Question Difficulty Ranking for Multiple-Choice Reading Comprehension
Vatsal Raina Mark Gales
Multiple-choice (MC) tests are an efficient method to assess English
learners. It is useful for test creators to rank candidate MC questions by
difficulty during exam curation. Typically, the difficulty is determined by
having human test takers trial the questions in a pretesting stage. However,
this is expensive and not scalable. Therefore, we explore automated approaches
to rank MC questions by difficulty. However, there is limited data for explicit
training of a system for difficulty scores. Hence, we compare task transfer and
zero-shot approaches: task transfer adapts level classification and reading
comprehension systems for difficulty ranking while zero-shot prompting of
instruction finetuned language models contrasts absolute assessment against
comparative. It is found that level classification transfers better than
reading comprehension. Additionally, zero-shot comparative assessment is more
effective at difficulty ranking than the absolute assessment and even the task
transfer approaches at question difficulty ranking with a Spearman's
correlation of 40.4%. Combining the systems is observed to further boost the
correlation.