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Cognitively complex assessments encourage students to prepare using deep learning strategies rather than surface learning, recallbased ones. In order to prepare such assessment tasks, it is necessary to have some way of measuring cognitive complexity. In the context of a student-generated MCQ writing task, we developed a rubric for assessing the cognitive complexity of MCQs based on Bloom’s taxonomy. We simplified the six-level taxonomy into a three-level rubric. Three rounds of moderation and rubric development were conducted, in which 10, 15 and 100 randomly selected student-generated MCQs were independently rated by three academic staff. After each round of marking, inter-rater reliability was calculated, qualitative analysis of areas of agreement and disagreement was conducted, and the markers discussed the cognitive processes required to answer the MCQs. Inter-rater reliability, defined by the intra-class correlation coefficient, increased from 0.63 to 0.94, indicating the markers rated the MCQs consistently. The three-level rubric was found to be effective for evaluating the cognitive complexity of MCQs generated by medical students.

The process of developing a rubric to assess the cognitive complexity of student-generated multiple choice questions in medical education