Deep Discourse Analysis for Generating Personalized Feedback in Intelligent Tutor Systems

TL;DR

This paper presents a neural discourse-based method for generating personalized, context-aware feedback in intelligent tutoring systems, significantly improving student learning outcomes by accurately identifying misconceptions.

Contribution

It introduces a novel neural discourse segmentation and classification approach to decompose student answers and generate personalized feedback, enhancing existing automated feedback methods.

Findings

High-quality, personalized feedback generated

Significant improvement in student learning gains

Effective identification of misconceptions

Abstract

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.