Bayesian Network Models for Adaptive Testing
This work addresses model selection for adaptive testing in education, but it appears incremental as it builds on existing Bayesian network approaches without claiming major breakthroughs.
The researchers tackled the problem of model selection in computerized adaptive testing by proposing and comparing several Bayesian network models using cross-validation on data from grammar school students, resulting in a clearer view on the model selection problem.
Computerized adaptive testing (CAT) is an interesting and promising approach to testing human abilities. In our research we use Bayesian networks to create a model of tested humans. We collected data from paper tests performed with grammar school students. In this article we first provide the summary of data used for our experiments. We propose several different Bayesian networks, which we tested and compared by cross-validation. Interesting results were obtained and are discussed in the paper. The analysis has brought a clearer view on the model selection problem. Future research is outlined in the concluding part of the paper.