  We introduce a high-quality dataset that contains 3,397 samples comprising
(i) multiple choice questions, (ii) answers (including distractors), and (iii)
their source documents, from the educational domain. Each question is phrased
in two forms, normal and close. Correct answers are linked to source documents
with sentence-level annotations. Thus, our versatile dataset can be used for
both question and distractor generation, as well as to explore new challenges
such as question format conversion. Furthermore, 903 questions are accompanied
by their cognitive complexity level as per Bloom's taxonomy. All questions have
been generated by educational experts rather than crowd workers to ensure they
are maintaining educational and learning standards. Our analysis and
experiments suggest distinguishable differences between our dataset and
commonly used ones for question generation for educational purposes. We believe
this new dataset can serve as a valuable resource for research and evaluation
in the educational domain. The dataset and baselines will be released to
support further research in question generation.
