A Rasch model for detecting learning while solving an intelligence test

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.