Learned student models with item to item knowledge structures

User Modeling and User-Adapted Interaction

Volumn 16, Issue 5, 2006, Pages 403-434

  Desmarais, Michel C ^a   Meshkinfam, Peyman ^a   Gagnon, Michel ^a  

^a ÉCOLE POLYTECHNIQUE DE MONTRÉAL   (Canada)

Author keywords

Adaptive testing; Bayesian inference; Bayesian networks; CAT; Empirical simulations; Knowledge assessment; Knowledge spaces; POKS; Probabilistic models; Student models

Indexed keywords

COMPUTATIONAL METHODS; COMPUTER SIMULATION; KNOWLEDGE BASED SYSTEMS; MATHEMATICAL MODELS; STUDENTS;

ADAPTIVE TESTING; BAYESIAN INFERENCE; BAYESIAN NETWORKS; CAT; EMPIRICAL SIMULATIONS; KNOWLEDGE ASSESSMENT; KNOWLEDGE SPACES; POKS; PROBABILISTIC MODELS; STUDENT MODELS;

LEARNING SYSTEMS;

EID: 33751306543     PISSN: 09241868     EISSN: 15731391     Source Type: Journal    
DOI: 10.1007/s11257-006-9016-3     Document Type: Article

References (38)

1. Aleven, V., McLaren, B.M., Sewall, J., Koedinger, K.R.: The cognitive tutor authoring tools (CTAT): versatile and increasingly rapid creation of tutors'. In: ITS'06: Proceedings of the 8th International Conference on Intelligent Tutoring Systems. Tapei, Taiwan (2006)
2. Carmona, C., Millán, E., de-la Cruz, J.-L.P., Trella, M., Conejo, R.: Introducing Prerequisite Relations in a Multi-layered Bayesian Student Model. In: Ardissono, L., Brna, P., Mitrovic, A. (eds.) User Modeling, vol. 3538. Lecture Notes in Computer Science, pp. 347-356. Springer, Berlin (2005)
3. Chow, C., Liu, C.: Approximating discrete probability distributions with dependence trees. IEEE Trans. Inform. Theory 14(11), 462-467 (1968)
4. Conati, C., Gertner, A., VanLehn, K.: Using bayesian networks to manage uncertainty in student modeling. User Model. User-Adapt. Int. 12(4), 371-417 (2002)
5. Cooper, G.F., Herskovits, E.: A Bayesian method for the induction of probabilistic networks from data. Mach. Learn. 9, 309-347 (1992)
6. Desmarais, M.C., Maluf, A., Liu, J.: User-expertise modeling with empirically derived probabilistic implication networks. User Model. User-Adapt. Int. 5(3-4), 283-315 (1996)
7. Desmarais, M.C., Pu, X.: A Bayesian inference adaptive testing framework and its comparison with item response theory. Int. J. Artif. Int. Educ. 15, 291-323 (2005)
8. Doignon, J.-P., Falmagne, J.-C.: Knowledge Spaces. Springer-Verlag, Berlin (1999)
9. Domingos, P., Pazzani, M.: On the optimality of the simple bayesian classifier under zero-one loss. Mach. Learn. 29, 103-130 (1997)
10. Dösinger, G.: Adaptive competence testing in elearning. Euro. J. Open Distance Learn. http://www.eu-rodl.org/ p. 12 (online publication) (2002)
11. Dowling, CE., Hockemeyer, C.: Automata for the Assessment of knowledge. IEEE Trans. Knowl. Data Eng. 13(3), 451-461 (2001)
12. Falmagne, J.-C., Cosyn, E., Doignon, J.-P., Thiéry, N.: The Assessment of knowledge, in theory and in practice. In: Missaoui, R., Schmid, J. (eds.) ICFCA, vol. 3874 of Lecture Notes in Computer Science, pp. 61-79. Springer, New York (2006)
13. Falmagne, J.-C., Koppen, M., Villano, M., Doignon, J.-P., Johannesen, L.: Introduction to knowledge spaces: how to build test and search them. Psychol. Rev. 97, 201-224 (1990)
14. François, O., Leray, P.: Etude comparative d'algorithmes d'apprentissage de structure dans les réseaux bayésiens. In: Proceedings of RJCIA03, Plate-Forme AFIA03, pp. 167-180. Lavai, France, 1-2 July 2003
15. Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Mach. Learn. 29(2-3), 131-163 (1997)
16. Heller, J., Steiner, C., Hockemeyer, C., Albert, D.: Competence-based knowledge structures for personalised learning. Int. J. E-Learn. 5(1), 75-88 (2006)
