Abstract
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 tod improved performance in both conceptual and procedural knowledge.
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Weerasinge, A., Mitrovic, A. Supporting Deep Learning in an Open-ended Domain. In: Gh. Negoita, M., Reusch, B. (eds) Real World Applications of Computational Intelligence. Studies in Fuzziness and Soft Computing, vol 179. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11364160_3
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DOI: https://doi.org/10.1007/11364160_3
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Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25006-7
Online ISBN: 978-3-540-32387-7
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