Abstract: Self-explanation is one of the most effective learning strategies, resulting in deep knowledge. In this paper, we discuss how NORMIT supports self-explanation. NORMIT is a constraint-based tutor that teaches data normalization. We present the system first, and then discuss how it supports self-explanation. We hypothesized the self-explanation support in NORMIT would result in increased problem solving skills and better conceptual knowledge. An evaluation study of the system was performed, the results of which show that both problem-solving performance and the understanding of the domain of students who self-explained increased. We also discuss our plans for future research.
Keywords: intelligent educational systems, data normalization, metacognitive skills, evaluation of teaching strategies