Eagle Eye: a progress measure for intelligent tutoring systems

Abstract

The aim of intelligent tutoring is to let a learner reach a specific learning outcome within the shortest time and least effort possible, while at the same time keeping the learner motivated. To effectively reach this goal, an accurate evaluation of a learner’s progress within an intelligent tutoring system is crucial in order to optimize learning content towards a learner’s needs. This thesis presents Eagle Eye, a sensitive progress measure which is easy to interpret by both human and machine. Eagle Eye has been implemented and tested in a goal-based intelligent tutoring system that aims to improve self-management skills of children with type 1 diabetes. Initial results indicate that Eagle Eye’s output enables human experts to evaluate a child’s progress and use that evaluation to ensure optimal learning.