Abstract:Recent works on Intelligent Tutoring Systems have focused on more complicated knowledge domains, which pose challenges in automated assessment of student performance. In particular, while the system can log every user action and keep track of the student's solution state, it is unable to determine the hidden intermediate steps leading to such state or what the student is trying to achieve. In this paper, we show that this information can be acquired through data mining, along with the type, frequency and conte… Show more
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