Abstract
In this paper, we present an empirical study with simulated students that allows to compare the accuracy of two models based on Bayesian networks in the context of student modelling: a dynamic model versus an static model. The results show that the performance of both models is very similar, being the dynamic much faster and easier to implement. A second study evaluates the use of adaptive item selection criteria, that can provide an increase on accuracy and a big reduction in test length.
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Millán, E., Pérez-de-la-Cruz, J.L., García, F. (2003). Dynamic versus Static Student Models Based on Bayesian Networks: An Empirical Study. In: Palade, V., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2003. Lecture Notes in Computer Science(), vol 2774. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45226-3_181
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DOI: https://doi.org/10.1007/978-3-540-45226-3_181
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