CamChoice: A Corpus of Multiple Choice Questions and Candidate Response Distributions
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Multiple Choice examinations are a ubiquitous form of assessment that is used to measure the ability of candidates across various domains and tasks. Maintaining the quality of proposed questions is of great importance to test designers, and therefore newly proposed questions go through several pre-test evaluation stages before they can be deployed into real-world exams. This process is currently quite manual, which can lead to time lags in the question development cycle. Automating this process would lead to a large improvement in efficiency, however, current datasets do not contain sufficient pre-test analysis information. In this paper, we introduce CamChoice; a multiple-choice comprehension dataset with questions at different target levels, where questions have the true candidate selected options distributions. We introduce the task of candidate distribution matching, propose several evaluation metrics for the task, and demonstrate that automatic systems trained on RACE++ can be leveraged as baselines for our task. We further demonstrate that these automatic systems can be used for practical pre-test evaluation tasks such as detecting underperforming distractors, where our detection systems can automatically identify poor distractors that few candidates select. We release the data publicly for future research.
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