Making decisions in an intelligent tutoring system

International Journal of Information Technology and Decision Making

Volumn 4, Issue 2, 2005, Pages 207-233

  Pek, Peng Kiat ^a   Poh, Kim Leng ^b  

^a SINGAPORE POLYTECHNIC   (Singapore)

^b NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

Bayesian network; Decision analysis; Item selection; Student model

Indexed keywords

EID: 33746242086     PISSN: 02196220     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0219622005001489     Document Type: Article

References (39)

1. E. H. Shortliffe, Computer-Based Medical Consultations: MYCIN(Elsevier, 1976).
2. Y. Matsubara and M. Nagamachi, Motivation system and human model for intelligent tutoring, in Proc. 3rd International Conference on Intelligent Tutoring Systems, Montréal, Canada (June 1996), pp. 139-147.
3. Y. Tang, K. C. C. Chan, A. Wu and P. Winoto, A fuzzy-based assessment for Perl tutoring system, in Proc. 8th International Conference on Computers in Education/International Conference on Computer-Assisted Instruction, Taipei, Taiwan (June 2000), pp. 1068-1075.
4. J. Reye, A belief net backbone for student modeling, in Proc. 3rd International Conference on Intelligent Tutoring Systems, Montréal, Canada, pp. 596-604.
5. K. VanLehn, Z. Niu, S. Siler and A. S. Gertner, Student modelling from conventional test data,: A Bayesian approach without priors, in Proc. 4th International Conference on Intelligent Tutoring Systems, Texas, USA (August 1998), pp. 434-443.
6. B. H. Far and A. H. Hashimoto, A computational model for learner's motivation states in individualized tutoring system, in Proc. 8th International Conference on Computers in Education/International Conference on Computer-Assisted Instruction, Taipei, Taiwan (November 2000), pp. 1059-1067.
7. J. Cheng and M. J. Druzdzel, AIS-BN: An adaptive importance sampling algorithm for evidential reasoning in large Bayesian networks, Journal of Artificial Intelligence Research 13 (2000) 155-188.
8. J. Reye, Two-phase updating of student models based on dynamic belief network, in Proc. 4th International Conference on Intelligent Tutoring Systems, Texas, USA (August 1998), pp. 274-283.
9. E. Millán, J. L. Pérez-de-la-Cruz and E. Suárez, Adaptive Bayesian netowrks for multilevel student modelling, in Proc. 5th International Conference on Intelligent Tutoring Systems, Montréal, Canada (June 2000), pp. 534-543.
10. M. Stern, J. Beck and B. Woolf, B, Naïve Bayes classifiers for user modeling, Center for Knowledge Communication, Computer Science Department, University of Massachusetts (1999).
11. M. Mayo, Bayesian student modelling and decision-theoretic selection of tutorial actions in intelligent tutoring systems, PhD thesis, University of Canterbury (2001).
12. A. Gertner and K. Vanlehn, ANDES: A coached problem solving environment for physics, in Proc. 5th International Conference on Intelligent Tutoring Systems, Montréal, Canada (June 2000), pp. 133-142.
13. A. S. Gertner, Providing feedback to equation entries in an intelligent tutoring system for physics, in Proc. 4th International, Conference on Intelligent Tutoring Systems, Texas, USA (August 1998), pp. 254-263.
14. A. Gertner, C. Conati and K. VanLehn, Procedural help in ANDES: Generating hints using a Bayesian network student model, in Proc. 15th National Conference on Artificial Intelligence, Wisconsin, USA (July 1998), pp. 106-111.
15. C. Conati, A. S. Gertner, K. Vanlehn and M. J. Druzdzel, On-line student modeling for coached problem solving using Bayesian networks, 6th International Conference on User Modeling, Sardinia, Italic (June 1997), pp. 231-242.
