Artificial neural network-based performance assessments

We have explored the ability of artificial neural network technologies to g enerate performance models of complex problem-solving tasks without the det ailed a priori knowledge of the nature of the task. To test the generalizib ility of this approach we applied this analysis to two diverse content doma ins-high school genetics and clinical patient management. In both domains, the artificial neural networks, using only the sequence of actions taken wh ile performing the task, generated multiple classification groups defining different levels of competence. The validity of these neural network perfor mance groupings was further established by the good concordance of these cl assifications with independently derived expert ratings. (C) 1999 Elsevier Science Ltd. All rights reserved.