Generating Plausible Distractors for Multiple-Choice Questions via Student Choice Prediction
This work addresses the need for more effective multiple-choice questions in education, particularly for computer science subjects, by improving distractor plausibility to better assess student misconceptions.
The study tackled the problem of generating plausible distractors for multiple-choice questions by training a model to predict student choices, resulting in a distractor generator that outperformed baselines and produced questions with a higher item discrimination index.
In designing multiple-choice questions (MCQs) in education, creating plausible distractors is crucial for identifying students' misconceptions and gaps in knowledge and accurately assessing their understanding. However, prior studies on distractor generation have not paid sufficient attention to enhancing the difficulty of distractors, resulting in reduced effectiveness of MCQs. This study presents a pipeline for training a model to generate distractors that are more likely to be selected by students. First, we train a pairwise ranker to reason about students' misconceptions and assess the relative plausibility of two distractors. Using this model, we create a dataset of pairwise distractor ranks and then train a distractor generator via Direct Preference Optimization (DPO) to generate more plausible distractors. Experiments on computer science subjects (Python, DB, MLDL) demonstrate that our pairwise ranker effectively identifies students' potential misunderstandings and achieves ranking accuracy comparable to human experts. Furthermore, our distractor generator outperforms several baselines in generating plausible distractors and produces questions with a higher item discrimination index (DI).