  We investigate how automated, data-driven, personalized feedback in a
large-scale intelligent tutoring system (ITS) improves student learning
outcomes. We propose a machine learning approach to generate personalized
feedback, which takes individual needs of students into account. We utilize
state-of-the-art machine learning and natural language processing techniques to
provide the students with personalized hints, Wikipedia-based explanations, and
mathematical hints. Our model is used in Korbit, a large-scale dialogue-based
ITS with thousands of students launched in 2019, and we demonstrate that the
personalized feedback leads to considerable improvement in student learning
outcomes and in the subjective evaluation of the feedback.
