Automatic Feedback Generation for Short Answer Questions using Answer Diagnostic Graphs

This paper develops and evaluates a system that automatically generates personalized feedback for student responses to short-answer reading comprehension questions using Answer Diagnostic Graphs (ADG) that align student responses to the logical structure of reading texts. An empirical study with students compares learning outcomes between those receiving model answers only versus those also receiving system-generated feedback.

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 tha

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