Probabilistic Models for Computerized Adaptive Testing: Experiments
This work addresses the need for more effective adaptive testing methods in educational or assessment contexts, but it is incremental as it builds on prior research in Bayesian networks for CAT.
The paper tackled the problem of improving computerized adaptive testing (CAT) by comparing probabilistic models including Item Response Theory, Bayesian networks, and neural networks on simulated tests with empirical data, presenting results for each model separately and in comparison.
This paper follows previous research we have already performed in the area of Bayesian networks models for CAT. We present models using Item Response Theory (IRT - standard CAT method), Bayesian networks, and neural networks. We conducted simulated CAT tests on empirical data. Results of these tests are presented for each model separately and compared.