Abstract
Two major goals in Educational Data Mining are determining students' state of knowledge and determining their affective state as students progress through the learning session. While many models and solutions have been explored for each of these problems, relatively little work has been done on examining these states in parallel, even though the psychology literature suggests that it is an interplay of both of these states that influences how a student performs and behaves. This work proposes a model that takes into account the performance and behavior of students when working with an Intelligent Tutoring System in order to track both knowledge and engagement and tests it on data from two different systems and explores the usefulness of such models.
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