Improving Automated Distractor Generation for Math Multiple-choice Questions with Overgenerate-and-rank
This work addresses a specific challenge in educational technology for math educators, though it is incremental as it builds on existing generation methods.
The paper tackled the problem of automatically generating distractors for math multiple-choice questions by proposing an overgenerate-and-rank method, which improved alignment with human-authored distractors but did not surpass human quality.
Multiple-choice questions (MCQs) are commonly used across all levels of math education since they can be deployed and graded at a large scale. A critical component of MCQs is the distractors, i.e., incorrect answers crafted to reflect student errors or misconceptions. Automatically generating them in math MCQs, e.g., with large language models, has been challenging. In this work, we propose a novel method to enhance the quality of generated distractors through overgenerate-and-rank, training a ranking model to predict how likely distractors are to be selected by real students. Experimental results on a real-world dataset and human evaluation with math teachers show that our ranking model increases alignment with human-authored distractors, although human-authored ones are still preferred over generated ones.