Abstract
Bayesian and probabilistic networks are claimed to offer powerful approaches to inferring an individual’s knowledge state from evidence of mastery of concepts or skills. A typical application where such tools can be useful is Computer Adaptive Testing (CAT). Bayesian networks have been proposed as an alternative to the traditional Item Response Theory (IRT), which has been the prevalent CAT approach for the last three decades. We compare the performance of one probabilistic network approach, named POKS, to the IRT two parameter logistic model. Experimental results over a 34 items UNIX test and a 160 items French language test show that both approaches can classify examinees as master or non master effectively and efficiently. Implications of these results for adaptive testing and student modeling are discussed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Desmarais, M.C., Maluf, A., Liu, J.: User-expertise modeling with empirically derived probabilistic implication networks. User Modeling and User-Adapted Interaction 5(3-4), 283–315 (1995)
van der Linden, W.J., Hambleton, R.K. (eds.): Handbook of Modern Item Response Theory. Springer, Heidelberg (1997)
Baker, F.B.: Item Response Theory Parameter Estimation Techniques. Marcel Dekker Inc., New York (1992)
Almond, R.G., Mislevy, R.J.: Graphical models and computerized adaptive testing. Applied Psychological Measurement 23(3), 223–237 (1999)
Mislevy, R.J., Chang, H.: Does adaptive testing violate local independence? Psychometrika 65, 149–156 (2000)
Giarratano, J., Riley, G.: Expert Systems: Principles and Programming, 3rd edn. PWS-KENT Publishing, Boston (1998)
Desmarais, M.C., Pu, X.: A bayesian inference adaptive testing framework and its comparison with item response theory, tech. rep., Ecole Polytechnique de Montreal, Montreal, Canada (2005)
Conati, C., Gertner, A., VanLehn, K.: Using bayesian networks to manage uncertainty in student modeling. User Modeling and User-Adapted Interaction 12(4), 371–417 (2002)
Mislevy, R.J., Gitomer, D.: The role of probability-based inference in an intelligent tutoring system. User Modeling and User-Adapted Interaction 42(5), 253–282 (1995)
Collins, J.A., Greer, J.E., Huang, S.X.: Adaptive assessment using granularity hierarchies and bayesian nets. In: Intelligent Tutoring Systems, Montreal, Canada, pp. 569–577 (1996)
Millán, E., 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)
Vomlel, J.: Bayesian networks in educational testing. International Journal of Uncertainty, Fuzziness and Knowledge Based Systems 12(Supplementary Issue 1), 83–100 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Desmarais, M.C., Pu, X. (2005). Computer Adaptive Testing: Comparison of a Probabilistic Network Approach with Item Response Theory. In: Ardissono, L., Brna, P., Mitrovic, A. (eds) User Modeling 2005. UM 2005. Lecture Notes in Computer Science(), vol 3538. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527886_51
Download citation
DOI: https://doi.org/10.1007/11527886_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27885-6
Online ISBN: 978-3-540-31878-1
eBook Packages: Computer ScienceComputer Science (R0)