ADVISOR: A Machine Learning Architecture for Intelligent Tutor Construction

Abstract

ADVISOR is a machine learning architecture for constructing intelligent tutoring systems (ITS). ADVISOR is able to automate some of the reasoning about how the student will probably perform, and all of the reasoning about which teaching action should be made in a particular context. The benefit of this approach is that it works by observing students using an ITS. By observing students, ADVISOR constructs a model of how a student will respond to a particular teaching action in a given situation. With this model, ADVISOR is able to experiment and determine a policy for presenting teaching actions that tries to achieve a customizable teaching goal. We experimented with a variety of approaches for constructing a model of how students behave, and we found that sophisticated approaches such as Multiple Adaptive Regression Splines (MARS) are only slightly better than linear regression. We also examined a variety of ways ADVISOR can reason with the model of student performance and determine how to teach. We used including temporal difference learning, heuristic search, and the use of rollouts. If little is known a prior about the teaching goal, rollouts are a strong choice as they require little prior knowledge and are robust. Given prior knowledge of the teaching goal, some type of temporal difference learning is a good option since this requires less computation time than using heuristic search or rollouts. ADVISOR was tested in the context of the AnimalWatch tutor for grade school arithmetic. However, the architecture is generic and applicable to a variety of ITS. As part of AnimalWatch, ADVISOR was tested in a grade school and achieved the specified teaching goal of minimizing the amount of time per problem. The ADVISOR architecture is also useful for evaluating what components of the tutoring system are responsible for performance, and what components of ADVISOR are constraining performance. In this way, engineering effort can be directed to where it is most profitable. Thus, the ADVISOR architecture has the potential to benefit a wide range of ITS (and possibly other adaptive systems) in several ways. In addition to determining which components limit performance, our hope is ADVISOR's ability to automate the construction of the knowledge of how to teach will result in a decreased cost to construct ITS.