Towards a General Model for Supporting Explanations to Enhance Learning
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Revisiting Ill-Definedness and the Consequences for ITSs
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Towards Individualized Dialogue Support for Ill-Defined Domains
International Journal of Artificial Intelligence in Education
Evaluating a general model of adaptive tutorial dialogues
AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education
Trace-Based framework for experience management and engineering
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Fifteen years of constraint-based tutors: what we have achieved and where we are going
User Modeling and User-Adapted Interaction
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Self-explanation has been used successfully in teaching Mathematics and Physics to facilitate deep learning. We are interested in investigating whether self-explanation can be used in an open-ended, ill-structured domain. For this purpose, we enhanced KERMIT, an intelligent tutoring system that teaches conceptual database design. The resulting system, KERMIT-SE, supports self-explanation by engaging students in tutorial dialogues when their solutions are erroneous. The results of an evaluation study indicate that self-explanation leads to improved performance in both conceptual and procedural knowledge.