16. R. J. Mislevy and D. H. Gitomer, The role of probability-based inference in an intelligent tutoring system, User Modelling and User-Adapted Interaction 5 (1996) 253-282.
17. R. C. Murray and K. VanLehn and D. T. Tutor, A decision-theoretic, dynamic approach for optimal selection of tutorial actions, in Proc. 5th International Conference on Intelligent Tutoring Systems, Montréal, Canada (June 2000), pp. 153-162.
18. R. Ganeshan, W. L. Johnson, E. Shaw and B. P. Wood, Tutoring diagnostic problem solving, in Proc. 5th International Conference on Intelligent Tutoring Systems, Montréal, Canada (June 2000), pp. 33-42.
19. D. Heckerman, A tutorial on learning with Bayesian networks, in Learning in Graphical Models, eds. M. I. Jordan (MIT Press, Cambridge, MA, 1999), pp. 301-354.
20. E. Horvitz, J. Breese and M. Henrion, Decision theory in expert systems and artificial intelligence, International Journal of Approximate Reasoning 2 (1988) 247-302, http://www.auai.org/auai-tutes.html.
21. L. Savage, The Foundations of Statistics (Wiley, 1954).
22. H. A. Simon, The New Science of Management Decisions (Harper & Row, New York, 1960).
23. M. Mayo and A. Mitrovic, Optimizing ITS behavior with Bayesian networks and decision theory, International Journal of Artificial Intelligence and Education 12 (2001) 124-153.
24. J. Beck, M. Stern and B. P. Woolf, Using the student model to control problem difficulty, in Proc. 6th International Conference on User Modeling, Sardinia, Italic (June 1997), pp. 277-289.
25. P. Litwin, K. Getz and M. Gunderloy, Access 2002: Enterprise Developer's Handbook (Sybex Inc., 2002).
26. Norsys, Netica API Programmer's Library Reference Manual, version 1.06 (Norsys Software Corp., 1999), http://www.norsys.com.
27. R. J. Oberg, Introduction to C# Using .NET (Prentice Hall, 2002).
28. J. A. Collins, J. E. Greer and S. X. Huang, Adaptive assessment using granularity hierarchies and Bayesian nets, in Proc. 3rd International Conference on Intelligent Tutoring Systems, eds. C. Frasson, G. Gauthier and A. Lesgold, Montreal, Canada, (1996), pp. 569-586.
29. J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference (Morgan Kaufmann Publishers, Inc., 1988).
30. R. L. Keeney and H. Raiffa, Decisions With Multiple Objectives: Preferences and Value Tradeoffs (John Wiley, New York, 1976).
31. F. B. Baker, Item Response Theory: Parameter Estimation Techniques (Marcel Dekker Inc., 1992).
32. G. Zweig and S. Russell, Compositional modeling with DPNs, Report No. UCB/USD97-970, September 1997, Computer Science Division (EECS), University of California, Berkeley (1997).
33. A. R. Cassandra, L. P. Kaelbling and M. L. Littman, Acting optimally in partially observable stochastic domains, in Proc. 12th National Conference on Artificial Intelligence, Washington, USA July-August 1994, pp. 1023-1028.
34. A. Corbett, Cognitive computer tutors: Solving the two-sigma problem, in Proc. 8th International Conference on User Modeling, July 2001, Sonthofen, Germany (July 2001), pp. 137-147.
35. J. Cohen, Statistical Power Analysis for the Behavioral Sciences (Hillsdale, Lawrence Earlbaum Associates, NJ, 1988).
36. B. S. Bloom, The 2 sigma problem: The research for methods of group instruction as effective as one-to-one tutoring, Educational Researcher 13 (1984) 3-15.
37. K. VanLehn, S. Ohlsson and R. Nason, Applications of simulated students: An exploration, Journal of Artificial Intelligence and Education 5(2) (1995) 135-175.
38. M. A. Mark and J. E. Greer, Evaluation methodologies for intelligent tutoring systems, Journal of Artificial Intelligence in Education 4(2-3) (1993) 129-153.
39. R. Gagné, L. Briggs and W. Wager, Principles of Instructional Design, 4th edn. (Fort Worth, HBJ College Publishers, TX, 1992).