Chapter

Artificial Intelligence in Education

Volume 6738 of the series Lecture Notes in Computer Science pp 394-402

Evaluating a General Model of Adaptive Tutorial Dialogues

  • Amali WeerasingheAffiliated withIntelligent Computer Tutoring Group, University of Canterbury
  • , Antonija MitrovicAffiliated withIntelligent Computer Tutoring Group, University of Canterbury
  • , David ThomsonAffiliated withIntelligent Computer Tutoring Group, University of Canterbury
  • , Pavle MoginAffiliated withVictoria University of Wellington
  • , Brent MartinAffiliated withIntelligent Computer Tutoring Group, University of Canterbury

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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 constraint-based tutors Ill-defined tasks well-defined tasks