Tracing Knowledge and Engagement in Parallel in an Intelligent Tutoring System

Two of the major goals in Educational Data Mining are determining students’ state of knowledge and determining whether students are affectively engaged with the task and in positive affective states. These two problems are usually examined separately and multiple methods have been proposed to solve each of them. However, little work has been done on tracing both of these states in parallel and the combined effect on a student’s performance. In this work, we propose a model for tracing student engagement in parallel with knowledge as the student uses an Intelligent Tutoring System. We then compare this model to existing methods of tracing student knowledge and engagement.