Understanding Student Interactions with Tutorial Dialogues in EER-Tutor (2014)
Type of ContentTheses / Dissertations
Thesis DisciplineComputer Science
Degree NameMaster of Science
PublisherUniversity of Canterbury. Department of Computer Science and Software Engineering
AuthorsElmadani, Myse Alishow all
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.