The Effect of Explaining on Learning: a Case Study with a Data Normalization Tutor

Authors:
Antonija Mitrovic
Affiliations:
Intelligent Computer Tutoring Group, Department of Computer Science and Software Engineering, University of Canterbury, New Zealand
Venue:
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
Year:
2005

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Abstract

Several studies have shown that explaining actions increases students' 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 confirmed our hypothesis. Students who self-explained learnt constraints significantly faster, and acquired more domain knowledge.