NEURAL NETWORKS APPLIED TO KNOWLEDGE ACQUISITION IN THE STUDENT MODEL

Knowledge acquisition for the student model of intelligent tutoring sy stems (ITSs) remains a difficult problem, partly because of the comple xity associated with understanding both how people learn and how it is best to tutor, much of which relates to metacognition and problem-sol ving skills. The bottleneck associated with this area significantly in creases the development times of ITSs. Neural networks have made a mar ked impact in many artificial intelligence areas such as pattern recog nition, speech learning, speech understanding, and hand-written charac ter recognition. Neural networks are noted for their ability to handle noisy and approximate data, to generalize over situations they have n ot handled before, and to be represented in a way amenable to parallel processing. In addition, they have the ability to learn, a characteri stic which should prove very useful in the development of ITSs. In thi s paper, we show that neural networks can address the knowledge acquis ition bottleneck associated with the student model. We demonstrate tha t incomplete knowledge obtained from the expert can be refined and exp anded by a neural network to provide a more complete, and hence more a ccurate, student model.