Using Optimally Selected Drill Practice to Train Basic Facts
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Explaining Self-Explaining: A Contrast between Content and Generation
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Learning probabilistic hierarchical task networks to capture user preferences
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Efficient cross-domain learning of complex skills
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Learning to perceive two-dimensional displays using probabilistic grammars
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Accelerated future learning, in which learning proceeds more effectively and more rapidly because of prior learning, is considered to be one of the most interesting measures of robust learning A growing body of studies have demonstrated that some instructional treatments lead to accelerated future learning However, little study has focused on under- standing the learning mechanisms that yield accelerated future learning In this paper, we present a computational model that demonstrates accelerated future learning through the use of machine learning techniques for feature recognition In order to understand the behavior of the proposed model, we conducted a controlled simulation study with four alternative versions of the model to investigate how both better prior knowledge learning and better learning strategies might independently yield accelerated future learning We measured the learning outcomes of the models by rate of learning and the fit to the pattern of errors made by real students We found out that both stronger prior knowledge and a better learning strategy can speed up the learning process Some model variations generate human-like error patterns, but others learn to avoid errors more quickly than students.