A Procedure for Identifying the Best Skill Multimap in the Gain-Loss Model

The gain-loss model is a formal model for assessing learning processes. For the assessment to be trustworthy, the skill multimap associating the problems with the skills has to be specified in an appropriate way. In practical applications, however, more than one skill multimap could be plausible, while none corresponds to the true one. The article investigates whether constraining the error probabilities is a way of uncovering the best skill assignment among a number of alternatives. A simulation study shows that this approach allows the detection of the models that are closest to the correct one.