Artificial neural network-based performance assessments

Abstract

We have explored the ability of artificial neural network technologies to generate performance models of complex problem-solving tasks without the detailed a priori knowledge of the nature of the task. To test the generalizibility of this approach we applied this analysis to two diverse content domains—high school genetics and clinical patient management. In both domains, the artificial neural networks, using only the sequence of actions taken while performing the task, generated multiple classification groups defining different levels of competence. The validity of these neural network performance groupings was further established by the good concordance of these classifications with independently derived expert ratings.