Intelligent Tutoring Systems (ITSs) have been shown to significantly improve students' learning in a variety of domains, including physics, mathematics, and thermodynamics. Tutorial dialogues is one of the strategies used by ITSs and has been empirically shown to significantly improve learning. This project investigates how different students interact with the tutorial dialogues in EER-Tutor, using both eye-gaze data and student-system interaction logs. EER-Tutor is a constraint-based ITS that teaches conceptual database design. In order to have a more comprehensive and accurate picture of a user's interactions with a learning environment, we need to know which interface features s/he visually inspected, what strategies s/he used and what cognitive efforts s/he made to complete tasks. Such knowledge allows intelligent systems to be proactive, rather than reactive, to users' actions. Eye-movement tracking is therefore a potential source of real-time adaptation in a learning environment. Our findings indicate that advanced students are selective of the interface areas they visually focus on whereas novices waste time by paying attention to interface areas that are inappropriate for the task at hand. Novices are also unaware that they need help with understanding the domain concepts discussed in the tutorial dialogues. We were able to accurately classify students, for example as novice or advanced students, using only eye-gaze or EER-Tutor log data as well as a combination of EER-Tutor and eye-gaze features. The cost of eye-tracking is justified as classifiers using only eye-gaze features sometimes perform as well as those utilising both EER-Tutor and eye-gaze data and outperform classifiers using only EER-Tutor data. The ability to classify students will therefore allow an ITS to intervene when needed and better guide students' learning if it detects sub-optimal behaviour.

Adaptive tutorial dialogues have been successfully employed by ITSs to facilitate deep learning of conceptual domain knowledge. But none of the approaches used for generating dialogues have been used across instructional domains and tasks. The objective of this project was twofold: (i) to propose a general model that provides adaptive dialogue support in both well- and ill-defined instructional tasks (ii) to explore whether adaptive tutorial dialogues are better than non-adaptive dialogues in acquiring domain knowledge. Our model provides adaptive dialogue support by identifying the concepts that the student has most difficulty with, and then selecting the tutorial dialogues corresponding to those concepts. The dialogues are customised based on the student’s knowledge and explanation skills, in terms of the length and the exact content of the dialogue. The model consists of three parts: an error hierarchy, tutorial dialogues and rules for adapting them. We incorporated our model into EER-Tutor, a constraint-based tutor that teaches database design. The effectiveness of adaptive dialogues compared to non-adaptive dialogues in learning this ill-defined task was evaluated in an authentic classroom environment. The results revealed that the acquisition of the domain knowledge (represented as constraints) of the experimental group who received adaptive dialogues was significantly higher than their peers in the control group with non-adaptive dialogues. We also incorporated our model into NORMIT, a constraint-based tutor that teaches data normalization. We repeated the experiment using NORMIT in a real-world class room environment with a much smaller group of students (18 in NORMIT study vs 65 in EER-Tutor study) but did not find significant differences. We also investigated whether our model could support dialogues in logical database design and fraction addition using paper-based methods. Our evaluation studies and investigations on paper indicated that our model can provide adaptive support for both ill-and well-defined tasks associated with a well-defined domain theory. The results also indicated that adaptive dialogues are more effective than non-adaptive dialogues in teaching the ill-defined task of database design.

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