A dynamic extension of the Rasch model (Verhelst & Glas, 1993, 1995) is dev
eloped from a Bayesian point of view, and it is shown how this permits appl
ication of the model in a wide variety of test settings. In particular, the
method allows for an adequate modeling of learning throughout a test, dete
rmining whether learning has occurred and whether individual differences in
learning rate should be assumed. An example is provided in which the model
is applied to a computer-administered intelligence test. A satisfactory fi
t of the model was found for these data. Results indicated that learning di
d occur, and that there might be individual differences in learning rate. I
ndex terms: Bayesian statistics, dynamic Rasch model, intelligence tests, l
earning, Rasch model.