  We explore creating automated, personalized feedback in an intelligent
tutoring system (ITS). Our goal is to pinpoint correct and incorrect concepts
in student answers in order to achieve better student learning gains. Although
automatic methods for providing personalized feedback exist, they do not
explicitly inform students about which concepts in their answers are correct or
incorrect. Our approach involves decomposing students answers using neural
discourse segmentation and classification techniques. This decomposition yields
a relational graph over all discourse units covered by the reference solutions
and student answers. We use this inferred relational graph structure and a
neural classifier to match student answers with reference solutions and
generate personalized feedback. Although the process is completely automated
and data-driven, the personalized feedback generated is highly contextual,
domain-aware and effectively targets each student's misconceptions and
knowledge gaps. We test our method in a dialogue-based ITS and demonstrate that
our approach results in high-quality feedback and significantly improved
student learning gains.
