Detecting Problematic Questions to Support Math Word Problem Design

Mao-Siang Chen, Yu-Hang Deng, An-Zi Yen

Keywords: Problematic Question Detection, Question Design Support, Self-Optimization Prompting

Abstract:

When designing math word problems, teachers must ensure the clarity and precision of the question to avoid multiple interpretations and unanswerable situations, thereby maintaining consistent grading standards and effectiveness. We address these issues to provide comprehensive support to teachers in creating clear, solvable, and formal math word problems. In this paper, we present MathError, a dataset of real-world math word problems annotated with error types to investigate the need for question correction. Our work explores how large language models (LLMs) can assist teachers in detecting problematic questions to support math word problem design in scenarios with limited data, simulating real-world conditions with minimal training samples. Preliminary results demonstrate the models' capabilities in detecting problematic questions and identify areas for further research and development in educational applications.

Primary Area: applications to computer vision, audio, language, and other modalities

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Submission Number: 758