Predicting problem-solving performance with concept maps: An information-theoretic approach

An increasing number of researchers and educational practitioners have shown their interest in the use of concept mapping techniques to elicit and represent individuals' knowledge structures. By extending previous research on concept map assessment, this study aims to develop a new evaluation metric enabling the "information uncertainty" embedded in concept maps to be assessed so as to predict individuals' problem-solving performance. In particular, our novel metric EntropyAvg is underpinned by the notion of entropy in information theory. A controlled experiment is carried out to evaluate the effectiveness of our proposed metric. Our experimental results demonstrate that the entropy values computed according to EntropyAvg strongly correlate to individuals' problem-solving performance and that the predictive power of EntropyAvg is significantly higher than that of existing widely used concept map evaluation metrics. Our research work opens the door to the application of concept mapping techniques in enterprise-wide knowledge management in general and enterprise-wide knowledge assessment in particular.