Bayesian student modeling

Studies in Computational Intelligence

Volumn 308, Issue , 2010, Pages 281-299

  Conati, Cristina ^a  

^a UNIVERSITY OF BRITISH COLUMBIA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 77957285040     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-14363-2_14     Document Type: Article

References (27)

1. Arroyo, I., Woolf, B.: Inferring learning and attitudes from a Bayesian Network of log file data. In: 12th International Conference on Artificial Intelligence in Education, AIED 2005 (2005)
2. Bunt, A., Conati, C., Hugget, M., Muldner, K.: On Improving the Effectiveness of Open Learning Environments through Tailored Support for Exploration. In: 10th World Conference of Artificial Intelligence and Education, AIED 2001 (2001)
3. Buntine, W.: A Guide to the Literature on Learning Probabilistic Networks from Data. IEEE Transactions on Knowledge and Data Engineering 8(2), 195-210 (1996) (Pubitemid 126776290)
4. Chi, M.: Self-explaining: The dual processes of generating inference and repairing mental models. In: Glaser, R. (ed.) Advances in instructional psychology: Educational design and cognitive science, vol. (5), pp. 161-238. Lawrence Erlbaum Associates, Mahwah (2000)
5. Conati, C., Maclaren, H.: Empirically Building and Evaluating a Probabilistic Model of User Affect. Modeling and User-Adapted Interaction 19(3), 267-303 (2009)
6. Conati, C., Merten, C.: Eye-Tracking for User Modeling in Exploratory Learning Environments: an Empirical Evaluation. Knowledge Based Systems 20(6), 557-574 (2007) (Pubitemid 47068960)
7. Conati, C., Gertner, A., Van Lehn, K.: Using Bayesian Networks to Manage Uncertainty in Student Modeling. Journal of User Modeling and User-Adapted Interaction 12(4), 371-417 (2002)
8. Conati, C., Merten, C., Muldner, K., Ternes, D.: Exploring Eye Tracking to Increase Bandwidth in User Modeling. In: Ardissono, L., Brna, P., Mitrovic, A. (eds.) UM 2005. LNCS (LNAI), vol. 3538, pp. 357-366. Springer, Heidelberg (2005)
9. Corbett, A. T., Anderson, J. R.: Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction 4(4), 253-278 (1995)
10. Costa, P., McRae, R.: Four ways five factors are. Personality and Individual Differences 13, 653-665 (1992)
11. Dean, T., Kanazawa, K.: A Model for REasoning About Persistence and Causation. Computational Intelligence 5(3), 142-150 (1989)
12. Dempster, A., Laird, N., Rubin, D.: Maximization-likelihood from Incomplete Data via the EM Algorithm. Journal of Royal Statistical Society, Series B (1977)
13. D'Mello, S., Craig, S., Witherspoon, A., McDaniel, B., Graesser, A.: Automatic detection of learner's affect from conversational cues. User Modeling and User-Adapted Interaction, 45-80 (2008)
14. Ferguson, K., Arroyo, Y., Mahadevan, S., Park Woolf, B., Barto, A.: Improving Intelligent Tutoring Systems: Using Expectation Maximization to Learn Student Skill Levels. In: Ikeda, M., Ashley, K. D., Chan, T.-W. (eds.) ITS 2006. LNCS, vol. 4053, pp. 453-462. Springer, Heidelberg (2006) (Pubitemid 44117617)
15. Henrion, M.: Some practical issues in constructing belief networks. In: 3rd Conference on Uncertainty in Artificial Intelligence, pp. 161-173 (1989)
16. Keeney, R. L., von Winterfeldt, D.: Eliciting probabilities from experts in complex technical problems. IEEE Transactions on Engineering Management 38, 191-201 (1991)
17. Martin, J., Van Lehn, K.: Student assessment using Bayesian nets. International Journal of Human-Computer Studies 42, 575-591 (1995)
18. Mayo, M., Mitrovic, T.: Optimising ITS Behaviour with Bayesian Networks and Decision Theory. International Journal of Artificial Intelligence in Education 12, 124-153 (2001)
19. Mislevy, R.: Probability-based inference in cognitive diagnosis. In: Nichols, P., Chipman, S., Brennan, R. (eds.) Cognitive Diagnostic Assessment, pp. 43-71. Erlbaum, Hillsdale (1995)
20. Moore, A., Wong, W.: Optimal Reinsertion: A New Search Operator for Accelerated and More Accurate Bayesian Network Structure Learning. In: ICML 2003, pp. 552-559 (2003)
21. Murray, C., Van Lehn, K., Mostov, J.: Looking Ahead to Select Tutorial Actions: A Decision-Theoretic Approach. International Journal of Artificial Intelligence in Education 14 (3-4), 235-278 (2004)
22. Pearl, J.: Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, San Mateo (1988)
23. Reye, J.: Two-phase updating of student models based on dynamic belief networks. In: Goettl, B. P., Halff, H. M., Redfield, C. L., Shute, V. J. (eds.) ITS 1998. LNCS, vol. 1452, pp. 274-283. Springer, Heidelberg (1998) (Pubitemid 128115403)
24. Russel, S., Norvig, P.: Artificial Intelligence - A Modern Approach, 3rd edn. Prentice Hall, Englewood Cliffs (2010)
25. Van Lehn, K., Niu, Z.: Bayesian student modeling, user interfaces and feedback: A sensitivity analysis. International Journal of Artificial Intelligence in Education 12, 154-184 (2001)
26. Zapata-Rivera, D., Greer, J.: Interacting with Inspectable Bayesian Student Models. International Journal of Artificial Intelligence in Education 14(2), 127-163 (2004)
27. Zhou, X., Conati, C.: Inferring User Goals from Personality and Behavior in a Causal Model of User Affect. In: UI 2003, International Conference on Intelligent User Interfaces, pp. 211-281. ACM Press, New York (2003)