An ACT-R List Learning Representation for Training Prediction

Abstract

This paper presents a representation of training based on an ACT-R model of list learning. The benefit of the list model representation for making training predictions can be seen in the accurate a priori predictions of trials to mastery given the number of task steps. The benefit of using accurate step times can be seen in the even more accurate post-hoc model results.


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