17. Horvitz, E.: Principles of mixed-initiative user interfaces. In: Proceedings of the ACM Conference on Human Factors in Computing Systems, CHI'99, pp. 159-166. Pittsburg, PA, USA, 1.5-20 May 1999
18. Jensen, F. V.: An Introduction to Bayesian Networks. UCL Press, London, England (1996)
19. Kambouri, M., Koppen, M., Villano, M., Falmagne, J.-C.: Knowledge assessment: tapping human expertise by the QUERY routine. Int. J. Hum.-Comput. Stud. 40(1), 119-151 (1994)
20. Kay, J., Kummerfeld, B., Lauder, P.: Personis: a Server for user models. In: Bra, P.D., Brusilovsky, P., Conejo, R. (eds.) AH, vol. 2347 of Lecture Notes in Computer Science, pp. 203-212 (2002)
21. Kobsa, A.: Generic user modeling systems. User Model. User-Adapt. Interact 11(1-2), 49-63 (2001)
22. Kodaganallur, V., Weitz, R.R., Rosenthal, D.: A comparison of model-tracing and constraint-based intelligent tutoring paradigms. Int. J. of Artif. Int. Educ. 15, 117-144 (2005)
23. Martin, B., Mitrovic, A.: Easing the ITS bottleneck with constraint-based modelling. New Ze. J. Comput. 8(3), 38-47 (2001)
24. Millán, E., Trella, M., Pérez-de-la-Cruz, J.-L., Conejo, R.: Using Bayesian networks in computerized adaptive tests. In: Ortega, M., Bravo, J. (eds.) Computers and Education in the 21st Century, pp. 217-228. Kluwer, Dordrecht (2000)
25. Mitrovic, A., Mayo, M., Suraweera, P., Martin, B.: Constraint-based tutors: a success story. In: Monostori, L., Váncza, J., Ali, M. (eds.) IEA/AIE, vol. 2070 of Lecture Notes in Computer Science, pp. 931-940. Springer, New York (2001)
26. Murphy, K.P.: The bayes net toolbox for MATLAB. Technical report. University of California at Berkeley, Berkeley, CA (2001)
27. Neapolitan, R.E.: Learning Bayesian Networks. Prentice Hall, New Jersey (2004)
28. Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo, CA (1988)
29. Pearl, J., Verma, T.: A theory of inferred causation. In: Knowledge Representation and Reasoning: Proceedings of the Second International Conference, pp. 441-452. Cambridge, MA. USA, April 1991
30. Rish, I.: An empirical study of the naive bayes classifier. In: IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, pp. 41-46. Seatle, WA, USA, 4-10 August (2001)
31. Spirtes, P., Glymour, C., Scheines, R.: Causation, Prediction, and Search, 2nd edn. The MIT Press, Cambridge, MA (2000)
32. Suraweera, P., Mitrovic, A., Martin, B.: A knowledge acquisition system for constraint-based intelligent tutoring systems. In: Looi, C., McCalla, G., Bredeweg, B., Breuker, J., (eds.) Proceedings of the 12th International Conference on Artificial Intelligence in Education, AEID'2005, pp. 638-645. Amsterdam (2005)
33. Tatsuoka, K.: Rule space: an approach for dealing with misconceptions based on item response theory. J. Educ. Meas. 20, 345-354 (1983)
34. VanLehn, K., Lynch, C., Schulze, K., Shapiro, J., Shelby, R., Taylor, L., Treacy, D., Weinstein, A., Wintersgill, M.: The andes physics tutoring system: five years of evaluation. In: Proceedings of AIED'05, pp. 678-685. Amsterdam, Netherland, 18-22 July 2005
35. VanLehn, K., Niu, Z., Siler, S., Gertner, A.S.: Student modeling from conventional test data: a Bayesian approach without priors. In: ITS'98: Proceedings of the 4th International Conference on Intelligent Tutoring Systems, pp. 434-443. London, UK (1998)
36. Vomlel, J.: Bayesian networks in educational testing. Int. J. Uncertain. Fuzz. Knowl. Based Syst. 12(Suppl. 1), 83-100 (2004)
37. Wang, W.-C., Chen, P.-H.: Implementation and measurement efficiency of multidimensional computerized adaptive testing. Appl. Psychol. Meas. 28(5), 295-316 (2004)
38. Zapata-Rivera, J.-D., Greer, J.: Inspectable bayesian student modelling servers in multi-agent tutoring systems. Int. J. Hum.-Comput. Stud. 61(4), 535-563 (2004)