Self-improving instructional plans on the level of student categories

This paper describes a learning process for the tutor of an intelligent tutoring system (ITS) to automatically learn models of student categories and self-improve its instructional plans on the level of these categories. Using real-world teaching scenarios as experiment data, we empirically show that for every category the tutor is able to efficiently learn effective instructional plans. Our experiment results also show that the absence of category background knowledge decreases the tutor´s learning performance as well the effectiveness of the learned instructional plans.