A computational model of accelerated future learning through feature recognition

Abstract

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. © 2010 Springer-Verlag.