EduQG: A Multi-Format Multiple-Choice Dataset for the Educational Domain

Abstract

Natural language processing technology has made significant progress in recent years, fuelled by increasingly powerful general language models. This has also inspired a sizeable body of work targeted specifically towards the educational domain, where the creation of questions (both for assessment and practice) is a laborious/expensive effort. Thus, automatic Question-Generation (QG) solutions have been proposed and studied. Yet, according to a recent survey of the educational QG community's progress, a common baseline dataset unifying multiple domains and question forms (e.g., multiple choice vs. fill-the-gap), including readily available baseline models to compare against, is largely missing. This is the gap we aim to fill with this paper. In particular, we introduce a high-quality dataset in the educational domain, containing over 3,000 entries, comprising (i) multiple-choice questions, (ii) the corresponding answers (including distractors), and (iii) associated passages from the course material used as sources for the questions. Each question is phrased in two forms, normal and cloze (i.e., fill-the-gap), and 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 are made available to support further research in question generation for education (https://github.com/hadifar/question-generation).