Enhancing Learning in a Constraint-Based Tutor

Amali Weerasinghe, Antonija Mitrovic and Pramuditha Suraweera

ABSTRACT

Self-explanation has been used in several intelligent tutoring systems in the domains of mathematics and physics to facilitate deep learning. As all these domains are well structured, the instructional material to self-explain can be clearly defined. We are interested in investigating whether self-explanation can facilitate deep learning in an open-ended domain. For this purpose, we enhanced KERMIT, an intelligent tutoring system that teaches conceptual database design and is based on constraint-based modelling approach. The resulting system, KERMIT-SE, supports self-explanation by engaging students in tutorial dialogues when their solutions are erroneous. An evaluation study was conducted in July 2002, to test the hypothesis that students will learn better with KERMIT-SE than without self-explanation.