Neural networks applied to knowledge acquisition in the student model

Knowledge acquisition for the student model of intelligent tutoring systems (ITSs) remains a difficult problem, partly because of the complexity associated with understanding both how people learn and how it is best to tutor, much of which relates to metacognition and problem-solving skills. The bottleneck associated with this area significantly increases the development times of ITSs. Neural networks have made a marked impact in many artificial intelligence areas such as pattern recognition, speech learning, speech understanding, and hand-written character recognition. Neural networks are noted for their ability to handle noisy and approximate data, to generalize over situations they have not handled before, and to be represented in a way amenable to parallel processing. In addition, they have the ability to learn, a characteristic which should prove very useful in the development of ITSs. In this paper, we show that neural networks can address the knowledge acquisition bottleneck associated with the student model. We demonstrate that incomplete knowledge obtained from the expert can be refined and expanded by a neural network to provide a more complete, and hence more accurate, student model.