Abstract
In this paper we present an extension of a previously developed generic student model based on Bayesian Networks. A new layer has been added to the model to include prerequisite relationships. The need of this new layer is motivated from different points of view: in practice, this kind of relationships are very common in any educational setting, but also their use allows for improving efficiency of both adaptation mechanisms and the inference process. The new prerequisite layer has been evaluated using two different experiments: the first experiment uses a small toy example to show how the BN can emulate human reasoning in this context, while the second experiment with simulated students suggests that prerequisite relationships can improve the efficiency of the diagnosis process by allowing increased accuracy or reductions in the test length.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Brusilovsky, P., Karagianidis, C., Sampson, D.: The benefits of layered evaluation of adaptive applications and services. In: Proceedings of the UM 2001 workshop on Empirical Evaluation of Adaptive Systems. Pedagogical Univ. of Freiburg, pp. 1–8 (2001)
Carmona, C., Conejo, R.: A Learner Model in a Distributed Environment. In: De Bra, P.M.E., Nejdl, W. (eds.) AH 2004. LNCS, vol. 3137, pp. 353–359. Springer, Heidelberg (2004)
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)
Conejo, R., Guzmán, E., Millán, E., Pérez, J.L., Trella, M.: SIETTE: A Web-Based Tool for Adaptive Testing. International Journal of Artificial Intelligence in Education 14(1), 29–61 (2004)
Dowling, C.E., Hockemeyer, C., Ludwig, A.H.: Adaptive Assessment and Training Using the Neighborhood of Knowledge States. In: Lesgold, A.M., Frasson, C., Gauthier, G. (eds.) ITS 1996. LNCS, vol. 1086, pp. 578–585. Springer, Heidelberg (1996)
Henze, N., Nedjl, W.: Student Modeling for the KBS Hyperbook System using Bayesian Networks. Technical report, University of Hannover (1998)
Horvitz, E., Breese, J., Heckerman, D., Hovel, D., Rommelse, K.: The Lumière Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users. In: Proceedings of UAI 1998, pp. 256–265. Morgan Kauffman, San Francisco (1998)
Mayo, M., Mitrovic, A.: Using a Probabilistic Student Model to Control Problem Difficulty. In: Gauthier, G., VanLehn, K., Frasson, C. (eds.) ITS 2000. LNCS, vol. 1839, pp. 525–533. Springer, Heidelberg (2000)
Millán, E., Pérez-de-la-Cruz, J.L., Suárez, E.: An Adaptive Bayesian Network for Multilevel Student Modeling. In: Gauthier, G., VanLehn, K., Frasson, C. (eds.) ITS 2000. LNCS, vol. 1839, pp. 534–543. Springer, Heidelberg (2000)
Millán, E., Pérez-de-la-Cruz, J.L.: A Bayesian Diagnostic Algorithm for Student Modeling. User Modeling and User-Adapted Interaction 12, 281–330 (2002)
Vassileva, J., McCalla, G., Greer, J.: Multi-Agent Multi-User Modeling in I-Help. User Modeling and User-Adapted Interaction 12, 179–210 (2003)
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
Carmona, C., Millán, E., Pérez-de-la-Cruz, J.L., Trella, M., Conejo, R. (2005). Introducing Prerequisite Relations in a Multi-layered Bayesian Student Model. 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_46
Download citation
DOI: https://doi.org/10.1007/11527886_46
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)