Deep Discourse Analysis for Generating Personalized Feedback in Intelligent Tutor Systems

Matt Grenander, Robert Belfer, Ekaterina Kochmar, Iulian V. Serban, Francois St-Hilaire, Jackie C. K. Cheung

Research output: Contribution to conferencePaperpeer-review


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.
Original languageEnglish
Publication statusPublished - 6 Feb 2021
EventEAAI-21: The 11th Symposium on Educational Advances in Artificial Intelligence (Collocated with AAAI-21) - Virtual Conference
Duration: 6 Feb 20217 Feb 2021


Abbreviated titleEAAI-21
Internet address


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