Self-Explanation in a Data Normalization Tutor
Self-explanation is one of the most effective learning strategies, resulting in deep knowledge. In this paper, we discuss how self-explanation is scaffolded in NORMIT, a data normalization tutor. We present the system first, and then discuss how it supports self-explanation. We hypothesized the self-explanation support in NORMIT will affect students problem solving skills, and also result in better conceptual knowledge. A preliminary evaluation study of the system was performed in October 2002, the results of which show that both problem-solving performance and the understanding of the domain of students who selfexplained increased. We also discuss our plans for future research.