons, 1991.
Cowell, R. G., Dawid, A. P., Lauritzen, S. L., and Spiegelhalter, D. J., Probabilistic Networks and Expert Systems, Springer-Verlag, New York, 1999.
Garthwaite, P. H., Jolliffe, I. T., and Jones, B., Statistical Inference, Prentice Hall, 1995.
Hambleton, R. K., Swaminathan, H., and Rogers, H. J., Fundamentals of Item Response Theory, Sage Publications, 1991.
Hatzilygeroudis, I., and Prentzas, J., Knowledge representation requirements for intelligent tutoring systems, Proceedings of the Seventh International Conference on Intelligent Tutoring Systems, 87−97, 2004.
Heckerman, D. and Breese, J. S., Causal independence for probability assessment and inference using bayesian networks, Institute of Electrical and Electronics Engineers Transactions on Systems, Man, and Cybernetics, 26(6), 826−831,1994.
Heckerman, D., Learning Bayesian networks, Technical Report MSR-TR-95-02, Microsoft Research, 1995a.
Heckerman, D., A tutorial on learning with Bayesian networks, Technical Report MSR-TR-95-06, Microsoft Research, 1995b.
Heckerman, D., Mamdani, A., and Wellman, M. P., Real world applications of Bayesian networks, Communications of the ACM, 38,1995.
Hsu, C.-N., Chung, H.-H, and Huang, H.-S., Mining skewed and sparse transaction data for personalize shopping recommendation, Machine Learning, 57(1-2), 35–59, 2004.
Hugin Expert A/S., HUGIN API Reference Manual Version 6.2, http://developer.hugin.com/documentation/API_Manuals, 2004
Jensen, F. V., Bayesian Networks and Decision Graphs, Springer, 2001.
Liu, C.-L., Wang, Y.-T., and Liu, Y.-C., A Bayesian network-based simulation environment for investigating assessment issues in intelligent tutoring systems, Proceedings of the International Computer Symposium 2004, 234−239, 2004.
Liu, C.-L., Using mutual information for adaptive item comparison and student assessment, Journal of Educational Technology & Society, 8(4), to appear.
MacQueen, J. B., Some methods for classification and analysis of multivariate observations, Proceedings of Fifth Berkeley Symposium on Mathematical Statistics and Probability, 1, 281−297, 1967.
Millán, E., Pérez-de-la-Cruz, J. L., and Suárez, E., Adaptive Bayesian networks for multilevel student modelling, Proceedings of the Fifth International Conference on Intelligent Tutoring Systems, 534–543, 2000.
Millán, E., and Pérez-de-la-Cruz, J. L., A Bayesian diagnostic algorithm for student modeling and its evaluation, User Modeling and User-Adapted Interaction, 12(2-3), 281−330, 2002.
Manning, S. H., “Foundations of Statistical Natural Language Processing”, MIT Press, 1999.
Martin, J., and VanLehn, K., Student assessment using Bayesian nets, International Journal of Human-Computer Studies, 42(6), 575−591, 1995.
Mislevy, R. J. Probability-based inference in cognitive diagnosis. In Nichols, P., Chipman, S., and Brennan, R., L., eds., Cognitively Diagnostic Assessment, Hillsdale, NJ: Erlbaum, 1995.
Mislevy, R. J., and Gitomer, G. H., The role of probability-based inference in an intelligent tutoring system, User Modeling and User-Adapted Interaction, 5, 253–282, 1996.
Misley, R. J., Almond, R. G., Yan, D., and Steinberg, L. S., Bayes nets in educational assessment: Where do the numbers come from?, Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, 437–446, 1999.
Neapolitan, R. E., Learning Bayesian Networks, Prentice Hall, 2004.
Pearl, J., Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann, 1988.
Reye, J., Student modeling based on belief networks, International Journal of Artificial Intelligence in Education, 14, 63−96, 2004.
Rijsbergen, C. J., Information Retrieval, Butterworths, 1979.
Rost, J. and Langeheine, R. (Eds.), Applications of Latent Trait and Latent Class Models in the Social Sciences, Wasmann, 1997.
Stirling, J., Methodus differentialis, sive tractatus de summation et interpolation serierum infinitarium, 1730. English translation by Holliday, J., The Differential Method: A Treatise of the Summation and Interpolation of Infinite Series, 1749.
Tatsuoka, K. K., Rule space: An approach for dealing with misconceptions based on item response theory, Journal of Educational Measurement, 20, 345–354, 1983.
VanLehn, K., Ohlsson, S., and Nason, R., Applications of simulated students: An exploration, International Journal of Artificial Intelligence in Education, 5(2), 135–175, 1994.
VanLehn, K. and Martin, J., Evaluation of an assessment system based on Bayesian student modeling, International Journal of Artificial Intelligence in Education, 8(2), 179–221, 1997.
VanLehn, K., Conceptual and meta learning during coached problem solving, Proceedings of the Third International Conference on Intelligent Tutoring Systems, 29–47, 1996.
Vomlel, J., Bayesian networks in educational testing, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 12, 83−100, 2004.
Walpole, R. E., Myers, R. H., Myers, S. L., and Ye, K., Probability and Statistics for Engineers and Scientists, seventh edition, Prentice Hall, 2002.
Yan, D., Almond, R. G., and Mislevy, R. J., Empirical comparisons of cognitive diagnostic models, Technical Report, Educational Testing Service, http://www.ets.org/research/dload/aera03-yan.pdf, 2003.
Zhou, Y. and Evens, M. W., A practical student model in an intelligent tutoring system, Proceedings of the Eleventh IEEE International Conference on Tools with Artificial Intelligence, 13–18, 1999. |
