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|Title:||Evaluating a general model of adaptive tutorial dialogues|
|Citation:||Lecture Notes in Artificial Intelligence, 2011, vol.6738 LNAI, pp.394-402|
|Publisher:||Springer Berlin Heidelberg|
|Series/Report no.:||Lecture Notes in Computer Science; vol. 6738|
|Conference Name:||15th International Conference on Artificial Intelligence in Education (28 Jun 2011 - 2 Jul 2011 : Auckland, New Zealand)|
|Amali Weerasinghe, Antonija Mitrovic, David Thomson, Pavle Mogin, Brent Martin|
|Abstract:||Tutorial dialogues are considered as one of the critical factors contributing to the effectiveness of human one-on-one tutoring. We discuss how we evaluated the effectiveness of a general model of adaptive tutorial dialogues in both an ill-defined and a well-defined task. The first study involved dialogues in database design, an ill-defined task. The control group participants received non-adaptive dialogues regardless of their knowledge level and explanation skills. The experimental group participants received adaptive dialogues that were customised based on their student models. The performance on pre- and post-tests indicate that the experimental group participants learned significantly more than their peers. The second study involved dialogues in data normalization, a well-defined task. The performance of the experimental group increased significantly between pre- and post-test, while the improvement of the control group was not significant. The studies show that the model is applicable to both ill- and well-defined tasks, and that they support learning effectively.|
|Keywords:||adaptive tutorial dialogues|
|Rights:||© Springer-Verlag Berlin Heidelberg 2011|
|Appears in Collections:||Aurora harvest 2|
Computer Science publications
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