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Eye-movement tracking and student-system interaction logs provide different types of information which can be used as a potential source of real-time adaptation in learning environments. By analysing student interactions with an intelligent tutoring system (ITS), we can identify sub-optimal behaviour such as not paying attention to important interface components. On the basis of such findings, ITSs can be enhanced to be proactive, rather than reactive, to users’ actions. Tutorial dialogues are one of the teaching strategies used in ITSs which has been shown empirically to significantly improve learning. Enhanced entity-relationship (EER)-Tutor is a constraint-based ITS that teaches conceptual database design. This paper presents the preliminary results of a project that investigates how students interact with the tutorial dialogues in EER-Tutor using both eye-gaze data and student-system interaction logs. 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 require help with the tutorial dialogues. Furthermore, we have demonstrated that the student’s prior knowledge, the problem complexity and the percentage of the dialogue’s prompts that are answered correctly are factors that can be used to predict future errors. The findings from our study can be used to further enhance EER-Tutor in order to support learning better, including real-time classification of students into novices and advanced students in order to adapt system feedback and interventions.
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