A simulated student is a machine learning agent that learns a set of cognitive skills by observing solutions demonstrated by human experts. The learned cognitive skills are converted into a cognitive model for a Cognitive Tutor that is a computerized tutor that teaches human students the cognitive skills. In this paper, we analyze the characteristics of the effective demonstrations that lead to quicker and more accurate learning. Results from empirical studies show that expressive demonstrations (as opposed to abbreviated demonstrations that involve implicit mental operations) are better for both speed and accuracy of learning. We also found that providing multiple demonstrations of the same cognitive skill with differing surface features accelerates learning. These findings imply that the ordering of training sequence as well as the level of detail in demonstration determines the efficiency with which a simulated student generates a cognitive model.

14 pages

*Human-Computer Interaction Institute, Carnegie Mellon University

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