Automatic Feedback Generation for Short Answer Questions using Answer Diagnostic Graphs

TL;DR

This paper presents a novel NLP-based system that generates targeted feedback for short-answer reading comprehension questions by using answer diagnostic graphs, aiming to improve student understanding and motivation.

Contribution

It introduces the first feedback generation system for short-answer reading comprehension leveraging answer diagnostic graphs and NLP techniques.

Findings

Feedback helped students identify errors and key points.

No significant score improvement was observed.

Motivation significantly increased with system feedback.

Abstract

Short-reading comprehension questions help students understand text structure but lack effective feedback. Students struggle to identify and correct errors, while manual feedback creation is labor-intensive. This highlights the need for automated feedback linking responses to a scoring rubric for deeper comprehension. Despite advances in Natural Language Processing (NLP), research has focused on automatic grading, with limited work on feedback generation. To address this, we propose a system that generates feedback for student responses. Our contributions are twofold. First, we introduce the first system for feedback on short-answer reading comprehension. These answers are derived from the text, requiring structural understanding. We propose an "answer diagnosis graph," integrating the text's logical structure with feedback templates. Using this graph and NLP techniques, we estimate students' comprehension and generate targeted feedback. Second, we evaluate our feedback through an experiment with Japanese high school students (n=39). They answered two 70-80 word questions and were divided into two groups with minimal academic differences. One received a model answer, the other system-generated feedback. Both re-answered the questions, and we compared score changes. A questionnaire assessed perceptions and motivation. Results showed no significant score improvement between groups, but system-generated feedback helped students identify errors and key points in the text. It also significantly increased motivation. However, further refinement is needed to enhance text structure understanding.