High-level student modeling with machine learning

Abstract

We have constructed a learning agent that models student behavior at a high level of granularity for a mathematics tutor. Rather than focusing on whether the student knows a particular piece of knowledge, the learning agent determines how likely the student is to answer a problem correctly and how long he will take to generate this response. To construct this model, we used traces from previous users of the tutor to train the machine learning agent. This agent used information about the student, the current topic, the problem, and the student’s efforts to solve this problem to make its predictions. This model was very accurate at predicting the time students required to generate a response, and was somewhat accurate at predicting the likelihood the student’s response was correct. We present two methods for integrating such an agent into an intelligent tutor.