A Theory-Driven Approach to Predict Frustration in an ITS

The importance of affect in learning has led many intelligent tutoring systems (ITS) to include learners' affective states in their student models. The approaches used to identify affective states include human observation, self-reporting, data from physical sensors, modeling affective states, and mining students' data in log files. Among these, data mining and modeling affective states offer the most feasible approach in real-world settings, which may involve a huge number of students. Systems using data mining approaches to predict frustration have reported high accuracy, while systems that predict frustration by modeling affective states, not only predict a student's affective state but also the reason for that state. In our approach, we combine these approaches. We begin with the theoretical definition of frustration, and operationalize it as a linear regression model by selecting and appropriately combining features from log file data. We illustrate our approach by modeling the learners' frustration in Mindspark, a mathematics ITS with large-scale deployment. We validate our model by independent human observation. Our approach shows comparable results to existing data mining approaches and also the clear interpretation of the reasons for the learners' frustration.