A New Score for Adaptive Tests in Bayesian and Credal Networks
  A test is adaptive when its sequence and number of questions is dynamically
tuned on the basis of the estimated skills of the taker. Graphical models, such
as Bayesian networks, are used for adaptive tests as they allow to model the
uncertainty about the questions and the skills in an explainable fashion,
especially when coping with multiple skills. A better elicitation of the
uncertainty in the question/skills relations can be achieved by interval
probabilities. This turns the model into a credal network, thus making more
challenging the inferential complexity of the queries required to select
questions. This is especially the case for the information theoretic quantities
used as scores to drive the adaptive mechanism. We present an alternative
family of scores, based on the mode of the posterior probabilities, and hence
easier to explain. This makes considerably simpler the evaluation in the credal
case, without significantly affecting the quality of the adaptive process.
Numerical tests on synthetic and real-world data are used to support this
claim.
